A large industrial user load feature extraction and power consumption mode analysis system and method

Abstract

The present application relates to the technical field of user load feature extraction and power consumption mode analysis, in particular to a large industrial user load feature extraction and power consumption mode analysis system and method, which improves data analysis and processing data through hierarchical processing of data, and through two-line transmission of result data and complete data, ensures data analysis and processing efficiency, reduces loss, and through separate storage of data and periodic comparison, reduces the occurrence of malicious tampering and loss of data; comprising: a data collection and arrangement module, a feature selection and extraction module, a data analysis module, a security module, a management module, an early warning module and a data deletion module.

Description

Technical Field

[0001] This invention relates to the technical field of user load feature extraction and electricity consumption pattern analysis, and in particular to a system and method for large industrial user load feature extraction and electricity consumption pattern analysis. Background Technology

[0002] With societal development, peak and off-peak electricity consumption patterns are becoming increasingly pronounced. To ensure normal electricity supply for industrial production and to allocate and utilize electricity rationally, it is necessary to extract the load characteristics of large industrial users and analyze their electricity consumption patterns. This will enable power companies to deliver appropriate amounts of electricity to large industrial users at different times, ensuring their normal production operations while reducing energy waste.

[0003] Existing methods for extracting electricity load features and analyzing electricity consumption patterns, such as the user electricity load feature extraction method based on empirical wavelet transform disclosed in the invention patent with authorization announcement number CN107491412B and the refined identification method of user electricity behavior categories and typical electricity consumption patterns disclosed in the invention patent with publication number CN113033596A, can all extract user load features and analyze their electricity consumption patterns.

[0004] However, existing systems analyze data slowly, making it inconvenient to handle some emergencies in a timely manner. Furthermore, due to sealing issues and electrical equipment problems in most workshops, the signal is poor, making it difficult for some data acquisition and processing modules to send data and receive adjustment commands. In addition, it is inconvenient to protect the collected and analyzed data, resulting in poor practicality. Therefore, there is an urgent need for a system and method for extracting load characteristics and analyzing power consumption patterns of large industrial users to improve the above problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a system and method for extracting load characteristics and analyzing electricity consumption patterns of large industrial users. This system improves data analysis and processing by performing hierarchical processing of data, and ensures data analysis and processing efficiency while reducing data loss by transmitting result data and complete data through two separate lines. Furthermore, it reduces the occurrence of malicious data tampering and loss by storing data separately and performing periodic comparisons.

[0006] The present invention provides a system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users, comprising:

[0007] Data acquisition and processing module: Multiple data acquisition and processing modules collect, process and store electricity consumption data from different electricity consumption areas of large industrial users. The data is then transmitted between the multiple data acquisition modules through data sharing. The processed results and complete data are sent to the feature selection and extraction module via two lines through a data acquisition module with good signal outside the factory or workshop.

[0008] Feature selection and extraction module: Extracts the required parts from the result data according to the features preset by the staff, stores the received complete data, and sends the extracted data and complete data to the data analysis module through two separate lines;

[0009] Data Analysis Module: By analyzing and processing the extracted data, it predicts the user's electricity consumption pattern for a period of time in the future, enabling staff to adjust the power transmission in a timely manner based on the analysis results, so as to achieve higher power utilization and reduce the impact of peak and off-peak electricity consumption on power plants, power storage stations and power distribution departments. At the same time, it stores the prediction results and the complete data received.

[0010] Security Module: Regularly compares the complete data in the data collection and processing module, feature selection and extraction module, and data analysis module to ensure that the complete data in these modules is identical. When modifying the complete data in these modules, managers from different departments must confirm and modify it simultaneously.

[0011] Management module: Verifies the information of logged-in personnel, displays the prediction results and complete data, adjusts the data collection and processing module, feature selection and extraction module and data analysis module, and records the operation process;

[0012] Early warning module: By setting a threshold, an alarm is issued to staff when the predicted result exceeds the threshold range, reminding them to adjust the power supply in a timely manner;

[0013] Data deletion module: Deletes data that exceeds the set time limit in the data collection and processing module, feature selection and extraction module, and data analysis module.

