Intelligent identification and statistics method and system for electricity bills

Abstract

The invention discloses an intelligent identification and statistics method and system for electricity bills, and the method comprises the steps: displaying an electricity bill uploading interface in response to a data uploading instruction, and importing electricity bills of at most 12 months in a cross-year manner; in response to an intelligent identification instruction, performing OCR identification on the electricity bill to obtain an electricity price type, and performing feature extraction on the electricity bill according to the electricity price type to obtain electricity bill data; in response to a data management instruction, displaying a data management interface, summarizing the electricity bill data, and generating an electricity bill list which is used for viewing or editing the electricity bill data; and in response to a statistical analysis instruction, displaying a statistical analysis interface, performing statistical analysis on the electricity bill list, and generating various types of visual charts. According to the method, one-key import, accurate identification, classified summarization and visual presentation can be realized, the database file is exported to reduce the manual operation cost, the data processing efficiency and precision are improved, and the method can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to an intelligent identification and statistical method and system for electricity bills. Background Technology

[0002] In the current electricity bill management scenario, the data processing of electricity bills mainly relies on manual entry or traditional simple tools, which has problems such as low efficiency, poor accuracy, weak aggregation capabilities, lack of visualization, easy data confusion, and inability to analyze aggregated data. Summary of the Invention

[0003] To address the aforementioned technical problems, the purpose of this application is to provide an intelligent identification and statistical method and system for electricity bills, which can reduce manual operation costs and improve data processing efficiency and accuracy.

[0004] To achieve the above objectives, one aspect of this application proposes an intelligent identification and statistical method for electricity bills, comprising the following steps: In response to the data upload command, the electricity bill upload interface is displayed, allowing the import of electricity bills for up to 12 months that can span across years; In response to the intelligent recognition command, the electricity bill is subjected to OCR recognition to obtain the electricity price type, and then the electricity bill is subjected to feature extraction based on the electricity price type to obtain the electricity bill data; In response to a data management command, a data management interface is displayed to summarize the electricity bill data and generate an electricity bill list, which is used to view or edit the electricity bill data. In response to the statistical analysis command, the statistical analysis interface is displayed, and statistical analysis is performed on the electricity bill list to generate various types of visualization charts.

[0005] In some embodiments, the step of responding to a data upload instruction by displaying an electricity bill upload interface and importing electricity bills for up to 12 months that can span across years specifically includes: Display the main interface; In response to a first data upload instruction to the first component in the main interface, the electricity bill upload interface is displayed, and the electricity bills for up to 12 months spanning multiple years are imported through the electricity bill upload interface; In response to the second data upload command of the second component in the main interface, historical data is cleared, and then the electricity bill upload interface is displayed. The electricity bills for up to 12 months that can span across years are imported through the electricity bill upload interface.

[0006] In some embodiments, the step of performing OCR recognition on the electricity bill in response to a smart recognition instruction to obtain the electricity price type specifically includes: In response to the intelligent recognition instruction of the third component in the electricity bill upload interface, the metering point number, year and month, electricity start time, electricity end time and electricity price identifier in the electricity bill are identified; Based on the electricity price identifier, the electricity price type of the electricity bill is determined, and the electricity price type includes large industrial electricity and non-industrial electricity.

[0007] In some embodiments, the electricity price type includes large industrial electricity and non-industrial electricity, and the step of extracting features from the electricity bill according to the electricity price type to obtain electricity bill data specifically includes: If the electricity price type of the electricity bill is the large industrial electricity consumption, extract the electricity volume, electricity price, and amount corresponding to the peak electricity price, mid-peak electricity price, flat-peak electricity price, and off-peak electricity price in the electricity bill; If the electricity price type of the electricity bill is non-industrial electricity, extract the total active power, electricity price, and amount corresponding to the total active power in the electricity bill; The electricity consumption, electricity price, and amount are rounded to obtain the electricity bill data.

[0008] In some embodiments, the electricity bill data includes the metering point number and year and month. The step of responding to a data management instruction, displaying a data management interface, summarizing the electricity bill data, and generating an electricity bill list specifically includes: Display the main interface; In response to the data management instruction on the fourth component in the main interface, the data management interface is displayed; Based on the metering point number and the year and month, the electricity bill data is correlated to generate a structured table; The header of the structured table is determined, and then the electricity bill data for each month is filled into the structured table according to the header to generate the electricity bill list.

[0009] In some embodiments, the step of responding to a statistical analysis command by displaying a statistical analysis interface, performing statistical analysis on the electricity bill list, and generating various types of visualization charts specifically includes: Display the main interface; In response to the statistical analysis command on the fifth component in the main interface, the statistical analysis interface is displayed; The total electricity cost, total electricity consumption, and average electricity cost for each item type under the same metering point number in the electricity bill list are calculated to obtain the statistical results. Based on the statistical results, a monthly electricity cost trend bar chart and a monthly electricity consumption trend curve are generated; The project types include peak electricity charges, mid-peak electricity charges, flat-peak electricity charges, off-peak electricity charges, and total active power charges.

[0010] In some embodiments, the method further includes: In response to the data export command of the sixth component in the data management interface, the electricity bill list is exported; In response to the chart export command of the seventh component in the statistical analysis interface, the statistical results, the monthly electricity cost trend bar chart, or the monthly electricity consumption trend curve chart are exported.

