Work order monitoring and early warning processing method and device, electronic equipment and storage medium

Abstract

The invention provides a work order monitoring and early warning processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed work order in a current preset duration, classifying the to-be-processed work order, and obtaining the classified to-be-processed work order; when it is determined that the work order number fluctuation degree of the classified to-be-processed work orders is abnormal, intention recognition is carried out on the classified to-be-processed work orders, and problems fed back in the classified to-be-processed work orders are obtained; based on the problems fed back in the classified work orders to be processed, work order early warning processing is carried out, and the work order early warning processing is used for prompting a user to solve the problems fed back in the classified work orders to be processed. The to-be-processed work order with the feedback problem can be timely and accurately monitored, so that a user can be reminded to timely solve the problem fed back in the to-be-processed work order.

Description

Technical Field

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

[0002] In the current software system development and operation and maintenance process, with the continuous iteration and rapid release of software systems, ensuring the stability and availability of the system has become a crucial link.

[0003] However, in actual operation, software systems often experience malfunctions or system unavailability due to various bugs. Once these problems arise, they usually need to be reported and fed back in the form of work orders by multiple role users. In the field of real estate transactions, these multiple role users may be brokers, store owners, and city operations, etc. Since the problems reported by multiple role users through work orders often require a long circulation cycle, this process may involve multiple levels of approval, transfer, and communication, which greatly reduces the efficiency of problem handling.

[0004] Therefore, finding a work order monitoring and early warning processing method that can timely and accurately monitor work orders with feedback problems has become a current research hotspot. Summary of the invention

[0005] The present invention provides a work order monitoring and early warning processing method, device, electronic device and storage medium, which can timely and accurately monitor the pending work orders with feedback problems, so as to remind users to solve the problems reported in the pending work orders in a timely manner.

[0006] The present invention provides a work order monitoring and early warning processing method, the method comprising: obtaining pending work orders within a current preset time period, and classifying the pending work orders to obtain classified pending work orders; when it is determined that there is an abnormality in the degree of fluctuation in the number of work orders of the classified work orders to be processed, performing intention recognition on the classified work orders to be processed to obtain problems fed back in the classified work orders to be processed, wherein the abnormality in the degree of fluctuation in the number of work orders of the classified work orders to be processed is used to characterize that the difference in the number of work orders of the classified work orders to be processed within the current preset time period relative to the number of work orders of the classified work orders to be processed within the previous month-on-month preset time period and\or the previous year-on-year preset time period exceeds a preset difference; based on the problems fed back in the classified work orders to be processed, performing work order early warning processing, wherein the work order early warning processing is used to prompt users to solve the problems fed back in the classified work orders to be processed.

[0007] According to a work order monitoring and early warning processing method provided by the present invention, it is determined that there is an abnormality in the degree of fluctuation in the number of work orders to be processed after the classification, and it is achieved in the following way: obtaining the first number of work orders to be processed after the classification within the current preset time length, and obtaining the second number of work orders to be processed after the classification within the previous preset time length; based on the first number of work orders and the second number of work orders, it is determined that there is an abnormality in the degree of fluctuation in the number of work orders to be processed after the classification.

[0008] According to a work order monitoring and early warning processing method provided by the present invention, the second work order number within the prior preset time period includes the second month-on-month work order number within the prior month-on-month preset time period, and the second year-on-year work order number within the prior year-on-year preset time period; the determining, based on the first work order number and the second work order number, whether the fluctuation degree of the work order number of the classified work orders is abnormal, specifically includes: determining a month-on-month abnormality score based on the first work order number and the second month-on-month work order number; determining a year-on-year abnormality score based on the first work order number and the second year-on-year work order number; determining a year-on-year abnormality score based on the first work order number and the second year-on-year work order number; when the month-on-month abnormality score exceeds a score threshold, and\or the year-on-year abnormality score exceeds a score threshold, determining that the fluctuation degree of the work order number of the classified work orders is abnormal.

[0009] According to a work order monitoring and early warning processing method provided by the present invention, the month-on-month abnormality score is determined based on the first work order number and the second month-on-month work order number, which specifically includes: calling a pre-maintained first mapping table, wherein the first mapping table includes a correspondence between different work order ratios and different month-on-month abnormality scores, and the work order ratio is used to characterize the ratio of the first work order number to the second month-on-month work order number; determining the work order ratio based on the ratio of the first work order number to the second month-on-month work order number; determining the month-on-month abnormality score based on the work order ratio and the correspondence between different work order ratios and different month-on-month abnormality scores in the first mapping table.

