Optimization method based on multi-dimensional thermal power plant energy-saving data analysis and related device

Abstract

The invention discloses an optimization method based on multi-dimensional thermal power plant energy-saving data analysis and a related device, and the method comprises the steps: obtaining parameter information in a thermal power plant region, and determining a multi-dimensional energy evaluation index according to the parameter information in the thermal power plant region; constructing a multi-dimensional optimization set of the thermal power plant, constructing a matter-element extension model according to the multi-dimensional energy evaluation indexes and the multi-dimensional optimization set, and constructing a multi-dimensional association sequence according to the matter-element extension model; calculating a gray correlation number of each multi-dimensional energy evaluation index based on the multi-dimensional correlation sequence; an optimal optimization scheme is selected according to the grey correlation number of each multi-dimensional energy evaluation index, the thermal power plant is optimized according to the optimal optimization scheme, and the method and the related device can optimize the thermal power plant from multiple dimensions.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy data analysis and optimization, and relates to an optimization method and related device based on multi-dimensional energy-saving data analysis of thermal power plants. Background Art

[0002] In the context of the increasingly serious global energy shortage and environmental pollution problems, thermal power plants, as the main energy consumers and carbon emission sources, their energy conservation and consumption reduction are particularly important. With the rapid development of industrialization and urbanization, the global energy demand has been continuously climbing, and the limited nature of energy resources has made the energy shortage problem increasingly prominent. At the same time, environmental pollution problems have also become increasingly serious. In particular, the emissions of greenhouse gases such as carbon dioxide have led to global warming and frequent extreme weather, posing a serious threat to the human living environment. As the main source of energy consumption and carbon emissions, the energy conservation and consumption reduction of thermal power plants are of great significance for alleviating energy shortages and reducing environmental pollution.

[0003] Existing energy-saving optimization technologies for thermal power plants often focus on single or a few indicators, making it difficult to comprehensively reflect the energy-saving effect and resulting in the one-sidedness and limitations of the optimization scheme. For example, some optimization schemes mainly focus on improving the energy conversion efficiency while ignoring the optimization of equipment operation efficiency and energy transmission efficiency; some schemes consider multiple indicators, but there is a lack of effective correlation analysis between the indicators, resulting in unsatisfactory optimization effects. Therefore, there is an urgent need for an optimization scheme that can perform multi-dimensional energy-saving data analysis of thermal power plants to achieve more comprehensive and accurate energy-saving optimization. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above-mentioned disadvantages of the prior art and provide an optimization method and related device based on multi-dimensional energy-saving data analysis of thermal power plants, which can optimize thermal power plants from multiple dimensions.

[0005] To achieve the above object, the present invention discloses an optimization method based on multi-dimensional energy-saving data analysis of thermal power plants, including:

[0006] Obtain parameter information within the thermal power plant area, and determine multi-dimensional energy evaluation indicators according to the parameter information within the thermal power plant area;

[0007] Construct a multi-dimensional optimization set for the thermal power plant, construct a matter-element extension model according to the multi-dimensional energy evaluation indicators and the multi-dimensional optimization set, and construct a multi-dimensional correlation sequence according to the matter-element extension model;

[0008] Based on the multi-dimensional correlation sequence, calculate the grey correlation numbers of each multi-dimensional energy evaluation indicator;

[0009] Select the optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation index, and optimize the thermal power plant according to the optimal optimization plan.

[0010] A further improvement of the optimization method based on multi-dimensional energy consumption data analysis of a thermal power plant according to the present invention lies in:

[0011] Furthermore, the parameter information within the thermal power plant area includes at least one of various energy data, equipment operation data, and environmental data within the thermal power plant area.

[0012] Furthermore, the process of obtaining the parameter information within the thermal power plant area and determining the multi-dimensional energy evaluation index according to the parameter information within the thermal power plant area is as follows:

[0013] Obtain the parameter information within the thermal power plant area, perform cleaning, denoising, and normalization processing on the parameter information within the thermal power plant area, and determine the multi-dimensional energy evaluation index according to the normalized parameter information. The multi-dimensional energy evaluation index includes at least one of energy conversion efficiency, equipment operation efficiency, carbon dioxide emissions, and energy transmission efficiency.

