A smart work order scheduling system, method, device and medium

Abstract

This invention relates to the field of intelligent work order scheduling platform design technology, and discloses an intelligent work order scheduling system, method, device, and medium. The method includes: the system integrates multiple functional modules to achieve efficient and intelligent scheduling of equipment failure work orders. First, the work order access module receives equipment failure information from different channels to ensure comprehensive and timely information. Next, the profile building module uses historical service data of maintenance personnel to build a multi-dimensional capability profile, providing a data foundation for intelligent matching. Then, the intelligent allocation module accurately matches the profile data and work order parameters, associating the most suitable maintenance personnel with the work order. The order grabbing interaction module generates a list of work orders to be grabbed and an order grabbing interface, enhancing scheduling flexibility and improving scheduling efficiency. The execution tracking module monitors the work order processing progress in real time, ensuring transparent and controllable scheduling. Finally, the knowledge base question-and-answer module provides professional knowledge support to maintenance personnel, helping them quickly resolve work order issues.

Description

Technical Field

[0001] This invention relates to the field of intelligent work order scheduling platform design technology, and in particular to an intelligent work order scheduling system, method, device and medium. Background Technology

[0002] In recent years, with the rapid development and widespread application of information technology, the demand for equipment fault handling has been increasing, placing higher demands on the intelligence and efficiency of work order scheduling systems. Traditional work order scheduling methods mainly rely on manual operation, resulting in problems such as low efficiency, untimely response, and unreasonable resource allocation. Specifically, this manifests in the following aspects:

[0003] Excessive human intervention: In traditional systems, from receiving and allocating work orders to tracking them, most of the work orders require human intervention for judgment and operation. This not only increases labor costs, but also easily leads to delays or errors due to human factors.

[0004] Insufficient information integration: Work order information from different sources has not been effectively integrated. Maintenance personnel often need to manually query multiple systems or databases to obtain the required information during the process, which reduces work efficiency.

[0005] Lack of accurate matching mechanism: The matching between work orders and maintenance personnel is usually based on simple geographical location or experience judgment, without fully considering the technical expertise of maintenance personnel, current workload and other multi-dimensional factors, resulting in unreasonable task allocation.

[0006] Service quality is difficult to guarantee: Due to the lack of effective monitoring methods and service evaluation systems, it is difficult to grasp the working status and service quality of maintenance personnel in real time, which in turn affects customer satisfaction. Summary of the Invention

[0007] In view of the aforementioned existing problems, the present invention is proposed.

[0008] Therefore, the present invention provides an intelligent work order scheduling system, method, device and medium that can solve the problems of low efficiency, untimely response and unreasonable resource allocation in traditional work order scheduling systems.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0010] In a first aspect, the present invention provides an intelligent work order scheduling system, comprising:

[0011] It includes a work order access module, a profile building module, an intelligent allocation module, an order grabbing interaction module, an execution tracking module, and a knowledge base question and answer module;

[0012] The work order access module is used to obtain equipment failure work orders from several different channels;

[0013] The profile building module is used to build a multi-dimensional capability profile based on the maintenance personnel's historical service data.

[0014] The intelligent allocation module is used to perform intelligent matching based on the profile data and work order parameters transmitted by the work order access module and the profile building module, and send the matching result to the order grabbing interaction module;

[0015] The order-grabbing interaction module is used to generate a list of work orders to be grabbed based on the matching results, and to generate an order-grabbing interface for interaction with the user through the work order list;

[0016] The execution tracking module is used to monitor the progress of user-side work order processing and update the status in real time.

[0017] The knowledge base question-and-answer module is used to match answers to questions related to professional knowledge in the field of information technology innovation during the fault work order processing.

[0018] As a preferred embodiment of the intelligent work order scheduling system of the present invention, the intelligent allocation module includes an urgency level judgment unit, a first parsing unit, a second parsing unit, a geographic filtering unit, and a strategy allocation unit.

[0019] The urgency level judgment unit is connected to the work order access module and performs feature extraction on the equipment failure work orders transmitted by the work order access module to obtain the urgency level of the failure work orders.

[0020] The first parsing unit is connected to the work order access module and performs a first quantization operation on the equipment fault work orders transmitted by the work order access module to obtain relevant parameters of the fault work orders;

[0021] The second parsing unit is connected to the portrait construction module and performs a second quantification operation on the multi-dimensional capability portrait to obtain the capability-related parameters of the maintenance personnel.

[0022] The geographic filtering unit is connected to the first parsing unit and the second parsing unit. Based on the output of the first parsing unit and the second parsing unit, it filters the geographic locations of qualified maintenance personnel and transmits the filtering results to the order-grabbing interaction module.

[0023] The urgency judgment unit, the first analysis unit, the second analysis unit, and the geographic filtering unit are all connected to the strategy allocation unit. The strategy allocation unit performs strategy allocation judgment based on the inputs of the urgency judgment unit, the first analysis unit, the second analysis unit, and the geographic filtering unit, and transmits the strategy allocation judgment result to the order grabbing interaction module.

[0024] As a preferred embodiment of the intelligent work order scheduling system of the present invention, the intelligent allocation module further includes:

[0025] The strategy allocation unit obtains the equipment model, fault level, and geographical location information from the work order parameters based on the data transmitted by the first parsing unit, and calculates the estimated arrival time of the maintenance personnel from their current location to the fault location.

[0026] The strategy allocation unit combines the multi-dimensional capability profile of the second analysis unit to perform multi-dimensional sorting, and the multi-dimensional factors include skill matching degree, service radius and current load.

[0027] The strategy allocation unit includes an emergency work order priority allocation strategy and a normal work order round-robin allocation strategy. The selection of the strategy is determined by the urgency judgment unit.

[0028] If the urgency determination unit determines that a work order is an urgent work order, then the urgent work order priority allocation strategy is used.

[0029] If the urgency determination unit determines that a work order is a normal work order, then the normal work order round-robin allocation strategy is used.

[0030] As a preferred embodiment of the intelligent work order scheduling system of the present invention, the intelligent allocation module further includes:

[0031] When the emergency work order priority allocation strategy is selected, the strategy allocation unit confirms that the work order is an emergency work order through the urgency judgment unit, and then performs a geographic filtering operation on all qualified maintenance personnel; it filters out maintenance personnel who can reach the fault site, and ranks the filtered maintenance personnel based on three key factors: skill matching degree, estimated arrival time, and current load; the strategy allocation unit selects the maintenance personnel with the highest score as the first choice, and immediately pushes the work order information to that maintenance personnel, while generating a list of work orders to be grabbed in the order grabbing interaction module;

[0032] When the ordinary work order polling allocation strategy is selected, after receiving a work order that is determined to be of the ordinary level by the urgency level judgment unit, the strategy allocation unit uses the first parsing unit and the second parsing unit to obtain the work order parameters and the capability profile of the maintenance personnel; the strategy allocation unit starts the polling mechanism and sends notifications to the eligible maintenance personnel according to the order of the time when the maintenance personnel last completed the task.