[0010] According to a work order monitoring and early warning processing method provided by the present invention, the year-on-year abnormality score is determined based on the first work order number and the second year-on-year work order number, specifically including: calling a pre-maintained second mapping table, wherein the second mapping table includes a correspondence between different work order ratios and different year-on-year abnormality scores, and the work order ratio is used to characterize the ratio of the first work order number and the second year-on-year work order number; determining the work order ratio based on the ratio of the first work order number and the second year-on-year work order number; determining the year-on-year abnormality score based on the work order ratio and the correspondence between different work order ratios and different year-on-year abnormality scores in the second mapping table.

[0011] According to a work order monitoring and early warning processing method provided by the present invention, after obtaining the classified work orders to be processed, the method also includes: obtaining a whitelist work order list, wherein the whitelist work order list includes multiple whitelist classified work orders; based on the whitelist work order list, determining whether the classified work order to be processed matches the whitelist classified work order; and when the classified work order to be processed matches the whitelist classified work order, stopping processing the classified work order.

[0012] According to a work order monitoring and early warning processing method provided by the present invention, the intent recognition of the classified work orders to be processed is performed to obtain the problems fed back in the classified work orders to be processed, which specifically includes: calling a pre-trained intent recognition model, wherein the intent recognition model is used to identify the problems fed back in the classified work orders to be processed; inputting the classified work orders to be processed into the intent recognition model to obtain the problems fed back in the classified work orders to be processed output by the intent recognition model.

[0013] The present invention also provides a work order monitoring and early warning processing device, the device comprising: an acquisition module, used to acquire the pending work orders within a current preset time period, and classify the pending work orders to obtain the classified pending work orders; an identification module, used to identify the intention of the classified work orders to be processed when it is determined that there is an abnormality in the degree of fluctuation in the number of work orders of the classified work orders to be processed, and obtain the problems fed back in the classified work orders to be processed, wherein the abnormality in the degree of fluctuation in the number of work orders of the classified work orders to be processed is used to characterize that the difference in the number of work orders of the classified work orders to be processed within the current preset time period relative to the number of work orders of the classified work orders to be processed within the previous month-on-month preset time period and\or the previous year-on-year preset time period exceeds a preset difference; a processing module, used to perform work order early warning processing based on the problems fed back in the classified work orders to be processed, wherein the work order early warning processing is used to prompt the user to solve the problems fed back in the classified work orders to be processed.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the work order monitoring and early warning processing method as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the work order monitoring and early warning processing method as described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements any of the work order monitoring and early warning processing methods described above.

[0017] The present invention provides a work order monitoring and early warning processing method, device, electronic device and storage medium, which obtain the pending work orders within the current preset time and classify the pending work orders to obtain the classified pending work orders; when it is determined that the fluctuation degree of the work order quantity of the classified pending work orders is abnormal, the classified pending work orders are intentionally identified to obtain the problems reported in the classified pending work orders; based on the problems reported in the classified pending work orders, the work order early warning processing is performed, wherein the work order early warning processing is used to prompt the user to solve the problems reported in the classified pending work orders. It is achieved that the pending work orders with feedback problems can be monitored in a timely and accurate manner, so that the user can be reminded to solve the problems reported in the pending work orders in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is one of the flow charts of the work order monitoring and early warning processing method provided by the present invention.

[0020] Figure 2 It is a flow chart of determining that the fluctuation degree of the number of work orders to be processed after classification is abnormal, provided by the present invention.

[0021] Figure 3 It is a flow chart provided by the present invention for determining that the degree of fluctuation of the number of work orders to be processed after classification is abnormal based on the first work order number and the second work order number.

[0022] Figure 4 This is the second flow chart of the work order monitoring and early warning processing method provided by the present invention.

[0023] Figure 5 It is a structural schematic diagram of the work order monitoring and early warning processing device provided by the present invention.

[0024] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. 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.

[0026] It is known from the related technology that the current work order monitoring and alarm method only issues an alarm message when the number of work orders exceeds a certain threshold, so as to ensure that when there is a certain backlog of work orders, R&D personnel can intervene and handle them in time. The current alarm method does not make dynamic judgments based on the number of work orders under each category. For one type of customer service work order, the number is very large during the daily time period, which is not a problem and does not require an early warning. For another type of customer service work order, which belongs to the consulting customer service work order, although there is a certain backlog, it does not require R&D personnel to intervene.