[0011] To achieve the above objectives, another aspect of this application proposes an intelligent electricity bill recognition and statistics system, comprising: The data upload module is used to respond to data upload commands, display the electricity bill upload interface, and import electricity bills for up to 12 months that can span across years; The data recognition module is used to respond to the intelligent recognition command, perform OCR recognition on the electricity bill to obtain the electricity price type, and then extract features from the electricity bill based on the electricity price type to obtain the electricity bill data. The data management module is used to respond to data management commands, display the data management interface, summarize the electricity bill data, and generate an electricity bill list. The electricity bill list is used to view or edit the electricity bill data. The data statistics module is used to respond to statistical analysis commands, display the statistical analysis interface, perform statistical analysis on the electricity bill list, and generate various types of visualization charts.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, it implements the method described above.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a storage medium, which is a computer-readable storage medium for computer-readable storage, wherein the storage medium stores one or more programs that can be executed by one or more processors to implement the method described above.

[0014] The beneficial effects of this application are as follows: The intelligent electricity bill recognition and statistical method and system of this application include: responding to a data upload command, displaying an electricity bill upload interface, and importing electricity bills spanning up to 12 months across years; responding to an intelligent recognition command, performing OCR recognition on the electricity bills to obtain the electricity price type, and then extracting features from the electricity bills based on the electricity price type to obtain electricity bill data; responding to a data management command, displaying a data management interface, summarizing the electricity bill data, and generating an electricity bill list, which is used to view or edit the electricity bill data; and responding to a statistical analysis command, displaying a statistical analysis interface, performing statistical analysis on the electricity bill list, and generating various types of visual charts. Based on user data upload commands, intelligent recognition commands, data management commands, and statistical analysis commands, this application automatically recognizes, summarizes, and statistically analyzes electricity bills, generating visual charts. It can achieve one-click import, accurate recognition, categorized summarization, visual presentation, and statistical data output, thereby reducing manual operation costs and improving data processing efficiency and accuracy. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments of this application are described below. It should be understood that the drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions in this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 A schematic diagram illustrating the implementation environment of the intelligent electricity bill recognition and statistics method provided in one embodiment of this application; Figure 2 This is a flowchart illustrating the steps of an intelligent electricity bill recognition and statistics method provided in one embodiment of this application. Figure 3 A schematic diagram of the main interface provided in one embodiment of this application; Figure 4 This is a schematic diagram illustrating the steps of uploading an electricity bill according to one embodiment of this application; Figure 5 This is a first partial schematic diagram of an electricity bill upload interface provided in one embodiment of this application; Figure 6 This is a second partial schematic diagram of an electricity bill upload interface provided in one embodiment of this application; Figure 7 This is a schematic diagram illustrating the steps of OCR recognition provided in one embodiment of this application; Figure 8 This is a third partial schematic diagram of an electricity bill upload interface provided in one embodiment of this application; Figure 9A schematic diagram illustrating data management steps provided in one embodiment of this application; Figure 10 A schematic diagram of a data management interface provided in one embodiment of this application; Figure 11 A schematic diagram illustrating the steps of statistical analysis provided in one embodiment of this application; Figure 12 A schematic diagram of a statistical analysis page provided in one embodiment of this application; Figure 13 A schematic diagram of the visualization chart area of ​​a statistical analysis page provided in one embodiment of this application; Figure 14 This is a schematic diagram of the structure of an intelligent electricity bill recognition and statistics system provided in one embodiment of this application; Figure 15 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] In the current electricity bill management scenario, the data processing of electricity bills mainly relies on manual entry or traditional simple tools, which has the following problems: 1. High reliance on manual labor and low efficiency: In existing technologies, enterprises or managers need to manually read key information (such as meter number, electricity consumption, electricity price, amount, etc.) from paper / electronic electricity bills (e.g., JPG, PNG format) and then manually enter it into spreadsheet tools such as Excel for statistical analysis. For electricity bill data from multiple consecutive months and multiple meter points, manual entry is time-consuming (averaging 5-10 minutes per bill) and is prone to data errors due to human error (such as misreading numbers or entry mistakes), with an error rate of 3%-5%.

[0021] 2. Poor OCR recognition adaptability and insufficient accuracy: Although some existing tools support OCR recognition, they can only recognize a single format (such as only supporting JPG) or a single electricity price type (such as only supporting residential electricity), and cannot distinguish the differentiated data requirements of different industrial electricity users; moreover, the recognition process often compresses or converts the image format, resulting in blurry text / tables, and low recognition accuracy of key data (such as 6 decimal places for electricity price and 2 decimal places for electricity consumption), which cannot meet the accuracy requirements of financial accounting.

[0022] 3. Weak data aggregation capabilities and lack of categorized statistics: Existing tools cannot automatically link data, requiring manual filtering and merging of data from different months; and they do not support categorization and aggregation by multiple project types, requiring secondary manual processing, and cannot directly generate structured tables that meet the needs of financial analysis.

[0023] 4. Lack of visualization and inconvenient result export: Existing technology can only generate basic data tables, and users cannot intuitively perceive the trend of data changes; at the same time, statistical results only support export in a single format (such as only Excel) and do not include visualization charts, which cannot meet the needs of multiple scenarios such as reporting and archiving.

