Work order processing method, device and storage medium

Abstract

The present application provides a work order processing method, device and storage medium, which extracts historical data of network fault work orders, classifies work order faults, initially locates work order faults and relocates work order faults for historical data, builds a work order fault identification model based on the classification information of work order faults, the initial location information of work order faults and the secondary location information of work order faults, matches the return data corresponding to the historical data with the output data of the work order fault identification model, and stores the work order fault identification model in a local server when the matching rate is greater than or equal to a preset threshold; and judges network faults through a web application testing tool based on the work order fault identification model stored in the local server to issue a fault work order. The network fault is identified in real time and a fault work order is issued, which reduces the impact of human factors, improves the accuracy and timeliness of fault identification and work order distribution, and improves the operational safety of the network.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a work order processing method, device and storage medium. Background Art

[0002] With the vigorous development of telecommunication services such as 4G and 5G services, the distribution of telecommunication equipment such as network service base stations, servers, and network element equipment is becoming more and more widespread, and the number is also increasing. These devices are set in different locations and areas, and various equipment failures are inevitable during their operation. In order to improve the working stability of the equipment, it is necessary for operation and maintenance personnel to promptly understand the causes of the failures and perform timely maintenance on them. At present, the general maintenance process for telecommunication service operation equipment in the industry is that the dispatching unit, such as the network operation and maintenance center, uses the experience of the operation and maintenance staff to make remote judgments, generate maintenance work orders, and then send the maintenance work orders to the fault handling unit after manual secondary screening. After receiving the maintenance work order, the fault handling unit conducts on-site processing according to the order information, investigates the real cause of the fault and provides feedback on the return order. After receiving the return order content fed back by the on-site processing personnel, the dispatching unit confirms whether the network has resumed normal operation. At the same time, it receives the alarm clearing information sent by each network element and network base station, confirms that the fault has been handled, and manually returns the order in the dispatching system. This traditional fault ticket processing process requires a lot of manpower and is easily limited by factors such as manpower, experience or time, resulting in untimely fault dispatch and misjudgment of faults. It is not convenient for the fault handling unit to repair network faults, which seriously affects the normal operation of telecommunications services. Summary of the invention

[0003] In view of this, one of the technical problems solved by the embodiments of the present application lies in a work order processing method, device and storage medium thereof, which are used to automatically detect network failures caused by the failure of network equipment such as network element equipment and base stations to work properly in real time and generate corresponding maintenance work orders, thereby reducing the need for manual fault judgment, improving the accuracy and efficiency of network fault judgment, and promptly repairing network failures to provide users with more stable telecommunications services.

[0004] In a first aspect, an embodiment of the present application provides a work order processing method, including:

[0005] Extract historical data of network fault work orders;

[0006] Performing work order fault classification, work order fault initial location, and work order fault secondary location on the historical data, and building a work order fault identification model based on the work order fault classification information, work order fault initial location information, and work order fault secondary location information;

[0007] Matching the receipt data corresponding to the historical data with the output data of the work order fault identification model, and storing the work order fault identification model in a local server when the matching rate is greater than or equal to a preset threshold;

[0008] According to the work order fault identification model stored in the local server, the network fault is judged through the web application testing tool to issue a fault work order according to the judgment result.

[0009] Optionally, in an embodiment of the present application, constructing a work order fault identification model according to the classification information of the work order fault, the initial location information of the work order fault, and the secondary location information of the work order fault includes:

[0010] Using a multi-class and / or multi-label algorithm to predict sample attributes of the historical data, and determine attribute prediction data of the sample data;

[0011] The classification information of the work order fault, the initial location information of the work order fault and the secondary location information of the work order fault are used as sample data, and combined with the attribute prediction data of the sample data, a work order fault identification model is constructed.

[0012] Optionally, in an embodiment of the present application, matching the receipt data corresponding to the historical data with the output data of the work order fault identification model includes:

[0013] The Python standard library Difflib is referenced to slice the receipt content corresponding to the historical data, and the sliced ​​work order data is matched with the output data of the work order fault identification model.