[0027] The work order monitoring and early warning processing method provided by the present invention can ensure to the greatest extent that when a customer service work order is dynamically warned, a problem has indeed occurred in the application system, and R&D personnel can intervene quickly, thereby shortening the system failure time, and then restoring system functions and improving user experience.

[0028] Figure 1 This is one of the flow charts of the work order monitoring and early warning processing method provided by the present invention.

[0029] The following will be combined Figure 1 The process of the work order monitoring and early warning processing method provided by the present invention is explained.

[0030] In an exemplary embodiment of the present invention, Figure 1 It can be seen that the work order monitoring and early warning processing method may include steps 110 to 130, and each step will be introduced below.

[0031] In step 110, the work orders to be processed within the current preset time period are obtained, and the work orders to be processed are classified to obtain the classified work orders to be processed.

[0032] In one embodiment, the pending work orders within the current preset time period can be obtained. The pending work orders can be the pending work orders of the Shared Service Center (SSC). The current preset time period can be considered as the pending work orders within the current half hour. It should be noted that the preset time period can be adjusted according to actual conditions, and in this embodiment, no specific limitation is made.

[0033] In another embodiment, the work orders to be processed may be classified to obtain the classified work orders to be processed. In the process of classifying the work orders to be processed, they may be classified and sorted according to the problem classification, so as to obtain the classified work orders to be processed.

[0034] In step 120, when it is determined that the fluctuation degree of the work order quantity of the classified work orders to be processed is abnormal, the classified work orders to be processed are subjected to intent recognition to obtain the problems fed back in the classified work orders to be processed.

[0035] In step 130, based on the problems reported in the classified work orders to be processed, work order warning processing is performed, wherein the work order warning processing is used to prompt the user to solve the problems reported in the classified work orders to be processed.

[0036] In another embodiment, it can be determined whether there is an abnormality in the degree of fluctuation of the number of work orders to be processed after classification. When it is determined that there is an abnormality in the degree of fluctuation of the number of work orders to be processed after classification, it means that the work order to be processed after classification may have problems to be processed. Furthermore, in order to further clarify whether the work order to be processed after classification has feedback that there are problems to be processed, the work order to be processed after classification can be identified with intent to obtain the problems fed back in the work order to be processed after classification. Among them, the abnormality in the degree of fluctuation of the number of work orders of the work orders to be processed after classification is used to characterize that the difference between the number of work orders of the work orders to be processed after classification within the current preset time period and the number of work orders of the work orders to be processed after classification within the previous preset time period and\or the previous preset time period exceeds the preset difference. It can be understood that the preset difference can be adjusted according to the actual situation, and the preset difference is not specifically limited in this embodiment.

[0037] It should be noted that the number of work orders to be processed after classification within the preset time period of the prior month-on-month comparison can be considered as the second month-on-month number of work orders described below; the number of work orders to be processed after classification within the preset time period of the prior year-on-year comparison can be considered as the second year-on-year number of work orders described below.

[0038] In another embodiment, work order warning processing can also be performed based on the problems reported in the classified pending work orders, so as to facilitate R&D personnel to solve the problems accordingly based on the problems reported in the pending work orders. In one example, the problems reported in the classified pending work orders can be system problems existing in the operating system used by the user. During the application process, R&D personnel can solve the system problems existing in the operating system in a timely manner, thereby improving the user experience.

[0039] The present invention provides a work order monitoring and early warning processing method, which obtains the pending work orders within the current preset time length and classifies the pending work orders to obtain the classified pending work orders; when it is determined that the fluctuation degree of the work order quantity of the classified pending work orders is abnormal, the classified pending work orders are intentionally identified to obtain the problems reported in the classified pending work orders; based on the problems reported in the classified pending work orders, the work order early warning processing is performed, wherein the work order early warning processing is used to prompt the user to solve the problems reported in the classified pending work orders. It is achieved that the pending work orders with feedback problems can be monitored in a timely and accurate manner, so that the user can be reminded to solve the problems reported in the pending work orders in a timely manner.

[0040] Figure 2 It is a flow chart of determining that the fluctuation degree of the number of work orders to be processed after classification is abnormal, provided by the present invention.

[0041] The following will be combined Figure 2 The process of determining that the fluctuation degree of the work order quantity of the work orders to be processed after classification provided by the present invention is abnormal is described.

