This paper takes the supply chain alliance under the decoupling of the front and back of the all-for-one tourism as the research object. Considering the three behavior stimuli of self-benefit, altruism, and invariance, this article resets the attributes such as environmental stimuli and response threshold of ants based on the characteristics of the all-for-one tourism supply chain with shared services as the core under the decoupling of the front and back. Moreover, it introduces dual intervention factors to coordinate the benefit distribution process of different member companies, takes fairness as the main goal of benefit distribution, introduces relative deprivation as the measure index of fairness, and establishes a dynamic all-for-one tourism supply chain alliance benefit distribution model. The experimental results show that the extended model has good flexibility of benefit distribution and realizes the fair distribution of supply chain benefits. In recent years, rural tourism has become an important model of China’s tourism industry under the support of the rural revitalization strategy . NowadayIn recent years, among the methods of studying the allocation problem, the swarm intelligence method with biological dynamic flexibility has attracted scholars’ extensive attention. Swarm intelligence refers to the macroscopic intelligent behaviors of social organisms such as ants, bees, and birds that have social characteristics when performing certain specific activities. It is an emergence phenomenon of simple subjects through microscopic interactions . DiffereWith the rapid development of information technology, transportation and storage, customer contact, and other related technologies, the mobility of backstage functions in all-for-one tourism attractions is getting stronger and stronger. Therefore, from the perspective of the overall process of all-for-one tourism, this paper introduces the separation strategy of the front and back stages in the design of the service process, reshapes its spatial structure based on the decoupling of the back stage, and divides the all-for-one tourism supply chain structure into tourism suppliers, indirect suppliers represented by service sharing centers (SSCs)  and publThe structure is based on decoupling the foreground business and background functions of the service function unit to release the maximum resources and efficiency. The front and back structure is a service system spatial structure design concept adopted by service organizations to deal with customer contact in the process of service provision; that is, the service system is spatially separated into two parts: the front stage and the back stage. The front stage refers to the part of the system that directly faces customers and provides services and can be perceived by customers, while the back stage refers to the part of the system that does not directly contact customers and provides efficient and centralized support work without interference for the front stage business. Therefore, the all-for-one tourism supply chain can be extended to the procedure of suppliers–SSC–service function units–travel agencies–tourists.The front stage of all-for-one tourism mainly includes catering and accommodation facilities, landscape sightseeing facilities, entertainment and shopping facilities, and other service function units directly facing tourists, mainly providing accommodation and catering, tourist guides, entertainment and souvenir sales, and other types of services that can be perceived by tourists. The back stage of all-for-one tourism mainly includes kitchen facilities, laundry facilities, warehouse facilities, fire-fighting facilities, drainage and sewage treatment systems, and other supportive units that are not directly oriented to tourists. It mainly provides catering semi-finished products and agricultural and sideline products’ processing, linen washing, cargo transportation and storage, fire prevention and disaster warning, emission environmental protection treatment, and other services that cannot or are difficult to be perceived by tourists.In recent years, research on front and back structures has mainly focused on the mobility of background functions in service systems, which led to a leap from the theory of front–background structure to the theory of front–background decoupling. That is, under the premise of dividing the front stage and back stage structures, the background business is relocated or outsourced, thereby realizing the intensive and industrialized operation of background functions. At present, the front and back structure decoupling theory has been successfully applied to many service industries such as telecommunications, finance, and IT.In the development process of the tourism industry, there are many problems in the operation mode of the traditional supply chain. For a long time, each member enterprise in the tourism supply chain is profitable. When carrying out business activities, it only follows the principle of maximizing its own benefits and rarely considers the overall economic, social, and environmental benefits, and some member enterprises have a serious tendency of opportunism during the peak tourism seasons, which brings great efficiency loss to the whole tourism supply chain. Therefore, how to coordinate the overall benefits of the supply chain and the individual benefits among the member enterprises has become one of the core issues of tourism supply chain management.Nowadays, with the continuous increase in the uncertainty of the tourism market environment and the change in the perception of member companies, more and more tourism suppliers and tourism operators form strategic alliances to carry out business activities together so as to achieve the goals of complementary advantages, cost reduction, information sharing, and collaborative innovation . Howevervice versa.