[0033] Each maintenance worker has a fixed time window to review and decide whether to accept a work order;

[0034] If no one accepts the offer within the specified fixed time window, it will automatically be transferred to the next candidate;

[0035] Once a maintenance worker accepts the work order, the assignment process is complete, and the status in the execution tracking module is updated.

[0036] As a preferred embodiment of the intelligent work order scheduling system of the present invention, the profile construction module includes a data acquisition unit, a text analysis unit, a scoring system construction unit, and a dynamic update unit.

[0037] The data acquisition unit is used to collect relevant information about maintenance personnel from multiple channels, including skill certificates, historical work order completion records, and service evaluation data.

[0038] The text analysis unit is connected to the data acquisition unit and performs natural language processing on the work order text to extract fault type features. By parsing the descriptions in historical work orders, it identifies and classifies different fault types.

[0039] The scoring system construction unit establishes a weighted scoring system, namely a multi-dimensional capability profile, based on the data provided by the data collection unit and the text analysis unit, which includes technical expertise, response speed, and service quality.

[0040] The dynamic update unit is equipped with a function of regular automatic updates to ensure that all data and scores can reflect the latest status in a timely manner. The dynamic update unit is connected to the scoring system construction unit to adjust the capability profile of maintenance personnel in real time.

[0041] As a preferred embodiment of the intelligent work order scheduling system of the present invention, the knowledge base question and answer module includes a question parsing and classification unit, a knowledge retrieval unit, an answer generation and optimization unit, a feedback and learning unit, and a real-time update and maintenance unit.

[0042] The problem parsing and classification unit is used to receive and parse query requests from maintenance personnel, extract the core keywords and related information of the problem, analyze the extracted information through natural language processing technology, and classify the problem into different types such as hardware failure, software problem, and network configuration according to its characteristics.

[0043] The knowledge retrieval unit searches a pre-built knowledge base based on the results of problem analysis and classification. It uses search algorithms and natural language processing technology to improve matching accuracy and ensure that the found answers are as relevant as possible to actual needs. For each query, the system retrieves the most relevant solutions or reference cases from the knowledge base.

[0044] The answer generation and optimization unit organizes the retrieved relevant information into an easy-to-understand answer format and performs personalized optimization based on the specific fault description. For complex problems, it integrates information from multiple sources to form a comprehensive solution.

[0045] The feedback and learning unit collects feedback from maintenance personnel on the provided answers, including satisfaction ratings and improvement suggestions. By analyzing this feedback data, the content and structure of the knowledge base are optimized.

[0046] The real-time update and maintenance unit is equipped with a function of regular automatic updates to ensure that all information can reflect the latest status in a timely manner and to remove outdated or no longer applicable information.

[0047] As a preferred embodiment of the intelligent work order scheduling system described in this invention, the order-grabbing interaction module includes a geographic location filtering and service capability matching unit, a dynamic sorting list generation unit, a work order information push unit, an order-grabbing countdown mechanism unit, and an automatic allocation compensation rule unit.

[0048] The geographic location filtering and service capability matching unit is used to further screen qualified maintenance personnel. Based on the filtering results of the geographic filtering unit in the intelligent allocation module, combined with the service quality rating provided by the scoring system construction unit, the service capability of each maintenance personnel is evaluated.

[0049] The dynamic sorting list generation unit generates a dynamic sorting list based on distance and service quality rating. First, it calculates the actual distance between the maintenance personnel and the fault location, and then calculates a comprehensive score for each maintenance personnel based on the service quality rating. The list is then sorted from high to low according to the comprehensive score, ensuring that maintenance personnel who are closer and have higher service quality are displayed first.

[0050] The work order information push unit pushes work order information to the terminals of idle maintenance personnel in the designated area in real time, monitors the status of maintenance personnel, and pushes detailed information including fault type, estimated arrival time suggestion and customer evaluation to the idle maintenance personnel located in the designated area.

[0051] The order-grabbing countdown mechanism unit is used to set a fixed order-grabbing countdown time window. During the order-grabbing countdown time window, qualified maintenance personnel can view the work order details and decide whether to accept the task.

[0052] If a repairman successfully accepts the order within the specified time, the countdown will stop immediately and a cancellation notification will be sent to other repairmen.

[0053] If no one responds, the automatic allocation of compensation rules is triggered through the automatic allocation compensation rule unit;

[0054] The automatic allocation compensation rule unit is used to automatically select a maintenance worker for task allocation according to the pre-set compensation rules when no one bids for the task. The second-best option is the maintenance worker ranked second or third in the comprehensive score.

[0055] Secondly, the present invention provides an intelligent work order scheduling method, comprising:

[0056] The work order access module and the profile building module obtain profile data and work order parameters within the target area.

[0057] The intelligent allocation module performs intelligent matching operations on the profile data and work order parameters within the target area;

[0058] The matching results are displayed to the repair personnel through the order-grabbing interaction module, and the order-grabbing process is initiated.

[0059] During the order-grabbing process, the execution tracking module monitors the order-grabbing progress and the status of the maintenance personnel's work order processing in real time;

[0060] If maintenance personnel encounter professional knowledge issues while processing work orders, they can quickly obtain answers through the knowledge base Q&A module.

[0061] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0062] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0063] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes an intelligent work order scheduling system. This system integrates multiple functional modules to achieve efficient and intelligent scheduling of equipment failure work orders. First, the work order access module can widely receive equipment failure information from different channels, ensuring the comprehensiveness and timeliness of the information. Next, the profile building module uses the historical service data of maintenance personnel to construct a multi-dimensional capability profile, providing a data foundation for subsequent intelligent matching. The intelligent allocation module performs precise matching based on the profile data and work order parameters, associating the most suitable maintenance personnel with the work order. The order-grabbing interaction module further enhances the flexibility of scheduling by generating a list of work orders to be grabbed and an order-grabbing interface, enabling maintenance personnel to actively participate in order grabbing, thus improving scheduling efficiency. The execution tracking module monitors the work order processing progress in real time, ensuring the transparency and controllability of scheduling. Finally, the knowledge base question-and-answer module provides maintenance personnel with professional knowledge support, helping them quickly solve problems during work order processing. Attached Figure Description

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a schematic diagram of the structure of an intelligent work order scheduling system provided in one embodiment of the present invention.

[0066] Figure 2 This is a schematic diagram of the profile construction module structure of an intelligent work order scheduling system provided in one embodiment of the present invention.

[0067] Figure 3 This is a schematic diagram of the intelligent allocation module structure of an intelligent work order scheduling system provided in one embodiment of the present invention.

[0068] Figure 4 This is a schematic diagram of the order-grabbing interaction module structure of an intelligent work order scheduling system provided in one embodiment of the present invention.

[0069] Figure 5 This is a schematic diagram of the knowledge base question-and-answer module structure of an intelligent work order scheduling system provided in one embodiment of the present invention.

