Electrical load contrast decision-making method based on load composition

Abstract

The invention discloses an electrical load contrast decision-making method based on load composition, and the method comprises the following steps: firstly, presetting a parameter set and a preset load threshold value; performing collection processing on the electrical load data to obtain an electrical load data set after collection processing; performing image acquisition on the electrical load data set to obtain an electrical load image; the electrical load image is preprocessed; setting a load unit sample interval in the electrical load image; classifying the target load units; the electrical load state is judged, and the analysis precision and reliability of an electrical load image can be remarkably improved by presetting a parameter set and a load threshold in combination with an image preprocessing step; through algorithms such as image enhancement and high-frequency noise removal, the visual effect and analysis efficiency of the electrical load image are improved, more scientific and efficient technical support is provided for electrical load evaluation, the time-consuming cost of manual evaluation is effectively reduced, and the evaluation efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power planning monitoring, and in particular, to a method for making a comparison decision on electricity consumption load based on load composition. Background Art

[0002] With the continuous development and intelligentization of the power system, electricity consumption load management has become a key link in ensuring the stable operation of the power system and improving energy utilization efficiency. Electricity consumption load management involves the collection, analysis, and evaluation of electricity consumption data to identify and predict high-load areas, so as to take corresponding measures for optimization and management.

[0003] Traditional electricity consumption load management mainly relies on manual monitoring and empirical judgment. This method is not only time-consuming and laborious but also prone to misjudgment. In recent years, with the development of smart meter and sensor technologies, real-time collection of electricity consumption load data has become a major trend. However, how to accurately identify and evaluate the load status from a large amount of data remains a challenge; existing electricity consumption load analysis methods usually lack effective steps for collection and processing, resulting in noise and outliers affecting the accuracy of the analysis results, relying too much on manual judgment, lacking a data-based evaluation standard, and being unfavorable for reducing evaluation errors. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing method for making a comparison decision on electricity consumption load based on load composition, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide a method for making a comparison decision on electricity consumption load based on load composition.

[0007] To solve the above technical problems, the present invention provides the following technical solution: A method for making a comparison decision on electricity consumption load based on load composition, comprising the following steps First, preset a parameter set and a preset load threshold; Perform collection and processing on the electricity consumption load data to obtain a dataset of electricity consumption load after collection and processing; Perform image acquisition on the electricity consumption load dataset to obtain an electricity consumption load image; Perform preprocessing on the electricity consumption load image, including background noise removal, load intensity correction, and detection and separation of load unit overlap; Set a load unit sample interval in the electricity consumption load image, and identify target load units within the load unit sample interval according to a preset parameter set; Classify the target load units and calculate the proportion; Compare the proportion with a preset load threshold to determine the electricity consumption load status; and, Dynamically adjust the parameter set and the load threshold according to the actual analysis results to optimize the accuracy of subsequent electricity consumption load analysis; The parameter set includes load power parameters, load type parameters, and load intensity parameters. The values of the parameter set are set according to historical data, regional data, and industry standards. The load threshold is set according to historical data, regional data, and industry standards and can be used to determine the electricity consumption load status.

[0008] As a preferred solution of the electricity consumption load comparison and decision-making method based on load composition according to the present invention, wherein: the values of the parameter set are set according to at least one of the following: the parameter range obtained from historical data; the parameter range in industry standards; the parameter range in published academic literature; The setting of the load threshold is based on at least one of the following: the high load rate load threshold of actual historical data; the experience setting of electricity consumption load management experts; the load threshold in published academic literature.

[0009] As a preferred solution of the electricity consumption load comparison and decision-making method based on load composition according to the present invention, wherein: the preprocessing step further includes using an image enhancement algorithm to improve the contrast of the electricity consumption load image; applying a filtering algorithm to remove high-frequency noise in the image.

[0010] As a preferred solution of the electricity consumption load comparison and decision-making method based on load composition according to the present invention, wherein: the step of classifying the target load units includes classifying the target load units to distinguish high load units and low load units; calculating the proportion of high load units in the target load units according to the classification results.