(1) The attributes of the stakeholders are determined by their contribution to the total benefit and their profitability. In the same tourism supply chain strategic alliance, the higher the contribution and profitability of the stakeholders, the greater their willingness to increase the benefits, and (2) Due to the characteristics of the tourism supply chain, the benefit distribution mechanism in the tourism supply chain alliance will be affected by the dual influence of tourism resource monopoly factors and time factors, so the benefit imbalance is inevitable in the process of benefit distribution. At this time, the stakeholders’ perception of the fairness of the benefit distribution process determines the stability of the supply chain alliance. The stakeholders’ perception of fairness is embodied by a sense of relative deprivation.(3) In the case of imbalance of benefits, most of the groups with impaired benefits will actively demand the restoration of their due benefits, while some groups with strong benefits will weaken their tendency to profit out of consideration for the continuous cooperation of the alliance and their own reputation building. At the same time, external policy intervention will also coordinate the distribution of benefits within the alliance.The benefit distribution of the all-for-one tourism supply chain alliance mainly involves three aspects: the attributes of the stakeholders, the perception of fairness by the stakeholders, and different behavior choices under the influence of internal and external conditions. The detailed description is as follows:The dynamic variability of the all-for-one tourism supply chain alliance makes it put forward high requirements for the adaptability of benefit distribution. In view of the adaptive distribution flexibility of the ant colony labor division model, this paper applies the ant colony labor division model to the benefit distribution of alliance members, establishes the mapping relationship between the model and benefit distribution, combines the characteristics of member companies and fairness requirements, and expands the basic ant colony labor division model. On this basis, an extended ant colony labor division model for the benefit distribution problem of the all-for-one tourism supply chain is proposed.s. The urgency of the task is proportional to the magnitude of the stimulus value. The more urgent the task, the higher the stimulus value; otherwise, the lower it is. Similarly, according to whether its own ability is sufficient to solve the task, each ant has a fixed response threshold θ corresponding to the task, and the response threshold is a fixed value in the FRTM. The response threshold level of the individual ant reflects the actual difference in behavioral response; that is, the threshold determines the response state of the individual ant to a task. When the stimulus intensity of a task in the environment exceeds the response threshold of an ant, the probability of it engaging in the task is high; on the contrary, it has a low probability of performing the task. In addition, the stimulus intensity of the task will vary with the number and efficiency of performing ants. When the task is not executed by the ants, the stimulus value of the task will increase to stimulate more ants. When the ants performing a specific task withdraw from task execution, the intensity of the environmental stimulus corresponding to the task will increase until it reaches the level of ants with a higher response threshold, thereby inspiring these ants to start the task.s varies with time(1) The environmental stimulus value The fixed response threshold model (FRTM) of the ant colony labor division was proposed by vice versa.Environmental stimulus is a direct factor of the individual’s response to a task. When a task appears, the environment will produce a stimulus value to affect the individual’s state. The environmental stimulus value is proportional to the task amount. The larger the task amount, the greater the stimulus value, and t is a discrete time variable, s (t) represents the environmental stimulus value at time t, δ represents the increase in unit time of the environmental stimulus, φ represents the amount of tasks completed by ants in unit time, and nact represents the number of ants performing this task in unit time.