Evolutionary analysis method for cloud manufacturing service low-carbon supervision and related products

Abstract

The invention relates to the technical field of cloud manufacturing services, in particular to an evolution analysis method for cloud manufacturing service low-carbon supervision and related products. According to limited rationality characteristics of suppliers and demanders, cloud platform low-carbon cooperative excitation is considered, a two-party game of the suppliers and the demanders is expanded into a three-party game of the suppliers, the demanders and the cloud platform, and stability conditions of three-party strategy evolution are discussed. And analyzing low-carbon strategy evolution processes of suppliers and demanders under different rewards and punishment of the cloud platform. By defining the reward and punishment range of the cloud platform for promoting the supply and demand parties to select the low-carbon cooperation, a reference is provided for the low-carbon cooperation decision of the supply and demand parties and the supervision decision of the cloud platform, so that the effect of low-carbon excitation can be better played, and the low-carbon cooperation of supply and demand enterprises is promoted.

Description

Technical Field

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

[0002] The market demand for low-carbon ship products and services is constantly increasing, and developing a low-carbon supply chain throughout the entire process from ship design to delivery is one of the important measures to achieve carbon emission reduction. The formation of a low-carbon supply chain requires close cooperation among enterprises at all levels, such as the research and application of low-carbon fuels like LNG (Liquefied Natural Gas) and methanol, green welding technology, and green painting technology. However, when enterprises cooperate to form a low-carbon supply chain, they face challenges such as high low-carbon investment costs, lack of transparency in carbon emission reduction technologies and information, leading to a dilemma in low-carbon transformation. With the development of industrial internet technology, cloud manufacturing service platforms have emerged. These platforms virtualize, encapsulate, and combine manufacturing resources such as intelligent equipment and production lines to form various services required throughout the manufacturing process, including design, testing, processing, simulation, and quality inspection. Based on these cloud manufacturing service platforms, supply and demand enterprises can interact on carbon emission reduction information, knowledge, and technologies.

[0003] 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.

[0004] Suppliers and consumers weigh benefits, costs, and risks when making decisions about low-carbon cooperation. This decision-making process is a long-term, dynamic game with inherent gains and losses. Initially, neither party can choose the optimal strategy; instead, they achieve the optimal strategy through continuous observation, learning, and imitation to maximize benefits. Currently, cloud platforms are not playing a role in incentivizing low-carbon development, thus failing to promote low-carbon cooperation between suppliers and consumers. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide an evolutionary analysis method and related products for low-carbon regulation of cloud manufacturing services, so that cloud platforms can better play the role of low-carbon incentives, thereby promoting low-carbon cooperation between supply and demand enterprises.

[0006] To achieve the above and other related objectives, this invention provides an evolutionary analysis method for low-carbon regulation of cloud manufacturing services, the evolutionary analysis method comprising:

[0007] Determine the strategy sets of suppliers, demanders, and cloud platforms, and analyze the game relationship among the three parties;

[0008] Determine the strategy combination for the three parties based on the strategy sets of suppliers, demanders, and cloud platforms;

[0009] Determine the parameters involved in the game between suppliers, demanders, and cloud platforms, and construct a payment matrix for the three parties under different strategy combinations, based on the strategy combinations of the three parties.

[0010] Based on the payout matrices of the three parties under different strategy combinations, the payout functions of the three parties under different strategies are determined, and the replication dynamic equations of the three parties are derived based on the payout functions.

[0011] Based on the three-party replication dynamic equation, the stability of the three-party strategy evolution is analyzed, and the equilibrium points and stability conditions of each equilibrium point in the three-party evolutionary game model are obtained.

[0012] This study analyzes the impact of cloud platform rewards and penalties on the evolution of low-carbon cooperation between supply and demand through numerical simulation, and provides recommendations for cloud platform reward and penalty management for low-carbon cooperation between supply and demand.

[0013] Optionally, the supplier's strategy set includes providing low-carbon manufacturing services (P) and providing traditional manufacturing services (NP);

[0014] The demanders' strategy set includes adopting low-carbon manufacturing service A and adopting traditional manufacturing service NA;

[0015] The cloud platform's strategy set includes regulated supply and demand sides (R) and unregulated supply and demand sides (NR).

