Block chain energy optimization method and device, equipment, medium and computer program product

Abstract

The invention relates to a block chain energy optimization method and device, equipment, a medium and a computer program product. The method comprises the following steps: acquiring alternative energy data, and establishing an evaluation criterion corresponding to the alternative energy data; respectively establishing a weighted sum model, a risk aversion decision model and a fairness criterion weight model according to the alternative energy data and the evaluation criterion; establishing a multi-target comprehensive optimization model according to the weighted sum model, the risk aversion decision model and the fairness criterion weight model; and determining an energy decision scheme of the current block chain through the multi-target comprehensive optimization model. By adopting the method, the block chain energy optimization efficiency can be improved, and robustness, fairness and optimization efficiency can be considered.

Description

Technical Field

[0001] This application relates to the field of blockchain technology, and in particular to a blockchain energy optimization method, apparatus, device, medium, and computer program product. Background Technology

[0002] Blockchain technology is widely used in supply chain management due to its advantages such as decentralization and security. However, the energy-intensive nature of blockchain, resulting from massive computation and data mining, poses significant challenges to business and the environment, leading to substantial operational and environmental risks. Integrating renewable energy has become a key solution to mitigate this problem. The evaluation of these alternatives involves various aspects, including sustainable energy transfer, tangible attributes, legal regulations, energy supply costs, technological infrastructure, and climate constraints.

[0003] Current research on integrating renewable energy optimization models with blockchain technology suffers from problems such as low optimization efficiency and an inability to balance robustness, fairness, and optimization efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a blockchain energy optimization method, device, equipment, medium, and computer program product that can improve the efficiency of blockchain energy optimization and balance robustness, fairness, and optimization efficiency, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a blockchain energy optimization method, the method comprising:

[0006] Acquire alternative energy data and establish evaluation criteria corresponding to the alternative energy data;

[0007] Based on the alternative energy data and the evaluation criteria, a weighted summation model, a risk aversion decision-making model, and a fairness criterion weight model are established respectively.

[0008] A multi-objective comprehensive optimization model is established based on the weighted summation model, the risk aversion decision-making model, and the fairness criterion weight model.

[0009] The energy decision scheme for the current blockchain is determined through the multi-objective comprehensive optimization model.

[0010] In some embodiments of the method, establishing a multi-objective comprehensive optimization model based on the weighted summation model, the risk aversion decision model, and the fairness criterion weight model includes:

[0011] The adjacent criterion weight difference of the alternative energy data is determined by the fairness criterion weight model.

[0012] The positive and negative biases of the alternative energy data are determined by the weighted summation model and the risk aversion decision model, and the total bias is determined based on the positive and negative biases.

[0013] The weighted sum of the adjacent criterion weight difference and the total deviation, combined with the corresponding weight factors, is then minimized.

[0014] In some embodiments of the method, establishing a multi-objective comprehensive optimization model based on the weighted summation model, the risk aversion decision model, and the fairness criterion weight model further includes:

[0015] The first score of the alternative energy data is determined using the risk aversion decision model.

[0016] The second score of the alternative energy data is determined using the weighted summation model.

[0017] The difference between the first score and the second score is dynamically adjusted by combining the dynamic adjustment factor corresponding to the current blockchain.

[0018] The first score represents the lowest score of the alternative energy data determined based on the risk aversion decision model, and the second score represents the highest score of the alternative energy data determined based on the weighted summation model.

[0019] In some embodiments of the method, the weighted summation model is used to maximize the summation of the alternative energy data in combination with the weighting factors corresponding to the evaluation criteria.

[0020] In some embodiments of the method, the risk-averse decision model is used to maximize the minimum score of all alternative energy options.

[0021] In some embodiments of the method, the fairness criterion weight model is used to minimize the difference in adjacent criterion weights of the alternative energy data.

[0022] According to a second aspect of the present disclosure, a blockchain energy optimization device is provided, the device comprising:

[0023] The evaluation criteria module is used to acquire alternative energy data and establish evaluation criteria corresponding to the alternative energy data.

