Co-evolution analysis method for cloud manufacturing low-carbon cooperation and related products

Abstract

The invention relates to the technical field of cloud manufacturing services, in particular to a common evolution analysis method for cloud manufacturing low-carbon cooperation and related products. According to the method, suppliers and demander groups are connected through a small world network, the suppliers select learning neighbors based on the companion effect, supplier group strategies are updated through a Fermi rule, and interaction between the suppliers is achieved. And the demanders update the demander group strategy through the Aspiration-drive rule, and update the willing level based on the conformity effect to realize interaction between the demanders. Therefore, in the double-layer network of the supplier group and the demander group, the common evolution process of low-carbon cooperation of the supplier and the demander group is analyzed, and strategy evolution rules of the supplier group and the demander group are designed based on the companion effect and the conformity effect. Reference is provided for the policy formulation of low-carbon cooperative excitation of the cloud platform to the supply and demand parties, and low-carbon transformation of enterprises is promoted.

Description

Technical Field

[0001] This invention relates to the technical field of cloud manufacturing services, and in particular to a co-evolutionary analysis method and related products for low-carbon collaboration in cloud manufacturing. Background Technology

[0002] Developing a low-carbon shipbuilding supply chain is a crucial measure for achieving carbon emission reduction. The formation of a low-carbon supply chain requires close cooperation among enterprises at all stages, including zero-margin manufacturing, green processing technologies such as laser cutting, and energy-saving devices such as drag reduction systems. However, when enterprises collaborate to form a low-carbon supply chain, they face challenges such as high low-carbon investment costs, lack of transparency in carbon reduction technologies and information, and insufficient government incentives for low-carbon initiatives, leading to a dilemma in their low-carbon transformation. With the development of industrial internet technology, cloud manufacturing platforms have emerged, which will integrate information from upstream and downstream of the supply chain and share low-carbon technologies and equipment.

[0003] The cloud manufacturing supply chain comprises suppliers, consumers, and cloud platform operators. When making low-carbon cooperation decisions, supply and demand companies face challenges such as conflicts of interest, opaque and uncertain market demand information. Their decisions are influenced by government low-carbon policies, market demand, economic benefits, and low-carbon costs, exhibiting characteristics of information asymmetry and bounded rationality. They cannot know each other's decisions and benefits, and their low-carbon decisions evolve with changes in the external environment and their own understanding. The entire decision-making process is constantly evolving. Both supply and demand sides face certain costs and risks when joining low-carbon cooperation, and their enthusiasm for such cooperation needs to be improved. To encourage both sides to actively participate in low-carbon cooperation, cloud platforms need to develop reasonable incentive strategies for low-carbon cooperation.

[0004] When making low-carbon cooperation decisions, cloud manufacturing service providers and consumers face an increasingly complex and uncertain environment, exhibiting characteristics of bounded rationality in their cognitive abilities and decision-making. Under the "dual-carbon" context, active participation in low-carbon cooperation by both suppliers and consumers has economic value and market competitiveness. However, considering the costs associated with low-carbon cooperation such as technology research and development and equipment upgrades, as well as uncertainties in market demand and free-riding issues, both parties, driven by self-interest, have a weak willingness to engage in low-carbon cooperation. To promote low-carbon cooperation between suppliers and consumers, cloud platforms need to implement incentive and penalty systems through regulation.

[0005] Furthermore, suppliers and consumers have formed distinct network topologies through information and knowledge sharing. Different network nodes represent heterogeneous individuals with varying low-carbon preferences and environmental awareness, making low-carbon cooperation decisions between supply and demand players more complex. Moreover, the strategy evolution of service suppliers is not isolated but influenced by the dynamic evolution of consumer strategies. Decisions within either the supplier or consumer group are interactive, exhibiting peer effects and conformity effects. Current low-carbon evolution analyses of both supply and demand cannot analyze the co-evolution of low-carbon cooperation within the two-layered network of service suppliers and consumers, nor can they design strategy evolution rules for suppliers and consumers based on peer and conformity effects. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a co-evolution analysis method and related products for low-carbon cooperation in cloud manufacturing, so as to analyze the co-evolution process of low-carbon cooperation between the supply and demand sides in a two-layer network of supplier and demand groups, and design strategy evolution rules for supplier and demand groups based on peer effect and conformity effect.