[0014] Furthermore, the multi-dimensional optimization set of the thermal power plant includes at least one of energy type optimization and equipment operation optimization.

[0015] Furthermore, the process of constructing the multi-dimensional correlation sequence according to the matter-element extension model is as follows:

[0016] According to the matter-element extension model, determine the grade correlation degree between each multi-dimensional energy evaluation index and each multi-dimensional optimization pair in the multi-dimensional optimization set, and then construct the multi-dimensional correlation sequence.

[0017] Furthermore, the process of selecting the optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation index is as follows:

[0018] According to the grey correlation numbers and weights of each multi-dimensional energy evaluation index, use the weighted summation method to solve the comprehensive score of each multi-dimensional energy evaluation index;

[0019] Select the optimal optimization plan according to the comprehensive scores of each multi-dimensional energy evaluation index.

[0020] The present invention discloses an optimization system based on multi-dimensional energy consumption data analysis of a thermal power plant, including:

[0021] An acquisition module, configured to acquire the parameter information within the thermal power plant area and determine the multi-dimensional energy evaluation index according to the parameter information within the thermal power plant area;

[0022] A construction module for constructing a multi-dimensional optimization set of a thermal power plant, constructing a matter-element extension model according to the multi-dimensional energy evaluation index and the multi-dimensional optimization set, and constructing a multi-dimensional correlation sequence according to the matter-element extension model;

[0023] A calculation module for calculating the grey correlation numbers of each multi-dimensional energy evaluation index based on the multi-dimensional correlation sequence;

[0024] An optimization module for selecting an optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation index and optimizing the thermal power plant according to the optimal optimization plan.

[0025] Further, the process of selecting the optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation index is as follows:

[0026] According to the grey correlation numbers and weights of each multi-dimensional energy evaluation index, the comprehensive scores of each multi-dimensional energy evaluation index are solved by means of weighted summation;

[0027] Select the optimal optimization plan according to the comprehensive scores of each multi-dimensional energy evaluation index.

[0028] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the optimization method based on multi-dimensional thermal power plant energy-saving data analysis are implemented.

[0029] The present invention discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the optimization method based on multi-dimensional thermal power plant energy-saving data analysis are implemented.

[0030] The present invention has the following beneficial effects:

[0031] When the optimization method and related device based on multi-dimensional thermal power plant energy-saving data analysis of the present invention are specifically operated, a matter-element extension model is constructed according to the multi-dimensional energy evaluation index and the multi-dimensional optimization set, a multi-dimensional correlation sequence is constructed according to the matter-element extension model, the grey correlation numbers of each multi-dimensional energy evaluation index are calculated based on the multi-dimensional correlation sequence, and the optimal optimization plan is selected accordingly, so as to realize the optimization of the thermal power plant from multiple dimensions, improve the energy conversion efficiency and equipment operation efficiency of the thermal power plant, reduce energy consumption, and significantly improve the energy utilization efficiency and reduce energy waste by optimizing the energy structure and equipment operation parameters. Provide technical support for the energy conservation and consumption reduction of thermal power plants. Through the analysis and optimization of multi-dimensional data, a more scientific and reasonable energy-saving optimization plan can be formulated, providing strong support for the sustainable development of thermal power plants. Description of the Drawings

[0032] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The schematic embodiments and descriptions thereof of the invention are used to explain the invention and shall not unduly limit the invention. In the drawings:

[0033] Figure 1 is a flowchart of the method of this invention. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0035] In the description of the present invention, it should be understood that the terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0036] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0037] It should be further understood that the term " / and" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations. For example, A and / or B may represent: the presence of A alone, the presence of both A and B, and the presence of B alone. In addition, the character " / " in the present invention generally indicates an "or" relationship between the related objects before and after.

