Work order monitoring method and device, electronic equipment and storage medium

Abstract

The invention discloses a work order monitoring method and device, electronic equipment and a storage medium, which are used for solving the technical problems of poor service quality and low efficiency of the existing work order monitoring method. The method comprises the following steps: reading a work order from a preset data source, and obtaining work order key data of the work order; preprocessing the work order key data to obtain preprocessed data; generating a macro form by adopting the preprocessed data of the work order; and generating early warning information according to the macro form, and performing early warning according to the early warning information. According to the invention, the work order is converted into the macro form, so that a user can visually check the processing progress of each work order, and early warning is carried out by monitoring the progress of the work order, thereby improving the service quality and efficiency of the work order monitoring process.

Description

Technical Field

[0001] The present invention relates to the technical field of work order monitoring, and in particular to a work order monitoring method, device, electronic equipment and storage medium. Background Art

[0002] With the rapid development and digital transformation of the power industry, the traditional work order management method can no longer meet the growing demand for power services. Work order monitoring, as an important part of the work of power teams, is crucial to improving service quality and user satisfaction. However, the existing work order monitoring process mainly relies on manual operation, which has many problems and affects the overall efficiency and quality of power services.

[0003] In recent years, with the development of economy and the progress of society, people's demand for power services has been growing, and the power industry has been constantly innovating technology and upgrading service models to adapt to market changes and user needs. In this process, digital transformation has become an important trend, aiming to improve the quality and efficiency of power services through information technology.

[0004] Work order monitoring refers to the process of tracking and managing various work orders generated during the power service process. These work orders cover a number of service activities, from user repair reports, maintenance requests to construction arrangements. Efficient work order monitoring can not only ensure the timeliness of power services, but also improve user satisfaction, thereby having a positive impact on the brand image of power companies.

[0005] Currently, work order monitoring is mainly done manually, which has the following main problems:

[0006] 1. Low tracking efficiency: Manual operation leads to low efficiency in work order monitoring, which affects the timely handling rate of faults and user satisfaction. In the traditional work order monitoring process, staff need to manually record and update the information of each work order, including the status of the work order, processing progress, etc. This method is not only time-consuming and labor-intensive, but also prone to errors, resulting in delayed work order processing and affecting service quality.

[0007] 2. System data is not connected: Data between departments and teams cannot be connected, and information transmission is not smooth, which affects the timeliness and accuracy of work order monitoring. Since there are usually multiple different departments and teams within the power company, each of them is responsible for different business scopes. In this case, if there is no effective data sharing mechanism between departments, it will lead to information islands, resulting in information lag or missing in the work order processing process, affecting the timeliness and accuracy of work order processing.

[0008] 3. Lack of initiative and timeliness: Manual monitoring and processing methods lack initiative and timeliness in archiving work orders, which is prone to delays and omissions. Since work order processing involves multiple links, from receiving repair information to the final completion of repairs, each link requires close cooperation. If manual monitoring is used, it is difficult to track all work orders in real time and handle them in a timely manner, especially when faced with a large number of work orders, the staff may miss some important information or miss key time nodes.

[0009] 4. Difficulty in identifying abnormal data: During the work order monitoring process, abnormal values ​​may appear in the data, such as extreme service response time, fault types beyond the normal range, etc. It is difficult for manual monitoring to effectively identify these abnormal data, resulting in the inability to take corrective measures in a timely manner, affecting service quality.

[0010] The existence of the above problems seriously restricts the ability of power companies to improve service quality and efficiency. Summary of the invention

[0011] The present invention provides a work order monitoring method, device, electronic equipment and storage medium, which are used to solve the technical problems of poor service quality and low efficiency of the existing work order monitoring method.

[0012] The present invention provides a work order monitoring method, comprising:

[0013] Read the work order from the preset data source and obtain the key work order data of the work order;

[0014] Preprocessing the key data of the work order to obtain preprocessed data;

[0015] Generating a macro form using the pre-processed data of the work order;

[0016] Generate warning information according to the macro form, and issue a warning according to the warning information.

[0017] Optionally, the work order key data includes a plurality of data points of different attributes; the step of preprocessing the work order key data to obtain preprocessed data includes:

[0018] Generate a density histogram of the key data of the work order;

[0019] Determine the neighborhood radius and the minimum number of points using the density histogram;

[0020] Determine the weight of each attribute;

[0021] Calculating the weighted Euclidean distance between the data points of each attribute according to the weight;

[0022] Based on the neighborhood radius, the minimum number of points and the weighted Euclidean distance, an improved DBSCAN algorithm is used to filter out outliers in the key data of the work order;

[0023] The abnormal value is subjected to abnormal processing to obtain preprocessed data.