[0016] Optionally, the analysis of the game relationship among the three parties includes:

[0017] When monitoring supply and demand on the cloud platform, if both parties join in low-carbon cooperation, the cloud platform will provide rewards; if neither party joins, the cloud platform will impose penalties.

[0018] Optionally, the parameters involved in the supplier game include the revenue G1 of the supplier choosing to provide traditional manufacturing services NP, the input cost C1 of the supplier choosing to provide low-carbon manufacturing services P, and the revenue growth rate β of the supplier choosing to provide low-carbon manufacturing services P.

[0019] The parameters involved in the demander game include the revenue G2 of the demander choosing to adopt traditional manufacturing service NA, the additional cost C2 of the demander choosing to adopt low-carbon manufacturing service A, and the revenue growth rate ρ of the demander choosing to adopt low-carbon manufacturing service A.

[0020] The parameters involved in the cloud platform game include the cloud platform's benefit G3 from choosing not to regulate both supply and demand sides NR, and the cloud platform's regulatory costs C. r The cloud platform provides the following rewards: S1 for suppliers choosing to provide low-carbon manufacturing services (P); S2 for customers choosing low-carbon manufacturing services (A); P1 for suppliers choosing to provide traditional manufacturing services (NP); and government subsidies to the cloud platform (T). i .

[0021] Optionally, if the proportion of suppliers choosing to provide low-carbon manufacturing services P is X, then the proportion of suppliers choosing to provide traditional manufacturing services NP is 1-X; if the proportion of demanders choosing low-carbon manufacturing services A is Y, then the proportion of demanders choosing traditional manufacturing services NA is 1-Y; if the proportion of the cloud platform choosing to regulate both supply and demand sides R is Z, then the proportion of the cloud platform choosing not to regulate both supply and demand sides NR is 1-Z. In this case, the supplier's replication dynamic equation is:

[0022] F(X) = X(1-X)(U X -U 1-X )=X(1-X)[Z(S1+P1)+βG1-C1]

[0023] The replication dynamic equation for demanders is:

[0024] H(Y)=Y(1-Y)(U Y -U 1-Y )=Y(1-Y)(ρG2+ZS2-C2)

[0025] The replication dynamic equation for the cloud platform is:

[0026] W(Z)=Z(1-Z)(U z -U 1-z )=Z(1-Z)[-C r +P1+T4+X(T2-T4-P1-S1)+Y(T3-T4-S2)+XY(T1-T2-T3+T4)].

[0027] Optionally, the impact of the cloud platform's reward and punishment mechanisms on the evolution of low-carbon cooperation between supply and demand sides includes the impact of the cloud platform's punishment P1 on the supplier's choice to provide traditional manufacturing services NP on the supplier's strategy evolution, the impact of the cloud platform's reward S1 on the supplier's choice to provide low-carbon manufacturing services P on the supplier's strategy evolution, and the impact of the cloud platform's reward S2 on the demander's choice to adopt low-carbon manufacturing services A on the demander's strategy evolution.

[0028] 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 the evolutionary analysis method for low-carbon regulation of cloud manufacturing services as described above.

[0029] 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 evolutionary analysis method for low-carbon regulation of cloud manufacturing services as described above.

[0030] In this invention's evolutionary analysis method for low-carbon regulation of cloud manufacturing services, considering the bounded rationality characteristics of suppliers and consumers, and taking into account the low-carbon cooperation incentives of the cloud platform, the two-party game between suppliers and consumers is expanded into a three-party game involving suppliers, consumers, and the cloud platform. The stability conditions of the three-party strategy evolution are explored, and the evolution process of low-carbon strategies for suppliers and consumers under different cloud platform rewards and penalties is analyzed. By defining the reward and penalty scope for the cloud platform to promote low-carbon cooperation between suppliers and consumers, a reference is provided for the low-carbon cooperation decisions of both parties and the regulatory decisions of the cloud platform, thereby better leveraging the role of low-carbon incentives and promoting low-carbon cooperation between supply and demand enterprises. Attached Figure Description

[0031] Figure 1 This is a flowchart of an evolutionary analysis method for low-carbon regulation of cloud manufacturing services according to an embodiment of the present invention;

[0032] Figure 2 This is a game diagram illustrating the evolution of supply and demand sides and the cloud platform according to an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram illustrating the strategy combination of the supply and demand parties and the cloud platform in one embodiment of the present invention;

[0034] Figure 4 This is a trajectory diagram illustrating the impact of a cloud platform's penalty P1 for non-low-carbon cooperation on supplier strategy evolution in one embodiment of the present invention.

