Network chain double-structure multi-body mutualism evolution analysis method

Abstract

The invention discloses a network chain double-structure multi-body mutualism evolution analysis method, which comprises the following steps: constructing a multi-index system to determine the weight of each index, and further obtaining the population scale of a value co-creation unit according to the weight; on the basis of obtaining the population scale of the value co-creation unit, realizing symbiotic coefficient measurement and calculation by combining a relational expression between an intermediate parameter in the converted multi-subject symbiotic evolution model constructed by the multi-subject symbiotic evolution model and a symbiotic coefficient in the multi-subject symbiotic evolution model; and determining a symbiotic mode among the value co-creation units in the current ecological subsystem according to the obtained symbiotic coefficient. Furthermore, through the obtained symbiotic mode, the stable state of the ecological subsystem in long-term operation is analyzed in combination with numerical simulation, and conditions and support are provided for guiding the optimal advancing direction of the ecological subsystem.

Description

Technical Field

[0001] The present invention relates to a network-chain dual-structure multi-agent mutually beneficial symbiotic evolution analysis method, belonging to the field of intelligent manufacturing. Background Art

[0002] With the interactive integration of distributed manufacturing's chain and mesh "dual structures," vertical linear collaboration systems limited to a single upstream and downstream are constrained in terms of security and stability. Various entities are realizing that by absorbing information from third-party cloud platforms, using value as the primary driver, and adopting vertical linear collaboration within a mesh value chain and horizontal collaboration between multiple parallel single-chain value chains, they can achieve dynamic cooperation among entities in different regions and build a digital ecological resource system to achieve sustainable competitive advantage. As an ecological resource system characterized by "chain structure" and "mesh structure" and focusing on building "complementarity and diversity," the healthy and stable evolution of this system is crucial to the value flow of various entities in the digital ecosystem.

[0003] In the mutually beneficial and symbiotic evolutionary ecosystem of multiple subjects in the automotive network chain, there are various value stakeholders such as suppliers, manufacturers, and dealers. The third-party platform is an effective medium for resource and information transmission in the integration activities of the automotive industry network chain. Therefore, it is very important to map "enterprise-resource flow-industry" and "population-value co-creation-ecology" to each other and study the changes in complex characteristics such as symbiotic patterns, evolutionary mechanisms, ecological individuals and ecological populations in digital ecological models.

[0004] Existing analytical methods are mostly targeted at traditional single-chain models of parts supply and fail to consider the symbiotic dynamics between populations in reality, making them difficult to apply to cross-chain and multi-chain value network ecosystems. For such complex ecosystems, the symbiotic dynamics between various units within the system, the future evolutionary direction of each unit, the maximum scale of each unit's growth when reaching stability, and how to guide the development of these symbiotic units cannot be applied to existing ecosystem analysis methods.

[0005] The present invention is proposed for this purpose. Summary of the Invention

[0006] The present invention provides a method for analyzing the mutually beneficial symbiotic evolution of a dual-structure network chain with multiple agents. By combining the symbiotic evolution model of the ecological subsystem, an evolutionary analysis is performed on the ecological subsystems in the mutually beneficial symbiotic evolution ecosystem of the automobile network chain with multiple agents to be analyzed, so as to realize the classification of the symbiotic patterns of the ecological subsystems.

[0007] The technical solution of the present invention is:

[0008] According to a first aspect of the present invention, a method for analyzing the evolution of a network-chain dual-structure multi-agent mutually beneficial symbiosis is provided, comprising the following steps:

[0009] Based on the extended Logistic model, a symbiotic evolution model of ecological subsystems in the multi-agent mutually beneficial symbiotic evolution ecosystem of the automotive network chain is established;

[0010] Simplify the symbiotic evolution model of ecological subsystems and establish a multi-agent symbiotic evolution model;

[0011] The ecological subsystem in the multi-agent mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain to be analyzed is regarded as a composite system S = {s k ,(k=1,2,...,K)},s k Represents the kth value co-creation unit; each value co-creation unit has I order parameter dimensions; each order parameter contains A i order parameter component indicators; take the order parameter component indicators of K value co-creation units obtained at time t as a sample, and construct a data set based on the samples obtained in T consecutive time periods; normalize the order parameter component indicators of each sample in the data set and calculate the order parameter component indicator weight at time t; calculate the comprehensive weighted score of the kth value co-creation unit at time t based on the normalized order parameter component indicators and the order parameter component indicator weights at time t, and record it as the population size of the kth value co-creation unit at time t;

[0012] The multi-agent symbiotic evolution model is transformed to obtain a transformed multi-agent symbiotic evolution model; based on the transformed multi-agent symbiotic evolution model and the multi-agent symbiotic evolution model, a relationship between an intermediate parameter in the transformed multi-agent symbiotic evolution model and a symbiotic coefficient in the multi-agent symbiotic evolution model is established; the transformed symbiotic evolution model is changed to obtain a grey mapping symbiotic model differential equation group; based on the obtained population size, the intermediate parameters in the grey mapping symbiotic model differential equation group are solved;

[0013] Substitute the intermediate parameters obtained by the solution into the relationship to obtain the symbiosis coefficient; establish a corresponding relationship between the value range of the symbiosis coefficient and the symbiosis pattern; and determine the symbiosis pattern between the value co-creation units in the current ecological subsystem based on the obtained symbiosis coefficient.

[0014] Furthermore, the symbiotic evolution model of the ecological subsystem is simplified to establish a multi-agent symbiotic evolution model of three value co-creation units; wherein, the three value co-creation units include at least suppliers and manufacturers, or at least manufacturers and distributors.

