Method, device and equipment for evaluating operation and maintenance work saturation and medium

Abstract

The invention discloses an operation and maintenance work saturation evaluation method and device, equipment and a medium, and relates to the technical field of operation and maintenance management, and the method comprises the steps: obtaining daily report data of a target operation and maintenance worker in a single-day operation and maintenance period, and the daily report data at least comprises an operation and maintenance action type, a shop address and an equipment state corresponding to each operation shop; predicting the operation and maintenance time consumption of the target operation and maintenance personnel in each operation shop according to the operation and maintenance action type and the equipment state, and determining the travel time consumption between any adjacent operation shops according to the shop address; accumulating the operation and maintenance time consumption and the travel time consumption to obtain the total working time length of the target operation and maintenance personnel in the single-day operation and maintenance period; and evaluating the operation and maintenance work saturation of the target operation and maintenance personnel according to the total working time length to obtain a saturation evaluation result. According to the method, the number of operation and maintenance actions, the spatial distribution of operation and maintenance work and the actual time consumption difference are concerned at the same time, and the actual workload and saturation of operation and maintenance personnel can be accurately quantified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of operation and maintenance, and in particular to an operation and maintenance work saturation evaluation method, device, equipment and medium. BACKGROUND

[0002] With the vigorous development of the sharing economy, the scale of offline intelligent devices represented by power banks has shown explosive growth, and its operation and maintenance management is facing challenges such as complex personnel scheduling and difficult work load evaluation. The traditional manual operation and maintenance mode has been difficult to meet the demand of large-scale and refined operation, and it is urgent to realize the intelligent management of operation and maintenance work through digital technology. Among them, how to scientifically and accurately evaluate the work saturation of operation and maintenance personnel has become a key technical problem for optimizing personnel allocation and improving operation and maintenance efficiency.

[0003] In the prior art, the evaluation of the work saturation of operation and maintenance personnel mainly adopts a simple quantitative operation and maintenance action quantity method. For example, it is directly stipulated that an operation and maintenance personnel needs to complete at least a certain number of operation and maintenance actions (such as network disconnection activation, power bank replenishment, fault maintenance, etc.) every day, and as long as the preset number is reached, it is considered that the work load meets the standard. This evaluation method only focuses on the number of actions, but completely ignores the spatial distribution and actual time difference of operation and maintenance work.

[0004] Specifically, when different operation and maintenance personnel complete the same number of operation and maintenance actions, due to the differences in the range of work area, path planning and traffic conditions, the actual time spent may be significantly different. For example, an operation and maintenance personnel only completes 8 operation and maintenance actions in a short distance, which may only take 2 hours to complete; while another operation and maintenance personnel needs to work across regions, and also completes 8 actions, which may take 8 hours. However, the existing evaluation method treats the performances of the two as the same, which leads to the inability to truly reflect the actual work load and saturation of operation and maintenance personnel, causing waste of human resources, difficulty in fairly evaluating the work efficiency of operation and maintenance personnel, and inability to provide a scientific basis for management decisions such as personnel scheduling and performance optimization. SUMMARY

[0005] Therefore, the present application provides an operation and maintenance work saturation evaluation method, device, equipment and medium, which can accurately quantify the actual work load and saturation of operation and maintenance personnel by simultaneously focusing on the number of operation and maintenance actions, the spatial distribution of operation and maintenance work and the actual time difference, and thus can provide a scientific basis for management decisions such as personnel scheduling and performance optimization.

[0006] According to a first aspect of the present application, an operation and maintenance work saturation evaluation method is provided, comprising:

[0007] obtaining daily report data of a target operation and maintenance personnel in a single-day operation and maintenance period, the daily report data at least containing operation and maintenance action types, shop addresses and device states corresponding to each work shop;

[0008] predicting, according to the operation action type and the device state, operation time consumption of the target operation personnel at each of the operation stores, and determining travel time consumption between any adjacent operation stores according to the store addresses;

[0009] adding the operation time consumption and the travel time consumption to obtain total work time of the target operation personnel in the single-day operation period;

[0010] evaluating, according to the total work time, operation work saturation of the target operation personnel to obtain a saturation evaluation result.

[0011] According to a second aspect of the present application, an operation work saturation evaluation device is provided, comprising:

[0012] an acquisition module configured to acquire daily report data of a target operation personnel in a single-day operation period, the daily report data at least including operation action type, store address and device state corresponding to each operation store;

[0013] a prediction module configured to predict, according to the operation action type and the device state, operation time consumption of the target operation personnel at each of the operation stores, and determine travel time consumption between any adjacent operation stores according to the store addresses;

[0014] a calculation module configured to add the operation time consumption and the travel time consumption to obtain total work time of the target operation personnel in the single-day operation period;

[0015] an evaluation module configured to evaluate, according to the total work time, operation work saturation of the target operation personnel to obtain a saturation evaluation result.

