Task processing method and device

Abstract

The invention provides a task processing method and device, and relates to the technical field of data processing. According to the invention, fault prediction is carried out on the target equipment based on the monitoring data of the target equipment, and the fault probability value of each candidate component in the target equipment in the future preset time period is obtained; according to the fault probability values corresponding to the candidate parts, target candidate parts with fault hidden dangers are identified from the candidate parts of the target equipment, and a first to-do task is generated according to the target candidate parts; obtaining a target skill label and a first position parameter corresponding to the first to-do task; determining a second candidate employee whose employee skill label is successfully matched with the target skill label from the first candidate employees, and obtaining a state parameter and a second position parameter of the second candidate employee; and according to the first position parameter, the state parameter and the second position parameter, determining a target employee from the second candidate employees, and allocating a first to-do task to a terminal device where the target employee is located.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a task processing method and device thereof. Background Art

[0002] With the acceleration of urbanization and the advancement of science and technology, property management faces growing challenges. Property management needs to ensure the normal operation of various equipment (such as elevators, fire protection systems, air conditioning systems, etc.). When a traditional property management system fails, it usually requires manual inspection or user repair to discover the fault. The response time is long, which affects the normal operation of the equipment and user experience. The equipment is often repaired only after the failure occurs, resulting in increased maintenance costs and shortened equipment life. In addition, the allocation of maintenance tasks relies on manual judgment and lacks a scientific basis for allocation, which easily leads to waste of resources and inefficiency. Summary of the invention

[0003] The present disclosure provides a task processing method and device thereof, so as to at least solve the problem in the related art that faults can only be discovered after manual inspection or user repair report, and the maintenance task allocation process relies on manual judgment and lacks scientific allocation basis.

[0004] The first aspect of the present application proposes a task processing method, including: predicting faults of a target device based on monitoring data of the target device, and obtaining a fault probability value of each candidate component in the target device within a preset time period in the future; identifying target candidate components with potential fault hazards from the candidate components of the target device according to the fault probability values ​​corresponding to the candidate components, and generating a first to-do task according to the target candidate components; obtaining a target skill label and a first position parameter corresponding to the first to-do task, wherein the target skill label is used to indicate the specific skills required to complete the first to-do task; determining a second candidate employee from the first candidate employees whose employee skill label successfully matches the target skill label, and obtaining a state parameter and a second position parameter of the second candidate employee; determining a target employee from the second candidate employees according to the first position parameter, the state parameter, and the second position parameter, and assigning the first to-do task to the terminal device where the target employee is located.

[0005] According to one embodiment of the present application, a first to-do task is generated based on the target candidate components, including: obtaining the component importance of each target candidate component, comparing the component importance with a preset importance threshold, and obtaining a first comparison result; in response to the first comparison result indicating that there is at least one component importance greater than or equal to the preset importance threshold, generating a first task as the first to-do task, the first task being used to instruct whole-machine maintenance of the target device.

[0006] According to one embodiment of the present application, the task processing method also includes: in response to a first comparison result indicating that the importance of all components is less than a preset importance threshold, obtaining the total number of target candidate components; comparing the total number with the preset number threshold to obtain a second comparison result; in response to the second comparison result indicating that the total number is less than the preset number threshold, generating a second task as a first to-do task, the second task is used to indicate maintenance of the target candidate components; in response to the second comparison result indicating that the total number is greater than or equal to the preset number threshold, generating a first task as a first to-do task, the first task is used to indicate whole-machine maintenance of the target device.

[0007] According to one embodiment of the present application, the state parameters include a target state, a continuous working time in a current working cycle, and a current load value, the target state being a working state or an idle state, and determining the target employee from the second candidate employees based on the first position parameter, the state parameter, and the second position parameter, including: judging whether there are employees in an idle state among the second candidate employees based on the target state; in response to all the second candidate employees being in a working state, screening out employees whose continuous working time is less than a preset working time threshold from the second candidate employees as third candidate employees; determining a third position parameter corresponding to each third candidate employee from the second position parameter; and determining the target employee from the third candidate employees based on the first position parameter, the third position parameter, and the current load value.

[0008] According to one embodiment of the present application, a target employee is determined from third candidate employees based on a first position parameter, a third position parameter, and a current load value, including: calculating a first commuting distance between each third candidate employee and a target device based on the first position parameter and the third position parameter in combination with map data; performing a weighted summation on the first commuting distance and the current load value to obtain a comprehensive scheduling value corresponding to each third candidate employee; and selecting the third candidate employee corresponding to the comprehensive scheduling value with the smallest numerical value as the target employee.

[0009] According to one embodiment of the present application, the status parameter also includes an employee score, and the method also includes: in response to the existence of a second candidate employee in an idle state, taking the second candidate employee in the idle state as a fourth candidate employee; obtaining the employee score of each fourth candidate employee, and taking the fourth candidate employees whose employee scores are greater than a preset score threshold as fifth candidate employees; determining the fourth position parameter corresponding to each fifth candidate employee from the second position parameter; and determining the target employee from the fifth candidate employees according to the first position parameter and the fourth position parameter.

[0010] According to one embodiment of the present application, a target employee is determined from the fifth candidate employees based on the first position parameter and the fourth position parameter, including: calculating the second commuting distance between each fifth candidate employee and the target device based on the first position parameter and the fourth position parameter in combination with map data; and selecting the fifth candidate employee corresponding to the second commuting distance with the smallest value as the target employee.

[0011] According to one embodiment of the present application, a method for determining a current load value includes: obtaining a to-do task set corresponding to each second candidate employee, wherein the to-do task set includes at least one second to-do task; obtaining to-do task set parameters corresponding to each to-do task set, wherein the to-do task set parameters include an estimated processing time, a fifth position parameter, and a task difficulty value corresponding to each second to-do task in the to-do task set; and calculating the current load value of the second candidate employee based on the to-do task set parameters.

[0012] According to one embodiment of the present application, the task processing method also includes: obtaining the target employee's maintenance record for the first to-do task; obtaining feedback information from the user corresponding to the target device on the first to-do task; and updating the employee score of the target employee based on the maintenance record and feedback information.

