Carbon emission reduction scheme recommendation evaluation method and device, equipment and storage medium

Abstract

The invention discloses a carbon emission reduction scheme recommendation evaluation method, device and equipment and a storage medium, and relates to the technical field of carbon emission reduction management, and the method comprises the steps: building a carbon emission reduction scheme recommendation evaluation environment state according to the carbon emission reduction scheme historical selection data and enterprise social learning information of a plurality of enterprises; based on the carbon emission reduction scheme recommendation evaluation environment state and the carbon emission reduction scheme recommended to the enterprise, constructing a state-action value function; and calculating an optimal carbon emission reduction scheme recommended to an enterprise through the state-action value function. By comprehensively considering the historical selection data of the carbon emission reduction scheme of the enterprise and the social learning information of the enterprise, the method can more accurately grasp the preferences and demands of the enterprise, can adapt to multi-dimensional scheme characteristics and a dynamic market environment, gives full play to the experience sharing advantage between the enterprises, and improves the experience sharing efficiency of the enterprises. Therefore, a proper carbon emission reduction scheme is recommended for an enterprise more effectively.

Description

Technical Field

[0001] The present application relates to the technical field of carbon emission reduction management, and more specifically, to a carbon emission reduction scheme recommendation and evaluation method, device, equipment and storage medium. Background Art

[0002] Against the backdrop of global climate change and environmental protection, carbon emission reduction has become the focus of attention of governments and enterprises around the world. The importance of carbon emission reduction plan recommendations has become increasingly prominent. By recommending appropriate carbon emission reduction plans to enterprises, carbon emissions can be reduced more effectively, energy efficiency can be improved, and the goal of "carbon peak and carbon neutrality" can be achieved. For enterprises, choosing an efficient carbon emission reduction plan can not only reduce operating costs, but also gain more benefits in the carbon trading market. Therefore, providing accurate carbon emission reduction plan recommendations is crucial to promoting the carbon emission reduction process of the entire society.

[0003] However, the current recommendation of carbon emission reduction solutions faces many challenges. First, enterprises have different needs and preferences for carbon emission reduction solutions. Different enterprises have different emphases on energy-saving effects, implementation costs, and technological maturity, which makes the personalization of recommended solutions particularly complicated. Secondly, the selection of carbon emission reduction solutions not only involves technical characteristics, but is also affected by market dynamics, policies and regulations, and social cognition. Therefore, how to integrate multi-dimensional information and provide accurate solution recommendations is an important problem facing the recommendation of carbon emission reduction solutions. In addition, when choosing a solution, enterprises often refer to the evaluation and experience of other enterprises, but the current system does not fully utilize this social learning process, resulting in the recommendation results being difficult to meet actual needs.

[0004] The current carbon reduction solution recommendation system still has certain shortcomings in solving the above problems. Traditional recommendation algorithms often find it difficult to adapt to complex multi-dimensional solution characteristics and dynamic market environments, and they also lack flexibility in dealing with changes in corporate preferences. In addition, existing recommendation systems often ignore information exchange and social influence between companies, which limits the actual effect of recommendation solutions and makes it difficult to give full play to the advantages of experience sharing between companies. Therefore, the existing technology urgently needs a more intelligent and more adaptable recommendation method for complex environments to improve the accuracy and applicability of recommendations. Summary of the invention

[0005] In response to at least one defect or improvement need in the prior art, the present invention provides a carbon emission reduction plan recommendation and evaluation method, device, equipment and storage medium, which are used to solve the problems that the recommendation algorithm in the prior art is difficult to adapt to the multi-dimensional plan characteristics and dynamic market environment, lacks flexibility in dealing with changes in corporate preferences, and ignores information exchange and social impact between enterprises, making it difficult to give full play to the advantages of experience sharing between enterprises.

[0006] To achieve the above object, according to a first aspect of the present invention, a carbon emission reduction scheme recommendation and evaluation method is provided, comprising: Establish carbon reduction plan recommendations and evaluate environmental status based on historical carbon reduction plan selection data of multiple companies and corporate social learning information; Based on the carbon reduction scheme recommendation, the environmental status is evaluated and the carbon reduction scheme recommended to the enterprise is constructed into a state-action value function; The optimal carbon reduction plan recommended to enterprises is calculated through the state-action value function.

