WeChat-based power grid enterprise shopping mall purchase order evaluation early warning method and system

Abstract

The invention provides a WeChat-based power grid enterprise shopping mall purchase order evaluation early warning method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a data table based on an RPA system, and downloading the details of a purchase request order which is ordered in the current year and is not evaluated, and an unevaluated order table; opening and deleting column content of a receiving time confirmation list in the unevaluated order table to obtain empty items; matching the factory name with the region corresponding table, and if the matching fails, classifying the region which fails to match into a to-be-determined region; if the matching is successful, dividing the successfully matched region into a target region; combining the to-be-determined area and the target area to obtain a to-be-detected area, obtaining a to-be-evaluated total entry number of each user in the to-be-detected area, and processing the to-be-evaluated total entry number based on a preset message pushing format to obtain a plurality of to-be-pushed messages; and writing the to-be-pushed message into the database table based on the message pushing tool, extracting an unsent message from the database table, and sending the unsent message to the WeChat group for early warning.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a WeChat-based power grid enterprise mall purchase order evaluation and early warning method and system. Background Art

[0002] In the current operating environment of power grid enterprises, mall procurement evaluation, as an important part of supply chain management, is of great significance for improving supplier service quality, optimizing procurement processes and enhancing internal management efficiency of enterprises.

[0003] However, at present, various units of power grid enterprises mainly rely on traditional manual operation modes when dealing with mall procurement evaluation situations. Each unit needs to arrange special personnel to log in to the mall system or background database regularly and manually download data tables containing purchase order evaluation information. However, the downloaded data tables often need to be circulated and processed by multiple departments. In order to facilitate analysis and supervision, enterprise staff usually need to use office software to classify, screen and summarize data. Although this process can achieve preliminary sorting of data, it is inefficient and prone to errors. Especially when processing large amounts of data, the tediousness of manual operation is particularly prominent, and the summarized evaluation information needs to be reported to the management level by level. This forwarding method will lead to delayed information transmission, low work efficiency and low real-time nature of early warning data.

[0004] Therefore, it is urgent to provide a solution to improve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for evaluating and warning purchase orders of power grid enterprise malls based on WeChat, so as to improve the problems of low working efficiency and low real-time performance of warning data in the prior art.

[0006] In the first aspect, the present invention provides a WeChat-based power grid enterprise mall purchase order evaluation and early warning method using the following technical solutions:

[0007] A data table is obtained based on a preset RPA system, and the purchase order details that have not been evaluated and placed in this year are automatically downloaded according to the data table, and an unevaluated order table is obtained based on the purchase order details. The line items of the unevaluated order table include: confirmation of receipt time and factory name;

[0008] Open and delete the column content of the confirmed delivery time list in the unevaluated order table based on a preset automated tool to obtain an empty entry, wherein the confirmed delivery time includes the delivery time of the undelivered order and the delivery time of the goods received within a preset time;

[0009] Based on the preset automation tool, each factory name is matched with the preset regional correspondence table. If the match fails, the area where the match fails is classified as the pending area; if the match succeeds, the area where the match succeeds is classified as the target area;

[0010] Combining the to-be-determined area and the target area to obtain an area to be detected, summing up the number of items to obtain a total number of items to be evaluated for each user in the area to be detected, and processing the total number of items to be evaluated based on a preset message push format to obtain a plurality of messages to be pushed;

[0011] The message to be pushed is written into a database table based on a message push tool, and unsent messages are extracted from the database table based on a data collection cycle, and the unsent messages are sent to a WeChat group for early warning.

[0012] The beneficial effect of the WeChat-based power grid enterprise mall purchase order evaluation and early warning method provided by the present invention is that the present invention can reduce the time for management personnel to organize and send information for a single supervision by automatically sending early warning messages in real time, thereby improving work efficiency, and grassroots employees can be informed of the early warning situation in real time, reducing the early warning notification circulation time, improving the problem closure efficiency, providing data support for supplier evaluation, improving management efficiency and early warning real-time, improving work efficiency, and ensuring the real-time nature of early warning data.

[0013] Optionally, the process of extracting unsent messages from the database table based on a data collection cycle and sending the unsent messages to a WeChat group for early warning includes:

[0014] Extracting unsent information from the database table based on a data collection cycle, and sending a push instruction to a message push platform using a preset automation tool;

[0015] After the message push platform receives the push instruction, the WeChat robot calls the WeChat dedicated interface to send the unsent message to the WeChat group for early warning.

