Method and device for adjusting service interaction behaviors in service interaction network and electronic equipment

Abstract

The invention discloses a method and a device for adjusting a service interaction behavior in a service interaction network, and electronic equipment. Relates to the field of artificial intelligence, and the method comprises the following steps: obtaining candidate interaction strategy data sets corresponding to a first account set and a second account set in a service interaction network, the first account set and the second account set having a service interaction behavior; simulating a service interaction behavior corresponding to the candidate interaction strategy data set by using the target model to obtain a simulation result; determining an interaction satisfaction degree corresponding to the simulation result, and determining target interaction strategy data from the candidate interaction strategy data set based on the interaction satisfaction degree; and according to the target interaction strategy data, adjusting a service interaction behavior in the service interaction network, so that the first account set and the second account set execute the adjusted service interaction behavior. Through the method and the device, the technical problem of low adjustment accuracy of the service interaction behavior in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, device and electronic device for adjusting business interaction behavior in a business interaction network. Background Art

[0002] In the related art, business interaction behaviors are adjusted by adopting static optimization models. The above-mentioned static optimization models are usually based on a limited set of information, such as historical business demand, service prices and performance indicators, to predict future demand and response. However, due to the constant changes in market dynamics, user preferences and competitive landscape, static optimization models often find it difficult to capture real-time market feedback and subtle changes in user behavior. Adjustments to business interaction behaviors may be based on outdated or inaccurate information, resulting in reduced accuracy of the adjustments. Therefore, there is still a technical problem of low accuracy in the adjustment of business interaction behaviors in the related art.

[0003] Currently, no effective solution has been proposed for the technical problem of low accuracy in adjusting business interaction behaviors in related technologies. Summary of the invention

[0004] The main purpose of the present application is to provide a method, device and electronic device for adjusting business interaction behavior in a business interaction network, so as to solve the technical problem of low accuracy in adjusting business interaction behavior in related technologies.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for adjusting business interaction behavior in a business interaction network is provided. The method includes: obtaining a candidate interaction strategy data set corresponding to a first account set and a second account set in the business interaction network, wherein there is a business interaction behavior between the first account set and the second account set, and the candidate interaction strategy data in the candidate interaction strategy data set is used to represent the behavior data generated by executing the corresponding business interaction behavior; using a target model, simulating the business interaction behavior corresponding to the candidate interaction strategy data set to obtain a simulation result, wherein the target model is used to simulate the business interaction behavior, and the competition behavior between the first user in the first account set and the competition behavior between the second user in the second account set during the execution of the business interaction behavior; determining the interaction satisfaction corresponding to the simulation result, and based on the interaction satisfaction, determining the target interaction strategy data from the candidate interaction strategy data set, wherein the interaction satisfaction is used to represent the satisfaction of the first account set and the second account set with the business interaction behavior, and the target interaction strategy data is used to represent that the execution state of the business interaction behavior is a balanced state; adjusting the business interaction behavior in the business interaction network according to the target interaction strategy data, so that the first account set and the second account set execute the adjusted business interaction behavior.

[0006] Optionally, the behavior data includes first behavior data and second behavior data, and the method further includes: obtaining, from the candidate interaction strategy set, first behavior data generated by the first account set for business interaction behavior, and obtaining second behavior data generated by the second account set for business interaction behavior, wherein the second behavior data is behavior data executed by the second account set as a result of the generation of the first behavior data; and determining an interaction satisfaction model based on the first behavior data and the second behavior data.

[0007] Optionally, the target model includes a deterministic model and a stochastic model, and the equilibrium state includes an evolutionary equilibrium state, wherein the method further includes: obtaining a deterministic model corresponding to the second account set, and solving the deterministic model to predict the evolutionary equilibrium state, wherein the deterministic model is used to represent the change of the second behavior data of the second account set over time; based on the stochastic model corresponding to the second account set, and solving the stochastic model, determining the stability probability, wherein the stochastic model is used to represent the random disturbance of the second behavior data of the second account set.

[0008] Optionally, solving the deterministic model and predicting the evolutionary equilibrium state includes: determining the net utility data of the business interaction behavior based on the interaction satisfaction model and the business price of the business in the first behavior data, wherein the net utility data is used to represent the revenue status of the business purchased by the second account set from the first account set through the business interaction behavior; solving the deterministic model based on the net utility data and predicting the evolutionary equilibrium state.

[0009] Optionally, the equilibrium state includes a Nash equilibrium state, wherein target interaction strategy data are determined from a candidate interaction strategy data set based on interaction satisfaction, including: constructing a strategy matrix corresponding to a strategy combination containing candidate interaction strategy data based on the interaction satisfaction data, wherein the strategy combination includes candidate interaction strategy data executed by a first account set under a business interaction behavior, and candidate interaction strategy data executed by a second account set; in the strategy matrix, detecting whether the candidate interaction strategy data is in a Nash equilibrium state by comparing the candidate interaction strategy data corresponding to the first account set with the candidate interaction strategy data corresponding to the second account set; and determining the candidate interaction strategy data in the Nash equilibrium state as the target interaction strategy data.

[0010] Optionally, the business interaction behavior in the business interaction network is adjusted according to the adjustment strategy corresponding to the target interaction strategy data, including: randomly selecting a first user in the first account set and a business corresponding to the first user for a second user in the second account set in the business interaction network; calculating the remuneration information of the second user in the second account set based on the business and the business price corresponding to the business; determining average remuneration information based on the remuneration information of each second user in the second account set; determining probability data corresponding to the second account set based on the average remuneration information and the remuneration information, wherein the probability data is used to represent the probability of selection opportunity for the second account set to change the business; and determining the adjustment strategy based on the probability data.

[0011] Optionally, based on the average reward information and the reward information, probability data corresponding to the second account set is determined, including: in response to the reward information being less than the average reward information, based on the average reward information and the reward information, probability data is determined; based on the probability data, an adjustment strategy is determined, including: in response to the probability data being greater than a probability threshold, a target business required by the second account set at a future time, and an adjustment strategy corresponding to the target business, wherein the adjustment strategy is used to replace the business with the target business.

[0012] In order to achieve the above objective, according to another aspect of the present application, a device for adjusting service interaction behavior in a service interaction network is provided. The device comprises: an acquisition unit, used for acquiring candidate interaction strategy data sets corresponding to a first account set and a second account set in a business interaction network, wherein there is a business interaction behavior between the first account set and the second account set, and the candidate interaction strategy data in the candidate interaction strategy data sets are used to represent the behavior data generated by executing the corresponding business interaction behavior; a simulation unit, used for simulating the business interaction behavior corresponding to the candidate interaction strategy data sets by using a target model to obtain a simulation result, wherein the target model is used to simulate the business interaction behavior, and the competition behavior between the first users in the first account set and the competition behavior between the second users in the second account set during the execution of the business interaction behavior; a determination unit, used for determining the interaction satisfaction corresponding to the simulation result, and based on the interaction satisfaction, determining the target interaction strategy data from the candidate interaction strategy data sets, wherein the interaction satisfaction is used to represent the satisfaction degree of the first account set and the second account set with the business interaction behavior, and the target interaction strategy data is used to represent that the execution state of the business interaction behavior is a balanced state; an adjustment unit, used for adjusting the business interaction behavior in the business interaction network according to the target interaction strategy data, so that the first account set and the second account set execute the adjusted business interaction behavior.

[0013] According to another aspect of the embodiment of the present application, a processor is further provided. The processor is used to run a program, wherein when the program is run by the processor, the method for adjusting the service interaction behavior in the service interaction network in the embodiment of the present application is executed.