[0016] According to a third aspect of the present application, a storage medium having a computer program stored thereon is provided, the program being executed by a processor to implement the operation work saturation evaluation method described above.

[0017] According to a fourth aspect of the present application, an electronic device is provided, comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, the processor executing the program to implement the operation work saturation evaluation method described above.

[0018] By the technical scheme, the method, device, equipment and medium for evaluating operation and maintenance work saturation provided by the application can accurately capture the spatial distribution and task difference of operation and maintenance work by obtaining daily report data containing operation store address, operation and maintenance action type and equipment state; can quantize the time cost caused by regional range, path planning and traffic conditions by calculating the travel time between adjacent operation stores according to the store address, and avoid the time consumption of cross-regional operation being ignored; can consider the actual difficulty difference of different tasks by predicting the operation time consumption in combination with the operation and maintenance action type and the equipment state, and change the extensive mode of evaluating by single action number; and can reflect the actual work load of operation and maintenance personnel, effectively avoid the performance misjudgment of close-range operation and cross-regional operation, and provide scientific basis for management decisions such as personnel scheduling and performance optimization based on actual work time, thereby solving the problems of unfair evaluation, resource waste and lack of data support for decision-making in the prior art.

[0019] The above description is only a summary of the technical scheme of the application, in order to more clearly understand the technical means of the application, the application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the application more obvious and easy to understand, the following specific embodiments of the application are described. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of a method for evaluating operation and maintenance work saturation provided by an embodiment of the application is shown;

[0021] Figure 2 A flowchart of a method for evaluating operation and maintenance work saturation provided by another embodiment of the application is shown;

[0022] Figure 3 A structural diagram of an evaluation device for operation and maintenance work saturation provided by an embodiment of the application is shown;

[0023] Figure 4 A structural diagram of another evaluation device for operation and maintenance work saturation provided by an embodiment of the application is shown. DETAILED DESCRIPTION

[0024] In the following, the application will be described in detail with reference to the accompanying drawings and embodiments. It should be noted that the embodiments and features in the embodiments in the application can be combined with each other without conflict.

[0025] In the prior art, the evaluation of the work saturation of operation and maintenance personnel mainly adopts a simple quantitative operation and maintenance action quantity. For example, it is directly stipulated that the operation and maintenance personnel need to complete at least a certain number of operation and maintenance actions (such as network disconnection activation, treasure supplement, fault maintenance, etc.) every day, and as long as the preset number is reached, it is considered that the workload is up to the standard. This evaluation method only focuses on the number of actions, but completely ignores the spatial distribution and actual time difference of operation and maintenance work.

[0026] Specifically, when different operation and maintenance personnel complete the same number of operation and maintenance actions, due to the differences in the range of work area, path planning and traffic conditions, the actual time input may have significant differences. For example, an operation and maintenance personnel only completes 8 operation and maintenance actions in a short distance area, and may only need 2 hours to complete; while another operation and maintenance personnel needs to work across areas, and also completes 8 actions, but may take 8 hours. However, the existing evaluation method treats the performances of the two as the same, which leads to the fact that the actual work load and saturation of the operation and maintenance personnel cannot be truly reflected, which not only may cause waste of human resources, but also is difficult to fairly evaluate the work efficiency of the operation and maintenance personnel, and cannot provide a scientific basis for management decisions such as personnel scheduling and performance optimization.

[0027] In order to solve the above problems, the embodiment of the present application provides an evaluation method for operation and maintenance work saturation, as shown in the formula (I), the method comprises the steps of: Figure 1

[0028] Step 110, obtaining the daily report data of the target operation and maintenance personnel in a single day operation and maintenance period, the daily report data at least containing the operation and maintenance action type, shop address and device state corresponding to each work shop.