[0016] Optionally, the collection period is 1 minute, and the data table is a preset JX_WX_MESSAGE_WARN database table, and the JX_WX_MESSAGE_WARN database table includes: date ID, time point when the data is created, message data, user name, and job type.

[0017] Optionally, the process of obtaining empty entries includes: automatically opening the order list to be evaluated based on an automated tool, deleting undelivered orders in the order list to be evaluated and delivered orders within a preset time, to obtain an updated order list to be evaluated.

[0018] Optionally, the preset message push format consists of a preset first message push format and a preset second message push format, the preset first message push format is determined according to the number of items to be evaluated and the ranking in each area to be detected, the preset second message push format is determined according to the number of items to be evaluated and the ranking under the purchasing account in each area to be detected, and each user corresponds to a purchasing account.

[0019] In a second aspect, the present invention further provides a WeChat-based power grid enterprise mall purchase order evaluation and early warning system, comprising:

[0020] The data acquisition module is used to obtain unevaluated orders from the power grid enterprise mall system;

[0021] A data preprocessing module, used to delete the unreceived orders and the received orders within a preset time from the unevaluated orders and obtain updated unevaluated orders;

[0022] A region matching module, used to match the updated unevaluated orders with a preset factory region correspondence table, and obtain the number and ranking of order items to be evaluated in each region;

[0023] A message generation module, used to process the number and ranking of the order items to be evaluated in each area according to a preset message push format to generate a message to be pushed;

[0024] A message push module is used to write the messages to be pushed into a database table, and to regularly send unsent messages to a WeChat group for early warning according to a data collection cycle;

[0025] The data storage module is used to store the unevaluated order table, the factory area correspondence table and the messages to be pushed.

[0026] For the beneficial effects of the second aspect, reference may be made to the description of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a WeChat-based power grid enterprise mall purchase order evaluation and early warning method provided by the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0029] First, the terms used in the following steps are explained as follows:

[0030] RPA system: Robotic process automation is an automation software based on artificial intelligence and machine learning technology. It can automate repetitive and tedious tasks by simulating the operations performed by humans on computers, such as clicking, inputting, copying, and pasting, thereby improving work efficiency and quality.

[0031] The embodiment of the present invention provides a method for evaluating and warning purchase orders of power grid enterprise malls based on WeChat, see Figure 1 ,include:

[0032] S1. Obtain a data table based on a preset RPA system, automatically download the purchase order details that have been placed and unevaluated this year according to the data table, and obtain an unevaluated order table based on the purchase order details. The line items of the unevaluated order table include: confirmation of receipt time and factory name.

[0033] S2. Open and delete the column content of the confirmed delivery time list in the unevaluated order table based on a preset automated tool to obtain an empty entry, wherein the confirmed delivery time includes the delivery time of the undelivered order and the delivery time of the delivery received within a preset time;

[0034] S3. Match each factory name with a preset regional correspondence table based on a preset automated tool. If the match fails, the region where the match fails is classified as a pending region; if the match succeeds, the region where the match succeeds is classified as a target region;

[0035] S4, combining the to-be-determined area and the target area to obtain an area to be detected, summing up the number of items to obtain a total number of items to be evaluated for each user in the area to be detected, and processing the total number of items to be evaluated based on a preset message push format to obtain multiple messages to be pushed;

[0036] S5. Writing the message to be pushed into a database table based on a message push tool, extracting unsent messages from the database table based on a data collection cycle, and sending the unsent messages to a WeChat group for early warning.

[0037] In some embodiments, the process of obtaining a data table based on a preset RPA system in step S1, automatically downloading purchase order details that have been placed and not evaluated in this year according to the data table, and obtaining an unevaluated order table based on the purchase order details includes:

[0038] The RPA system simulates the manager to automatically expand the menu list and click "Provincial Company Side Statistical Query" -> "User Evaluation" in the menu list;

[0039] The RPA system simulates the manager to automatically click the "Unrated Order List (Online Province)" menu in "User Evaluation";

[0040] The RPA system simulates the manager to automatically fill in the query condition "order time" according to the "Unrated Order List (Online Province)" menu, and sets the start time of the "order time" to January 1 of the current year and the end time to the current date;

[0041] The RPA system simulates the manager to automatically click the "Query" button to obtain the query results;

[0042] Based on the query results, the RPA system simulates the administrator to automatically click the "Download Center Export" button;

[0043] The RPA system simulates the manager to automatically select the file storage address (computer desktop) and then automatically click the "Confirm" button, and automatically generates the "Unevaluated Order Report Export.xlsx" file on the computer desktop to obtain the unevaluated order table.