[0014] According to another aspect of an embodiment of the present application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein when the program is running, the method for adjusting the service interaction behavior in the service interaction network in each embodiment of the present application is executed.

[0015] According to another aspect of the embodiment of the present application, a computer-readable storage medium is provided, which includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for adjusting the service interaction behavior in the service interaction network of the embodiment of the present application.

[0016] According to another aspect of an embodiment of the present application, a computer program product is further provided. The computer program product includes a computer program. When the computer program is executed by a processor, the method for adjusting service interaction behavior in a service interaction network in the embodiment of the present application is implemented.

[0017] According to another aspect of an embodiment of the present application, a computer program product is also provided, including a non-volatile computer-readable storage medium, the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method for adjusting business interaction behavior in a business interaction network in an embodiment of the present application is implemented.

[0018] According to another aspect of the embodiments of the present application, the embodiments of the present application further provide a computer program, which, when executed by a processor, implements the method for adjusting service interaction behavior in the service interaction network in the embodiments of the present application described above.

[0019] In an embodiment of the present application, if it is necessary to adjust the business interaction behavior, the candidate interaction strategy data sets corresponding to the first account set and the second account set in the business interaction network can be obtained. The target model can be used to simulate the business interaction behavior corresponding to the candidate interaction strategy data set to obtain a simulation result. The interaction satisfaction corresponding to the simulation result can be determined, and based on the interaction satisfaction, the target interaction strategy data can be determined from the candidate interaction strategy data set. The business interaction behavior in the business interaction network can be adjusted according to the target interaction strategy data so that the first account set and the second account set perform the adjusted business interaction behavior. In this embodiment, the behavior data generated by the corresponding business interaction behavior between the bank alliance (first account set) and the user (second account set) is used to simulate the business interaction behavior using an evolutionary game model (target model), and the dynamic strategy optimization and market equilibrium of the two account sets in the business interaction network are solved by an iterative algorithm, so as to achieve the purpose of accurately adjusting the business interaction behavior according to the dynamic evolution and learning effect of user behavior and market disturbance, thereby solving the technical problem of low accuracy of adjustment of business interaction behavior in related technologies, and achieving the technical effect of improving the accuracy of adjustment of business interaction behavior in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0021] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for adjusting a service interaction behavior in a service interaction network is shown;

[0022] Figure 2 is a flow chart of a method for adjusting a service interaction behavior in a service interaction network provided in an embodiment of the present application;

[0023] Figure 3 It is a schematic diagram of establishing a business collection transaction market provided in accordance with an embodiment of the present application;

[0024] Figure 4 It is a schematic diagram of an iterative process of primary user policy adjustment and an evolutionary process of secondary user policy selection provided according to an embodiment of the present application;

[0025] Figure 5 is a schematic diagram of a two-level competition provided according to an embodiment of the present application;

[0026] Figure 6 is a schematic diagram of the relationship between the number of secondary users and the opportunity to purchase different services provided according to an embodiment of the present application;

[0027] Figure 7 is a schematic diagram of a device for adjusting service interaction behavior in a service interaction network provided according to an embodiment of the present application;

[0028] Figure 8 It is a structural block diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Example 1

[0032] According to an embodiment of the present application, an embodiment of a method for adjusting business interaction behavior in a business interaction network is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing a method for adjusting service interaction behavior in a service interaction network is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor or a programmable logic device), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface, a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0034] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for adjusting the business interaction behavior in the business interaction network in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the method for adjusting the business interaction behavior in the business interaction network described above is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] The display may be, for example, a touch screen liquid crystal display that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0038] Under the above operating environment, this application provides Figure 2 A method for adjusting business interaction behavior in a business interaction network is shown. Figure 2 It is a flowchart of a method for adjusting business interaction behavior in a business interaction network provided according to an embodiment of the present application.

[0039] Step S201: Acquire a candidate interaction strategy data set corresponding to a first account set and a second account set in a business interaction network.

[0040] In step S201 of the embodiment of the present application, a candidate interaction strategy data set corresponding to the first account set and the second account set in the business interaction network can be obtained. The business interaction network can be a bank alliance business interaction network or a bank alliance-customer group business interaction network. The first account set can be a primary user, a primary user, a bank alliance, a main user, or a master user. The second account set can be a secondary user, a customer, or a secondary user. Among them, there is a business interaction behavior between the first account set and the second account set, and the candidate interaction strategy data in the candidate interaction strategy data set can be used to represent the behavior data generated by executing the corresponding business interaction behavior. For example, data on business opportunities of different sizes and sales or business transactions at different prices can be used as candidate interaction strategy data.

[0041] In this embodiment, the bank alliance business interaction network is a financial business transaction network between banks and between banks and customers. The bank alliance can share resources, data or services to provide more efficient and extensive services to customers. The bank alliance-customer group business interaction network is a business transaction network between the bank alliance and multiple customer groups. The customer groups may include different types of users, such as individuals, enterprises or institutions, who can conduct financial transactions with the bank alliance through the network. The first account set is a service provider in the business interaction network and can provide business opportunities, such as fund transactions, data sharing, etc. to the second account set. The second account set is a service consumer in the business interaction network and can select business opportunities that meet their needs for purchase or use based on the candidate interaction strategy data set provided by the first account set.

[0042] In this embodiment, in the bank alliance-customer group business interaction network, by obtaining the candidate interaction strategy data sets corresponding to the first account set and the second account set in the business interaction network, the detailed information of various business opportunities provided by primary users, including price, scale and possible market feedback, can be obtained. At the same time, the preferences and selection patterns of secondary users for the above business opportunities are obtained, which provides necessary input for building subsequent target models.

[0043] Optionally, the demand information of primary and secondary users, including preferences for business scale, price, and performance, can be collected through questionnaires, user interviews, or online feedback. It should be noted that this is only a preferred implementation method for obtaining behavioral data generated by the corresponding business interaction behaviors of primary and secondary users, and does not specifically limit the process and method of obtaining behavioral data.

[0044] Step S202: using the target model, simulating the business interaction behavior corresponding to the candidate interaction strategy data set to obtain a simulation result.

[0045] In step S202 of the embodiment of the present application, after obtaining the candidate interaction strategy data sets corresponding to the first account set and the second account set in the business interaction network, the business interaction behavior corresponding to the candidate interaction strategy data sets can be simulated using the target model to obtain a simulation result. The target model can be an evolutionary game model. The target model can be used to simulate business interaction behavior, and the competitive behavior between the first user in the first account set and the competitive behavior between the second user in the second account set during the execution of the business interaction behavior.

[0046] In this embodiment, the evolutionary game model can be used as a mathematical framework for analyzing the evolution of strategies of individuals or groups in repeated games. In a bank alliance-customer group business interaction network, the evolutionary game model can be used to simulate business interaction behaviors corresponding to the candidate interaction strategy data set, such as competition behaviors between first users and competition behaviors between second users.

[0047] In the embodiment of the present application, through the simulation of the evolutionary game model, the complexity of the strategic interaction in the business interaction network can be deeply analyzed, and the market evolution trend under different strategies can be predicted, which is of great value for optimizing business processes, improving network efficiency and user satisfaction. The simulation results can provide a theoretical basis and practical guidance for the adjustment and optimization of subsequent business interaction behaviors. Thereby solving the technical problem of low accuracy of adjustment of business interaction behaviors in related technologies, and achieving the technical effect of improving the accuracy of adjustment of business interaction behaviors in related technologies.