[0038] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present invention to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0039] Depending on the context, as used herein, the word "if" can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0040] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0041] Structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the drawings. These drawings are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the drawings and their relative sizes and positional relationships are merely exemplary. In practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art can design regions / layers with different shapes, sizes and relative positions according to actual needs.

[0042] Embodiment 1

[0043] Reference Figure 1 , the optimization method based on multi-dimensional energy-saving data analysis of thermal power plants according to the present invention includes the following steps:

[0044] 1) Data collection;

[0045] Sensors and data acquisition devices acquire various types of energy data, equipment operation data and environmental data within the thermal power plant area, specifically including energy type data such as coal and natural gas, equipment operation data such as generator sets and boilers, and environmental data such as environmental temperature and humidity.

[0046] The specific operation of step 1) is:

[0047] 01) Install high-precision sensors and data acquisition devices to cover all key energy and equipment operation nodes in the thermal power plant. Select sensors with high sensitivity and low power consumption, such as model A123, to ensure high-precision data acquisition. The data acquisition device adopts a distributed architecture to improve the stability and reliability of the system.

[0048] 02) Establish a real-time data transmission system to ensure that data can be transmitted to the central data warehouse in a timely manner. Specifically, adopt 5G or industrial Ethernet technology to ensure high-speed and low-latency data transmission.

[0049] 03) Regularly inspect and maintain sensors and data acquisition devices to ensure their normal operation.

[0050] 2) Data preprocessing;

[0051] Clean, denoise, and normalize the collected data to eliminate outliers and redundant information in the data, ensuring data quality and consistency. At the same time, perform time series analysis on the data to identify trends and periodic changes in the data, providing a basis for subsequent analysis.

[0052] The specific operations of step 2) are as follows:

[0053] 21) Use data cleaning algorithms to automatically detect and remove outliers. Specifically, adopt the statistical-based Z-score algorithm and set thresholds to automatically identify and remove abnormal data.

[0054] 22) Adopt data smoothing techniques to remove noise and ensure data smoothness. Use the Savitzky-Golay filter for data smoothing to maintain the original characteristics of the data.

[0055] 23) Normalize the data to make it in the same dimension for subsequent analysis. Specifically, adopt the Min-Max normalization method to scale the data between 0 and 1.

[0056] 24) Conduct time series analysis to identify trends and periodic changes in the data. Specifically, use the ARIMA model for time series prediction to analyze the change trends and periodic patterns of the data.

[0057] 3) Determine multi-dimensional energy evaluation indicators;

[0058] Based on the collected data, determine energy evaluation indicators in multiple dimensions, including energy conversion efficiency, equipment operation efficiency, carbon dioxide emissions, and energy transmission efficiency. Analyze and discuss these indicators through an expert team, and determine them in combination with the actual situation of the thermal power plant and industry standards to ensure the quantifiability and operability of the indicators.

[0059] The specific operation of step 3) is as follows:

[0060] 31) Organize an expert team consisting of energy experts, data analysts, and thermal power plant technicians. The expert team should include at least 5 experts with more than 10 years of relevant experience.

[0061] 32) Hold multiple seminars to analyze and screen potential evaluation indicators. The seminars should be held at least 3 times, each lasting 2 days, to ensure sufficient discussion and screening.

[0062] 33) Develop detailed index evaluation criteria and calculation methods. The evaluation criteria should refer to the relevant standards of the International Energy Agency (IEA) and the National Energy Administration.

[0063] 34) Verify the quantifiability and operability of the determined indicators. Through simulation experiments and actual data tests, ensure the feasibility and effectiveness of the indicators.

[0064] 4) Allocate index weights;

[0065] According to the importance of each evaluation index to energy conservation optimization, use methods such as expert scoring method and analytic hierarchy process to determine the weights of each index.

[0066] The specific process of step 4) is as follows:

[0067] 41) Design an expert scoring questionnaire, clarify the scoring criteria and basis. Among them, the questionnaire should include detailed scoring instructions and scoring criteria.

[0068] 42) Invite experts in relevant fields to score. Among them, invite at least 10 experts to score to ensure the authority and representativeness of the results.