[0044] In some embodiments, when executing step S2, the process of obtaining empty entries includes: automatically opening the order list to be evaluated based on an automated tool, deleting the undelivered orders in the order list to be evaluated and the delivered orders within a preset time to obtain an updated order list to be evaluated.

[0045] In fact, the preset time is 3 days.

[0046] Furthermore, the "unevaluated order list" is processed again through the automation tool developed by .NET. According to the classification rules of power grid enterprises, the data of the unit and the district and county units under its jurisdiction are summarized by prefecture-level cities and directly affiliated units. Then, according to the summary data and ranking of each unit, according to the early warning rules, it is processed into an early warning message that needs to be sent to the instant messaging, and the early warning message is automatically stored in the agreed database table.

[0047] Specifically, .NET is a free open source development platform for building a variety of applications, and .NET applications can be written using C#, F#, or Visual Basic.

[0048] .NET is used to generate various types of applications and libraries to develop web applications, web APIs and microservices, serverless functions in the cloud, cloud native applications, mobile applications, desktop applications, Windows WPF, Windows Forms, Universal Windows Platform (UWP), games, Internet of Things (IoT), machine learning, console applications, Windows services. The Windows service developed in C# can be directly executed on the Windows operating system computer.

[0049] In some embodiments, when executing step S4 to process the total number of items to be evaluated based on a preset message push format to obtain multiple messages to be pushed, the preset message push format consists of a preset first message push format and a preset second message push format, and the preset first message push format is determined according to the number and ranking of items to be evaluated in each area to be detected, and the preset second message push format is determined according to the number and ranking of items to be evaluated under the purchasing account in each area to be detected, and each user corresponds to a purchasing account.

[0050] Specifically, the preset first message push format is as follows:

[0051] (1) [E-commerce purchases in the areas to be tested without evaluation]:

[0052] From January 1, 2024 to August 20, 2024, there were a total of M e-commerce purchases and receipts that were not evaluated in the province. The details of the number of e-commerce purchases and receipts that were not evaluated in each city in the province are as follows: 1. Pingxiang order a, 2. Jiujiang order b, 3. Ganzhou order c..., "others" J orders, among which M>a>b>c, M>J.

[0053] Furthermore, the preset second message push format is as follows:

[0054] (2) [Prompt for no evaluation of purchasing account e-commerce purchase]:

[0055] 1. Pingxiang A order, details are as follows:

[0056] 1.1 Zhang San e order, 1.2. Li Si f order, ...

[0057] 2. Jiujiang B order, details are as follows:

[0058] 2.1 Wang Wu g single, 2.2. Zhao Liu h single, ...

[0059] 3. “Other” orders, details are as follows:

[0060] 3.1 Sun Qi P single, 3.2. Zhou Ba Q single, ...

[0061] Among them, a>e>f, b>g>h, J>p>q.

[0062] In some embodiments, when S5 is executed, the process of extracting unsent messages from the database table based on the data collection cycle and sending the unsent messages to the WeChat group for early warning includes:

[0063] S51, extracting unsent information from the database table based on a data collection cycle, and sending a push instruction to a message push platform using a preset automation tool;

[0064] S52: After the message push platform receives the push instruction, the WeChat robot calls the WeChat dedicated interface to send the unsent message to the WeChat group for early warning.

[0065] Specifically, when executing step S51, the data collection cycle is 1 minute, and the database table is the preset JX_WX_MESSAGE_WARN database table, which specifically includes: date ID, time point when the data is created, message data, user name, and job type.

[0066] Furthermore, when executing step S52, the process of sending the unsent message to the WeChat group for early warning includes:

[0067] The existing packaged message push platform interface is automatically called through the preset automation tool, and the "unsent" message is used as the interface transmission content to send a message push request to the message push platform;

[0068] After receiving the message push request, the message push platform automatically calls the WeChat dedicated interface. The WeChat robot sends the message to the agreed WeChat group through the WeChat dedicated interface, and all users in the WeChat group can view the relevant message.