[0024] 5. Poor data independence and easy confusion: In existing tools, electricity bill data from different batches and different metering points are easily stored together, leading to incorrect statistical results; and it does not support separate calculation of data from multiple metering points, requiring manual splitting, which further increases the complexity of operation.

[0025] In view of this, this application proposes an intelligent electricity bill recognition and statistical method and system. The method includes: responding to a data upload command, displaying an electricity bill upload interface, and importing electricity bills spanning up to 12 months across years; responding to an intelligent recognition command, performing OCR recognition on the electricity bills to obtain the electricity price type, and then extracting features from the electricity bills based on the electricity price type to obtain electricity bill data; responding to a data management command, displaying a data management interface, summarizing the electricity bill data, and generating an electricity bill list, which is used to view or edit the electricity bill data; and responding to a statistical analysis command, displaying a statistical analysis interface, performing statistical analysis on the electricity bill list, and generating various types of visual charts. Based on user data upload commands, intelligent recognition commands, data management commands, and statistical analysis commands, this application automatically recognizes, summarizes, and statistically analyzes electricity bills, generating visual charts. It can achieve one-click import, accurate recognition, categorized summarization, visual presentation, and statistical data output, thereby reducing manual operation costs and improving data processing efficiency and accuracy. This intelligent electricity bill identification and statistical method can be widely applied to the analysis of new energy power generation and energy storage consumption, and can also be applied to scenarios such as corporate financial management, property electricity management, and industrial production electricity accounting, but it is not limited to these.

[0026] Figure 1 This is a schematic diagram illustrating the implementation environment of an intelligent electricity bill recognition and statistics method provided in one embodiment of this application. For example... Figure 1 As shown, this implementation environment includes a terminal 101 and a server 102. The terminal 101 is equipped with an intelligent electricity bill recognition and statistics tool, which can import, recognize, summarize, and statistically analyze electricity bills. The main interface, electricity bill upload interface, data management interface, and statistical analysis interface of the intelligent electricity bill recognition and statistics tool are all visual interfaces, including various components. In this implementation environment, the terminal 101 can be any electronic product that allows human-computer interaction through one or more methods such as a keyboard, touchpad, touchscreen, remote control, voice interaction, or handwriting device. This electronic product can receive user commands through its intelligent electricity bill recognition and statistics tool and display the results of the commands in real time. For example, as... Figure 1 As shown, the terminal 101 can be a personal computer (PC), mobile phone, smartphone, personal digital assistant (PDA), wearable device, handheld computer (PPC), tablet computer, etc.

[0027] Reference Figure 2 , Figure 2This is a flowchart illustrating the steps of an intelligent electricity bill identification and statistics method according to an embodiment of this application. The embodiment of this application proposes an intelligent electricity bill identification and statistics method, which includes steps S101 to S104: Step S101: In response to the data upload command, display the electricity bill upload interface and import electricity bills for up to 12 months that can span across years; As an optional implementation, step S101 can be further divided into the following steps S1011 to S1013: Step S1011: Display the main interface; Step S1012: In response to the first data upload instruction of the first component in the main interface, display the electricity bill upload interface, and import electricity bills for up to 12 months that can span across years through the electricity bill upload interface. Step S1013: In response to the second data upload command of the second component in the main interface, clear historical data and then display the electricity bill upload interface, and import electricity bills for up to 12 months that can span across years through the electricity bill upload interface.

[0028] Specifically, such as Figure 3 The diagram shown is a schematic of the main interface. Figure 4 The diagram illustrates the steps for uploading an electricity bill. The main interface of the intelligent electricity bill recognition and statistics tool provides functional components such as electricity bill uploading, OCR recognition, data management, and statistical summarization. The first data upload command is executed by the user in the main interface for the first component (…). Figure 3 The operation performed by the user in the "Start Using" section is captured and triggered by the main interface; the second data upload command is the user's action on the second component (in the main interface) Figure 3 The operation performed by "Start a new statistic" is captured by the main interface and triggered by the command.

[0029] In some optional embodiments, when the user clicks the "Start Using" component on the main interface, a first data upload instruction is triggered, displaying, as shown below. Figure 5 The electricity bill upload interface shown below; when the user clicks the "Start New Statistics" component on the main interface, a second data upload command is triggered, and after clearing historical data, the following is displayed: Figure 5 The image shows the electricity bill upload interface. Next, drag and drop files or click to select files in the "File Upload Area" of the electricity bill upload interface. A maximum of 12 electricity bill files (including those spanning multiple years) corresponding to the same metering point number can be uploaded at a time, without needing to perform batch operations. Electricity bill files can be in formats such as jpg, jpeg, png, and bmp, with a single file size ≤ 10Mb to meet the format requirements of electricity bills in different scenarios. Uploaded files must also be named using year-month, year-month, or a 6-digit year-month format. The system displays the name and format of each uploaded file.

[0030] When uploading files, users can either select the "metering point number" first and then upload the corresponding file, or upload the file first and then associate it with the "metering point number". Changing the order will not affect the data summary results.

[0031] It should be noted that the embodiments of this application have a data clearing mechanism: each time a user logs in again, they can choose to automatically or manually clear all uploaded statistical data from the previous operation to avoid data mixing from different batches and different metering points and to ensure data independence.