[0048] Step S203, determining the interaction satisfaction level corresponding to the simulation result, and determining target interaction strategy data from the candidate interaction strategy data set based on the interaction satisfaction level.

[0049] In step S203 of the embodiment of the present application, after simulating the business interaction behavior corresponding to the candidate interaction strategy data set using the target model and obtaining the simulation result, the interaction satisfaction corresponding to the simulation result can be determined, and based on the interaction satisfaction, the target interaction strategy data, that is, the interaction data in Nash equilibrium and the strategy combination that meets the requirements, can be determined from the candidate interaction strategy data set. The interaction satisfaction can be used to indicate the satisfaction of the first account set and the second account set with the business interaction behavior, and the target interaction strategy data can be used to indicate that the execution state of the business interaction behavior is an equilibrium state, such as Nash equilibrium.

[0050] In this embodiment, interaction satisfaction is the degree of satisfaction of the first account set (e.g., bank alliance) and the second account set (e.g., customer group) with the business interaction behavior. In the framework of the evolutionary game model, interaction satisfaction can be determined by evaluating the benefits of the bank alliance and the net utility of the customer group. For the bank alliance, interaction satisfaction is related to the sales volume, price and cost of the business opportunity; while for the customer group, satisfaction is based on the business quality, price and actual benefits (or utility). Nash equilibrium is a strategic state in which each user (bank alliance member or customer group member) will not unilaterally change the strategy. In the business interaction network, when the bank alliance and the customer group reach Nash equilibrium in strategy selection, it means that a stable state has been reached in the scale, price and distribution of business opportunities, and all users have accepted the current interaction conditions.

[0051] In this embodiment, determining the target interaction strategy data (i.e., interaction data in Nash equilibrium) can identify a strategy combination that enables the interaction satisfaction of bank alliance members and customer group members to reach a stable point. After identifying the interaction strategy that reaches Nash equilibrium, a strategy that can maximize the overall revenue of the bank alliance and the overall satisfaction of the customer group can be selected. The bank alliance can adjust the scale and price of its business opportunities to attract more customers and maximize revenue. The customer group can make business choices based on the strategy combination that meets the requirements to obtain a good business quality and price ratio.

[0052] Optionally, the target interaction strategy data may be determined by comparing the interaction satisfaction in different Nash equilibrium states and evaluating other factors such as stability, efficiency, and fairness. This is for illustrative purposes only and is not specifically limited here.

[0053] In the embodiment of the present application, by determining the target interaction strategy data based on interaction satisfaction, the bank alliance can optimize its business strategy to maintain stability and attractiveness in a highly competitive market while meeting the needs of the customer group. It can ensure that the resource allocation between the bank alliance and the customer group is efficient, avoiding resource waste and imbalance between supply and demand.

[0054] Step S204: adjusting the service interaction behavior in the service interaction network according to the target interaction strategy data, so that the first account set and the second account set execute the adjusted service interaction behavior.

[0055] In step S204 of the embodiment of the present application, after determining the interaction satisfaction level corresponding to the simulation result and determining the target interaction strategy data from the candidate interaction strategy data set based on the interaction satisfaction level, the business interaction behavior in the business interaction network can be adjusted according to the target interaction strategy data so that the first account set and the second account set execute the adjusted business interaction behavior.

[0056] In this embodiment, after determining the target interaction strategy data (i.e., the strategy combination that meets the requirements in Nash equilibrium), the first account set and the second account set can adjust their respective business interaction behaviors according to the target interaction strategy data. For the bank alliance, it means adjusting the scale and price of the business opportunities provided to achieve the required benefits in equilibrium. For the customer group, it means selecting and purchasing business opportunities according to the strategy combination that meets the requirements to achieve a suitable business quality and price ratio.

[0057] Optionally, the business interaction behavior in the business interaction network can be adjusted by the bank alliance members adjusting the scale of business opportunities (for example, adjusting the size of the shared spectrum) and setting the price of business transactions to ensure consistency with the target interaction strategy data. It can be that the customer group members re-evaluate their purchase decisions based on the new business opportunity scale and price to achieve their maximum utility.

[0058] In this embodiment, by adjusting the service strategy, the service interaction behavior between the bank alliance and the customer group can be made to tend to a better state and achieve Nash equilibrium, which can improve network performance, increase user satisfaction, and ensure the maximum benefit of the bank alliance.

[0059] In steps S201 to S204 of the embodiment of the present application, if the business interaction behavior needs to be adjusted, the candidate interaction strategy data sets corresponding to the first account set and the second account set in the business interaction network can be obtained. The business interaction behavior corresponding to the candidate interaction strategy data set can be simulated using the target model to obtain a simulation result. The interaction satisfaction corresponding to the simulation result can be determined, and based on the interaction satisfaction, the target interaction strategy data can be determined from the candidate interaction strategy data set. The business interaction behavior in the business interaction network can be adjusted according to the target interaction strategy data so that the first account set and the second account set perform the adjusted business interaction behavior. In this embodiment, the behavior data generated by the corresponding business interaction behaviors between the bank alliance (first account set) and the user (second account set) are used to simulate the business interaction behaviors using an evolutionary game model (target model), and an iterative algorithm is used to solve the dynamic strategy optimization and market equilibrium of the two account sets in the business interaction network, thereby achieving the purpose of accurately adjusting the business interaction behaviors according to the dynamic evolution and learning effects of user behaviors, as well as market disturbances, thereby solving the technical problem of low accuracy in adjusting the business interaction behaviors in related technologies, and achieving the technical effect of improving the accuracy of adjusting the business interaction behaviors in related technologies.

[0060] The above method of this embodiment is further introduced below.

[0061] As an optional implementation mode, the behavior data includes first behavior data and second behavior data, and the method further includes: obtaining, from a candidate interaction strategy set, first behavior data generated by a first account set for business interaction behavior, and obtaining second behavior data generated by a second account set for business interaction behavior, wherein the second behavior data is behavior data executed by the second account set as a result of the generation of the first behavior data; and determining an interaction satisfaction model based on the first behavior data and the second behavior data.

[0062] In this embodiment, the behavior data may include first behavior data and second behavior data. The first behavior data generated by the first account set for the business interaction behavior can be obtained from the candidate interaction strategy set, and the second behavior data generated by the second account set for the business interaction behavior can be obtained; the interaction satisfaction model can be determined based on the first behavior data and the second behavior data. The first behavior data can be the scale and price of the business opportunity, the size of the business opportunity, the business efficiency, the opportunity to purchase the business, etc. It reflects the active behavior of the bank alliance in the market and how to adjust its strategy according to market dynamics. The second behavior data can be the behavior data executed by the second account set due to the generation of the first behavior data, that is, the behavior of the customer group is based on the strategy and conditions of the bank alliance. For example, business selection (such as which bank's service is selected), purchase volume, payment behavior, feedback information (such as satisfaction evaluation of the service), strategy adjustment, etc. The interaction satisfaction model can be established through the benefit function.

[0063] In this embodiment, the interaction satisfaction model can use evolutionary game theory, combined with historical transaction records, user preferences, market trends and other information, to establish a model that can quantify the interaction satisfaction between the bank alliance and the customer group. The interaction satisfaction model can consider the interaction between the first behavior data and the second behavior data, that is, how the strategy of the bank alliance affects the satisfaction of the customer group, and how the response behavior of the customer group in turn affects the strategic success and benefits of the bank alliance.

[0064] Alternatively, the primary user or secondary user satisfaction can be quantified by the following logarithmic utility function (revenue function):

[0065]

[0066] Among them, b=ck; b can be used to represent the reputation policy quality (ie, reputation indicator); c can be used to represent the size of the allocated service set; k can be used to represent the service set efficiency of the service set; u1 and u2 are constants depending on the application type.