(2) Responses of non-performing ants to environmental stimuliThe task will be completed gradually with the addition of the individual and the corresponding environmental stimulus will be gradually weakened. However, as long as the task is not completely completed, the environmental stimulus will keep increasing with the duration of the task, thus stimulating more individuals to participate in the task. The rules for increasing environmental stimuli are as follows:i will determine the probability of participating in the task based on its own attribute characteristics.si represents the activity state of individual ant i and si = 1 represents the ant participating in performing a task. si = 0 indicates that the ant does not participate in the task. P (Si = 0→Si = 1) represents the probability that ant i, who has not performed the task, is affected by the environmental stimulus and changes from the non-executing state to the executing state, and θi is the response threshold of individual ant i. In the model proposed by n is a constant controlling the curve shape of the threshold function, and in general, n = 2. It can be seen from θi is constant, the larger the s is, the larger the P is. This shows that the stronger the environmental stimulus is, the greater the probability of the ants responding to the task are, and when s is constant, the larger the θi is, the smaller the P is, indicating that the higher the threshold of the ant is, the smaller the probability of the individual responding to the task is.(3) The probability that the performing ant quits the taskOnce the task appears, the environment will give the ant a stimulus value and the ant i participates in task execution, after every period of time, it will decide whether to quit or not according to the following probability:When ant P (Si = 1→Si = 0) represents the probability that the status of ant i performing the task changes from 1 to 0, that is, the probability of quitting the task. To simplify the model, p is generally a constant. In the FRTM, the probability of an ant participating in a task and the probability of quitting a task are independent of each other.In the formula, In the entire operation process of the all-for-one tourism scenic spot, member companies can be divided into tourism operators represented by travel agencies and tourism suppliers represented by service function units  and service sharing centers (SSCs) according to the different structures and scales. In order to maximize their efficiency, travel agencies usually outsource their business to local travel agencies at the destination. At this time, travel agencies can be divided into group travel agencies and ground travel agencies. Therefore, the membership in the supply chain alliance is shown in (1) The member enterprises in the tourism destination supply chain are defined as ants, including service sharing centers, group travel agencies, and ground travel agencies(2) The profitability of member enterprises is defined as the response threshold of ants to tasks(3) The member enterprises' perception of benefit distributive justice is defined as environmental stimulus(4) Both ants and members of the supply chain alliance decide whether to join the execution of the task or not according to the task and their actual ability(5) Whether individuals and companies participate or not is spontaneous, and there is no mandatory contribution/membership/activity(6) The characteristics of environmental stimulus changes over time in the ant colony labor division model are similar to the changes in perceptions of fairness by member companies in the all-for-one tourism supply chain allianceThe mapping relationship between the benefit distribution of the all-for-one tourism supply chain and the ant colony labor division model can be summarized as follows:The aforementioned analysis and explanation on the similarities between the two are aimed at demonstrating and clarifying the feasibility of research ideas in this article.(1) In the basic ant colony labor division model, the threshold of ants remains fixed once determined, which obviously cannot fully fit the complex benefit distribution process. In this paper, the inhibition factor and the government subsidy factor are considered to coordinate the benefit distribution process of the supply chain alliance so that the response threshold of member enterprises will change in the changing environment, and then, it is more in line with the market benefit coordination mechanism.(2) In the basic ant colony labor division model, the environmental stimulus value of the task is determined by the corresponding task quantity. However, in the tourism supply chain alliance, because the contribution size, perception of fairness, and requirements for the stability of the alliance of different member enterprises are different, it is necessary to consider that different member enterprises have different incentives to increase or decrease the benefit behavior.(3) In the basic ant colony labor division model, there are only two states of ant joining and exiting, and the probability of ant quitting is usually a preset value, which does not meet the actual requirements. In the benefit distribution of the tourism supply chain alliance, it is necessary to consider the three situations of increasing, decreasing, and unchanged benefit of member enterprises, and the corresponding probability model should be constructed.The basic ant colony labor division FRTM is relatively simple, and the attributes of individual ant such as stimulus and threshold are simple too. If it is directly used in the modeling and simulation of tourism supply chain alliance benefit distribution, it will be difficult to reflect the complex relationship between the supply chain and the member enterprises. Therefore, this paper redesigned the basic attributes, response behavior, and benefit adjustment rules of ants. By