[0007] In one possible implementation, the established carbon reduction program recommends assessing the state of the environment for: ; in, Indicates the enterprise The characteristic vector of Indicates carbon reduction solutions , where the solution number , provided by Carbon Reduction Solutions Management, Indicates that the scheme The characteristic parameters on the features, Represents the number of solution features, that is, the number of dimensions of the multidimensional vector, Indicates the enterprise In time The social learning information of each plan is obtained based on the social learning model.

[0008] In one possible implementation, social learning information for: ; in, Indicates the enterprise In time Time for decision making The prior confidence of It represents the enterprise's perceived probability of the current solution, which is calculated by the solution characteristics and the perception model.

[0009] In one possible implementation, the state-action value function The update formula is: ; Among them, action Express to enterprises Recommend a solution , represents the learning rate, represents the discount factor, represents the reward function, Indicates in status The state-action value function corresponding to the optimal action when are the network parameters of the DQN model.

[0010] In one possible implementation, the reward function for: ; in, Indicates the instant reward value.

[0011] In one possible implementation, when considering the satisfaction of the enterprise with the recommended carbon reduction plan, the reward function Updated to: ; in, Indicates the satisfaction adjustment value.

[0012] In one possible implementation, based on the state-action value function The update formula is trained to determine the best carbon reduction solution:

[0013] in, is a random number sampled uniformly from the range [0,1]. represents the exploration probability, Represents the optional action space, which contains all the carbon emission reduction solutions that can be recommended.

[0014] According to a second aspect of the present invention, there is also provided a carbon emission reduction scheme recommendation and evaluation device, comprising: An environmental status establishment module, which is configured to establish a carbon emission reduction scheme recommendation evaluation environmental status based on the carbon emission reduction scheme historical selection data of multiple enterprises and the enterprise social learning information; a function building module configured to build a state-action value function based on the carbon reduction solution recommendation evaluation environmental state and the carbon reduction solution recommended to the enterprise; The recommendation calculation module is configured to calculate the best carbon emission reduction plan recommended to the enterprise through a state-action value function.

[0015] According to the third aspect of the present invention, there is also provided a carbon emission reduction scheme recommendation and evaluation device, which includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit executes the steps of any one of the above-mentioned carbon emission reduction scheme recommendation and evaluation methods.

[0016] According to the fourth aspect of the present invention, there is also provided a storage medium storing a computer program executable by an access authentication device, wherein when the computer program runs on the access authentication device, the access authentication device executes any of the steps of the carbon emission reduction scheme recommendation and evaluation method described above.

[0017] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art: The carbon reduction scheme recommendation and evaluation method provided by the present invention can more accurately grasp the preferences and needs of enterprises by comprehensively considering the historical selection data of carbon reduction schemes of enterprises and the social learning information of enterprises, and can adapt to the characteristics of multi-dimensional schemes and dynamic market environments. At the same time, it takes into account the information exchange and social impact between enterprises, and gives full play to the experience sharing advantages between enterprises, which helps to improve the acceptance of enterprises to the recommended schemes, thereby more effectively recommending suitable carbon reduction schemes to enterprises. The use of state-action value function to calculate the best carbon reduction scheme is a scientific decision-making method based on data and algorithms, which can comprehensively consider the effects of different carbon reduction schemes under different environmental conditions, and help to maximize the carbon reduction effect under limited resource conditions and improve resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic diagram of a flow chart of an embodiment of a carbon emission reduction scheme recommendation and evaluation method provided by the present invention; Figure 2 A schematic diagram of the structure of an embodiment of a device for recommending and evaluating a carbon emission reduction scheme provided by the present invention; Figure 3 A schematic diagram of the structure of a carbon emission reduction solution recommendation and evaluation device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0021] The terms "first", "second", "third", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0022] The present invention provides a carbon emission reduction scheme recommendation evaluation method, device, equipment and storage medium, which are described below respectively.

[0023] See also Figure 1 , Figure 1 This is a flow chart of an embodiment of a method for recommending and evaluating a carbon emission reduction scheme provided by the present invention. In a specific embodiment of the present invention, a method for recommending and evaluating a carbon emission reduction scheme is disclosed, including: S101. Establish a carbon reduction plan recommendation and evaluate the environmental status based on the historical selection data of carbon reduction plans of multiple enterprises and the social learning information of enterprises; S102, constructing a state-action value function based on the carbon emission reduction scheme recommendation evaluation of the environmental state and the carbon emission reduction scheme recommended to the enterprise; S103. Calculate the best carbon emission reduction plan recommended to the enterprise through the state-action value function.