[0013] According to one embodiment of the present application, fault prediction is performed on a target device based on the monitoring data of the target device, including: obtaining a monitoring data time series, a fault repair record time series, and an equipment maintenance manual corresponding to the target device; performing feature extraction on the monitoring data time series, the fault repair record time series, and the equipment maintenance manual to obtain device features of the target device; and inputting the device features into a pre-trained fault prediction model for fault prediction.

[0014] According to one embodiment of the present application, the task processing method also includes: in response to monitoring the task administrator's related operations on the first to-do task, determining whether the task administrator has the operation authority for the related operations; in response to the task administrator having the operation authority for the related operations, performing the related operations.

[0015] The second aspect of the present application proposes a task processing device, including: a prediction module, which is used to predict the fault of the target device based on the monitoring data of the target device, and obtain the fault probability value of each candidate component in the target device within a preset time period in the future; a generation module, which is used to identify the target candidate components with potential fault hazards from the candidate components of the target device according to the fault probability values ​​corresponding to the candidate components, and generate a first to-do task according to the target candidate components; an acquisition module, which is used to obtain the target skill label and the first position parameter corresponding to the first to-do task, wherein the target skill label is used to indicate the specific skills required to complete the first to-do task; a matching module, which is used to determine the second candidate employee whose employee skill label successfully matches the target skill label from the first candidate employees, and obtain the state parameter and the second position parameter of the second candidate employee; a determination module, which is used to determine the target employee from the second candidate employees according to the first position parameter, the state parameter and the second position parameter, and assign the first to-do task to the terminal device where the target employee is located.

[0016] According to one embodiment of the present application, the generation module is also used to: obtain the component importance of each target candidate component, compare the component importance with a preset importance threshold, and obtain a first comparison result; in response to the first comparison result indicating that there is at least one component importance greater than or equal to the preset importance threshold, generate a first task as a first to-do task, and the first task is used to instruct the whole machine maintenance of the target equipment.

[0017] According to one embodiment of the present application, the generation module is also used to: in response to a first comparison result indicating that the importance of all components is less than a preset importance threshold, obtain the total number of target candidate components; compare the total number with the preset number threshold to obtain a second comparison result; in response to the second comparison result indicating that the total number is less than the preset number threshold, generate a second task as a first to-do task, the second task is used to indicate maintenance of the target candidate components; in response to the second comparison result indicating that the total number is greater than or equal to the preset number threshold, generate a first task as a first to-do task, the first task is used to indicate whole-machine maintenance of the target device.

[0018] According to one embodiment of the present application, the state parameters include a target state, a continuous working time in a current working cycle and a current load value, the target state is a working state or an idle state, and the determination module is further used to: determine whether there are employees in an idle state among the second candidate employees according to the target state; in response to all the second candidate employees being in a working state, screen out employees whose continuous working time is less than a preset working time threshold from the second candidate employees as third candidate employees; determine a third position parameter corresponding to each third candidate employee from the second position parameter; and determine a target employee from the third candidate employees according to the first position parameter, the third position parameter and the current load value.

[0019] According to one embodiment of the present application, the determination module is also used to: calculate the first commuting distance between each third candidate employee and the target device based on the first position parameter and the third position parameter in combination with the map data; perform weighted summation on the first commuting distance and the current load value to obtain the comprehensive scheduling value corresponding to each third candidate employee; and select the third candidate employee corresponding to the comprehensive scheduling value with the smallest numerical value as the target employee.

[0020] According to one embodiment of the present application, the determination module is further used to: in response to the existence of a second candidate employee in an idle state, use the second candidate employee in an idle state as a fourth candidate employee; obtain an employee score for each fourth candidate employee, and use the fourth candidate employees whose employee scores are greater than a preset score threshold as fifth candidate employees; determine the fourth position parameter corresponding to each fifth candidate employee from the second position parameter; and determine the target employee from the fifth candidate employees according to the first position parameter and the fourth position parameter.

[0021] According to one embodiment of the present application, the determination module is further used to: calculate the second commuting distance between each fifth candidate employee and the target device based on the first position parameter and the fourth position parameter in combination with the map data; and select the fifth candidate employee corresponding to the second commuting distance with the smallest value as the target employee.

[0022] According to one embodiment of the present application, the determination module is also used to: obtain the to-do task set corresponding to each second candidate employee, wherein the to-do task set includes at least one second to-do task; obtain the to-do task set parameters corresponding to each to-do task set, the to-do task set parameters including the estimated processing time, the fifth position parameter and the task difficulty value corresponding to each second to-do task in the to-do task set; and calculate the current load value of the second candidate employee according to the to-do task set parameters.

[0023] According to one embodiment of the present application, the task processing device also includes an update module, which is used to: obtain the target employee's maintenance record for the first to-do task; obtain feedback information from the user corresponding to the target device on the first to-do task; and update the employee score of the target employee based on the maintenance record and feedback information.

[0024] According to one embodiment of the present application, the prediction module is also used to: obtain the monitoring data time series, fault repair record time series and equipment maintenance manual corresponding to the target device; perform feature extraction on the monitoring data time series, fault repair record time series and equipment maintenance manual to obtain device features of the target device; and input the device features into a pre-trained fault prediction model for fault prediction.

[0025] According to one embodiment of the present application, the task processing device also includes a monitoring module, which is used to: in response to monitoring the task administrator's related operations on the first to-do task, determine whether the task administrator has the operation authority for the related operations; in response to the task administrator having the operation authority for the related operations, perform the related operations.

[0026] The third aspect embodiment of the present application proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the task processing method described in the first aspect embodiment of the present application.

[0027] The fourth aspect embodiment of the present application proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the task processing method as described in the first aspect embodiment of the present application.

[0028] The fifth aspect embodiment of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the task processing method described in the first aspect embodiment of the present application.

[0029] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects: by predicting faults of target equipment, the present application can identify components with potential faults in advance, thereby achieving early warning; once components with potential faults are identified, the first to-do task is immediately generated and quickly assigned to appropriate maintenance personnel, thereby improving response speed, and preventing maintenance before the components actually fail, reducing the need for emergency repairs, thereby reducing maintenance costs, reducing equipment downtime, and avoiding operational interruptions and economic losses caused by equipment failures; by matching employee skill tags and status parameters, ensuring that tasks are assigned to the most appropriate candidates, avoiding waste of resources, improving overall work efficiency, enhancing the professionalism and reliability of maintenance work, and improving customer satisfaction and trust.