[0007] To achieve the above and other related objectives, this invention provides a co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing, the evolutionary analysis method comprising:

[0008] Determine the group strategies for suppliers and customers. The group strategies for suppliers include providing low-carbon manufacturing services (P) and providing traditional manufacturing services (NP). The group strategies for customers include adopting low-carbon manufacturing services (A) and adopting traditional manufacturing services (NA).

[0009] Constructing a game network between supply and demand groups, that is, constructing a small-world network between supply and demand;

[0010] Determine the parameters involved in the game between suppliers and demanders, construct the game payoff matrix between suppliers and demanders, and determine the payoff function of individual suppliers and demanders after playing against their neighbors.

[0011] Set evolution rules for supplier low-carbon strategies, i.e. update supplier group strategies by considering the Fermi rule of node degree;

[0012] Set evolution rules for demanders' low-carbon strategies, that is, update the demander group's strategies by updating the aspiration-driven rules at the vision level;

[0013] This study analyzes the impact of low-carbon cooperation incentives on the evolution trend of low-carbon cooperation between supply and demand sides under different evolutionary models, and outputs the proportion of low-carbon cooperation between supply and demand sides.

[0014] Optionally, the construction of a small-world network between supply and demand sides includes:

[0015] Set the number of network nodes, the average number of neighbors connected to each node, and the probability of randomized reconnection;

[0016] The networkx library, based on Python, generates small-world networks for both supply and demand sides.

[0017] Optionally, the method of updating the supplier group strategy by considering the Fermi rule based on node degree includes:

[0018] When choosing learning targets, each supplier tends to learn from its most influential neighbors;

[0019] Update the policy using Fermi rules.

[0020] Optionally, the strategy of updating the demand group by updating the aspiration-driven rule of the vision level includes:

[0021] By introducing the bandwagon effect, the vision level of demanders is updated;

[0022] Use aspiration-driven rules for policy updates.

[0023] Optionally, the policy update using aspiration-driven rules includes:

[0024] Demanders will increase their own profits by π i With vision level π α Comparing, if π i >π α If demander i maintains its current strategy, then π i ≤π α If so, the policy is updated according to the Fermi rules.

[0025] Optionally, the impact of low-carbon cooperation incentives under different evolutionary modes on the evolutionary trend of low-carbon cooperation between supply and demand sides includes the impact of carbon tax rates and green subsidies on the proportion of low-carbon cooperation by suppliers and the impact of consumption subsidies on the proportion of low-carbon cooperation by demanders.

[0026] In another aspect, the present invention provides a machine-readable storage medium having a machine-executable program stored thereon, wherein the machine-executable program, when executed by a processor, implements any of the above-described methods for co-evolutionary analysis of cloud manufacturing low-carbon cooperation.

[0027] In another aspect, the present invention provides a computer device including a memory, a processor, and a machine-executable program stored in the memory and running on the processor, wherein the processor, when executing the machine-executable program, implements the co-evolutionary analysis method for cloud manufacturing low-carbon cooperation described above.

[0028] In this invention, a co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing connects supplier and demander groups through small-world networks. Suppliers learn neighbor selection based on peer effects and update their group strategies using Fermi rules, enabling interaction among suppliers. Demanders update their group strategies using aspiration-driven rules and update their vision levels based on conformity effects, achieving interaction among demanders. This allows for the analysis of the co-evolutionary process of low-carbon cooperation between suppliers and demanders within a two-layer network, and the design of strategy evolution rules for both groups based on peer and conformity effects. This provides a reference for cloud platforms to formulate policies incentivizing low-carbon cooperation between suppliers and demanders, promoting the low-carbon transformation of enterprises. Attached Figure Description

[0029] Figure 1 This is a flowchart of a co-evolution analysis method for low-carbon cloud manufacturing cooperation according to an embodiment of the present invention;

[0030] Figure 2 This is a flowchart of an embodiment of the present invention for updating the supplier group strategy by taking into account the degree of nodes in Fermi rules;

[0031] Figure 3 This is a flowchart illustrating an embodiment of the present invention of updating the demander group strategy by updating the aspiration-driven rules at the vision level;

[0032] Figure 4 This is a graph showing the impact of carbon tax rate on the proportion of low-carbon cooperation of suppliers under the self-evolution mode in one embodiment of the present invention.