[0015] Furthermore, the order parameters include resource symbiosis order parameters, collaborative symbiosis order parameters, service symbiosis order parameters, and platform symbiosis order parameters. The order parameter component indicators include positive indicators and negative indicators. The order parameter component indicators of each sample in the data set are normalized, and the expression is: in, It represents the normalized value of the jth order parameter component index under the ith order parameter of the kth value co-creation unit at time t; is the index of the order parameter component at time t upper and lower limits.

[0016] Furthermore, the comprehensive weighted score x of the kth value co-creation unit at time t is calculated based on the normalized order parameter component indicators and order parameter component indicator weights at time t. k (t), the expression is:

[0017]

[0018]

[0019] in, represents the weighted score of the i-th order parameter of the k-th value co-creation unit at time t; is the weight of the i-th order parameter at time t; It represents the weight of the j-th order parameter component index under the i-th order parameter at time t.

[0020] Furthermore, based on the transformed multi-agent symbiotic evolution model and the multi-agent symbiotic evolution model, a relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model is established, as follows:

[0021]

[0022] Among them, α 12 , α 13 They represent the symbiotic effect coefficient of value co-creation unit 1 on value co-creation unit 2 and the symbiotic coefficient of value co-creation unit 1 on value co-creation unit 3, β 21 , β 23 They represent the symbiotic coefficient of value co-creation unit 2 to value co-creation unit 1 and the symbiotic coefficient of value co-creation unit 2 to value co-creation unit 3, respectively. 31 ,θ 32 They represent the symbiotic coefficient of value co-creation unit 3 to value co-creation unit 1 and the symbiotic coefficient of value co-creation unit 3 to value co-creation unit 2 respectively; r1, r2, and r3 represent the natural growth rates of the population sizes of the three value co-creation units; N1, N2, and N3 represent the maximum values ​​of the three population sizes; k 12 、k 13 、k 21 、k 23 、k 31 、k 32 is the intermediate parameter.

[0023] Furthermore, based on the mapping relationship between grey derivatives and even logarithms, the transformed symbiotic evolution model is changed to obtain a grey mapping symbiotic model differential equation group; according to the obtained population size, the least squares method is used to solve the intermediate parameters in the grey mapping symbiotic model differential equation group.

[0024] According to a second aspect of the present invention, a network chain dual-structure multi-agent mutually beneficial symbiotic evolution analysis device is provided, comprising a module of any one of the network chain dual-structure multi-agent mutually beneficial symbiotic evolution analysis methods described above.

[0025] According to the third aspect of the present invention, an evolution analysis device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the network-chain dual-structure multi-agent mutually beneficial symbiotic evolution analysis method as described in any one of the above items are implemented.

[0026] The beneficial effects of the present invention are as follows: the present invention fully considers the characteristics of different value co-creation units, constructs a multi-indicator system to determine the weight of each indicator, and then obtains the population size of the value co-creation unit based on the weight; on the basis of obtaining the population size of the value co-creation unit, the symbiosis coefficient is calculated by combining the intermediate parameters in the transformed multi-agent symbiosis evolution model constructed by combining the multi-agent symbiosis evolution model and the relationship between the symbiosis coefficient in the multi-agent symbiosis evolution model; and then, based on the obtained symbiosis coefficient, the symbiosis pattern between the value co-creation units in the current ecological subsystem is determined. Furthermore, through the obtained symbiosis pattern, combined with numerical simulation analysis of the stable state of the ecological subsystem in long-term operation, conditions and support are provided for guiding the optimal direction of the ecological subsystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the present invention.

[0028] Figure 2 This is a simulation analysis diagram of independent coexistence and symbiotic evolution.

[0029] Figure 3 This is a simulation diagram of competitive symbiotic evolution; Figure 3 (a) is the normal competition symbiotic evolution simulation, Figure 3 (b) is the simulation of vicious competition symbiotic evolution.

[0030] Figure 4 This is a simulation diagram of the evolution of parasitic symbiosis; Figure 4 (a) Evolutionary simulation where any supplier benefits from development while the manufacturer suffers from development losses, Figure 4 (b) Evolutionary simulation where the first type of suppliers benefit from development while the second type of suppliers suffer from development.

[0031] Figure 5 For the simulation of partial symbiotic evolution, Figure 5 (a) Evolution simulation for any supplier's development benefiting from it while the manufacturer's development has no impact, Figure 5 (b) Evolutionary simulation where the development of the first type of suppliers benefits while the development of the second type of suppliers has no impact.

[0032] Figure 6 This is a simulation diagram of the evolution of mutualism. Figure 6 (a) is the simulation of symmetric mutualistic evolution, Figure 6 (b) Simulation of asymmetric mutualistic evolution.

[0033] Figure 7 This is a simulation diagram of the mutualistic evolution with an increasing coefficient. Figure 7 (a) is the simulation of symmetric mutualism evolution with increasing coefficients, Figure 7 (b) Simulation of asymmetric mutualistic evolution with increasing coefficient. DETAILED DESCRIPTION

[0034] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other in any way.

[0035] Example 1: Figure 1-Figure 7 As shown, according to a first aspect of an embodiment of the present invention, a method for analyzing the evolution of a network-chain dual-structure multi-agent mutually beneficial symbiosis is provided, which can be performed by an evolution analysis device, that is, one or more processors in the evolution analysis device execute the following steps 1 to 7, specifically including the following steps:

[0036] Step 1: Based on the extended logistic model, establish the symbiotic evolution model of each ecological subsystem in the multi-agent mutually beneficial symbiotic evolution ecosystem of the automotive network chain; the ecological subsystem includes the first ecological subsystem constructed by suppliers and manufacturers, and the second ecological subsystem constructed by manufacturers and dealers.