[0032] Step S102: In response to the intelligent recognition instruction, perform OCR recognition on the electricity bill to obtain the electricity price type, and then extract features from the electricity bill according to the electricity price type to obtain the electricity bill data; As an optional implementation, the step of performing OCR recognition on the electricity bill in response to the smart recognition command to obtain the electricity price type can be further divided into the following steps S1021 to S1022: Step S1021: In response to the intelligent recognition instruction of the third component in the electricity bill upload interface, identify the metering point number, year and month, electricity start time, electricity end time and electricity price identifier in the electricity bill; Step S1022: Determine the electricity price type of the electricity bill based on the electricity price label. The electricity price type includes large industrial electricity and non-industrial electricity.

[0033] Specifically, intelligent recognition commands are commands given by the user, such as... Figure 6 The electricity bill upload interface shown above shows the third component ( Figure 6 The operation performed by "Start Identification" in the electricity bill upload interface is captured by the instruction triggered by the operation.

[0034] In some alternative embodiments, such as Figure 7 The diagram illustrates the OCR recognition steps. When a user clicks the "Start Recognition" component on the electricity bill upload interface, the intelligent recognition command is triggered, and the system automatically starts OCR recognition. First, the original, uncompressed or unconverted image is read. Then, time information recognition and long image pagination recognition are performed: automatically recognizing the "year and month" in the file name, and the "electricity start time," "electricity end time," and "electricity price" within the electricity bill image, providing a basis for the time dimension association of data aggregation; and for long electricity bill images exceeding the single-page display range, automatically recognizing and extracting the table data for each page to avoid data omission. Finally, as shown in the diagram... Figure 8 The electricity bill upload interface shows the recognition status of each month's electricity bill (including recognition in progress, recognition successful, recognition failed + reason for failure).

[0035] It should be noted that this application embodiment does not compress or convert the uploaded electricity bill image, but directly performs OCR recognition based on the original image to ensure the clarity of text and table data and improve recognition accuracy; it can also adapt to electricity bills with different layouts (such as horizontal and vertical) and different printing quality (such as slightly blurry or tilted), and supports Chinese and other multilingual text detection and recognition, covering various electricity bill scenarios.

[0036] As an optional implementation, the electricity price type includes large industrial electricity and non-industrial electricity. The step of extracting features from the electricity bill according to the electricity price type to obtain the electricity bill data can be further divided into the following steps S1023 to S1025: Step S1023: If the electricity price type of the electricity bill is large industrial electricity, extract the electricity volume, price and amount corresponding to the peak electricity price, mid-peak electricity price, flat-peak electricity price and off-peak electricity price in the electricity bill; Step S1024: If the electricity price type of the electricity bill is non-industrial electricity, extract the total active power, electricity price and amount corresponding to the electricity bill. Step S1025: Round off the electricity consumption, electricity price, and amount to obtain the electricity bill data.

[0037] Specifically, such as Figure 7 As shown, when the image contains the electricity price label "large industrial electricity", the system automatically locates the table below the electricity bill information and extracts the electricity consumption (kWh), electricity price (yuan / kWh), and amount (yuan) data corresponding to "peak electricity price, peak electricity price, flat electricity price, and off-peak electricity price" for each electricity bill. When the image contains the electricity price label "non-industrial electricity", the system extracts the electricity consumption (kWh), electricity price (yuan / kWh), and amount (yuan) data corresponding to "total active power" for each electricity bill.

[0038] After successful recognition, the electricity consumption (kWh) data is accurate to two decimal places, the electricity price (yuan / kWh) data is accurate to six decimal places, and the amount (yuan) data is accurate to two decimal places. The project types are arranged in the order of "Peak Electricity Charge → Peak Electricity Charge → Flat Electricity Charge → Off-Peak Electricity Charge," and the electricity consumption (kWh), electricity price (yuan / kWh), and amount (yuan) are directly taken from the original data in the corresponding columns of the image, without using serial numbers to replace them, thus obtaining the electricity bill data.

[0039] Step S103: In response to the data management command, display the data management interface, summarize the electricity bill data, and generate an electricity bill list. The electricity bill list is used to view or edit the electricity bill data. As an optional implementation, the electricity bill data includes the metering point number and the year and month. Step S103 can be further divided into the following steps S1031 to S1034: Step S1031: Display the main interface; Step S1032: In response to the data management command of the fourth component in the main interface, display the data management interface; Step S1033: Based on the metering point number and year and month, perform data association on the electricity bill data to generate a structured table; Step S1034: Determine the title bar of the structured table, and then fill in the electricity bill data for each month into the structured table according to the title bar to generate an electricity bill list. Specifically, data management commands are commands that users use on the main interface to access the fourth component (…). Figure 3 The operation performed in "Data Management" is captured by the main interface and triggered by the command.