comparing with the characteristics of tourism supply chain alliance, the basic ant colony labor division model was extended from the following aspects:i can be expressed asCi represents the contribution of member i in the supply chain and N represents the number of members in the alliance.Different social roles and capabilities of enterprises lead to great differences in their contribution levels they make in the process of creating total social benefits. Their contribution can be measured by their investment in transportation, labor, facilities, equipment, technology, environmental protection, and other indicators in the process of creating the total benefits of the tourism supply chain. The contribution rate of member enterprise i generates the expected value Qi for the distribution of total benefit according to its contribution in transportation, labor, facilities, equipment, technology, environmental protection, and other aspects, and Qi = ci·G . When Gi = Qi, the expected benefit of member enterprise i matches the actual benefit, which is the most fair and reasonable distribution method. When Gi< Qi, the actual benefit of some member enterprises is lower than the expected benefit. Since the total benefit is fixed, that is, G = ∑Gi = ∑Qi, it can be known that the actual benefit of some member enterprises must be higher than the expected benefit, that is, Gi> Qi. As a result, the distribution of benefits is not balanced, which causes the dissatisfaction of member enterprises and then affects the service level and tourist experience of the all-for-one tourism scenic spot.Member enterprise i is defined as the degree of realization of the expected benefit Di; then,The gap between the actual benefit and the expected benefit of member enterprise The total degree of realization of the expected benefit isRelative deprivation is a social psychological theory about group behavior, which was proposed by sociologist Stouffer  in 1949.i after comparing the realization degree of its expected benefits with the reference group is defined asAD (Di) represents the sum of the parts in the reference group whose expected benefit degree value is higher than Di. N represents the number of members in the supply chain alliance.Drawing on the research of (1) Environmental stimulusAccording to the applicability and defects of the ant colony labor division FRTM applied to the aforementioned benefit distribution of the tourism supply chain alliance, this paper proposes a new dynamic threshold model applied to the benefit distribution problem. The specific derivation process is as follows:Environmental stimulus values reflect the response of ants to different tasks. The stronger the task stimulates, the more likely the ant is to engage in the task, and the weaker the task stimulates, the less likely the ant is to engage in the task. In the benefit distribution of the all-for-one tourism supply chain, the member enterprises have three behavioral choices, namely, increase in benefit, decrease in benefit, and unchanged benefit, which correspond to three behavioral stimuli, respectively. As the total benefit is fixed, the benefit of some member enterprises increases, which will inevitably lead to the benefit reduction of some other member enterprises. When the member enterprises' contribution is greater than their benefit and their sense of relative deprivation is high, they have a greater tendency to pursue benefit increase. At the same time, member enterprises sometimes choose to reduce their benefits based on their long-term development or establishing a good reputation. The change of member enterprises' selection tendency is related to the change of benefit and the factors of inhibition and subsidy.Ci is the contribution of member enterprise i in the supply chain and Gi is the actual benefit of member enterprise i at time t. It can be seen from The environmental incentives for member enterprises to increase their benefits are as follows:In order to create a healthy tourism environment under the reasonable profit-making competition mechanism and the individual consideration that some subjects emphasize win–win cooperation over independent profit, some member enterprises will also have a tendency to reduce their benefits. The corresponding environmental incentives to reduce their benefits ares changes over time(2) Stimulus It can be seen from Di = 1. When Di > 1, the process of environmental stimulus of member enterprises over time is as follows:In the basic ant colony labor division model, when the task is not fully completed, the environmental stimulus value will increase by a constant at every moment so as to stimulate more ants to participate in the task. With the continuous addition of ant individuals, the task is gradually decomposed and completed, and the environmental stimulus of the task will gradually weaken until it disappears. In the process of benefit distribution, in general, when the actual benefit is greater than the expected benefit, the willingness of member enterprises to continue to increase the benefit will be weakened, and the willingness to reduce the benefit will be relatively enhanced. However, when the actual benefit is lesser than the expected benefit, the willingness of member enterprises to continue to reduce the benefit will be weakened, and the willingness to increase the benefit will be relatively enhanced. The target task of each member enterprise is defined as the actual benefit equals to the expected benefit, that is, Di< 1, the process of environmental stimulus of member enterprises over time is as follows:δ is the independent variable of environmental stimulus.