[0024] In the above embodiment, the historical selection data is the information such as the type of carbon reduction schemes, carbon reduction targets, budgets, preferences for scheme features, etc. selected by multiple enterprises in the past period of time. These data are helpful to understand the preferences, behavioral habits and emission reduction results of enterprises. Social learning information includes energy-saving efficiency, carbon emission reduction, implementation cost, etc. By integrating these data, enterprises are segmented and a multi-dimensional carbon emission reduction scheme recommendation and evaluation environment status is constructed. The carbon emission reduction scheme recommendation and evaluation environment status not only reflects the individual characteristics of the enterprise, but also integrates the social environment and market dynamics, providing a solid foundation for subsequent recommendation calculations.

[0025] In the state-action value function, the state is defined as a quantitative representation of the environmental state of the carbon reduction plan recommendation assessment, including the individual characteristics, historical behavior, social learning information, etc. of the enterprise. The action refers to the carbon reduction plan recommended to the enterprise, which can be a specific emission reduction measure (such as using energy-saving lamps, buying electric vehicles, etc.) or a more macro emission reduction strategy (such as reducing energy consumption, adopting renewable energy, etc.). Express to enterprises Recommend a solution .

[0026] Use Deep Q-Network (DQN) to estimate the state-action value function. The DQN model is a multi-layer neural network whose input is the state vector and output is the expected benefit (i.e., Q value) corresponding to each action. The structure of the model can be adjusted according to the complexity of the problem, and usually includes an input layer, a hidden layer, and an output layer.

[0027] The constructed state-action value function is used to calculate the best carbon reduction plan recommended to the enterprise. The current environmental state of the enterprise's carbon reduction plan recommendation is used as input and passed to the state-action value function. The function calculates and outputs the value of each carbon reduction plan (i.e., expected benefits or effects) based on the input state. These values ​​reflect the pros and cons of performing a specific action under a given state. According to the size of the output value, the carbon reduction plan with the largest value is selected as the recommendation result. This plan not only meets the individual characteristics and preferences of the enterprise, but also achieves the best emission reduction effect under the current environmental conditions. In the recommendation process, some additional factors need to be considered, such as cost, feasibility, sustainability, etc.

[0028] Compared with the prior art, the carbon reduction scheme recommendation and evaluation method provided in this embodiment can more accurately grasp the preferences and needs of enterprises by comprehensively considering the historical selection data of carbon reduction schemes of enterprises and the social learning information of enterprises, and can adapt to the characteristics of multi-dimensional schemes and dynamic market environments. At the same time, it considers the information exchange and social impact between enterprises, gives full play to the advantages of experience sharing between enterprises, and helps to improve the acceptance of enterprises to the recommended schemes, thereby more effectively recommending suitable carbon reduction schemes for enterprises. Using the state-action value function to calculate the best carbon reduction scheme is a scientific decision-making method based on data and algorithms, which can comprehensively consider the effects of different carbon reduction schemes under different environmental conditions, and help to maximize the carbon reduction effect under limited resource conditions and improve resource utilization efficiency.

[0029] In some embodiments of the present invention, the established carbon reduction plan recommends assessing the environmental status for: ; in, Indicates the enterprise The characteristic vector of Indicates carbon reduction solutions , where the solution number , provided by Carbon Reduction Solutions Management, Indicates that the scheme The characteristic parameters on the features, Represents the number of solution features, that is, the number of dimensions of the multidimensional vector, Indicates the enterprise In time The social learning information of each plan is obtained based on the social learning model.

[0030] In the above embodiment, when constructing a deep Q network (DQN), these state information will be used as input features and processed by a neural network to estimate the expected benefits (i.e., Q values) of each action under different states. Actions can represent the selection of different carbon reduction options, and the goal of DQN is to learn a strategy that selects the action with the maximum Q value (i.e., the optimal emission reduction option) under a given state.

