Collaborative optimization method and system for industrial chain of electric power system

Abstract

The invention relates to the technical field of collaborative optimization, and discloses a collaborative optimization method and system for an industrial chain of a power system, and the method comprises the steps: collecting target industrial chain data, and constructing a hierarchical data scene; generating a promotion strategy according to the hierarchical data scene and verifying; and collecting verification data of the promotion strategy, analyzing the effect of the promotion strategy according to the verification data and performing improvement, thereby realizing collaborative optimization of the target industry chain. According to the method, a data-strategy-application-feedback closed-loop optimization mechanism is formed by constructing a hierarchical data scene, formulating and verifying a popularization strategy and collecting feedback data, so that the collaboration efficiency and benefit of an industrial chain of a power system are improved. Core requirements of an industrial chain are identified through a hierarchical data scene, and a scientific basis is provided for formulation of a popularization strategy; practical case verification and data feedback ensure the practicability of the popularization strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative optimization, and in particular to a method and system for collaborative optimization of an industrial chain of a power system. Background Art

[0002] Accelerating the application of scientific and technological achievements and improving the level of transformation of scientific and technological achievements will play an important role in promoting the high-quality development of enterprises. In view of the urgent need for the digital transformation of the State Grid, the development of traditional power grids towards digitalization and intelligence will inevitably be accompanied by the birth of new major technological achievements. However, in the process of transformation of scientific and technological achievements, there are problems such as imbalance between output and industrialization and low efficiency of information transmission, which will restrict the release of the potential of scientific and technological achievements. Therefore, conducting research on the layout capabilities and cultivation strategies of major achievements for the development of the new power system industrial chain suitable for standard digital transformation, building a classification and grading research on key achievements, improving the multi-party collaboration model, and optimizing the output path of achievement content can better play the role of the major achievement system, promote a closer integration of science and technology with economic and social development, and accelerate the transformation of scientific and technological achievements into real productivity. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is that the process of transforming scientific and technological achievements faces problems such as an imbalance in the ratio of output to industrialization and low efficiency of information transmission.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a method for collaborative optimization of the industrial chain of an electric power system, comprising: collecting target industrial chain data and constructing a layered data scenario; generating and verifying a promotion strategy based on the layered data scenario; collecting verification data of the promotion strategy, analyzing the effect of the promotion strategy based on the verification data and making improvements to achieve collaborative optimization of the target industrial chain.

[0006] As a preferred solution of the method for collaborative optimization of the power system industrial chain described in the present invention, the generation and verification of promotion strategies based on layered data scenarios includes establishing promotion goals, and producing corresponding promotion strategies based on the promotion goals and layered data scenarios.

[0007] As a preferred solution of the collaborative optimization method of the power system industrial chain described in the present invention, the generation and verification of promotion strategies based on layered data scenarios also includes constructing empirical research scenarios, selecting typical regions or enterprises for empirical research, and verifying the feasibility and effectiveness of the promotion strategies through analysis and comparison of actual cases.

[0008] As a preferred solution of the collaborative optimization method of the power system industry chain described in the present invention, the feedback optimization includes establishing a continuous improvement scenario, combining verification data and industry development conditions, and continuously improving the promotion strategy.

[0009] As a preferred scheme of the collaborative optimization method of the power system industrial chain described in the present invention, wherein: the target industrial chain includes but is not limited to the power system industrial chain; the power industry chain data includes but is not limited to power generation data, transmission data, distribution data, power user electricity consumption data, equipment operation and maintenance data, market and economic data and external environment data.

[0010] As a preferred solution of the collaborative optimization method of the power system industrial chain described in the present invention, the layered data scenarios include the first-level power equipment and power design links; the second-level power generation, transmission and distribution, and power sales links; the third-level power application scenarios, including industrial power consumption, commercial power consumption, and residential power consumption links.

[0011] As a preferred solution of the collaborative optimization method of the power system industrial chain described in the present invention, the generation and verification of promotion strategies based on layered data scenarios include: constructing empirical research scenarios, collecting actual case data from various regions or power companies; cleaning and screening the collected actual case data, and selecting representative case data from typical regions or power companies for empirical research; based on the obtained typical case data, analyzing the adaptability of the promotion strategy in different regions or companies, economic benefit evaluation, power system efficiency improvement effect, and actual application effect in different power links, and evaluating the application value and promotion potential of the promotion strategy in the entire power system.