[0070] Figure 6 This is an internal structure diagram of an electronic device for an intelligent work order scheduling system provided in one embodiment of the present invention. Detailed Implementation

[0071] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0072] Example 1, referring to Figures 1-5 This is the first embodiment of the present invention, which provides an intelligent work order scheduling system, including:

[0073] Existing technologies have several drawbacks. For instance, traditional work order scheduling systems often rely on manual allocation, which is not only inefficient but also prone to misallocation due to human factors, impacting repair efficiency and service quality. Furthermore, traditional systems lack intelligent matching mechanisms, failing to accurately match repair personnel's capabilities with the specific needs of the work order. This leads to uneven resource allocation, with some repair personnel overloaded while others remain idle. Moreover, traditional systems are inadequate in information exchange; repair personnel struggle to obtain work order information promptly and cannot proactively participate in order bidding, further reducing scheduling efficiency.

[0074] This invention provides a method that can effectively solve the problems mentioned above. The following will describe in detail how to implement the intelligent work order scheduling system with reference to several embodiments.

[0075] Figure 1 A schematic diagram of an intelligent work order scheduling system is shown, including: a work order access module 100, a profile building module 200, an intelligent allocation module 300, a bid-grabbing interaction module 400, an execution tracking module 500, and a knowledge base question-and-answer module 600;

[0076] The work order access module 100 is used to obtain several equipment failure work orders from different channels;

[0077] The profile building module 200 is used to build a multi-dimensional capability profile based on the historical service data of maintenance personnel.

[0078] The intelligent allocation module 300 is used to perform intelligent matching based on the profile data and work order parameters transmitted by the work order access module and the profile building module, and send the matching result to the order grabbing interaction module;

[0079] The order-grabbing interaction module 400 is used to generate a list of work orders to be grabbed based on the matching results, and to generate an order-grabbing interface for interaction with the user terminal through the work order list;

[0080] The execution tracking module 500 is used to monitor the progress of user-end work order processing and update the status in real time.

[0081] The knowledge base question-and-answer module 600 is used to match answers to professional knowledge questions in the field of information technology innovation during the fault work order processing.

[0082] It should be noted that different channels refer to various sources from which device malfunction reports can be received, such as email, telephone hotlines, online customer service platforms, mobile applications, etc.

[0083] It should be noted that the multi-dimensional capability profile is a comprehensive evaluation system built upon the historical service data of maintenance personnel, encompassing aspects including but not limited to technical expertise, response speed, and service quality. It uses a quantitative scoring method to comprehensively evaluate the capabilities of maintenance personnel.

[0084] It should be noted that intelligent matching refers to using algorithms to analyze work order parameters (such as fault type, urgency, geographical location, etc.) from the work order access module and the maintenance personnel capability profile from the profile building module, thereby achieving the best match between the work order and the maintenance personnel most suitable to handle the work order.

[0085] It should be noted that the list of pending work orders is a list of work orders generated based on the results of the intelligent allocation module. It lists the available work orders for maintenance personnel to view and decide whether to accept.

[0086] It should be noted that the order-grabbing interface is a user interface that allows maintenance personnel to browse a list of work orders to be grabbed and provides options for maintenance personnel to choose to accept or reject specific work orders.

[0087] It should be noted that real-time monitoring means that the system can continuously track the progress of work orders and update status information in a timely manner so that managers can understand the latest work progress.

[0088] It should be noted that "IT innovation" mentioned in the answers to questions about professional knowledge in the IT innovation field refers to the IT application innovation industry. This module is mainly designed to solve technical problems encountered in processing work orders, especially those involving professional issues in the field of IT application innovation. It obtains solutions or suggestions by querying a pre-built knowledge base.

[0089] In some specific real-time scenarios, different modules can be designed by combining different units. However, the combined result should all be used to achieve the functions described above. Other relevant technical personnel can still make appropriate adjustments and optimizations to the specific implementation of each module without deviating from the core ideas of this invention. For example, in the profile building module, in addition to considering factors such as technical expertise, response speed, and service quality, other evaluation dimensions, such as maintenance experience and user reviews, can be introduced to more comprehensively reflect the ability level of maintenance personnel. In the intelligent allocation module, more advanced algorithm models, such as deep learning and machine learning, can be used to improve the accuracy and efficiency of matching. Furthermore, the design of the order-grabbing interaction module can be made more user-friendly, providing rich interactive functions and a good user experience to further enhance the participation and satisfaction of maintenance personnel.

[0090] In this embodiment of the invention, the intelligent allocation module 300 includes an urgency level judgment unit 301, a first parsing unit 302, a second parsing unit 303, a geographic filtering unit 304, and a strategy allocation unit 305, such as... Figure 3 As shown;

[0091] The urgency level judgment unit 301 is connected to the work order access module 100 and performs feature extraction on the equipment failure work orders transmitted by the work order access module 100 to obtain the urgency level of the failure work orders.

[0092] The first parsing unit 302 is connected to the work order access module 100 and performs a first quantization operation on the equipment fault work orders transmitted by the work order access module 100 to obtain relevant parameters of the fault work orders;

[0093] The second parsing unit 303 is connected to the portrait construction module 200 and performs a second quantification operation on the multi-dimensional capability portrait to obtain the capability-related parameters of the maintenance personnel.

[0094] The geographic filtering unit 304 is connected to the first parsing unit 302 and the second parsing unit 303. It filters the geographic locations of qualified maintenance personnel based on the outputs of the first parsing unit 302 and the second parsing unit 303, and transmits the filtering results to the order-grabbing interaction module 400.

[0095] The urgency level judgment unit 301, the first parsing unit 302, the second parsing unit 303, and the geographic filtering unit 304 are all connected to the strategy allocation unit 305. The strategy allocation unit 305 performs strategy allocation judgment based on the inputs of the urgency level judgment unit 301, the first parsing unit 302, the second parsing unit 303, and the geographic filtering unit 304, and transmits the strategy allocation judgment result to the order grabbing interaction module 400.

[0096] In some specific implementations, feature extraction of equipment fault work orders transmitted by the work order access module 100 can be achieved through the following specific steps:

[0097] Step 1: Collect equipment malfunction work orders from various channels (such as phone, email, online forms, etc.). These work orders may contain information in various forms, including text descriptions, pictures, and videos.

[0098] Step 2: Preprocessing, removing irrelevant characters from the work order, such as punctuation marks and special characters, and standardizing the text format.

[0099] Transforming unstructured data (such as images or audio recordings) into an analyzable format. For example, using optical character recognition (OCR) technology to extract text information from images, or using speech recognition technology to convert speech into text.

[0100] Step 3: Use Natural Language Processing (NLP) technology to identify and extract key information from the work order, such as equipment model, fault type, and location information.

[0101] Further identify and categorize the specific entities mentioned in the work order, such as time, location, person (if it is about a service request), and specific technical terms or fault codes.

[0102] Step 4: Based on the extracted key information and entities, construct features that will aid in subsequent analysis. For example, infer possible causes of the failure based on the fault description, or calculate the estimated time required for maintenance personnel to arrive on-site.

[0103] Transform qualitative descriptions into quantitative data. For example, classify the severity of a fault into several levels, or assess the average response time in a certain area based on historical data.

[0104] For each work order, its urgency is assessed based on its characteristics (such as fault type, location, and scope of impact). This step usually requires automated decision-making by combining pre-set standards or algorithm models. This invention designs a model for assessing urgency based on fault type, location, and scope of impact using existing neural networks. Since it uses existing technology, this application will not elaborate on it.