[0031] In order to train DQN, a large number of state-action-reward pairs need to be collected as training data. This data can be obtained through simulation environments (such as simulators based on enterprise characteristics and emission reduction scheme characteristics) or actual operating systems (such as collecting data in actual carbon emission reduction recommendation systems). During the training process, DQN will continuously adjust its weights to minimize the difference between the predicted Q value and the actual Q value, thereby gradually optimizing its decision-making ability.

[0032] In some embodiments of the invention, social learning information for: ; in, Indicates the enterprise In time Time for decision making The prior confidence of It represents the enterprise's perceived probability of the current solution, which is calculated by the solution characteristics and the perception model.

[0033] In the above embodiment, in the process of building a carbon reduction scheme recommendation and evaluation system, social learning information It can be used as an important input feature to estimate the expected benefits (i.e., Q-value) of each action under different states. By combining the individual characteristics of the enterprise, the characteristics of the carbon reduction plan, and social learning information, the system can more accurately recommend emission reduction plans that meet the needs and preferences of the enterprise.

[0034] Based on information obtained from other companies, companies Update its perceived probability of each solution : ; in, Indicates the enterprise and The social distance weight between companies reflects the degree of trust between companies. The smaller the distance, the higher the trust.

[0035] In some embodiments of the present invention, the state-action value function The update formula is: ; Among them, action Express to enterprises Recommend a solution , represents the learning rate, represents the discount factor, represents the reward function, Indicates in status The state-action value function corresponding to the optimal action when are the network parameters of the DQN model.

[0036] In the above embodiment, DQN is used to estimate the state-action value function , that is, in the state Next select action The expected reward of . The learning rate Used to control the update step size, discount factor The importance of controlling future rewards, immediate rewards In Status Next action After obtaining, the network parameters Mainly weights and biases, which are used for training and prediction of deep neural networks.

[0037] In some embodiments of the invention, the reward function for: ; in, Indicates the instant reward value.

[0038] In the above embodiment, the instant reward value g is a positive number used to reward the enterprise for accepting the recommendation. Correspondingly, if the enterprise rejects the recommendation, a negative reward is given − g ,This design encourages the system to recommend more solutions that are accepted by enterprises.

[0039] In some embodiments of the present invention, when considering the satisfaction of the enterprise with the recommended carbon emission reduction plan, the reward function Updated to: ; in, Indicates the satisfaction adjustment value.

[0040] In the above embodiment, in actual application, only considering whether the enterprise accepts the recommendation plan may not be enough to fully reflect the enterprise's satisfaction and the performance of the system. Therefore, in the subsequent embodiments, the reward function Updates can be made based on companies’ satisfaction with the recommended carbon reduction plans.

[0041] Satisfaction adjustment value y is a positive number used to reward the enterprise for accepting and being satisfied with the recommended solution. If the enterprise accepts the recommended solution but is not satisfied, no reward will be given (i.e., the reward is 0). If the enterprise rejects the recommended solution, a negative reward will be given − y This design not only takes into account the acceptance behavior of enterprises, but also their satisfaction with the solution, thus reflecting the performance of the system more comprehensively.

[0042] In practical applications, it is necessary to determine g and y The specific value of may need to be adjusted according to the actual situation of the system and the feedback from the enterprise. In addition, in order to accurately evaluate the enterprise's satisfaction with the recommended solution, the system may need to collect the enterprise's feedback data, such as ratings, comments, etc. These data can be used to update the enterprise's satisfaction model and further optimize the reward function and recommendation strategy.

[0043] In some embodiments of the present invention, based on the state-action value function The update formula is trained to determine the best carbon reduction solution:

[0044] in, is a random number sampled uniformly from the range [0,1]. represents the exploration probability, Represents the optional action space, which contains all the carbon emission reduction solutions that can be recommended.

[0045] In the above embodiment, Used to determine whether to explore or exploit. When Exploring new recommendation solutions can help the system avoid local optimal solutions. When the system selects the current state The action with the largest value , that is, make recommendations based on the currently learned optimal strategy. Control the balance between exploration and exploitation. The larger the value, the system tends to explore more to obtain more information. During the training process, gradually reduce , which makes the system shift from exploration to utilization, that is, it is more inclined to use the optimal strategy that has been learned.

[0046] By setting the maximum number of cycles to iteratively train the state-action value function, the optimal action is calculated. , to enterprises The recommended carbon reduction solution is .