[0042] In an exemplary embodiment of the present invention, Figure 2 It can be known that determining that the fluctuation degree of the work order quantity of the work orders to be processed after classification is abnormal may include steps 210 to 230, and each step will be introduced below.

[0043] In step 210, the first number of classified work orders to be processed within the current preset time period is obtained.

[0044] In step 220, the number of second work orders of the classified work orders to be processed within a previously preset time period is obtained.

[0045] In step 230, based on the first work order quantity and the second work order quantity, it is determined that there is an abnormality in the degree of fluctuation of the work order quantity of the classified work orders to be processed.

[0046] In one embodiment, the number of first work orders within the current preset time period and the number of second work orders within the previous preset time period can be obtained respectively. The current preset time period is the same as the previous preset time period. The previous preset time period is the time period corresponding to the current preset time period.

[0047] It should be noted that since the first work order quantity and the second work order quantity are dynamic representations of the quantity of the classified work orders to be processed, based on the first work order quantity and the second work order quantity, it can be effectively determined that there is an abnormality in the degree of fluctuation in the quantity of the classified work orders to be processed.

[0048] Figure 3It is a flow chart provided by the present invention for determining that the degree of fluctuation of the number of work orders to be processed after classification is abnormal based on the first work order number and the second work order number.

[0049] The following will be combined Figure 3 The process provided by the present invention of determining that there is an abnormality in the degree of fluctuation of the work order quantity of the classified work orders to be processed according to the first work order quantity and the second work order quantity is described.

[0050] In an exemplary embodiment of the present invention, the second work order quantity within the previous preset time period may include the second month-on-month work order quantity within the previous month-on-month preset time period and the second year-on-year work order quantity within the previous year-on-year preset time period.

[0051] The previous comparative preset duration may refer to a time period of the same preset duration in a previous time period corresponding to the current preset duration. For example, if the current preset duration refers to the current half-hour duration, the corresponding previous comparative preset duration may be a half-hour duration in the previous 60 minutes. The number of second work orders within the previous comparative preset duration may be referred to as the second comparative work order number.

[0052] The previous year-on-year preset duration may refer to a time period in the same time period in the previous time period in the past corresponding to the current preset duration. For example, if the current preset duration refers to the time period corresponding to the current 9:00 a.m. to 9:30 a.m., the corresponding previous year-on-year preset duration may be the time period corresponding to the previous week's 9:00 a.m. to 9:30 a.m. The number of second work orders within the previous year-on-year preset duration may be referred to as the second year-on-year work order number.

[0053] Combination Figure 3 It can be seen that, according to the first work order quantity and the second work order quantity, determining that there is an abnormality in the fluctuation degree of the work order quantity of the classified work orders to be processed may include steps 310 to 330, and each step will be introduced below.

[0054] In step 310, a month-on-month abnormality score is determined according to the first work order quantity and the second month-on-month work order quantity.

[0055] In step 320, a year-on-year anomaly score is determined according to the first work order quantity and the second year-on-year work order quantity.

[0056] In step 330, when the month-on-month abnormality score exceeds the score threshold, and / or the year-on-year abnormality score exceeds the score threshold, it is determined that there is an abnormality in the fluctuation degree of the work order quantity of the classified work orders to be processed.

[0057] In one embodiment, the month-on-month anomaly score can be determined based on the ratio of the first work order number to the second month-on-month work order number. And the year-on-year anomaly score can be determined based on the ratio of the first work order number to the second year-on-year work order number. Further, when it is determined that the month-on-month anomaly score exceeds the score threshold, and / or the year-on-year anomaly score exceeds the score threshold, it can be determined that there is an abnormality in the fluctuation degree of the work order quantity of the classified pending work orders. In this scenario, the classified pending work orders need to be further processed to determine whether the classified pending work orders feedback system operation problems that need to be resolved.

[0058] In another exemplary embodiment of the present invention, continuing with the above Figure 3 Taking the above embodiment as an example, determining the month-on-month abnormality score according to the first work order quantity and the second month-on-month work order quantity can be implemented in the following manner: Calling a pre-maintained first mapping table, wherein the first mapping table includes a correspondence between different work order ratios and different month-on-month anomaly scores, and the work order ratio is used to represent a ratio between the first work order quantity and the second month-on-month work order quantity; Determine a work order ratio according to a ratio of the first work order quantity to the second work order quantity; The month-on-month anomaly score is determined according to the work order ratio and the corresponding relationship between different work order ratios and different month-on-month anomaly scores in the first mapping table.