[0069] 43) Use the analytic hierarchy process to construct an index weight model. Among them, use yaahp software to construct an analytic hierarchy model to ensure the scientific nature of weight allocation.

[0070] 44) Verify and adjust the weight allocation results. Among them, verify the consistency of weight allocation through consistency test (CI / CR) and make necessary adjustments.

[0071] 5) Construct a multi-dimensional optimization set;

[0072] According to the energy optimization set and equipment optimization set, construct a multi-dimensional optimization set. Specifically, it includes energy type optimization, equipment operation optimization, etc. Through detailed analysis of energy types and equipment operation, formulate specific optimization measures, such as adjusting the energy structure, improving equipment operation parameters, etc., to form a multi-dimensional optimization set.

[0073] The specific operation of step 5) is as follows:

[0074] 51) Analyze the existing energy structure and determine the optimization direction. Through data analysis, identify the high-energy-consuming and low-efficiency parts in the current energy structure and formulate the optimization direction.

[0075] 52) Develop an energy type adjustment plan. For example, increase the proportion of clean energy, set specific goals, such as raising the proportion of clean energy to 30%, and formulate corresponding implementation plans.

[0076] 53) Conduct a detailed analysis of the operating parameters of the equipment and formulate optimization measures. Specifically, through data analysis, identify the inefficient links in the equipment operation and formulate specific optimization measures, such as adjusting parameters like operating temperature and pressure.

[0077] 54) Integrate the optimization of energy types and equipment operation to form a multi-dimensional optimization set. Specifically, integrate the optimization measures of energy types and equipment operation to form a complete optimization set.

[0078] 6) Refine the optimization plan;

[0079] Conduct a detailed design of each optimization measure, including implementation steps, technical requirements, expected effects, etc. At the same time, evaluate the cost and benefit of each optimization measure to ensure the feasibility and economy of the optimization plan.

[0080] The specific operation of step 6) is as follows:

[0081] 61) Develop a detailed implementation plan and schedule for each optimization measure, and clarify the time nodes and responsible persons for each step.

[0082] 62) Define the technical requirements and standards for each measure, and develop detailed technical specifications and standards to ensure that the implementation of the optimization measures meets the relevant technical requirements and standards.

[0083] 63) Predict the expected effects and energy-saving benefits of each measure. Through simulation experiments and data analysis, predict the expected effects and energy-saving benefits of each optimization measure.

[0084] 64) Conduct a cost-benefit analysis to ensure the feasibility of the optimization plan. Through detailed cost analysis and benefit evaluation, ensure the feasibility and economy of the optimization plan.

[0085] 7) Construct a matter-element extension model;

[0086] Based on the multi-dimensional optimization set and multi-dimensional energy evaluation indicators, construct a matter-element extension model, determine the grade correlation degree between each multi-dimensional energy evaluation indicator and each multi-dimensional optimization pair in the multi-dimensional optimization set, and then construct a multi-dimensional correlation sequence. The matter-element extension model is an effective multi-dimensional data analysis method that can fully consider the relevance and interaction between various indicators.

[0087] The specific process of step 7) is as follows:

[0088] 71) Construct the theoretical framework of the matter-element extension model. According to the matter-element extension theory, establish the basic framework and mathematical model of the model.

[0089] 72) Determine the hierarchical correlation degree between each index and the optimization measures. Through expert scoring and data analysis, determine the hierarchical correlation degree between each index and the optimization measures.

[0090] 73) Construct a multi-dimensional correlation sequence. According to the hierarchical correlation degree, construct a multi-dimensional correlation sequence to reflect the correlation relationship between each index and the optimization measures.

[0091] 74) Verify the accuracy and reliability of the model. Through simulation experiments and actual data verification, ensure the accuracy and reliability of the model.

[0092] 8) Model verification and adjustment;

[0093] Verify and adjust the constructed matter-element extension model to ensure the accuracy and reliability of the matter-element extension model. Through comparison of simulation experiments and actual data, continuously optimize the parameters of the matter-element extension model to improve the prediction ability and adaptability of the matter-element extension model.