[0047] After receiving the recommendation, the enterprise adjusts the perceived probability based on the social learning model And make decisions based on your own preferences. Purchase probability Expressed as: ; in, It represents the enterprise’s perception of the recommended solution and follows a normal distribution. Indicates perceived volatility.

[0048] The feedback data of each enterprise is recorded for further training and optimization of the model. Through multiple rounds of interaction and feedback, the DQN model continuously updates the recommendation strategy to make it closer to the actual preferences of the enterprise.

[0049] Use gradient descent to optimize the network parameters of the DQN model : ; in, is the learning rate.

[0050] In order to better implement the carbon emission reduction scheme recommendation and evaluation method in the embodiment of the present invention, based on the carbon emission reduction scheme recommendation and evaluation method, please refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of an embodiment of a carbon emission reduction scheme recommendation and evaluation device provided by the present invention. The embodiment of the present invention provides a carbon emission reduction scheme recommendation and evaluation device 200, including: An environmental status establishment module 210 is configured to establish a carbon emission reduction solution recommendation evaluation environmental status based on the carbon emission reduction solution historical selection data of multiple enterprises and the enterprise social learning information; A function construction module 220 configured to construct a state-action value function based on the carbon reduction solution recommendation evaluation environmental state and the carbon reduction solution recommended to the enterprise; The recommendation calculation module 230 is configured to calculate the best carbon emission reduction plan recommended to the enterprise through the state-action value function.

[0051] It should be noted here that: the device 200 provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above method embodiments, which will not be repeated here.

[0052] See also Figure 3 , Figure 3A schematic diagram of the structure of a carbon emission reduction scheme recommendation and evaluation device provided in an embodiment of the present invention. Based on the above carbon emission reduction scheme recommendation and evaluation method, the present invention also provides a carbon emission reduction scheme recommendation and evaluation device, which can be a computing device such as a mobile terminal, a desktop computer, a notebook, a PDA, and a server. The carbon emission reduction scheme recommendation and evaluation device 300 includes a processor 310, a memory 320, and a display 330. Figure 3 Only some components of the synchronous tracking flying welding equipment for real-time measurement of battery height are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0053] In some embodiments, the memory 320 may be an internal storage unit of the carbon emission reduction scheme recommendation and evaluation device 300, such as a hard disk or memory of the carbon emission reduction scheme recommendation and evaluation device 300. In other embodiments, the memory 320 may also be an external storage device of the carbon emission reduction scheme recommendation and evaluation device 300, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the carbon emission reduction scheme recommendation and evaluation device 300. Further, the memory 320 may also include both an internal storage unit and an external storage device of the carbon emission reduction scheme recommendation and evaluation device 300. The memory 320 is used to store application software and various data installed in the carbon emission reduction scheme recommendation and evaluation device 300, such as the program code for installing the carbon emission reduction scheme recommendation and evaluation device 300. The memory 320 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a carbon emission reduction scheme recommendation and evaluation program 340 is stored in the memory 320 , and the carbon emission reduction scheme recommendation and evaluation program 340 can be executed by the processor 310 , thereby implementing the carbon emission reduction scheme recommendation and evaluation method of each embodiment of the present application.

[0054] In some embodiments, the processor 310 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 320, such as executing a carbon emission reduction scheme recommendation evaluation method.

[0055] In some embodiments, the display 330 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 330 is used to display information on the carbon emission reduction scheme recommendation and evaluation device 300 and to display a visual user interface. The components 310-330 of the carbon emission reduction scheme recommendation and evaluation device 300 communicate with each other via a system bus.

[0056] In one embodiment, when the processor 310 executes the carbon emission reduction scheme recommendation and evaluation program 340 in the memory 320 , the steps in the above carbon emission reduction scheme recommendation and evaluation method are implemented.

[0057] This embodiment further provides a computer-readable storage medium on which a carbon emission reduction scheme recommendation and evaluation program is stored. When the carbon emission reduction scheme recommendation and evaluation program is executed by a processor, the following steps are implemented: Establish carbon reduction plan recommendations and evaluate environmental status based on historical carbon reduction plan selection data of multiple companies and corporate social learning information; Based on the carbon reduction scheme recommendation, the environmental status is evaluated and the carbon reduction scheme recommended to the enterprise is constructed into a state-action value function; The optimal carbon reduction plan recommended to enterprises is calculated through the state-action value function.

