Electricity utilization acquisition abnormity analysis processing method and system

Abstract

The invention provides a power utilization acquisition abnormity analysis processing method and system, and the method comprises the following steps: S1, collecting system power utilization data, and carrying out the preprocessing; s2, big data mining analysis is carried out on the collected data according to a metering principle innovation intelligent analysis algorithm, multi-dimensional configuration and superposition can be carried out on data items of data analysis, such as voltage, current or electric quantity data metered by a user or other metering point data of the user, and key thresholds are determined according to different data items in analysis; and S3, on the basis of the key thresholds of different data items, constructing a corresponding analysis model to screen and discriminate abnormal operation of the electric energy metering device and suspected abnormal electricity utilization users. According to the invention, the timeliness and accuracy of power utilization acquisition exception processing are improved.

Description

Technical Field

[0001] The invention relates to the technical field of computer software for electric power systems, and in particular to a method and system for analyzing and processing abnormal power consumption collection. Background Art

[0002] In the actual transmission process of electricity, due to the complexity of the transmission network, each power branch may cause partial power loss under various factors (such as line heating loss, power loss caused by power passing through transformer equipment, etc.). In theory, line loss cannot be avoided, but by strengthening line loss management to reduce power transmission loss, the utilization rate of electric energy can be effectively improved. In terms of line loss management, my country has currently formulated relevant management measures, one of which is sub-station management. The main management object of sub-station management is low-voltage power pipe network users. By monitoring the power consumption of power users in the entire substation area, the accurate positioning of the line loss area can be achieved, and effective measures can be taken in time to improve the efficiency of each station in the low-voltage distribution network. For low-voltage substations, affected by various factors, the line loss value often has unstable problems, and sometimes even abnormal line loss phenomena, which increases unnecessary power consumption on the one hand, and also has a serious impact on the stability of power transmission and distribution lines; therefore, by analyzing the abnormal line loss problems in low-voltage distribution substations, strengthening the management of line loss problems, thereby reducing the losses of power supply companies and providing users with better and more stable power supply services. Abnormal line loss in the substation area can be divided into two categories: one is high loss and the other is negative loss. Substation line loss management is a systematic project, involving various disciplines such as business, reading and collection, metering, load control, usage inspection, distribution network construction and operation and maintenance. Management personnel are required to analyze and investigate on-site and industry data.

[0003] In the process of promoting and deepening the application of the electricity information collection system, there are also some problems that need to be solved. For example, the curve data collection of voltage, current, demand, power, etc. of special transformer users and public transformer users failed or the data collection was incomplete, resulting in the failure of the success rate of full data collection to meet the requirements of practical indicators and unable to provide data support for various professional management. It takes a long time to manually analyze and screen abnormal information of huge collected data, and manual analysis often has omissions, and some analysis tools are difficult to meet the requirements. Therefore, it is necessary to innovate intelligent analysis algorithms based on the measurement principle to deeply analyze the collected data, screen and identify abnormal operation of electric energy metering devices and suspected abnormal electricity users. Moreover, due to the untimely distribution of abnormal work orders or the lack of timely response feedback after issuance, abnormalities exist for a long time, affecting the improvement of indicators. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a method and system for analyzing and processing anomalies in electricity consumption collection, which helps to improve the timeliness and accuracy of anomaly processing in electricity consumption collection.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for analyzing and processing abnormal power consumption collection, comprising the following steps:

[0006] Step S1: Collect system power consumption data and pre-process it;

[0007] Step S2: Innovate intelligent analysis algorithms based on metrological principles to conduct big data mining and analysis on the collected data. The data items of data analysis can be configured and superimposed in multiple dimensions; the analysis adopts the method of determining critical thresholds based on different data items;

[0008] Step S3: Based on the critical thresholds of different data items, a corresponding analysis model is constructed to screen and identify abnormal operation of electric energy metering devices and users with suspected abnormal electricity consumption.

[0009] In a preferred embodiment, the analysis algorithm includes a regularity discontinuity algorithm and a classification continuous difference algorithm.