[0033] Figure 5 This is a graph showing the impact of carbon tax rate on the proportion of low-carbon cooperation of suppliers under the co-evolution mode in one embodiment of the present invention.

[0034] Figure 6 This is a graph showing the impact of green subsidies on the proportion of low-carbon cooperation with suppliers under a self-evolutionary mode in one embodiment of the present invention.

[0035] Figure 7 This is a graph showing the impact of green subsidies on the proportion of low-carbon cooperation among suppliers under a co-evolutionary model in one embodiment of the present invention.

[0036] Figure 8This is a graph showing the impact of low-carbon consumption subsidies on the proportion of low-carbon cooperation among demanders under a self-evolution or co-evolution mode in one embodiment of the present invention.

[0037] Figure 9 This is a schematic diagram of a machine-readable storage medium according to an embodiment of the present invention;

[0038] Figure 10 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0039] The following reference Figures 1 to 10 This invention describes a co-evolutionary analysis method for low-carbon cloud manufacturing collaboration and related products.

[0040] like Figure 1 As shown, this embodiment of the invention provides a co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing. The evolutionary analysis method includes:

[0041] Step S1: Determine the group strategies for suppliers and customers. Specifically, the supplier group strategy includes providing low-carbon manufacturing services (P) and providing traditional manufacturing services (NP). The customer group strategy includes adopting low-carbon manufacturing services (A) and adopting traditional manufacturing services (NA).

[0042] Step S2: Construct a game network between the supply and demand groups, that is, construct a small-world network between the supply and demand groups.

[0043] Because suppliers are heterogeneous, each supplier differs in manufacturing capabilities, company size, and environmental awareness. Suppliers do not exist in isolation but are closely connected through information and technology. Furthermore, there is only a game-theoretic relationship with a subset of suppliers; complex networks can reflect the intricate relationships among suppliers. A complex supplier network consists of a heterogeneous group of suppliers with bounded rationality, denoted as G=(V,E). The nodes in the network are V={V1,V2,V3,…,V…}. N} indicates that the complex network has N suppliers. E = {E1, E2, E3, ..., E} N} represents a direct connection between service providers. E ij =1 indicates that supplier i and supplier j are connected, meaning they are neighbors and have a game-theoretic relationship; otherwise, E ij=0. Supplier networks exhibit general correlation (i.e., relatively short average path length) and clustering of similar entities (i.e., high clustering density), with strong competition among suppliers, resulting in a small-world characteristic. Furthermore, when supplier groups change their strategies based on a certain update rule, they are influenced by government policies. Therefore, the network structure of supplier groups exhibits both regularity and randomness. Small-world networks are a type of complex network between random and regular networks; thus, they can be used to represent the information interaction structure among suppliers.

[0044] Demanders will adjust their strategies based on the strategies and profits of demanders with whom they have close relationships, which also has the characteristics of a small-world network. Therefore, a small-world network can also be used to identify the information exchange structure between demanders.

[0045] Furthermore, constructing a small-world network for both supply and demand includes:

[0046] Set the number of network nodes, the average number of neighbors connected to each node, and the probability of randomized reconnection;

[0047] The networkx library, based on Python, generates small-world networks for both supply and demand sides.

[0048] Optionally, the Watts-Strogatz model can be used for small-world network construction.

[0049] Step S3: Determine the parameters involved in the game between suppliers and demanders, construct the game payoff matrix for suppliers and demanders, and determine the payoff function of individual suppliers and demanders after playing against their neighbors.

[0050] Specifically, the parameters involved in the supplier game include:

[0051] If the total market demand for cloud manufacturing services is Q, and the demand share of low-carbon manufacturing services is τ, then the market demand for low-carbon manufacturing services is τQ. If the number of suppliers is N, then the average manufacturing service demand that each supplier needs to meet is...

[0052] The price of traditional manufacturing services is p t The cost of traditional manufacturing services is c t The price of low-carbon manufacturing services is p. l The cost of low-carbon manufacturing services is c l The initial investment cost of the supplier in low-carbon technologies, equipment, processes, etc. is C1.