[0059] In one embodiment, a pre-maintained first mapping table may be called. The first mapping table may include a correspondence between different work order ratios and different month-on-month anomaly scores. During the application process, the work order ratio may be determined based on the ratio of the second month-on-month work order number to the first work order number. Further, based on the work order ratio and the correspondence between different work order ratios and different month-on-month anomaly scores in the first mapping table, the month-on-month anomaly score may be obtained. In one example, when the first work order number is greater than 50% of the second month-on-month work order number, that is, the work order ratio is 150%, it can be determined that the month-on-month anomaly score is 8. When the first work order number is greater than 50% of the second month-on-month work order number and less than 100%, that is, the work order ratio is greater than 150% and less than 200%, it can be determined that the month-on-month anomaly score is 16.

[0060] In another exemplary embodiment of the present invention, continuing with the above Figure 3 Taking the above embodiment as an example, the year-on-year abnormality score is determined according to the first work order quantity and the second year-on-year work order quantity, which can be implemented in the following manner: Calling a pre-maintained second mapping table, wherein the second mapping table includes a correspondence between different work order ratios and different year-on-year anomaly scores, and the work order ratio is used to represent a ratio between the first work order quantity and the second year-on-year work order quantity; Determine a work order ratio according to a ratio of the first work order quantity to the second work order quantity; The year-on-year anomaly score is determined according to the work order ratio and the corresponding relationship between different work order ratios and different year-on-year anomaly scores in the second mapping table.

[0061] In one embodiment, a pre-maintained second mapping table may be called. The second mapping table may include a correspondence between different work order ratios and different year-on-year anomaly scores. During the application process, the work order ratio may be determined based on the ratio of the second year-on-year work order number in the first work order number. Further, based on the work order ratio and the correspondence between different work order ratios and different year-on-year anomaly scores in the second mapping table, the year-on-year anomaly score may be obtained. In one example, when the first work order number is greater than 50% of the second year-on-year work order number, that is, the work order ratio is 150%, it can be determined that the year-on-year anomaly score is 8. When the first work order number is greater than 50% of the second year-on-year work order number and less than 100%, that is, the work order ratio is greater than 150% and less than 200%, it can be determined that the year-on-year anomaly score is 16.

[0062] Figure 4 This is the second flow chart of the work order monitoring and early warning processing method provided by the present invention.

[0063] In an exemplary embodiment of the present invention, Figure 4 It can be seen that the work order monitoring and early warning processing method may include steps 410 to 440, wherein step 410 is the same as or similar to step 110. For its specific implementation and beneficial effects, please refer to the previous description, which will not be repeated in this embodiment. Steps 420 to 440 will be introduced separately below.

[0064] In step 420, a whitelist work order list is obtained, wherein the whitelist work order list includes a plurality of whitelist classification work orders.

[0065] In step 430, based on the whitelist work order list, it is determined whether the classified to-be-processed work order matches the whitelist classified work order.

[0066] In step 440, when the classified work order to be processed matches the classified work order in the whitelist, the processing of the classified work order to be processed is stopped.

[0067] In one embodiment, a whitelist work order list may also be obtained, wherein the whitelist work order list may include multiple whitelist classified work orders. Whitelist classified work orders may be considered as work orders that do not require problem solving.

[0068] Furthermore, based on the whitelist work order list, it can be determined whether the classified pending work order matches the whitelist classified work order. In the case where it is determined that the classified pending work order matches the whitelist classified work order, the processing of the classified pending work order can be stopped. In this embodiment, in the case where it is determined that the classified pending work order matches the whitelist classified work order, it means that the classified pending work order is a work order that does not require problem solving, so there is no need to perform subsequent processing operations, that is, stop processing the classified pending work order. Thereby, work efficiency can be effectively improved.

[0069] In another exemplary embodiment of the present invention, the above-mentioned embodiment is continued as an example for explanation, wherein the intent recognition is performed on the classified work orders to be processed, and the feedback problems in the classified work orders to be processed are obtained, which can be implemented in the following manner: Call the pre-trained intent recognition model, where the intent recognition model is used to identify the feedback issues in the classified work orders to be processed; The classified pending work orders are input into the intent recognition model to obtain the problems reported in the classified pending work orders output by the intent recognition model.