[0030] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0032] Figure 1 It is a schematic diagram of an exemplary implementation of a task processing method shown in an embodiment of the present application.

[0033] Figure 2 It is a schematic diagram of an exemplary implementation of a task processing method shown in an embodiment of the present application.

[0034] Figure 3 It is a schematic diagram of an exemplary implementation of a task processing method shown in an embodiment of the present application.

[0035] Figure 4 It is a schematic diagram of a task processing device shown in an embodiment of the present application.

[0036] Figure 5 It is a schematic diagram of an electronic device shown in one embodiment of the present application. DETAILED DESCRIPTION

[0037] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0038] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations.

[0039] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0040] Figure 1 is a schematic diagram of an exemplary implementation of a task processing method shown in the present application, such as Figure 1 As shown, the method for processing the task includes the following steps:

[0041] S101, performing fault prediction on a target device based on monitoring data of the target device, and obtaining a fault probability value of each candidate component in the target device within a future preset time period.

[0042] This application can be applied to a variety of scenarios, such as property management scenarios, manufacturing management scenarios, and medical equipment management scenarios. This application does not limit the scenarios to which this solution is applicable.

[0043] Taking the property management scenario as an example, the target equipment may be elevator equipment, fire fighting equipment, air conditioning equipment, heating equipment, etc. Each device may include multiple candidate components that need to be predicted for faults. For example, if the target equipment is an elevator, the candidate components may include elevator doors, motors, transmission components, etc.

[0044] Among them, the future preset time period can be specifically set according to the actual situation. For example, it can be set to the time period within the next week, the next month, etc.

[0045] In the present application, monitoring data of the target device is acquired in real time, and fault prediction of the target device is performed based on the monitoring data, and the fault probability value of each candidate component in the target device within a preset time period in the future is obtained respectively.

[0046] S102, identifying target candidate components with potential failure hazards from candidate components of the target device according to the failure probability values ​​corresponding to the candidate components, and generating a first to-do task according to the target candidate components.

[0047] Obtain the probability threshold corresponding to each candidate component. For any candidate component, compare the failure probability value corresponding to the candidate component with the probability threshold corresponding to the candidate component. If the failure probability value corresponding to the candidate component is greater than the probability threshold corresponding to the candidate component, it is determined that the candidate component has a potential failure, and the candidate component with the potential failure is used as the target candidate component.

[0048] Exemplarily, the elevator equipment may include a total of five candidate components, including elevator doors, motors, transmission components, etc. If it is determined that the motor has a potential fault, the motor will be used as a target candidate component and a first to-do task corresponding to the motor will be generated. The first to-do task is used to instruct maintenance of the motor.

[0049] The template of the task list of the first to-do task can be customized by the administrator, and the task list may include information such as task name, fault potential type, fault potential description, expected time of occurrence, urgency, maintenance deadline, maintenance priority, etc. Administrators can add custom fields to meet specific task management needs.

[0050] S103, obtaining a target skill tag and a first position parameter corresponding to the first to-do task, wherein the target skill tag is used to indicate a specific skill required to complete the first to-do task.

[0051] Exemplarily, if the first to-do task is to maintain the motor, the target skill tag corresponding to the first to-do task may be “good at motor maintenance”.

[0052] Among them, the first position parameter refers to the position of the target candidate component to be maintained corresponding to the first to-do task, which can also be understood as the location of the target device. Taking the motor on the elevator that needs maintenance as an example, the first position parameter can be the motor position corresponding to the elevator in Unit 2, Building 1, Community A.

[0053] S104: Determine, from the first candidate employees, a second candidate employee whose employee skill tag successfully matches the target skill tag, and obtain a state parameter and a second position parameter of the second candidate employee.

[0054] In this application, employees in the employee database are taken as first candidate employees.

[0055] It is not difficult to understand that each first candidate employee has different areas of expertise, which is reflected in the employee skill label of each first candidate employee. For example, if an employee is good at motor maintenance, then the employee skill label of the employee includes "good at motor maintenance"; for example, if an employee is good at transmission component maintenance, then the employee skill label of the employee includes "good at transmission component maintenance".

[0056] In the present application, each first candidate employee may correspond to one or more employee skill tags to indicate that the employee corresponds to one or more proficient skills.

[0057] In this application, the employee skill label of each first candidate employee is matched with the target skill label, and the first candidate employees who are successfully matched are screened out as second candidate employees, which means that the second candidate employees are employees who are good at maintaining the target candidate components corresponding to the first to-do task.

[0058] In the present application, after determining the second candidate employee, the state parameter and the second position parameter of each second candidate employee are obtained. The state parameter includes but is not limited to the target state (for example, the target state is the working state or the idle state), the continuous working time in the current working cycle, the current load value and the employee score and other parameters. The second position parameter refers to the current position of the second candidate employee.

[0059] S105: Determine a target employee from the second candidate employees according to the first position parameter, the state parameter, and the second position parameter, and assign a first to-do task to the terminal device where the target employee is located.

[0060] In the present application, an employee who is in an idle state can be determined from the second candidate employees according to the state parameter, and an employee who is in an idle state and has the shortest commuting distance can be preferentially selected as a target employee according to the first position parameter and the second position parameter.

[0061] In the present application, if it is determined according to the status parameters that each second candidate employee is in a busy state, the second candidate employees whose commuting distance meets the preset distance interval can be determined according to the first position parameter and the second position parameter, and the employee with the smallest current load value can be selected from the second candidate employees whose commuting distance meets the preset distance interval as the target employee.