[0053] The greenness of low-carbon manufacturing services is denoted by g, which refers to the degree to which low-carbon manufacturing services are environmentally friendly. The cloud platform's subsidy for each unit of greenness in a manufacturing service is s, therefore the cloud platform's subsidy for each unit of low-carbon manufacturing service is gs. The carbon emissions per unit of traditional manufacturing service are μ. μ The carbon tax rate is p c The carbon tax per unit of traditional manufacturing service is μ μ p c If the carbon emission reduction rate of low-carbon manufacturing services is σ, then the carbon tax per unit of low-carbon manufacturing services is μ. μ p c (1-σ).

[0054] Based on the parameters involved in the supplier game described above, a payoff matrix for the game between the supplier and its neighboring suppliers is constructed, including:

[0055] When both the supplier and its neighboring suppliers choose strategy P, and all demanders choose strategy A, the average manufacturing service demand that each supplier needs to meet is Q. ave Then the payment matrix of the supplier and its neighboring suppliers is {[p l +gs-c l -μ μ p c (1-σ)]Q ave -C1,[p l +gs-c l -μ μ p c (1-σ)]Q ave If all demanders choose strategy NA, then the payoff matrix for the supplier and its neighboring suppliers is {-C1, -C1}.

[0056] When a supplier chooses strategy P and neighboring suppliers choose strategy NP, if the proportion of suppliers choosing strategy P is... The number of supplier selection strategies P is If all demanders adopt strategy A, then the payment matrix of the supplier and its neighboring suppliers will be as follows: If all demanders adopt strategy NA, then the payment matrix of the supplier and its neighboring suppliers is as follows:

[0057] When a supplier chooses strategy NP, neighboring suppliers choose strategy P. The proportion of suppliers choosing strategy P is... The number of supplier selection strategies P is If all demanders adopt strategy A, then the payment matrix of the supplier and its neighboring suppliers is as follows: If all demanders choose strategy NA, then the payment matrix for suppliers and their neighboring suppliers is as follows:

[0058] When both the supplier and its neighboring suppliers choose strategy NP, if all demanders adopt strategy A, the payoff matrix for the supplier and its neighboring suppliers is {0,0}. If all demanders choose strategy NA, the payoff matrix for the supplier and its neighboring suppliers is {(p t -μ μ p c -c t )Q ave ,(p t -μ μ p c -c t )Q ave}

[0059] There are N suppliers i There are N neighbors, and the number of neighbor suppliers adopting strategy P is N. ilc Then the proportion of neighboring suppliers adopting strategy P is The proportion of neighboring suppliers adopting the strategy NP is If the proportion of demanders choosing strategy A is τ, then the proportion of demanders choosing strategy NA is 1-τ.

[0060] At this point, the expected return of supplier i choosing strategy P is shown in formula (1).

[0061]

[0062] The expected return of supplier i's selection strategy NP is shown in formula (2).

[0063]

[0064] Similarly, the game between demanders and their neighboring demanders includes:

[0065] Both the demander and its neighboring demanders adopt strategy NA. The demander's utility per unit of traditional manufacturing service is u. d , with p t If the price for obtaining the unit's manufacturing services is [amount], then the payment from the demander for low-carbon manufacturing services is [amount]. d -p t .

[0066] Both the demander and its neighboring demanders adopt strategy A. They are low-carbon conscious. After purchasing low-carbon manufacturing services, the efficiency of demanders will increase. The demander will receive after purchasing low-carbon manufacturing services. The utility of low-carbon manufacturing services. The price paid by the demander for low-carbon manufacturing services is p. l The low-carbon consumption subsidy received from the cloud platform is v. Therefore, the payment for low-carbon manufacturing services by the demander is:

[0067] The demander adopts strategy NA, while its neighboring demander adopts strategy A. When the demander provides traditional manufacturing services and its neighboring demander provides low-carbon manufacturing services, the demander's reputational loss is R1, and the demander's payment is u. d -p t -R1, while the neighboring demander will gain reputation R1, and the neighbor's payment is

[0068] In summary, the payment matrix for demanders is shown in Table 1.