[0024] The first model building module is used to build a weighted summation model, a risk aversion decision model, and a fairness criterion weight model based on the alternative energy data and the evaluation criteria, respectively.

[0025] The second model building module is used to build a multi-objective comprehensive optimization model based on the weighted summation model, the risk aversion decision model, and the fairness criterion weight model.

[0026] The output module is used to determine the energy decision scheme of the current blockchain through the multi-objective comprehensive optimization model.

[0027] According to a third aspect of the present disclosure, a computer device is provided. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0028] Acquire alternative energy data and establish evaluation criteria corresponding to the alternative energy data;

[0029] Based on the alternative energy data and the evaluation criteria, a weighted summation model, a risk aversion decision-making model, and a fairness criterion weight model are established respectively.

[0030] A multi-objective comprehensive optimization model is established based on the weighted summation model, the risk aversion decision-making model, and the fairness criterion weight model.

[0031] The energy decision scheme for the current blockchain is determined through the multi-objective comprehensive optimization model.

[0032] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0033] Acquire alternative energy data and establish evaluation criteria corresponding to the alternative energy data;

[0034] Based on the alternative energy data and the evaluation criteria, a weighted summation model, a risk aversion decision-making model, and a fairness criterion weight model are established respectively.

[0035] A multi-objective comprehensive optimization model is established based on the weighted summation model, the risk aversion decision-making model, and the fairness criterion weight model.

[0036] The energy decision scheme for the current blockchain is determined through the multi-objective comprehensive optimization model.

[0037] According to a fifth aspect of the present disclosure, a computer program product is provided. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0038] Acquire alternative energy data and establish evaluation criteria corresponding to the alternative energy data;

[0039] Based on the alternative energy data and the evaluation criteria, a weighted summation model, a risk aversion decision-making model, and a fairness criterion weight model are established respectively.

[0040] A multi-objective comprehensive optimization model is established based on the weighted summation model, the risk aversion decision-making model, and the fairness criterion weight model.

[0041] The energy decision scheme for the current blockchain is determined through the multi-objective comprehensive optimization model.

[0042] The blockchain energy optimization scheme provided in this application can establish a weighted summation model, a risk-averse decision-making model, and a fairness criterion weight model by combining alternative energy data and evaluation criteria. The weighted summation model can screen out the most suitable alternative energy for high-load blockchain scenarios. By introducing the concept of risk aversion, the worst-case scenario of energy substitution schemes can be optimized, thereby improving the robustness of the system. Furthermore, by balancing the importance of different criteria, the blockchain design can avoid over-reliance on a single criterion, ensuring the comprehensive sustainability of energy selection. The multi-objective comprehensive optimization model can combine the weighted summation principle, risk-averse decision-making, and fairness principle to dynamically balance the multi-dimensional needs of blockchain energy optimization scenarios.

[0043] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0045] Figure 1 This is a flowchart illustrating a blockchain energy optimization method according to an exemplary embodiment;

[0046] Figure 2 This is a flowchart illustrating the steps of establishing a multi-objective integrated optimization model according to an exemplary embodiment;

[0047] Figure 3 This is a flowchart illustrating the steps of establishing a multi-objective integrated optimization model according to another exemplary embodiment;

[0048] Figure 4 This is a structural block diagram of a blockchain energy optimization device according to an exemplary embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0050] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure. The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitation, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. For example, the use of terms such as "first," "second," etc., is to denote names and does not indicate any specific order.

[0051] In some implementations provided in this disclosure, the execution of the blockchain energy optimization method can be controlled by a unified controller or by multiple controllers. These controllers may include controllers on local terminals or controllers on remote servers. In some implementations, the controllers on local terminals and the controllers on servers may work together to complete the blockchain energy optimization scheme. The local terminal mentioned in this disclosure may include, but is not limited to, various robotic devices, in-vehicle devices, personal computers, laptops, smartphones, tablets, wearable devices, medical devices, VR (Virtual Reality) devices, etc. The server may also be a server, server cluster, distributed subsystem, cloud processing platform, server containing blockchain nodes, or a combination thereof. The controllers described in this disclosure may include various control units capable of implementing logic processing functions, including but not limited to CPU (Central Processing Unit), PLC (Programmable Logic Controller), ECU (Electronic Control Unit), MCU (Microcontroller Unit), FPGA (Field Programmable Gate Array), and CPLD (Complex Programmable Logic Device), as well as controllers composed of one or more logic function units, chips, etc.