[0062] An embodiment of the present application proposes a task processing method, including: predicting faults of a target device based on monitoring data of the target device, and obtaining a fault probability value of each candidate component in the target device within a preset time period in the future; identifying target candidate components with potential fault hazards from the candidate components of the target device according to the fault probability values ​​corresponding to the candidate components, and generating a first to-do task according to the target candidate components; obtaining a target skill label and a first position parameter corresponding to the first to-do task, wherein the target skill label is used to indicate a specific skill required to complete the first to-do task; determining a second candidate employee from the first candidate employees whose employee skill label successfully matches the target skill label, and obtaining a state parameter and a second position parameter of the second candidate employee; determining a target employee from the second candidate employees according to the first position parameter, the state parameter, and the second position parameter, and assigning the first to-do task to the terminal device where the target employee is located. This application can predict faults of target equipment and identify components with potential failures in advance, thereby achieving early warning. Once a component with potential failures is identified, the first to-do task is immediately generated and quickly assigned to the appropriate maintenance personnel, which improves the response speed. Preventive maintenance can be performed before the component actually fails, reducing the need for emergency repairs, thereby reducing maintenance costs, reducing equipment downtime, and avoiding operational interruptions and economic losses caused by equipment failures. By matching employee skill tags and status parameters, it ensures that tasks are assigned to the most appropriate person, avoiding waste of resources, improving overall work efficiency, enhancing the professionalism and reliability of maintenance work, and improving customer satisfaction and trust.

[0063] Figure 2 is a schematic diagram of an exemplary implementation of a task processing method shown in the present application, such as Figure 2 As shown, the method for processing the task includes the following steps:

[0064] S201 , performing fault prediction on the target device based on the monitoring data of the target device, and obtaining a fault probability value of each candidate component in the target device within a future preset time period.

[0065] Obtain the monitoring data time series, fault repair record time series and equipment maintenance manual corresponding to the target equipment; extract features from the monitoring data time series, fault repair record time series and equipment maintenance manual to obtain the equipment features of the target equipment; input the equipment features into a pre-trained fault prediction model for fault prediction, and obtain the failure probability value of each candidate component in the target equipment within a preset time period in the future.

[0066] Among them, taking the target equipment as an elevator as an example, the monitoring data time series may include the time series of parameters such as elevator operation time, operation times, load conditions, door opening and closing times, motor temperature, current and voltage fluctuations, and vibration sensor data. The equipment features of the elevator obtained after feature extraction may include average load, maximum operation time, fault precursor signals (such as abnormal noise, vibration), etc. After obtaining the equipment features, the equipment features are normalized or standardized to ensure that different features are at the same order of magnitude in terms of values, which is convenient for model prediction or model training.

[0067] Preferably, in the present application, a fault prediction model is constructed based on a long short-term memory network (LSTM), historical sample data of the target device is obtained, and feature extraction is performed based on the historical sample data to form a feature data set. The data set is divided into a training set, a validation set, and a test set. The performance of the fault prediction model is evaluated by cross-validation and other methods until the fault prediction model training is completed and used for online target device fault prediction.

[0068] S202, identifying target candidate components with potential failure hazards from candidate components of the target device according to the failure probability values ​​corresponding to the candidate components, and generating a first to-do task according to the target candidate components.

[0069] After identifying target candidate components with potential failure hazards from candidate components of the target device, the component importance of each target candidate component is obtained, and the component importance is compared with a preset importance threshold to obtain a first comparison result corresponding to each target candidate component.

[0070] If the first comparison result indicates that there is at least one target candidate component whose component importance is greater than or equal to the preset importance threshold, it means that the target candidate component with the hidden danger of failure is very important for the operation of the target device. The hidden danger of this target candidate component is often accompanied by hidden dangers of other candidate components or may cause huge losses. Therefore, for safety reasons, the entire target device needs to be comprehensively maintained. Therefore, the first task is generated as the first to-do task, and the first task is used to indicate the maintenance of the entire target device.

[0071] If the first comparison result indicates that the component importance of all target candidate components is less than the preset importance threshold, it means that the importance of the target candidate components with potential fault hazards is average. At this time, it is necessary to obtain the total number of target candidate components and compare the total number with the preset number threshold to obtain the second comparison result.

[0072] If the second comparison result indicates that the total number of target candidate components is less than the preset number threshold, it means that the target candidate components are of average importance and the number is relatively small, so only the target candidate components need to be maintained. A second task is generated as the first to-do task, and the second task is used to indicate the maintenance of the target candidate components.

[0073] If the second comparison result indicates that the total number of target candidate components is greater than or equal to the preset number threshold, it means that the number of target candidate components with potential faults is relatively large, and the potential fault of the target device is very large. Therefore, for safety reasons, the entire target device needs to be comprehensively maintained. In this case, a first task needs to be generated as the first to-do task, and the first task is used to instruct the entire target device to be maintained.

[0074] S203, obtaining a target skill tag and a first position parameter corresponding to the first to-do task, wherein the target skill tag is used to indicate a specific skill required to complete the first to-do task.

[0075] For example, taking the whole machine maintenance of the elevator as an example, the target skill label corresponding to the first to-do task may be "all-round elevator maintenance worker".

[0076] For example, taking the target candidate component as a motor, the target skill label corresponding to the first to-do task may be “good at motor repair”.

[0077] S204, determining a second candidate employee whose employee skill tag successfully matches the target skill tag from the first candidate employees, and obtaining a state parameter and a second position parameter of the second candidate employee, wherein the state parameter includes a target state, continuous working time in the current working cycle, and a current load value, and the target state is a working state or an idle state.

[0078] Among them, the method for determining the current load value of the second candidate employee includes: obtaining the to-do task set corresponding to each second candidate employee, wherein the to-do task set includes at least one second to-do task; obtaining the to-do task set parameters corresponding to each to-do task set, the to-do task set parameters including the estimated processing time corresponding to each second to-do task in the to-do task set, the fifth position parameter corresponding to each second to-do task and the task difficulty value; and calculating the current load value of the second candidate employee according to the to-do task set parameters.

[0079] Among them, for any second candidate employee, when calculating the current load value of the second candidate employee according to the to-do task set parameters, the estimated processing time corresponding to all second to-do tasks included in the to-do task set corresponding to the second candidate employee can be summed up to obtain the sum of the processing time; then, according to the fifth position parameter corresponding to all second to-do tasks included in the to-do task set corresponding to the second candidate employee, the commuting distance corresponding to the second candidate employee when processing all the second to-do tasks included in the to-do task set is obtained; then, the difficulty average of the task difficulty values ​​corresponding to all second to-do tasks included in the to-do task set corresponding to the second candidate employee is obtained; finally, the sum of the processing time corresponding to the to-do task set corresponding to the second candidate employee, the commuting distance and the difficulty average are weightedly summed to obtain the current load value of the second candidate employee.

[0080] S205: Determine whether there is an idle employee among the second candidate employees according to the target status.