[0105] Step 5: Integrate all extracted and generated feature data and store it in the database for use by the subsequent intelligent allocation module.

[0106] In some specific implementations, the first quantification operation mainly involves feature extraction and quantification of equipment failure work orders obtained from the work order access module 100 to facilitate the subsequent intelligent matching process. Its purpose is to transform unstructured work order information into a numerical form that can be understood and processed by a computer, thereby providing a basis for strategy allocation.

[0107] For example, the specific steps of the first quantization operation can be as follows:

[0108] Remove irrelevant characters from the text and standardize formatting.

[0109] Furthermore, natural language processing (NLP) technology is used to identify and extract key information, such as device model, fault level, and geographical location.

[0110] Furthermore, new feature variables are constructed based on the extracted keywords. For example, specific maintenance guidelines or solutions to common problems can be determined based on "equipment model"; urgency scores can be set based on "fault level".

[0111] Furthermore, qualitative descriptions are converted into quantitative data. For example, fault levels such as "high," "medium," and "low" are converted into specific numerical values ​​(e.g., 3, 2, 1), or the estimated time for maintenance personnel to arrive on site is calculated.

[0112] Furthermore, in order to ensure comparability between different features, it is usually necessary to standardize or normalize numerical features.

[0113] In some specific implementations, the second quantification operation targets the historical service data of maintenance personnel, constructing a multi-dimensional capability profile to assess each maintenance worker's capabilities across multiple dimensions, including professional skills, response speed, and service quality. This step provides a crucial basis for intelligent allocation, ensuring that tasks are assigned to the most suitable maintenance personnel.

[0114] For example, the specific steps of the second quantization operation can be as follows:

[0115] We collect relevant information about maintenance personnel from multiple channels, including but not limited to skills certificates, historical work order completion records, and service evaluations.

[0116] Furthermore, NLP technology is used to parse the work order text, extract fault type features, and classify different fault types. This helps to understand which types of faults maintenance personnel are good at handling.

[0117] Furthermore, based on the collected data, a weighted scoring system is established. This system may include multiple dimensions such as technical expertise, response speed, and service quality, with each dimension assigned a corresponding weight.

[0118] For example, a maintenance worker who has successfully solved a large number of complex technical problems in the past year will score highly in the "technical expertise" dimension; and who can always respond to work order requests in a short time will score highly in the "response speed" dimension.

[0119] Furthermore, a function for regular automatic updates is set up to ensure that all data and ratings reflect the latest status in a timely manner. This means that as new work orders are completed and customer feedback increases, the skill profiles of maintenance personnel will also be adjusted accordingly.

[0120] Furthermore, based on the scoring results of each dimension, a comprehensive score is calculated as a standard to measure the overall capability of maintenance personnel. This comprehensive score will play an important role in the intelligent allocation process.

[0121] It's important to note that the benefits of these two quantification operations are that they make the entire system more intelligent, enabling it to automatically process large amounts of complex information and make optimal decisions. Precise task allocation avoids resource waste, ensuring that each work order is handled by the most suitable person, thus improving overall resource utilization efficiency. Rapid and accurate responses and high-quality service significantly enhance the user experience, strengthening customer trust and loyalty.

[0122] In some specific implementations, the geographic filtering unit 304 extracts the geographical location (e.g., latitude and longitude coordinates) of the fault from the equipment fault work order obtained by the first parsing unit 302. It also extracts the current location (also represented by latitude and longitude coordinates) of all maintenance personnel from the multi-dimensional capability profile obtained by the second parsing unit 303.

[0123] Furthermore, geographic calculation methods (such as the Haversine formula or the more precise Vincenty formula) are used to calculate the straight-line distance between each maintenance worker and the fault location.

[0124] Furthermore, for each maintenance worker, calculate the actual driving distance from their current location to the fault location (this can be done using map service APIs such as Google Maps API, Baidu Maps API, etc., taking into account actual road conditions and traffic conditions).

[0125] Furthermore, a maximum response time threshold can be set based on company policy or service quality standards (e.g., arriving on-site within 60 minutes).

[0126] Furthermore, the estimated arrival time for each maintenance worker to reach the fault location from their current location is estimated based on the current traffic conditions.

[0127] Furthermore, based on the above information, a reasonable service radius is determined, within which maintenance personnel can reach the fault location within a specified time.

[0128] Furthermore, the estimated arrival time of each maintenance worker is compared with the set maximum response time threshold.

[0129] Furthermore, only maintenance personnel whose expected arrival time is less than or equal to the maximum response time threshold are retained, forming a preliminary candidate list.

[0130] Furthermore, based on the initial candidate list, maintenance personnel are further ranked and scored comprehensively according to other key factors (such as skill matching, current workload, etc.).

[0131] Furthermore, skill matching can be assessed by comparing the types of faults that maintenance personnel are good at handling with the types of faults in the current work order.

[0132] Furthermore, the current load reflects the number of tasks that maintenance personnel are currently handling, and personnel with lower loads are prioritized to ensure service efficiency.

[0133] Furthermore, the list of maintenance personnel, which has been filtered geographically and sorted by comprehensive score, along with their relevant information (such as estimated arrival time, skill matching degree, etc.), is transmitted to the order-grabbing interaction module 400 to generate a list of work orders to be grabbed and push notifications to eligible maintenance personnel.

[0134] It should be noted that if there are not enough computing servers or the computing requirements are low, the geographic filtering unit 304 can directly use the distance from each maintenance worker's current location to the fault location for geographic filtering.

[0135] Through the above steps, the geographic filtering unit 304 can effectively narrow down the range of candidate maintenance personnel, ensuring that only those maintenance personnel who are geographically appropriate and can arrive at the fault site within the specified time participate in the subsequent allocation process, thereby improving the response speed and service quality of the entire system.

[0136] In this embodiment of the invention, the intelligent allocation module 300 further includes:

[0137] The strategy allocation unit 305 obtains the equipment model, fault level, and geographical location information from the work order parameters based on the data transmitted by the first parsing unit 302, and calculates the estimated arrival time of the maintenance personnel from their current location to the fault location.

[0138] The strategy allocation unit 305 combines the multi-dimensional capability profile of the second parsing unit 303 to perform multi-dimensional sorting, the multi-dimensional factors including skill matching degree, service radius and current load.

[0139] The strategy allocation unit 305 includes an emergency work order priority allocation strategy and a normal work order round-robin allocation strategy. The selection of the strategy is determined by the urgency judgment unit 301.

[0140] If the urgency determination unit 301 determines that a work order is an urgent work order, then the urgent work order priority allocation strategy is used.

[0141] If the urgency determination unit 301 determines that a work order is a normal work order, then the normal work order round-robin allocation strategy is used.