[0010] In a preferred embodiment, the regular discontinuity algorithm is specifically as follows: when analyzing data, the abnormal state of the data collected and saved according to the data freezing density continues for a period of time and then returns to normal, and then this abnormal phenomenon continues to occur in a certain time period; in this case, it is considered that the user's electricity consumption is abnormal; when the user performs this behavior, the curve will fluctuate and change regularly; discontinuity means that the data suddenly changes at a certain point and maintains this state for a certain period of time.

[0011] In a preferred embodiment, the classified continuous difference algorithm is specifically as follows: for the analysis part according to voltage and current, for the user meter voltage data, classification refers to analyzing different data items according to different types; the difference refers to when the difference between the voltage value included in the analysis and the rated voltage of the user meter exceeds the threshold, it is considered abnormal; continuity refers to when the analyzed data value is a voltage curve, when the number of continuous data abnormality points reaches and exceeds a threshold, it is considered abnormal; for the user meter current data, classification refers to analyzing different data items according to different types; the difference refers to when the current value included in the analysis, where the ratio of the maximum current value to the minimum current value exceeds a threshold, it is considered abnormal; continuity refers to when the analyzed data value is a current curve, when the number of continuous data abnormality points reaches and exceeds a threshold, it is considered abnormal.

[0012] In a preferred embodiment, the data items of data analysis can be configured and superimposed in multiple dimensions, and the analysis adopts the following method to determine the critical threshold according to different data items:

[0013] The current data item is used as the judgment basis, and the threshold is the current imbalance ratio and a certain current value; after the threshold is selected, the data system is operated, and the system performs abnormal analysis according to the designed algorithm to screen out users with abnormal current;

[0014] The voltage data item is used as the judgment basis, and the threshold is the upper limit ratio of the difference and the lower limit ratio of the difference; after the threshold is selected, the data system is operated, and the system performs abnormal analysis according to the designed algorithm to screen out users with abnormal voltage;

[0015] The judgment is based on the electricity data item, and the threshold is 30 consecutive days of no electricity use. After the threshold is selected, the data system is run. The system performs anomaly analysis according to the designed algorithm to screen out users with abnormal electricity use.

[0016] The present invention also provides an abnormal analysis and processing system for electricity consumption collection, including a client and a server; when the server is running, it implements the abnormal analysis and processing method for electricity consumption collection, and is also responsible for database connection storage, Excel table processing and analysis, work order formation and communication with the electricity user; after connecting to the server main station, the client receives the message sent by the main station and sends messages to it.

[0017] In a preferred embodiment, the server side includes a transmission communication module, a data mining module, an Excel operation module and a data storage module; the transmission communication module is connected to the data mining module, the Excel operation module and the data storage module respectively.

[0018] In a preferred embodiment, the transmission communication module adopts a connection-oriented TCP / IP protocol, and the reception and transmission of data call the Receive and Send methods of Socket; whenever a client module requests access, such a connection is established; the transmission communication module adopts a multi-threaded program structure, each connection is maintained by a thread, and each thread receives and sends data through a connection implementation module. The thread transmits the received information to the main form for display, and at the same time receives control information from the main form and sends it to the client module, thereby realizing communication between the server and the client.

[0019] In a preferred embodiment, the data mining module uses the DataGridView control in Visual C# to perform data binding and perform data operations such as querying, modifying, adding and deleting.

[0020] In a preferred embodiment, the Excel operation module uses COM components in C# to complete the import of Excel files, and exporting Excel is to export the DataTable object to an Excel worksheet.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. The present invention can monitor the electricity consumption behavior of electricity users in real time, and automatically give an alarm when abnormalities are found, thus improving the efficiency of electricity consumption monitoring of power supply companies;

[0023] 2. The present invention effectively improves the timeliness and accuracy of abnormal processing of electric energy metering devices and avoids waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a schematic diagram of a method flow in one embodiment of the present invention;

[0025] Figure 2 is a schematic diagram of a system architecture in one embodiment of the present invention;

[0026] Figure 3 This is a system transmission module process in one embodiment of the present invention;

[0027] Figure 4 is a flow chart of a system data mining module in one embodiment of the present invention;

[0028] Figure 5 is a flow chart of a regular non-continuous algorithm in one embodiment of the present invention;