[0014] Preferably, the data acquisition and processing module:

[0015] Data acquisition module: Collects users' electricity consumption data;

[0016] Data processing module: Processes users' electricity consumption data, obtains the total value for each time period, and packages the collected complete data;

[0017] First data storage module: Stores the complete packaged data;

[0018] First result transmission module: Transmits the total value;

[0019] The first complete data transmission module: transmits the packaged complete data.

[0020] Preferably, the feature selection and extraction module includes:

[0021] Feature selection module: Staff can set the feature selection mode according to the user's situation;

[0022] Feature extraction module: Extracts the required feature data from the total number of values ​​using the set feature extraction mode;

[0023] Second data storage module: Stores the complete packaged data;

[0024] The second result transmission module transmits the extracted feature data.

[0025] The second complete data transmission module: transmits the packaged complete data.

[0026] Preferably, the data analysis module includes:

[0027] Processing module: Analyzes and processes the extracted feature data to predict the user's electricity consumption patterns over a future period of time;

[0028] Database: Stores complete data packages of prediction results and structures.

[0029] Preferably, the processing module uses the PyTorch framework and a convolutional neural network, and the model used is a pre-trained model.

[0030] Preferably, the security module includes:

[0031] Data comparison module: Regularly compares the complete data in the data collection and processing module, feature selection and extraction module, and data analysis module to ensure that the complete data in these modules is identical;

[0032] Alarm module: Issues an alarm to staff when different situations occur, alerting them to the alarm.

[0033] Preferably, the management module includes:

[0034] Display module: Provides staff with a port to interact with the system, displaying prediction results, complete data, and settings interface;

[0035] Settings module: Manages and adjusts the data acquisition and processing module, feature selection and extraction module, and data analysis module;

[0036] Login Management Module: Verifies the information of logged-in users;

[0037] Recording module: Records the operation process of logged-in users.

[0038] Preferably, the data deletion module is equipped with a time-limited recovery unit to recover accidentally deleted data within a set period.

[0039] Preferably, the data acquisition module, data processing module, first data storage module, first result transmission module, and first complete data transmission module are all equipped with quick-connect components to facilitate the assembly of the data acquisition and processing module.

[0040] A preferred method for load characteristic extraction and power consumption pattern analysis in a large industrial user load characteristic extraction and power consumption pattern analysis system includes the following steps:

[0041] S1. The login management module verifies the login personnel information, the settings module manages and adjusts the data collection and processing module, the feature selection and extraction module, and the data analysis module, and the recording module records the login personnel's operation process. Then, multiple sets of data collection modules collect electricity consumption data from different electricity consumption areas of large industrial users, process the user's electricity consumption data to obtain the total value for each time period, and package the collected complete data. Then, the multiple sets of data collection modules transmit the data through data sharing, and a set of data collection modules with good signal outside the factory or workshop sends the processed results and complete data to the feature selection and extraction module through two lines.

[0042] S2. Staff set the feature selection mode according to the user's situation, extract the required feature data from the total value through the set feature extraction mode, store the received complete data, and send the extracted data and complete data to the data analysis module again through two lines.

[0043] S3. The extracted feature data is analyzed and processed by the processing module to predict the user's electricity consumption pattern in the future, and the complete packaged data of the prediction results and structure is stored.

[0044] S4. The data comparison module regularly compares the complete data in the data collection and processing module, feature selection and extraction module, and data analysis module to ensure that the complete data in these modules are identical. If any discrepancies occur, the alarm module will issue an alarm to the staff to remind them.

[0045] S5. The display module provides a port for staff to interact with the system, displaying the prediction results, complete data and settings interface. The early warning module will issue an alarm to the staff when the prediction results exceed the threshold range, reminding them to adjust the power supply in time.

[0046] S6. Delete data that exceeds the set time limit in the data collection and processing module, feature selection and extraction module, and data analysis module through the data deletion module.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] 1. By classifying and processing data, we can improve data analysis and processing capabilities, allowing power sector staff sufficient time to adjust power transmission.