[0142] In this embodiment of the invention, the intelligent allocation module 300 further includes:

[0143] When the emergency work order priority allocation strategy is selected, the strategy allocation unit 305 confirms that the work order is an emergency work order through the urgency judgment unit 301, and then performs a geographic filtering unit 304 operation on all qualified maintenance personnel; it filters out maintenance personnel who can reach the fault site, and performs a comprehensive score ranking on the filtered maintenance personnel based on three key factors: skill matching degree, estimated arrival time, and current load; the strategy allocation unit 305 selects the maintenance personnel with the highest score as the first choice, and immediately pushes the work order information to the maintenance personnel, while generating a list of work orders to be grabbed in the order grabbing interaction module 400;

[0144] When the ordinary work order polling allocation strategy is selected, after receiving a work order that is determined to be of the ordinary level by the urgency level judgment unit 301, the strategy allocation unit 305 uses the first parsing unit 302 and the second parsing unit 303 to obtain the work order parameters and the capability profile of the maintenance personnel; the strategy allocation unit 305 starts the polling mechanism and sends notifications to the eligible maintenance personnel according to the order of the time when the maintenance personnel last completed the task.

[0145] Each maintenance worker has a fixed time window to review and decide whether to accept a work order;

[0146] If no one accepts the offer within the specified fixed time window, it will automatically be transferred to the next candidate;

[0147] Once a maintenance worker accepts the work order, the assignment process is completed, and the status in the execution tracking module 500 is updated.

[0148] For example, suppose an IT service company provides maintenance services for servers and network equipment. The company uses an intelligent work order scheduling system to efficiently manage and allocate work orders.

[0149] When a corporate customer reported a critical server startup failure via an online platform, affecting data access for multiple departments, the issue was marked as an urgent work order.

[0150] At this point, the emergency work order priority allocation strategy can be implemented. The steps for implementing the emergency work order priority allocation strategy are as follows:

[0151] The urgency assessment unit 301 determines the work order to be of an urgency level based on factors such as the fault type (server cannot start) and the scope of impact (data access to multiple departments is blocked).

[0152] Furthermore, the geolocation information indicates that the fault occurred in the computer room on the 5th floor of Building B, No. 123 XX Road, Pudong New Area, Shanghai.

[0153] Furthermore, the geographic filtering unit 304 filters out all qualified maintenance personnel who can arrive on-site within a specified time (e.g., within 60 minutes). Assume there are a total of 5 qualified maintenance personnel.

[0154] Further evaluation of these five maintenance personnel:

[0155] Skill matching: Reviewing the historical records of each maintenance worker, it was found that 3 of them were skilled in handling server hardware failures.

[0156] Estimated arrival time: Calculated using the map API, the estimated arrival time for 2 people is 30 minutes, and for the other 3 people it is 45 minutes.

[0157] Current workload: Checking the current workload of each maintenance worker, it was found that one person had no other tasks, while the other four people had a small number of tasks.

[0158] Furthermore, maintenance personnel were ranked and scored based on the three factors mentioned above. The highest score was given to a maintenance personnel whose estimated arrival time was 30 minutes and who had no other tasks.

[0159] Furthermore, the strategy allocation unit 305 selects the maintenance personnel with the highest score as the first choice and immediately pushes the work order information to that maintenance personnel.

[0160] Furthermore, the order-grabbing interaction module 400 generates a list of work orders to be grabbed and displays them to other qualified but unselected maintenance personnel, in case the preferred maintenance personnel fail to respond in a timely manner.

[0161] A small business reported network connectivity issues on several computers in its office. Because the problem had a relatively small impact, the ticket was marked as a "normal" level.

[0162] At this point, a standard work order round-robin allocation strategy is implemented. The steps for the standard work order round-robin allocation strategy are as follows:

[0163] The first parsing unit 302 extracts the work order parameters from the work order access module 100: the fault type is unstable network connection, and the geographical location is the 12th floor of a building in Chaoyang District, Beijing.

[0164] Furthermore, the second analysis unit 303 obtains the capability profiles of all maintenance personnel from the profile construction module 200, including their technical expertise, historical work order completion records, and service evaluations.

[0165] Furthermore, the strategy allocation unit 305 sends notifications to eligible maintenance personnel according to the order in which they last completed their tasks. Assume there are 8 maintenance personnel capable of handling network problems.

[0166] First, contact the repairman A who completed the task earliest and give them a fixed time window (e.g., 15 minutes) to decide whether to accept the work order.

[0167] If maintenance personnel A accepts the work order within 15 minutes, the task assignment is completed, and the status in the execution tracking module 500 is updated.

[0168] If maintenance worker A does not accept the work order, the system will automatically transfer the call to the next candidate (i.e., maintenance worker B, whose last task was completed the second earlier) and repeat the above process.

[0169] If maintenance personnel C accepts the work order on the third attempt, the polling process is immediately stopped, and the status in the execution tracking module 500 is updated to reflect the new task assignment.

[0170] It should be noted that skill matching refers to the degree of match between a repair technician's professional skills and the requirements of the current work order. For example, if the work order involves network configuration issues, a repair technician with extensive network configuration experience will have a high skill matching degree.

[0171] It should be noted that the estimated arrival time refers to the time it would take for maintenance personnel to reach the fault location based on their current location and traffic conditions.

[0172] It should be noted that the current load indicates the number of tasks or workload that maintenance personnel are currently handling. Maintenance personnel with lower loads are generally able to respond to new task requests more quickly.

[0173] It should be noted that the comprehensive score ranking is based on the three key factors mentioned above (skill matching, estimated arrival time, and current load) after the qualified maintenance personnel are screened out, and the system scores each maintenance personnel and ranks them according to their scores. The maintenance personnel with the highest scores are considered the best candidates.

[0174] It should be noted that when using the order-grabbing mechanism, the system will generate a list of available work orders for maintenance personnel to view and select. This list will be displayed to all eligible maintenance personnel, allowing them a certain period of time to decide whether to accept a specific work order.

[0175] It should be noted that the polling mechanism is a task allocation method that sends notifications to qualified maintenance personnel in a certain order until someone accepts the task.

[0176] It should be noted that the order of task completion time refers to the order in which maintenance personnel last completed a task; those who completed their tasks first will be given priority for new assignments. This method helps to balance the workload of various maintenance personnel.

[0177] It should be noted that a fixed time window refers to giving each repair technician a fixed period of time (e.g., 30 minutes) during which they can review the work order details and decide whether to accept it. If no repair technician accepts the work order within this time period, it is automatically forwarded to the next candidate.

[0178] It should be noted that the intelligent allocation module 300 can automatically select appropriate allocation strategies based on different levels of urgency, improving the efficiency and accuracy of task allocation. For urgent work orders, a priority allocation strategy ensures a rapid response and dispatch of the most suitable maintenance personnel, minimizing the impact of faults on operations. For regular work orders, a round-robin allocation strategy is used, allocating tasks according to the order in which maintenance personnel last completed their tasks. This ensures fairness and helps balance the workload of maintenance personnel, preventing some maintenance personnel from being overloaded while others are idle. In addition, the design of the intelligent allocation module 300 also considers various practical situations and factors, such as geographical location, skill matching, estimated arrival time, and current load, ensuring the comprehensiveness and accuracy of task allocation.

[0179] In this embodiment of the invention, the profile construction module 200 includes a data acquisition unit 201, a text analysis unit 202, a scoring system construction unit 203, and a dynamic update unit 204, such as... Figure 2 As shown;

[0180] The data acquisition unit 201 is used to collect relevant information about maintenance personnel from multiple channels, including skill certificates, historical work order completion records, and service evaluation data.