[0029] Figure 6 It is a flow chart of a classification continuous difference algorithm in one embodiment of the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0032] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0033] Please refer to Figure 1-6 The present invention provides a method for analyzing and processing abnormal power consumption data, comprising the following steps:

[0034] Step S1: Collect system power consumption data and pre-process it;

[0035] Step S2: Innovate intelligent analysis algorithms based on metering principles to perform big data mining and analysis on the collected data. The data items of data analysis can be configured and superimposed in multiple dimensions, such as the voltage, current or electricity data of the user's meter, or other metering point data of the user, etc. The analysis adopts the method of determining the critical threshold according to different data items;

[0036] Step S3: Based on the critical thresholds of different data items, a corresponding analysis model is constructed to screen and identify abnormal operation of electric energy metering devices and users with suspected abnormal electricity consumption.

[0037] In this embodiment, reference Figure 2 , provides an abnormal analysis and processing system for electricity consumption collection, including a client and a server; the server is responsible for database connection storage, Excel spreadsheet processing and analysis, work order formation and communication with the electricity user; after connecting to the server main station, the client receives the message sent by the main station and sends messages to it.

[0038] In this embodiment, a serial communication program is created using C# in the Visual Studio 2022 software development environment. Standard RS232 is used for two-way communication, and the Serial Port class provided in .NET 3.0 is used for programming to read and configure parameters.

[0039] The software communication control system is realized by using multi-threaded program structure technology. The acquisition master station plays the role of server in the system. The TcpListener class is used in programming to listen and receive the connection requests from the client module, so as to realize the software to establish communication connection with multiple client power terminals at the same time.

[0040] SQL server database connection technology and Microsoft Excel connection technology are introduced to realize two-way data transmission, user management, data saving and printing functions between the main acquisition station and the user end through the network. The following is a detailed introduction to the four parts of the system transmission communication module, data mining module, data storage module and Excel operation module.

[0041] In this embodiment, the server side includes a transmission communication module, a data mining module, an Excel operation module and a data storage module; the transmission communication module is connected to the data mining module, the Excel operation module and the data storage module respectively.

[0042] Preferably, reference Figure 3The transmission communication module adopts the connection-oriented TCP / IP protocol, and the reception and transmission of data call the Receive and Send methods of Socket; whenever a client module requests access, such a connection is established; the transmission communication module adopts a multi-threaded program structure, each connection is maintained by a thread, and each thread receives and sends data through a connection implementation module. The thread transmits the received information to the main form for display, and at the same time receives control information from the main form and sends it to the client module, thereby realizing communication between the server and the client.

[0043] File transfer is mainly divided into single file transfer and directory file transfer (multi-file transfer). The present invention uses two-way flow for file stream transfer. If you need to transfer a file to the other party, you can directly select the file in the local directory of the system window, so that the user can confirm the file transfer. First send a confirmation message to the other party to receive the file and start the TCP / IP protocol. Calculate the file size before transferring the file, and always record the size and path of the file that has been transferred. File transfer can also realize the one-time transfer of multiple files in a folder. Before the transfer, the system will first transfer a directory file formed by multiple file names to be transferred, and then transfer each file in turn. Every time the other party receives a file, it first judges the local storage directory of this file. If this directory does not exist, it will create this directory, and then send a confirmation to receive the file. After successful reception, the next file is received in a cycle.

[0044] Preferably, reference Figure 4 ,The data mining module uses the DataGridView control in Visual C# for data binding and ,performs data operations such as querying, modifying, adding and deleting.

[0045] It is mainly used for query and data addition operations. In the classification query, the Select() method of the DataTable class is used to query and filter data. In the adding operation, the SQL statement of the Command object can have parameters, and the parameters are identified by "@parameter name". Add parameter objects to the parameter set of the command object. Each parameter object needs to specify the parameter name and type, and then assign values ​​to the parameters. In addition, the present invention also has the functional characteristics of adding collected data in real time, and common data operation functions such as query, modify, add and delete.

