Multi-process background field comprehensive evaluation method and related device
By constructing a multi-process background field comprehensive evaluation method and adopting the Gaussian attenuation method and dynamic weight allocation mechanism, the problems of sensitivity imbalance and weight equalization in the existing technology are solved, achieving a more accurate and reliable background field evaluation and improving the accuracy and reliability of power weather forecasts.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-17
- Publication Date
- 2026-04-03
AI Technical Summary
Existing background field assessment technologies suffer from sensitivity imbalance and weight equalization issues, leading to a decline in the accuracy of power weather forecasts and failing to meet the refined meteorological support needs of high-proportion renewable energy and power electronic equipment.
A multi-process background field comprehensive evaluation method is adopted. By constructing a multi-process characterization element set oriented towards the needs of power meteorological forecasting, and combining the Gaussian decay method and dynamic weight allocation mechanism, nonlinear scoring and dynamic weighted evaluation are carried out to screen out the background field that matches the needs of power meteorological forecasting.
It improves the sensitivity and accuracy of background field assessment, reduces systematic bias, enhances the accuracy and reliability of power weather forecasts, and supports the collaborative solution of multiple dimensions and processes, such as wind turbine wake effect simulation, transmission line meteorological disaster characterization, and photovoltaic module surface irradiance attenuation.
Smart Images
Figure CN121786537A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power meteorological forecasting technology, and relates to a multi-process background field comprehensive evaluation method and related devices. Background Technology
[0002] As new power systems develop towards higher proportions of renewable energy and power electronic equipment, the demand for refined meteorological support services for power system operation is becoming increasingly urgent. Numerical weather forecasting, as a core technology for grid security dispatch, new energy power prediction, and disaster early warning, relies on the ability to dynamically model multi-scale atmospheric processes to improve its accuracy. Background field data, as the initial state field for initializing numerical models, must not only satisfy the mass-wind field balance relationship in meteorological principles but also accurately characterize the coupled characteristics of multiple processes, such as atmospheric thermodynamic stratification stability and dynamic vertical motion conditions. Especially in the application scenario of power meteorology numerical forecasting, it is necessary to simultaneously support the collaborative solution of multiple dimensions and processes, such as wind turbine wake effect simulation, transmission line meteorological disaster characterization, and photovoltaic module surface irradiance attenuation.
[0003] Current background field assessment techniques mainly follow traditional meteorological operational standards, employing an assessment paradigm of "single-element deviation statistics + linear weighted scoring." Typical methods include: constructing indicator systems such as root mean square error (RMSE) and correlation coefficient (CC) for key elements like wind speed, temperature, and humidity; and weighting and summing the scores of each element using empirical weighting coefficients to form a comprehensive assessment index. Some improved schemes introduce principal component analysis (PCA) for dimensionality reduction or use fuzzy comprehensive evaluation methods to handle nonlinear relationships, but they are essentially still based on a static weight allocation mechanism.
[0004] Existing technologies suffer from two major technical bottlenecks: First, there is an imbalance in assessment sensitivity. When multiple background fields differ only slightly in key indicators (such as boundary layer wind speed prediction error) (e.g., background field A scores 1.0, while background field B scores 0.99), the forced use of discrete hierarchical scoring (3 points vs. 2 points) leads to an abnormal amplification of noise errors, affecting the optimal selection of background fields. Second, there is the issue of equal weighting. Existing methods assign the same weight coefficients to all assessment indicators (such as precipitation forecast accuracy and temperature standard deviation), failing to reflect the priority differences of different physical processes in power meteorology scenarios. For example, wind power prediction is significantly more sensitive to near-surface wind fields than to mid-tropospheric temperature fields. These deficiencies result in a significant systematic deviation between background field selection and the needs of power meteorology forecasting, leading to a decrease in the accuracy of power meteorology forecasts and hindering the engineering application effectiveness of numerical weather prediction products in the power industry. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-process background field comprehensive evaluation method and related device. The background field screened by this method and related device has a low systematic deviation from the requirements of power meteorological forecasting.
[0006] To achieve the above objectives, this invention discloses a multi-process background field comprehensive evaluation method, comprising: Constructing a multi-process characterization element set to meet the needs of power meteorological forecasting; The multi-process characterization elements in the multi-process characterization element set are input into the comprehensive evaluation model based on the classification of evaluation indicators and different standardization techniques to obtain the values of each background field indicator after evaluation. The Gaussian decay method is used to nonlinearly assign scores to the values of each background field index after evaluation, and weighting coefficients are assigned to the values of each background field index after evaluation of each process in each background field. Based on the nonlinear scoring results of the background field index values after evaluation and the weighting coefficients assigned to the background field index values after evaluation of each process in each background field, the comprehensive score of each background field is calculated, and each background field is evaluated based on the comprehensive score of each background field.