[0081] S206: In response to all the second candidate employees being in a working state, select employees whose continuous working hours are less than a preset working hour threshold from the second candidate employees as third candidate employees.

[0082] If all second candidate employees are in working status, in this application, in order to avoid employees working too long hours, it is necessary to conduct another round of screening for all second candidate employees, that is, to screen out from the second candidate employees employees whose continuous working hours are less than the preset working hour threshold as third candidate employees.

[0083] S207: Determine a third position parameter corresponding to each third candidate employee from the second position parameters.

[0084] Since the third candidate employees are selected from the second candidate employees, the third position parameter corresponding to each third candidate employee can be determined from the second position parameter corresponding to each second candidate employee determined above.

[0085] S208: Determine a target employee from the third candidate employees according to the first position parameter, the third position parameter, and the current load value.

[0086] According to the first position parameter and the third position parameter, combined with the map data, the first commuting distance between each third candidate employee and the target equipment is calculated; the first commuting distance and the current load value are weightedly summed to obtain the comprehensive scheduling value corresponding to each third candidate employee; the third candidate employee corresponding to the smallest comprehensive scheduling value is selected as the target employee.

[0087] S209: Allocate the first to-do task to the terminal device where the target employee is located.

[0088] The embodiments of the present application mainly introduce that by predicting faults of target equipment, components with potential fault hazards can be identified in advance, thereby achieving early warning. Once components with potential fault hazards are identified, the first to-do task is immediately generated and quickly assigned to appropriate maintenance personnel, thereby improving the response speed. Preventive maintenance can be performed before the components actually fail, reducing the need for emergency repairs, thereby reducing maintenance costs, reducing equipment downtime, and avoiding operational interruptions and economic losses due to equipment failures. By matching employee skill tags and status parameters, when all successfully matched employees are in working status, a balance is achieved between the employee commuting distance and the current load value to ensure that tasks are assigned to the most appropriate candidates, thereby avoiding waste of resources, improving overall work efficiency, improving the professionalism and reliability of maintenance work, and improving customer satisfaction and trust.

[0089] Figure 3 is a schematic diagram of an exemplary implementation of a task processing method shown in the present application, such as Figure 3 As shown, the method for processing the task includes the following steps:

[0090] S301, performing fault prediction on the target device based on the monitoring data of the target device, and obtaining a fault probability value of each candidate component in the target device within a future preset time period.

[0091] S302 , identifying target candidate components with potential failure hazards from candidate components of the target device according to the failure probability values ​​corresponding to the candidate components, and generating a first to-do task according to the target candidate components.

[0092] S303, obtaining a target skill tag and a first position parameter corresponding to the first to-do task, wherein the target skill tag is used to indicate a specific skill required to complete the first to-do task.

[0093] Regarding the specific implementation of steps S301 to S303, reference may be made to the specific introduction of the relevant parts in the above embodiments, which will not be elaborated here.

[0094] S304, determining a second candidate employee whose employee skill tag successfully matches the target skill tag from the first candidate employees, and obtaining a state parameter and a second position parameter of the second candidate employee, where the state parameter includes the target state and the employee score.

[0095] The target state may include a working state and an idle state.

[0096] S305: Determine whether there is an idle employee among the second candidate employees according to the target status.

[0097] S306: In response to the existence of the second candidate employee in an idle state, the second candidate employee in an idle state is used as a fourth candidate employee.

[0098] If there is a second candidate employee who is in an idle state, the first to-do task is assigned to the second candidate employee who is in an idle state first. At this time, the second candidate employees need to be screened, and the second candidate employee who is in an idle state is screened out as the fourth candidate employee.

[0099] S307, obtaining an employee score of each fourth candidate employee, and selecting the fourth candidate employees whose employee scores are greater than a preset score threshold as fifth candidate employees.

[0100] When there are multiple fourth candidate employees, this application gives priority to assigning the first to-do task to employees with higher employee scores to improve maintenance efficiency. Therefore, it is necessary to screen the fourth candidate employees at this time, and select the fourth candidate employees whose employee scores are greater than the preset score threshold as the fifth candidate employees.

[0101] S308: Determine a fourth position parameter corresponding to each fifth candidate employee from the second position parameter.

[0102] Since the fifth candidate employee is selected from the second candidate employees, the fourth position parameter corresponding to each fifth candidate employee can be determined from the second position parameter corresponding to each second candidate employee determined above.

[0103] S309: Determine a target employee from the fifth candidate employees according to the first position parameter and the fourth position parameter.

[0104] The second commuting distance between each fifth candidate employee and the target device is calculated based on the first position parameter and the fourth position parameter in combination with the map data; the fifth candidate employee corresponding to the second commuting distance with the smallest value is taken as the target employee.

[0105] S310, allocating a first to-do task to the terminal device where the target employee is located.

[0106] The embodiments of the present application mainly introduce that by predicting faults of target equipment, components with potential fault hazards can be identified in advance, thereby achieving early warning. Once components with potential fault hazards are identified, the first to-do task is immediately generated and quickly assigned to appropriate maintenance personnel, thereby improving the response speed. Preventive maintenance can be performed before the components actually fail, reducing the need for emergency repairs, thereby reducing maintenance costs, reducing equipment downtime, and avoiding operational interruptions and economic losses caused by equipment failures. By matching employee skill tags and status parameters, when there are idle employees among the successfully matched employees, employees with short commuting distances and high employee scores are given priority to ensure that tasks are assigned to the most appropriate candidates, avoiding waste of resources, improving overall work efficiency, enhancing the professionalism and reliability of maintenance work, and improving customer satisfaction and trust.

[0107] Furthermore, in this application, after the target employee completes the first to-do task, the target employee's maintenance record for the first to-do task (such as maintenance duration, maintenance effect, etc.) is obtained; feedback information from the user of the target device on the first to-do task (such as the owner's satisfaction) is obtained; and the target employee's employee rating is updated based on the maintenance record and feedback information. By evaluating maintenance records and user feedback, the employee's work performance can be more accurately reflected, and dynamically updating the rating helps to monitor employee performance in real time, adjust management strategies in a timely manner, and ensure the achievement of team goals.