[0094] The specific process of step 8) is as follows:

[0095] 81) Design simulation experiments to verify the effectiveness of the matter-element extension model. Design multiple groups of simulation experiments to verify the performance of the matter-element extension model under different conditions.

[0096] 82) Apply the matter-element extension model to actual data and compare and analyze the results. Apply the matter-element extension model to actual data, compare and analyze the results with the actual effects, and verify the accuracy of the matter-element extension model.

[0097] 83) Adjust the parameters of the matter-element extension model according to the verification results. According to the verification results, make necessary adjustments and optimizations to the model parameters to improve the prediction ability and adaptability of the matter-element extension model.

[0098] 84) Continuously optimize the matter-element extension model to ensure its adaptability and prediction ability. Establish a model optimization mechanism, regularly optimize and update the matter-element extension model to ensure its continuous adaptation to the operation conditions of thermal power plants.

[0099] 9) Calculate the grey correlation coefficient;

[0100] Based on the multi-dimensional correlation sequence, use the grey correlation coefficient to calculate the correlation numbers of each multi-dimensional energy evaluation index. The grey correlation coefficient is a method for analyzing the correlation degree between system factors and can quantify the influence degree of each index on the optimization scheme.

[0101] The specific process of step 9) is as follows:

[0102] 91) Calculate the grey relational coefficients of each index. Specifically, adopt the grey relational analysis method to calculate the grey relational coefficients between each index and the optimization plan.

[0103] 92) Analyze the relational coefficients to determine the influence degree of each index on the optimization plan. Specifically, according to the magnitudes of the relational coefficients, analyze the influence degree of each index on the optimization plan and identify the key indicators.

[0104] 93) Visualize the analysis results for the convenience of decision-makers to understand. Adopt the forms of charts and reports to visually display the analysis results, which is convenient for decision-makers to understand and apply.

[0105] 10) Correlation analysis;

[0106] Analyze the calculated grey relational coefficients to identify the key indicators and optimization measures that have the greatest impact on the optimization plan, providing a basis for subsequent plan selection and adjustment.

[0107] The specific process of step 10) is as follows:

[0108] 101) Sort the grey relational coefficients to identify the key indicators. Specifically, according to the magnitudes of the relational coefficients, sort each index to identify the key indicators.

[0109] 102) Analyze the relationship between the key indicators and the optimization measures. Analyze the correlation relationship between the key indicators and the optimization measures to determine the key directions of the optimization measures.

[0110] 103) Form suggestions for adjusting the optimization plan. According to the analysis results, form specific suggestions for adjusting the optimization plan, providing a basis for subsequent plan selection and adjustment.

[0111] 11) Select the best optimization plan:

[0112] According to the correlation numbers of the carbon dioxide emission sequence, energy conversion rate sequence, and energy transmission efficiency sequence, select the best multi-dimensional energy optimization plan according to actual needs. When selecting the best plan, factors such as energy-saving effect, economic benefit, and environmental protection requirements need to be comprehensively considered.

[0113] The specific process of step 11) is as follows:

[0114] 111) Synthesize the correlation numbers of each sequence and calculate the comprehensive score. According to the correlation numbers of each sequence, adopt the weighted average method to calculate the comprehensive score of each sequence.

[0115] 112) Select the best optimization plan according to the comprehensive scores of each sequence. According to the comprehensive scores, select the optimization plan with the highest score as the best plan.

[0116] 113) Develop an implementation plan to clarify the implementation steps and time nodes. Develop an implementation plan to clarify the specific implementation steps and time nodes to ensure the smooth implementation and effective execution of the optimization plan.

[0117] 12) Plan evaluation and optimization;

[0118] Conduct a detailed evaluation of the selected best plan, including technical feasibility, economic benefit analysis, environmental impact assessment, etc. Optimize and adjust the plan according to the evaluation results to ensure the comprehensiveness and effectiveness of the plan.

[0119] The specific process of step 12) is as follows:

[0120] 121) Conduct a technical feasibility analysis to ensure the implementability of the plan. Through technical analysis and experimental verification, ensure the feasibility and technical reliability of the plan.