[0040] In some alternative embodiments, such as Figure 9 The diagram illustrates the steps of data management. When a user clicks the "Data Management" component on the main interface, a data management command is triggered, displaying the following... Figure 10 The data management interface shown demonstrates how the system associates data using a two-dimensional approach: "meter point number + year and month." It automatically aggregates data for 12 consecutive months (including spanning years) corresponding to the same meter point number, while separating data from different meter point numbers to avoid confusion. Next, a structured table is generated. The table column headings are fixed as "Serial Number, Meter Point Number, Year and Month, Item Type (Peak / Side / Valley / Total Active Power), Electricity (kWh), Electricity Price (Yuan / kWh), Amount (Yuan)." The "Year and Month" directly uses the time information from the file name to ensure accurate data-time correspondence. Then, the electricity bill data obtained from OCR is filled into the corresponding table, and this process is repeated to complete the 12-month data aggregation. After aggregation, the entered data is verified against the table data to obtain the final result. Figure 10 The list of electricity bills shown.

[0041] Step S104: In response to the statistical analysis command, display the statistical analysis interface, perform statistical analysis on the electricity bill list, and generate various types of visualization charts.

[0042] As an optional implementation, step S104 can be further divided into the following steps S1041 to S1044: Step S1041: Display the main interface; Step S1042: In response to the statistical analysis command of the fifth component in the main interface, display the statistical analysis interface; Step S1043: Calculate the total electricity cost, total electricity consumption, and average electricity cost for each item type under the same metering point number in the electricity bill list, and obtain the statistical results; Step S1044: Based on the statistical results, generate a monthly electricity cost trend bar chart and a monthly electricity consumption trend curve chart; The project types include peak electricity charges, mid-peak electricity charges, flat-peak electricity charges, off-peak electricity charges, and total active power charges.

[0043] Specifically, the statistical analysis command is a command that the user uses on the main interface to access the fifth component (…). Figure 3 The operation performed by "Statistical Analysis" in the main interface is captured by the main interface and the command is triggered.

[0044] In some alternative embodiments, such as Figure 11 The diagram illustrates the steps of statistical analysis. When a user clicks the "Statistical Analysis" component on the main interface, the statistical analysis command is triggered, and the following is displayed: Figure 12 The statistical analysis interface shown demonstrates how the system automatically calculates the "Total Electricity Cost (Yuan)," "Total Electricity Consumption (kWh)," and "Average Electricity Cost (Yuan / kWh, calculated as: Total Electricity Cost ÷ Total Electricity Consumption)" for each item type (e.g., peak, average, valley, total active power) under the same metering point number. The results are then embedded in a summary table for easy viewing by the user. Next, in... Figure 13 The visualization chart area shown generates a monthly electricity consumption trend curve (horizontal axis for months, vertical axis for electricity consumption) and a monthly electricity cost trend bar chart (horizontal axis for months, vertical axis for electricity cost) for 12 consecutive months (including years), intuitively presenting the data change trend.

[0045] Furthermore, in addition to line charts and bar charts, users can also add pie charts of electricity consumption percentages (such as peak / slow / valley electricity consumption percentages for large industrial users) or other types of visualization charts. Users can select the type of chart to be generated through the interface.

[0046] As an optional implementation, the intelligent identification and statistical method for electricity bills further includes the following steps S105 to S106: Step S105: In response to the data export command of the sixth component in the data management interface, export the electricity bill list; Step S106: In response to the chart export command of the seventh component in the statistical analysis interface, export the statistical results, monthly electricity cost trend bar chart or monthly electricity consumption trend curve chart.

[0047] Specifically, the data export command is a command that the user makes in the data management interface for the sixth component ( Figure 10 The "Export CSV" operation is captured and triggered by the data management interface; the chart export command is executed by the user in the statistical analysis interface on the seventh component (…). Figure 12The operation performed by "Export Report" in the statistical analysis interface is captured by the statistical analysis interface and triggered by the instruction.

[0048] In some optional embodiments, when a user clicks the "Export CSV" component in the data management interface, the system will export the electricity bill list and statistical data into a CSV file and store it on the user's computer. The exported file can be directly used for electricity bill data statistics. When a user clicks the "Export Report" component in the statistical analysis interface, the system will export the summary table, statistical results, and visualization charts (line chart + bar chart) together, supporting Excel, CSV, and PDF formats to meet different needs such as financial archiving and reporting.

[0049] Furthermore, in addition to supporting Excel, CSV, and PDF, the results export function also supports JSON format to meet the data import needs of enterprise system integration.

[0050] The following describes the intelligent identification and statistical method for electricity bills in this application in more detail with specific examples, but the implementation of this application is not limited thereto.

[0051] Example 1: Large industrial electricity consumption scenario (single metering point in a factory): In this example, the application scenario is "a manufacturing plant (large industrial electricity user) needs to process electricity bills for the same metering point (e.g., metering point number DFD-001) from January 2024 to December 2024". The specific operation steps are as follows: 1. File import: After the user logs in to the tool, the system automatically clears the historical data; in the "File Upload Area", 12 electricity bill files (format: jpg, jpeg, png, bmp, single file size less than 10Mb) are uploaded by dragging or manually clicking. The system displays "Waiting for processing, file list (12)".

[0052] 2. OCR Recognition Processing: Click the "Start Recognition" component on the main interface, and the system will automatically start OCR recognition. First, it reads the image, and then extracts "2024-01" to "2024-12" from each file. When recognizing the image content, the "Large Industrial Electricity" label is detected. Then, the system extracts the electricity consumption (accurate to 2 decimal places), electricity price (accurate to 6 decimal places), and amount (accurate to 2 decimal places) corresponding to "Electricity Fee (Peak / Side / Valley)" from each electricity bill. Finally, the data accuracy is checked. If there is no error, the system proceeds to the next step.