[0011] As a preferred solution of the electricity consumption load comparison and decision-making method based on load composition according to the present invention, wherein: the dynamic adjustment step includes adjusting each parameter in the parameter set according to the deviation between the actual analysis result and the prediction result; adjusting the preset load threshold according to the deviation between the actual analysis result and the prediction result.

[0012] As a preferred solution of the electricity consumption load comparison and decision-making method based on load composition according to the present invention, wherein: it further includes, Compare the electricity consumption load image with a known standard electricity consumption load image for verification to evaluate the accuracy of the analysis result; Further optimize the parameter set and load threshold according to the results of comparative verification.

[0013] As a preferred embodiment of the method for comparing and making decisions on electricity consumption loads based on load composition according to the present invention, wherein: in the preprocessing step, the specific algorithm for background noise removal is Calculate the local mean μ and local standard deviation σ of the electricity consumption load image, where μ represents the average pixel value within the local area, and σ represents the pixel standard deviation within the local area; For each pixel point , calculate its noise level ;

[0014] Where represents the pixel value at the position (x, y) of the image; If , then mark this pixel point as a noise point, where k is a preset noise load threshold that can control the sensitivity of noise detection; Then, use median filtering to smooth the noise points, which can reduce the impact of noise on the image.

[0015] As a preferred embodiment of the method for comparing and making decisions on electricity consumption loads based on load composition according to the present invention, wherein: in the preprocessing step, the specific algorithm for load intensity correction is Calculate the global average negative intensity of the electricity consumption load image, where represents the average value of all pixel values in the image; For each pixel point , calculate its corrected load intensity ;

[0016] Where represents the original pixel value at the position (x, y) of the image, and T I represents a preset target load intensity for normalizing the load intensity.

[0017] As a preferred embodiment of the method for comparing and making decisions on electricity consumption loads based on load composition according to the present invention, wherein: in the preprocessing step, the specific algorithm for detecting and separating overlapping load units includes Use an edge detection algorithm to detect the edges of the load units and generate an edge image; For each detected load unit area, calculate its area A and perimeter P, where A represents the area of the load unit area and P represents the perimeter of the load unit area; If , it is determined that there may be an overlap in the load unit area, where C is a preset overlapping load threshold; Use morphological operations to separate the overlapping load units to ensure the independence of each load unit area.

[0018] As a preferred solution of the method for making an electricity consumption load comparison and decision based on load composition according to the present invention, wherein: the image enhancement algorithm improves the contrast of the electricity consumption load image, including by histogram equalization or adaptive contrast enhancement method; The application of the filtering algorithm removes high-frequency noise in the image, and a low-pass filter or a bilateral filter can be used.

[0019] The beneficial effects of the present invention: By presetting a parameter set and a load threshold, combined with the image preprocessing steps, the analysis accuracy and reliability of the electricity consumption load image can be significantly improved; Through algorithms such as image enhancement and high-frequency noise removal, the visual effect and analysis efficiency of the electricity consumption load image are improved, providing more scientific and efficient technical support for the evaluation of electricity consumption load. By digitalizing the judgment conditions, the evaluation is made more standard, effectively reducing the time-consuming cost of manual evaluation and improving the evaluation efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them: Figure 1 It is a schematic diagram of the overall process of the method for making an electricity consumption load comparison and decision based on load composition according to the present invention.

[0021] Figure 2 It is a computer device of the electricity consumption load comparison and decision system based on load composition according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the above objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings in the specification.

[0023] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Second, as used herein, "an embodiment" or "embodiments" refer to specific features, structures, or characteristics that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that are mutually exclusive of other embodiments.