[0024] Optionally, the step of generating a macro form using the pre-processed data of the work order includes:

[0025] Obtaining preset rules and processing logic for the work order;

[0026] The preset rules and the processing logic are used to associate the pre-processed data to generate a macro form.

[0027] Optionally, the step of generating warning information according to the macro form and issuing a warning according to the warning information includes:

[0028] Monitor the work order progress and key node information in the macro form;

[0029] When the progress of the work order does not reach the preset progress or triggers key node information, an early warning message is generated, and an early warning is issued according to the early warning information.

[0030] Optionally, the key data of the work order includes: user repair report information, work order status information, and construction progress information; the user repair report information includes repair report time, fault description, and contact information; the work order status information includes pending, in progress, and completed; the construction progress information includes start time, estimated completion time, and actual completion time.

[0031] The present invention also provides a work order monitoring device, comprising:

[0032] A work order key data acquisition module is used to read the work order from a preset data source and obtain the work order key data of the work order;

[0033] A preprocessing module, used for preprocessing the key data of the work order to obtain preprocessed data;

[0034] A macro form generation module, used to generate a macro form using the pre-processed data of the work order;

[0035] The early warning module is used to generate early warning information according to the macro form and issue an early warning according to the early warning information.

[0036] Optionally, the work order key data includes several data points with different attributes; the preprocessing module includes:

[0037] A density histogram generation submodule, used to generate a density histogram of the key data of the work order;

[0038] A neighborhood radius and minimum point number determination submodule, used to determine the neighborhood radius and minimum point number using the density histogram;

[0039] The weight determination submodule is used to determine the weight of each attribute;

[0040] A weighted Euclidean distance calculation submodule, used to calculate the weighted Euclidean distance between the data points of each attribute according to the weight;

[0041] An outlier screening submodule, for screening outliers in the key data of the work order by using an improved DBSCAN algorithm based on the neighborhood radius, the minimum number of points and the weighted Euclidean distance;

[0042] The exception processing submodule is used to perform exception processing on the abnormal value to obtain preprocessed data.

[0043] Optionally, the macro form generation module includes:

[0044] A preset rule and processing logic acquisition submodule, used to acquire the preset rule and processing logic of the work order;

[0045] The macro form generation submodule is used to associate each of the pre-processed data with the preset rules and the processing logic to generate a macro form.

[0046] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0047] The memory is used to store program code and transmit the program code to the processor;

[0048] The processor is used to execute the work order monitoring method as described in any one of the above items according to the instructions in the program code.

[0049] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the work order monitoring method as described in any one of the above items.

[0050] It can be seen from the above technical solutions that the present invention has the following advantages: The present invention provides a work order monitoring method, and specifically discloses: reading a work order from a preset data source and obtaining the work order key data of the work order; preprocessing the work order key data to obtain preprocessed data; using the preprocessed data of the work order to generate a macro form; generating warning information according to the macro form, and issuing a warning according to the warning information. The present invention converts the work order into a macro form, so that the user can intuitively view the processing progress of each work order, and issue a warning by monitoring the progress of the work order, thereby improving the service quality and efficiency of the work order monitoring process. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0052] Figure 1 A flowchart of a work order monitoring method provided by an embodiment of the present invention;

[0053] Figure 2 A flowchart of a work order monitoring method provided by another embodiment of the present invention;

[0054] Figure 3 A structural block diagram of a work order monitoring device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The embodiments of the present invention provide a work order monitoring method, device, electronic device and storage medium, which are used to solve the technical problems of poor service quality and low efficiency of the existing work order monitoring method.

[0056] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only 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.

[0057] See also Figure 1 , Figure 1 A flowchart of the steps of a work order monitoring method provided by an embodiment of the present invention.

[0058] The present invention provides a work order monitoring method, which may specifically include the following steps:

[0059] Step 101, read the work order from the preset data source, and obtain the key work order data of the work order;

[0060] A work order is a simple maintenance or manufacturing plan consisting of one or more operations.

[0061] In the embodiment of the present invention, the work order can be read from a preset data source, and the key work order data of the work order can be obtained.

[0062] In actual applications, the preset data sources may include user repair reporting systems, work order management systems, and construction team management systems.

[0063] Key data of a work order may include but is not limited to user repair information, work order status information, and construction progress information.

[0064] User repair reporting information may include reporting time, fault description, contact information, etc.