[0035] Figure 5 This is a trajectory diagram illustrating the impact of the cloud platform's penalty P1 for suppliers' non-low-carbon cooperation on the evolution of the three-party strategy in one embodiment of the present invention.

[0036] Figure 6 This is a trajectory diagram illustrating the impact of the cloud platform's reward S1 for low-carbon cooperation with suppliers on the evolution of supplier strategies in one embodiment of the present invention.

[0037] Figure 7 This is a trajectory diagram illustrating the impact of the cloud platform's reward S1 for supplier low-carbon cooperation on the evolution of the three-party strategy in one embodiment of the present invention.

[0038] Figure 8 This is a trajectory diagram illustrating the impact of the cloud platform's reward S2 for low-carbon cooperation with demanders on the evolution of demander strategies in one embodiment of the present invention.

[0039] Figure 9 This is a trajectory diagram illustrating the impact of the cloud platform's reward S2 for low-carbon cooperation with demanders on the evolution of the three-party strategy in one embodiment of the present invention.

[0040] Figure 10 This is a schematic diagram of a machine-readable storage medium according to an embodiment of the present invention;

[0041] Figure 11 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0042] The following reference Figures 1 to 11 This invention describes an evolutionary analysis method for low-carbon regulation of cloud manufacturing services and related products.

[0043] like Figure 1 As shown, this embodiment of the invention provides an evolutionary analysis method for low-carbon regulation of cloud manufacturing services. The evolutionary analysis method includes:

[0044] Step S1: Determine the strategy sets of suppliers, demanders, and cloud platforms, and analyze the game relationship among the three parties.

[0045] Specifically, refer to Figure 2 The supplier's strategy set includes providing low-carbon manufacturing services (P) and providing traditional manufacturing services (NP). The demander's strategy set includes adopting low-carbon manufacturing services (A) and adopting traditional manufacturing services (NA). The cloud platform's strategy set includes regulating both supply and demand (R) and not regulating either (NR). When the cloud platform regulates both supply and demand, it will reward them if they join in low-carbon cooperation. Conversely, it will penalize them to encourage them to participate in low-carbon cooperation.

[0046] The proportion of service providers choosing strategy P is denoted by X. When X = 1, it indicates that the service provider offers low-carbon manufacturing services. When X = 0, it indicates that the service provider does not offer low-carbon manufacturing services. The proportion of service demanders choosing strategy A is denoted by Y. When Y = 1, it indicates that the demander adopts low-carbon manufacturing services. When Y = 0, it indicates that the demander does not adopt low-carbon manufacturing services. The proportion of cloud platform monitoring both supply and demand sides is denoted by Z. When Z = 1, it indicates that the cloud platform monitors both supply and demand sides. When Z = 0, it indicates that the cloud platform does not monitor either service supply or demand sides.

[0047] Step S2: Determine the strategy combination for the three parties based on their strategy sets. Specifically, as follows... Figure 3 As shown.

[0048] Step S3: Determine the parameters involved in the three-party game and construct the payoff matrix for the three parties under different strategy combinations, based on the strategy combinations of the three parties.

[0049] Specifically, the parameters involved in the three-way game are shown in Table 1.

[0050] Table 1. Main parameters and meanings of the three-way evolutionary game.

[0051]

[0052]

[0053] The payment matrix for the three parties under different strategy combinations is shown in Table 2.

[0054] Table 2 Payment Matrix for the Three Parties under Different Strategy Combinations

[0055]

[0056] Step S4: Based on the payoff matrices of the three parties under different strategy combinations, determine the payoff functions of the three parties under different strategies, and derive the replication dynamic equations of the three parties based on the payoff functions.

[0057] Specifically, the expected revenue of a supplier providing low-carbon manufacturing services is shown in Equation (1).