[0121] 122) Conduct an economic benefit analysis to evaluate the cost-benefit of the plan. Through detailed cost analysis and benefit evaluation, ensure the feasibility and economy of the plan.

[0122] 123) Conduct an environmental impact assessment to analyze the environmental protection effect of the plan. Through environmental impact assessment, analyze the environmental protection effect of the plan to ensure that the plan meets the environmental protection requirements.

[0123] 124) Optimize and adjust the plan according to the evaluation results. According to the evaluation results, conduct necessary optimization and adjustment of the plan to ensure the comprehensiveness and effectiveness of the plan.

[0124] Embodiment 2

[0125] The optimization system based on multi-dimensional energy-saving data analysis of thermal power plants according to the present invention includes:

[0126] An acquisition module, configured to acquire parameter information within the thermal power plant area and determine multi-dimensional energy evaluation indicators according to the parameter information within the thermal power plant area;

[0127] A construction module, configured to construct a multi-dimensional optimization set of the thermal power plant, construct a matter-element extension model according to the multi-dimensional energy evaluation indicators and the multi-dimensional optimization set, and construct a multi-dimensional correlation sequence according to the matter-element extension model;

[0128] A calculation module, configured to calculate the grey correlation numbers of each multi-dimensional energy evaluation indicator based on the multi-dimensional correlation sequence;

[0129] An optimization module, configured to select an optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation indicator and optimize the thermal power plant according to the optimal optimization plan.

[0130] In the embodiments of the present application, the division of modules is illustrative. It is only a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present application, each functional module may be integrated in a processor, may exist separately physically, or two or more modules may be integrated in one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0131] Embodiment III

[0132] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the optimization method based on multi-dimensional energy-saving data analysis of a thermal power plant. For example, it includes: obtaining parameter information within the thermal power plant area, determining multi-dimensional energy evaluation indicators according to the parameter information within the thermal power plant area; constructing a multi-dimensional optimization set of the thermal power plant, constructing a matter-element extension model according to the multi-dimensional energy evaluation indicators and the multi-dimensional optimization set, and constructing a multi-dimensional correlation sequence according to the matter-element extension model; calculating the grey correlation numbers of each multi-dimensional energy evaluation indicator based on the multi-dimensional correlation sequence; selecting the optimal optimization scheme according to the grey correlation numbers of each multi-dimensional energy evaluation indicator, and optimizing the thermal power plant according to the optimal optimization scheme. Among them, the memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc.; the processor, network interface, and memory are interconnected through an internal bus, which may be an Industry Standard Architecture bus, a Peripheral Component Interconnect standard bus, an Extended Industry Standard Architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0133] Embodiment IV

[0134] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the optimization method based on multi-dimensional energy-saving data analysis of a thermal power plant. For example, it includes: obtaining parameter information within the area of the thermal power plant, and determining multi-dimensional energy evaluation indicators according to the parameter information within the area of the thermal power plant; constructing a multi-dimensional optimization set for the thermal power plant, constructing a matter-element extension model according to the multi-dimensional energy evaluation indicators and the multi-dimensional optimization set, and constructing a multi-dimensional correlation sequence according to the matter-element extension model; based on the multi-dimensional correlation sequence, calculating the grey correlation numbers of each multi-dimensional energy evaluation indicator; selecting an optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation indicator, and optimizing the thermal power plant according to the optimal optimization plan. Specifically, the computer-readable storage medium includes but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disc, magnetic disk, etc.

[0135] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0137] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more flows and / or blocksFigure 1 The functions specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 process or multiple processes and / or boxes Figure 1 or more boxes.

[0139] After considering the specification and the disclosure of the invention, those skilled in the art will readily conceive of other embodiments of the present invention. This application is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include known common knowledge or conventional technical means in the technical field not disclosed by the present invention. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the following claims.