[0007] A further improvement of the multi-process background field comprehensive evaluation method described in this invention lies in: Furthermore, the processes in the background field include meteorological processes, kinetic processes, and thermodynamic processes.
[0008] Furthermore, the background field indicators in the comprehensive evaluation model based on the classification of evaluation indicators and different standardization techniques include continuous indicators, graded indicators, and core indicators. The continuous indicators include positive indicators and negative indicators.
[0009] Furthermore, the process of assigning weighting coefficients to the values of each background field index after evaluating each process in each background field is as follows: Based on a dynamic weight allocation mechanism driven by power meteorological scenarios, weight coefficients are assigned to the index values of each background field after the evaluation of each process in each background field.
[0010] Furthermore, the process of evaluating each background field based on the comprehensive score of each background field is as follows: Based on the comprehensive score of each background field, all background fields are ranked to obtain the initial ranking results of all background fields; The initial ranking results of all background fields are subjected to Monte Carlo perturbation tests to obtain the final ranking results of all background fields; Each background field is evaluated based on the final ranking results of all background fields.
[0011] This invention discloses a multi-process background field comprehensive evaluation system, comprising: The module is used to build a set of multi-process characterization elements to meet the needs of power meteorological forecasting; The estimation module is used to input the multi-process characterization elements from the multi-process characterization element set into the comprehensive evaluation model based on the classification of evaluation indicators and different standardization techniques, so as to obtain the values of each background field indicator after evaluation. The assignment module is used to perform nonlinear scoring on the evaluation values of each background field index using the Gaussian decay method, and to assign weight coefficients to the evaluation values of each background field index of each process in each background field. The evaluation module is used to calculate the comprehensive score of each background field based on the nonlinear scoring results of the background field index values after evaluation and the weight coefficients assigned to the background field index values of each process in each background field after evaluation, and to evaluate each background field based on the comprehensive score of each background field.
[0012] A further improvement of the multi-process background field comprehensive evaluation system of the present invention is as follows: Furthermore, the processes in the background field include meteorological processes, kinetic processes, and thermodynamic processes.
[0013] Furthermore, the background field indicators in the comprehensive evaluation model based on the classification of evaluation indicators and different standardization techniques include continuous indicators, graded indicators, and core indicators. The continuous indicators include positive indicators and negative indicators.
[0014] Furthermore, the process of assigning weighting coefficients to the values of each background field index after evaluating each process in each background field is as follows: Based on a dynamic weight allocation mechanism driven by power meteorological scenarios, weight coefficients are assigned to the index values of each background field after the evaluation of each process in each background field.
[0015] Furthermore, the evaluation module includes: The ranking unit is used to rank all the background fields based on the comprehensive score of each background field, and obtain the initial ranking results of all background fields. The test unit is used to perform Monte Carlo perturbation tests on the initial ranking results of all background fields to obtain the final ranking results of all background fields. An evaluation unit is used to evaluate each background field based on the final ranking results of all background fields.
[0016] 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, wherein the processor executes the computer program to implement the steps of the multi-process background field comprehensive evaluation method.
[0017] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-process background field comprehensive evaluation method.
[0018] The present invention has the following beneficial effects: In practical operation, the multi-process background field comprehensive evaluation method and related device of the present invention employs the Gaussian decay method to nonlinearly assign scores to the evaluated background field index values, and assigns weight coefficients to the evaluated background field index values of each process in each background field. This avoids the problems of sensitivity imbalance and weight equalization. Then, based on the nonlinear scoring results of the evaluated background field index values and the weight coefficients assigned to the evaluated background field index values of each process in each background field, the comprehensive score of each background field is calculated. As a result, the selected background fields have a lower systematic deviation from the requirements of power meteorological forecasting, thereby improving the accuracy of power meteorological forecasting. Attached Figure Description
[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system structure diagram of the present invention. Detailed Implementation
[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0022] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.
[0023] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.
[0024] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.
[0025] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. 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.
[0026] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."
[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0028] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.
[0029] Example 1 refer to Figure 1 The multi-process background field comprehensive evaluation method of the present invention includes the following steps: 1) Establish a multi-process characterization element set oriented towards the needs of power meteorological forecasting; The multi-process includes at least meteorological processes, dynamic processes, and thermodynamic processes. The characteristic elements of the meteorological processes include at least surface weather maps and upper-air weather maps. The characteristic elements of the dynamic processes include at least vorticity, divergence, and vertical velocity. The characteristic elements of the thermodynamic processes include at least pseudo-equivalent potential temperature and total temperature. The current characteristic element data of each process are detected, and a multi-process characterization element set for power meteorological forecasting needs is constructed based on this data. It should be noted that a background field is formed by the integration of multiple processes, and one process corresponds to multiple characteristic elements.