[0058] U X =ZS1+[(1+β)G1-C1] (1)

[0059] The expected benefits of suppliers not providing low-carbon manufacturing services are shown in equation (2).

[0060] U 1-X =G1-ZP1 (2)

[0061] Based on formulas (1) and (2), the average expected revenue of the supplier can be obtained as shown in formula (3).

[0062] U X,1-X =XU X +(1-X)U 1-X =G1-ZP1+XZ(S1+P1)-XC1+XβG1 (3)

[0063] The supplier's replication dynamic equation is shown in equation (4).

[0064] F(X) = X(1-X)(U X -U 1-X )=X(1-X)[Z(S1+P1)+βG1-C1] (4)

[0065] Where F(X) represents the rate of change of supplier selection strategy P.

[0066] The expected benefits of demanders adopting low-carbon manufacturing services are shown in equation (5).

[0067] U Y =ZS2+[(1+ρ)G2-C2] (5)

[0068] The expected benefits for demanders who do not adopt low-carbon manufacturing services are shown in equation (6).

[0069] U 1-Y =G2(6)

[0070] Based on formulas (5) and (6), the average expected revenue of the demander is shown in formula (7).

[0071] U Y,1-Y =YU Y +(1-Y)U 1-Y =G2+Y(ρG2+ZS2-C2) (7)

[0072] The replication dynamic equation of the demander is shown in equation (8).

[0073] H(Y)=Y(1-Y)(U Y -U 1-Y )=Y(1-Y)(ρG2+ZS2-C2) (8)

[0074] Where H(Y) represents the rate of change of demanders adopting strategy A.

[0075] The expected benefits of cloud platform supervision for both supply and demand sides are shown in equation (9).

[0076] U Z =G3-C r +P1+T4+X(T2-T4-P1-S1)

[0077] +Y(T3-T4-S2)+XY(T1-T2-T3+T4) (9)

[0078] The cloud platform does not regulate the expected benefits of both supply and demand sides as shown in equation (10).

[0079] U 1-Z =G3(10)

[0080] Based on formulas (9) and (10), the average expected revenue of the cloud platform is shown in formula (11).

[0081] U Z,1-z =ZU Z +(1-Z)U 1-Z =G3+Z(P1+T4-Cr )

[0082] +ZX(T2-T4-P1-S1)+ZY(T3-T4-S2)+ZXY(T1-T2-T3+T4) (11)

[0083] The replication dynamic equation of the cloud platform is shown in equation (12).

[0084] W(Z)=Z(1-Z)(U z -U 1-z )=Z(1-Z)[-C r +P1+T4+X(T2-T4-P1-S1)

[0085] +Y(T3-T4-S2)+XY(T1-T2-T3+T4)] (12)

[0086] Where W(Z) represents the rate of change of strategy R adopted by the cloud platform.

[0087] In summary, the three-dimensional dynamic system consisting of suppliers, demanders, and cloud platforms can be derived as shown in equation (13).

[0088]

[0089] Step S5: Based on the replication dynamic equations of the three parties, analyze the stability of the strategy evolution of the three parties to obtain the equilibrium points and stability conditions of each equilibrium point in the three-party evolutionary game model.

[0090] Specifically, as can be seen from formula (13), the following equilibrium points exist in the game between the supplier, the demander, and the cloud platform: E1(0,0,0), E2(0,1,0), E3(0,0,1), E4(0,1,1), E5(1,0,0), E6(1,1,0), E7(1,0,1), E8(1,1,1).

[0091] When all eigenvalues ​​of the Jacobian matrix of each equilibrium point are less than 0, the equilibrium point is stable. When all eigenvalues ​​of the Jacobian matrix of each equilibrium point are greater than 0, the equilibrium point is unstable. When the eigenvalues ​​of the Jacobian matrix of each equilibrium point are both positive and negative, the equilibrium point is a saddle point. The Jacobian matrix of this game system is shown in equation (14):

[0092]