[0012] A power system industry chain collaborative optimization system using any of the methods described in the present invention, wherein: a scenario construction module collects power system industry chain data and organizes it in layers to form a basic data scenario that supports a promotion strategy; a strategy generation module formulates a promotion strategy based on the layered data scenario and verifies it in typical regions or enterprises; a feedback optimization module collects verification data of the promotion strategy, analyzes the effect of the promotion strategy based on the verification data and improves it, so as to achieve collaborative optimization of the target industry chain.

[0013] A computer device comprises: a memory and a processor; the memory stores a computer program, comprising: the steps of implementing any one of the methods of the present invention when the processor executes the computer program.

[0014] A computer-readable storage medium stores a computer program, comprising: when the computer program is executed by a processor, the steps of implementing any one of the methods of the present invention are implemented.

[0015] Beneficial effects of the present invention: The method of the present invention forms a closed-loop optimization mechanism of data-strategy-application-feedback by constructing layered data scenarios, formulating and verifying promotion strategies, and collecting feedback data to improve the collaborative efficiency and benefits of the power system industry chain. The core needs of the industrial chain are identified through layered data scenarios to provide a scientific basis for the formulation of promotion strategies; actual case verification and data feedback ensure the practicality of the promotion strategy. Continuous data collection and strategy optimization enable the promotion plan to adapt to market demand and policy changes, achieve coordinated development of all links in the power industry chain, and improve effectiveness and flexibility in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0017] Figure 1 An overall flow chart of a method for collaborative optimization of an electric power system industry chain provided by an embodiment of the present invention;

[0018] Figure 2 A diagram of the application steps of a method for collaborative optimization of an electric power system industry chain provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] Example 1, reference Figure 1 and Figure 2 , which is an embodiment of the present invention, provides a method for collaborative optimization of an industrial chain of a power system, comprising:

[0021] S1: Collect target industry chain data and build a layered data scenario.

[0022] Furthermore, establish scenarios for collecting data and information, collect relevant data and information of the target industrial chain, and provide basic information and support for research; construct scenarios by collecting market analysis, technological development status, policies and regulations, and corporate needs to obtain layered data scenarios.

[0023] The target industrial chain includes but is not limited to manufacturing, agricultural industrial chain, pharmaceutical industry, and logistics and supply chain management. In one embodiment of the present invention, the target industrial chain is the power system industrial chain, and the power industrial chain data includes but is not limited to power generation data, transmission data, distribution data, power user power consumption data, equipment operation and maintenance data, market and economic data, and external environment data.

[0024] Specifically, the hierarchical data scenarios include the first-level power equipment and power design links; the second-level power generation, transmission and distribution, and power sales links; and the third-level power application scenarios, including industrial power consumption, commercial power consumption, and residential power consumption links.

[0025] In another embodiment of the present invention, the target industrial chain may also be an agricultural industrial chain.

[0026] Specifically, data from multiple parties in the agricultural industry chain are collected, including crop planting, breeding, production and processing, circulation and sales, to form the basic data source of the agricultural industry chain.

[0027] The layered data scenarios include the first-level planting and production links, which are used to optimize planting decisions, predict yields, and analyze farmland productivity; the second-level processing and primary production links, which are used to improve agricultural product processing efficiency, optimize production processes, and conduct quality control management; the third-level circulation and consumption links, which are used to improve the logistics and supply chain management of agricultural products, analyze changes in market demand, and adjust production and circulation to reduce losses and improve economic benefits.

[0028] It should be noted that the construction of layered data scenarios is not limited to three-level stratification. The number of layers and the function of each layer can be adjusted according to the required target strategy. It is also not limited to the layered construction of the entire industrial chain. For example, when the target strategy focuses on increasing the productivity of agricultural products, the layered scenario can be constructed only for the production link of agricultural products, and the ultimate evaluation goal is the quantity and quality of agricultural products produced.

[0029] S2: Generate and verify promotion strategies based on hierarchical data scenarios.

[0030] Furthermore, a promotion strategy is generated based on the stratified data scenario, an empirical research scenario is constructed, typical regions or enterprises are selected for empirical research, and the feasibility and effectiveness of the promotion strategy are verified through analysis and comparison of actual cases.

[0031] To generate promotion strategies, we first need to establish promotion goals. We need to analyze the core needs and pain points of the industry from the collected data. For example, for the power system industry chain, it can be to improve power transmission efficiency, reduce equipment failure rate, etc.; for other industry chains, such as agriculture, it can be to increase production or reduce resource waste.

[0032] According to the promotion goals, determine the applicable promotion strategy type, and then formulate a specific promotion strategy. For example, adopt a new technology application promotion strategy (such as intelligent equipment or automated management system), or a strategy to optimize resource allocation. Combined with the collection of data and information scenarios, integrate the data on the market, policies and regulations, technology status and user needs to ensure that the strategy design is based on sufficient information.