[0046] According to the voltage data or current data of the user meter, or the data of other user metering points, the critical threshold is determined and the corresponding analysis model is established. Two intelligent analysis methods, regular discontinuous algorithm and classified continuous difference algorithm, are designed to adapt to different types of terminals.

[0047] refer to Figure 5The regular discontinuity algorithm means that when analyzing data, the abnormal state of the data collected and saved according to the data freezing density continues for a period of time and then returns to normal, and then this abnormal phenomenon continues to occur in a certain period of time. In this case, it is considered that the user's electricity consumption is abnormal. When the user performs this behavior, the curve will fluctuate and change regularly. Discontinuity means that the data suddenly changes at a certain point and maintains this state for a certain period of time.

[0048] Take high supply and low meter voltage as an example:

[0049] (UA>pvU·ratedV)||(UA<pvD·ratedV)||(UB> pvU·ratedV)||(UB<pvD·ratedV)||(UC> pvU·ratedV)||(UC <pvD·ratedV)(1)

[0050] Where UA is the A-phase voltage of the user meter; UB is the B-phase voltage of the user meter; UC is the C-phase voltage of the user meter; pvU is the upper limit ratio of the difference; pvD is the lower limit ratio of the difference; ratedV is the rated voltage. When the result of formula (1) is a Boolean value of TRUE, the user voltage meter is abnormal.

[0051] Take high supply and low meter current as an example:

[0052] Max(IA,IB,IC) / Min(IA,IB,IC)>rt (2)

[0053] Where IA is the A-phase current of the user meter; IB is the B-phase current of the user meter; IC is the

[0054] The C phase current; rt is the difference threshold ratio. When the result of formula (2) is a Boolean value TRUE, the user voltage meter is abnormal.

[0055] refer to Figure 6 , the classification continuous difference algorithm analyzes the voltage and current parts. For the voltage data of the user meter, classification refers to analyzing different data items according to different types. Difference means that when the difference between the voltage value included in the analysis and the rated voltage of the user meter exceeds the threshold, it is considered abnormal. Continuous means that when the analyzed data value is a voltage curve, it is considered abnormal when the number of continuous data abnormal points reaches and exceeds a threshold. For the current data of the user meter, classification refers to analyzing different data items according to different types. Difference means that when the current value included in the analysis, the ratio of the maximum current value (IMax) to the minimum current value (IMin) exceeds a threshold, it is considered abnormal. Continuous means that when the analyzed data value is a current curve, it is considered abnormal when the number of continuous data abnormal points reaches and exceeds a threshold.

[0056] Take high supply and low meter voltage as an example:

[0057] (UAQ+1>pvU·ratedV)||(UAQ+1 <pvD·ratedV)||

[0058] (UBQ+1>pvU·ratedV)||(UBQ+1 <pvD·ratedV)||

[0059] (UCQ+1>pvU·ratedV)||(UCQ+1 <pvD·ratedV)…

[0060] (UAQ+K>pvU·ratedV)||(UAQ+K <pvD·ratedV)||

[0061] (UBQ+K>pvU·ratedV)||(UBQ+K <pvD·ratedV)||

[0062] (UCQ+K>pvU·ratedV)||(UCQ+K <pvD·ratedV)&&(K≥vpoint)(3)

[0063] Where UAQ is the A-phase voltage of the user meter at a certain point with an interval of 15 minutes (96 points in 24 hours) from zero; UBQ is the B-phase voltage of the user meter at a certain point with an interval of 15 minutes (96 points in 24 hours) from zero; UCQ is the C-phase voltage of the user meter at a certain point with an interval of 15 minutes (96 points in 24 hours) from zero; pvU is the upper limit ratio of the difference; pvD is the lower limit ratio of the difference; K is the number of points analyzed; vPoint is the number of consecutive abnormal points. When the result of formula (3) is a Boolean value of TRUE, the user voltage meter is abnormal.