[0052] In some embodiments of this disclosure, a blockchain energy optimization method is provided, such as... Figure 1 As shown, the method includes:

[0053] S20. Obtain alternative energy data and establish evaluation criteria corresponding to the alternative energy data.

[0054] In some embodiments of this disclosure, alternative energy data typically refers to information on alternative energy sources. Alternative energy sources can include at least one of wind, solar, biomass, geothermal, and hydropower. Each alternative energy source has its unique advantages and potential risks in a blockchain scenario. In some examples, wind energy can be defined as A1, with advantages of low space requirements and continuous power generation; however, its risks in a blockchain scenario include high initial costs and instability due to wind speed fluctuations. Solar energy can be defined as A2, with advantages of flexible deployment and low maintenance costs; however, its risks in a blockchain scenario include susceptibility to fluctuations in sunlight (e.g., power outages at night / on cloudy days). Biomass energy can be defined as A3, with advantages of simple storage and strong dispatchability; however, its risks in a blockchain scenario include long production cycles and the need for large amounts of arable land. Geothermal energy can be defined as A4, with advantages of being unaffected by climate and high stability; however, its risks in a blockchain scenario include high secondary processing costs. Hydropower can be defined as A5, with advantages of low installation costs and high power generation efficiency; however, its risks in a blockchain scenario include significant ecological impact.

[0055] In some embodiments of this disclosure, the evaluation criteria typically refer to criteria that quantify the suitability of alternative energy sources in blockchain applications. Evaluation criteria may include at least one of the following: sustainable energy transfer criteria, geographical adaptability criteria, climate constraint criteria, legal compliance criteria, energy cost criteria, and technical infrastructure criteria. In some examples, the sustainable energy transfer criterion can be defined as C1, requiring a continuous and stable energy supply for the blockchain's 24 / 7 operation, addressing the hard requirement of uninterrupted blockchain node operation. The geographical adaptability criterion can be defined as C2, corresponding to energy adaptability to the distributed layout of blockchain nodes, matching the decentralized architecture and avoiding single points of failure caused by centralized energy. The climate constraint criterion can be defined as C3, corresponding to the reliability of energy under extreme weather conditions, preventing blockchain network downtime due to climate fluctuations. The legal compliance criterion can be defined as C4, corresponding to compliance with regional renewable energy regulations, meeting the compliance requirements for cross-border blockchain deployment and avoiding policy risks. The energy cost criterion can be defined as C5, corresponding to the economic feasibility of blockchain under high energy consumption requirements, directly related to operating costs and affecting the willingness of blockchain nodes to participate. The technical infrastructure criterion can be defined as C6, corresponding to the engineering capabilities supporting the integration of energy and blockchain, ensuring seamless access of energy to blockchain hardware facilities.

[0056] S22. Based on the alternative energy data and the evaluation criteria, establish a weighted summation model, a risk aversion decision-making model, and a fairness criterion weight model, respectively.

[0057] In some embodiments of this disclosure, a weighted summation model can be established based on alternative energy data and evaluation criteria. This model filters out the most suitable alternative energy sources for high-load blockchain scenarios through weighted summation. A risk-averse decision-making model can also be established based on alternative energy data and evaluation criteria. This model optimizes the worst-case scenario of energy substitution solutions by introducing the concept of risk aversion, thereby improving the robustness of the system. Furthermore, a fairness criterion weighting model can be established based on alternative energy data and evaluation criteria. This model balances the importance of different criteria, preventing the blockchain design from over-relying on a single criterion and ensuring the comprehensive sustainability of energy selection.

[0058] S24. Establish a multi-objective comprehensive optimization model based on the weighted summation model, the risk aversion decision model, and the fairness criterion weight model.