[0181] The text analysis unit 202 is connected to the data acquisition unit 201 and performs natural language processing on the work order text to extract fault type features. By parsing the descriptions in historical work orders, it identifies and classifies different fault types.

[0182] The scoring system construction unit 203 establishes a weighted scoring system, including technical expertise, response speed, and service quality, based on the data provided by the data acquisition unit 201 and the text analysis unit 202, namely a multi-dimensional capability profile.

[0183] The dynamic update unit 204 is equipped with a regular automatic update function to ensure that all data and scores can reflect the latest status in a timely manner. The dynamic update unit 204 is connected to the scoring system construction unit 203 to adjust the capability profile of maintenance personnel in real time.

[0184] It should be noted that the regular automatic update function allows the system to automatically check and update all relevant data and ratings by setting a fixed time interval (such as daily or weekly), ensuring the timeliness and accuracy of the information.

[0185] It should be noted that as new data is added (such as new work order completion records, customer reviews, etc.), the system will dynamically recalculate the score and update the maintenance personnel's competency profile accordingly. This means that the maintenance personnel's competency profile will always reflect their latest capabilities and performance.

[0186] In some specific implementations, the steps for establishing a weighted scoring system can be as follows:

[0187] The data acquisition unit 201 collects relevant information about maintenance personnel from multiple channels, including but not limited to: skills certificates (proving their technical expertise), historical work order completion records (used to analyze response speed and service quality), and customer service evaluations (directly reflecting service quality).

[0188] Furthermore, remove or correct incomplete, duplicate, or erroneous data. Ensure all data is formatted consistently, for example, standardize dates to the same format and convert scores to the same scale (e.g., 0-10).

[0189] Furthermore, by analyzing skills certificates and historical work order types, the technical areas of expertise for maintenance personnel are determined. Each technical area is assigned a weighted score based on certificate level and the number of work orders successfully resolved.

[0190] Furthermore, the time interval from receiving the work order to starting processing is calculated. The response time is then converted into a score, with shorter response times resulting in higher scores.

[0191] Furthermore, the ratings and comments in customer service evaluations were analyzed. Natural Language Processing (NLP) techniques were used to perform sentiment analysis on the text evaluations, resulting in positive, negative, or neutral sentiment scores.

[0192] The total score is calculated based on skills certificates and technical field scores, with a weight of W1. The average response time score is weighted as W2. The combined customer review score and sentiment analysis results are weighted as W3.

[0193] Ensure that the sum of the weights of each dimension is 1 (W1+W2+W3=1).

[0194] The scoring formula is set as follows: ;

[0195] TS, RS, and SS are the specific scores for each dimension.

[0196] Furthermore, set up an automatic update cycle (such as weekly or monthly) to recollect the latest data and update the scores. Whenever a new work order is completed or a customer feedback is received, immediately update the scores for the relevant dimensions and recalculate the total score.

[0197] Furthermore, historical data is used to test the effectiveness of the scoring system, ensuring that the scores accurately reflect the true abilities and performance of maintenance personnel. The weights of each dimension are adjusted based on actual application results to improve the accuracy and practicality of the scoring system.

[0198] Furthermore, the final multi-dimensional capability profile is applied to the intelligent allocation module 300 to help the system more accurately match maintenance personnel with work order requirements.

[0199] Furthermore, we will collect feedback from maintenance personnel and customers to further optimize the scoring system and allocation strategy.

[0200] For example, suppose a repairman A has a technical expertise score of 8, a response speed score of 7, and a service quality score of 9. The weights assigned are: technical expertise 0.4, response speed 0.3, and service quality 0.3.

[0201] The total score is calculated as follows: ;

[0202] This means that maintenance worker A has a comprehensive score of 8.0 under the current scoring system, which can be used to assign tasks.

[0203] It should be noted that the profile building module 200 comprehensively and accurately reflects the multidimensional capabilities of maintenance personnel, providing strong data support for the intelligent allocation module 300. Through this module, the system can quickly identify the strengths and weaknesses of maintenance personnel, thereby allocating work orders more scientifically and improving work efficiency and service quality. It also helps promote the personal growth of maintenance personnel. Maintenance personnel can clearly understand their own performance ratings, allowing them to improve their skills and service levels in a targeted manner. Simultaneously, by comparing themselves with other maintenance personnel, they can identify their own shortcomings, motivating them to learn and improve. Furthermore, it helps improve customer satisfaction. Based on the maintenance personnel's profiles, the system can recommend the most suitable maintenance personnel to customers, thereby providing more professional and efficient services. This not only enhances customer trust and loyalty but also earns the company a good reputation and brand image.

[0204] In this embodiment of the invention, the knowledge base question-answering module 600 includes a question parsing and classification unit 601, a knowledge retrieval unit 602, an answer generation and optimization unit 603, a feedback and learning unit 604, and a real-time update and maintenance unit 605, such as... Figure 5 As shown;

[0205] The problem parsing and classification unit 601 is used to receive and parse query requests from maintenance personnel, extract the core keywords and related information of the problem, analyze the extracted information through natural language processing technology, and classify the problem into different types such as hardware failure, software problem, and network configuration according to its characteristics.

[0206] The knowledge retrieval unit 602 searches the pre-built knowledge base based on the results of problem analysis and classification, and uses search algorithms and natural language processing technology to improve matching accuracy, ensuring that the found answers are as consistent as possible with actual needs. For each query, the system retrieves the most relevant solutions or reference cases from the knowledge base.

[0207] The answer generation and optimization unit 603 organizes the retrieved relevant information into an easy-to-understand answer format and performs personalized optimization in combination with the specific fault description. For complex problems, it integrates information from multiple sources to form a comprehensive solution.

[0208] The feedback and learning unit 604 collects feedback from maintenance personnel on the provided answers, including satisfaction ratings and improvement suggestions. By analyzing this feedback data, the content and structure of the knowledge base are optimized.

[0209] The real-time update and maintenance unit 605 is equipped with a function of regular automatic updates to ensure that all information can reflect the latest status in a timely manner and to remove outdated or no longer applicable information.

[0210] It should be noted that a query request refers to a question raised by maintenance personnel to the system during the troubleshooting process, such as: "The server cannot start; could it be a power supply problem?"

[0211] It should be noted that core keywords are key information extracted from the problem, such as "server," "cannot start," and "power," which are used to determine the type of problem.

[0212] It's important to note that hardware failure refers to problems with the physical components of a device, such as a damaged hard drive or a loose memory module. Software problems refer to abnormalities in the system, applications, or drivers, such as blue screens or program crashes. Network configuration involves network connectivity issues such as IP settings, router configuration, and DNS resolution.

[0213] It should be noted that the pre-built knowledge base refers to a structured database stored internally by the system, containing a large number of common problems and their solutions related to the field of information technology innovation, such as troubleshooting manuals, case libraries, official documents, etc.

[0214] It should be noted that an easy-to-understand answer format transforms complex technical content into easily understandable language, such as using step-by-step instructions, combining text and images, and highlighting keywords.