[0093] (1) When βG1-C1<0, ρG2-C2<0, C r>When P1 + T4, E1(0, 0, 0) is an ESS (Evolutionary Stable Strategy), that is, the strategy is {Supplier selection strategy NP, Demander selection strategy NA, Cloud platform selection strategy NR}. The increased revenue of the supplier selection strategy P is lower than the low-carbon input cost of the supplier (βG1 - C1 < 0), so the supplier selects the strategy NP. The increased revenue of the demander selection strategy A is lower than the additional cost of selecting the strategy A (ρG2 - C2 < 0), so the demander selects the strategy NA. The regulatory cost of the cloud platform is higher than the sum of the penalty for service providers and the subsidy from the government to the cloud platform (C r >P1 + T4), that is, the revenue of the cloud platform selection strategy NR is higher than the revenue of the cloud platform selection strategy R, so the cloud platform adopts an unregulated strategy.

[0094] (2) When βG1 - C1 < 0, ρG2 - C2 > 0, C r >P1 - S2 + T3, E2(0, 1, 0) is an ESS, that is, the strategy is {Supplier selection strategy NP, Demander selection strategy A, Cloud platform selection strategy NR}. The increased revenue of the supplier selection strategy P is lower than the low-carbon input cost of the supplier (βG1 - C1 < 0), so the supplier selects the strategy NP. The increased revenue of the demander selection strategy A is higher than the additional cost of selecting the strategy A (ρG2 - C2 > 0), so the demander tends to select the strategy A. The regulatory cost of the cloud platform is higher than the additional revenue of selecting the strategy R (C r >P1 - S2 + T3), so the cloud platform will choose not to regulate both the service supply and demand sides.

[0095] (3) When P1 < C1 - S1 - βG1, S2 + ρG2 < C2, P1 + T4 > C r When, E3(0, 0, 1) is an ESS, that is, the strategy is {Supplier selection strategy NP, Demander selection strategy NA, Cloud platform selection strategy R}. When the loss of the supplier selection strategy NP is less than the loss of the selection strategy P (P1 < C1 - S1 - βG1), the supplier chooses not to provide low-carbon manufacturing services. The additional cost of the demander selection strategy NA is higher than the sum of the reward and increased revenue of the cloud platform (S2 + ρG2 < C2), so the demander tends to select the strategy NA. The regulatory cost of the cloud platform is lower than the additional revenue when selecting the strategy R (P1 + T4 > C r ), so the cloud platform will choose to regulate both the service supply and demand sides.

[0096] (4) When P1 < C1 - S1 - βG1, C2 < S2 + ρG2, C rWhen P1 + T3 - S2, E4(0, 1, 1) is the ESS, and the strategy combination is {Supplier selection strategy NP, Demander selection strategy A, Cloud platform selection strategy R}. When the loss of the supplier's selection strategy NP is less than the loss of the selection strategy P (P1 < C1 - S1 - βG1), the supplier selects the strategy NP. When the reward from the cloud platform to the demander and the increased revenue of the demander are higher than the additional cost of selecting strategy A (C2 < S2 + ρG2), the service demander selects the strategy A. The additional revenue obtained by the cloud platform through supervision is higher than the supervision cost (P1 + T3 - S2 > C r ), so the cloud platform tends to select the strategy R.

[0097] (5) When βG1 - C1 > 0, ρG2 - C2 < 0, C r > T2 - S1, E5(1, 0, 0) is the ESS, that is, the strategy is {Supplier selection strategy P, Demander selection strategy NA, Cloud platform selection strategy NR}. When the low-carbon input cost of the supplier is lower than its increased revenue (C1 < βG1), the supplier chooses to provide low-carbon manufacturing services. When the additional cost of the demander's selection strategy A is higher than its increased revenue (C2 > ρG2), the demander tends to select the strategy NA. The supervision cost of the cloud platform is higher than the additional revenue when selecting the strategy R (C r > T2 - S1), so the cloud platform will choose not to supervise both the service supplier and the demander.

[0098] (6) When βG1 - C1 > 0, ρG2 - C2 > 0, C r > T1 - S2 - S1, E6(1, 1, 0) is the ESS, that is, the strategy is {Supplier selection strategy P, Demander selection strategy A, Cloud platform selection strategy NR}. When the additional revenue of the supplier's selection strategy P is positive (βG1 - C1 > 0), the supplier chooses to provide low-carbon manufacturing services. When the additional revenue of the demander's selection strategy A is positive (ρG2 - C2 > 0), the demander tends to select the strategy A. When the additional revenue of the cloud platform's selection strategy R is negative (C r > T1 - S2 - S1), the cloud platform chooses not to supervise both the service supplier and the demander.