[0059] In some embodiments of this disclosure, a multi-objective comprehensive optimization model can be established based on the weighted summation model, the risk-averse decision-making model, and the fairness criterion weight model. This multi-objective comprehensive optimization model combines the weighted summation principle, risk-averse decision-making, and fairness principle to dynamically balance the multi-dimensional needs of blockchain energy optimization scenarios.

[0060] S26. Determine the energy decision scheme for the current blockchain through the multi-objective comprehensive optimization model.

[0061] In some embodiments of this disclosure, a final energy score corresponding to the current blockchain can be generated through a multi-objective comprehensive optimization model. This final energy score can be used to assess the applicability of alternative energy sources in current blockchain applications.

[0062] The blockchain energy optimization scheme provided in this application can establish a weighted summation model, a risk-averse decision-making model, and a fairness criterion weight model by combining alternative energy data and evaluation criteria. The weighted summation model can screen out the most suitable alternative energy for high-load blockchain scenarios. By introducing the concept of risk aversion, the worst-case scenario of energy substitution schemes can be optimized, thereby improving the robustness of the system. Furthermore, by balancing the importance of different criteria, the blockchain design can avoid over-reliance on a single criterion, ensuring the comprehensive sustainability of energy selection. The multi-objective comprehensive optimization model can combine the weighted summation principle, risk-averse decision-making, and fairness principle to dynamically balance the multi-dimensional needs of blockchain energy optimization scenarios.

[0063] In some embodiments of this disclosure, the weighted summation model is used to maximize the summation of alternative energy data with weighted factors of corresponding evaluation criteria.

[0064] In some embodiments of this disclosure, the weighted summation model filters out the most suitable alternative energy sources for high-load blockchain scenarios through weighted summation. In some examples, the alternative energy data can be maximized by weighting and summing the weights of the corresponding evaluation criteria using the following formula (1):

[0065] (1)

[0066] In equation (1), w i x is the weighting factor for evaluation criterion i. ij Let j be the score of alternative energy on evaluation criterion i. The most suitable alternative energy for high-load blockchain scenarios can be selected by maximizing the weighted sum of the total score using equation (1).

[0067] In other examples, the total score can be normalized using the following formula (2) to prevent over-optimization of a single evaluation criterion:

[0068] (2)

[0069] In equation (2), It is a very small positive number to prevent the weight factor of evaluation criterion i from reaching zero.

[0070] In some embodiments of this disclosure, over-optimization of a single evaluation criterion can be prevented by normalizing the total score, while alternative energy sources most suitable for high-load blockchain scenarios can be screened by maximizing the weighted sum of the total score.

[0071] In some embodiments of this disclosure, the risk-averse decision model is used to maximize the minimum score of all alternative energy options.

[0072] In some embodiments of this disclosure, the worst-case scenario of energy alternatives can be optimized by introducing the concept of risk aversion, thereby improving the robustness of the system. In some examples, the worst-case scenario of energy alternatives can be optimized using the following equations (3) and (4):

[0073] (3)

[0074] (4)

[0075] In equation (3), ρ is the minimum score among all alternative energy sources, and ρ characterizes the energy adaptability in the worst-case scenario. In equation (4), w i x is the weighting factor for evaluation criterion i. ij Let j be the score on the evaluation criterion i. Equations (3) and (4) can be used to address the stability requirements of the blockchain (such as preventing downtime) and ensure that the alternative energy can still meet the minimum supply threshold even in harsh environments (such as insufficient wind).

[0076] In some embodiments of this disclosure, the worst-case scenario of energy alternatives can be optimized by introducing the concept of risk aversion, ensuring that the worst-case scenario of the alternatives still meets the corresponding requirements of the blockchain criteria, thereby improving the robustness of energy optimization.

[0077] In some embodiments of this disclosure, the fairness criterion weight model is used to minimize the adjacent criterion weight difference of the alternative energy data.