[0037] Specifically:

[0038] The equation of the extended logistic model is: N(t) is the population size at time t, r is the natural growth rate of the population, The consumption of limited resources hinders the growth of its own population size, N mis the maximum environmental capacity of the population under certain conditions; under the initial conditions N(t0) = N0 and parameters (r, N m >0) is known, N(t) is uniquely determined, and the change in population size represents the growth of each value co-creation unit; N0 represents the initial population size. The extended logistic model is used to describe the symbiotic evolution model of the interaction between the value co-creation units of E-type suppliers sup, F-type manufacturers mf, and H-type distributors ser based on the third-party service platform:

[0039]

[0040] The first set of equations is the impact between Class E suppliers and Class F manufacturers in the ecological subsystem of Class E suppliers and Class F manufacturers. The second set of equations is the impact between Class F manufacturers and Class H dealers in the ecological subsystem of Class F manufacturers and Class H dealers. e 、u f Represents the natural growth rate of suppliers and manufacturers in the E-category supplier and F-category manufacturer ecosystems respectively; y v 、z h are the natural growth rates of manufacturers and dealers in the F-type manufacturer and H-type dealer ecological subsystems respectively; sup e 、mf f are the unit sizes of suppliers and manufacturers in the E-category supplier and F-category manufacturer ecosystems, respectively; mf v 、ser h are the unit sizes of manufacturers and dealers in the F-category manufacturer and H-category dealer ecological subsystems, respectively; Respectively represent the maximum population size of the e-th and g-th supplier and f-th manufacturer units in the E-th supplier and F-th manufacturer ecological subsystems; They represent the maximum population size of the vth and pth manufacturers and hth dealer units in the F-type manufacturer and H-type dealer ecological subsystems respectively; in the E-type supplier and F-type manufacturer ecological subsystems, They represent the symbiosis coefficient of the e-th supplier to the f-th manufacturer and the symbiosis coefficient of the f-th manufacturer to the e-th supplier, respectively. represents the symbiosis coefficient of the g-th supplier to the e-th supplier, They represent the impact of various manufacturers on the size of the e-th category supplier population, and the impact of other categories of suppliers except e on the size of the e-th category supplier population, Indicates the impact of various suppliers on the size of the f-type manufacturer population; in the F-type manufacturer and H-type dealer ecological subsystem, They represent the symbiotic coefficient of the vth type manufacturer to the hth type dealer, and the symbiotic coefficient of the hth type dealer to the vth type manufacturer, represents the symbiosis coefficient of the p-th manufacturer to the v-th manufacturer, They represent the impact of various types of distributors on the size of the v-th manufacturer population, and the impact of other types of manufacturers except v-th manufacturer on the size of the v-th manufacturer population, Represents the impact of various manufacturers on the size of the h-th type of dealer population.

[0041] Step 2: Simplify the symbiotic evolution model of ecological subsystems and establish a multi-agent symbiotic evolution model.

[0042] For example:

[0043] For the mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain, most of the ecosystems are network chain systems between three value co-creation units. Therefore, the value co-creation units involved in the symbiotic evolution model of the ecological subsystem can be simplified, and a multi-agent symbiotic evolution model with three value co-creation units is established as follows:

[0044]

[0045] The above formula can represent the multi-agent symbiotic evolution model between the three value co-creation units in any ecological subsystem (any ecological subsystem is the first ecological subsystem and the second ecological subsystem); X 10 、X 20 、X 30 They represent the initial population sizes of the three value co-creation units; r1, r2, and r3 represent the natural growth rates of the population sizes of the three value co-creation units; x 1, x 2, x3 is the population size of each value co-creation unit; N1, N2, and N3 represent the maximum values ​​of the three population sizes; is the Logistic coefficient, which indicates that the consumption of limited resources by the three value co-creation units has a hindering effect on the growth of their own population size; α 12 , α 13 They represent the symbiotic effect coefficient of value co-creation unit 1 on value co-creation unit 2 and the symbiotic coefficient of value co-creation unit 1 on value co-creation unit 3, β 21 , β 23 They represent the symbiotic coefficient of value co-creation unit 2 to value co-creation unit 1 and the symbiotic coefficient of value co-creation unit 2 to value co-creation unit 3, respectively. 31 ,θ 32 They respectively represent the symbiosis coefficient of value co-creation unit 3 to value co-creation unit 1 and the symbiosis coefficient of value co-creation unit 3 to value co-creation unit 2.

[0046] Step 3: Based on the collaborative thinking of complex systems, the ecological subsystems in the multi-agent mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain to be analyzed are regarded as a composite system S = {sk ,(k=1,2,...,K)},s k represents the kth value co-creation unit, K represents the number of value co-creation units; each value co-creation unit has I order parameter dimensions, and the i-th order parameter of the kth value co-creation unit is recorded as w ki ; Each order parameter contains A i Order parameter component index, at time t, the jth order parameter component index under the ith order parameter of the kth value co-creation unit is marked as Among them, t=1,2,...,T; i=1,2,...,I; j=1,2,...,A i ; The order parameters include resource symbiosis order parameters, collaborative symbiosis order parameters, service symbiosis order parameters, and platform symbiosis order parameters, and the order parameter component indicators include positive indicators and negative indicators; the order parameter component indicators of the K value co-creation units obtained at time t are taken as a sample, and a data set is constructed based on the samples obtained in T consecutive time periods; further, the Euclidean distance outlier detection method can be used to delete abnormal data from the samples in the data set; and then the mean value checking and filling method is used to fill in the missing data.