[0067] In this embodiment, constructing an interaction satisfaction model based on the first behavior data and the second behavior data can improve the strategy optimization capability of the business interaction network, promote market stability and fairness, while enhancing user satisfaction and trust, and achieving a win-win situation for the banking alliance and the customer group.

[0068] As an optional implementation mode, the target model includes a deterministic model and a stochastic model, and the equilibrium state includes an evolutionary equilibrium state, wherein the method further includes: obtaining a deterministic model corresponding to the second account set, and solving the deterministic model to predict the evolutionary equilibrium state, wherein the deterministic model is used to represent the change of the second behavior data of the second account set over time; based on the stochastic model corresponding to the second account set, and solving the stochastic model, determining the stability probability, wherein the stochastic model is used to represent the random disturbance of the second behavior data of the second account set.

[0069] In this embodiment, the target model may include a deterministic model and a stochastic model, and the equilibrium state may include an evolutionary equilibrium state. The deterministic model corresponding to the second account set may be obtained, and the deterministic model may be solved to predict the evolutionary equilibrium state, wherein the deterministic model is used to represent the change of the second behavior data of the second account set over time; the stochastic model corresponding to the second account set may be solved to determine the stability probability, wherein the stochastic model may be used to represent the random disturbance state of the second behavior data of the second account set, and may be constructed by the element Q of the Markov transition matrix.

[0070] In this embodiment, a deterministic model of the second account set is established, and the changes in the behavioral data of the second account set over time can be quantified. The deterministic model can use replicator dynamics to describe the evolution of the strategy selection of the customer group. In a dynamic evolutionary game, the strategy used by individuals in the group can be copied by other individuals through the ability to learn. The replication process of this strategy over time can be simulated by a set of differential equations, which is called replicator dynamics.

[0071] Alternatively, the dynamic definition of a replicator can be described by the following formula:

[0072]

[0073] in, The average return rate can be used to represent the average return rate of the secondary users in the group, which can be expressed by the average net utility Sure; can be used to represent net utility; the population state can be represented by the vector Indicates; σ can be used to indicate the change speed of secondary users' service selection; p i Can be used to indicate the service price of the service provided by the secondary user; j Can be used to indicate the service price of the service provided by the primary user.

[0074] in, It can be used to represent the proportion of business opportunities purchased by secondary users from primary users, which can be determined by the following formula:

[0075]

[0076] in, Can be used to represent the number of secondary users in the group who purchased business opportunities from the primary user; N (a) It can be used to represent the total number of secondary users in a group, which can be determined by the following formula:

[0077]

[0078] Here, M may be used to represent the total number of primary users who provide business opportunities.

[0079] In this embodiment, the deterministic model of the dynamic evolutionary game requires that the number of secondary users is continuous (i.e., real number). In addition, the replicator dynamics in this case do not include perturbation effects. For example, in the case of a small probability, a secondary user may choose to purchase a business opportunity with lower performance and / or higher price. This perturbation may be caused by the irrationality of the secondary user, possibly due to incorrect (noisy) reward information. In this case, a random model based on Markov chain (stochastic model) can be used to simulate the evolution of the group.

[0080] Alternatively, the state space Ψ of the Markov chain of the secondary user evolution can be described by the following formula:

[0081]

[0082] in, is a random variable that can be used to represent the number of secondary users within the group who have the opportunity to purchase business from primary users. The definition of the element Q of the transition matrix of the Markov chain can be described by the following formula:

[0083]

[0084] Wherein, ρ can be used to represent the disturbance rate. Generally speaking, the disturbance rate is relatively small (for example, ρ = 10 -4 ), the disturbance rate corresponds to the small probability that a secondary user irrationally switches from a service with a higher reward to a service with a lower reward.

[0085] In this embodiment, by solving the stochastic model, the stable probability of the customer group reaching and maintaining the evolutionary equilibrium state under the consideration of random disturbances can be determined. The stable probability can reflect the possibility that the business interaction network dynamically maintains a stable state when random disturbances exist. The steady-state probability (i.e., vector e) can be described by the following formula:

[0086]

[0087] The steady-state probability of the Markov chain can be obtained by solving eQ=0, e1=1.

[0088] Where 1 = [1,…,1] T , T can be used to represent transpose.

[0089] The average proportion of secondary users purchasing services from primary users in the group can be obtained as follows:

[0090]

[0091] Thus, we can get the evolutionary equilibrium, which can be expressed by the following formula:

[0092]

[0093] In this embodiment, the game model can ensure that it does not deviate from market dynamics, and evolutionary simulation can be performed to ensure market stability.

[0094] As an optional implementation method, the deterministic model is solved to predict the evolutionary equilibrium state, including: determining the net utility data of the business interaction behavior based on the interaction satisfaction model and the business price of the business in the first behavior data, wherein the net utility data is used to represent the revenue status of the business purchased by the second account set from the first account set through the business interaction behavior; solving the deterministic model based on the net utility data to predict the evolutionary equilibrium state.

[0095] In this embodiment, if the deterministic model is to be solved and the evolutionary equilibrium state is to be predicted, the net utility data of the business interaction behavior can be determined based on the interaction satisfaction model and the business price of the business in the first behavior data, and the deterministic model can be solved based on the net utility data to predict the evolutionary equilibrium state. The net utility data can be used to represent the revenue status of the business purchased by the second account set from the first account set through the business interaction behavior.

[0096] Alternatively, the opportunity for the secondary user to purchase services from the primary user in the secondary user's net utility may be defined as:

[0097]

[0098] Among them, b i It can be used to represent the information of the service allocated by the primary user to the secondary user (i.e., the service size multiplied by the service efficiency), p i It can be used to represent the price of a business. Assuming that all secondary users who purchase business opportunities from the same primary user are assigned to the same size of business and are charged the same price, the net utility can be defined by the following formula:

[0099]

[0100] in, Can be used to indicate the total number of secondary users in a group. i It can be used to indicate the size of the service allocated to the primary user. i Can be used to represent the set of groups that purchase business opportunities from the primary user, a∈A i ;k i It can be used to indicate the corresponding service efficiency of secondary users.

[0101] As an optional implementation method, the equilibrium state includes a Nash equilibrium state, wherein step S203, based on the interaction satisfaction, determines the target interaction strategy data from the candidate interaction strategy data set, including: based on the interaction satisfaction data, constructing a strategy matrix corresponding to the strategy combination containing the candidate interaction strategy data, wherein the strategy combination includes the candidate interaction strategy data executed by the first account set under the business interaction behavior, and the candidate interaction strategy data executed by the second account set; in the strategy matrix, by comparing the candidate interaction strategy data corresponding to the first account set with the candidate interaction strategy data corresponding to the second account set, detecting whether the candidate interaction strategy data is in a Nash equilibrium state; and determining the candidate interaction strategy data in the Nash equilibrium state as the target interaction strategy data.

[0102] In this embodiment, the equilibrium state may include a Nash equilibrium state. In the process of determining the target interaction strategy data from the candidate interaction strategy data set based on the interaction satisfaction, a strategy matrix corresponding to the strategy combination containing the candidate interaction strategy data may be constructed based on the interaction satisfaction data; in the strategy matrix, by comparing the candidate interaction strategy data corresponding to the first account set with the candidate interaction strategy data corresponding to the second account set, it may be detected whether the candidate interaction strategy data is in a Nash equilibrium state; the candidate interaction strategy data in the Nash equilibrium state may be determined as the target interaction strategy data. The strategy matrix may be a Jacob matrix; the strategy combination may include the candidate interaction strategy data executed by the first account set under the business interaction behavior, and the candidate interaction strategy data executed by the second account set.