[0078] In some embodiments of this disclosure, the overall sustainability of energy selection can be ensured by balancing the importance of different criteria to avoid over-reliance on a single criterion in blockchain design. In some examples, the weight difference between adjacent criteria for alternative energy data can be minimized using the following equation (5):

[0079] (5)

[0080] In equation (5), The weight difference between adjacent criteria is used to achieve fair allocation by minimizing the weight difference between adjacent criteria.

[0081] In some embodiments of this disclosure, the importance of different criteria can be balanced by minimizing the weight difference between adjacent criteria of alternative energy data, avoiding excessive reliance on a single criterion in blockchain design, and ensuring the comprehensive sustainability of energy selection.

[0082] In some embodiments of this disclosure, reference is made to Figure 2 S24 includes:

[0083] S241. Determine the adjacent criterion weight difference of the alternative energy data through the fairness criterion weight model;

[0084] S242. Determine the positive and negative biases of the alternative energy data using the weighted summation model and the risk aversion decision model, and determine the total bias based on the positive and negative biases;

[0085] S243. The adjacent criterion weight difference and the total deviation are weighted and summed together with the corresponding weight factors, and then minimized.

[0086] In some embodiments of this disclosure, a multi-objective comprehensive optimization model can be used to combine the weighted sum principle, risk-averse decision-making, and fairness principle to dynamically balance the multi-dimensional needs of blockchain energy optimization scenarios. In some examples, the following formula (6) can be used to minimize the weighted sum of the adjacent criterion weight difference and the total deviation combined with the corresponding weight factors:

[0087] (6)

[0088] In equation (6), The difference in weights between adjacent criteria. , These are the negative and positive biases mentioned above. The negative bias represents the negative difference between the actual score and the target value, while the positive bias represents the positive difference between the actual score and the target value. K1 and K2 are the weighting factors corresponding to the weight difference between adjacent criteria and the total bias, respectively. By minimizing the weighted sum of the weight difference between adjacent criteria and the total bias combined with the corresponding weighting factors, the balance of weight allocation can be ensured, while making the final result close to the ideal score range of the three models: the fairness criterion weight model, the weighted sum model, and the risk aversion decision model.

[0089] In some embodiments of this disclosure, the weight distribution can be balanced by minimizing the weighted sum of the adjacent criterion weight differences and the total deviation combined with the corresponding weight factors. This ensures the final result approximates the ideal score range of the fairness criterion weight model, the weighted sum model, and the risk-averse decision model. The optimization results of these three models can be inherited and integrated to obtain a result that balances robustness, fairness, and efficiency.

[0090] In some embodiments of this disclosure, reference is made to Figure 3 S24 also includes:

[0091] S244. Determine the first score of the alternative energy data using the risk aversion decision model;

[0092] S245. Determine the second score of the alternative energy data using the weighted summation model;

[0093] S246. The difference between the first score and the second score is dynamically adjusted by combining the dynamic adjustment factor corresponding to the current blockchain.

[0094] Wherein, the first score represents the lowest score of the alternative energy data determined based on the risk aversion decision model, and the second score represents the highest score of the alternative energy data determined based on the weighted summation model.

[0095] In some embodiments of this disclosure, a first score for the alternative energy data can be determined using a risk aversion decision model. , The lowest score for alternative energy source j in the risk aversion model represents the worst-case scenario for alternative energy source j in the risk aversion model; a second score for alternative energy source data can also be determined using a weighted summation model. , The highest score for alternative energy source j in the weighted summation model represents the optimal state of alternative energy source j in the weighted summation model. This can provide (…). ~ The score range is defined by equation (7). In some examples, the score range can be dynamically adjusted using the following equation (7) to adapt to the energy requirements of the blockchain in different scenarios:

[0096] (7)

[0097] In equation (7), This represents the lowest score for alternative energy source j in the risk aversion model. The highest score for alternative energy j in the weighted summation model. , These refer to the negative and positive deviations mentioned above, respectively. This is a dynamically adjusted factor. In some examples, ; .