[0047] Step 4: Normalize the order parameter component indicators of each sample in the data set: in, It represents the normalized value of the jth order parameter component index under the ith order parameter of the kth value co-creation unit at time t; is the index of the order parameter component at time t upper and lower limits; if the order parameter component is a positive indicator, the larger the index value, the higher the order of the value co-creation unit; if the order parameter component is a negative indicator, the larger the index value, the lower the order of the value co-creation unit.

[0048] Step 5: Calculate the order parameter component index weight at time t, the expression is: By introducing equal time series weights, we can obtain the weights of each order parameter component index, which can be expressed as follows: in, represents the weight of the jth order parameter component index under the i-th order parameter at time t, When t The proportion of all value co-creation units under the jth order parameter component index under the i-th order parameter, is the entropy value of the jth order parameter component under the i-th order parameter at time t, is the difference degree of the jth order parameter component under the i-th order parameter at time t; λ ij It represents the weight of the jth order parameter component index under the i-th order parameter.

[0049] Step 6: At time t, for the kth value co-creation unit s k The weighted score of the i-th order parameter The comprehensive weighted score of the kth value co-creation unit at time t is Denote as the population size of the kth value co-creation unit at time t; where, is the weight of the i-th order parameter at time t,

[0050] Step 7: Transform the multi-agent symbiotic evolution model constructed by multiple value co-creation units to obtain a transformed multi-agent symbiotic evolution model; establish a relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model based on the transformed multi-agent symbiotic evolution model and the multi-agent symbiotic evolution model; based on the mapping relationship between grey derivatives and even logarithms, change the transformed symbiotic evolution model to obtain a grey mapping symbiotic model differential equation group; use the least squares criterion to solve the intermediate parameters in the grey mapping symbiotic model differential equation group; substitute the solved intermediate parameters into the relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model to obtain the symbiotic coefficient.

[0051] For example, the multi-agent symbiotic evolution model constructed by the three value co-creation units is transformed to obtain the transformed multi-agent symbiotic evolution model, which is expressed as follows:

[0052]

[0053] Among them, k 10 、k 11 、k 12 、k 13 、k 20 、k 21 、k 22 、k 23 、k 30 、k 31 、k 32 、k 33 is the intermediate parameter (k 10 、k 11 、k 12 、k 13 Corresponding to the intermediate parameter of the first value co-creation unit, k 20 、k 21 、k 22 、k 23 Corresponding to the intermediate parameter of the second value co-creation unit, k 30 、k 31 、k 32 、k33 Corresponding to the intermediate parameter of the third value co-creation unit), x k ={x 1, x 2, x3} is the population size of the kth value co-creation unit;

[0054] According to the transformed multi-agent symbiotic evolution model and the multi-agent symbiotic evolution model, a relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model is established, which is specifically as follows:

[0055]

[0056] Based on the mapping relationship between grey derivatives and even logarithms, the transformed symbiotic evolution model is changed to obtain the grey mapping symbiotic model differential equations as shown below:

[0057]

[0058] In the formula, x1(t+1), x2(t+1), and x3(t+1) are the population sizes of the 1st, 2nd, and 3rd value co-creation units at t+1, respectively, and the same applies to the others.

[0059] The least squares method is used to solve the intermediate parameter k in the difference equations of the grey mapping symbiosis model. 10 、k 11 、k 12 、k 13 、k 20 、k 21 、k 22 、k 23 、k 30 、k 31 、k 32 、k 33 ; Substitute the intermediate parameters into the "relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model" to obtain the symbiotic coefficient α 12 , α 13 , β 21 , β 23 ,θ 31 ,θ 32 .

[0060] Step 8: Establish the corresponding relationship between the range of symbiosis coefficient values ​​and the symbiosis model. Based on the obtained symbiosis coefficient, determine the symbiosis model between each value co-creation unit in the current ecological subsystem as the actual symbiosis model.

[0061] Furthermore, based on the multi-agent symbiotic evolution model, the symbiotic evolution of the ecological subsystem under ideal conditions is studied to obtain the ideal symbiotic pattern between the value co-creation units in the ecological subsystem; based on the ideal symbiotic pattern, the weight adjustment of the order parameter components in the data set is guided as the direction for optimizing the ecological subsystem in the mutually beneficial symbiotic evolution ecosystem of the multi-agent automotive network chain to be analyzed.

[0062] In the above, the ideal symbiotic model between the value co-creation units in the ecological subsystem is determined as follows:

[0063] Based on the multi-agent symbiotic evolution model, let We get eight equilibrium points of the three value co-creation units, including: E1(0,0,0), E2(N1,0,0), E3(0,N2,0), E4(0,0,N3), The equilibrium point E1(0,0,0) where (0,0,0) represents x 1, x 2, The value of x3 is 0, and the same applies to the others;

[0064] Based on the multi-agent symbiotic evolution model, the Jacobian matrix of the multi-agent symbiotic evolution model is established to analyze the asymptotic stability of the model. The Jacobian matrix is:

[0065]

[0066] Based on the Jacobian matrix, the eigenvalues ​​of the Jacobian matrix are calculated for different equilibrium points. If all eigenvalues ​​of the Jacobian matrix for different equilibrium points are negative, the equilibrium point is stable; otherwise, the equilibrium point is unstable.