[0014] Optionally, in an embodiment of the present application, the work order processing method further includes: reading the content of the receipt, and confirming the receipt according to the content of the receipt and the result of judging the network fault.

[0015] Optionally, in an embodiment of the present application, the reading of the receipt content and confirming the receipt according to the receipt content and the result of judging the network fault include:

[0016] Capture the receipt content on the system display interface through a web crawler, and display the result of whether the network failure is repaired based on the receipt content and the judgment result of the network failure;

[0017] The display result is captured, and the display interface is awakened through a POST request to confirm the receipt according to the content of the captured packet.

[0018] In a second aspect, based on the work order processing method described in the first aspect of the present application, an embodiment of the present application further provides a work order processing device, including:

[0019] Extraction module, used to extract historical data of network fault work orders;

[0020] A construction module is used to classify work order faults, perform initial work order fault location and secondary work order fault location on the historical data, and construct a work order fault identification model based on the classification information of the work order faults, the initial work order fault location information and the secondary work order fault location information;

[0021] A verification module, used to match the return data corresponding to the historical data with the output data of the work order fault identification model, and when the matching rate is greater than or equal to a preset threshold, store the work order fault identification model in a local server;

[0022] The processing module is used to judge the network fault according to the work order fault identification model stored in the local server through the web application testing tool, so as to issue a fault work order according to the judgment result.

[0023] Optionally, in one embodiment of the present application, the construction module is also used to adopt a multi-class and / or multi-label algorithm to perform sample attribute prediction on the historical data to determine the attribute prediction data of the sample data; the classification information of the work order fault, the initial location information of the work order fault and the secondary location information of the work order fault are used as sample data, combined with the attribute prediction data of the sample data, to construct a work order fault identification model.

[0024] Optionally, in one embodiment of the present application, the verification module is also used to reference the Python standard library Difflib to slice the receipt content corresponding to the historical data, and match the sliced ​​work order data with the output data of the work order fault identification model.

[0025] Optionally, in an embodiment of the present application, the work order processing device further includes a confirmation module, and the confirmation module is used to read the content of the return slip and confirm the return slip according to the content of the return slip and the result of judging the network fault.

[0026] In the third aspect, based on the work order processing method provided in the first aspect of the present application, an embodiment of the present application also provides a storage medium, on which a computer program is stored. When the processor executes the computer program stored on the storage medium, the work order processing method described in any embodiment of the first aspect of the present application is implemented.

[0027] The present application provides a work order processing method, device and storage medium. The work order processing method extracts historical data of network fault work orders, classifies work order faults, initially locates work order faults and relocates work order faults for the historical data, builds a work order fault identification model based on the classification information of the work order faults, the initial location information of the work order faults and the secondary location information of the work order faults, matches the return data corresponding to the historical data with the output data of the work order fault identification model, and stores the work order fault identification model in a local server when the matching rate is greater than or equal to a preset threshold. The network fault is judged by the work order fault identification model stored in the local server through a web application testing tool, so as to issue a fault work order according to the judgment result. This work order processing method counts and summarizes the work order faults of historical data, builds a work order fault identification model, and detects network faults in real time, effectively reducing the human influence factors in the process of fault identification work order distribution, improving the accuracy and timeliness of work order distribution, and improving the operation safety of networks such as network element equipment and base stations, so as to provide users with more stable telecommunications services. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Hereinafter, some specific embodiments of the present application will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0029] Figure 1 A flowchart of a work order processing method provided in an embodiment of the present application;

[0030] Figure 2 A schematic diagram of the structure of a work order processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. The described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should belong to the protection scope of the embodiments of the present application.

[0032] Embodiment 1

[0033] Embodiment 1 of the present application provides an image processing method, such as Figure 1 As shown, Figure 1 A flowchart of a work order processing method provided in an embodiment of the present application, the work order processing method comprising:

[0034] S101. Extract historical data of network fault work orders.