[0065] Work order status information may include pending, processing, completed, etc.

[0066] Construction progress information may include start time, estimated completion time, actual completion time, and other information.

[0067] Step 102, preprocessing the key data of the work order to obtain preprocessed data;

[0068] After collecting work orders from different data sources, they can be pre-processed, including but not limited to cleaning the data, deleting duplicate or invalid data, and ensuring the accuracy and consistency of the data.

[0069] Step 103, using the pre-processed data of the work order to generate a macro form;

[0070] Macro forms can visualize work order data, including but not limited to visualizing the geographic location, completion time, and processing flow time nodes of work orders in the form of charts and maps. The specific implementation method is to use bar charts, pie charts, line charts, etc. to display key indicators such as the processing time and completion quantity of work orders, and use geographic information system (such as GIS) technology to mark the geographic location of work orders on the map to help members view the distribution of work orders.

[0071] Through visualization through macro forms, users can intuitively understand the processing progress and distribution of each work order, thereby improving work efficiency and management effectiveness.

[0072] Step 104, generating warning information according to the macro form, and issuing a warning according to the warning information.

[0073] After generating the macro form of the work order, you can generate warning information based on the real-time situation on the macro form when the warning conditions are met, and select the corresponding warning method based on the warning information.

[0074] The present invention reads the work order from a preset data source and obtains the key data of the work order; pre-processes the key data of the work order to obtain pre-processed data; generates a macro form using the pre-processed data of the work order; generates warning information according to the macro form, and issues a warning according to the warning information. Thus, the user can intuitively view the processing progress of each work order, and issue a warning by monitoring the progress of the work order, thereby improving the service quality and efficiency of the work order monitoring process.

[0075] See also Figure 2 , Figure 2 A flowchart of a work order monitoring method provided by another embodiment of the present invention. Specifically, the following steps may be included:

[0076] Step 201, read the work order from the preset data source, and obtain the key work order data of the work order;

[0077] In the embodiment of the present invention, the work order can be read from a preset data source, and the key work order data of the work order can be obtained.

[0078] In actual applications, the preset data sources may include user repair reporting systems, work order management systems, and construction team management systems.

[0079] Key data of a work order may include but is not limited to user repair information, work order status information, and construction progress information.

[0080] User repair reporting information may include reporting time, fault description, contact information, etc.

[0081] Work order status information may include pending, processing, completed, etc.

[0082] Construction progress information may include start time, estimated completion time, actual completion time, and other information.

[0083] Step 202, preprocessing the key data of the work order to obtain preprocessed data;

[0084] After collecting work orders from different data sources, they can be pre-processed, including but not limited to cleaning the data, deleting duplicate or invalid data, and ensuring the accuracy and consistency of the data.

[0085] In one example, the work order key data includes a number of data points with different attributes; the step of preprocessing the work order key data to obtain preprocessed data includes:

[0086] S21, generating a density histogram of key data of the work order;

[0087] S22, using density histogram to determine the neighborhood radius and minimum number of points;

[0088] S23, determining the weight of each attribute;

[0089] S24, calculating the weighted Euclidean distance between the data points of each attribute according to the weight;

[0090] S25, based on the neighborhood radius, minimum number of points and weighted Euclidean distance, the improved DBSCAN algorithm is used to filter outliers in the key data of the work order;

[0091] S26, performing abnormal processing on the abnormal value to obtain preprocessed data.

[0092] In the embodiment of the present invention, the work order key data may be clustered by using an improved DBSCAN algorithm to identify abnormal values ​​in the work order key data.

[0093] The improved points of the improved DBSCAN algorithm of the present invention are mainly in the distance measurement and parameter selection method.

[0094] In the electric power industry, different attributes may have different importance. The present invention adjusts the distance metric according to the specific situation, and the distance metric used is the weighted Euclidean distance.

[0095] In the embodiment of the present invention, parameter selection involves the neighborhood radius The present invention determines the neighborhood radius in an adaptive manner. And the minimum number of points MinPts.

[0096] The DBSCAN algorithm process is as follows:

[0097] Given a data set D = {x1, x2, ..., x n}, neighborhood radius And the minimum number of points MinPts.

[0098] Neighborhood: .

[0099] Key point: If , then p is a core point.

[0100] Boundary point: If p is not a core point but belongs to the neighborhood of a core point, then p is a boundary point.

[0101] Noise point: A point that is neither a core point nor a boundary point is a noise point.