[0029] ​The target operation and maintenance personnel is an operation and maintenance personnel to be evaluated for operation and maintenance work saturation. The single-day operation and maintenance cycle refers to a complete time interval from when the target operation and maintenance personnel starts to perform a first operation and maintenance task on a day to when the target operation and maintenance personnel completes all operation and maintenance tasks on the day and ends work. The single-day operation and maintenance cycle takes a natural day as a unit to define a work time range, and is used as a time boundary for standardizing data statistics and analysis. The operation and maintenance action type refers to a specific task classification performed by the operation and maintenance personnel in a work store. The operation and maintenance action type usually includes but is not limited to network disconnection activation, treasure replenishment, fault repair, inspection, data collection, software upgrade, and the like. The operation and maintenance action type can reflect the specific task nature of operation and maintenance work, and the time and effort required for different types of actions are different. The store address can be the latitude and longitude of the work store, and is used to determine the spatial position of the operation and maintenance work. The store address is crucial for subsequent calculation of travel time between adjacent stores and analysis of the work path of the operation and maintenance personnel, and can effectively reflect the time cost difference caused by different work area ranges, path planning, and traffic conditions. In the operation and maintenance management scenario, the device state usually refers to the current operating condition or performance index of the device to be maintained, and is used to reflect whether the device is working normally, whether there is a fault or potential risk, which directly affects the execution difficulty and time consumption of the operation and maintenance action. For example, the repair time consumption of a device with a severe fault is usually significantly higher than that of a device with a slight fault. Including the device state in the data collection range can help to more accurately predict the operation and maintenance time consumption of each work store.

[0030] In a specific application scenario, after the target operation and maintenance personnel completes the operation and maintenance of each work store, the target operation and maintenance personnel can upload daily report data to the system in various ways: 1. Real-time collection through a mobile terminal using an operation and maintenance application program, selection of an operation and maintenance action type through a pull-down menu, acquisition of a store address through GPS positioning or code scanning, confirmation of a device state through preset options or image recognition, and real-time synchronization to the cloud. 2. Automatic reporting relying on Internet of Things devices, triggering of data uploading through smart terminals, sensors, or RFID. 3. Voice interaction inputting, generation of structured data through NLP analysis of voice content, and reporting. Correspondingly, for the embodiments of the present disclosure, the background server can receive and aggregate all daily report data uploaded by the target operation and maintenance personnel within a single-day operation and maintenance cycle.

[0031] Step 120: predicting operation and maintenance time consumption of the target operation and maintenance personnel at each work store according to the operation and maintenance action type and the device state, and determining travel time between any adjacent work stores according to the store address.

[0032] The operation and maintenance time consumption is the total time of the target operation and maintenance personnel to complete all operation actions in a single store, that is, the time difference from starting the operation to confirming the completion.

[0033] For the embodiments of the present disclosure, a prediction model can be established by analyzing the correlation rules of different operation and maintenance actions, device states and actual time consumption in historical data, the operation and maintenance time consumption of the target operation and maintenance personnel in a single store is estimated based on the prediction model, the physical distance is calculated by using the geographic location information, and the moving time consumption of the target operation and maintenance personnel between different operation stores is estimated by combining factors such as traffic conditions and distribution routes.

[0034] By predicting the operation and maintenance time consumption according to the operation and maintenance action type and the device state, and determining the moving time consumption according to the store address, accurate prediction of the operation and maintenance operation time consumption and scientific quantification of the moving time consumption can be realized. The manager can be provided with fine decision basis based on the actual working time and the space-time characteristics, so as to avoid waste of human resources and improve operation efficiency and evaluation fairness.

[0035] Step 130, the operation and maintenance time consumption and the moving time consumption are accumulated to obtain the total working time of the target operation and maintenance personnel in a single daily operation period.

[0036] For example, a target operation and maintenance personnel A needs to complete the tasks of three stores in a working day: performing device inspection in store A (operation and maintenance time consumption 30 minutes), then driving to store B 3 kilometers away to repair the faulty device (moving time consumption 15 minutes, repair time consumption 60 minutes), and finally walking to store C 1 kilometer away to perform software upgrade (moving time consumption 10 minutes, upgrade time consumption 45 minutes). At this time, the total working time of the target operation and maintenance personnel in the day is the sum of the time consumption of each link, that is, (30+60+45)+(15+10)=160 minutes.

[0037] By accumulating the operation and maintenance time consumption and the moving time consumption in a single daily operation period, the actual working load of the target operation and maintenance personnel in a single daily operation period can be comprehensively and accurately calculated. Compared with only counting the number of operation and maintenance actions, the present application can fully consider the space-time difference in work, effectively avoid underestimating the time cost due to large operation area span and complex path planning, and thus provide a real and reliable data basis for subsequent scientific evaluation of operation and maintenance work saturation, optimization of personnel scheduling and performance evaluation.

[0038] Step 140, evaluating the operation and maintenance work saturation of the target operation and maintenance personnel according to the total working time to obtain a saturation evaluation result.