(3) Dynamic response thresholdWhen s and make behavioral choices. The higher the response threshold of a member enterprise to increase its benefits, the lower its probability of taking benefit-increasing behaviors. Similarly, the higher the response threshold of a member enterprise’s benefit-reducing behavior, the lower its probability of taking benefit-reducing behaviors. The response threshold of member enterprises is determined by their profitability. In this paper, the functional relationship between the profitability of member enterprises and the response threshold is established. The response threshold of member enterprises to increase their benefits isλ is the threshold control coefficient to ensure the comparability between the threshold and the stimulus. EGCi indicates the profitability of member enterprise i in the current all-for-one tourist attractions, which is determined by factors such as the size of the member enterprises, the social division of labor undertaken, and the degree of dependence on the season. κ(n) is an inhibitory factor, used to weaken the benefit realization ability of member enterprises that continues to increase benefits when Di is greater than 1, and chooses benefit reduction behaviors for n consecutive times but Di is still greater than 1. Taking into account the timeliness of benefit distribution, n is 3 here. ρ represents the number of times that Di is still greater than 1 when it chooses the benefit reduction behavior for n consecutive times. N is the number of members in the supply chain alliance.The response threshold is an internal factor that determines whether member enterprises respond to stimulus ω(n) is an enhancement factor, which is used to enhance the benefit realization ability of member enterprises that continues to reduce benefits when Di is less than 1 and choose the behavior of increasing benefits for n consecutive times but Di is still less than 1. Similarly, n = 3, and υ refers to the number of times when Di is still less than 1 when it chooses the behavior of increasing benefits for n consecutive times.(4) Probability of behavioral choiceThe response threshold of member enterprises to reduce benefits isP+, the probability of profit decrease as P−, and the probability of unchanged profit as P*, and then, P++ P− + P* = 1.In the basic ant colony labor division model, ants perceive all environmental stimuli with equal probability. The sum of the selection probabilities of the ants that have not performed the task to participate in the task and the non-participation of the task is 1, and the sum of the selection probabilities of the ants that perform the task to exit the task and continue to perform the task is 1. In the model of this article, there are three different options: increase in benefit, decrease in benefit, and unchanged benefit. We define the probability of profit increase as i responds to the increase in benefit isThe probability that member enterprise i responds to the decrease in benefit isThe probability that member enterprise i responds to the unchanged benefit is(5) Change in benefitThe probability that member enterprise H1, H2, and H3 are all decision variables. H1 = 1 and H2 and H3 = 0 when the member enterprise is not coordinated by the inhibitory factor κ and the enhancement factor ω. H2 = 1 and H1 and H3 = 0 when member enterprises are coordinated by the inhibitory factor κ. H3 = 1 and H1 and H2 = 0 when member enterprises are coordinated by the enhancement factor ω. EGCi*κ and EGCi *ω reflect the benefit realization ability of member enterprise i under its self-inhibition and external intervention. τ1 is the average value of increased profits of member enterprises, and rand (τ1) stands for a random number between 0 and τ1. τ2 is the average value of reduction profit of member enterprises, and rand (τ2) stands for a random number between 0 and τ2 , and government subsidy factor ω (n) are initialized.The initial discrete simulation time variable Step 2i is calculated according to The contribution rate of member enterprise Step 3The environmental stimulus value and response threshold are calculated according to Step 4P+, P−, and P* are calculated according to The individual behavior selection probability Step 5Gi is updated according to The individual benefit Step 6Di, TDi, and the relative deprivation RDi are updated according to The expected benefit realization degree Step 7s (t + 1) is updated according to TDi =N, go to Step 8; otherwise, go to Step 3.The individual environmental stimulus value Step 8Statistics and output the simulation results.R is a national tourism demonstration zone city. In response to the call of the local government to deepen all-for-one tourism and accelerate the development of tourism, as well as the self-requirement of promoting the upgrading and transformation of the local tourism industry, City R has completed the comprehensive upgrading of the tourism supply chain, transforming the traditional independent node relationship of profitability into a win–win alliance relationship. Under the goal of sustainable development of tourism