[0064] Take high supply and low meter current as an example:

[0065] Max(IAQ+1,IBQ+1,ICQ+1) / Min(IAQ+1,IBQ+1,ICQ+1)>rt……Max(IAQ+K,IBQ+K,ICQ+K) / Min

[0066] (IAQ+K,IBQ+K,ICQ+K)>rt (4)

[0067] Where IAQ is the A-phase current of the user meter at a certain point at an interval of 15 minutes (96 points in 24 hours) from zero; IBQ is the B-phase current of the user meter at a certain point at an interval of 15 minutes (96 points in 24 hours) from zero; ICQ is the C-phase current of the user meter at a certain point at an interval of 15 minutes (96 points in 24 hours) from zero; K is the number of points analyzed; rt is the difference threshold ratio. When the result of formula (4) is a Boolean value of TRUE, the user current meter is abnormal.

[0068] In this embodiment, the three data of current, voltage and power are analyzed. The analysis determines the critical threshold according to different data items, establishes the corresponding analysis model, and screens and identifies the abnormal operation of the electric energy metering device and the users with suspected abnormal electricity consumption.

[0069] Based on the current data item, the difference threshold ratio is selected: the threshold is the current imbalance ratio and a certain current value. Current imbalance ratio: 3, a certain current value is greater than: 5A. After selecting the threshold, the data system is run. The system performs abnormal analysis according to the designed algorithm and screens out users with abnormal current. The current of one user has a large difference between the A and B phase currents and the C phase current. The A and B phase currents are always 10 -2 A level and not zero, suspected of using shunt to steal electricity. On-site inspection found that the household used iron wire to short-circuit the lower end of the junction box to shunt the A and B phase currents.

[0070] Based on the voltage data item, the value of the difference threshold ratio is as follows: the threshold is the upper limit ratio of the difference and the lower limit ratio of the difference. The upper limit ratio of the difference is 1.2, and the lower limit ratio of the difference is 0.3. After selecting the threshold, the data system is run. The system performs abnormal analysis according to the designed algorithm to screen out users with abnormal voltage. The voltage of one user's A phase intermittently produces continuous low voltage (concentrated from 19:00 to 23:00 in the evening, and normal at other times), and it is suspected that the A phase voltage is disconnected to steal electricity. On-site inspection found that the household used a loose voltage connector to short-circuit the junction box, causing the A phase voltage to lose voltage.

[0071] The judgment is based on the electricity data item, and the threshold is taken as 30 consecutive days of electricity usage and no electricity consumption. After the threshold is selected, the data system is run. The system performs anomaly analysis according to the designed algorithm and screens out abnormal users. When conducting on-site electricity usage inspection of low-voltage users, it is found that the user has stolen electricity and circumvented the metering electricity usage without authorization.

[0072] In this embodiment, an Excel spreadsheet is provided to connect to the database to save the collected information in real time, and a human-computer dialogue application with a user-friendly interface provides users with real-time query, control and system management functions anytime and anywhere.

[0073] The design of the login interface uses SQL Server 2022 to create an mdf file, and then embeds the mdf file in the project application to perform SQL Server database, which is convenient for porting and easy to use. The method used is to let the program connect to the mdf file in the project in the WPF program instead of connecting to the mdf file under bin / Debug.

[0074] The Excel module in the present invention uses COM components in C# to complete the import of Excel files. Although the COM component is very different from the .Net Framework SDK in structure, the COM component can easily realize the import of Excel data. The core of calling the COM component to operate Excel is to use the namespace Excel in the Office 2022 assembly, in which four classes, Application, Workbook, Worksheet, and Range, are defined.

[0075] The basic principle of Excel export programming is to export the DataTable object to an Excel worksheet. The method is to create an Excel object instance in memory, then fill the data in the DataTable into the memory Excel worksheet, and then save the memory to a local path. It is used for user archive protection and supports Microsoft Excel 2023 version. In the operation control interface, there are also functions such as "Page Setup" and "Print" for easy use by operators.

[0076] The above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made according to the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A method for analyzing and processing abnormal power consumption collection, characterized in that: The following steps are involved: Step S1: Collect system power consumption data and pre-process it; Step S2: Innovate intelligent analysis algorithms based on metrological principles to conduct big data mining and analysis on the collected data. The data items of data analysis can be configured and superimposed in multiple dimensions; the analysis adopts the method of determining critical thresholds based on different data items; Step S3: Based on the critical thresholds of different data items, a corresponding analysis model is constructed to screen and identify abnormal operation of electric energy metering devices and users with suspected abnormal electricity consumption.