[0108] Furthermore, the task administrator can manually reassign the first pending task or modify related parameters. Different task administrators may have different management permissions. If the task administrator's related operations on the first pending task (such as manually reassigning tasks or modifying related parameters) are monitored, it is determined whether the task administrator has the operation permission for the related operation; if the task administrator has the operation permission for the related operation, the related operation is performed. Through permission review, it is ensured that only administrators with corresponding qualifications can perform specific operations, reducing the risk of errors or improper operations.

[0109] Furthermore, the permission management of task administrators can be flexibly configured through a variety of permission templates and can be adjusted according to actual needs, including adding, deleting and modifying permissions. Task administrators can inherit some permissions of the previous level task administrator and have the ability to manage employees in their own department. In addition, fine-grained authorization management can be used to strictly control permissions for each operation link of the task. For example, it can be set to allow only specific employees to view the progress of the task but not to modify the task content. For important permission change operations, they need to go through the approval process before they can take effect to ensure the rationality and compliance of the changes. At the same time, when the permissions of employees change, they will be notified in a timely manner so that they can clearly understand the adjustment of their permissions. Improve the flexibility and security of permission management, thereby enhancing overall management efficiency.

[0110] Furthermore, the entire process of the first to-do task (including task creation, editing, deletion, etc.) is recorded to generate a task log so that it can be traced and investigated when a security incident occurs. At the same time, the task log is audited regularly to check whether there are abnormal operations or potential security risks, and take corresponding measures to prevent them.

[0111] Furthermore, the execution status of the first to-do task, including progress, completion status and any relevant feedback, can be displayed in real time on a large visualization screen.

[0112] Furthermore, in addition to users providing feedback or suggestions on the first to-do task, employees can also provide feedback or suggestions on the first to-do task, and display them in the task list of the first to-do task, so that the task manager can make necessary updates and improvements to the work list.

[0113] Furthermore, the fault prediction model may be retrained from time to time based on the relevant data of the first to-be-done task.

[0114] Figure 4 is a schematic diagram of a task processing device shown in this application, such as Figure 4 As shown, the processing device 400 of the task includes a prediction module 401, a generation module 402, an acquisition module 403, a matching module 404 and a determination module 405, wherein:

[0115] The prediction module 401 is used to perform fault prediction on the target device based on the monitoring data of the target device, and obtain the fault probability value of each candidate component in the target device within a preset time period in the future.

[0116] The generating module 402 is used to identify target candidate components with potential failures from the candidate components of the target device according to the failure probability values ​​corresponding to the candidate components, and generate a first to-do task according to the target candidate components.

[0117] The acquisition module 403 is used to acquire the target skill tag and the first position parameter corresponding to the first to-do task, wherein the target skill tag is used to indicate the specific skills required to complete the first to-do task.

[0118] The matching module 404 is used to determine a second candidate employee whose employee skill tag successfully matches the target skill tag from the first candidate employees, and obtain the state parameter and the second position parameter of the second candidate employee.

[0119] The determination module 405 is used to determine the target employee from the second candidate employees according to the first position parameter, the state parameter and the second position parameter, and allocate the first to-do task to the terminal device where the target employee is located.

[0120] By predicting faults of target equipment, this device can identify components with potential failures in advance, thereby achieving early warning. Once a component with potential failures is identified, the first to-do task is immediately generated and quickly assigned to the appropriate maintenance personnel, which improves the response speed. Preventive maintenance can be performed before the component actually fails, reducing the need for emergency repairs, thereby reducing maintenance costs, reducing equipment downtime, and avoiding operational interruptions and economic losses caused by equipment failures. By matching employee skill tags and status parameters, it ensures that tasks are assigned to the most appropriate person, avoiding waste of resources, improving overall work efficiency, enhancing the professionalism and reliability of maintenance work, and improving customer satisfaction and trust.

[0121] Furthermore, the generation module 402 is also used to: obtain the component importance of each target candidate component, compare the component importance with a preset importance threshold, and obtain a first comparison result; in response to the first comparison result indicating that there is at least one component importance greater than or equal to the preset importance threshold, generate a first task as a first to-do task, and the first task is used to indicate the whole machine maintenance of the target equipment.

[0122] Furthermore, the generation module 402 is also used to: in response to a first comparison result indicating that the importance of all components is less than a preset importance threshold, obtain the total number of target candidate components; compare the total number with the preset number threshold to obtain a second comparison result; in response to the second comparison result indicating that the total number is less than the preset number threshold, generate a second task as a first to-do task, the second task is used to indicate maintenance of the target candidate components; in response to the second comparison result indicating that the total number is greater than or equal to the preset number threshold, generate a first task as a first to-do task, the first task is used to indicate whole-machine maintenance of the target device.

[0123] Furthermore, the state parameters include a target state, continuous working time in the current working cycle and a current load value, the target state being a working state or an idle state, and the determination module 405 is further used to: determine whether there are employees in an idle state among the second candidate employees according to the target state; in response to all the second candidate employees being in a working state, select employees whose continuous working time is less than a preset working time threshold from the second candidate employees as third candidate employees; determine a third position parameter corresponding to each third candidate employee from the second position parameter; and determine a target employee from the third candidate employees according to the first position parameter, the third position parameter and the current load value.

[0124] Furthermore, the determination module 405 is also used to: calculate the first commuting distance between each third candidate employee and the target device based on the first position parameter and the third position parameter in combination with the map data; perform weighted summation on the first commuting distance and the current load value to obtain the comprehensive scheduling value corresponding to each third candidate employee; and select the third candidate employee corresponding to the comprehensive scheduling value with the smallest numerical value as the target employee.

[0125] Furthermore, the determination module 405 is also used to: in response to the existence of a second candidate employee in an idle state, use the second candidate employee in an idle state as a fourth candidate employee; obtain the employee score of each fourth candidate employee, and use the fourth candidate employees whose employee scores are greater than a preset score threshold as fifth candidate employees; determine the fourth position parameter corresponding to each fifth candidate employee from the second position parameter; and determine the target employee from the fifth candidate employees according to the first position parameter and the fourth position parameter.

[0126] Furthermore, the determination module 405 is also used to: calculate the second commuting distance between each fifth candidate employee and the target device based on the first position parameter and the fourth position parameter in combination with the map data; and select the fifth candidate employee corresponding to the second commuting distance with the smallest value as the target employee.