destinations, a new SSC node is added to the supply chain. The tourism supply chain alliance is generally composed of tourism operators and tourism suppliers. At present, a total of four enterprises have established alliance partnerships with one SSC, two travel suppliers represented by service function units, and one travel operator represented by travel agencies, which are, respectively, represented by Agent 1, Agent 2(a), Agent 2(b), and Agent 3.City Tmax = 100 times, the environmental stimulus variable δ = 0.1, the contributions of the three groups are 68, 75, and 57, and the profitability EGCi is 10, 7, and 3, respectively. The inhibitory factor κ = 0.4, and the enhancement factor ω = 1.1.This paper takes the benefit distribution of 10 tourism supply chain alliances as an example to verify the feasibility of the model. The total benefit and initial benefit of the 10 alliances are given in MATLAB programming was carried out according to the established model, and the running results are shown in Every change in the benefits of member enterprises is a game similar to bargaining. In the case of constant total benefit, member enterprises are inclined to increase their benefit as much as possible on the basis of maintaining the overall stable cooperation of the supply chain. With the increase in the number of games, the fluctuation of the benefits of the four enterprises gradually weakened. Taking The moments X = 7, 22, 50, and 53 in the figure are taken as examples. When X = 7, Agent 1 reaches the maximum value of benefit under the action of its own will. In order to maintain stable cooperation in the supply chain, the inhibiting factor starts to work. It can be seen from the figure that after the inhibitory factor takes effect, the first time, the benefit of Agent 1 drops to the lowest point 42.02 and the last time, the benefit drops to the lowest point 43.91. It shows that as the number of games increases, the stimulus for Agent 1 to reduce its benefit becomes smaller each time, and the manifestation is that the reduction is getting smaller. The return of Agent 1 at the first time increase to the maximum point is 60.76, and the return at the last time increase to the maximum point is 46.79, indicating that with the increase in the number of games, the incentive for Agent 1 to increase the return each time will become smaller, which is manifested in the smaller increase range. In the cyclical game of increasing and decreasing benefit, Agent 1 gradually achieves the balance between expected benefit and actual benefit, which shows that as the number of iterations increases, the model runs better.In the game process of benefit increase and decrease, the member enterprises are jointly affected by the internal effects of the environmental stimulus and the external effects of inhibiting factors and enhancement factors, which is manifested in the different incentives to increase or decrease benefits with the change of their own actual benefits and expected degree of realization. When the benefit increases, the member enterprises have the willingness to reduce the benefit; and when the benefit decreases, the member enterprises have the willingness to increase the benefit. On the other hand, in order to maintain the long-term stable cooperation of the tourism supply chain alliance, apart from relying on the self-adjustment of the alliance members, a certain form of benefit distribution mechanism can also be established within the alliance.Considering the supervision role of the government and public departments in the all-for-one tourism supply chain, the model in this paper introduces inhibiting factors and enhancing factors to conduct some external intervention in the benefit distribution process. Taking the changes in the benefits of Agent 1 and Agent 3 as an example, The aforementioned analysis shows that the inhibitory factors and enhancement factors set by the model in this paper have a good effect on the pursuit of fairness and efficiency in the benefit distribution process.(1) In several experiments with different initial benefits, the model in this paper can realize the fair distribution of benefits by setting the fairness criteria and double factors.(2) Based on their own capabilities, there are groups with strong interests, intermediate groups, and disadvantaged groups in the alliance. In the absence of the benefit distribution mechanism, the strong groups always tend to increase their profits, while the weak groups can only choose to continue to reduce their profits in the exploitation. The unfair distribution of benefits can easily damage the stability of the alliance.(3) Considering the long-term stable development of the alliance, some powerful groups will have the intention to reduce their own benefits, but the effect is not significant in the initial stage of benefit distribution of the whole alliance, but will become the main factor to maintain the stability in the later stages.(4) The extended model considers the introduction of inhibitory factors and enhancement factors to act on the benefit distribution process, and the setting of this dual factor can quantitatively explain the intervention effect of regulatory authorities in reality. With a certain degree of intervention by the regulatory authorities, the fair and just distribution of benefits in the supply chain alliance can be realized quickly and efficiently.