2. The method for analyzing and processing abnormal power consumption collection according to claim 1, characterized in that: The analysis algorithm includes a regularity discontinuous algorithm and a classification continuous difference algorithm.

3. The method for analyzing and processing abnormal power consumption collection according to claim 2, characterized in that: The regular discontinuity algorithm is specifically as follows: when analyzing data, the abnormal state of the data collected and saved according to the data freezing density continues for a period of time and then returns to normal, and then this abnormal phenomenon continues to occur in a certain time period; in this case, it is considered that the user's electricity consumption is abnormal; when the user performs this behavior, the curve will fluctuate and change regularly; discontinuity means that the data suddenly mutates at a certain point and maintains this state for a certain period of time.

4. The method for analyzing and processing abnormal power consumption collection according to claim 2, characterized in that: The classification continuous difference algorithm is specifically as follows: for the analysis part according to voltage and current, for the user meter voltage data, classification refers to analyzing different data items according to different types; the difference refers to when the difference between the voltage value included in the analysis and the rated voltage of the user meter exceeds the threshold, it is considered abnormal; continuity means that when the analyzed data value is a voltage curve, it is considered abnormal when the number of continuous data abnormality points reaches and exceeds a threshold; for the user meter current data, classification refers to analyzing different data items according to different types; the difference refers to when the current value included in the analysis, among which the ratio of the maximum current value to the minimum current value exceeds a threshold, it is considered abnormal; continuity means that when the analyzed data value is a current curve, it is considered abnormal when the number of continuous data abnormality points reaches and exceeds a threshold.

5. The method for analyzing and processing abnormal power consumption collection according to claim 1, characterized in that: The data items of data analysis can be configured and superimposed in multiple dimensions. The analysis uses the key thresholds determined according to different data items as follows: The current data item is used as the judgment basis, and the threshold is the current imbalance ratio and a certain current value; after the threshold is selected, the data system is operated, and the system performs abnormal analysis according to the designed algorithm to screen out users with abnormal current; The voltage data item is used as the judgment basis, and the threshold is the upper limit ratio of the difference and the lower limit ratio of the difference; after the threshold is selected, the data system is operated, and the system performs abnormal analysis according to the designed algorithm to screen out users with abnormal voltage; The judgment is based on the electricity data item, and the threshold is 30 consecutive days of no electricity use. After the threshold is selected, the data system is run. The system performs anomaly analysis according to the designed algorithm to screen out users with abnormal electricity use.

6. A power consumption collection abnormality analysis and processing system, characterized in that: It includes a client and a server; when the server is running, it implements a method for analyzing and processing abnormal power consumption collection as described in any one of claims 1-5, and is also responsible for database connection storage, excel table processing and analysis, work order formation and communication with the power user; after connecting to the server main station, the client receives the message sent by the main station and sends messages to it.

7. The power consumption collection abnormality analysis and processing system according to claim 6, characterized in that: The server end includes a transmission communication module, a data mining module, an Excel operation module and a data storage module; the transmission communication module is connected to the data mining module, the Excel operation module and the data storage module respectively.

8. The power consumption collection abnormality analysis and processing system according to claim 7, characterized in that: The transmission communication module adopts the connection-oriented TCP / IP protocol, and the reception and transmission of data call the Receive and Send methods of Socket; whenever a client module requests access, such a connection is established; the transmission communication module adopts a multi-threaded program structure, each connection is maintained by a thread, and each thread receives and sends data through a connection implementation module. The thread transmits the received information to the main form for display, and at the same time receives control information from the main form and sends it to the client module, thereby realizing communication between the server and the client.

9. The power consumption collection abnormality analysis and processing system according to claim 7, characterized in that: The data mining module uses the DataGridView control in Visual C# to perform data binding and perform data operations such as querying, modifying, adding and deleting.

10. The power consumption collection abnormality analysis and processing system according to claim 7, characterized in that: The Excel operation module uses COM components in C# to complete the import of Excel files, and exporting Excel is to export the DataTable object to an Excel worksheet.

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