[0053] 3. Data Summary and Table Generation: Click the "Data Management" component on the main interface. The system will associate data according to "Metering Point Number DFD-001 + Year and Month" to generate a standard table title bar. Enter the identified data into the corresponding title bar: "Serial Number, Metering Point Number, Year and Month, Item Type (Electricity Fee (Peak), Electricity Fee (Rest), Electricity Fee (Side), Electricity Fee (Valley)), Electricity Consumption (kWh), Electricity Price (Yuan / kWh), Amount (Yuan)". Continue this process to complete the 12-month data summary. After the summary is complete, verify the entered data against the identified data in the table. Click the "Export CSV" component in the Data Management interface. The system will export the summary table and statistical results to a CSV file, which will be stored on the user's computer. The exported file can be directly used for electricity fee data statistics.

[0054] 4. Statistics and Visualization: Click the “Statistical Analysis” component in the main interface, and the system will automatically calculate the total electricity cost (yuan), total electricity consumption (kWh), and average electricity cost (yuan / kWh) for each item type of “2024 DFD-001 metering point” for 12 consecutive months (peak, flat, and valley periods). The system will also generate “Monthly Electricity Consumption Trend Curve Chart for 12 Consecutive Months (Surveyable Across Years)” and “Monthly Electricity Cost Trend Bar Chart for 12 Consecutive Months (Surveyable Across Years)” in the “Visualization Chart Area”.

[0055] 5. Results Export: When users click the "Export Report" component in the statistical analysis interface, the system will export the summary table and statistical results to an Excel spreadsheet and store it on the user's computer. The exported file can be directly used for factory financial accounting.

[0056] Example 2: Non-industrial electricity consumption scenario (single metering point in an office building): In this example, the application scenario is "an office building (non-industrial electricity) needs to process electricity bills for the same metering point (e.g., metering point number DFD-002) from March 2024 to February 2025". The specific operation steps are as follows: 1. File import: After the user logs in to the tool, the system automatically clears the historical data; in the "File Upload Area", 12 electricity bill files (format: jpg, jpeg, png, bmp, single file size less than 10Mb) are uploaded by dragging or manually clicking. The system displays "Waiting for processing, file list (12)".

[0057] 2. OCR Recognition Processing: Click the "Start Recognition" component on the main interface. The system will automatically start OCR recognition, first reading the image, then extracting "2024-01" to "2024-12" from each file. When recognizing the image content, if the "non-industrial" label is detected, the system will then extract the electricity consumption (accurate to 2 decimal places), electricity price (accurate to 6 decimal places), and amount (accurate to 2 decimal places) corresponding to the "Total Active Power" in each electricity bill. Finally, the system will verify the accuracy of the data. If there are no errors, the system will proceed to the next step.

[0058] 3. Data Summary and Table Generation: Click the "Data Management" component on the main interface. The system will associate data according to "Metering Point Number DFD-002 + Year and Month" to generate a standard table title bar. Enter the identified data into the corresponding headings: "Serial Number, Metering Point Number, Year and Month, Project Type (Total Active Power), Electricity Consumption (kWh), Electricity Price (Yuan / kWh), Amount (Yuan)". Continue this process to complete the 12-month data summary. After the summary is complete, verify the entered data against the identified data in the table. Click the "Export CSV" component in the Data Management interface. The system will export the summary table and statistical results to a CSV file, which will be stored on the user's computer. The exported file can be directly used for electricity billing statistics.

[0059] 4. Statistics and Visualization: Click the “Statistical Analysis” component in the main interface, and the system will automatically calculate the total electricity cost (yuan), total electricity consumption (kWh), and average electricity cost (yuan / kWh) for each project type of “2024 DFD-002 metering point” for 12 consecutive months. The system will also generate “Monthly Electricity Consumption Trend Curve Chart for 12 Consecutive Months (Surveyable Across Years)” and “Monthly Electricity Cost Trend Bar Chart for 12 Consecutive Months (Surveyable Across Years)” in the “Visualization Chart Area”.

[0060] 5. Results Export: When users click the "Export Report" component in the statistical analysis interface, the system will export the summary table and statistical results to an Excel spreadsheet and store it on the user's computer. The exported file can be directly used for factory financial accounting.

[0061] Example 3: Multi-meter point scenario (two metering points for a company): In this example, the application scenario is "a company (including large industrial electricity metering point XFD-003 and non-industrial electricity metering point XDF-004) needs to process the electricity bills for January to December 2024 for both metering points separately." The specific operation steps are as follows: 1. File import: After the user logs in to the tool, the system automatically clears the historical data; in the "File Upload Area", 12 electricity bill files (format: jpg, jpeg, png, bmp, single file size less than 10Mb) are uploaded by dragging or manually clicking. The system displays "Waiting for processing, file list (12)".

[0062] 2. OCR Recognition Processing: Click the "Start Recognition" component on the main interface, and the system will automatically start OCR recognition. First, it reads the image, and then extracts "2024-01" to "2024-12" from each file. When recognizing the image content, it detects the "Large Industrial Electricity" and "Non-Industrial" labels, and then extracts the electricity consumption (accurate to 2 decimal places), electricity price (accurate to 6 decimal places), and amount (accurate to 2 decimal places) corresponding to "Electricity Fee (Peak / Side / Valley / Total Active Power)" from each electricity bill. Finally, it performs data accuracy verification. If there is no error, it proceeds to the next step.