[0025] Next, the present invention will be described in detail with reference to schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0026] Embodiment 1, referring to Figure 1 , provides an overall flow schematic diagram of a power consumption load comparison decision-making method based on load composition, as shown in Figure 1 , a power consumption load comparison decision-making method based on load composition includes the following steps. First, preset a parameter set and a preset load threshold; Collect and process the power consumption load data to obtain a dataset of power consumption load after collection and processing; Collect images of the power consumption load dataset to obtain power consumption load images; Preprocess the power consumption load images, including background noise removal, load intensity correction, and load unit overlap detection and separation; Set a load unit sample interval in the power consumption load image, and identify target load units within the load unit sample interval according to the preset parameter set; Classify the target load units and calculate the proportion; the steps of classifying the target load units include classifying the target load units to distinguish high-load units and low-load units; calculating the proportion of high-load units in the target load units according to the classification results; Compare the proportion with the preset load threshold to judge the power consumption load status; and, Dynamically adjust the parameter set and the load threshold according to the actual analysis results to optimize the accuracy of subsequent power consumption load analysis; The parameter set includes load power parameters, load type parameters, and load intensity parameters. The values of the parameter set are set according to historical data, regional data, and industry standards. The load threshold is set according to historical data, regional data, and industry standards and can be used to judge the state of the electrical load. The values of the parameter set are set based on at least one of the following: the parameter range obtained from historical data; the parameter range in industry standards; the parameter range in published academic literature. The setting of the load threshold is based on at least one of the following: the high load rate load threshold of actual historical data; the experience setting of electrical load management experts; the load threshold in published academic literature. Among them, in the field of electrical load management, industry standards usually include quality standards for electrical load images, standards for load unit identification and classification, standards for aggregation processing, etc. For example, the quality standard for electrical load images requires clear images, moderate contrast, no obvious noise and artifacts. The standard for load unit identification and classification requires accurate classification according to the shape, size, aggregation processing characteristics, etc. of the load unit. The references include but are not limited to local historical statistical data, industry standard documents and data such as "Technical Specification for Electrical Load Management" and "Technical Guide for Electrical Load Analysis". Such documents detail the specific methods and standards for the acquisition, processing, analysis of electrical load images, and the identification and classification of load units.

[0027] Furthermore, the preprocessing step also includes using an image enhancement algorithm to improve the contrast of the electrical load image; applying a filtering algorithm to remove high-frequency noise in the image. Since the high-frequency noise is of a different type from the background noise, different methods are generally used for processing. It also includes comparing and validating the electrical load image with a known standard electrical load image to evaluate the accuracy of the analysis result. According to the result of the comparison and validation, the parameter set and the load threshold are further optimized.

[0028] Furthermore, the dynamic adjustment step includes adjusting each parameter in the parameter set according to the deviation between the actual analysis result and the predicted result; adjusting the preset load threshold according to the deviation between the actual analysis result and the predicted result. Here, statistical indicators such as the mean and standard deviation of historical evaluation data are calculated to dynamically adjust the load threshold. For example, if the average value of the proportion of high-load units in historical data is 30% and the standard deviation is 5%, the load threshold can be set between 25% - 35%. Calculate the mean or median within the sliding window in real time and use it as the current load threshold. For example, set a sliding window of size 10, calculate the mean of the proportion of high-load units within the window in real time, and use it as the current load threshold. Furthermore, future trends can also be predicted through time series analysis or machine learning models, and the load threshold can be dynamically adjusted accordingly. The above are the specific methods of dynamic adjustment, and the basis for dynamic adjustment is: (1) Adjust each parameter in the parameter set and the preset load threshold according to the deviation between the actual analysis result and the predicted result. If the deviation between the actual analysis result and the predicted result is large, it indicates that the current parameter set and load threshold may not be accurate enough and need to be adjusted; (2) Feedback information: The accuracy feedback information provided by the user to the processing platform after evaluating the accuracy of the proportion value through the client. According to the user's feedback information, adjust the parameter set and load threshold to improve the accuracy of subsequent electricity load analysis; (3) Comparison and verification results: Compare the electricity load image with the known standard electricity load image for verification. According to the comparison and verification results, further optimize the parameter set and load threshold. If the comparison and verification results show that there is a large difference between the current analysis result and the standard electricity load image, it indicates that the parameter set and load threshold need to be adjusted.

[0029] Specifically, in the preprocessing step, the specific algorithm for background noise removal is Calculate the local mean μ and local standard deviation σ of the electricity load image, where μ represents the pixel average value within the local area, and σ represents the pixel standard deviation within the local area; For each pixel point , calculate its noise level ;

[0030] where represents the pixel value at the position (x, y) of the image; If , then mark this pixel point as a noise point, where k is a preset noise load threshold that can control the sensitivity of noise detection; Then, use median filtering to smooth the noise points, which can reduce the impact of noise on the image.