[0049] 2. By transmitting the result data and the complete data through two separate lines, the efficiency of data analysis and processing is ensured. Furthermore, by transmitting the same data in different modules and performing regular checks, the occurrence of malicious data tampering and loss is reduced.

[0050] 3. Transmit the collected data through data sharing to reduce interference from surrounding electrical equipment on the signal. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the structure of the large industrial user load characteristic extraction and power consumption pattern analysis system of the present invention;

[0052] Figure 2 This is a schematic diagram of the data acquisition and processing module of the present invention;

[0053] Figure 3 This is a schematic diagram of the feature selection and extraction module of the present invention;

[0054] Figure 4 This is a schematic diagram of the data analysis module of the present invention;

[0055] Figure 5 This is a schematic diagram of the security module of the present invention;

[0056] Figure 6 This is a structural diagram of the management module of the present invention. Detailed Implementation

[0057] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. Example

[0058] like Figures 1 to 6 As shown, it includes:

[0059] Data acquisition and processing module: Multiple data acquisition and processing modules collect, process and store electricity consumption data from different electricity consumption areas of large industrial users. The data is then transmitted between the multiple data acquisition modules through data sharing. The processed results and complete data are sent to the feature selection and extraction module via two lines through a data acquisition module with good signal outside the factory or workshop.

[0060] Feature selection and extraction module: Extracts the required parts from the result data according to the features preset by the staff, stores the received complete data, and sends the extracted data and complete data to the data analysis module through two separate lines;

[0061] Data Analysis Module: By analyzing and processing the extracted data, it predicts the user's electricity consumption pattern for a period of time in the future, enabling staff to adjust the power transmission in a timely manner based on the analysis results, so as to achieve higher power utilization and reduce the impact of peak and off-peak electricity consumption on power plants, power storage stations and power distribution departments. At the same time, it stores the prediction results and the complete data received.

[0062] Security Module: Regularly compares the complete data in the data collection and processing module, feature selection and extraction module, and data analysis module to ensure that the complete data in these modules is identical. When modifying the complete data in these modules, managers from different departments must confirm and modify it simultaneously.

[0063] Management module: Verifies the information of logged-in personnel, displays the prediction results and complete data, adjusts the data collection and processing module, feature selection and extraction module and data analysis module, and records the operation process;

[0064] Early warning module: By setting a threshold, an alarm is issued to staff when the predicted result exceeds the threshold range, reminding them to adjust the power supply in a timely manner;

[0065] Data deletion module: Deletes data that exceeds the set time limit in the data collection and processing module, feature selection and extraction module, and data analysis module;

[0066] The data acquisition and processing module:

[0067] Data acquisition module: Collects users' electricity consumption data;

[0068] Data processing module: Processes users' electricity consumption data, obtains the total value for each time period, and packages the collected complete data;

[0069] First data storage module: Stores the complete packaged data;

[0070] First result transmission module: Transmits the total value;

[0071] The first complete data transmission module: transmits the packaged complete data;

[0072] The feature selection and extraction module includes:

[0073] Feature selection module: Staff can set the feature selection mode (such as peak electricity consumption and off-peak electricity consumption at different times) according to the user's situation;

[0074] Feature extraction module: Extracts the required feature data from the total number of values ​​using the set feature extraction mode;

[0075] Second data storage module: Stores the complete packaged data;

[0076] The second result transmission module transmits the extracted feature data.

[0077] The second complete data transmission module: transmits the packaged complete data;

[0078] The data analysis module includes:

[0079] Processing module: Analyzes and processes the extracted feature data to predict the user's electricity consumption patterns over a future period of time;

[0080] Database: Stores complete, packaged data of prediction results and structures;

[0081] The processing module uses the PyTorch framework and convolutional neural networks, and the model used is a pre-trained model.