[0215] It should be noted that the regular automatic update function allows the system to automatically check and update the knowledge base content at fixed time intervals (such as daily or weekly) to ensure its timeliness.

[0216] In this embodiment of the invention, the order-grabbing interaction module 400 includes a geographic location filtering and service capability matching unit 401, a dynamic sorting list generation unit 402, a work order information push unit 403, an order-grabbing countdown mechanism unit 404, and an automatic compensation rule allocation unit 405, such as... Figure 4 As shown;

[0217] The geographic location filtering and service capability matching unit 401 is used to further screen qualified maintenance personnel. Based on the filtering results of the geographic filtering unit 304 in the intelligent allocation module 300, combined with the service quality rating provided by the scoring system construction unit 203, the service capability of each maintenance personnel is evaluated.

[0218] The dynamic sorting list generation unit 402 generates a dynamic sorting list based on distance and service quality rating. First, it calculates the actual distance between the maintenance personnel and the fault location, and then calculates a comprehensive score for each maintenance personnel based on the service quality rating. The list is then arranged from high to low according to the comprehensive score, ensuring that maintenance personnel who are closer and have higher service quality are displayed first.

[0219] The work order information push unit 403 pushes work order information to the terminals of idle maintenance personnel in the designated area in real time, monitors the status of maintenance personnel, and pushes detailed information including fault type, estimated arrival time suggestion and customer evaluation to the idle maintenance personnel located in the designated area.

[0220] The order-grabbing countdown mechanism unit 404 is used to set a fixed order-grabbing countdown time window. During the order-grabbing countdown time window, qualified maintenance personnel can view the work order details and decide whether to accept the task.

[0221] If a repairman successfully accepts the order within the specified time, the countdown will stop immediately and a cancellation notification will be sent to other repairmen.

[0222] If no one responds, the automatic allocation compensation rule is triggered through the automatic allocation compensation rule unit 405;

[0223] The automatic allocation compensation rule unit 405 is used to automatically select a maintenance worker for task allocation according to the preset compensation rules when no one bids for the task. The second-best option is the maintenance worker ranked second or third in the comprehensive score.

[0224] In summary, this invention proposes an intelligent work order scheduling system. This system integrates multiple functional modules to achieve efficient and intelligent scheduling of equipment failure work orders. First, the work order access module can widely receive equipment failure information from different channels, ensuring the comprehensiveness and timeliness of the information. Next, the profile building module utilizes the historical service data of maintenance personnel to construct a multi-dimensional capability profile, providing a data foundation for subsequent intelligent matching. The intelligent allocation module performs precise matching based on the profile data and work order parameters, associating the most suitable maintenance personnel with the work order. The order-grabbing interaction module further enhances the flexibility of scheduling by generating a list of work orders to be grabbed and an order-grabbing interface, enabling maintenance personnel to actively participate in order grabbing, thus improving scheduling efficiency. The execution tracking module monitors the work order processing progress in real time, ensuring the transparency and controllability of scheduling. Finally, the knowledge base question-and-answer module provides maintenance personnel with professional knowledge support, helping them quickly resolve problems during work order processing.

[0225] Example 2, refer to Figure 6 This embodiment also provides an intelligent work order scheduling method, including:

[0226] The work order access module 100 and the profile construction module 200 obtain profile data and work order parameters within the target area.

[0227] The intelligent allocation module 300 performs intelligent matching operations on the profile data and work order parameters within the target area;

[0228] The matching results are displayed to the maintenance personnel through the order-grabbing interaction module 400, and the order-grabbing process is initiated.

[0229] During the order-grabbing process, the execution tracking module 500 monitors the order-grabbing progress and the status of maintenance personnel handling the work orders in real time;

[0230] If maintenance personnel encounter professional knowledge issues while processing work orders, they can quickly obtain answers through the knowledge base Q&A module 600.

[0231] This embodiment also provides an electronic device, which can be a terminal, and its internal structure diagram can be as follows: Figure 6As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an intelligent work order scheduling method. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0232] This embodiment also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps:

[0233] The work order access module 100 and the profile construction module 200 obtain profile data and work order parameters within the target area.

[0234] The intelligent allocation module 300 performs intelligent matching operations on the profile data and work order parameters within the target area;

[0235] The matching results are displayed to the maintenance personnel through the order-grabbing interaction module 400, and the order-grabbing process is initiated.

[0236] During the order-grabbing process, the execution tracking module 500 monitors the order-grabbing progress and the status of maintenance personnel handling the work orders in real time;

[0237] If maintenance personnel encounter professional knowledge issues while processing work orders, they can quickly obtain answers through the knowledge base Q&A module 600.

[0238] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0239] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0240] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An intelligent work order dispatch system, characterized by, The system comprises a work order access module (100), an image construction module (200), an intelligent allocation module (300), a work order grabbing interaction module (400), an execution tracking module (500), and a knowledge base question answering module (600); The work order access module (100) is configured to obtain device fault work orders from different channels; The image construction module (200) is configured to construct a multi-dimensional capability image according to historical service data of the maintenance personnel; The image construction module (200) comprises a data collection unit (201), a text analysis unit (202), a scoring system construction unit (203), and a dynamic updating unit (204); The image construction module (200) collects skill certificates, historical work order completion records, and service evaluation data of the maintenance personnel, removes or corrects incomplete, repetitive, or erroneous data, and converts the data into the same scale; The skill certificates and historical work order types are analyzed to determine the technical fields in which the maintenance personnel are skilled, and a weight score is assigned to each technical field based on the certificate level and the number of successfully solved work orders; The time interval from receiving the work order to starting processing is calculated, and the response time is converted into a score; The scores and comment contents in the customer service evaluation are analyzed, the text evaluation is subjected to sentiment analysis, and a positive, negative, or neutral sentiment score is obtained; a weighted scoring system, i.e., a multi-dimensional capability image, is established; The intelligent allocation module (300) is configured to intelligently match the image data and work order parameters transmitted by the work order access module and the image construction module, and send the matching results to the work order grabbing interaction module; The intelligent allocation module (300) comprises an urgency judgment unit (301), a first analysis unit (302), a second analysis unit (303), a geographical filtering unit (304), and a strategy allocation unit (305); If the urgency judgment unit (301) determines that a work order is an urgent work order, an urgent work order priority allocation strategy is used; If the urgency judgment unit (301) determines that a work order is a normal work order, a normal work order polling allocation strategy is used; When the urgent work order priority allocation strategy is selected, the strategy allocation unit (305) performs geographical filtering unit (304) operation on all maintenance personnel who meet the qualifications after confirming that the work order is an urgent work order by the urgency judgment unit (301); When the normal work order polling allocation strategy is selected, the strategy allocation unit (305) obtains the work order parameters and the capability image of the maintenance personnel by using the first analysis unit (302) and the second analysis unit (303) after receiving the work order determined by the urgency judgment unit (301) as a normal level; The work order grabbing interaction module (400) is configured to generate a work order list to be grabbed according to the matching results, and generate a work order grabbing interface for interaction with the user end through the work order list. The order grabbing interaction module (400) comprises a geographical position filtering and service capability matching unit (401) configured to further screen the qualified maintenance personnel, based on the filtering result of the geographical filtering unit (304) in the intelligent allocation module (300) and in combination with the service quality rating provided by the scoring system construction unit (203), to evaluate the service capability of each maintenance personnel; The execution tracking module (500) is configured to monitor the order processing progress of the user end in real time and update the status; The knowledge base question and answer module (600) is configured to match the professional knowledge question and answer in the process of processing the fault order.