[0099] (7) When P1 > C1 - S1 - βG1, C2 > S2 + ρG2, C rWhen \(T_2 - S_1\), \(E_7(1, 0, 1)\) is an ESS, and the strategy is {Supplier selection strategy \(P\), Demander selection strategy \(NA\), Cloud platform selection strategy \(R\)}. The loss of the supplier selection strategy \(NP\) is higher than the loss of the selection strategy \(P\) (\(P_1 > C_1 - S_1 - \beta G_1\)), so the supplier will choose to provide low-carbon manufacturing services. The additional cost of the demander's selection strategy \(A\) is higher than the sum of the reward and growth benefit of the cloud platform to the demander (\(C_2 > S_2 + \rho G_2\)), so the demander chooses not to adopt low-carbon manufacturing services. The additional benefit obtained by the cloud platform through supervision is higher than the supervision cost (\(C\ r \((T_2 - S_1)\), so the cloud platform will choose to supervise both the supply and demand sides.

[0100] (8) When \(P_1 > C_1 - S_1 - \beta G_1\), \(C_2 < S_2 + \rho G_2\), \(C r When \(T_1 - S_2 - S_1\), \(E_8(1, 1, 1)\) is an ESS, and the strategy is {Supplier selection strategy \(P\), Demander selection strategy \(A\), Cloud platform selection strategy \(R\)}. When the loss of the service supplier's selection strategy \(NP\) is higher than the loss of the selection strategy \(P\) (\(P_1 > C_1 - S_1 - \beta G_1\)), the supplier chooses to provide low-carbon manufacturing services. The additional cost of the demander's selection strategy \(A\) is lower than the sum of the reward and growth benefit of the cloud platform to the demander (\(C_2 < S_2 + \rho G_2\)), so the demander chooses to accept low-carbon manufacturing services. The additional benefit obtained by the cloud platform through supervision is higher than the supervision cost (\(C r \((T_1 - S_2 - S_1)\), so the cloud platform tends to choose to supervise both the supply and demand sides.

[0101] Step S6, analyze the influence of the reward and punishment intensity of the cloud platform on the low-carbon cooperation evolution process of both the supply and demand sides through numerical simulation, and give suggestions on the reward and punishment management of the cloud platform for the low-carbon cooperation of both the supply and demand sides.

[0102] Specifically, based on the replicator dynamic equations of the service supplier, demander, and cloud platform, analyze the influence of the rewards and punishments of the cloud platform on both the supply and demand sides on the strategy evolution process of the three parties. Optionally, set the initial parameters of the payoff matrix as: \(G_1 = 1000\), \(G_2 = 1500\), \(G_3 = 250\), \(C_1 = 370\), \(C_2 = 380\), \(C r = 90, \(T_1 = 300\), \(T_2 = 180\), \(T_3 = 120\), \(T_4 = 60\), \(S_1 = 80\), \(S_2 = 90\), \(P_1 = 100\), \(\beta = 0.2\), \(\rho = 0.2\). The three parties in the game are all boundedly rational and randomly select the initial proportion, and the initial proportion is set to \((0.5, 0.5, 0.5)\).

[0103] (1) Influence of the cloud platform's punishment \(P_1\) on the strategy evolution of the three parties

[0104] The punishment \(P_1\) of the cloud platform for the supplier's non-low-carbon cooperation has no significant influence on the strategy evolution of the demander and the cloud platform. Therefore, only analyze the influence of \(P_1\) on the strategy evolution of the supplier.