[0069] Table 1 Payment Matrix of Demanders

[0070]

[0071] Based on the payment matrix of the demanders in Table 1, the expected returns of the demanders choosing strategy A and strategy NA are calculated as shown in formula (3):

[0072]

[0073] In the formula, the demand side has m j There are M neighbors, and the number of neighbors who require strategy A is M. jlc Then the proportion of neighbors adopting strategy A is The proportion of neighboring demanders adopting strategy NA is...

[0074] Step S4: Set the evolution rules for the supplier's low-carbon strategy, that is, update the supplier group strategy by taking into account the degree of nodality of Fermi rules.

[0075] Furthermore, such as Figure 2 As shown, the supplier group strategy is updated by considering the Fermi rule based on node degree, including:

[0076] Step S41: Select a learning target. Each supplier tends to learn from its most influential neighbor, i.e., the peer effect. The influence of a node supplier in the network can be measured by its degree, which reflects the degree of information interaction between the node and other nodes. The higher the degree of a node supplier, the more extensive the supplier's connections with other suppliers, and the greater the supplier's influence in the small-world network. The probability that supplier i selects neighbor supplier j as the policy imitation target is shown in Equation (4).

[0077]

[0078] In the formula, Ω represents the sum of the degrees of the neighbors of node supplier i. i Let d represent the set of all neighbors of node supplier i. j(t) represents the degree of neighbor supplier j. The greater the degree of neighbor supplier j, the more likely it is to be selected as a learning object.

[0079] Step S42: Update the policy using the Fermi rule. The Fermi rule is introduced to characterize the policy update process of suppliers on a small-world network. The Fermi rule is a stochastic evolutionary rule. Supplier i selects its neighbor supplier j for payoff comparison; if the payoff is lower than its neighbor's, in the next round of the game, it chooses to replace the supplier with the neighbor j. The probability of learning the current strategy of neighbor supplier j is shown in Equation (5).

[0080]

[0081] Where u i u represents the gain of supplier i in the previous round of the game. j Let s represent the payoff of supplier j, a neighbor of supplier i, in the previous round of the game. i This represents the strategy of supplier i in the previous round, s j This represents the strategy of neighbor supplier j in the previous round. When u i >u j Service provider i is unlikely to imitate its neighbor supplier j, and vice versa. k represents the environmental noise level, referring to interference from uncontrollable factors, and measures the supplier's bounded rationality; generally, k = 0.1. k→0 indicates that the supplier has a high level of rationality, and strategy updates are deterministic. k→∞ indicates that the supplier cannot rationally choose a strategy, strategy updates are random, and high-yield strategies may not be chosen.

[0082] Step S5: Set the evolution rules for the demanders' low-carbon strategy, that is, update the demanders' group strategy by updating the aspiration-driven rules at the vision level.

[0083] Furthermore, such as Figure 3 As shown, the demand group strategy is updated by updating the aspiration-driven rules at the vision level, including:

[0084] Step S51: Update the policy using aspiration-driven rules. In complex environments, service providers often have incomplete information, and there may be discrepancies between expected and actual payoffs. This necessitates individual self-coordination. Aspiration-driven rules are a self-learning policy update mechanism in evolutionary game models, and therefore can be used for policy updates.

[0085] Furthermore, using aspiration-driven rules for policy updates specifically refers to: demander i updating its own revenue π... i With vision level (personal expectations) π α Comparing, if π i >π α Demander i maintains its current strategy. If π i ≤π α Then, the policy is updated according to the Fermi rule. That is, according to formula (5), demander i updates its policy with a certain probability. π α It serves as a benchmark for demander i's tolerance or dissatisfaction, assessing an individual's level of greed and reflecting the demander's expectations for returns.

[0086] Step S52: Based on the bandwagon effect, update the vision level of the demander. That is, in the next game, demander i will update its vision level according to the average payoff of the neighbors who choose the most certain strategies, as shown in Equation (6).

[0087]

[0088] In the formula, Ω si Let represent the set of neighbors of demander i using its selection strategy s. Let n represent the number of neighbors of demander i. s This represents the number of neighbors of demander i's choice strategy s. Let represent the average payoff of the neighbors of service demander i who chooses strategy s. If this average payoff is higher than the vision level of demander i in the previous game, then demander i adopts this average payoff as its own vision level. Otherwise, demander i's vision level remains unchanged.