[0102] in, is the weighted Euclidean distance, calculated as follows:

[0103]

[0104] in, is the weight of the ith attribute, and The values ​​of p and q on the i-th attribute respectively.

[0105] Neighborhood radius The minimum number of points MinPts can be determined by preprocessing the data set and using methods such as density histogram.

[0106] After the noise points are determined by the DBSCAN algorithm, the noise points can be determined as outliers and then subjected to outlier processing. The outlier processing can be methods such as ignoring the noise points and correcting the noise points. The noise points can be corrected by filling the outliers with interpolation.

[0107] The improved DBSCAN algorithm with adaptive neighborhood radius can more accurately identify true outliers, reduce the possibility of false positives, and improve data reliability. It helps to increase the speed of data processing, especially in the case of large amounts of data, and can complete data cleaning and anomaly detection more quickly. High-quality data supports more accurate decision-making, helps improve service quality and quickly respond to customer needs.

[0108] Step 203, using the pre-processed data of the work order to generate a macro form;

[0109] In the embodiment of the present invention, after the abnormal data is processed, the pre-processed data of each work order may be processed and integrated to generate a unified macro form, so as to determine the abnormal situation in the work order through the macro form.

[0110] In one example, the steps of generating a macro form using pre-processed data of a work order include:

[0111] S31, obtaining preset rules and processing logic of the work order;

[0112] S32, using preset rules and processing logic to associate various pre-processed data to generate a macro form.

[0113] In a specific implementation, different preset rules and processing logics can be set according to different work order processes, such as associating user repair information with corresponding work order status to generate a complete work order processing process, etc. The present invention does not specifically limit the preset rules and processing logic.

[0114] By integrating data from various data sources and generating a unified macro form, data silos can be broken and seamless integration of key work order data can be achieved. Departments and teams can share work order information through macro forms, reducing communication costs and information transmission errors, and improving overall work collaboration capabilities.

[0115] Step 204, monitoring the work order progress and key node information in the macro form;

[0116] Step 205: When the progress of the work order does not reach the preset progress or triggers the key node information, an early warning message is generated, and an early warning is issued according to the early warning message.

[0117] After generating the macro form of the work order, you can generate warning information based on the real-time situation on the macro form when the warning conditions are met, and select the corresponding warning method based on the warning information.

[0118] In the embodiment of the present invention, the data in the macro form can be monitored. When the processing progress of a work order lags behind or is about to reach a critical node, the monitoring system can automatically send a reminder to notify relevant personnel to follow up in time. This centralized early warning function ensures that the construction team can complete the task on time and avoids delays and omissions.

[0119] The present invention reads the work order from a preset data source and obtains the key data of the work order; pre-processes the key data of the work order to obtain pre-processed data; generates a macro form using the pre-processed data of the work order; generates warning information according to the macro form, and issues a warning according to the warning information. Thus, the user can intuitively view the processing progress of each work order, and issue a warning by monitoring the progress of the work order, thereby improving the service quality and efficiency of the work order monitoring process.

[0120] See also Figure 3 , Figure 3 A structural block diagram of a work order monitoring device provided in an embodiment of the present invention.

[0121] An embodiment of the present invention provides a work order monitoring device, comprising:

[0122] The work order key data acquisition module 301 is used to read the work order from the preset data source and obtain the work order key data of the work order;

[0123] A preprocessing module 302 is used to preprocess the key data of the work order to obtain preprocessed data;

[0124] A macro form generation module 303, for generating a macro form using pre-processed data of a work order;

[0125] The warning module 304 is used to generate warning information according to the macro form and issue a warning according to the warning information.

[0126] In the embodiment of the present invention, the work order key data includes a number of data points with different attributes; the pre-processing module 302 includes:

[0127] Density histogram generation submodule, used to generate density histograms of key data of work orders;

[0128] A neighborhood radius and minimum point number determination submodule, used to determine the neighborhood radius and minimum point number using a density histogram;

[0129] The weight determination submodule is used to determine the weight of each attribute;

[0130] The weighted Euclidean distance calculation submodule is used to calculate the weighted Euclidean distance between the data points of each attribute according to the weight;

[0131] The outlier screening submodule is used to screen outliers in the key data of work orders using the improved DBSCAN algorithm based on the neighborhood radius, minimum number of points and weighted Euclidean distance;

[0132] The exception handling submodule is used to perform exception handling on abnormal values ​​to obtain preprocessed data.

[0133] In the embodiment of the present invention, the macro form generation module 303 includes:

[0134] The preset rules and processing logic acquisition submodule is used to obtain the preset rules and processing logic of the work order;

[0135] The macro form generation submodule is used to associate various pre-processed data using preset rules and processing logic to generate a macro form.