[0098] In some embodiments of this disclosure, the score range can be adjusted dynamically by adjusting factors, thereby balancing the priorities among different models. This dynamic adjustment of the score range can also adapt to the energy demands of blockchain in different scenarios, achieving flexible adjustment of blockchain energy optimization. Furthermore, the blockchain can be used to record first and second score data, supporting credible traceability of the energy optimization solution's origin. The optimization results of three models—the fairness criterion weight model, the weighted summation model, and the risk-averse decision model—can be inherited and integrated to achieve multi-objective integration and dynamic adjustment, thus obtaining results that balance robustness, fairness, and efficiency.

[0099] The blockchain energy optimization methods disclosed herein can establish weighted summation models, risk-averse decision-making models, and fairness criterion weight models by combining alternative energy data and evaluation criteria. The weighted summation model can screen out the most suitable alternative energy for high-load blockchain scenarios. By introducing the concept of risk aversion, the worst-case scenario of energy substitution can be optimized, thereby improving the robustness of the system. Furthermore, by balancing the importance of different criteria, the blockchain design can avoid over-reliance on a single criterion, ensuring the comprehensive sustainability of energy selection. The multi-objective comprehensive optimization model can combine the weighted summation principle, risk-averse decision-making, and fairness principle to dynamically balance the multi-dimensional needs of blockchain energy optimization scenarios.

[0100] It is understood that the various embodiments of the methods described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. Relevant details can be found in the descriptions of other method embodiments.

[0101] It should be understood that although the steps in the flowcharts shown in the accompanying drawings are displayed sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise expressly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the accompanying drawings may include multiple steps or stages, which are not necessarily completed at the same time, but may be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least a portion of the steps or stages of other steps.

[0102] Based on the description of the above-described embodiments of the blockchain energy optimization method, this disclosure also provides a blockchain energy optimization device for implementing the aforementioned blockchain energy optimization method. The device may include a system (including a distributed system), software (application), module, component, controller, server, terminal, etc., using the method described in the embodiments of this specification, combined with necessary implementation hardware. Based on the same innovative concept, the devices in one or more embodiments provided in this disclosure are as described in the following embodiments. Since the implementation schemes and methods for solving the problem by the devices are similar, the implementation of specific devices in the embodiments of this specification can refer to the implementation of the aforementioned method, and repeated details will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0103] Figure 4 This is a schematic block diagram illustrating a blockchain energy optimization device according to an exemplary embodiment. The device can be the aforementioned terminal, a server, or a module, component, device, control unit, etc., integrated into the terminal. For details, please refer to... Figure 4 The device may include: an evaluation criteria module for acquiring alternative energy data and establishing evaluation criteria corresponding to the alternative energy data; a first model building module for establishing a weighted summation model, a risk aversion decision model, and a fairness criterion weight model based on the alternative energy data and the evaluation criteria; a second model building module for establishing a multi-objective comprehensive optimization model based on the weighted summation model, the risk aversion decision model, and the fairness criterion weight model; and an output module for determining the energy decision scheme of the current blockchain through the multi-objective comprehensive optimization model.

[0104] In some embodiments of the device, the second model building module is further configured to determine the adjacent criterion weight difference of the alternative energy data through the fairness criterion weight model; and to determine the positive and negative deviations of the alternative energy data through the weighted summation model and the risk aversion decision model, and to determine the total deviation based on the positive and negative deviations; and to perform a minimization calculation by weighting and summing the adjacent criterion weight difference and the total deviation with the corresponding weight factors.

[0105] In some embodiments of the device, the second model building module is further configured to determine a first score of the alternative energy data using the risk-averse decision model; and to determine a second score of the alternative energy data using the weighted summation model; and to dynamically adjust the difference between the first score and the second score by incorporating a dynamic adjustment factor corresponding to the current blockchain. The first score represents the lowest score of the alternative energy data determined based on the risk-averse decision model, and the second score represents the highest score of the alternative energy data determined based on the weighted summation model.

[0106] In some embodiments of the device, the weighted summation model is used to maximize the summation of the alternative energy data in combination with the weighting factors corresponding to the evaluation criteria.

[0107] In some embodiments of the device, the risk-averse decision model is used to maximize the minimum score of all alternative energy options.