[0031] In the preprocessing step, the specific algorithm for load intensity correction is as follows: Calculate the global average load intensity of the electricity consumption load image , where represents the average value of pixel values in the image; For each pixel point , calculate its corrected load intensity ;

[0032] where represents the original pixel value at the position (x, y) of the image, and T I represents the preset target load intensity for normalizing the load intensity; In the preprocessing step, the specific algorithm for load unit overlap detection and separation includes Use an edge detection algorithm to detect the edges of load units and generate an edge image; For each detected load unit area, calculate its area A and perimeter P, where A represents the area of the load unit area and P represents the perimeter of the load unit area; If , then it is determined that there may be an overlap in this load unit area, where C is a preset overlap load threshold; Use morphological operations to separate overlapping load units to ensure the independence of each load unit area; The image enhancement algorithm improves the contrast of the electricity consumption load image, including through histogram equalization or adaptive contrast enhancement methods; Apply a filtering algorithm to remove high-frequency noise in the image, and low-pass filters or bilateral filters can be used.

[0033] Operation process: Preset parameter sets and loads. Parameter set part: Load power parameter; Set the load unit size range, for example, the sampling diameter of the load unit is between 5 and 20. Load type parameter; Set the sampling shape characteristics of the load unit, for example, the circularity of the load unit is between 0.7 and 1.0. Load intensity parameter; Set the load intensity range of the load unit. Load unit type parameter; Set the type characteristics of the load unit, for example, the relative size ratio of the load unit core and the load unit mass.

[0034] Load threshold part: High load unit ratio load threshold; Set the ratio load threshold of high load units in the target load units, with a value of 30%; Preprocess the electricity consumption load data, including data cleaning, outlier processing, and data normalization, to obtain a preprocessed electricity consumption load data set; Use an electrical load data graphical acquisition and monitoring device to perform high-resolution image acquisition on the electrical load data set to obtain an electrical load image; Perform background noise removal, load intensity correction, load unit overlap detection and separation, image enhancement, and high-frequency noise removal; Perform load unit identification and classification: Set a load unit sample interval in the preprocessed electrical load image; Identify target load units within the load unit sample interval according to a preset parameter set; Classify the target load units to distinguish high-load units and low-load units; Distinguish high-load units: The load intensity is higher than the preset load threshold.

[0035] Distinguish low-load units: The load intensity is lower than the preset load threshold.

[0036] Calculate the proportion of high-load units in the target load units.

[0037] Then perform electrical load status judgment: Compare the calculated proportion of high-load units with the preset load threshold (30%). If the proportion of high-load units is higher than the load threshold, it is judged as a high-load state; otherwise, it is judged as a low-load state; Dynamic adjustment: Adjust the parameters in the parameter set and the preset load threshold according to the deviation between the actual analysis result and the prediction result; If the deviation between the actual analysis result and the prediction result is large, it indicates that the current parameter set and load threshold may not be accurate enough and need to be adjusted.

[0038] Comparison and verification: Compare the electrical load image with a known standard electrical load image for comparison and verification to evaluate the accuracy of the analysis result; According to the result of the comparison and verification, further optimize the parameter set and the load threshold to ensure the reliability and accuracy of the analysis result.

[0039] Example 2. What is different from the first example in this example is that this method further includes the following content; Data acquisition and preprocessing: Collect electrical load data, including but not limited to real-time monitoring of parameters such as current, voltage, and power; Perform preprocessing on the collected data, including data cleaning, outlier processing, data normalization, etc., to ensure the accuracy and reliability of the data.

[0040] Load feature extraction: Extract load features from the preprocessed data, such as power factor, load rate, peak load, etc. These features will be used for subsequent load status evaluation.

[0041] Load status assessment: Based on the extracted load characteristics and combined with preset thresholds, the power consumption load status is evaluated. For example, if the power factor is lower than a certain threshold, it may indicate a problem of insufficient power factor; if the load rate exceeds a certain threshold, it may indicate that the system is in an overloaded state.