[0136] In the embodiment of the present invention, the early warning module 304 includes:

[0137] The work order progress and key node information monitoring submodule is used to monitor the work order progress and key node information in the macro form;

[0138] The early warning submodule is used to generate early warning information when the progress of the work order does not reach the preset progress or triggers key node information, and issue an early warning based on the early warning information.

[0139] In an embodiment of the present invention, the key data of a work order include: user repair report information, work order status information, and construction progress information; user repair report information includes repair report time, fault description, and contact information; work order status information includes pending, in process, and completed; construction progress information includes start time, estimated completion time, and actual completion time.

[0140] An embodiment of the present invention further provides an electronic device, the device comprising a processor and a memory:

[0141] The memory is used to store the program code and transmit the program code to the processor;

[0142] The processor is used to execute the work order monitoring method of the embodiment of the present invention according to the instructions in the program code.

[0143] The embodiment of the present invention further provides a computer-readable storage medium, which is used to store program codes, and the program codes are used to execute the work order monitoring method of the embodiment of the present invention.

[0144] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0145] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0146] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0147] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable terminal device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0151] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0152] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A work order monitoring method, characterized in that: include: Read the work order from the preset data source and obtain the key work order data of the work order; Preprocessing the key data of the work order to obtain preprocessed data; Generating a macro form using the pre-processed data of the work order; Generate warning information according to the macro form, and issue a warning according to the warning information.

2. The method according to claim 1, characterized in that: The work order key data includes a number of data points with different attributes; the step of preprocessing the work order key data to obtain preprocessed data includes: Generate a density histogram of the key data of the work order; Determine the neighborhood radius and the minimum number of points using the density histogram; Determine the weight of each attribute; Calculating the weighted Euclidean distance between the data points of each attribute according to the weight; Based on the neighborhood radius, the minimum number of points and the weighted Euclidean distance, an improved DBSCAN algorithm is used to filter out outliers in the key data of the work order; The abnormal value is subjected to abnormal processing to obtain preprocessed data.

3. The method according to claim 1, characterized in that: The step of generating a macro form using the pre-processed data of the work order comprises: Obtaining preset rules and processing logic for the work order; The preset rules and the processing logic are used to associate the pre-processed data to generate a macro form.

4. The method according to claim 1, characterized in that: The step of generating warning information according to the macro form and issuing a warning according to the warning information includes: Monitor the work order progress and key node information in the macro form; When the progress of the work order does not reach the preset progress or triggers key node information, an early warning message is generated, and an early warning is issued according to the early warning information.

5. The method according to claim 1, characterized in that The key data of the work order include: user repair report information, work order status information, and construction progress information; the user repair report information includes repair report time, fault description, and contact information; the work order status information includes pending, in progress, and completed; the construction progress information includes start time, estimated completion time, and actual completion time.

6. A work order monitoring device, characterized in that: include: A work order key data acquisition module is used to read the work order from a preset data source and obtain the work order key data of the work order; A preprocessing module, used for preprocessing the key data of the work order to obtain preprocessed data; A macro form generation module, used to generate a macro form using the pre-processed data of the work order; The early warning module is used to generate early warning information according to the macro form and issue an early warning according to the early warning information.

7. The device according to claim 6, characterized in that The work order key data includes a number of data points with different attributes; the preprocessing module includes: A density histogram generation submodule, used to generate a density histogram of the key data of the work order; A neighborhood radius and minimum point number determination submodule, used to determine the neighborhood radius and minimum point number using the density histogram; The weight determination submodule is used to determine the weight of each attribute; A weighted Euclidean distance calculation submodule, used to calculate the weighted Euclidean distance between the data points of each attribute according to the weight; An outlier screening submodule, for screening outliers in the key data of the work order by using an improved DBSCAN algorithm based on the neighborhood radius, the minimum number of points and the weighted Euclidean distance; The exception processing submodule is used to perform exception processing on the abnormal value to obtain preprocessed data.

8. The device according to claim 6, characterized in that The macro form generation module includes: A preset rule and processing logic acquisition submodule, used to acquire the preset rule and processing logic of the work order; The macro form generation submodule is used to associate each of the pre-processed data with the preset rules and the processing logic to generate a macro form.

9. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program codes and transmit the program codes to the processor; The processor is used to execute the work order monitoring method described in any one of claims 1-5 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the work order monitoring method according to any one of claims 1-5.

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