[0033] The core of the promotion strategy is to optimize according to the data of each level of the industrial chain to ensure that it meets the actual needs of the industry. In one embodiment of the present invention, the promotion strategy can be a smart grid promotion strategy. For the promotion of smart grid systems, strategies are formulated to improve grid efficiency, promote solutions such as demand response management and distributed energy integration, and conduct empirical research through smart grid demonstration areas in typical regions.

[0034] The promotion strategy can also be technology promotion, such as the installation of smart devices, the application of automated control in power systems, etc. For other industries, it can be the promotion of automatic irrigation systems or smart logistics; process optimization, optimizing existing processes or production processes, reducing resource waste, and improving efficiency; policies and incentive mechanisms, designing promotion plans that comply with regulations based on policies and regulations, which may include incentives to enhance the adoption enthusiasm of enterprises or users.

[0035] In another embodiment of the present invention, when the target industrial chain is an agricultural industrial chain, the promotion strategy may be an intelligent manufacturing promotion strategy. A promotion strategy related to intelligent manufacturing is formulated, including the implementation and promotion of industrial robots, automated production lines, etc., and representative enterprises are selected as demonstration cases to verify the role of the strategy in improving production efficiency.

[0036] Furthermore, we construct empirical research scenarios, select typical regions or enterprises for empirical research, and verify the feasibility and effectiveness of promotion strategies through analysis and comparison of actual cases.

[0037] The specific steps to verify the promotion strategy are:

[0038] 1. Establish empirical research scenarios and collect actual case data from various regions or power companies, including actual operation conditions, equipment application conditions and economic benefit data of power generation, transmission, distribution and sales, to provide basic information for field application verification of promotion strategies.

[0039] 2. Clean and screen the collected actual case data, remove abnormal data and duplicate information, and select representative case data of typical regions or power companies to ensure that the research objects are generally representative and have application value. The cleaned and screened data should include equipment operating parameters, cost-effectiveness data, and power demand information.

[0040] 3. Based on the typical case data obtained, analyze the adaptability of the promotion strategy in different regions or enterprises, economic benefit evaluation, power system efficiency improvement effect, and actual application effect in different power links, and evaluate the application value and promotion potential of the promotion strategy in the entire power system.

[0041] S3: Collect verification data of the promotion strategy, analyze the effectiveness of the promotion strategy based on the verification data and make improvements to achieve coordinated optimization of the target industrial chain.

[0042] Furthermore, establish application information collection scenarios, collect information on the results of applying promotion strategies in actual production and management, and collect information on the promotion and application of promotion strategies to other regions or enterprises.

[0043] Specifically, taking the electric power industry chain as an example, the operating data, equipment status monitoring data and production process optimization information of the power system after the application of the promotion strategy are collected, including but not limited to key indicators such as power generation efficiency, transmission loss, distribution reliability, and power sales service quality; equipment failure rate, maintenance cost, and equipment life extension; production process improvements brought about by the promotion strategy, such as scheduling optimization, load management improvement, and energy utilization improvement.

[0044] At the same time, it is also necessary to collect information on the promotion and application of the promotion strategy to other regions or enterprises. Statistics on the application of the promotion strategy in different regions, power grid companies or power companies, as well as special problems and solutions encountered when applying the promotion strategy.

[0045] Clean all types of collected data, remove outliers and noise, store the sorted data in a unified database, and classify and manage them according to different links and levels of the power industry chain to facilitate subsequent analysis and call-up.

[0046] Furthermore, based on the application information collection scenarios, continuous improvement scenarios are established, and the actual application effects and industry development conditions are taken into consideration to continuously improve and perfect the promotion strategies and continuously enhance their adaptability and effectiveness.

[0047] Specifically, based on the collected operating data and results information, the actual effect of the promotion strategy is evaluated, including technical performance, economic benefits and social benefits, and problems or shortcomings that arise in actual applications are located, such as the poor applicability of the strategy in a specific environment and problems reported by users.

[0048] Combined with the new technology development, new policies and regulations, and market changes in the power industry, evaluate their impact on the promotion strategy. Based on the analysis results, modify and improve the content of the promotion strategy, pilot the improved promotion strategy in selected regions or enterprises, collect data and feedback information from the pilot application again, and verify the effectiveness of the improvement measures. Through continuous feedback and improvement, the promotion strategy can better adapt to the needs of different regions, enterprises, and power links, forming a closed-loop optimization mechanism of data-strategy-application-feedback, and promoting the continuous coordinated development of the power industry chain.