[0039] For the embodiments of the present disclosure, the target operation and maintenance personnel is evaluated for operation and maintenance work saturation according to the total working time, that is, the total working time obtained by accumulating the operation and maintenance time and the travel time of the target operation and maintenance personnel in a single day operation and maintenance cycle is compared and analyzed with a pre-set reasonable working time threshold, and the operation and maintenance work saturation is divided into low saturation, medium saturation, high saturation, overload and the like according to different intervals of the total working time.

[0040] The evaluation process is based on quantitative data and can intuitively reflect the actual work load of the operation and maintenance personnel, providing a scientific and objective decision basis for enterprise optimization of resource scheduling, adjustment of task allocation, protection of personnel health, improvement of work efficiency and fair performance evaluation, and effectively solving the problem of inaccurate evaluation caused by ignoring the space-time difference in the traditional evaluation method.

[0041] In summary, according to the evaluation method of operation and maintenance work saturation provided by the present application, first, by obtaining daily report data containing job store address, operation and maintenance action type and device state, the spatial distribution and task difference of operation and maintenance work can be accurately captured; then the travel time between adjacent job stores is calculated according to the store address, which can quantify the time cost caused by regional range, path planning and traffic conditions, and avoid ignoring the time consumption of cross-regional operation; and the operation and maintenance time is predicted in combination with the operation and maintenance action type and the device state, which can consider the actual difficulty difference of different tasks and change the extensive mode of single action number evaluation; finally, the total working time is obtained by accumulating the operation and maintenance time and the travel time, and the saturation is evaluated accordingly, which can truly reflect the actual work load of the operation and maintenance personnel, effectively avoid the performance misjudgment of close-range operation and cross-regional operation, and further provide a scientific basis for personnel scheduling, performance optimization and other management decisions based on actual working time, solving the problems of unfair evaluation, resource waste and lack of data support in the prior art.

[0042] Further, as a refinement and expansion of the above embodiment, in order to fully describe the implementation of the present embodiment, the present embodiment further provides another evaluation method of operation and maintenance work saturation, as shown in Figure 2 The method comprises:

[0043] Step 210, obtaining daily report data of the target operation and maintenance personnel in a single day operation and maintenance cycle, the daily report data at least containing operation and maintenance action type, store address and device state corresponding to each job store.

[0044] For the embodiments of the present disclosure, the specific implementation process can be referred to the related description in embodiment step 110, which will not be repeated here.

[0045] Step 220, inputting the operation and maintenance action type and the device state corresponding to each job store into the pre-trained time consumption prediction model respectively to obtain the operation and maintenance time of the target operation and maintenance personnel in the corresponding job store.

[0046] The time-consuming prediction model is an algorithm model trained based on historical data for estimating the time consumption of operation and maintenance tasks, which can be any machine learning model or deep learning model suitable for performing time-consuming prediction tasks, and is not specifically limited here.

[0047] In the training process, the time-consuming prediction model can continuously learn the relationship between historical operation and maintenance action types, historical device states and actual time consumption, and optimize internal parameters until the loss function reaches a minimum value, thereby having the ability to predict time consumption according to input data. For the embodiments of the present disclosure, when new operation and maintenance action types and device state data are input, the time-consuming prediction model will quickly calculate the operation and maintenance time consumption of the target operation and maintenance personnel in the corresponding job store based on the learned rules, providing reliable basic data for subsequent evaluation of total working time and work saturation, effectively avoiding errors caused by traditional experience-based estimation, and making the calculation of operation and maintenance time consumption more scientific and accurate.

[0048] The training process of the time-consuming prediction model aims to learn the correlation rules between operation and maintenance actions, device states and actual time consumption through historical data, thereby achieving accurate prediction. First, a preset sliding window is used as a data collection period (for example, a week or a month as a window, and data is updated every day by sliding back), and the system collects historical operation and maintenance data of sample operation and maintenance personnel, which includes historical operation and maintenance action types (such as device maintenance, inspection, etc.), historical device states (normal, fault, etc.) and actual time consumption. The actual time consumption is accurately calculated by the difference between the store check-in time and the log upload time, which can be used to ensure the authenticity and timeliness of the data; then, the historical operation and maintenance action types and the historical device states are used as the input data of the model, and the actual time consumption is used as the training label to construct the training data set. The model is trained by continuous iteration, trying to extract the mapping relationship between the features and the labels from the input data. Each iteration will calculate the loss function (normalized mean square error or cross-entropy loss) according to the difference between the predicted value and the actual label. As the training progresses, the model adjusts the internal parameters to reduce the loss function value. When the loss function is less than a preset threshold, it means that the time-consuming prediction model has reached a high prediction accuracy on the training data, and at this time it is judged that the time-consuming prediction model training is completed and can be used to predict the time consumption of future operation and maintenance tasks.