[0108] In some embodiments of the device, the fairness criterion weight model is used to minimize the difference in adjacent criterion weights of the alternative energy data.

[0109] Each module in the aforementioned blockchain energy optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0110] Based on the foregoing description of the relevant methods and apparatus embodiments, this disclosure also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the blockchain energy optimization method described in any embodiment of this specification.

[0111] Based on the foregoing description of the relevant methods and apparatus embodiments, this disclosure also provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by the processor of a computer device, enables the computer device to implement the blockchain energy optimization method as described in any embodiment of this disclosure.

[0112] Based on the foregoing description of the relevant methods and apparatus embodiments, this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the blockchain energy optimization method described in any embodiment of this specification.

[0113] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, for hardware + program embodiments, since they are basically similar to the method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0114] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0115] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0116] It should be noted that the apparatus, computer equipment, storage medium, and computer program products described above may also include other implementation methods according to the description of the method embodiments. Specific implementation methods can be found in the description of the relevant method embodiments. Furthermore, new embodiments formed by combinations of features from various methods, apparatuses, devices, and server embodiments still fall within the scope of this disclosure and will not be elaborated upon here.

[0117] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, when implementing one or more of these specifications, the functions of each module can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling and communication connections between the devices or units shown or described can be implemented through direct and / or indirect coupling / connection, through standard or custom interfaces or protocols, and can be implemented electrically, mechanically, or in other forms.

[0118] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0119] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A blockchain energy optimization method, characterized in that, The method includes: Acquire alternative energy data and establish evaluation criteria corresponding to the alternative energy data; Based on the alternative energy data and the evaluation criteria, a weighted summation model, a risk aversion decision-making model, and a fairness criterion weight model are established respectively. A multi-objective comprehensive optimization model is established based on the weighted summation model, the risk aversion decision-making model, and the fairness criterion weight model. The energy decision scheme for the current blockchain is determined through the multi-objective comprehensive optimization model.

2. The method according to claim 1, characterized in that, The establishment of a multi-objective comprehensive optimization model based on the weighted summation model, the risk aversion decision-making model, and the fairness criterion weight model includes: The adjacent criterion weight difference of the alternative energy data is determined by the fairness criterion weight model. The positive and negative biases of the alternative energy data are determined by the weighted summation model and the risk aversion decision model, and the total bias is determined based on the positive and negative biases. The weighted sum of the adjacent criterion weight difference and the total deviation, combined with the corresponding weight factors, is then minimized.

3. The method according to claim 2, characterized in that, The step of establishing a multi-objective comprehensive optimization model based on the weighted summation model, the risk aversion decision-making model, and the fairness criterion weight model further includes: The first score of the alternative energy data is determined using the risk aversion decision model. The second score of the alternative energy data is determined using the weighted summation model. The difference between the first score and the second score is dynamically adjusted by combining the dynamic adjustment factor corresponding to the current blockchain. The first score represents the lowest score of the alternative energy data determined based on the risk aversion decision model, and the second score represents the highest score of the alternative energy data determined based on the weighted summation model.

4. The method according to claim 1, characterized in that, The weighted summation model is used to maximize the summation of the alternative energy data in combination with the weighting factors corresponding to the evaluation criteria.

5. The method according to claim 1, characterized in that, The risk-averse decision model is used to calculate the minimum score for all alternative energy options.

6. The method according to claim 1, characterized in that, The fairness criterion weight model is used to minimize the difference in adjacent criterion weights for the alternative energy data.

7. A blockchain energy optimization device, characterized in that, The device includes: The evaluation criteria module is used to acquire alternative energy data and establish evaluation criteria corresponding to the alternative energy data. The first model building module is used to build a weighted summation model, a risk aversion decision-making model, and a fairness criterion weight model based on the alternative energy data and the evaluation criteria, respectively. The second model building module is used to build a multi-objective comprehensive optimization model based on the weighted summation model, the risk aversion decision model, and the fairness criterion weight model. The output module is used to determine the energy decision scheme of the current blockchain through the multi-objective comprehensive optimization model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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