[0069] The embodiment of the present invention also provides a WeChat-based power grid enterprise mall purchase order evaluation and early warning system, including:

[0070] The data acquisition module is used to obtain unevaluated orders from the power grid enterprise mall system;

[0071] A data preprocessing module, used to delete the unreceived orders and the received orders within a preset time from the unevaluated orders and obtain updated unevaluated orders;

[0072] A region matching module, used to match the updated unevaluated orders with a preset factory region correspondence table, and obtain the number and ranking of order items to be evaluated in each region;

[0073] A message generation module, used to process the number and ranking of the order items to be evaluated in each area according to a preset message push format to generate a message to be pushed;

[0074] A message push module is used to write the messages to be pushed into a database table, and to regularly send unsent messages to a WeChat group for early warning according to a data collection cycle;

[0075] The data storage module is used to store the unevaluated order table, the factory area correspondence table and the messages to be pushed.

[0076] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A WeChat-based power grid enterprise mall purchase order evaluation and early warning method, characterized in that: include: A data table is obtained based on a preset RPA system, and the purchase order details that have not been evaluated and placed in this year are automatically downloaded according to the data table, and an unevaluated order table is obtained based on the purchase order details. The line items of the unevaluated order table include: confirmation of receipt time and factory name; Open and delete the column content of the confirmed delivery time list in the unevaluated order table based on a preset automated tool to obtain an empty entry, wherein the confirmed delivery time includes the delivery time of the undelivered order and the delivery time of the goods received within a preset time; Based on the preset automation tool, each factory name is matched with the preset regional correspondence table. If the match fails, the area where the match fails is classified as the pending area; if the match succeeds, the area where the match succeeds is classified as the target area; Combining the to-be-determined area and the target area to obtain an area to be detected, summing up the number of items to obtain a total number of items to be evaluated for each user in the area to be detected, and processing the total number of items to be evaluated based on a preset message push format to obtain a plurality of messages to be pushed; The message to be pushed is written into a database table based on a message push tool, and unsent messages are extracted from the database table based on a data collection cycle, and the unsent messages are sent to a WeChat group for early warning.

2. According to a WeChat-based power grid enterprise mall purchase order evaluation and early warning method according to claim 1, it is characterized in that: The process of extracting unsent messages from the database table based on a data collection cycle and sending the unsent messages to a WeChat group for early warning includes: Extracting unsent information from the database table based on a data collection cycle, and sending a push instruction to a message push platform using a preset automation tool; After the message push platform receives the push instruction, the WeChat robot calls the WeChat dedicated interface to send the unsent message to the WeChat group for early warning.

3. According to a WeChat-based power grid enterprise mall purchase order evaluation and early warning method according to claim 2, it is characterized in that: The collection cycle is 1 minute, and the data table is a preset JX_WX_MESSAGE_WARN database table. The JX_WX_MESSAGE_WARN database table includes: date ID, time point when the data is created, message data, user name, and job type.

4. According to a WeChat-based power grid enterprise mall purchase order evaluation and early warning method according to claim 1, it is characterized in that: The process of obtaining the empty entries includes: automatically opening the order list to be evaluated based on an automated tool, deleting the undelivered orders and the delivered orders within a preset time in the order list to be evaluated to obtain an updated order list to be evaluated.

5. According to a WeChat-based power grid enterprise mall purchase order evaluation and early warning method according to claim 1, it is characterized in that: The preset message push format consists of a preset first message push format and a preset second message push format. The preset first message push format is determined based on the number and ranking of items to be evaluated in each area to be detected. The preset second message push format is determined based on the number and ranking of items to be evaluated under the purchasing account in each area to be detected. Each user corresponds to a purchasing account.

6. A WeChat-based power grid enterprise mall purchase order evaluation and early warning system, including: The data acquisition module is used to obtain unevaluated orders from the power grid enterprise mall system; A data preprocessing module, used to delete the unreceived orders and the received orders within a preset time from the unevaluated orders and obtain updated unevaluated orders; A region matching module is used to match the updated unevaluated orders with a preset factory region correspondence table to obtain the number and ranking of order items to be evaluated in each region; A message generation module, used to process the number and ranking of the order items to be evaluated in each area according to a preset message push format to generate a message to be pushed; A message push module is used to write the messages to be pushed into a database table, and to regularly send unsent messages to a WeChat group for early warning according to a data collection cycle; The data storage module is used to store the unevaluated order table, the factory area correspondence table and the messages to be pushed.

Images