[0070] In one embodiment, the pre-trained intent recognition model can be called, and the classified pending work orders can be input into the intent recognition model, so that the problems reported in the classified pending work orders output by the intent recognition model can be accurately and quickly obtained. This lays the foundation for the work order early warning processing based on the problems reported in the classified pending work orders in the following text.

[0071] According to the foregoing description, the present invention provides a work order monitoring and early warning processing method, which obtains the pending work orders within the current preset time length and classifies the pending work orders to obtain the classified pending work orders; when it is determined that the fluctuation degree of the work order quantity of the classified pending work orders is abnormal, the classified pending work orders are intentionally identified to obtain the problems reported in the classified pending work orders; based on the problems reported in the classified pending work orders, work order early warning processing is performed, wherein the work order early warning processing is used to prompt the user to solve the problems reported in the classified pending work orders. It is achieved that the pending work orders with feedback problems can be monitored in a timely and accurate manner, so that the user can be reminded to solve the problems reported in the pending work orders in a timely manner.

[0072] The work order monitoring and early warning processing device provided by the present invention is described below. The work order monitoring and early warning processing device described below and the work order monitoring and early warning processing method described above can be referenced to each other.

[0073] Figure 5 It is a structural schematic diagram of the work order monitoring and early warning processing device provided by the present invention.

[0074] The following will be combined Figure 5 The structure of the work order monitoring and early warning processing device is explained.

[0075] In an exemplary embodiment of the present invention, Figure 5 It can be seen that the work order monitoring and early warning processing device can include an acquisition module 510, an identification module 520, and a processing module 530. Each module will be introduced below.

[0076] The acquisition module 510 may be configured to acquire the pending work orders within a current preset time period, and classify the pending work orders to obtain the classified pending work orders; The identification module 520 may be configured to, when it is determined that the fluctuation degree of the work order quantity of the classified work orders to be processed is abnormal, perform intent identification on the classified work orders to be processed to obtain the problems fed back in the classified work orders to be processed, wherein the fluctuation degree of the work order quantity of the classified work orders to be processed is abnormal for representing that the difference between the number of work orders of the classified work orders to be processed within the current preset time period and the number of work orders of the classified work orders to be processed within the previous preset period of month-on-month comparison and\or the previous preset period of year-on-year comparison exceeds the preset difference; The processing module 530 may be configured to perform work order warning processing based on the problems reported in the classified work orders to be processed, wherein the work order warning processing is used to prompt the user to solve the problems reported in the classified work orders to be processed.

[0077] In an exemplary embodiment of the present invention, the identification module 520 may determine whether the fluctuation degree of the number of work orders to be processed after the classification is abnormal by: Obtain the first number of work orders to be processed after the classification within the current preset time period, and Obtain the number of second work orders of the classified to-be-processed work orders within a previously preset time period; According to the first work order quantity and the second work order quantity, it is determined that there is an abnormality in the fluctuation degree of the work order quantity of the classified work orders to be processed.

[0078] In an exemplary embodiment of the present invention, the second work order quantity within the previously preset time period includes the second month-on-month work order quantity within the previously preset month-on-month time period, and the second year-on-year work order quantity within the previously preset year-on-year time period; The identification module 520 may determine that the fluctuation degree of the work order quantity of the classified to-be-processed work orders is abnormal according to the first work order quantity and the second work order quantity in the following manner: Determine a month-on-month abnormality score according to the first work order quantity and the second month-on-month work order quantity; Determine a year-on-year anomaly score according to the first work order quantity and the second year-on-year work order quantity; When the month-on-month abnormality score exceeds the score threshold, and / or the year-on-year abnormality score exceeds the score threshold, it is determined that there is an abnormality in the fluctuation degree of the work order quantity of the classified work orders to be processed.

[0079] In an exemplary embodiment of the present invention, the identification module 520 may determine the month-on-month abnormality score according to the first work order quantity and the second month-on-month work order quantity in the following manner: Calling a pre-maintained first mapping table, wherein the first mapping table includes a correspondence between different work order ratios and different month-on-month anomaly scores, and the work order ratio is used to represent a ratio between a first work order quantity and a second month-on-month work order quantity; Determining a work order ratio according to a ratio of the first work order quantity to the second comparative work order quantity; The month-on-month anomaly score is determined according to the work order ratio and the corresponding relationship between different work order ratios and different month-on-month anomaly scores in the first mapping table.