[0049] Example 2, the following is an embodiment of the present invention, which provides a method for collaborative optimization of the industrial chain of a power system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0050] This experiment selected power stations, transmission networks, distribution companies, and industrial, commercial, and residential users in a typical area as test points. The experiment mainly collects real-time operation data of each link, analyzes the effect of the promotion strategy, and conducts feedback optimization.

[0051] Install intelligent monitoring equipment at power stations and transmission networks in the experimental area to collect real-time data such as power generation, transmission voltage, current, and equipment temperature. Upgrade the data collection systems of distribution companies and electricity users to obtain information such as power supply reliability, power loss, and user load. Collect basic data for the experimental objects and collect benchmark data within one week as a reference for experimental comparison.

[0052] The implementation strategy is to set up an intelligent response system to dynamically adjust the power generation according to real-time load information. During peak periods, industrial power consumption is restricted to reduce distribution pressure; during low periods, users are encouraged to increase power consumption to improve power generation efficiency.

[0053] The experiment lasted for 4 weeks. The monitoring system recorded the power generation, transmission and distribution efficiency, equipment failure rate, power loss and power load data every week, and organized the data into a structured table. After the implementation of demand response, the adjustment range of industrial power consumption during peak and trough periods and its impact on the overall power supply pressure were recorded. Under the distributed energy integration strategy, the replacement rate of distributed energy for the main grid and its relief of the power station load were recorded. The collected experimental data were averaged and organized, and the obtained data are shown in Table 1.

[0054]

[0055] The experimental data shows that the power generation capacity is reduced from the benchmark 1000MW to 940MW week by week, indicating that with the participation of distributed energy, the power generation burden of the main grid is gradually reduced. At the same time, the transmission loss is reduced from the benchmark 8.2% to 6.5%, which significantly improves the power transmission efficiency. This shows that the reasonable integration of distributed energy not only reduces the power generation demand of the main grid, but also effectively reduces power loss.

[0056] With the introduction of demand response management, the reliability of power distribution has increased from 92.0% to 97.0%. By adjusting the power consumption time of industrial users through demand response, the pressure on the power distribution system is reduced, and the reliability of power distribution is gradually improved. This reliability improvement shows that the regulation effect of the promotion strategy of the present invention during peak power consumption has effectively reduced power outages caused by overload and significantly improved the stability of power supply. In addition, there are also obvious optimizations in equipment failure rate and power load balance indicators.

[0057] The experimental results of this embodiment verify the innovation and effectiveness of the present invention in the collaborative optimization of the power system industrial chain. The introduction of demand response management and distributed energy integration strategies not only significantly improves the efficiency of the power grid and the reliability of power supply, but also effectively reduces the equipment failure rate and power loss. Compared with the prior art, the innovation of the strategy of the present invention is that through intelligent demand response and distributed energy optimization, load balancing and resource conservation are achieved, overcoming the shortcomings of traditional power grids in load regulation and equipment maintenance. These data further illustrate that the present invention can provide significant optimization effects in different power links, and provide effective technical support for the realization of an intelligent and efficient power system industrial chain.

[0058] Embodiment 3: In an exemplary embodiment, a power system industry chain collaborative optimization system is also provided, including a scenario construction module, a strategy generation module and a feedback optimization module.

[0059] Among them, the scenario construction module is responsible for collecting various types of data in the power system industry chain and organizing them in layers to form basic data scenarios to support promotion strategies.

[0060] Specifically, the scenario construction module includes a data acquisition subsystem, a data processing subsystem, and a hierarchical scenario construction subsystem. The data acquisition subsystem obtains real-time data from power equipment, power generation, power transmission and distribution, power sales, and power applications, covering power generation data, power distribution data, power consumption data, equipment status, market and economic data, and environmental data. The data processing subsystem is responsible for data cleaning, screening, and format standardization to ensure data accuracy and consistency. The hierarchical scenario construction subsystem is hierarchically organized according to different levels of the power industry chain (equipment and design, power generation and transmission, and application links) to generate hierarchical data scenarios. The data acquisition and scenario construction system is connected to the promotion strategy generation and verification system to provide basic data support for it.

[0061] Furthermore, the strategy generation module formulates promotion strategies based on hierarchical data scenarios and verifies them in typical regions or enterprises to ensure the feasibility and adaptability of the strategies.

[0062] The strategy generation module includes a promotion strategy generation subsystem, a promotion goal setting subsystem, and an empirical research subsystem. The promotion goal setting subsystem combines market demand and policy goals to clarify the promotion direction and strategic goals. The promotion strategy generation subsystem uses layered data scenarios to generate corresponding promotion strategies. The empirical research subsystem selects representative regional or enterprise case data, verifies the feasibility by analyzing the promotion effects in different regions or enterprises, and provides feedback data. The system is connected to the data collection and scenario construction system to obtain layered data from it, and is also connected to the feedback optimization system to pass the verification data to the feedback optimization system.