[0067] Based on the eight equilibrium points of the symbiotic evolution of the ecological subsystem, the equilibrium points and stable conditions for the coexistence of the three types of value units in the ecological subsystem are determined.

[0068] Based on the multi-agent symbiotic evolution model, simulation analysis is used to obtain the evolutionary process between the value co-creation units corresponding to different symbiotic patterns under different symbiotic coefficients, and the maximum population size when the ecological subsystem reaches a stable state; the symbiotic pattern when the ecological subsystem reaches the maximum population size of the stable state is recorded as the ideal symbiotic pattern.

[0069] The ideal symbiotic model is used to guide the weight adjustment of the order parameter components in the dataset, specifically:

[0070] According to the corresponding relationship between the symbiosis coefficient and the symbiosis mode, the value range of the symbiosis coefficient under the ideal symbiosis mode is determined;

[0071] Based on the range of the symbiosis coefficient under the ideal symbiosis model, the optimization objective function is established: minimize-(total contribution -penalty-weight penalty );in, , total contribution is the sum of the contributions of all ordered parameter components of the three types of value co-creation units to the symbiosis coefficient. Penalty is the penalty for symbiosis coefficients that are not within the range of the symbiosis coefficient under the ideal symbiosis model (for example, when the ideal symbiosis model is a mutually beneficial symbiosis model, the symbiosis coefficient values ​​are all greater than 0. For symbiosis coefficients that are not within the range of the symbiosis coefficient under the ideal symbiosis model, that is, the symbiosis coefficient values ​​are less than or equal to 0, the penalty is 6 times the absolute value of the symbiosis coefficient that is less than or equal to 0 plus a penalty term). penalty It is a penalty for weight imbalance;

[0072] Based on the optimization objective function, the weight constraints of the order parameter components in the data set are established:

[0073] When the actual symbiosis pattern is different from the ideal symbiosis pattern, the SLSQP method is used to solve the optimization objective function.

[0074] According to a second aspect of an embodiment of the present invention, a network chain dual-structure multi-agent mutually beneficial symbiotic evolution analysis device is provided, comprising an establishment module for simplifying the ecological subsystem symbiotic evolution model and establishing a multi-agent symbiotic evolution model; a calculation module for treating the ecological subsystem in the automobile network chain multi-agent mutually beneficial symbiotic evolution ecosystem to be analyzed as a composite system S = {s k ,(k=1,2,...,K)},s k Represents the kth value co-creation unit; each value co-creation unit has I order parameter dimensions; each order parameter contains A iorder parameter component indicators; take the order parameter component indicators of K value co-creation units obtained at time t as a sample, and construct a data set based on the samples obtained in T consecutive time periods; normalize the order parameter component indicators of each sample in the data set and calculate the order parameter component indicator weight at time t; calculate the comprehensive weighted score of the kth value co-creation unit at time t based on the normalized order parameter component indicators and the order parameter component indicator weights at time t, and record it as the population size of the kth value co-creation unit at time t; obtain a module for transforming the multi-agent symbiotic evolution model to obtain the transformed multi-agent symbiotic evolution model; based on the transformed multi-agent The symbiotic evolution model and the multi-agent symbiotic evolution model are used to establish a relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model; the transformed symbiotic evolution model is changed to obtain a gray mapping symbiotic model differential equation group; based on the obtained population size, the intermediate parameters in the gray mapping symbiotic model differential equation group are solved; the intermediate parameters obtained by the solution are substituted into the relationship to obtain the symbiotic coefficient; a determination module is used to establish a corresponding relationship between the range of symbiotic coefficient values ​​and the symbiotic pattern; based on the obtained symbiotic coefficient, the symbiotic pattern between each value co-creation unit in the current ecological subsystem is determined.

[0075] Example 2:

[0076] Taking the ecological subsystem composed of two types of suppliers and one type of vehicle manufacturer in a third-party cloud platform as an example, the evolutionary analysis method of a network chain dual-structure multi-agent mutually beneficial symbiosis is described as follows:

[0077] Step (1): Based on the extended Logistic model, establish an ecological subsystem symbiotic evolution model of two types of suppliers and one type of manufacturer in the multi-agent mutually beneficial symbiotic evolution ecosystem of the automotive network chain. Simplify the ecological subsystem symbiotic evolution model and establish a multi-agent symbiotic evolution model of three value co-creation units as follows:

[0078]

[0079] Among them, r1, r2, and r3 represent the natural growth rates of the population sizes of the three value co-creation units, namely, the first type of supply, the second type of supplier, and the manufacturer. 1, x 2, x3 is the current population size of each value co-creation unit. N1, N2, and N3 represent the maximum values ​​of the three population sizes. 10 、X 20 、X 30 They represent the initial population sizes of the three value co-creation units respectively. is the Logistic coefficient, which indicates that the consumption of limited resources by the three value co-creation units has a hindering effect on the growth of their own population size. 12 , α 13 They represent the symbiosis coefficient of the first-category supplier to the second-category supplier and the symbiosis coefficient of the first-category supplier to the manufacturer; β 21 , β 23 They represent the symbiosis coefficient of the second-category supplier to the first-category supplier and the symbiosis coefficient of the second-category supplier to the manufacturer; θ 31 ,θ 32 They represent the symbiosis coefficient of the manufacturer to the first category of suppliers and the symbiosis coefficient of the manufacturer to the second category of suppliers respectively.