[0049] Correspondingly, the training process of the time consumption prediction model includes: taking a preset sliding window as a data collection period, obtaining historical operation and maintenance data of a sample operation and maintenance personnel, the historical operation and maintenance data including a historical operation and maintenance action type, a historical device state, and actual time consumption of the sample operation and maintenance personnel when performing a historical operation and maintenance task, the actual time consumption being a time difference between a store check-in time and a log upload time; taking the historical operation and maintenance action type and the historical device state corresponding to each actual time consumption as input data, and taking the actual time consumption as a training label, iteratively training the time consumption prediction model until a loss function of the time consumption prediction model is less than a preset threshold, and determining that the time consumption prediction model is trained.

[0050] The preset sliding window refers to a fixed length time window (such as 1 hour, 1 day, 1 week, etc.) that is set in advance when data is collected or analyzed, and the window is sequentially slid on the time axis according to a specified step (such as 1 hour, 30 minutes, etc.), thereby realizing segmented collection, processing or feature extraction of dynamic data. By training or real-time updating the time consumption prediction model by using the dynamically collected historical operation and maintenance data, the latest operation and maintenance mode, device state changes (such as rising failure rate of old devices), personnel efficiency fluctuations (such as improvement of proficiency of new employees) and other information can be continuously incorporated, so that the model learns the time consumption rule in the current environment in real time, and the prediction accuracy of the time consumption prediction model is ensured; the preset threshold is a critical value set by a person for judging whether the result meets the expectation before model training or task execution, and the specific value can be set according to the actual application scenario.

[0051] Step 230, determining the travel time consumption between any adjacent work stores according to the store addresses.

[0052] For the embodiments of the present disclosure, the determination of the travel time consumption between any adjacent work stores according to the store addresses in step 230 can include the following steps:

[0053] Step 230-1, generating an operation and maintenance work path of the target operation and maintenance personnel in a single-day operation and maintenance period based on the store addresses, and determining a path distance between any adjacent work stores on the operation and maintenance work path.

[0054] For the embodiments of the present disclosure, the target operation and maintenance personnel's single-day work path is planned and the distance between each work point is quantified through geographic information and algorithm optimization, so as to improve the scheduling efficiency. First, according to the address information (such as latitude and longitude coordinates or detailed address) of each work shop, combined with the priority, time requirement and other constraint conditions of the operation and maintenance task, the optimal or better operation and maintenance work path is generated by using a path planning algorithm (such as Dijkstra algorithm, genetic algorithm, etc.), so as to ensure that the total time or total distance is the shortest. After the path is generated, the straight-line distance or actual traffic distance (considering road, traffic rules and other factors) between any adjacent work shops on the operation and maintenance work path is calculated by a geographic information system (GIS), so as to provide a data basis for subsequent calculation of travel time and evaluation of work load.

[0055] For example, an operation and maintenance personnel A needs to go to three shops A, B and C to perform equipment inspection tasks on a working day. After the system obtains the address information of the three shops, a path planning algorithm is used to determine the work path of "A→B→C" in combination with real-time traffic data, so as to avoid congested road sections. Then, the system calculates that the path distance from A shop to B shop is 8 kilometers and the path distance from B shop to C shop is 5 kilometers.

[0056] Step 230-2, predicting a first travel time under each path distance by using a preset map interface.

[0057] The preset map interface integrates rich geographic information data and real-time traffic information, and can calculate the predicted time under different travel modes based on the input path distance, combined with road conditions, traffic flow, travel time period and other dynamic factors. After the preset map interface is called, accurate travel time data can be obtained, which is used as the first travel time. The time data can reflect the time required by the target operation and maintenance personnel on each path under the current actual situation, and provides a key basis for subsequent planning of operation and maintenance work path, evaluation of total work time and optimization of personnel scheduling, which helps to improve the overall efficiency and resource utilization rate of operation and maintenance work.

[0058] For the embodiments of the present disclosure, step 230-2 can include the following steps: by calling the preset map interface, real-time traffic time prediction values of each preset travel mode are obtained for the path distance between any adjacent work shops in the current period; the plurality of real-time traffic time prediction values corresponding to the plurality of preset travel modes are arranged in descending order, and the arithmetic mean of the remaining real-time traffic time prediction values is calculated after removing the maximum real-time traffic time prediction value, which is used as the first travel time between the corresponding adjacent work shops.