[0063] Furthermore, the feedback optimization module collects verification data of the promotion strategy, analyzes the effectiveness of the promotion strategy based on the verification data and makes improvements to achieve coordinated optimization of the target industrial chain.

[0064] The feedback optimization system includes a verification data collection subsystem, a continuous improvement subsystem, and an industry dynamics analysis subsystem. The verification data collection subsystem receives data feedback from empirical research from the promotion strategy generation and verification system. The continuous improvement subsystem combines feedback data with industry development to continuously optimize the adaptability of the promotion strategy. The industry dynamics analysis subsystem monitors policy changes, market trends, and technological progress to provide external information support for continuous improvement. The feedback optimization system feeds back the optimized strategy to the promotion strategy generation and verification system to achieve iterative improvement of the promotion strategy, and connects with the data collection and scenario construction system to ensure that the data scenario can support the optimized promotion strategy.

[0065] The collaborative optimization data collection and scenario construction system provides layered data scenarios to the promotion strategy generation and verification system, providing data support for strategy generation and verification.

[0066] The collaborative optimization promotion strategy generation and verification system feeds the verification data back to the feedback optimization system to support the continuous improvement of the promotion strategy.

[0067] The promotion strategy optimized by the collaborative optimization feedback optimization system is then transmitted back to the promotion strategy generation and verification system, and when necessary, fed back to the data collection and scenario construction system for scenario update, forming a complete closed-loop collaborative optimization process.

[0068] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0069] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0070] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0071] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0072] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for collaborative optimization of the power system industrial chain, characterized in that: include: Collect target industry chain data and build layered data scenarios; Generate and verify promotion strategies based on hierarchical data scenarios; Collect verification data of promotion strategies, analyze the effectiveness of promotion strategies based on the verification data and make improvements to achieve coordinated optimization of the target industrial chain.

2. The method for collaborative optimization of the power system industry chain according to claim 1, characterized in that: The generating and verifying of promotion strategies according to the layered data scenarios includes establishing promotion goals, and producing corresponding promotion strategies based on the promotion goals and the layered data scenarios.

3. The method for collaborative optimization of the power system industry chain according to claim 2, characterized in that: The generation and verification of promotion strategies based on layered data scenarios also includes constructing empirical research scenarios, selecting typical regions or enterprises for empirical research, and verifying the feasibility and effectiveness of the promotion strategies through analysis and comparison of actual cases.

4. The method for collaborative optimization of the power system industry chain according to claim 3, characterized in that: The feedback optimization includes establishing continuous improvement scenarios and continuously improving promotion strategies based on verification data and industry development conditions.

5. The method for collaborative optimization of the power system industry chain according to claim 1, characterized in that: The target industrial chain includes but is not limited to the power system industrial chain; Power industry chain data includes but is not limited to power generation data, transmission data, distribution data, power user electricity consumption data, equipment operation and maintenance data, market and economic data, and external environment data.

6. The method for collaborative optimization of the power system industry chain according to claim 5, characterized in that: The hierarchical data scenario includes the first level of power equipment and power design links; The second level of power generation, transmission and distribution, and power sales; The third-level electricity application scenarios include industrial electricity, commercial electricity, and residential electricity.

7. The method for collaborative optimization of the power system industry chain according to claim 6, characterized in that: The generation and verification of promotion strategies based on hierarchical data scenarios includes building empirical research scenarios and collecting actual case data from various regions or power companies; Clean and screen the collected actual case data, and select representative typical regions or power enterprise case data for empirical research; Based on the typical case data obtained, the adaptability of the promotion strategy in different regions or enterprises, economic benefit evaluation, power system efficiency improvement effect, and actual application effect in different power links are analyzed to evaluate the application value and promotion potential of the promotion strategy in the entire power system.

8. A power system industry chain collaborative optimization system using any of the methods of claims 1 to 7, characterized in that: include, The scenario construction module collects data from the power system industry chain and organizes it in layers to form basic data scenarios to support promotion strategies; Strategy generation module, which formulates promotion strategies based on hierarchical data scenarios and verifies them in typical regions or enterprises; The feedback optimization module collects verification data of the promotion strategy, analyzes the effectiveness of the promotion strategy based on the verification data and makes improvements to achieve coordinated optimization of the target industrial chain.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the power system industry chain collaborative optimization method as described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power system industry chain collaborative optimization method as described in any one of claims 1-7 are implemented.

Images