[0082] The security module includes:

[0083] Data comparison module: Regularly compares the complete data in the data collection and processing module, feature selection and extraction module, and data analysis module to ensure that the complete data in these modules is identical;

[0084] Alarm module: Issues an alarm to staff when different situations occur, alerting them accordingly;

[0085] The management module includes:

[0086] Display module: Provides staff with a port to interact with the system, displaying prediction results, complete data, and settings interface;

[0087] Settings module: Manages and adjusts the data acquisition and processing module, feature selection and extraction module, and data analysis module;

[0088] Login Management Module: Verifies the information of logged-in users;

[0089] Recording module: Records the operation process of logged-in users;

[0090] The data deletion module is equipped with a time-limited recovery unit to recover accidentally deleted data within a set period.

[0091] The data acquisition module, data processing module, first data storage module, first result transmission module, and first complete data transmission module are all equipped with quick-connect components to facilitate the assembly of the data acquisition and processing module.

[0092] All quick-connect components are pluggable, facilitating power supply and transmission, and making replacement easy. In case of a fault, the faulty component must be replaced.

[0093] Methods for load characteristic extraction and power consumption pattern analysis in large industrial user load characteristic extraction and power consumption pattern analysis systems:

[0094] S1. The login management module verifies the login personnel information, the settings module manages and adjusts the data collection and processing module, the feature selection and extraction module, and the data analysis module, and the recording module records the login personnel's operation process. Then, multiple sets of data collection modules collect electricity consumption data from different electricity consumption areas of large industrial users, process the user's electricity consumption data to obtain the total value for each time period, and package the collected complete data. Then, the multiple sets of data collection modules transmit the data through data sharing, and a set of data collection modules with good signal outside the factory or workshop sends the processed results and complete data to the feature selection and extraction module through two lines.

[0095] S2. Staff set the feature selection mode according to the user's situation, extract the required feature data from the total value through the set feature extraction mode, store the received complete data, and send the extracted data and complete data to the data analysis module again through two lines.

[0096] S3. The extracted feature data is analyzed and processed by the processing module to predict the user's electricity consumption pattern in the future, and the complete packaged data of the prediction results and structure is stored.

[0097] S4. The data comparison module regularly compares the complete data in the data collection and processing module, feature selection and extraction module, and data analysis module to ensure that the complete data in these modules are identical. If any discrepancies occur, the alarm module will issue an alarm to the staff to remind them.

[0098] S5. The display module provides a port for staff to interact with the system, displaying the prediction results, complete data and settings interface. The early warning module will issue an alarm to the staff when the prediction results exceed the threshold range, reminding them to adjust the power supply in time.

[0099] S6. Delete data exceeding the set time limit from the data acquisition and processing module, feature selection and extraction module, and data analysis module using the data deletion module.

[0100] Technical personnel in this industry only need to operate it according to the accompanying instruction manual, without requiring any creative effort from those skilled in the art.

[0101] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users, characterized in that, include: Data acquisition and processing module: Multiple data acquisition and processing modules collect, process and store electricity consumption data from different electricity consumption areas of large industrial users. The data is then transmitted between the multiple data acquisition modules through data sharing. The processed results and complete data are sent to the feature selection and extraction module via two lines through a data acquisition module with good signal outside the factory or workshop. Feature selection and extraction module: Extracts the required parts from the result data according to the features preset by the staff, stores the received complete data, and sends the extracted data and complete data to the data analysis module through two separate lines; Data Analysis Module: By analyzing and processing the extracted data, it predicts the user's electricity consumption pattern for a period of time in the future, enabling staff to adjust the power transmission in a timely manner based on the analysis results, so as to achieve higher power utilization and reduce the impact of peak and off-peak electricity consumption on power plants, power storage stations and power distribution departments. At the same time, it stores the prediction results and the complete data received. Security Module: Regularly compares the complete data in the data collection and processing module, feature selection and extraction module, and data analysis module to ensure that the complete data in these modules is identical. When modifying the complete data in these modules, managers from different departments must confirm and modify it simultaneously. Management module: Verifies the information of logged-in personnel, displays the prediction results and complete data, adjusts the data collection and processing module, feature selection and extraction module and data analysis module, and records the operation process; Early warning module: By setting a threshold, an alarm is issued to staff when the predicted result exceeds the threshold range, reminding them to adjust the power supply in a timely manner; Data deletion module: Deletes data that exceeds the set time limit in the data collection and processing module, feature selection and extraction module, and data analysis module.