[0030] 2) Construct a comprehensive evaluation model based on the classification of evaluation indicators and different standardization techniques; Since the elements in the multi-process characterization element set cover multiple different types with different units and orders of magnitude, this invention adopts a method that combines evaluation index classification with different standardization techniques for evaluation. The comprehensive evaluation index in the comprehensive evaluation model includes continuous indicators, hierarchical indicators, and core indicators. The element data in the multi-process characterization element set are classified according to the type of comprehensive evaluation index.
[0031] Specifically, for continuous indicators, which include root mean square error and correlation coefficient, the corresponding element data needs to be standardized, and the standardized results are used as the background field indicator values after evaluation. For graded indicators, which are subjective scores based on power meteorological forecasting needs, standardization is not required, and the corresponding element data are directly used as the background field indicator values after evaluation. For core indicators, which are core element indicators for power meteorological forecasting needs, standardization is also not required, and the corresponding element data are used as the background field indicator values after evaluation. At the same time, asymmetric enhancement processing is needed on the element data corresponding to the core indicators by increasing the weight coefficient of the element data corresponding to the core indicators.
[0032] Furthermore, this invention employs a dynamic weight allocation mechanism driven by power meteorological scenarios. This mechanism assigns weight coefficients to the evaluated background field index values of each process in each background field. The dynamic weight allocation mechanism driven by power meteorological scenarios is a strategy that combines real-time meteorological data with the power grid operating status and dynamically adjusts the weight coefficients of each factor to optimize the effectiveness of power meteorological services. Specifically, a weight allocation model is constructed based on machine learning algorithms. The weight allocation model can dynamically adjust the weight coefficients of each evaluated background field index value according to historical data and real-time feedback to adapt to complex and ever-changing power meteorological scenarios.
[0033] The continuous indicators are divided into positive and negative indicators, and are standardized to the [0, 1] interval using different standardization methods. Positive indicators, such as correlation coefficient and hit rate, are better the higher the value, and are standardized using an interval standardization method, specifically:
[0034] Negative indicators such as root mean square error and positional deviation are better the smaller the value. They are standardized using an inverse standardization method, specifically:
[0035] in, To evaluate the values of the background field index, and These are the minimum and maximum values of the feature element data corresponding to this continuous indicator. The data consists of feature elements that need to be standardized.
[0036] 3) Calculate the overall score for each background field; A nonlinear scoring method based on actual discrepancies and a dynamic weighted comprehensive evaluation method based on electricity demand are employed. For different background scenarios under evaluation, scores on each indicator are dynamically assigned based on standardized values, weights, and the distance between actual and optimal values, avoiding the sensitivity loss or distortion of difference differentiation caused by simple ranking methods. Based on this, a comprehensive score for different background scenarios is calculated using dynamic weighting.
[0037] Step 3) is as follows: 31) The background field is nonlinearly assigned using the Gaussian decay method; The Gaussian decay method, based on the decay characteristics of a normal distribution, assigns nonlinear scores to the background field index values after evaluation in step 2), specifically as follows:
[0038] in, For the i-th background field involved in the th The nonlinear scoring value of the background field index after the evaluation. For this indicator item, the full score is... The standard deviation of the background field index value after this evaluation is used to control the decay rate and support the 95th percentile truncation of extreme outliers. Let be the value of the background field index after evaluation for the i-th background field. The optimal background field index value for all background fields on this indicator.
[0039] 32) A comprehensive background score based on dynamically weighted demand; To assign scores to different elements across multiple processes, including synoptic, thermodynamic, and kinetic processes, in power meteorological forecasting, a comprehensive score based on demand-driven dynamic weighting is used to calculate different background fields. Specifically:
[0040] in, For the first A comprehensive score for each background scene; For the first The weighting coefficients of the background field index values after the evaluation.
[0041] After the above comprehensive evaluation, not only can small differences be amplified, high scores be assigned to indicators that are close to the optimal value, and the gap with poor indicators be widened; but also the robustness of extreme values is guaranteed, and the excessive influence of a single extreme value on the comprehensive score is avoided.
[0042] 4) Rank each background scene based on its overall score.
[0043] In addition, based on this ranking, a Monte Carlo perturbation is randomly implemented to test the robustness of the total score and the sensitivity of the ranking in the evaluation system. The robustness probability of the ranking is statistically output. If the ranking changes frequently, the aforementioned three steps need to be returned to further check the weight settings and outlier situations until the overall background score ranking no longer changes.