[0063] 3. Continue to import files: After completing the previous step, click "Home" to display the main interface. Select the "Start Using" component in the main interface. In the "File Upload Area", select the other 12 electricity bill files (format: jpg, jpeg, png, bmp, single file size less than 10Mb) to be uploaded by dragging or manually clicking. The system displays "Waiting for processing, file list (12)".

[0064] 4. Continued OCR Recognition Processing: Click the "Start Recognition" component on the main interface. The system will automatically start OCR recognition, first reading the image, then extracting "2024-01" to "2024-12" from each file. When recognizing the image content, the system detects the "Large Industrial Electricity" and "Non-Industrial" labels, and then extracts the electricity consumption (accurate to 2 decimal places), electricity price (accurate to 6 decimal places), and amount (accurate to 2 decimal places) corresponding to "Electricity Fee (Peak / Side / Valley / Total Active Power)" from each electricity bill. Finally, the system verifies the accuracy of the data. If there are no errors, it proceeds to the next step.

[0065] 5. Data Summary and Table Generation: Click the "Data Management" component on the main interface. The system will associate data using "Metering Point Number DFD-003 + Year and Month" and "Metering Point Number DFD-004 + Year and Month" to generate a standard table title bar. Enter the identified data into the corresponding title bar: "Serial Number, Metering Point Number, Year and Month, Item Type (Electricity Fee (Peak), Electricity Fee (Rest), Electricity Fee (Side), Electricity Fee (Valley), Total Active Power), Electricity (kWh), Electricity Price (Yuan / kWh), Amount (Yuan)". Continue this process to complete the 12-month data summary. After the summary is complete, verify the entered data against the identified data in the table. Click the "Export CSV" component in the Data Management interface. The system will export the summary table and statistical results to a CSV file, which will be stored on the user's computer. The exported file can be directly used for electricity fee data statistics.

[0066] 6. Statistics and Visualization: Click the “Statistical Analysis” component in the main interface, and the system will automatically calculate the total electricity cost (yuan), total electricity consumption (kWh), and average electricity cost (yuan / kWh) for each item type of “2024 DFD-003 metering point” and “2024 DFD-004 metering point” for 12 consecutive months. The system will also generate “Monthly Electricity Consumption Trend Curve Chart for 12 Consecutive Months (Spanning Years)” and “Monthly Electricity Cost Trend Bar Chart for 12 Consecutive Months (Spanning Years)” in the “Visualization Chart Area”.

[0067] 7. Results Export: When users click the "Export Report" component in the statistical analysis interface, the system will export the summary table and statistical results to an Excel spreadsheet and store it on the user's computer. The exported file can be directly used for factory financial accounting.

[0068] The above describes the intelligent identification and statistical method for electricity bills according to embodiments of this application. It can be recognized that, compared with existing electricity bill data processing methods, embodiments of this application have the following advantages: First, it adopts a file import module and a data clearing mechanism, which supports the one-time upload of large files in multiple formats. It also automatically clears historical data when logging in again, avoiding the mixing of data from different batches and metering points. Compared with manual batch upload and manual data clearing, the operation efficiency is improved by more than 80%, and the data independence is strong.

[0069] Second, it adopts original image OCR recognition + electricity price type differentiation extraction + precision control, without compressing images and distinguishing between large industrial and non-industrial electricity prices. The recognition accuracy of key data reaches over 99.5%. Compared with traditional OCR recognition that compresses images and does not differentiate between types, the error rate is reduced by 90%, and it is compatible with various types of layout and printing quality of electricity bills.

[0070] Third, by adopting dual-dimensional data association and structured table generation, the system automatically summarizes 12 consecutive months of data by measurement point number and time, generating standard tables that meet financial requirements, thus automating data aggregation and improving table standardization by 100%.

[0071] Fourth, it adopts multi-dimensional statistics, visualization charts, and multi-format export to automatically calculate total electricity costs and average electricity costs, and generate line charts / bar charts. The data analysis is intuitive and supports multi-format export. Compared with traditional tools without tables or visualization, the user's data analysis efficiency is improved by 70%. The results can be directly used for reporting and archiving without secondary processing.

[0072] Reference Figure 14 This application also provides an intelligent electricity bill recognition and statistics system, including: The data upload module is used to respond to data upload commands, display the electricity bill upload interface, and import electricity bills for up to 12 months that can span across years; The data recognition module is used to respond to intelligent recognition instructions, perform OCR recognition on the electricity bill to obtain the electricity price type, and then extract features from the electricity bill based on the electricity price type to obtain the electricity bill data. The data management module is used to respond to data management commands, display the data management interface, summarize the electricity bill data, and generate an electricity bill list. The electricity bill list is used to view or edit the electricity bill data. The data statistics module is used to respond to statistical analysis commands, display the statistical analysis interface, perform statistical analysis on the electricity bill list, and generate various types of visualization charts.