[0105] The impact of the cloud platform's punishment P1 for the supplier's non-low-carbon cooperation on the evolution trajectory of the supplier's strategy is as follows Figure 4 shown. When P1 < C1 - S1 - βG1, the supplier stabilizes to the equilibrium state "0", that is, the supplier is not willing to adopt the strategy P. When P1 > C1 - S1 - βG1, as P1 increases, the speed at which the supplier stabilizes to the equilibrium state "1" accelerates, and the supplier tends to provide low-carbon services. The impact of P1 on the three-party strategy evolution is as follows Figure 5 shown. When P1 = 70 and 80, the system finally converges to E4(0, 1, 1), that is, {the supplier selects the strategy NP, the demander selects the strategy A, and the cloud platform selects the strategy R}. When P1 > C1 - S1 - βG1, the stable conditions of the equilibrium point E8(1, 1, 1) are satisfied, so the system finally converges to E8(1, 1, 1). Through simulation analysis, the cloud platform's punishment for the supplier's non-low-carbon cooperation can promote the supplier's low-carbon cooperation

[0106] (2) The impact of the cloud platform's rewards S1 and S2 on the three-party strategy evolution

[0107] The cloud platform's low-carbon cooperation reward S1 for the service supplier has basically no impact on the strategy evolution of the demander and the cloud platform. Therefore, only the impact of S1 on the supplier's strategy evolution is analyzed. Similarly, only the impact of the cloud platform's low-carbon cooperation reward S2 for the demander on the demander's strategy evolution is analyzed

[0108] The impact of the cloud platform's reward S1 for the supplier's low-carbon cooperation on the supplier's strategy evolution is as follows Figure 6 shown. When S1 < C1 - P1 - βG1, the supplier converges to the equilibrium state "0". When S1 > C1 - P1 - βG1, the increase in S1 promotes the supplier to quickly stabilize to the equilibrium state "1". As can be seen from Figure 7 it, when S1 = 50 and 60, the system stabilizes to E4(0, 1, 1), that is, {the supplier selects the strategy NP, the demander selects the strategy A, and the cloud platform selects the strategy R}. As S1 increases, the system finally stabilizes to E8(1, 1, 1), that is, {the supplier selects the strategy P, the demander selects the strategy A, and the cloud platform selects the strategy R}. The increase in S1 has a positive effect on the service supplier's selection of low-carbon strategies

[0109] The impact of the cloud platform's low-carbon cooperation reward S2 for the demander on the demander's strategy evolution is as follows Figure 8 shown. As S2 increases, the demander changes from the equilibrium state "0" to the equilibrium state "1". The increase in S2 accelerates the demander's evolution speed towards the equilibrium state "1". The impact of S2 on the three-party strategy evolution is as follows Figure 9As shown, when S2 > C2 - ρG2, the system eventually converges to a steady state: {suppliers provide low-carbon services, consumers adopt low-carbon services, and the cloud platform monitors both supply and demand}. The cloud platform's improvement of S2 can encourage consumers to choose low-carbon strategies.

[0110] In summary, considering the bounded rationality characteristics of both suppliers and consumers, and taking into account the low-carbon cooperation incentives of cloud platforms, this study expands the two-party game between suppliers and consumers into a three-party game involving suppliers, consumers, and the cloud platform. It explores the stability conditions of the three-party strategy evolution and analyzes the evolution process of low-carbon strategies for suppliers and consumers under different cloud platform rewards and penalties. By defining the scope of rewards and penalties for cloud platforms to promote low-carbon cooperation between suppliers and consumers, this study provides a reference for both parties' low-carbon cooperation decisions and the cloud platform's regulatory decisions, thereby better leveraging the role of low-carbon incentives and promoting low-carbon cooperation between supply and demand enterprises.

[0111] refer to Figure 10 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 evolutionary analysis method for low-carbon regulation of cloud manufacturing services in the above embodiments.

[0112] refer to Figure 11 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 evolutionary analysis method for low-carbon regulation of cloud manufacturing services described in the above embodiments.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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. An evolutionary analysis method for low-carbon regulation of cloud manufacturing services, characterized in that, The evolutionary analysis method includes: Determine the strategy sets of suppliers, demanders, and cloud platforms, and analyze the game relationship among the three parties; Determine the strategy combination for the three parties based on the strategy sets of suppliers, demanders, and cloud platforms; Determine the parameters involved in the game between suppliers, demanders, and cloud platforms, and construct a payment matrix for the three parties under different strategy combinations, based on the strategy combinations of the three parties. Based on the payout matrices of the three parties under different strategy combinations, the payout functions of the three parties under different strategies are determined, and the replication dynamic equations of the three parties are derived based on the payout functions. Based on the three-party replication dynamic equation, the stability of the three-party strategy evolution is analyzed, and the equilibrium points and stability conditions of each equilibrium point in the three-party evolutionary game model are obtained. This study analyzes the impact of cloud platform rewards and penalties on the evolution of low-carbon cooperation between supply and demand through numerical simulation, and provides recommendations for cloud platform reward and penalty management for low-carbon cooperation between supply and demand.