2. The intelligent work order dispatch system of claim 1, wherein, The intelligent allocation module (300) further comprises: The emergency degree judgment unit (301) is connected with the order access module (100) and is configured to extract the features of the equipment fault order transmitted by the order access module (100) to obtain the emergency degree of the fault order; The first analysis unit (302) is connected with the order access module (100) and is configured to perform a first quantitative operation on the equipment fault order transmitted by the order access module (100) to obtain the related parameters of the fault order; The second analysis unit (303) is connected with the portrait construction module (200) and is configured to perform a second quantitative operation on the multi-dimensional capability portrait to obtain the related parameters of the maintenance personnel capability; The geographical filtering unit (304) is connected with the first analysis unit (302) and the second analysis unit (303), and is configured to perform geographical position filtering on the qualified maintenance personnel according to the outputs of the first analysis unit (302) and the second analysis unit (303), and transmit the filtering result to the order grabbing interaction module (400); The emergency degree judgment unit (301), the first analysis unit (302), the second analysis unit (303), and the geographical filtering unit (304) are all connected with the strategy allocation unit (305), and the strategy allocation unit (305) is configured to perform strategy allocation judgment according to the inputs of the emergency degree judgment unit (301), the first analysis unit (302), the second analysis unit (303), and the geographical filtering unit (304), and transmit the strategy allocation judgment result to the order grabbing interaction module (400).

3. The intelligent work order dispatch system of claim 2, wherein, The intelligent allocation module (300) further comprises: The strategy allocation unit (305) acquires the device model, the fault level, and the geographical position information in the order parameter from the data transmitted by the first analysis unit (302), and calculates the estimated arrival time of the current position of the maintenance personnel to the fault location, The strategy allocation unit (305) performs multi-dimensional sorting in combination with the multi-dimensional capability portrait of the second analysis unit (303), wherein the multi-dimensions include the skill matching degree, the service radius, and the current load; The strategy allocation unit (305) comprises an emergency order priority allocation strategy and a general order polling allocation strategy, and the selection of the strategy is determined by the emergency degree judgment unit (301).

4. The intelligent work order dispatch system of claim 3, wherein, The intelligent allocation module (300) further comprises: Screening out the maintenance personnel who can reach the fault site, and based on the skill matching degree, the estimated arrival time and the current load capacity three key factors, the screened maintenance personnel are comprehensively scored and sorted; the strategy allocation unit (305) selects the maintenance personnel with the highest score as the first choice, and immediately pushes the work order information to the maintenance personnel, while generating a to-be-claimed work order list in the claim interaction module (400); The strategy allocation unit (305) starts a polling mechanism, and sends a notification to the maintenance personnel who meet the conditions according to the time sequence of the last task completed by the maintenance personnel; Each maintenance personnel has a fixed time window to view and decide whether to accept the work order; If no one accepts within the specified fixed time window, it automatically goes to the next candidate; Once a maintenance personnel accepts the work order, the allocation process is completed, and the state in the execution tracking module (500) is updated.

5. The intelligent work order dispatch system of claim 4, wherein, The knowledge base question and answer module (600) includes a question analysis and classification unit (601), a knowledge retrieval unit (602), an answer generation and optimization unit (603), a feedback and learning unit (604), and a real-time updating and maintenance unit (605); The question analysis and classification unit (601) is used to receive and analyze the query request from the maintenance personnel, extract the core keywords and related information of the question, analyze the extracted information through natural language processing technology, and classify the question into different types such as hardware failure, software problem, and network configuration according to its characteristics; The knowledge retrieval unit (602) retrieves in the pre-constructed knowledge base based on the results of question analysis and classification, uses search algorithms and natural language processing technology to improve matching accuracy, and ensures that the found answers meet the actual needs as much as possible. For each query, the system retrieves the most relevant solution or reference case from the knowledge base; The answer generation and optimization unit (603) arranges the retrieved related information into an easy-to-understand answer format, and optimizes it individually combined with specific fault description. For complex problems, it integrates information from multiple sources to form a comprehensive solution; The feedback and learning unit (604) collects feedback opinions of the maintenance personnel on the provided answers, including satisfaction scores and improvement suggestions, and optimizes the content and structure of the knowledge base by analyzing these feedback data; The real-time updating and maintenance unit (605) sets a function of automatic updating at regular intervals to ensure that all information can timely reflect the latest state and is responsible for removing outdated or no longer applicable information.

6. The intelligent work order dispatch system of claim 5, wherein, The claim interaction module (400) also includes a dynamic sorting list generation unit (402), a work order information pushing unit (403), a claim countdown mechanism unit (404), and an automatic allocation compensation rule unit (405); The dynamic sorting list generation unit (402) generates a dynamic sorting list according to the distance and service quality rating. First, the actual distance between the maintenance personnel and the fault site is calculated, and a comprehensive score is calculated for each maintenance personnel combined with the service quality rating, and arranged in descending order of comprehensive score to ensure that maintenance personnel with shorter distance and higher service quality are displayed first; The work order information pushing unit (403) pushes work order information to idle maintenance personnel terminals in a designated area in real time, monitors the state of the maintenance personnel, and pushes detailed information including fault type, estimated arrival time suggestion, and customer evaluation to idle maintenance personnel located in the designated area; The order grabbing countdown mechanism unit (404) is used to set a fixed order grabbing countdown time window, and during the order grabbing countdown time window time period, maintenance personnel who meet the conditions can view the work order details and decide whether to accept the task; If a maintenance personnel successfully grabs the order within the specified time, the countdown is immediately stopped and a cancellation notice is sent to other maintenance personnel; If no one responds, the automatic allocation compensation rule unit (405) is triggered to trigger the automatic allocation compensation rule; The automatic allocation compensation rule unit (405) is used to automatically select a maintenance personnel for task allocation according to the pre-set compensation rule when no one grabs the order, and the second or third ranked maintenance personnel is selected as the sub-optimal option.

7. An intelligent work order scheduling method using the system of claim 1, using the system of any one of claims 1-6, wherein, It comprises: According to the work order access module (100) and the portrait construction module (200), the portrait data and work order parameters in the target area are obtained; According to the intelligent allocation module (300), the portrait data and work order parameters in the target area are intelligently matched; The matching result is displayed to the maintenance personnel through the order grabbing interaction module (400), and the order grabbing process is started; During the order grabbing process, the tracking module (500) monitors the order grabbing progress and the state of the maintenance personnel processing the work order in real time; If the maintenance personnel encounters a professional knowledge problem during the processing of the work order, the knowledge base question and answer module (600) can be used to quickly obtain the answer. 8.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to realize the steps of the intelligent work order scheduling method in claim 7.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the intelligent work order scheduling method in claim 7.

Images