[0127] Furthermore, the determination module 405 is also used to: obtain a to-do task set corresponding to each second candidate employee, wherein the to-do task set includes at least one second to-do task; obtain to-do task set parameters corresponding to each to-do task set, wherein the to-do task set parameters include an estimated processing time, a fifth position parameter and a task difficulty value corresponding to each second to-do task in the to-do task set; and calculate the current load value of the second candidate employee based on the to-do task set parameters.

[0128] Furthermore, the task processing device 400 also includes an update module, which is used to: obtain the target employee's maintenance record for the first to-do task; obtain feedback information from the user corresponding to the target device on the first to-do task; and update the employee score of the target employee based on the maintenance record and feedback information.

[0129] Furthermore, the prediction module 401 is also used to: obtain the monitoring data time series, fault repair record time series and equipment maintenance manual corresponding to the target equipment; perform feature extraction on the monitoring data time series, fault repair record time series and equipment maintenance manual to obtain equipment features of the target equipment; and input the equipment features into a pre-trained fault prediction model for fault prediction.

[0130] Furthermore, the task processing device 400 also includes a monitoring module, which is used to: in response to monitoring the task administrator's related operations on the first to-do task, determine whether the task administrator has the operation authority for the related operations; in response to the task administrator having the operation authority for the related operations, perform the related operations.

[0131] Furthermore, the task processing device 400 can be deeply integrated with other devices belonging to the same system, such as a financial management device, so as to uniformly display the data of the same system on a large visualization screen.

[0132] In order to implement the above embodiment, the present application embodiment also proposes an electronic device 500, such as Figure 5 As shown, the electronic device 500 includes: a processor 501 and a memory 502 communicatively connected to the processor, the memory 502 stores instructions executable by at least one processor, and the instructions are executed by at least one processor 501 to implement the task processing method shown in the above embodiment.

[0133] In order to implement the above embodiments, the embodiments of the present application also propose a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to implement a processing method for a task as shown in the above embodiments.

[0134] In order to implement the above embodiments, the embodiments of the present application further provide a computer program product, including a computer program, which implements the task processing method shown in the above embodiments when executed by a processor.

[0135] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0136] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0137] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0138] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0139] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A task processing method, characterized in that: include: Performing fault prediction on the target device based on the monitoring data of the target device to obtain a fault probability value of each candidate component in the target device within a future preset time period; According to the failure probability values ​​corresponding to the candidate components, identifying target candidate components with potential failure hazards from the candidate components of the target device, and generating a first to-do task according to the target candidate components; Acquire a target skill tag and a first position parameter corresponding to the first to-do task, wherein the target skill tag is used to indicate a specific skill required to complete the first to-do task; Determine, from the first candidate employees, a second candidate employee whose employee skill tag successfully matches the target skill tag, and obtain a state parameter and a second position parameter of the second candidate employee; According to the first position parameter, the state parameter and the second position parameter, a target employee is determined from the second candidate employees, and the first to-do task is allocated to the terminal device where the target employee is located.

2. The method according to claim 1, characterized in that The generating a first to-do task according to the target candidate component includes: Obtaining the component importance of each of the target candidate components, comparing the component importance with a preset importance threshold, and obtaining a first comparison result; In response to the first comparison result indicating that there is at least one component whose importance is greater than or equal to the preset importance threshold, a first task is generated as a first to-do task, wherein the first task is used to instruct to perform whole-machine maintenance on the target device.

3. The method according to claim 2, characterized in that The method further comprises: In response to the first comparison result indicating that the importance of all the components is less than the preset importance threshold, obtaining the total number of the target candidate components; Compare the total quantity with a preset quantity threshold to obtain a second comparison result; In response to the second comparison result indicating that the total quantity is less than the preset quantity threshold, generating a second task as the first to-do task, wherein the second task is used to instruct maintenance of the target candidate component; In response to the second comparison result indicating that the total quantity is greater than or equal to the preset quantity threshold, a first task is generated as a first to-do task, wherein the first task is used to instruct to perform whole-machine maintenance on the target device.

4. The method according to any one of claims 1 to 3, characterized in that The state parameters include a target state, a continuous working time in a current working cycle, and a current load value, the target state is a working state or an idle state, and determining a target employee from the second candidate employees according to the first position parameter, the state parameter, and the second position parameter includes: Determining whether there is an idle employee among the second candidate employees according to the target status; In response to all the second candidate employees being in a working state, selecting employees whose continuous working hours are less than a preset working hours threshold from the second candidate employees as third candidate employees; Determine a third position parameter corresponding to each of the third candidate employees from the second position parameter; The target employee is determined from the third candidate employees according to the first position parameter, the third position parameter and the current load value.

5. The method according to claim 4, characterized in that The step of determining the target employee from the third candidate employees according to the first position parameter, the third position parameter, and the current load value includes: Calculate a first commuting distance between each of the third candidate employees and the target device according to the first location parameter and the third location parameter in combination with map data; Performing a weighted summation on the first commuting distance and the current load value to obtain a comprehensive scheduling value corresponding to each of the third candidate employees; The third candidate employee corresponding to the comprehensive scheduling value with the smallest numerical value is used as the target employee.

6. The method according to claim 4, characterized in that The status parameter also includes an employee score, and the method further includes: In response to the existence of the second candidate employee in an idle state, taking the second candidate employee in an idle state as the fourth candidate employee; Obtaining an employee score of each of the fourth candidate employees, and selecting the fourth candidate employees whose employee scores are greater than a preset score threshold as fifth candidate employees; Determine a fourth position parameter corresponding to each of the fifth candidate employees from the second position parameter; The target employee is determined from the fifth candidate employees according to the first position parameter and the fourth position parameter.

7. The method according to claim 6, characterized in that The determining the target employee from the fifth candidate employees according to the first position parameter and the fourth position parameter includes: Calculate the second commuting distance between each of the fifth candidate employees and the target device according to the first location parameter and the fourth location parameter in combination with map data; The fifth candidate employee corresponding to the second commuting distance with the smallest value is selected as the target employee.

8. The method according to claim 4, characterized in that The method for determining the current load value includes: Obtaining a to-do task set corresponding to each of the second candidate employees, wherein the to-do task set includes at least one second to-do task; Obtaining to-do task set parameters corresponding to each of the to-do task sets, the to-do task set parameters including an estimated processing time, a fifth position parameter, and a task difficulty value corresponding to each second to-do task in the to-do task set; The current load value of the second candidate employee is calculated based on the to-be-done task set parameters.