[0044] 5) Use the highest-ranked background field for power weather forecasting.
[0045] It should be noted that the present invention has the following characteristics: This invention improves the sensitivity and discriminativeness of the evaluation: by using a continuous function instead of discrete graded scoring, i.e., the Gaussian decay method, it effectively avoids the abnormal amplification of noise errors caused by small differences in indicators (such as 0.99 vs 1.0), and can more finely capture the subtle differences between different background fields, thereby improving the sensitivity and discriminativeness of the evaluation. This allows for effective differentiation of superior and inferior background fields when their quality is similar, providing a more reliable basis for the selection of the best background field.
[0046] This invention provides more accurate and reliable background field assessment results: it comprehensively utilizes multi-process characterization elements, a hierarchical and classified assessment index system, nonlinear dynamic scoring, and a dynamic weighting mechanism, enabling the assessment results to more comprehensively and accurately reflect the matching degree between background field data and power meteorological forecasting needs, significantly improving the engineering application efficiency of numerical forecasting products in the power industry, and providing more reliable support for power grid safety dispatch, new energy power prediction, and disaster early warning.
[0047] This invention enhances the robustness and credibility of evaluation results: through robustness testing and iterative optimization mechanisms, Monte Carlo perturbation tests are conducted on the comprehensive background field score ranking to ensure the stability of the evaluation system, reduce the sensitivity of the ranking to random factors, and further enhance the credibility of the evaluation results.
[0048] Example 2 refer to Figure 2 The multi-process background field comprehensive evaluation system of the present invention includes: The module is used to build a set of multi-process characterization elements to meet the needs of power meteorological forecasting; The estimation module is used to input the multi-process characterization elements in the multi-process characterization element set into a comprehensive evaluation model based on evaluation index classification and different standardization techniques to obtain the values of each background field index after evaluation. The assignment module is used to perform nonlinear assignment on the values of each background field index after evaluation using the Gaussian decay method, and to assign weight coefficients to the values of each background field index after evaluation of each process in each background field. The evaluation module is used to calculate the comprehensive score of each background field based on the nonlinear scoring results of the background field index values after evaluation and the weight coefficients assigned to the background field index values of each process in each background field after evaluation, and to evaluate each background field based on the comprehensive score of each background field.
[0049] In this embodiment, the processes in the background field include meteorological processes, dynamic processes, and thermodynamic processes.
[0050] In this embodiment, the background field indicators in the comprehensive evaluation model based on evaluation index classification and different standardization techniques include continuous indicators, graded indicators, and core indicators. The continuous indicators include positive indicators and negative indicators.
[0051] In this embodiment, the process of assigning weighting coefficients to the values of each background field index after evaluating each process in each background field is as follows: Based on a dynamic weight allocation mechanism driven by power meteorological scenarios, weight coefficients are assigned to the index values of each background field after the evaluation of each process in each background field.
[0052] In this embodiment, the evaluation module includes: The ranking unit is used to rank all the background fields based on the comprehensive score of each background field, and obtain the initial ranking results of all background fields. The test unit is used to perform Monte Carlo perturbation tests on the initial ranking results of all background fields to obtain the final ranking results of all background fields. An evaluation unit is used to evaluate each background field based on the final ranking results of all background fields.
[0053] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.
[0054] Example 3 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 multi-process background field comprehensive evaluation method. For example, the method includes: constructing a multi-process characterization element set for power meteorological forecasting needs; inputting the multi-process characterization elements from the multi-process characterization element set into a comprehensive evaluation model based on evaluation index classification and different standardization techniques to obtain evaluated background field index values; applying a Gaussian decay method to nonlinearly assign scores to the evaluated background field index values, and assigning weight coefficients to the evaluated background field index values of each process in each background field; calculating a comprehensive score for each background field based on the linear scoring results of the evaluated background field index values and the assigned weight coefficients to the evaluated background field index values of each process in each background field; and evaluating each background field based on its comprehensive score. The memory may include main memory, such as high-speed random access memory (RAM), or non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, or an extended industry-standard architecture bus. The bus can be categorized as an address bus, data bus, or control bus. The memory stores programs; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.
[0055] Example 4 A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the multi-process background field comprehensive evaluation method. For example, the method includes: constructing a multi-process characterization element set for power meteorological forecasting needs; inputting the multi-process characterization elements from the set into a comprehensive evaluation model based on evaluation index classification and different standardization techniques to obtain evaluated background field index values; applying a Gaussian decay method to nonlinearly score the evaluated background field index values, assigning weight coefficients to the evaluated background field index values of each process in each background field; calculating a comprehensive score for each background field based on the linear scoring results of the evaluated background field index values and the weight coefficients assigned to the evaluated background field index values of each process in each background field; and evaluating each background field based on the comprehensive score. Specifically, the computer-readable storage medium includes, but is not limited to, 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 disk, magnetic disk, etc.