[0103] Alternatively, in the case of two primary users and two groups of secondary users, the evolutionary equilibrium can be solved as follows:

[0104]

[0105] The items on the left side and the right side of the equal sign can be used to represent the net utility of the secondary user purchasing the fund interaction services from the two primary users. It can be expressed by the following formula:

[0106]

[0107] in,

[0108] The stability of evolutionary equilibrium can be analyzed using the following Jacob matrix:

[0109]

[0110] in,

[0111]

[0112]

[0113] in,

[0114]

[0115] The two eigenvalues ​​of J can be obtained from It is determined that if two eigenvalues ​​have negative real parts, then the evolutionary equilibrium is stable.

[0116] As an optional implementation method, step S204 adjusts the business interaction behavior in the business interaction network according to the adjustment strategy corresponding to the target interaction strategy data, including: randomly selecting the first user in the first account set and the business corresponding to the first user for the second user in the second account set in the business interaction network; calculating the remuneration information of the second user in the second account set based on the business and the business price corresponding to the business; determining the average remuneration information based on the remuneration information of each second user in the second account set; determining the probability data corresponding to the second account set based on the average remuneration information and the remuneration information, wherein the probability data is used to represent the probability of the selection opportunity of the second account set to change the business; and determining the adjustment strategy based on the probability data.

[0117] In this embodiment, in the process of adjusting the service interaction behavior in the service interaction network according to the adjustment strategy corresponding to the target interaction strategy data, the first user in the first account set and the service corresponding to the first user can be randomly selected from the service interaction network for the second user in the second account set; the remuneration information of the second user in the second account set can be calculated based on the service and the service price corresponding to the service; the average remuneration information can be determined based on the remuneration information of each second user in the second account set; the probability data corresponding to the second account set can be determined based on the average remuneration information and the remuneration information; the adjustment strategy can be determined based on the probability data. The probability data can be used to indicate the probability of selecting a chance to change the service of the second account set.

[0118] In this embodiment, a secondary user randomly selects a service i. For a given scale of allocated services and service prices, each secondary user u can Formula for calculating compensation The above information about remuneration can be maintained by each secondary user. After the secondary user receives the remuneration information of all secondary users in the same group, the average remuneration information can be determined by the following formula:

[0119]

[0120] If a sub-user's return is lower than the average return of the same group of sub-users (i.e., ), then the probability that the secondary user randomly changes the service selection opportunity is

[0121] Optionally, an evolutionary balance may be achieved through an iterative algorithm of a game model.

[0122] As an optional implementation method, based on the average reward information and the reward information, probability data corresponding to the second account set is determined, including: in response to the reward information being less than the average reward information, based on the average reward information and the reward information, probability data is determined; based on the probability data, an adjustment strategy is determined, including: in response to the probability data being greater than a probability threshold, a target business required by the second account set at a future time, and an adjustment strategy corresponding to the target business, wherein the adjustment strategy is used to replace the business with the target business.

[0123] In this embodiment, in the process of determining the probability data corresponding to the second account set based on the average remuneration information and the remuneration information, if the remuneration information is less than the average remuneration information, the probability data can be determined based on the average remuneration information and the remuneration information. In the process of determining the adjustment strategy based on the probability data, the target business required by the second account set at a future time can be determined when the probability data is greater than the probability threshold, and the adjustment strategy corresponding to the target business can be determined. The adjustment strategy can be used to replace the business with the target business, for example, choosing to stop purchasing the business from the current primary user and turn to other primary users who may provide better quality or more reasonable prices.

[0124] Optionally, if the reward information is less than the average reward information, that is, the reward of a secondary user is lower than the average reward of the same group of secondary users (i.e., ), then the probability of the secondary user randomly changing the service selection opportunity can be If the secondary user decides to change the service selection, then the service j provided by the primary user is selected (for j≠i and ).

[0125] Optionally, after determining the adjustment strategy, secondary users can take corresponding measures, such as changing business suppliers, to seek business opportunities that provide better returns in terms of business size, price and quality, until the user's individual reward is close to or equal to the average reward of the group, at which point the user's strategy adjustment tends to stabilize and the system reaches an evolutionary equilibrium state.

[0126] In this embodiment, by implementing a mechanism based on average remuneration information and individual remuneration information adjustment strategy, the dynamic optimization and balance of the business interaction network can be effectively promoted. This mechanism not only enhances the adaptability of secondary users to market changes, but also promotes information transparency and fair competition between bank alliances and customer groups, which helps to improve overall market efficiency and user satisfaction. In addition, by continuously adjusting strategies, secondary users can use resources more effectively and avoid excessive concentration or waste of business opportunities, thereby promoting the long-term stability and healthy development of the business interaction network.

[0127] In an embodiment of the present application, if it is necessary to adjust the business interaction behavior, the candidate interaction strategy data sets corresponding to the first account set and the second account set in the business interaction network can be obtained. The target model can be used to simulate the business interaction behavior corresponding to the candidate interaction strategy data set to obtain a simulation result. The interaction satisfaction corresponding to the simulation result can be determined, and based on the interaction satisfaction, the target interaction strategy data can be determined from the candidate interaction strategy data set. The business interaction behavior in the business interaction network can be adjusted according to the target interaction strategy data so that the first account set and the second account set perform the adjusted business interaction behavior. In this embodiment, the behavior data generated by the corresponding business interaction behavior between the bank alliance (first account set) and the user (second account set) is used to simulate the business interaction behavior using an evolutionary game model (target model), and the dynamic strategy optimization and market equilibrium of the two account sets in the business interaction network are solved by an iterative algorithm, so as to achieve the purpose of accurately adjusting the business interaction behavior according to the dynamic evolution and learning effect of user behavior and market disturbance, thereby solving the technical problem of low accuracy of adjustment of business interaction behavior in related technologies, and achieving the technical effect of improving the accuracy of adjustment of business interaction behavior in related technologies.

[0128] The technical solution of the embodiment of the present application is illustrated below in conjunction with preferred implementation modes.

[0129] In related technologies, due to the simplification of the user demand response model, it is assumed that all users respond to the service in a homogeneous manner, ignoring individual differences and dynamic learning effects of users. For example, it is assumed that users select services based only on a single indicator of price or performance, without considering that users may make decisions based on multiple factors such as their personal experience, trust, and market cognition. This simplified demand response model cannot accurately reflect the real behavior of users, thereby affecting the accuracy of the adjustment of business interaction behavior. Since a sound information feedback mechanism has not been established, service providers can learn from user behavior and adjust their strategies. In the absence of effective feedback, service providers may not be able to understand changes in user satisfaction, quality perception, or price sensitivity in a timely manner, resulting in blindness and lag in strategy adjustment. Not only does it reduce the accuracy of business interaction adjustment, it may also lead to waste or mismatch of resources.

[0130] The embodiment of the present application considers the problem of multiple bank alliances with business interactions (i.e., major banks) conducting financial business transactions with multiple customers (i.e., secondary customers) who need to conduct financial business docking. Among them, secondary customers can adjust their purchasing behavior by observing the changes in the cost value required for the business (i.e., by observing the changes in the price and quality of the services provided by different primary banks or service providers, and secondary customers can adjust their business purchasing behavior (i.e., evolutionary strategy behavior). The primary bank alliance entity or the primary service provider can adjust their behavior of selling business opportunities to secondary customers to achieve higher utility. Therefore, a game model of a bank alliance-customer group business interaction network is proposed, and the evolution and dynamic behavior of secondary customers are modeled through the theory of evolutionary games. In order to simulate the competition between primary users, an iterative convergence algorithm is formulated, and the solution is determined when the convergence equilibrium is reached.