[0059] Specifically, the preset map interface can be called to obtain real-time road condition time consumption prediction values of multiple preset travel modes (such as driving, riding, and walking) between adjacent work stores based on path distances between the adjacent work stores. The prediction values are affected by dynamic factors such as real-time traffic flow, road construction, and weather conditions. To reduce the interference of extreme abnormal values on the results, the system arranges the obtained multiple real-time road condition time consumption prediction values in descending order, removes the largest time consumption prediction value (to avoid deviation caused by accidental severe congestion), takes an arithmetic mean of the remaining prediction values, and finally obtains a relatively stable and reliable first travel time consumption between the adjacent stores, which is used for subsequent total working time calculation and scheduling optimization.

[0060] For example, an operator needs to go from store A to store B, and the path distance between the two stores is 10 kilometers. After the system calls the map interface, real-time road condition time consumption prediction values of three preset travel modes in the current period are obtained: 25 minutes for driving, 40 minutes for riding, and 90 minutes for walking. The three values are arranged in descending order as 90 minutes, 40 minutes, and 25 minutes, the largest 90 minutes (walking prediction value, which may be greatly affected by sudden factors due to long-distance walking) is removed, and an arithmetic mean of the remaining 40 minutes and 25 minutes is taken, that is, (40+25) / 2=32.5 minutes, which is taken as the first travel time consumption from store A to store B. The value is closer to the actual possible travel time and can provide a more reasonable time reference for operation scheduling.

[0061] Step 230-3, according to the cumulative travel data of the target operator at historical time, calculating the second travel time consumption of each path distance.

[0062] For the embodiments of the present disclosure, the cumulative travel data of the target operator between each work store at different historical times can be collected, which covers the specific path distance, actual time consumption, and various environmental factors (such as traffic congestion, weather conditions, etc.) at the time of travel. On this basis, for each specific path distance, the travel time consumption of the same or similar path distance in the historical data can be analyzed, the interference of abnormal values is removed through statistical analysis (such as calculating the average value, median, etc.), and finally the second travel time consumption of the path distance is obtained. For example, an operator repeatedly travels between store X and store Y, the distance is 8 kilometers, the time consumption in the historical data fluctuates between 12-20 minutes, and after removing the abnormal long time consumption of 20 minutes, the average value of the remaining data is 14 minutes, which is the second travel time consumption of the path distance. The time consumption reflects the general rule of the operator's travel on the path, and can be used as a supplementary reference for travel time prediction, making the overall working time evaluation more personalized and accurate.

[0063] As a possible implementation, after obtaining the cumulative travel data of the target operation personnel at the historical time, including the cumulative travel distance and the cumulative travel time, based on these data, the average travel speed of the operation personnel in the historical journey can be calculated by the formula "average travel speed = cumulative travel distance ÷ cumulative travel time", which comprehensively reflects the driving habits, route familiarity and ability to deal with traffic conditions. After obtaining the average travel speed, for each path distance to be calculated, the second travel time required to pass through the path at the average travel speed can be calculated using the formula "second travel time = path distance ÷ average travel speed". For example, if the cumulative travel distance of an operation personnel is 500 kilometers and the cumulative travel time is 10 hours, the average travel speed is 50 kilometers / hour. When there is a 15-kilometer path in a new task, the second travel time calculated based on the average speed is 15 ÷ 50 = 0.3 hours (18 minutes). The second travel time calculated in this way, combined with the personal historical travel characteristics, can more accurately estimate the actual time spent by the operation personnel on each path, providing strong support for scientific planning of operation routes and reasonable arrangement of work load.

[0064] Correspondingly, step 230-3 can include the following steps: based on the cumulative travel distance and the cumulative travel time, calculating the average travel speed of the target operation personnel. Based on the average travel speed, the second travel time under each path distance is calculated.

[0065] Step 230-4, according to the preset weighting weight, the first travel time and the second travel time corresponding to the same adjacent operation store are weighted and summed to obtain the third travel time.

[0066] Among them, the first travel time is generated by the preset map interface combined with real-time traffic, which can reflect the instantaneous state of the current traffic environment; the second travel time is calculated based on the historical cumulative travel data of the target operation personnel, which reflects the long-term characteristics of the personal driving habits, route familiarity, etc. The preset weighting weight is a proportion parameter preset according to the actual business needs, which is used to balance the importance of the two kinds of time data.