[0080] Step (2): Consider the ecological subsystem in the multi-agent mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain to be analyzed as a composite system S = {s k ,(k=1,2,...,3)},s k represents the kth value co-creation unit; each value co-creation unit has 4 order parameter dimensions, and the i-th order parameter of the kth value co-creation unit is denoted as w ki ; Each order parameter contains A i Order parameter component index, at time t, the jth order parameter component index under the ith order parameter of the kth value co-creation unit is marked as The order parameters include resource symbiosis order parameter, collaborative symbiosis order parameter, service symbiosis order parameter, and platform symbiosis order parameter, totaling four order parameter components. The order parameter component indicators include positive indicators and negative indicators; specifically, as shown in Table 1:

[0081] Table 1

[0082]

[0083] Step (3): Normalize the order parameter component indicators of each sample in the data set; calculate the order parameter component indicator weights at time t based on the normalized values; introduce equal time series weights to obtain the weights of each order parameter component indicator (extract the data set of three types of value co-creation units on the third-party platform, and obtain the weights of each order parameter component indicator: "Accessory type" 0.123650, "Number of transfer stations owned" 0.162274, "Distance between enterprises" 0.120159, "Enterprise inventory similarity" 0.118874, "Collaborative process interruption rate" 0.114423, "Order processing time" 0.130995, "Satisfaction level" 0.112716, "Actual service application volume" 0.116909, that is, the initial weights); Based on the order parameter component indicator weights at time t, obtain the comprehensive weighted score of the kth value co-creation unit at time t Denote the population size of the kth value co-creation unit at time t. Table 2 below shows some examples.

[0084] Table 2 Population size of value co-creation units

[0085]

[0086] Step (4): transform the multi-agent symbiotic evolution model constructed by multiple value co-creation units to obtain a transformed multi-agent symbiotic evolution model; based on the transformed multi-agent symbiotic evolution model and the multi-agent symbiotic evolution model, establish a relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model; based on the mapping relationship between grey derivatives and even logarithms, change the transformed symbiotic evolution model to obtain a grey mapping symbiotic model differential equation group; use the least squares criterion to solve the intermediate parameters in the grey mapping symbiotic model differential equation group; substitute the solved intermediate parameters into the relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model to obtain the symbiotic coefficient.

[0087] The specific symbiosis coefficient is: 12 =-0.314284, α 13 =-0.209988, β 21 =-0.349021, β 23 =-1.236370, θ 31 =-0.946506, θ 32 =-1.056576.

[0088] Step (5): Establish the corresponding relationship between the range of symbiosis coefficient values ​​and the symbiosis model, as shown in Table 3. Based on the obtained symbiosis coefficient, determine the symbiosis model between each value co-creation unit in the current ecological subsystem as the actual symbiosis model. The symbiosis coefficient between two value co-creation units is called the same-group symbiosis coefficient.

[0089] Table 3

[0090]

[0091] Based on α 12 =-0.314284, α 13 =-0.209988, β 21 =-0.349021, β 23 =-1.236370, θ 31 =-0.946506, θ 32=-1.056576, we can see that at this time, the competition between the two types of suppliers is in a normal mode, the competition between type 1 suppliers and manufacturers is in a normal mode, and the competition between type 2 suppliers and manufacturers is in a vicious mode.

[0092] Step (6): Further, based on the multi-agent symbiotic evolution model, the symbiotic evolution of the ecological subsystem is studied under ideal conditions to obtain the ideal symbiotic model between the value co-creation units in the ecological subsystem; based on the ideal symbiotic model, the weight adjustment of the order parameter components in the data set is guided as the direction for optimizing the ecological subsystem in the mutually beneficial symbiotic evolution ecosystem of the automotive network chain multi-agent to be analyzed.

[0093] Specifically:

[0094] Based on the multi-agent symbiotic evolution model, let It can obtain eight equilibrium points of three types of value co-creation entities, including:

[0095] E1(0,0,00;

[0096] E2(N1,0,0);

[0097] E3(0,N2,0);

[0098] E4(0,0,N3);

[0099]

[0100] Based on the Jacobian matrix, calculate the stability of each equilibrium point. If all eigenvalues ​​of the Jacobian matrix are negative, the equilibrium point tends to be stable; otherwise, the equilibrium point is unstable. The Jacobian matrix is:

[0101]

[0102] The eight equilibrium points and their stability analysis are shown in Table 4:

[0103] Table 4

[0104]

[0105] Considering the complexity of the calculation after the E8 equilibrium point is brought into the matrix, the eigenvalue calculation formula is: So we can calculate the determinant det(J), trace tr(J), quadratic trace tr(J 2 ) derive the relevant conditions, namely det(J)>0, tr(J)<0.

[0106] Based on the 8 equilibrium points and their stability conditions, it is concluded that only E8(x1 * ,x2 * ,x3* ) is the conclusion of the equilibrium point where the three types of value co-creation units coexist.

[0107] Furthermore, based on the multi-agent symbiotic evolution model, the evolutionary process between the value co-creation units corresponding to different symbiotic patterns under different symbiotic coefficients and the maximum population size when the ecological subsystem reaches a stable state are obtained through simulation analysis; the symbiotic pattern when the ecological subsystem reaches the maximum population size of the stable state is recorded as the ideal symbiotic pattern.