[0058] In summary, the carbon reduction scheme recommendation and evaluation method provided by the present invention can more accurately grasp the preferences and needs of enterprises by comprehensively considering the historical selection data of carbon reduction schemes of enterprises and the social learning information of enterprises, and can adapt to the multi-dimensional scheme characteristics and dynamic market environment. At the same time, it takes into account the information exchange and social impact between enterprises, and gives full play to the experience sharing advantages between enterprises, which helps to improve the acceptance of enterprises to the recommended schemes, thereby more effectively recommending suitable carbon reduction schemes to enterprises. Using the state-action value function to calculate the best carbon reduction scheme is a scientific decision-making method based on data and algorithms, which can comprehensively consider the effects of different carbon reduction schemes under different environmental conditions, and help to maximize the carbon reduction effect under limited resource conditions and improve resource utilization efficiency.

[0059] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0060] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0061] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0062] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0063] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0065] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0066] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0067] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0068] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A carbon emission reduction scheme recommendation and evaluation method, characterized in that: include: Establish carbon reduction plan recommendations and evaluate environmental status based on historical carbon reduction plan selection data of multiple companies and corporate social learning information; Constructing a state-action value function based on the carbon emission reduction scheme recommendation evaluation environmental state and the carbon emission reduction scheme recommended to the enterprise; The optimal carbon emission reduction plan recommended to the enterprise is calculated through the state-action value function.

2. The carbon emission reduction scheme recommendation and evaluation method according to claim 1, characterized in that: The carbon reduction program established is recommended to assess the environmental status for: ； in, Indicates the enterprise The characteristic vector of Indicates carbon reduction solutions , where the solution number , provided by Carbon Reduction Solutions Management, Indicates that the scheme The characteristic parameters on the features, Represents the number of solution features, that is, the number of dimensions of the multidimensional vector, Indicates the enterprise In time The social learning information of each plan is obtained based on the social learning model.

3. The carbon emission reduction scheme recommendation and evaluation method according to claim 2, characterized in that: The social learning information for: ； in, Indicates the enterprise In time Time for decision making The prior confidence of It represents the enterprise's perceived probability of the current solution, which is calculated by the solution characteristics and the perception model.

4. The carbon emission reduction scheme recommendation and evaluation method according to claim 1, characterized in that: The state-action value function The update formula is: ； Among them, action Express to enterprises Recommend a solution , represents the learning rate, represents the discount factor, represents the reward function, Indicates in status The state-action value function corresponding to the optimal action when are the network parameters of the DQN model.

5. The carbon emission reduction scheme recommendation and evaluation method according to claim 4, characterized in that: The reward function for: ； in, Indicates the instant reward value.

6. The carbon emission reduction scheme recommendation and evaluation method according to claim 5, characterized in that: When considering the satisfaction of enterprises with the recommended carbon reduction solutions, the reward function Updated to: ； in, Indicates the satisfaction adjustment value.

7. The carbon emission reduction scheme recommendation and evaluation method according to claim 4, characterized in that: Based on the state-action value function The update formula is trained to determine the best carbon reduction solution: in, is a random number sampled uniformly from the range [0,1]. represents the exploration probability, Represents the optional action space, which contains all the carbon emission reduction solutions that can be recommended.

8. A carbon emission reduction scheme recommendation and evaluation device, characterized in that: include: An environmental status establishment module, which is configured to establish a carbon emission reduction scheme recommendation evaluation environmental status based on the carbon emission reduction scheme historical selection data of multiple enterprises and the enterprise social learning information; A function building module configured to build a state-action value function based on the carbon emission reduction solution recommendation evaluation environmental state and the carbon emission reduction solution recommended to the enterprise; The recommendation calculation module is configured to calculate the best carbon emission reduction plan recommended to the enterprise through the state-action value function.

9. A carbon emission reduction scheme recommendation and evaluation device, characterized in that: It comprises at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit executes the steps of the carbon emission reduction scheme recommendation and evaluation method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: It stores a computer program executable by an access authentication device. When the computer program runs on the access authentication device, the access authentication device executes the steps of the carbon emission reduction scheme recommendation and evaluation method according to any one of claims 1 to 7.

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