(5) In order to facilitate the study, the model in this paper is based on the determination of demand and information symmetry in all aspects of the global tourism supply chain. Further research is needed on the specific effects of enhancing factors and inhibiting factors.In the all-for-one tourism supply chain, the most fair and ideal state is to distribute benefits according to the respective contributions of member enterprises. However, in the actual situation, the influence of the off-peak season, the degree of cooperation between the upstream and downstream of the supply chain, the social status of member enterprises, and the widespread speculation in the tourism supply chain are all likely to affect the final actual benefit. If a fair and reasonable benefit distribution mechanism is not established, it is likely to form a situation in the supply chain where the strong becomes stronger and the weak becomes weaker, which will cause an imbalance of benefit distribution in the whole supply chain and seriously affect the stability of the supply chain. In this paper, a benefit distribution model of the extended ant colony division of labor for all-for-one tourism supply chain alliance is established by introducing inhibitory factors and enhancement factors and taking the expected realization degree of member enterprises as the criterion of fairness. The results show the following:Based on the aforementioned analysis, this paper believes that the supply chain alliance can adopt the extended ant colony labor division model to distribute the overall benefits.


Bemisia tabaci MED (Hemiptera: Aleyrodidae), is an omnivorous agricultural pest, which causes huge economic losses to agriculture and is highly resistant to many pesticides. The overexpression of cytochrome P450 may play an important role in host adaptation and insecticide resistance in B. tabaci MED. Therefore, the present study systematically analyzed the cytochrome P450 gene family at the genome-wide level to understand its function in B. tabaci MED. Our analysis identified 58 cytochrome P450 genes in B. tabaci MED, among which 24 were novel. Phylogenetic analysis revealed broad functional and species-specific diversification in B. tabaci MED P450, suggesting the role of multiple P450 genes in detoxifying. Reverse transcription-real time quantitative PCR (RT-qPCR) showed that CYP4CS2, CYP4CS5, CYP4CS6, CYP4CS8, CYP6DW4, CYP6DW5, CYP6DW6, CYP6DZ8, and CYP6EN1 genes increased significantly after two days of exposure to imidacloprid. Interestingly, all nine genes belonged to the CYP4 and CYP6 families. A decrease in the expression of five genes  via RNA interference (RNAi) resulted in a significant increase in the mortalities of whiteflies when exposed to imidacloprid. These results indicate that the overexpression of the P450 genes may play an essential role in imidacloprid tolerance of B. tabaci MED. Thus, the present study provides basic information on P450 genes in B. tabaci MED, which will further help elucidate the insecticide resistance mechanism in the agricultural pest whitefly.The whitefly,  Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae), is the main pest worldwide that is resistant to many insecticides. Therefore, it is necessary to develop new and effective compounds against B. tabaci. We sequenced the whole genome of B. tabaci to detect cytochrome P450 protein, and knocked out five genes by RNA interference, which led to an increase in mortality after imidacloprid exposure, and confirmed their role in insecticide resistance. These findings provide a theoretical basis for further studying the specific function of cytochrome P450 protein and clarifying the protein–compound interaction to develop effective pesticides.The whitefly, B. tabaci, is one of the most destructive polyphagous phloem-feeders worldwide [B. tabaci is a cryptic species complex including the species “MEAM1” and “MED” , acquiring a globally invasive insect status and are widely distributed throughout China, Southern United States, and Africa [B. tabaci do direct damage by feeding on plant sap and indirect damage by transmitting plant viruses to vegetable crops [The whitefly, orldwide . B. tabad Africa ,3. B. tale crops ,5,6. Becle crops ,8,9,10, le crops ,12.B. tabaci MED.Cytochrome P450 contributes to growth, development, nutrition, and detoxification of xenobiotics, and is used to metabolize a variety of endogenous and exogenous compounds, forming a polygenic enzyme superfamily ,14. CytoB. tabaci MED has a high level of resistance to neonicotinoid insecticides, which may contribute to genetic cluster changes [As an alternative to organophosphates and pyrethroids, neonicotinoid insecticides have been used to control whiteflies in fields and greenhouses; however, the extensive use has led to the rapid development of neonicotinoid resistance ,21. B. t changes .B. tabaci MED have become extremely tolerant to the commercial products of this insecticide [B. tabaci leads to imidacloprid resistance [B. tabaci compared with other insects, such as Bombyx mori (84) [Leptinotarsa decemlineata (96) [Rhynchophorus ferrugineus (77) [Drosophila melanogaster (85) [B. tabaci MED, there is a need to identify more P450 genes of B. tabaci MED.Neonicotinoid insecticides were introduced in 1991, with imidacloprid being the first product in the market. Imidacloprid