[0108] In the simulation analysis, the natural growth rates of the value co-creation unit populations for Category 1 supplier x1, Category 2 supplier x2, and manufacturer x3 are assumed to be 0.15, 0.2, and 0.2, respectively; all have an initial population size of 150. Given limited resources, the maximum population size of each unit is 1200, and the evolution period T is 1000. PeakX1 represents the peak value of the Category 1 supplier population during the symbiotic evolution simulation, PeakX2 represents the peak value of the Category 2 supplier population during the symbiotic evolution simulation, and PeakX3 represents the peak value of the manufacturer population during the symbiotic evolution simulation.

[0109] like Figure 2 As shown in the figure, when the value co-creation units in the multi-agent mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain are in independent coexistence, there is no symbiotic effect between the three types of value co-creation units, and the development speed is only affected by its own intrinsic growth rate. At this time, α 12 =α 13 =β 21 =β 23 =θ 31 =θ 32 = 0. After a period of time, the three types of value co-creation units are in a stable state and reach the maximum population size for independent development, with the maximum population size of each being 1200.

[0110] like Figure 3 As shown in (a), when the value co-creation subjects in the multi-subject mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain are in normal competition, at this time -1<α 12 <0,-1<β 21 <0, the population peaks were 673.16, 712.58, and 879.04 respectively. Among them, the second type of suppliers showed growth in the short term, but after the peak, there was a slight decline and then stabilized. The final stable scale of the three types of value co-creation units was smaller than the stable scale of 1200 when they developed independently, that is, the three types of value co-creation units did not receive the best development. Figure 3 As shown in (b), when the value co-creation entities are in vicious competition, α 12 <-1,β 21<-1. Although the two types of suppliers can achieve population growth in a short period of time under a vicious competition environment, the two types of value co-creation units will eventually destroy each other.

[0111] like Figure 4 As shown in the figure, when the value co-creation subjects in the multi-subject mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain are in a parasitic symbiotic mode, at this time, among any two types of value co-creation units, any value co-creation unit shows that it hopes to benefit from the other type of unit, that is, any one type of development benefits and the other type of development is damaged. Figure 4 (a) indicates that any supplier's development benefits while the manufacturer's development suffers, Figure 4 (b) indicates that the first type of suppliers benefit from development, while the second type of suppliers suffer. In a parasitic symbiotic model, as resources are utilized, the parasitized unit's resources will briefly grow and then gradually deplete, while the parasitic unit's resources will accumulate. At this point, the final stable size of the parasitized unit will be smaller than the population size during independent coexistence, while the final stable size of the parasitic unit will be larger than the population size during independent coexistence.

[0112] like Figure 5 As shown in the figure, when the value co-creation subjects in the multi-subject mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain are in a partial symbiotic mode, at this time, among any two types of value co-creation units, the symbiotic coefficient of any one type is 0, and the symbiotic coefficient of the other type is greater than 0. Figure 5 (a) Indicates that any supplier's development benefits, while the manufacturer's development has no impact, Figure 5 (b) indicates that the development of the first type of suppliers benefits, while the development of the second type of suppliers has no impact. The benefiting value co-creation units, because they have access to resources from other units, have their own population growth exceeding the upper limit of their independent period population size. The unbeneficial units, because their symbiosis coefficients are not zero, have their own population growth at the same scale as during their independent period, constrained only by their own growth rate.

[0113] like Figure 6 、 7 As shown in the figure, when the value co-creation subjects in the multi-subject mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain are in a mutually beneficial symbiotic mode, the symbiotic coefficients of any two types of value co-creation units are greater than 0, and the final stable population size of the three types of value co-creation units is greater than the population size when they coexist independently. The final equilibrium point is E8(x1 * ,x2 * ,x3 * ).like Figure 6 (a) and 7(a), when the symbiosis coefficients have the same value, the symbiosis mode is a symmetrical mutualistic symbiosis mode; Figure 6 (b) and 7(b), the symbiosis coefficients are different at this time, and the symbiosis mode is an asymmetric mutualistic symbiosis mode. Figure 6(a), 6(b), 7(a), the symbiosis coefficients of the three types of value co-creation units gradually become the largest, and the larger the final stable population size, the more the three types of value co-creation units benefit from the collaboration. Figure 7 As shown in (b), the value co-creation unit at this time cannot ultimately reach a stable state.

[0114] like Figure 2-Figure 7 According to the simulation results under different symbiotic modes, it can be concluded that under the mutually beneficial symbiotic mode, the final stable scale of each value co-creation unit is larger than the population size during independent coexistence, and the population size is the largest, which is the optimal direction of the network chain multi-agent mutually beneficial symbiotic evolution model based on the extended Logistic model.

[0115] It should be noted that the change in the population size of each value co-creation unit represents its growth process. A larger population size indicates a higher share of system resources and higher value generated, and vice versa. When the population size approaches zero during the evolution of a value co-creation unit, it indicates that the unit is close to extinction.

[0116] Step (7): In the mutually beneficial symbiotic model, the expression of the symbiotic coefficient is that the symbiotic coefficient is positive, and the larger the final stable population size is, the more symbiotic characteristics the objective function is constructed. The optimal weights are solved by the SLSQP method: "Accessory type" 0.215483, "Number of transfer stations owned" 0.050000, "Distance between enterprises" 0.050000, "Enterprise inventory similarity" 0.409516, "Collaborative process interruption rate" 0.125000, "Order processing time" 0.050000, "Satisfaction level" 0.050000, "Actual service application volume" 0.050000. Comparing the initial weights of step (3), it can be seen that at this time, the three types of value co-creation units are realizing a mutually beneficial symbiotic model in the network chain multi-subject mutually beneficial symbiotic evolutionary ecosystem. The most important thing to pay attention to is the changes in "Accessory type", "Enterprise inventory similarity" and "Collaborative process interruption rate" to achieve the optimal direction of the ecosystem.