[0056] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.
[0057] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0058] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0059] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0060] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention 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 the invention are indicated by the following claims.
[0061] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
[0062] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.
Claims
1. A comprehensive evaluation method for multi-process background fields, characterized in that, include: Constructing a multi-process characterization element set to meet the needs of power meteorological forecasting; The multi-process characterization elements in the multi-process characterization element set are input into the comprehensive evaluation model based on the classification of evaluation indicators and different standardization techniques to obtain the values of each background field indicator after evaluation. The Gaussian decay method is used to nonlinearly assign scores to the values of each background field index after evaluation, and weighting coefficients are assigned to the values of each background field index after evaluation of each process in each background field. Based on the nonlinear scoring results of the background field index values after evaluation and the weighting coefficients assigned to the background field index values of each process in each background field after evaluation, a comprehensive score for each background field is calculated, and each background field is evaluated based on the comprehensive score of each background field.
2. The multi-process background field comprehensive evaluation method according to claim 1, characterized in that, The processes in the background field include meteorological processes, dynamic processes, and thermodynamic processes.
3. The multi-process background field comprehensive evaluation method according to claim 1, characterized in that, The background field indicators in the comprehensive evaluation model based on the classification of evaluation indicators and different standardization techniques include continuous indicators, hierarchical indicators and core indicators. The continuous indicators include positive indicators and negative indicators.
4. The multi-process background field comprehensive evaluation method according to claim 1, characterized in that, The process of assigning weighting coefficients to the values of each background field index after evaluating each process in each background field is as follows: Based on a dynamic weight allocation mechanism driven by power meteorological scenarios, weight coefficients are assigned to the index values of each background field after the evaluation of each process in each background field.
5. The multi-process background field comprehensive evaluation method according to claim 1, characterized in that, The process of evaluating each background field based on the comprehensive score of each background field is as follows: Based on the comprehensive score of each background field, all background fields are ranked to obtain the initial ranking results of all background fields; The initial ranking results of all background fields are subjected to Monte Carlo perturbation tests to obtain the final ranking results of all background fields; Each background field is evaluated based on the final ranking results of all background fields.
6. A multi-process background field comprehensive evaluation system, characterized in that, include: The module is used to build a set of multi-process characterization elements to meet the needs of power meteorological forecasting; The estimation module is used to input the multi-process characterization elements from the multi-process characterization element set into the comprehensive evaluation model based on the classification of evaluation indicators and different standardization techniques, so as to obtain the values of each background field indicator after evaluation. The assignment module is used to perform nonlinear scoring on the evaluation values of each background field index using the Gaussian decay method, and to assign weight coefficients to the evaluation values of each background field index of each process in each background field. The evaluation module is used to calculate the comprehensive score of each background field based on the nonlinear scoring results of the background field index values after evaluation and the weight coefficients assigned to the background field index values of each process in each background field after evaluation, and to evaluate each background field based on the comprehensive score of each background field.
7. The multi-process background field comprehensive evaluation system according to claim 6, characterized in that, The processes in the background field include meteorological processes, dynamic processes, and thermodynamic processes.
8. The multi-process background field comprehensive evaluation system according to claim 6, characterized in that, The background field indicators in the comprehensive evaluation model based on the classification of evaluation indicators and different standardization techniques include continuous indicators, hierarchical indicators and core indicators. The continuous indicators include positive indicators and negative indicators.
9. The multi-process background field comprehensive evaluation system according to claim 6, characterized in that, The process of assigning weighting coefficients to the values of each background field index after evaluating each process in each background field is as follows: Based on a dynamic weight allocation mechanism driven by power meteorological scenarios, weight coefficients are assigned to the index values of each background field after the evaluation of each process in each background field.
10. The multi-process background field comprehensive evaluation system according to claim 6, characterized in that, The evaluation module includes: The ranking unit is used to rank all the background fields based on the comprehensive score of each background field, and obtain the initial ranking results of all background fields. The test unit is used to perform Monte Carlo perturbation tests on the initial ranking results of all background fields to obtain the final ranking results of all background fields. An evaluation unit is used to evaluate each background field based on the final ranking results of all background fields.
11. 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 multi-process background field comprehensive evaluation method as described in any one of claims 1-5.
12. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-process background field comprehensive evaluation method as described in any one of claims 1-5.