[0042] Uncertain load status judgment rule: When the value of the load characteristic is between the low load threshold and the high load threshold, according to the fuzzy load status judgment rule, the uncertainty of the load status is further evaluated. For example, if the load rate is between normal and overloaded, it is necessary to further analyze whether there is local overload or non-specific load.

[0043] Result feedback and dynamic adjustment: According to the evaluation results, the preset thresholds and load characteristic extraction methods are dynamically adjusted to optimize the subsequent load status assessment process. For example, if it is found that misjudgments often occur in a certain area, the relevant thresholds can be adjusted or the load characteristic extraction method can be improved.

[0044] Embodiment 3, referring to Figure 2 , what is different from the above embodiments in this embodiment is that the present invention also provides a power consumption load comparison decision-making system based on load composition, including: Parameter preset module: Used to preset a parameter set, the parameter set includes load power parameters, load type parameters, and load intensity parameters, and the values of the parameter set are set according to historical data and industry standards.

[0045] Threshold preset module: Used to preset thresholds, the thresholds are set according to historical data and industry standards, and are used to judge the power consumption load status.

[0046] Data acquisition module: Used to collect power consumption load data, including but not limited to real-time monitoring of parameters such as current, voltage, and power, to obtain a power consumption load data set.

[0047] Collection and processing module: Used to preprocess the power consumption load data set, including background noise removal, load intensity correction, and load unit overlap detection and separation.

[0048] Load characteristic extraction module: Used to extract load characteristics from the preprocessed power consumption load data, such as power factor, load rate, peak load, etc.

[0049] Load status assessment module: Used to evaluate the power consumption load status according to the extracted load characteristics and preset thresholds, and judge whether there is a high load status or a low load status.

[0050] Dynamic adjustment module: Used to dynamically adjust the parameter set and thresholds according to the actual power consumption load diagnosis results to optimize the accuracy of subsequent power consumption load analysis.

[0051] Result feedback module: used to feedback the evaluation results to the user, and further optimize the parameter set and threshold according to the user's feedback information.

[0052] Comparison and verification module: used to compare and verify the electricity consumption load data with the known standard electricity consumption load data to evaluate the accuracy of the analysis results.

[0053] It also includes a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented; It also includes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, the steps of the above method are implemented; Furthermore, if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0054] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0055] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0056] It is important to note that the construction and arrangement of the present application shown in multiple different exemplary embodiments are merely illustrative. Although only a few embodiments are described in detail in this disclosure, those skilled in the art who refer to this disclosure should readily understand that many modifications are possible without materially departing from the novel teachings and advantages of the subject matter described in this application (e.g., variations in the dimensions, scales, structures, shapes, and proportions of various elements, as well as parameter values (e.g., temperature, pressure, etc.), installation arrangements, use of materials, colors, orientations, etc.). For example, an element shown as integrally formed can be composed of multiple parts or elements, the position of the element can be inverted or otherwise changed, and the nature, number, or position of discrete elements can be altered or changed. Accordingly, all such modifications are intended to be included within the scope of the present invention. The order or sequence of any process or method steps can be changed or reordered according to alternative embodiments. In the claims, any "means-plus-function" clause is intended to cover the structures that perform the recited function herein, and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of the present invention. Thus, the present invention is not limited to the specific embodiments but extends to various modifications that still fall within the scope of the appended claims.

[0057] Moreover, to provide a concise description of the exemplary embodiments, all features of the actual embodiments may not be described (i.e., those features that are not relevant to the currently contemplated best mode of carrying out the present invention or those features that are not relevant to the implementation of the present invention).