[0035] In one implementation scenario of this embodiment, the historical data includes data such as network base stations, transmissions, and data network element equipment alarms within a preset area of ​​the system. In this embodiment, the larger the amount of historical data extracted and the more types, the more accurate the work order processing method described in this embodiment can be.

[0036] Optionally, in one implementation of this embodiment, in order to ensure that the extracted historical data contains more comprehensive network fault information while improving data processing efficiency, the historical data can be screened and compared for the same type and scenario based on the alarm data, and the historical data can be compressed to remove redundant data.

[0037] S102, classify work order faults, perform initial location and secondary location on the historical data, and build a work order fault identification model based on the work order fault classification information, work order fault initial location information and work order fault secondary location information.

[0038] In one implementation of the present embodiment, the present embodiment is exemplified by a network base station. Due to the limitation of the location of the base station and the service area it radiates, the network signal in the area closer to the base station is relatively stable, and the network signal in the area farther from the base station is relatively poor. At this time, the area farther from the base station is prone to false alarms of network failures due to unstable signal transmission. At this time, in order to improve the accuracy of historical data, the historical data can be used to classify the faults of work orders, and the network failures can be further refined based on the initial location of the work order failures and the secondary location of the work order failures, so as to construct a fault identification model based on the classified and refined historical data to ensure that the constructed fault identification model has a higher accuracy and reduce the work efficiency of operation and maintenance personnel in repairing network failures.

[0039] Optionally, in an implementation of this embodiment, a work order fault identification model is constructed according to the classification information of the work order fault, the initial location information of the work order fault, and the secondary location information of the work order fault, including:

[0040] A multi-class and / or multi-label algorithm is used to predict sample attributes of the historical data, determine the attribute prediction data of the sample data, take the classification information of the work order fault, the initial location information of the work order fault and the secondary location information of the work order fault as sample data, and combine the attribute prediction data of the sample data to build a work order fault identification model.

[0041] In one implementation of this embodiment, in order to improve the accuracy of the constructed work order fault identification model, a multi-class and / or multi-label algorithm can be used to perform attribute prediction on each sample data in the historical data, that is, to assign a series of target values ​​to each sample data in the historical data, and predict the various possibilities that may cause the network failure, and then further refine it based on the classification information of the historical data, the work order fault initial location information and the work order fault secondary location information, so that the work order fault identification model can be constructed based on more comprehensive fault information, thereby improving the accuracy of the work order fault identification construction.

[0042] S103: Match the return data corresponding to the historical data with the output data of the work order fault identification model, and when the matching rate is greater than or equal to a preset threshold, store the work order fault identification model in a local server.

[0043] In this embodiment, the receipt data corresponding to the historical data is matched with the output data of the constructed work order recognition model, and the matching result is verified. When the probability of the matching result is greater than the preset threshold, it means that the constructed work order fault recognition model can meet the current work requirements, so the constructed work order recognition model is verified and the work order fault recognition model that passes the verification is stored in the local server, so as to better ensure that the constructed work order recognition model has a higher accuracy when performing data recognition.

[0044] S104. According to the work order fault identification model stored in the local server, the network fault is judged by the web application testing tool, so as to issue a fault work order according to the judgment result.

[0045] In one application scenario of the embodiment, a verified work order fault identification model is stored in a local server, and a web application testing tool is called, such as a browsing browser through the Selenium web driver library, to achieve the purpose of calling the browser, so as to realize the purpose of real-time detection and judgment of the current network fault, and at the same time, a corresponding fault work order is issued according to the results of the detection and judgment, and the work order is distributed, thereby realizing automatic real-time detection of network faults and work order distribution, greatly improving the efficiency and accuracy of fault work order processing.

[0046] Optionally, in an implementation of the present embodiment, the work order processing method further includes: reading the content of the receipt, and confirming the receipt according to the content of the receipt and the result of judging the network fault.