targeted nicotinic acetylcholine receptor (nAChR) receptors in the insect nervous system . Becauseecticide . Studiessistance . Howeverori (84) , Leptinoata (96) , Rhynchoeus (77) , and Droter (85) . TherefoB. tabaci MED and assess their roles in insecticide resistance. We conducted a genome-wide analysis of P450 sequences in the complete genome of B. tabaci MED and investigated their roles in insecticide resistance using expression analysis, RNAi approach, and a bioassay.The present study aimed to identify the cytochrome P450 gene family in B. tabaci library on the PacBio platform. The total sequencing depth was about 166.16 x, the read N50 was 23.42 Kb, and the average read length was 14.74 Kb  significantly upregulated after imidacloprid treatment were selected as candidates for RNAi. The expression of these five genes of the CYP4 and CYP6 family was suppressed considerably after two days of dsRNA feeding, as shown by qPCR . Furthermore, the known down of these genes resulted in a significant increase in mortality of ompounds ,43,44. TB. tabaci MED P450 and a comprehensive picture of the B. tabaci MED P450 family based on gene identification and imidacloprid-induced expression profiling. We systematically analyzed 24 B. tabaci MED P450 genes, which provide a basis for further molecular and functional characterization of P450. The phylogenetic tree divided the B. tabaci MED P450 into four clans, of which clan 2 is dominant. Our research provides a solid foundation for future insecticide design. However, to guide the rational development of pesticide-related compounds and improve the accuracy and efficiency of computational-based drug construction, a deeper understanding of the interaction of imidacloprid-related heterocyclic compounds with P450s is required. Our study reveals various roles of B. tabaci MED P450 and provides a reference for better research on the functions of B. tabaci MED P450.The current study provides a genomic database of B. tabaci MED Lingshui population was collected from Lingshui City, Hainan Province, in January 2017. Since the collection, all populations have not been exposed to pesticides, and they are raised under laboratory conditions, without any further selection pressure, and each generation is about every 25 days. These insects were reared on the common tobacco variety NC89 for a long time in insect-proof cages .The B. tabaci samples were obtained from Lingshui, Hainan, in 2017 and identified as MED using the mtCOI gene [The experimental process is carried out according to the standard protocol provided by Pacbio, and the library construction includes six steps: (1) use g-TUBE to interrupt the DNA sample; (2) repair the damaged DNA sample; (3) terminal repair of DNA; (4) connect the dumbbell-shaped connector; (5) exonuclease digestion of nucleic acid; and (6) screening the target fragment with BluePippin to obtain the sequencing library. The present study used three strategies to predict the gene structure: de novo prediction, homologous species-based prediction, and Unigene-based prediction. De novo prediction was carried out using Genscan , AugustuCOI gene ,62. GeneB. tabaci. The phylogenetic tree was analyzed by MEGA7 NJ method and corrected for 1000 times. Then the conserved motifs of 24 P450 sequences were analyzed by MEME, and the screened P450 genes were classified according to the phylogenetic tree, and further named according to the molecular weight, and then handed over to Dr. David Nielsen (CYP Nomenclature Committee) for gene naming.In order to analyze the phylogenetic tree, P450 amino acid sequences of various insects were downloaded from NCBI, and then merged with 24 P450 amino acid sequences of Then, to express P450 under imidacloprid stress, whiteflies were treated with imidacloprid at a concentration of 50 ppm for two days, using a blank control and triplicates per treatment; approximately 100 adults were maintained per replicate.−ΔΔCT method [The RNA samples were extracted from 100 adults using TRIzol reagent , and the first-strand cDNA was prepared with the PrimeScript RT Kit , following the manufacturer’s protocol. The cDNA was used as a template for full-sequence cloning and real-time PCR (qPCR). Use (Takara) thermal cycle program for PCR amplification: 94 °C for 5 min (pre-denaturation), 94 °C for 1 min (deformation), 55 °C for 30 s, 72 °C for 40 s, then 40 cycles, and finally 72 °C for 72 °C 10 min. The amplicon was checked by agarose gel electrophoresis (1%) and recycled with SteadyPure Agarose Gel DNA Purification Kit  to ensure the accuracy of the operation. The relative expression levels of the P450 genes were calculated using the 2T method . The priUsing primers containing T7 RNA polymerase promoter, cytochrome P450 and enhanced green fluorescent protein (EGFP) genes were amplified by PCR. The PCR products were purified by Accurate Biology, and dsRNA was synthesized by using T7 RiboMAX Express RNAi kit  according to the manufacturer’s instructions. The concentration of dsRNA was determined by nano-drop spectrophotometer, and its integrity was verified by agarose gel electrophoresis (1.5%).B. tabaci MED adults in a feeding chamber (5 cm × 10 cm) according to a previously reported method [t-test and analysis of variance (ANOVA) using SPSS (v.21) , and considered significant at p < 0.05.The RNAi assay was performed by feeding dsRNA to d method . After 4