[0089] Step S6 analyzes the impact of low-carbon cooperation incentives on the evolution trend of low-carbon cooperation between supply and demand sides under different evolutionary models, including the impact of carbon tax rates and green subsidies on the proportion of low-carbon cooperation by suppliers and the impact of consumption subsidies on the proportion of low-carbon cooperation by demanders. The low-carbon cooperation proportions of both supply and demand sides are then output.

[0090] Specifically, considering the actual parameters: Q ave =100,p t =25,c t =6,p l =30,c l =20, s=15, g=0.3507, μ u =10,p c =2, σ=0.34, C1=200, u d =35, v = 5, R1 = 5, π α =35, τ = 0.5. A small-world network for the supplier group, with 300 nodes, each node having an average of 4 connected neighbors, and a randomized reconnection probability of 0.1. A small-world network for the demand group, with 100 nodes, each node having an average of 4 connected neighbors, and a randomized reconnection probability of 0.1.

[0091] Depend on Figure 4 It can be seen that at the carbon tax rate p c When p = 1, the service provider cooperation ratio fluctuates slightly and has not reached a stable value. c The percentage of service providers engaging in low-carbon cooperation increased from 2 to 4, stabilizing at 100%. Figure 5 It can be seen that as the carbon tax rate increases, the proportion of suppliers engaging in low-carbon cooperation begins to decline. (Comparison) Figure 4 and Figure 5 It can be seen that the co-evolution model can increase the proportion of low-carbon cooperation among service providers under the low-carbon tax.

[0092] refer to Figure 6 and Figure 7 When s=5, service providers are unwilling to provide low-carbon manufacturing services. As s increases, the proportion of service providers' low-carbon cooperation increases from 0 to 100%, and the rate at which it stabilizes at 100% accelerates. (Comparison) Figure 6 and Figure 7 When s=10, the proportion of low-carbon cooperation changed slightly. The evolutionary mode (supplier self-evolution and co-evolution of supply and demand sides) had no significant impact on the diffusion of supplier low-carbon strategies.

[0093] refer to Figure 8 As consumption subsidies increase from v=1 to v=4, the proportion of low-carbon cooperation among demanders stabilizes at 100%, with the stabilization rate increasing with v. Different evolutionary models (demander self-evolution and supply-demand co-evolution) have no impact on the proportion of low-carbon cooperation among demanders. This is because, under the co-evolution model, the diffusion of suppliers' low-carbon strategies does not affect the proportion of low-carbon cooperation among demanders.

[0094] In summary, by connecting suppliers and demanders through small-world networks, suppliers learn to select neighbors based on peer effects and update their group strategies using Fermi rules, thus achieving interaction among suppliers. Demanders update their group strategies using aspiration-driven rules and update their vision levels based on bandwagon effects, thus achieving interaction among demanders. This allows for the analysis of the co-evolution of low-carbon cooperation between suppliers and demanders within a two-layer network, and the design of strategy evolution rules for both groups based on peer and bandwagon effects. This provides a reference for cloud platforms to formulate policies incentivizing low-carbon cooperation between suppliers and demanders, promoting the low-carbon transformation of enterprises.

[0095] refer to Figure 9 The present invention also provides a machine-readable storage medium 400 on which a machine-executable program 410 is stored. When the machine-executable program 410 is executed by a processor, it implements the co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing as described in the above embodiments.

[0096] refer to Figure 10 The present invention also provides a computer device 500, including a memory 520, a processor 510, and a machine-executable program 410 stored in the memory and running on the processor. Furthermore, when the processor 510 executes the machine-executable program 410, it implements the co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing described in the above embodiments.

[0097] For the purposes of this embodiment, the machine-readable storage medium 400 can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the machine-readable storage medium 400 include: an electrical connection (electronic device) having one or more wires, a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the machine-readable storage medium 400 can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0098] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0099] Computer device 500 can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer device 500 can be a cloud computing node. Computer device 500 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. Computer device 500 can be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud computing environment, program modules can reside on local or remote computing system storage media, including storage devices.