[0047] In the actual application scenario of this embodiment, after the operation and maintenance personnel repair the fault on-site according to the dispatched work order, they transmit the content of the return slip for repairing the fault to the operation and maintenance center. At this time, in order to further reduce the labor cost of this process and improve the automation of the entire process of the work order processing method described in this embodiment, a comparison and judgment can be made based on the content of the return slip and the fault data contained in the dispatched work order. For example, the network fault location information in the work order is compared and judged to be equal to the location information in the return slip content, and the return slip is confirmed based on the result of the comparison and judgment, thereby forming a closed-loop work order processing.

[0048] Optionally, in an implementation of the present embodiment, the reading of the receipt content and the confirmation of the receipt based on the receipt content and the result of judging the network fault include: crawling the receipt content on the system display interface through a web crawler, displaying the result of whether the network fault is repaired based on the receipt content and the result of judging the network fault, capturing the displayed result, waking up the display interface through a POST request, and confirming the receipt based on the captured content.

[0049] In an actual application scenario of the present embodiment, when confirming the return slip, in order to further improve the convenience of return slip confirmation during the work order processing, a web crawler such as a web crawler tool can be used to capture the return slip content of the display interface of the system such as the network service reimbursement system used by the operator, and compare and judge the return slip content with the judgment result of the network fault contained in the issued work order to determine whether the network fault corresponding to the dispatched work order has been repaired, and display the result, and capture the network data content of the display data, such as the fault type, fault cause, fault handling instructions and other information of the return slip, and use the system to wake up the display interface of the actual system in real time by sending a POST request, and submit the form, so as to confirm the return slip according to the captured content, so that the work order processing process can further reduce the manual processing links in the return slip confirmation process, and better improve the efficiency of the return slip process.

[0050] The work order processing method provided in the present implementation extracts historical data of network fault work orders, classifies work order faults, initially locates work order faults, and secondary locates work order faults for the historical data, builds a work order fault identification model based on the classification information of the work order faults, the initial location information of the work order faults, and the secondary location information of the work order faults, matches the return order data corresponding to the historical data with the output data of the work order fault identification model, and when the matching rate is greater than or equal to a preset threshold, stores the work order fault identification model in a local server, and uses the work order fault identification model stored in the local server to judge the network fault through a web application testing tool, so as to issue a fault work order based on the judgment result. This work order processing method constructs a work order fault identification model by statistics and summary of work order faults in historical data, and performs accuracy verification on the processing results. After the verification, the work order fault identification model is used to detect network faults in real time, and the web application testing tool is called to call the browser to judge the work order fault, and a fault work order is issued in real time and distributed, thereby effectively reducing the factors affected by human factors in the process of fault identification and maintenance work order distribution, improving the accuracy and timeliness of fault identification and work order distribution, and improving the operation safety and working stability of networks such as network element equipment and base stations, so as to provide users with more stable telecommunications services.

[0051] Implementation column 2

[0052] Based on the work order processing method described in the first embodiment of the present application, the second embodiment of the present application provides a work order processing device, such as Figure 2 As shown, Figure 2 A schematic diagram of the structure of a work order processing device 20 provided in an embodiment of the present application, the work order processing device 20 includes:

[0053] Extraction module 201, used to extract historical data of network fault work orders;

[0054] A construction module 202 is used to classify work order faults, perform initial work order fault location and secondary work order fault location on the historical data, and construct a work order fault identification model according to the classification information of the work order faults, the initial work order fault location information and the secondary work order fault location information;

[0055] Verification module 203, used to match the return data corresponding to the historical data with the output data of the work order fault identification model, and when the matching rate is greater than or equal to a preset threshold, store the work order fault identification model in the local server;

[0056] The processing module 204 is used to judge the network fault according to the work order fault identification model stored in the local server through the web application testing tool, so as to issue a fault work order according to the judgment result.

[0057] Optionally, in one embodiment of the present application, the construction module 202 is also used to adopt a multi-class and / or multi-label algorithm to perform sample attribute prediction on the historical data to determine the attribute prediction data of the sample data; the classification information of the work order fault, the initial location information of the work order fault and the secondary location information of the work order fault are used as sample data, and combined with the attribute prediction data of the sample data, a work order fault identification model is constructed.