[0067] For the embodiments of the present disclosure, the first travel time and the second travel time corresponding to the same adjacent operation store can be multiplied by the respective weighting weights and then summed to obtain the third travel time. For example, if the preset weights are 60% for the first travel time and 40% for the second travel time, and the first travel time between certain adjacent stores is 20 minutes and the second travel time is 18 minutes, then the third travel time = 20*60% + 18*40% = 12 + 7.2 = 19.2 minutes. The third travel time thus obtained not only takes into account the dynamic changes of real-time road conditions, but also incorporates the historical travel characteristics of the operation and maintenance personnel, and can more comprehensively and accurately reflect the actual travel time required, providing a more scientific basis for subsequent operation and maintenance scheduling and work duration assessment.

[0068] Step 230-5: determining any one of the first travel time, the second travel time and the third travel time as the travel time between the adjacent operation stores.

[0069] Through this flexible selection mechanism, the enterprise can optimize the calculation logic of the travel time in different business scenarios, avoid the limitations of a single method, and thus more accurately support the time management and saturation assessment of the operation and maintenance work.

[0070] Step 240: accumulating the operation and maintenance time and the travel time to obtain the total work duration of the target operation and maintenance personnel in the single-day operation and maintenance cycle.

[0071] For the embodiments of the present disclosure, the specific implementation process can be referred to the related description in the embodiment step 130, which will not be repeated here.

[0072] Step 250: evaluating the operation and maintenance work saturation of the target operation and maintenance personnel according to the total work duration to obtain a saturation evaluation result.

[0073] In order to evaluate the saturation, the duration thresholds corresponding to different work intensities (such as a single-day standard work duration of 8 hours, more than 10 hours as overloading, and less than 6 hours as under-saturation) can be preset, and the total work duration is compared with these thresholds to divide the saturation levels. For example, if the total work duration of the target operation and maintenance personnel is 11 hours, which exceeds the overloading threshold, it can be determined that it is in an “overload” state; if the total work duration is 5 hours, it belongs to “low saturation”. The evaluation result can help the enterprise to intuitively grasp the personnel work state, provide an important basis for optimizing task allocation, adjusting scheduling strategies, ensuring employee health and improving work efficiency, and avoid low efficiency or personnel fatigue caused by uneven work load.

[0074] For the embodiments of the present disclosure, the evaluation of the operation and maintenance work saturation of the target operation and maintenance personnel according to the total work duration in step 250 to obtain the saturation evaluation result can include the following steps:

[0075] Step 250-1, compare the total working time with the first preset time threshold and the second preset time threshold respectively, the first preset time threshold is less than the second preset time threshold.

[0076] Wherein, the first preset time threshold and the second preset time threshold constitute the evaluation scale, and the first preset time threshold is less than the second preset time threshold, forming an interval standard for measuring working time. By comparing the total working time with the two thresholds, the working state can be divided into different levels: if the total working time is less than the first preset time threshold, the workload of the operation and maintenance personnel is low; if the total working time is between the two thresholds, the working intensity is in a reasonable range; if the total working time exceeds the second preset time threshold, the workload is too high. For example, set the first preset time threshold to 6 hours and the second preset time threshold to 8 hours, when the total working time of a certain operation and maintenance personnel is 5 hours, it is determined that the work is not saturated; if the time is 7 hours, the working intensity is reasonable; if the time reaches 9 hours, it is determined as overloading work.

[0077] Step 250-2, if the total working time is less than or equal to the first preset time threshold, it is determined that the saturation evaluation result corresponding to the target operation and maintenance personnel is not up to standard.

[0078] When the total working time of the target operation and maintenance personnel is less than or equal to the first preset time threshold, it means that the actual work input of the target operation and maintenance personnel in the single day operation and maintenance cycle is insufficient, and the basic work amount standard set by the enterprise or industry is not reached, so the saturation evaluation result of the target operation and maintenance personnel is determined as "not up to standard". This indicates that the target operation and maintenance personnel may have insufficient task allocation, low work efficiency or resource idling, etc., and the enterprise can accordingly reallocate tasks to avoid waste of human resources.

[0079] Step 250-3, if the total working time is greater than the first preset time threshold and less than the second preset time threshold, it is determined that the saturation evaluation result corresponding to the target operation and maintenance personnel is up to standard.

[0080] When the total working time exceeds the first preset time threshold but has not reached the second preset time threshold, it means that the workload of the target operation and maintenance personnel is within the normal range recognized by the enterprise. At this time, the working intensity not only guarantees sufficient business output, but also does not exceed the reasonable labor intensity, and the work efficiency and resource utilization are balanced, so the corresponding saturation evaluation result is determined as "up to standard".