[0117] The specific embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

Claims

1. A method for analyzing the evolution of network-chain dual-structure multi-agent mutualistic symbiosis, characterized by: The following steps are involved: Based on the extended Logistic model, a symbiotic evolution model of ecological subsystems in the multi-agent mutually beneficial symbiotic evolution ecosystem of the automotive network chain is established; Simplify the symbiotic evolution model of ecological subsystems and establish a multi-agent symbiotic evolution model; The ecological subsystem in the multi-agent mutually beneficial symbiotic evolutionary ecosystem of the automotive network chain to be analyzed is regarded as a composite system S = {s k ,(k=1,2,...,K)},s k Represents the kth value co-creation unit; each value co-creation unit has I order parameter dimensions; each order parameter contains A i order parameter component indicators; take the order parameter component indicators of K value co-creation units obtained at time t as a sample, and construct a data set based on the samples obtained in T consecutive time periods; normalize the order parameter component indicators of each sample in the data set and calculate the weight of the order parameter component indicators at time t; calculate the comprehensive weighted score of the kth value co-creation unit at time t based on the normalized order parameter component indicators and the order parameter component indicator weights at time t, and record it as the population size of the kth value co-creation unit at time t; The multi-agent symbiotic evolution model is transformed to obtain a transformed multi-agent symbiotic evolution model; based on the transformed multi-agent symbiotic evolution model and the multi-agent symbiotic evolution model, a relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model is established; the transformed symbiotic evolution model is changed to obtain a grey mapping symbiotic model differential equation group; based on the obtained population size, the intermediate parameters in the grey mapping symbiotic model differential equation group are solved; the solved intermediate parameters are substituted into the relationship to obtain the symbiotic coefficient; Establish the corresponding relationship between the range of symbiosis coefficient values ​​and the symbiosis pattern; based on the obtained symbiosis coefficient, determine the symbiosis pattern between each value co-creation unit in the current ecological subsystem.

2. The method for analyzing the mutualistic symbiosis evolution of a network chain dual structure and multiple agents according to claim 1 is characterized in that: The symbiotic evolution model of the ecological subsystem is simplified to establish a multi-agent symbiotic evolution model of three value co-creation units; wherein, the three value co-creation units include at least suppliers and manufacturers, or at least manufacturers and distributors.

3. The method for analyzing the mutualistic symbiosis evolution of a network chain dual structure and multiple agents according to claim 1 is characterized in that: The order parameters include resource symbiosis order parameters, collaborative symbiosis order parameters, service symbiosis order parameters, and platform symbiosis order parameters. The order parameter component indicators include positive indicators and negative indicators. The order parameter component indicators of each sample in the data set are normalized, and the expression is: in, It represents the normalized value of the jth order parameter component index under the ith order parameter of the kth value co-creation unit at time t; is the index of the order parameter component at time t upper and lower limits.

4. The method for analyzing the evolution of network chain dual-structure multi-agent mutualistic symbiosis according to claim 1 is characterized in that: According to the normalized order parameter component index and order parameter component index weight at time t, the comprehensive weighted score x of the kth value co-creation unit at time t is calculated. k (t), the expression is: in, represents the weighted score of the i-th order parameter of the k-th value co-creation unit at time t; is the weight of the i-th order parameter at time t; It represents the weight of the j-th order parameter component index under the i-th order parameter at time t.

5. The method for analyzing the mutualistic symbiosis evolution of a network chain dual structure and multiple agents according to claim 2 is characterized in that: According to the transformed multi-agent symbiotic evolution model and the multi-agent symbiotic evolution model, a relationship between the intermediate parameters in the transformed multi-agent symbiotic evolution model and the symbiotic coefficient in the multi-agent symbiotic evolution model is established, which is specifically as follows: Among them, α 12 , α 13 They represent the symbiotic effect coefficient of value co-creation unit 1 on value co-creation unit 2 and the symbiotic coefficient of value co-creation unit 1 on value co-creation unit 3, β 21 , β 23 They represent the symbiotic coefficient of value co-creation unit 2 to value co-creation unit 1 and the symbiotic coefficient of value co-creation unit 2 to value co-creation unit 3, respectively. 31 ,θ 32 They represent the symbiotic coefficient of value co-creation unit 3 to value co-creation unit 1 and the symbiotic coefficient of value co-creation unit 3 to value co-creation unit 2 respectively; r1, r2, and r3 represent the natural growth rates of the population sizes of the three value co-creation units; N1, N2, and N3 represent the maximum values ​​of the three population sizes; k 12 、k 13 、k 21 、k 23 、k 31 、k 32 is the intermediate parameter.

6. The method for analyzing the evolution of network chain dual-structure multi-agent mutualistic symbiosis according to claim 1 is characterized in that: Based on the mapping relationship between grey derivatives and even logarithms, the transformed symbiotic evolution model is changed to obtain the grey mapping symbiotic model differential equation group; according to the obtained population size, the least squares method is used to solve the intermediate parameters in the grey mapping symbiotic model differential equation group.

7. A network-chain dual-structure multi-agent mutually beneficial symbiotic evolution analysis device, characterized in that: A module comprising the network chain dual-structure multi-agent mutually beneficial symbiotic evolution analysis method as described in any one of claims 1-5.

8. An evolution analysis device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the network chain dual-structure multi-agent mutually beneficial symbiotic evolution analysis method as described in any one of claims 1 to 5 are implemented.

Images