[0058] It should be understood that in the development of any actual implementation, as in any engineering or design project, numerous specific implementation decisions may be made. Such development efforts may be complex and time-consuming, but for those of ordinary skill in the art who benefit from this disclosure, the development efforts will be a routine task of design, fabrication, and production without undue experimentation.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A load comparison decision method based on load composition, characterized in that: The following steps are included: First, a preset parameter set and a preset load threshold; The power load data is aggregated and processed to obtain a power load data set; Performing image acquisition on the power load data set to obtain a power load image; Preprocessing the power load image, including background noise removal, load intensity correction, and load unit overlap detection and separation; Setting a load unit sample interval in the power load image, and identifying a target load unit in the load unit sample interval according to a preset parameter set; classifying the target load units and calculating the ratios; Comparing the ratio with a preset load threshold to determine the power load status; as well as, Dynamically adjusting the parameter set and load threshold according to actual analysis results to optimize the accuracy of subsequent power load analysis; Comparative verification: comparing the power load image with a known standard power load image to evaluate the accuracy of the analysis results; The parameter set includes load power parameters, load type parameters and load intensity parameters. The value of the parameter set is set according to historical data, regional data and industry standards. The load threshold is set according to historical data, regional data and industry standards and can be used to determine the power load status.

2. The load composition-based power load comparison decision method according to claim 1, characterized in that: The value of the parameter set is set according to at least one of the following: a parameter range obtained from historical data; a parameter range in an industry standard; a parameter range in a published academic literature; The setting of the load threshold is based on at least one of the following: a high load rate load threshold of actual historical data; an experience setting by power load management experts; or a load threshold in published academic literature.

3. The load composition-based power load comparison decision method according to claim 1, characterized in that: The pre-processing step further comprises: Use image enhancement algorithms to improve the contrast of power load images; Apply filtering algorithms to remove high frequency noise from the image.

4. The method for comparing and deciding power loads based on load composition according to claim 1, characterized in that: The step of classifying the target load unit comprises: Classify the target load units and distinguish between high-load units and low-load units; The proportion of high load units to target load units is calculated based on the classification results.

5. The method for comparing and deciding power loads based on load composition according to claim 1, characterized in that: The dynamic adjustment step includes: Adjust the parameters in the parameter set according to the deviation between the actual analysis results and the predicted results; Adjust the preset load threshold according to the deviation between the actual analysis results and the predicted results.

6. The method for comparing and deciding power load based on load composition according to claim 1 or 5, characterized in that: Also includes, Comparing and verifying the power load image with a known standard power load image to evaluate the accuracy of the analysis result; According to the results of the comparative verification, the parameter set and the load threshold are further optimized.

7. The method for comparing and deciding power load based on load composition according to claim 1 or 3, characterized in that: In the preprocessing step, the specific algorithm for removing background noise is: Calculating a local mean μ and a local standard deviation σ of the power load image, where μ represents an average value of pixels in a local area, and σ represents a standard deviation of pixels in a local area; For each pixel , calculate its noise level ; in, Represents the pixel value of the image at position (x, y); like , then the pixel is marked as a noise point, where k is the preset noise load threshold, which can control the sensitivity of noise detection; Then, the noise points are smoothed using median filtering to reduce the impact of noise on the image.

8. The method for comparing and deciding power loads based on load composition according to claim 1, characterized in that: In the preprocessing step, the specific algorithm for load intensity correction is: Calculate the global average load intensity of the power load image ,in, Represents the average value of all pixel values ​​in the image; For each pixel , calculate the corrected load intensity ; in, Represents the original pixel value of the image at position (x, y), T I Indicates the preset target load intensity, which is used to standardize the load intensity.

9. The method for comparing and deciding power loads based on load composition according to claim 8, characterized in that: In the preprocessing step, the specific algorithm for detecting and separating the overlap of load cells includes: Use edge detection algorithm to detect the edge of load cell and generate edge image; For each detected load cell area, calculate its area A and perimeter P, where A represents the area of ​​the load cell area and P represents the perimeter of the load cell area; like , it is determined that the load unit area may overlap, where C is the preset overlapping load threshold; Overlapping load cells were separated using morphological operations to ensure the independence of each load cell region.

10. The load composition-based power load comparison decision method according to claim 3, characterized in that: The image enhancement algorithm improves the contrast of the power load image, including through histogram equalization or adaptive contrast enhancement method; The filtering algorithm is applied to remove high-frequency noise in the image, and a low-pass filter or a bilateral filter may be used.

Images