[0073] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0074] This application also provides an electronic device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for communication between the processor and the memory. When the program is executed by the processor, it implements the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0075] like Figure 15 The diagram shown is a hardware structure schematic of the electronic device provided in an embodiment of this application. (Refer to...) Figure 15 This application provides an electronic device, including: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0076] This application also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the above-described method.

[0077] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0078] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform... Figure 2 The method shown.

[0079] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0080] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the realm of conventional skill for an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0081] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0083] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0084] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0085] In the foregoing description of this specification, the references to terms such as "one embodiment," "another embodiment," or "some embodiments," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0086] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

[0087] The above is a detailed description of the preferred embodiments of this application, but this application is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for intelligent identification and statistical analysis of electricity bills, characterized in that, Includes the following steps: In response to the data upload command, the electricity bill upload interface is displayed, allowing the import of electricity bills for up to 12 months that can span across years; In response to the intelligent recognition command, the electricity bill is subjected to OCR recognition to obtain the electricity price type, and then the electricity bill is subjected to feature extraction based on the electricity price type to obtain the electricity bill data; In response to a data management command, a data management interface is displayed to summarize the electricity bill data and generate an electricity bill list, which is used to view or edit the electricity bill data. In response to a statistical analysis command, a statistical analysis interface is displayed to perform statistical analysis on the electricity bill list and generate various types of visualization charts.

2. The method according to claim 1, characterized in that, In response to the data upload command, the electricity bill upload interface is displayed, importing electricity bills for up to 12 months that can span across years, specifically including: Display the main interface; In response to a first data upload instruction to the first component in the main interface, the electricity bill upload interface is displayed, and the electricity bills for up to 12 months spanning multiple years are imported through the electricity bill upload interface; In response to the second data upload command of the second component in the main interface, historical data is cleared, and then the electricity bill upload interface is displayed. The electricity bills for up to 12 months that can span across years are imported through the electricity bill upload interface.

3. The method according to claim 1, characterized in that, In response to the intelligent recognition command, the electricity bill is subjected to OCR recognition to obtain the electricity price type, specifically including: In response to the intelligent recognition instruction of the third component in the electricity bill upload interface, the metering point number, year and month, electricity start time, electricity end time and electricity price identifier in the electricity bill are identified; Based on the electricity price identifier, the electricity price type of the electricity bill is determined, and the electricity price type includes large industrial electricity and non-industrial electricity.

4. The method according to claim 1, characterized in that, The electricity price type includes large industrial electricity and non-industrial electricity. The step of extracting features from the electricity bill based on the electricity price type to obtain electricity bill data specifically includes: If the electricity price type of the electricity bill is the large industrial electricity consumption, extract the electricity volume, electricity price, and amount corresponding to the peak electricity price, high-peak electricity price, flat-peak electricity price, and low-peak electricity price in the electricity bill; If the electricity price type of the electricity bill is non-industrial electricity, extract the total active power, electricity price, and amount corresponding to the total active power in the electricity bill; The electricity consumption, electricity price, and amount are rounded to obtain the electricity bill data.

5. The method according to claim 1, characterized in that, The electricity bill data includes the metering point number and year / month. In response to a data management command, a data management interface is displayed to summarize the electricity bill data and generate an electricity bill list, specifically including: Display the main interface; In response to the data management instruction on the fourth component in the main interface, the data management interface is displayed; Based on the metering point number and the year and month, the electricity bill data is correlated to generate a structured table; The header of the structured table is determined, and then the electricity bill data for each month is filled into the structured table according to the header to generate the electricity bill list.

6. The method according to claim 1, characterized in that, In response to a statistical analysis command, a statistical analysis interface is displayed to perform statistical analysis on the electricity bill list and generate various types of visualization charts, including: Display the main interface; In response to the statistical analysis command on the fifth component in the main interface, the statistical analysis interface is displayed; The total electricity cost, total electricity consumption, and average electricity cost for each item type under the same metering point number in the electricity bill list are calculated to obtain the statistical results. Based on the statistical results, a monthly electricity cost trend bar chart and a monthly electricity consumption trend curve are generated; The project types include peak electricity charges, mid-peak electricity charges, flat-peak electricity charges, off-peak electricity charges, and total active power charges.

7. The method according to claim 6, characterized in that, The method further includes: In response to the data export command of the sixth component in the data management interface, the electricity bill list is exported; In response to the chart export command of the seventh component in the statistical analysis interface, the statistical results, the monthly electricity cost trend bar chart, or the monthly electricity consumption trend curve chart are exported.

8. An intelligent recognition and statistics system for electricity bills, characterized in that, include: The data upload module is used to respond to data upload commands, display the electricity bill upload interface, and import electricity bills for up to 12 months that can span across years; The data recognition module is used to respond to the intelligent recognition command, perform OCR recognition on the electricity bill to obtain the electricity price type, and then extract features from the electricity bill based on the electricity price type to obtain the electricity bill data. The data management module is used to respond to data management commands, display the data management interface, summarize the electricity bill data, and generate an electricity bill list. The electricity bill list is used to view or edit the electricity bill data. The data statistics module is used to respond to statistical analysis commands, display the statistical analysis interface, perform statistical analysis on the electricity bill list, and generate various types of visualization charts.

9. An electronic device, characterized in that, The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 7.

10. A storage medium, said storage medium being a computer-readable storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the method as described in any one of claims 1 to 7.

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