[0131] Figure 3 is a schematic diagram of establishing a business collection transaction market according to an embodiment of the present application, such as Figure 3 As shown, the primary users (primary users) include primary user 1, primary user 2, ..., primary user M. Among them, the service set owned by primary user 1 can be expressed as F1, the service set owned by primary user 2 can be expressed as F2, and the service set owned by primary user M can be expressed as F M , the service quality can be expressed as C i. A primary user can sell part of the available service set to secondary users who are willing to purchase the service set opportunity, which can be secondary user 1 (group 1), group 2, group 3, etc. In this case, the primary user can broadcast the availability of service set opportunities and the price to be paid for obtaining the service set to the secondary users through a dedicated control reputation policy behavior. If a secondary user wants to purchase the service set opportunity from the primary user, a request message can be sent to the primary user through the control reputation policy behavior. After receiving the request message, the primary user allocates the time slot to the secondary user. It is assumed that secondary users in the same group can communicate with each other, for example, to exchange information about available service set opportunities. This communication can be achieved by using a central controller (such as a base station or an access point of the secondary user) or by broadcasting messages on a dedicated control reputation policy behavior. However, the above-mentioned secondary users will make independent decisions to purchase service set opportunities. As a result, a multi-seller, multi-buyer service set trading market is established, with the sellers and buyers corresponding to the primary user and the secondary user respectively.

[0132] Figure 4 is a schematic diagram of an iterative process of adjusting a primary user policy and an evolutionary process of selecting a secondary user policy according to an embodiment of the present application, such as Figure 4 As shown in Figure 2, as time goes by, the iteration of primary users and the evolution of secondary users continue. If there are A secondary user groups, and N (a) It can represent the number of secondary users in the group. In this application, the superscript (a) can represent a variable related to the secondary user group, and a and the subscript i can represent the primary user. These groups can correspond to user groups in different geographical areas. In this market, the business interaction between the primary user (primary user, such as the bank alliance entity) and the secondary user (secondary user, such as the customer group) is achieved through the buying and selling of "business pool opportunities". The business pool opportunities that the primary user can provide to the market c i , total available service quality C i , and the service price p charged to secondary users i .

[0133] Figure 5 is a schematic diagram of a two-level competition provided according to an embodiment of the present application, such as Figure 5As shown, there are two levels of competition in the service set trading market with multiple sellers and multiple buyers: primary user price competition (non-cooperative game) and secondary user spectrum selection (evolutionary game). The primary user price competition allocates the price and size of the spectrum to the secondary user spectrum selection, and the secondary user spectrum selection selects spectrum opportunities from the primary user to the primary user price competition. Specifically, the primary user price competition sells the service set opportunities to the secondary users. If the service set size provided by a primary user is small and / or the service set price is high, the secondary user will prefer to purchase the service set opportunities from other primary users. Therefore, each primary user must carefully allocate the service set size shared with the secondary user and set the price to maximize the return of each primary user. In addition, the solution must be stable, that is, the primary user will not unilaterally change its strategy. Secondary user spectrum selection is a competition among multiple secondary users to select service set opportunities provided by different primary users (i.e., service set selection). In this case, if many secondary users choose to purchase the service set opportunities provided by the same primary user, the corresponding service set will become crowded, which may lead to an increase in the service set price and / or a decrease in performance. Therefore, secondary users will evolve (i.e., churn) to purchase lower-priced and / or better-performing business set opportunities. When the return of a secondary user becomes the same as the average return of the group to which the secondary user belongs, the secondary user's strategic behavior will stop evolving.

[0134] In the embodiment of the present application, the primary or secondary user satisfaction can be quantified by the following logarithmic utility function (revenue function):

[0135]

[0136] Among them, b=ck; b can be used to represent the reputation policy quality (ie, reputation indicator); c can be used to represent the size of the allocated service set; k can be used to represent the service set efficiency of the service set; u1 and u2 are constants depending on the application type.

[0137] Since the function is concave, it can represent that user satisfaction reaches saturation as the transmission rate increases. Since the parameters u1 and u2 are adjustable, flexibility is provided, and the benefit function can be adapted to different types of user application experience data.

[0138] In the embodiments of the present application, the behavior of secondary users and the factors that influence the decision of secondary users to purchase business opportunities from primary users are described. A deterministic and stochastic dynamic evolutionary game model of secondary users is proposed, and the solution of the model, i.e., evolutionary equilibrium, is obtained. In addition, an iterative algorithm for achieving this evolutionary equilibrium is also proposed for secondary users.

[0139] Secondary users arranged in multiple group categories are assumed to have the following behaviors: (1) A secondary user chooses a primary user to purchase a business opportunity from, thereby obtaining higher satisfaction (i.e., net utility) in terms of performance and price. (2) Secondary users learn and gradually change their decisions on business selection (e.g., due to lack of information about the scale of business provided by the primary user, the price of the business, and the decisions of other secondary users). (3) Secondary users do not intend to influence the decisions of other secondary users in the future. By observing the payoffs of different strategies adopted by other users to purchase different business opportunities, a secondary user will change his decision on business selection without considering the impact on other secondary users.

[0140] The above behavior of secondary users is modeled by using dynamic evolutionary game theory with multiple populations. The evolutionary game was originally developed to describe the behavior of biological strategy replication. It is also used to simulate the behavior of humans in society and entities in market environments. In an evolutionary game, players are grouped into groups. A group corresponds to a group of secondary users in a specific location. A set of strategies associated with each player in the group is a decision.

[0141] The opportunity for a secondary user to purchase services from a primary user in the secondary user's net utility can be defined as:

[0142]

[0143] Among them, b i It can be used to represent the information of the service allocated by the primary user to the secondary user (i.e., the service size multiplied by the service efficiency), p i It can be used to represent the price of a business. Assuming that all secondary users who purchase business opportunities from the same primary user are assigned to the same size of business and are charged the same price, the net utility can be defined by the following formula:

[0144]

[0145] in, Can be used to indicate the total number of secondary users in a group. i It can be used to indicate the size of the service allocated to the primary user. i Can be used to represent the set of groups that purchase business opportunities from the primary user, a∈A i ;k i It can be used to represent the corresponding business efficiency of secondary users. In dynamic evolutionary games, the strategies used by individuals in a group can be copied by other individuals through learning capabilities. This strategy replication process over time can be simulated by a set of differential equations, which is called replicator dynamics. Replicator dynamics can be derived separately for each population (i.e., secondary user group).

[0146] The dynamic definition of a replica can be described by the following formula:

[0147]

[0148] in, The average return rate can be used to represent the average return rate of the secondary users in the group, which can be expressed by the average net utility Sure; can be used to represent net utility; the population state can be represented by the vector Indicates; σ can be used to indicate the change speed of secondary users' service selection; p i Can be used to indicate the service price of the service provided by the secondary user; j Can be used to indicate the service price of the service provided by the primary user.

[0149] in, It can be used to represent the proportion of business opportunities purchased by secondary users from primary users, which can be determined by the following formula:

[0150]

[0151] Can be used to represent the number of secondary users in the group who purchased business opportunities from the primary user; N (a) It can be used to represent the total number of secondary users in a group, which can be determined by the following formula:

[0152]

[0153] Here, M may be used to represent the total number of primary users who provide business opportunities.