9. The method according to claim 7, characterized in that: The method further comprises: Obtaining the target employee's maintenance record for the first to-do task; Obtaining feedback information of a user corresponding to the target device on the first to-do task; The employee rating of the target employee is updated according to the maintenance record and the feedback information.

10. The method according to claim 1, characterized in that The performing fault prediction on the target device based on the monitoring data of the target device includes: Obtain the monitoring data time series, fault repair record time series and equipment maintenance manual corresponding to the target equipment; Performing feature extraction on the monitoring data time series, the fault repair record time series and the equipment maintenance manual to obtain equipment features of the target equipment; The device characteristics are input into a pre-trained fault prediction model for fault prediction.

11. The method according to claim 9, characterized in that The method further comprises: In response to monitoring the task administrator's operation on the first to-do task, determining whether the task administrator has the operation authority for the related operation; In response to the task administrator having the operation authority for the related operation, the related operation is performed.

12. A task processing device, characterized in that: include: A prediction module, used to predict the failure of the target device based on the monitoring data of the target device, and obtain the failure probability value of each candidate component in the target device within a preset time period in the future; A generating module, configured to identify a target candidate component with potential failure from the candidate components of the target device according to the failure probability value corresponding to the candidate component, and generate a first to-do task according to the target candidate component; an acquisition module, configured to acquire a target skill tag and a first position parameter corresponding to the first to-do task, wherein the target skill tag is used to indicate a specific skill required to complete the first to-do task; A matching module, used to determine, from the first candidate employees, a second candidate employee whose employee skill tag successfully matches the target skill tag, and obtain a state parameter and a second position parameter of the second candidate employee; A determination module is used to determine a target employee from the second candidate employees according to the first position parameter, the state parameter and the second position parameter, and to assign the first to-do task to the terminal device where the target employee is located.

13. The device according to claim 12, characterized in that The generating module is further used for: Obtaining the component importance of each of the target candidate components, comparing the component importance with a preset importance threshold, and obtaining a first comparison result; In response to the first comparison result indicating that there is at least one component whose importance is greater than or equal to the preset importance threshold, a first task is generated as a first to-do task, wherein the first task is used to instruct to perform whole-machine maintenance on the target device.

14. The device according to claim 13, characterized in that The generating module is further used for: In response to the first comparison result indicating that the importance of all the components is less than the preset importance threshold, obtaining the total number of the target candidate components; Compare the total quantity with a preset quantity threshold to obtain a second comparison result; In response to the second comparison result indicating that the total quantity is less than the preset quantity threshold, generating a second task as the first to-do task, wherein the second task is used to instruct maintenance of the target candidate component; In response to the second comparison result indicating that the total quantity is greater than or equal to the preset quantity threshold, a first task is generated as a first to-do task, wherein the first task is used to instruct to perform whole-machine maintenance on the target device.

15. The device according to any one of claims 12 to 14, characterized in that The state parameters include a target state, a continuous working time in a current working cycle, and a current load value. The target state is a working state or an idle state. The determination module is further used to: Determining whether there is an idle employee among the second candidate employees according to the target status; In response to all the second candidate employees being in a working state, selecting employees whose continuous working hours are less than a preset working hours threshold from the second candidate employees as third candidate employees; Determine a third position parameter corresponding to each of the third candidate employees from the second position parameter; The target employee is determined from the third candidate employees according to the first position parameter, the third position parameter and the current load value.

16. The device according to claim 15, characterized in that The determining module is further used for: Calculate a first commuting distance between each of the third candidate employees and the target device according to the first location parameter and the third location parameter in combination with map data; Performing a weighted summation on the first commuting distance and the current load value to obtain a comprehensive scheduling value corresponding to each of the third candidate employees; The third candidate employee corresponding to the comprehensive scheduling value with the smallest numerical value is used as the target employee.

17. The device according to claim 15, characterized in that The determining module is further used for: In response to the existence of the second candidate employee in an idle state, taking the second candidate employee in an idle state as the fourth candidate employee; Obtaining an employee score of each of the fourth candidate employees, and selecting the fourth candidate employees whose employee scores are greater than a preset score threshold as fifth candidate employees; Determine a fourth position parameter corresponding to each of the fifth candidate employees from the second position parameter; The target employee is determined from the fifth candidate employees according to the first position parameter and the fourth position parameter.

18. The device according to claim 17, characterized in that The determining module is further used for: Calculate the second commuting distance between each of the fifth candidate employees and the target device according to the first location parameter and the fourth location parameter in combination with map data; The fifth candidate employee corresponding to the second commuting distance with the smallest value is selected as the target employee.

19. The device according to claim 15, characterized in that The determining module is further used for: Obtaining a to-do task set corresponding to each of the second candidate employees, wherein the to-do task set includes at least one second to-do task; Obtaining to-do task set parameters corresponding to each of the to-do task sets, the to-do task set parameters including an estimated processing time, a fifth position parameter, and a task difficulty value corresponding to each second to-do task in the to-do task set; The current load value of the second candidate employee is calculated based on the to-be-done task set parameters.

20. The device according to claim 18, characterized in that The device further comprises an updating module, wherein the updating module is configured to: Obtaining the target employee's maintenance record for the first to-do task; Obtaining feedback information of a user corresponding to the target device on the first to-do task; The employee rating of the target employee is updated according to the maintenance record and the feedback information.

21. The device according to claim 12, characterized in that The prediction module is further used for: Obtain the monitoring data time series, fault repair record time series and equipment maintenance manual corresponding to the target equipment; Performing feature extraction on the monitoring data time series, the fault repair record time series and the equipment maintenance manual to obtain equipment features of the target equipment; The device characteristics are input into a pre-trained fault prediction model for fault prediction.

22. The device according to claim 20, characterized in that The device further comprises a monitoring module, wherein the monitoring module is used for: In response to monitoring the task administrator's operation on the first to-do task, determining whether the task administrator has the operation authority for the related operation; In response to the task administrator having the operation authority for the related operation, the related operation is performed.

23. An electronic device, comprising: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 11.

24. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-11.

25. A computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 11.

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