[0100] Computer device 500 may include a processor 510 adapted to execute stored instructions and a memory 520 that provides temporary storage space for the operation of said instructions during operation. The processor 510 may be a single-core processor, a multi-core processor, a computing cluster, or any other configuration. The memory 520 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0101] The processor 510 can be connected via a system interconnect (e.g., PCI, PCI-Express, etc.) to an I / O interface (input / output interface) suitable for connecting the computer device 500 to one or more I / O devices (input / output devices). I / O devices may include, for example, a keyboard and indicating devices, where indicating devices may include a touchpad or touchscreen, etc. I / O devices may be built into the computer device 500 or may be external devices connected to the computing device.

[0102] The processor 510 can also be linked via a system interconnect to a display interface suitable for connecting the computer device 500 to a display device. The display device may include a display screen as a built-in component of the computer device 500. The display device may also include an external computer monitor, television, or projector connected to the computer device 500. Furthermore, a network interface controller (NIC) may be adapted to connect the computer device 500 to a network via a system interconnect. In some embodiments, the NIC may use any suitable interface or protocol (such as an Internet Minicomputer System Interface) to transmit data. The network may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, etc. Remote devices can connect to the computing device via the network.

[0103] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing, characterized in that, The evolutionary analysis method includes: Determine the group strategies for suppliers and customers. The group strategies for suppliers include providing low-carbon manufacturing services (P) and providing traditional manufacturing services (NP). The group strategies for customers include adopting low-carbon manufacturing services (A) and adopting traditional manufacturing services (NA). Constructing a game network between supply and demand groups, that is, constructing a small-world network between supply and demand; Determine the parameters involved in the game between suppliers and demanders, construct the game payoff matrix between suppliers and demanders, and determine the payoff function of individual suppliers and demanders after playing against their neighbors. Set evolution rules for supplier low-carbon strategies, i.e. update supplier group strategies by considering the Fermi rule of node degree; Set evolution rules for demanders' low-carbon strategies, that is, update the demander group's strategies by updating the aspiration-driven rules at the vision level; This study analyzes the impact of low-carbon cooperation incentives on the evolution trend of low-carbon cooperation between supply and demand sides under different evolutionary models, and outputs the proportion of low-carbon cooperation between supply and demand sides.

2. The co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing according to claim 1, characterized in that, The construction of a small-world network for both supply and demand includes: Set the number of network nodes, the average number of neighbors connected to each node, and the probability of randomized reconnection; The networkx library, based on Python, generates small-world networks for both supply and demand sides.

3. The co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing according to claim 1, characterized in that, The aforementioned update of the supplier group strategy by considering the Fermi rule based on node degree includes: When choosing learning targets, each supplier tends to learn from its most influential neighbors; Update the policy using Fermi rules.

4. The co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing according to claim 1, characterized in that, The aforementioned strategy for updating the demand group by updating aspiration-driven rules at the vision level includes: Use aspiration-driven rules for policy updates; Based on the bandwagon effect, update the vision level of demanders.

5. The co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing according to claim 4, characterized in that, The aforementioned policy update using aspiration-driven rules includes: Demanders will increase their own profits by π i With vision level π α Comparing, if π i >π α If demander i maintains its current strategy, then π i ≤π α If so, the policy is updated according to the Fermi rules.

6. The co-evolutionary analysis method for low-carbon cooperation in cloud manufacturing according to claim 1, characterized in that, The impact of low-carbon cooperation incentives under different evolutionary models on the evolutionary trend of low-carbon cooperation between supply and demand sides includes the impact of carbon tax rates and green subsidies on the proportion of low-carbon cooperation by suppliers and the impact of consumption subsidies on the proportion of low-carbon cooperation by demanders.

7. A machine-readable storage medium, characterized in that, It stores a machine-executable program, which, when executed by a processor, implements the co-evolution analysis method for low-carbon cloud manufacturing cooperation as described in any one of claims 1-6.

8. A computer device, characterized in that, It includes a memory, a processor, and a machine-executable program stored on the memory and running on the processor, wherein the processor, when executing the machine-executable program, implements the co-evolutionary analysis method for low-carbon cloud manufacturing cooperation as described in any one of claims 1-6.

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