[0154] The deterministic model of the dynamic evolutionary game requires that the number of secondary users is continuous (i.e., real). In addition, the replicator dynamics in this case do not include perturbation effects. For example, with a small probability, a secondary user may choose to purchase a business opportunity with lower performance and / or higher price. Such perturbations may arise due to the irrationality of the secondary users, perhaps due to incorrect (noisy) reward information. In this case, a stochastic model based on Markov chains (stochasticity model) can be used to simulate the evolution of the population.

[0155] Alternatively, the state space of the Markov chain of secondary user evolution can be described by the following formula:

[0156]

[0157] in, is a random variable that can be used to represent the number of secondary users within the group who have the opportunity to purchase business from primary users. The definition of the element Q of the transition matrix of the Markov chain can be described by the following formula:

[0158]

[0159] Where ρ can be used to represent the disturbance rate; generally speaking, the disturbance rate is relatively small (for example, ρ = 10 -4 ), the disturbance rate corresponds to the small probability that a secondary user irrationally switches from a service with a higher reward to a service with a lower reward.

[0160] In this embodiment, by solving the stochastic model, the stable probability of the customer group reaching and maintaining the evolutionary equilibrium state under the consideration of random disturbances can be determined. The stable probability can reflect the possibility that the business interaction network dynamically maintains a stable state when random disturbances exist. The steady-state probability (i.e., vector e) can be described by the following formula:

[0161]

[0162] The steady-state probability of the Markov chain can be obtained by solving eQ=0, e1=1.

[0163] Where 1 = [1,…,1] T , T can be used to represent transpose.

[0164] The average proportion of secondary users purchasing services from primary users in the group can be obtained

[0165] Thus, we can get the evolutionary equilibrium, which can be expressed by the following formula:

[0166]

[0167] Alternatively, in the case of two primary users (M=2) and two groups of secondary users (A=2), the evolutionary equilibrium can obtain the solution of the following equation:

[0168]

[0169] The items on the left side and the right side of the equal sign can be used to represent the net utility of the secondary user purchasing the fund interaction services from the two primary users. It can be expressed by the following formula:

[0170]

[0171] in,

[0172] The stability of evolutionary equilibrium can be analyzed using the following Jacob matrix:

[0173]

[0174] in,

[0175]

[0176]

[0177] in,

[0178]

[0179] The two eigenvalues ​​of J can be obtained from It is determined that if two eigenvalues ​​have negative real parts, then the evolutionary equilibrium is stable.

[0180] To achieve evolutionary balance, this application proposes an evolutionary algorithm for each secondary user. In this algorithm, it is assumed that the average reward of the secondary users in a group is what each secondary user in the group can get. The evolutionary algorithm can be described as follows:

[0181] A secondary user randomly selects a service i. For a given scale of assigned services and service prices, each secondary user u calculates the reward The above formula can be used The above information about remuneration can be maintained by each secondary user. After the secondary user receives the remuneration information of all secondary users in the same group, the average remuneration information can be determined by the following formula:

[0182]

[0183] If the reward information is less than the average reward information, that is, the reward of a sub-user is lower than the average reward of the same group of sub-users (i.e., ), then the probability of the secondary user randomly changing the service selection opportunity can be If the secondary user decides to change the service selection, then the service j provided by the primary user is selected (for j≠i and ).

[0184] Figure 6 is a schematic diagram of the relationship between the number of secondary users and the opportunity to purchase different services provided according to an embodiment of the present application, such as Figure 6 As shown, the fitness of the evolutionary equilibrium in terms of the number of secondary users in all groups (i.e. ) to purchase opportunities for different services. Although the service price offered by primary user 2 is fixed (i.e., p2=0.1), the service price of primary user 1 is variable. When the total number of secondary users in group 2 increases, the number of secondary users that select available service opportunities increases linearly. When secondary users observe that the transmission performance of a service has degraded, some secondary users will tend to purchase service opportunities that offer better performance. This performance degradation may be due to the smaller scale of service opportunities offered when many secondary users purchase service opportunities from the same primary user. In addition, when the service price of one group of service opportunities is much higher (e.g., p1=0.3) than another group (e.g., p2=0.1), more secondary users will purchase the cheaper service opportunities (i.e., ), even if there are multiple solutions (i.e., evolutionary equilibrium), for and There are multiple solutions (i.e., evolutionary equilibria), but there is a single solution if and

[0185] Example 2

[0186] The embodiment of the present application also provides a device for adjusting the service interaction behavior in a service interaction network. It should be noted that the device for adjusting the service interaction behavior in the service interaction network in the embodiment of the present application can be used to execute the method for adjusting the service interaction behavior in the service interaction network provided in the embodiment of the present application. The device for adjusting the service interaction behavior in the service interaction network provided in the embodiment of the present application is introduced below.

[0187] According to an embodiment of the present application, a device for implementing the method for adjusting service interaction behavior in the above service interaction network is also provided. Figure 7 is a schematic diagram of a device for adjusting service interaction behavior in a service interaction network according to an embodiment of the present application, such as Figure 7 As shown, the device 700 for adjusting the service interaction behavior includes: an acquisition unit 702 , a simulation unit 704 , a determination unit 706 and an adjustment unit 708 .

[0188] The acquisition unit 702 is used to acquire a candidate interaction strategy data set corresponding to a first account set and a second account set in a business interaction network, wherein there is a business interaction behavior between the first account set and the second account set, and the candidate interaction strategy data in the candidate interaction strategy data set is used to represent the behavior data generated by executing the corresponding business interaction behavior.

[0189] The simulation unit 704 is used to use the target model to simulate the business interaction behavior corresponding to the candidate interaction strategy data set to obtain a simulation result, wherein the target model is used to simulate the business interaction behavior, and the competition behavior between the first users in the first account set and the competition behavior between the second users in the second account set during the execution of the business interaction behavior.

[0190] The determination unit 706 is used to determine the interaction satisfaction level corresponding to the simulation result, and based on the interaction satisfaction level, determine the target interaction strategy data from the candidate interaction strategy data set, wherein the interaction satisfaction level is used to indicate the satisfaction level of the first account set and the second account set with the business interaction behavior, and the target interaction strategy data is used to indicate that the execution state of the business interaction behavior is a balanced state.

[0191] The adjustment unit 708 is configured to adjust the service interaction behavior in the service interaction network according to the target interaction strategy data, so that the first account set and the second account set execute the adjusted service interaction behavior.

[0192] The device for adjusting business interaction behavior in a business interaction network provided by the embodiment of the present application obtains a candidate interaction strategy data set corresponding to a first account set and a second account set in the business interaction network through an acquisition unit 702, wherein business interaction behavior exists between the first account set and the second account set, and the candidate interaction strategy data in the candidate interaction strategy data set is used to represent the behavior data generated by executing the corresponding business interaction behavior; the business interaction behavior corresponding to the candidate interaction strategy data set is simulated by a simulation unit 704 using a target model to obtain a simulation result, wherein the target model is used to simulate the business interaction behavior, and the competition behavior between the first users in the first account set and the competition behavior between the second users in the second account set during the execution of the business interaction behavior; the interaction satisfaction corresponding to the simulation result is determined by a determination unit 706, and based on the interaction satisfaction, the target interaction strategy data is determined from the candidate interaction strategy data set, wherein the interaction satisfaction is used to represent the satisfaction degree of the first account set and the second account set with the business interaction behavior, and the target interaction strategy data is used to represent that the execution state of the business interaction behavior is a balanced state; the business interaction behavior in the business interaction network is adjusted according to the target interaction strategy data by an adjustment unit 708, so that the first account set and the second account set execute the adjusted business interaction behavior. Thereby, the technical problem of low accuracy in adjusting business interaction behaviors in related technologies is solved, and the technical effect of improving the accuracy in adjusting business interaction behaviors in related technologies is achieved.