[0080] In an exemplary embodiment of the present invention, the identification module 520 may determine the year-on-year anomaly score according to the first work order quantity and the second year-on-year work order quantity in the following manner: Calling a pre-maintained second mapping table, wherein the second mapping table includes a correspondence between different work order ratios and different year-on-year anomaly scores, and the work order ratio is used to represent a ratio of a first work order quantity to a second year-on-year work order quantity; Determining a work order ratio according to a ratio of the first work order quantity to the second work order quantity; The year-on-year anomaly score is determined according to the work order ratio and the corresponding relationship between different work order ratios and different year-on-year anomaly scores in the second mapping table.

[0081] In an exemplary embodiment of the present invention, the identification module 520 may also be configured to: Obtain a whitelist work order list, wherein the whitelist work order list includes multiple whitelist classification work orders; Based on the whitelist work order list, determining whether the classified to-be-processed work order matches the whitelist classified work order; When the classified work order to be processed matches the classified work order in the whitelist, the processing of the classified work order to be processed is stopped.

[0082] In an exemplary embodiment of the present invention, the identification module 520 may implement the intent identification of the classified to-be-processed work orders and obtain the feedback problems in the classified to-be-processed work orders in the following manner: Calling a pre-trained intent recognition model, wherein the intent recognition model is used to identify problems reported in the work orders to be processed after the classification; The classified work orders to be processed are input into the intention recognition model to obtain the problems fed back in the classified work orders to be processed output by the intention recognition model.

[0083] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor (processor) 610 , a communication interface (Communications Interface) 620 , a memory (memory) 630 and a communication bus 640 , wherein the processor 610 , the communication interface 620 , and the memory 630 communicate with each other through the communication bus 640 . The processor 610 can call the logic instructions in the memory 630 to execute the work order monitoring and early warning processing method, which includes: obtaining the pending work orders within the current preset time length, and classifying the pending work orders to obtain the classified pending work orders; when it is determined that the fluctuation degree of the work order quantity of the classified work orders is abnormal, the intent of the classified work orders to be processed is identified to obtain the problems fed back in the classified work orders to be processed, wherein the abnormal fluctuation degree of the work order quantity of the classified work orders to be processed is used to characterize that the difference between the number of work orders of the classified work orders to be processed within the current preset time length and the number of work orders of the classified work orders to be processed within the previous month-on-month preset time length and\or the previous year-on-year preset time length exceeds the preset difference; based on the problems fed back in the classified work orders to be processed, work order early warning processing is performed, wherein the work order early warning processing is used to prompt the user to solve the problems fed back in the classified work orders to be processed.

[0084] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0085] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the work order monitoring and early warning processing method provided by the above methods, the method including: obtaining the pending work orders within the current preset time period, and classifying the pending work orders to obtain the classified pending work orders; when it is determined that the fluctuation degree of the work order quantity of the classified pending work orders is abnormal, the classified pending work orders are intentionally identified to obtain the problems reported in the classified pending work orders, wherein the fluctuation degree of the work order quantity of the classified pending work orders is abnormal, which is used to characterize that the difference between the number of work orders of the classified pending work orders within the current preset time period and the number of work orders of the classified pending work orders within the previous month-on-month preset time period and\or the previous year-on-year preset time period exceeds the preset difference; based on the problems reported in the classified pending work orders, work order early warning processing is performed, wherein the work order early warning processing is used to prompt the user to solve the problems reported in the classified pending work orders.

[0086] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the processor executes the work order monitoring and early warning processing method provided by the above-mentioned methods, the method comprising: obtaining the pending work orders within the current preset time period, and classifying the pending work orders to obtain the classified pending work orders; when it is determined that the fluctuation degree of the work order quantity of the classified pending work orders is abnormal, performing intention recognition on the classified pending work orders to obtain the problems fed back in the classified pending work orders, wherein the fluctuation degree of the work order quantity of the classified pending work orders is abnormal for characterizing that the difference between the number of work orders of the classified pending work orders within the current preset time period and the number of work orders of the classified pending work orders within the previous month-on-month preset time period and\or the previous year-on-year preset time period exceeds the preset difference; based on the problems fed back in the classified pending work orders, performing work order early warning processing, wherein the work order early warning processing is used to prompt the user to solve the problems fed back in the classified pending work orders.