[0058] Optionally, in one embodiment of the present application, the verification module 203 is also used to reference the Python standard library Difflib to slice the receipt content corresponding to the historical data, and match the sliced ​​work order data with the output data of the work order fault identification model.

[0059] Optionally, in an embodiment of the present application, the work order processing device further includes a confirmation module, and the confirmation module is used to read the content of the return slip and confirm the return slip according to the content of the return slip and the result of judging the network fault.

[0060] Optionally, in one implementation of this embodiment, the confirmation module is further used to crawl the receipt content of the system display interface through a web crawler, display the result of whether the network fault is repaired based on the receipt content and the judgment result of the network fault, capture the displayed result, wake up the display interface through a POST request, and confirm the receipt based on the content of the captured packet.

[0061] The work order processing device provided in the embodiment of the present application is configured to extract historical data of network fault work orders through an extraction module; a construction module is configured to classify work order faults, initially locate work order faults, and secondary locate work order faults on the historical data; a work order fault identification model is constructed according to the classification information of the work order faults, the initial location information of the work order faults, and the secondary location information of the work order faults; a verification module is configured to match the return data corresponding to the historical data with the output data of the work order fault identification model; when the matching rate is greater than or equal to a preset threshold, the work order fault identification model is stored in a local server; a processing module is configured to judge the network fault through the work order fault identification model stored in the local server through a web application testing tool, so as to issue a fault work order according to the judgment result. Thus, by statistically summarizing the work order failures in historical data, a work order failure identification model is constructed, and the processing results are verified for accuracy. After the verification, the work order failure identification model is used to detect network failures in real time, and the web application testing tool calls the browser to judge the work order failure, and a fault work order is issued in real time to be sent to the operation and maintenance personnel for fault repair, thereby effectively reducing the influence of human factors in the process of fault identification and maintenance work order distribution, and realizing a fully closed-loop work order processing process through different functional modules, thereby improving the accuracy and timeliness of fault identification and work order distribution. The work order processing device has a simple structure and is easy to implement.

[0062] Embodiment 3

[0063] Based on the work order processing method described in the first embodiment of the present application, the embodiment of the present application further provides a storage medium, on which a computer program is stored. When a processor executes the computer program stored on the storage medium, the work order processing method described in the first embodiment of the present application is implemented. The work order processing method includes but is not limited to:

[0064] Extract historical data of network fault work orders;

[0065] Performing work order fault classification, work order fault initial location, and work order fault secondary location on the historical data, and building a work order fault identification model based on the work order fault classification information, work order fault initial location information, and work order fault secondary location information;

[0066] Matching the receipt data corresponding to the historical data with the output data of the work order fault identification model, and storing the work order fault identification model in a local server when the matching rate is greater than or equal to a preset threshold;

[0067] According to the work order fault identification model stored in the local server, the network fault is judged through the web application testing tool to issue a fault work order according to the judgment result.

[0068] So far, the application has described specific embodiments of the subject matter. Other embodiments are within the scope of the appended claims. In some cases, the actions recorded in the claims can be performed in different orders and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing can be advantageous.

[0069] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0070] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0071] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0072] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0073] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0074] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0075] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.