[0193] It should be noted that the acquisition unit 702, simulation unit 704, determination unit 706 and adjustment unit 708 correspond to steps S201 to S204 in the embodiment, and the four units and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules or units can be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the above modules can also be part of the device and can be run in the computer terminal 10 provided in Example 1.

[0194] Example 3

[0195] An embodiment of the present application may provide an electronic device, Figure 8 is a structural block diagram of an electronic device provided according to an embodiment of the present application. Figure 8 As shown, the electronic device 800 may include: one or more ( Figure 8 (only one is shown) processor 802, memory 804, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0196] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0197] The processor can call the information and application programs stored in the memory through the transmission device to execute the method for adjusting the service interaction behavior in the service interaction network.

[0198] It can be understood by those skilled in the art that Figure 8 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 8 The structure of the electronic device is not limited. Figure 8More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 8 Different configurations are shown.

[0199] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0200] Example 4

[0201] According to an embodiment of the present application, a processor is further provided, the processor being used to run a program, wherein when the program is run by the processor, the method for adjusting a service interaction behavior in a service interaction network in the embodiment is executed.

[0202] According to an embodiment of the present application, a storage medium is further provided. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the method for adjusting the service interaction behavior in the service interaction network provided in the above embodiment 1.

[0203] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0204] According to an embodiment of the present application, a computer program product is also provided, which, when executed on a data processing device, is suitable for executing the program steps of the method for adjusting service interaction behavior in a service interaction network.

[0205] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0206] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0207] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0208] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0209] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0210] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0211] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for adjusting service interaction behavior in a service interaction network, characterized in that: include: Acquire a candidate interaction strategy data set corresponding to a first account set and a second account set in a business interaction network, wherein there is a business interaction behavior between the first account set and the second account set, and the candidate interaction strategy data in the candidate interaction strategy data set is used to represent behavior data generated by executing the corresponding business interaction behavior; Using the target model, simulating the business interaction behavior corresponding to the candidate interaction strategy data set to obtain a simulation result, wherein the target model is used to simulate the business interaction behavior, and the competition behavior between the first accounts in the first account set and the competition behavior between the second accounts in the second account set during the execution of the business interaction behavior; Determine the interaction satisfaction degree corresponding to the simulation result, and determine the target interaction strategy data from the candidate interaction strategy data set based on the interaction satisfaction degree, wherein the interaction satisfaction degree is used to indicate the satisfaction degree of the first account set and the second account set with the business interaction behavior, and the target interaction strategy data is used to indicate that the execution state of the business interaction behavior is a balanced state; The service interaction behavior in the service interaction network is adjusted according to the target interaction strategy data, so that the first account set and the second account set execute the adjusted service interaction behavior.

2. The method according to claim 1, characterized in that The behavior data includes first behavior data and second behavior data, and the method further includes: From the candidate interaction strategy set, obtaining the first behavior data generated by the first account set for the business interaction behavior, and obtaining the second behavior data generated by the second account set for the business interaction behavior, wherein the second behavior data is behavior data executed by the second account set as a result of the generation of the first behavior data; An interaction satisfaction model is determined based on the first behavior data and the second behavior data.

3. The method according to claim 2, characterized in that The target model includes a deterministic model and a stochastic model, the equilibrium state includes an evolutionary equilibrium state, wherein the method further includes: Acquire the deterministic model corresponding to the second account set, and solve the deterministic model to predict the evolutionary equilibrium state, wherein the deterministic model is used to represent the change of the second behavior data of the second account set over time; Based on a randomness model corresponding to the second account set, the randomness model is solved to determine a stable probability, wherein the randomness model is used to represent a random disturbance condition of the second behavior data of the second account set.

4. The method according to claim 3, characterized in that Solving the deterministic model to predict the evolutionary equilibrium state includes: Determine net utility data of the business interaction behavior based on the interaction satisfaction model and the business price of the business in the first behavior data, wherein the net utility data is used to represent the revenue status of the business purchased by the second account set from the first account set through the business interaction behavior; The deterministic model is solved based on the net utility data to predict the evolutionary equilibrium state.

5. The method according to claim 1, characterized in that The equilibrium state includes a Nash equilibrium state, wherein, based on the interaction satisfaction, target interaction strategy data is determined from the candidate interaction strategy data set, including: Based on the interaction satisfaction data, construct a strategy matrix corresponding to a strategy combination including the candidate interaction strategy data, wherein the strategy combination includes the candidate interaction strategy data executed by the first account set under the business interaction behavior, and the candidate interaction strategy data executed by the second account set; In the strategy matrix, by comparing the candidate interaction strategy data corresponding to the first account set with the candidate interaction strategy data corresponding to the second account set, detecting whether the candidate interaction strategy data is in the Nash equilibrium state; The candidate interaction strategy data in the Nash equilibrium state is determined as the target interaction strategy data.

6. The method according to claim 1, characterized in that Adjusting the service interaction behavior in the service interaction network according to the adjustment strategy corresponding to the target interaction strategy data includes: randomly selecting the first user in the first account set and the service corresponding to the first user for the second user in the second account set from the service interaction network; Calculating remuneration information of the second user in the second account set based on the service and the service price corresponding to the service; determining average remuneration information based on the remuneration information of each of the second users in the second account set; Determine probability data corresponding to the second account set based on the average remuneration information and the remuneration information, wherein the probability data is used to indicate a probability of selection opportunity of the second account set changing the business; Based on the probability data, the adjustment strategy is determined.

7. The method according to claim 6, characterized in that Determining probability data corresponding to the second account set based on the average reward information and the reward information includes: In response to the reward information being less than the average reward information, determining the probability data based on the average reward information and the reward information; Determining the adjustment strategy based on the probability data includes: In response to the probability data being greater than a probability threshold, a target service required by the second account set at a future time is determined, and the adjustment strategy corresponding to the target service is determined, wherein the adjustment strategy is used to replace the service with the target service.

8. A device for adjusting service interaction behavior in a service interaction network, characterized in that: include: an acquisition unit, configured to acquire a candidate interaction strategy data set corresponding to a first account set and a second account set in a business interaction network, wherein there is a business interaction behavior between the first account set and the second account set, and the candidate interaction strategy data in the candidate interaction strategy data set is used to represent behavior data generated by executing the corresponding business interaction behavior; a simulation unit, configured to simulate the business interaction behavior corresponding to the candidate interaction strategy data set using a target model to obtain a simulation result, wherein the target model is used to simulate the business interaction behavior, and during the execution of the business interaction behavior, the competition behavior between the first users in the first account set, and the competition behavior between the second users in the second account set; a determination unit, configured to determine an interaction satisfaction degree corresponding to the simulation result, and determine target interaction strategy data from the candidate interaction strategy data set based on the interaction satisfaction degree, wherein the interaction satisfaction degree is used to indicate the satisfaction degree of the first account set and the second account set with the business interaction behavior, and the target interaction strategy data is used to indicate that the execution state of the business interaction behavior is a balanced state; An adjustment unit is used to adjust the service interaction behavior in the service interaction network according to the target interaction strategy data, so that the first account set and the second account set execute the adjusted service interaction behavior.

9. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 7 when running.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

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