2. The evolutionary analysis method for low-carbon regulation of cloud manufacturing services according to claim 1, characterized in that, The supplier's strategy set includes providing low-carbon manufacturing services (P) and providing traditional manufacturing services (NP); The demanders' strategy set includes adopting low-carbon manufacturing service A and adopting traditional manufacturing service NA; The cloud platform's strategy set includes regulated supply and demand sides (R) and unregulated supply and demand sides (NR).

3. The evolutionary analysis method for low-carbon regulation of cloud manufacturing services according to claim 2, characterized in that, The analysis of the game relationship among the three parties includes: When monitoring supply and demand on the cloud platform, if both parties join in low-carbon cooperation, the cloud platform will provide rewards; if neither party joins, the cloud platform will impose penalties.

4. The evolutionary analysis method for low-carbon regulation of cloud manufacturing services according to claim 2, characterized in that, The parameters involved in the supplier game include the revenue G1 of the supplier choosing to provide traditional manufacturing services NP, the input cost C1 of the supplier choosing to provide low-carbon manufacturing services P, and the revenue growth rate β of the supplier choosing to provide low-carbon manufacturing services P. The parameters involved in the demander game include the revenue G2 of the demander choosing to adopt traditional manufacturing service NA, the additional cost C2 of the demander choosing to adopt low-carbon manufacturing service A, and the revenue growth rate ρ of the demander choosing to adopt low-carbon manufacturing service A. The parameters involved in the cloud platform game include the cloud platform's benefit G3 from choosing not to regulate both supply and demand sides NR, and the cloud platform's regulatory costs C. r The cloud platform provides rewards (S1) to suppliers who choose to provide low-carbon manufacturing services (P), rewards (S2) to customers who choose low-carbon manufacturing services (A), penalties (P1) to suppliers who choose to provide traditional manufacturing services (NP), and government subsidies (T) to the cloud platform. i .

5. The evolutionary analysis method for low-carbon regulation of cloud manufacturing services according to claim 4, characterized in that, If the proportion of suppliers choosing to provide low-carbon manufacturing services P is X, then the proportion of suppliers choosing to provide traditional manufacturing services NP is 1-X. If the proportion of demanders choosing low-carbon manufacturing services A is Y, then the proportion of demanders choosing traditional manufacturing services NA is 1-Y. If the proportion of the cloud platform choosing to regulate both supply and demand sides R is Z, then the proportion of the cloud platform choosing not to regulate both supply and demand sides NR is 1-Z. In this case, the supplier's replication dynamic equation is: F(X)=X(1-X)(U X -U 1-X )=X(1-X)[Z(S1+P1)+βG1-C1] The replication dynamic equation for demanders is: H(Y)=Y(1-Y)(U Y -OR 1-Y )=Y(1-Y)(ρG2+ZS2-C2) The replication dynamic equation for the cloud platform is: W(Z)=Z(1-Z)(U z -AT 1-z )=Z(1-Z)[-C r +P1+T4+X(T2-T4-P1-S1)+Y(T3-T4-S2)+XY(T1-T2-T3+T4)]。 6. The evolutionary analysis method for low-carbon regulation of cloud manufacturing services according to claim 4, characterized in that, The analysis of the impact of the cloud platform's reward and punishment mechanisms on the evolution of low-carbon cooperation between supply and demand includes the impact of the cloud platform's punishment P1 on suppliers choosing to provide traditional manufacturing services NP on the evolution of supplier strategies, the impact of the cloud platform's reward S1 on suppliers choosing to provide low-carbon manufacturing services P on the evolution of supplier strategies, and the impact of the cloud platform's reward S2 on demanders choosing to adopt low-carbon manufacturing services A on the evolution of demander strategies.

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

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