2. The system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users as described in claim 1, characterized in that, The data acquisition and processing module: Data acquisition module: Collects users' electricity consumption data; Data processing module: Processes users' electricity consumption data, obtains the total value for each time period, and packages the collected complete data; First data storage module: Stores the complete packaged data; First result transmission module: Transmits the total value; The first complete data transmission module: transmits the packaged complete data.

3. The system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users as described in claim 1, characterized in that, The feature selection and extraction module includes: Feature selection module: Staff can set the feature selection mode according to the user's situation; Feature extraction module: Extracts the required feature data from the total number of values ​​using the set feature extraction mode; Second data storage module: Stores the complete packaged data; The second result transmission module transmits the extracted feature data. The second complete data transmission module: transmits the packaged complete data.

4. The system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users as described in claim 1, characterized in that, The data analysis module includes: Processing module: Analyzes and processes the extracted feature data to predict the user's electricity consumption patterns over a future period of time; Database: Stores complete data packages of prediction results and structures.

5. The system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users as described in claim 4, characterized in that, The processing module uses the PyTorch framework and convolutional neural networks, and the model used is a pre-trained model.

6. The system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users as described in claim 1, characterized in that, The security module includes: Data comparison module: Regularly compares the complete data in the data collection and processing module, feature selection and extraction module, and data analysis module to ensure that the complete data in these modules is identical; Alarm module: Issues an alarm to staff when different situations occur, alerting them to the alarm.

7. The system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users as described in claim 1, characterized in that, The management module includes: Display module: Provides staff with a port to interact with the system, displaying prediction results, complete data, and settings interface; Settings module: Manages and adjusts the data acquisition and processing module, feature selection and extraction module, and data analysis module; Login Management Module: Verifies the information of logged-in users; Recording module: Records the operation process of logged-in users.

8. The system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users as described in claim 1, characterized in that, The data deletion module is equipped with a time-limited recovery unit to recover accidentally deleted data within a set period.

9. The system for extracting load characteristics and analyzing electricity consumption patterns of large industrial users as described in claim 2, characterized in that, The data acquisition module, data processing module, first data storage module, first result transmission module, and first complete data transmission module are all equipped with quick-connect components to facilitate the assembly of the data acquisition and processing module.

10. The method for load characteristic extraction and power consumption pattern analysis of the large industrial user load characteristic extraction and power consumption pattern analysis system according to any one of claims 1 to 9, comprising the following steps: S1. The login management module verifies the login personnel information, the settings module manages and adjusts the data collection and processing module, the feature selection and extraction module, and the data analysis module, and the recording module records the login personnel's operation process. Then, multiple sets of data collection modules collect electricity consumption data from different electricity consumption areas of large industrial users, process the user's electricity consumption data to obtain the total value for each time period, and package the collected complete data. Then, the multiple sets of data collection modules transmit the data through data sharing, and a set of data collection modules with good signal outside the factory or workshop sends the processed results and complete data to the feature selection and extraction module through two lines. S2. Staff set the feature selection mode according to the user's situation, extract the required feature data from the total value through the set feature extraction mode, store the received complete data, and send the extracted data and complete data to the data analysis module again through two lines. S3. The extracted feature data is analyzed and processed by the processing module to predict the user's electricity consumption pattern in the future, and the complete packaged data of the prediction results and structure is stored. S4. The data comparison module regularly compares the complete data in the data collection and processing module, feature selection and extraction module, and data analysis module to ensure that the complete data in these modules are identical. If any discrepancies occur, the alarm module will issue an alarm to the staff to remind them. S5. The display module provides a port for staff to interact with the system, displaying the prediction results, complete data and settings interface. The early warning module will issue an alarm to the staff when the prediction results exceed the threshold range, reminding them to adjust the power supply in time. S6. Delete data that exceeds the set time limit in the data collection and processing module, feature selection and extraction module, and data analysis module through the data deletion module.

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