[0076] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific transactions or implement specific abstract data types. The present application may also be practiced in distributed computing environments where transactions are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0077] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0078] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A work order processing method, It is characterized in that include: Extract historical data of network fault work orders; Performing work order fault classification, work order fault initial location, and work order fault secondary location on the historical data; Constructing a work order fault identification model according to the classification information of the work order fault, the initial location information of the work order fault and the secondary location information of the work order fault; The network base station is limited by the location of the base station and the service area it radiates. The network signal in the area close to the base station is relatively stable, while the network signal in the area far from the base station is relatively poor. The area far from the base station is prone to false alarms of network failures due to unstable signal transmission. In order to improve the accuracy of historical data, the historical data is classified into fault categories of work orders, and the network faults are further refined based on the initial location and secondary location information of the work order faults, so as to build a work order fault identification model based on the classified and refined historical data; The step of constructing a work order fault identification model according to the classification information of the work order fault, the initial location information of the work order fault and the secondary location information of the work order fault further includes: using a multi-class and / or multi-label algorithm to perform sample attribute prediction on the historical data to determine attribute prediction data of the sample data, that is, by assigning a series of target values ​​to the sample data in each historical data, predicting multiple possibilities of causing the network fault; The classification information of the work order fault, the initial location information of the work order fault and the secondary location information of the work order fault are used as sample data, and the attribute prediction data of the sample data are combined to build a work order fault identification model; Matching the receipt data corresponding to the historical data with the output data of the work order fault identification model; When the matching rate is greater than or equal to a preset threshold, the work order fault identification model is stored in a local server; According to the work order fault identification model stored in the local server, the network fault is judged through the web application testing tool to issue a fault work order according to the judgment result.

2. The work order processing method according to claim 1, It is characterized in that The matching of the receipt data corresponding to the historical data with the output data of the work order fault identification model includes: The Python standard library Difflib is referenced to slice the receipt content corresponding to the historical data, and the sliced ​​receipt data is matched with the output data of the work order fault identification model.

3. The work order processing method according to claim 1, It is characterized in that After judging the network fault through the web application testing tool according to the work order fault identification model stored in the local server and issuing a fault work order according to the judgment result, it also includes: reading the return ticket content, and confirming the return ticket according to the return ticket content and the result of judging the network fault.

4. The work order processing method according to claim 3, It is characterized in that The reading of the receipt content and confirming the receipt according to the receipt content and the result of judging the network fault include: Capture the receipt content on the system display interface through a web crawler, and display the result of whether the network failure is repaired based on the receipt content and the judgment result of the network failure; The display result is captured, and the display interface is awakened through a POST request to confirm the receipt according to the content of the captured packet.

5. A work order processing device, It is characterized in that include: Extraction module, used to extract historical data of network fault work orders; A construction module is used to classify work order faults, perform initial work order fault location and secondary work order fault location on the historical data, and construct a work order fault identification model based on the classification information of the work order faults, the initial work order fault location information and the secondary work order fault location information; The network base station is limited by the location of the base station and the service area it radiates. The network signal in the area close to the base station is relatively stable, while the network signal in the area far from the base station is relatively poor. The area far from the base station is prone to false alarms of network failures due to unstable signal transmission. In order to improve the accuracy of historical data, the historical data is classified into fault categories of work orders, and the network faults are further refined based on the initial location and secondary location information of the work order faults, so as to build a work order fault identification model based on the classified and refined historical data; The construction module is further used to use a multi-class and / or multi-label algorithm to perform sample attribute prediction on the historical data and determine attribute prediction data of the sample data, that is, to predict multiple possibilities causing the network failure by assigning a series of target values ​​to each sample data in the historical data; The construction module also uses the classification information of the work order fault, the initial location information of the work order fault and the secondary location information of the work order fault as sample data, and combines the attribute prediction data of the sample data to construct a work order fault identification model; A verification module, used to match the return data corresponding to the historical data with the output data of the work order fault identification model, and when the matching rate is greater than or equal to a preset threshold, store the work order fault identification model in a local server; The processing module is used to judge the network fault according to the work order fault identification model stored in the local server through the web application testing tool, so as to issue a fault work order according to the judgment result.

6. The work order processing device according to claim 5, It is characterized in that The verification module is also used to reference the Python standard library Difflib to slice the receipt content corresponding to the historical data, and match the sliced ​​receipt data with the output data of the work order fault identification model.

7. The work order processing device according to claim 5, It is characterized in that The system also includes a confirmation module, which is used to read the content of the receipt and confirm the receipt according to the content of the receipt and the result of judging the network failure.

8. A storage medium, It is characterized in that The storage medium stores a computer program, and when the processor executes the computer program stored in the storage medium, the work order processing method according to any one of claims 1 to 4 is implemented.

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