[0087] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0088] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 and early warning processing method, characterized in that: The method comprises: Obtaining the pending work orders within the current preset time period, and classifying the pending work orders to obtain the classified pending work orders; In the case where it is determined that the fluctuation degree of the work order quantity of the classified work orders to be processed is abnormal, the classified work orders to be processed are subjected to intention recognition to obtain the problems fed back in the classified work orders to be processed, wherein the fluctuation degree of the work order quantity of the classified work orders to be processed is abnormal for representing that the difference between the number of work orders of the classified work orders to be processed within the current preset time period and the number of work orders of the classified work orders to be processed within the previous month-on-month preset time period and\or the previous year-on-year preset time period exceeds the preset difference; Based on the problems reported in the classified work orders to be processed, work order warning processing is performed, wherein the work order warning processing is used to prompt the user to solve the problems reported in the classified work orders to be processed.

2. The work order monitoring and early warning processing method according to claim 1 is characterized in that: It is determined that the fluctuation degree of the number of work orders to be processed after the classification is abnormal, which is achieved by the following method: Obtain the first number of work orders to be processed after the classification within the current preset time period, and Obtain the number of second work orders of the classified to-be-processed work orders within a previously preset time period; According to the first work order quantity and the second work order quantity, it is determined that there is an abnormality in the fluctuation degree of the work order quantity of the classified work orders to be processed.

3. The work order monitoring and early warning processing method according to claim 2 is characterized in that: The second work order quantity within the previously preset time period includes the second month-on-month work order quantity within the previously preset month-on-month time period, and the second year-on-year work order quantity within the previously preset year-on-year time period; Determining, based on the first work order quantity and the second work order quantity, that there is an abnormality in the fluctuation degree of the work order quantity of the classified work orders to be processed specifically includes: Determine a month-on-month abnormality score according to the first work order quantity and the second month-on-month work order quantity; Determine a year-on-year anomaly score according to the first work order quantity and the second year-on-year work order quantity; When the month-on-month abnormality score exceeds the score threshold, and / or the year-on-year abnormality score exceeds the score threshold, it is determined that there is an abnormality in the fluctuation degree of the work order quantity of the classified work orders to be processed.

4. The work order monitoring and early warning processing method according to claim 3 is characterized in that: The determining, according to the first work order quantity and the second work order quantity compared with the previous period, a month-on-month abnormality score specifically includes: Calling a pre-maintained first mapping table, wherein the first mapping table includes a correspondence between different work order ratios and different month-on-month anomaly scores, and the work order ratio is used to represent a ratio between a first work order quantity and a second month-on-month work order quantity; Determining a work order ratio according to a ratio of the first work order quantity to the second comparative work order quantity; The month-on-month anomaly score is determined according to the work order ratio and the corresponding relationship between different work order ratios and different month-on-month anomaly scores in the first mapping table.

5. The work order monitoring and early warning processing method according to claim 3 is characterized in that: The determining, according to the first work order quantity and the second year-on-year work order quantity, a year-on-year anomaly score specifically includes: Calling a pre-maintained second mapping table, wherein the second mapping table includes a correspondence between different work order ratios and different year-on-year anomaly scores, and the work order ratio is used to represent a ratio of a first work order quantity to a second year-on-year work order quantity; Determining a work order ratio according to a ratio of the first work order quantity to the second work order quantity; The year-on-year anomaly score is determined according to the work order ratio and the corresponding relationship between different work order ratios and different year-on-year anomaly scores in the second mapping table.

6. The work order monitoring and early warning processing method according to any one of claims 1 to 5, characterized in that: After obtaining the classified work orders to be processed, the method further includes: Obtain a whitelist work order list, wherein the whitelist work order list includes multiple whitelist classification work orders; Based on the whitelist work order list, determining whether the classified to-be-processed work order matches the whitelist classified work order; When the classified work order to be processed matches the classified work order in the whitelist, the processing of the classified work order to be processed is stopped.

7. The work order monitoring and early warning processing method according to any one of claims 1 to 5, characterized in that: The performing of intent recognition on the classified work orders to be processed to obtain the problems reported in the classified work orders to be processed specifically includes: Calling a pre-trained intent recognition model, wherein the intent recognition model is used to identify problems reported in the work orders to be processed after the classification; The classified work orders to be processed are input into the intention recognition model to obtain the problems fed back in the classified work orders to be processed output by the intention recognition model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the work order monitoring and early warning processing method as described in any one of claims 1 to 7 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the work order monitoring and early warning processing method as described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by the processor, the work order monitoring and early warning processing method as described in any one of claims 1 to 7 is implemented.

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