[0140] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

[0141] As described above, the above are only preferred embodiments of the present invention, and do not impose any limitations on the present invention. Any simple modifications, changes, and equivalent structural changes made to the above embodiments according to the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An optimization method based on multi-dimensional energy-saving data analysis of thermal power plants, characterized in that, Including: Obtain parameter information within the thermal power plant area, and determine multi-dimensional energy evaluation indicators based on the parameter information within the thermal power plant area; Construct a multi-dimensional optimization set for the thermal power plant, construct a matter-element extension model according to the multi-dimensional energy evaluation indicators and the multi-dimensional optimization set, and construct a multi-dimensional correlation sequence according to the matter-element extension model; Based on the multi-dimensional correlation sequence, calculate the grey correlation numbers of each multi-dimensional energy evaluation indicator; Select the optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation indicator, and optimize the thermal power plant according to the optimal optimization plan.

2. The optimization method based on multi-dimensional energy-saving data analysis of thermal power plants according to claim 1, wherein The parameter information within the thermal power plant area includes at least one of various energy data, equipment operation data, and environmental data within the thermal power plant area.

3. The optimization method based on multi-dimensional energy-saving data analysis of thermal power plants according to claim 1, characterized in that, The process of obtaining parameter information within the thermal power plant area and determining multi-dimensional energy evaluation indicators based on the parameter information within the thermal power plant area is as follows: Obtain parameter information within the thermal power plant area, perform cleaning, denoising, and normalization processing on the parameter information within the thermal power plant area, and determine multi-dimensional energy evaluation indicators according to the normalized parameter information. The multi-dimensional energy evaluation indicators include at least one of energy conversion efficiency, equipment operation efficiency, carbon dioxide emissions, and energy transmission efficiency.

4. The optimization method based on multi-dimensional energy-saving data analysis of thermal power plants according to claim 1, characterized in that, The multi-dimensional optimization set for the thermal power plant includes at least one of energy type optimization and equipment operation optimization.

5. The optimization method based on multi-dimensional energy-saving data analysis of thermal power plants according to claim 1, characterized in that The process of constructing a multi-dimensional correlation sequence according to the matter-element extension model is as follows: According to the matter-element extension model, determine the grade correlation degrees between each multi-dimensional energy evaluation indicator and each multi-dimensional optimization pair in the multi-dimensional optimization set, and then construct a multi-dimensional correlation sequence.

6. The optimization method based on multi-dimensional energy-saving data analysis of thermal power plants according to claim 1, wherein The process of selecting the optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation indicator is as follows: According to the grey correlation numbers and weights of each multi-dimensional energy evaluation indicator, use the weighted summation method to solve the comprehensive scores of each multi-dimensional energy evaluation indicator; Select the optimal optimization plan according to the comprehensive scores of each multi-dimensional energy evaluation indicator.

7. An optimization system based on multi-dimensional energy-saving data analysis of thermal power plants, characterized in that, Including: An acquisition module for obtaining parameter information within the thermal power plant area and determining multi-dimensional energy evaluation indicators based on the parameter information within the thermal power plant area; A construction module for constructing a multi-dimensional optimization set for the thermal power plant, constructing a matter-element extension model according to the multi-dimensional energy evaluation indicators and the multi-dimensional optimization set, and constructing a multi-dimensional correlation sequence according to the matter-element extension model; A calculation module for calculating the grey correlation numbers of each multi-dimensional energy evaluation indicator based on the multi-dimensional correlation sequence; An optimization module for selecting the optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation indicator and optimizing the thermal power plant according to the optimal optimization plan.

8. The optimization system based on multi-dimensional energy-saving data analysis of thermal power plants according to claim 7, characterized in that The process of selecting the optimal optimization plan according to the grey correlation numbers of each multi-dimensional energy evaluation indicator is as follows: According to the grey correlation numbers and weights of each multi-dimensional energy evaluation indicator, use the weighted summation method to solve the comprehensive scores of each multi-dimensional energy evaluation indicator; Select the optimal optimization plan according to the comprehensive scores of each multi-dimensional energy evaluation indicator.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the optimization method based on multi-dimensional thermal power plant energy-saving data analysis according to any one of claims 1-6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the optimization method based on multi-dimensional energy-saving data analysis of thermal power plants according to any one of claims 1-6.

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