[0081] Step 250-4, if the total working time is greater than or equal to the second preset time threshold, it is determined that the saturation evaluation result corresponding to the target operation and maintenance personnel is overloading.

[0082] If the total working time of the target operation and maintenance personnel is greater than or equal to the second preset time threshold, it indicates that the daily work input of the target operation and maintenance personnel exceeds a reasonable range and is in an overworked state. This "overload saturation evaluation result means that the personnel may face problems such as excessive work pressure, decreased efficiency, or increased health risks, and the enterprise can intervene in time to reduce the task amount, optimize the work path, or adjust the personnel arrangement to relieve the work load of the target operation and maintenance personnel and ensure the sustainability of the operation and maintenance work and the health of the personnel.

[0083] In step 260, an activity distribution map of the target operation and maintenance personnel is generated according to the store address, the operation and maintenance time of the target operation and maintenance personnel at each work store, and the travel time between any adjacent work stores, and an abnormal event is marked in the activity distribution map.

[0084] According to the embodiments of the present disclosure, the spatial position, time consumption, and abnormal event data can be integrated to intuitively present the work trajectory and state of the target operation and maintenance personnel. First, the positions of all work stores can be marked on an electronic map based on the address information (such as latitude and longitude or address coordinates) of each work store, and the operation and maintenance time of the target operation and maintenance personnel at each store (such as 45 minutes of maintenance at store A) and the travel time between adjacent stores (such as 20 minutes of driving from A to B) are combined to form a complete daily activity path in time sequence. These data can be visualized into an activity distribution map, in which the travel route is usually represented by a line, and the operation and maintenance time of each store is marked by different colors or icons to form a clear time-space trajectory.

[0085] At the same time of generating the activity distribution map, abnormal events can also be automatically identified and marked. For example, if a certain travel time significantly exceeds the historical average level (such as 1 hour of travel time for a regular 20-minute journey), or the operation and maintenance time of a certain store exceeds the predicted value by a certain threshold (such as 1 hour of inspection time for a predicted 30-minute inspection), an abnormal symbol (such as a red exclamation mark) can be marked at the corresponding route or store position, and the abnormal type (such as "traffic congestion" or "device fault escalation") and specific time deviation value can be attached. This visualization method can help managers quickly locate inefficient links or sudden problems in the work process. For example, by viewing the activity distribution map, it can be intuitively found that a certain operation and maintenance personnel is delayed due to sudden road construction, or a certain store is delayed due to the complexity of device fault exceeding the expected value, so that the subsequent scheduling plan can be adjusted in time or support can be provided.

[0086] Correspondingly, when automatically identifying and labeling abnormal events in the activity distribution map, the embodiment steps can specifically include: obtaining an activity distribution map of a target operation and maintenance personnel, the activity distribution map being generated based on a store address, operation and maintenance time of the target operation and maintenance personnel at each work store, and travel time between any adjacent work stores; setting a preset rule for identifying abnormal events for the activity distribution map, the preset rule including a first abnormality judgment rule based on operation and maintenance time and a second abnormality judgment rule based on travel time; analyzing operation and maintenance time data in the activity distribution map according to the first abnormality judgment rule, and if operation and maintenance time of a certain work store exceeds a preset operation and maintenance time threshold, determining that the operation and maintenance event of the store is an abnormal event; analyzing travel time data in the activity distribution map according to the second abnormality judgment rule, and if travel time between any adjacent work stores exceeds a preset travel time threshold, determining that the travel event of the segment is an abnormal event; and for the determined abnormal event, using a preset labeling method to label in the activity distribution map, the labeling method including but not limited to using a specific color, a special symbol, or a pop-up prompt box.

[0087] The first abnormality judgment rule is to compare the operation and maintenance time with the preset operation and maintenance time threshold, and if the operation and maintenance time of a certain work store exceeds the preset operation and maintenance time threshold, it is determined that the operation and maintenance event of the store is an abnormal event. The second abnormality judgment rule is to compare the travel time with the preset travel time threshold, and if the travel time between any adjacent work stores exceeds the preset travel time threshold, it is determined that the travel event of the segment is an abnormal event. In addition, the abnormal event labeling also includes detailed description of the type, occurrence time, involved work store or path of the abnormal event, which is not specifically limited here.

[0088] Step 270, output a work saturation report containing the activity distribution map, operation and maintenance time of the target operation and maintenance personnel at each work store, travel time between any adjacent work stores, and saturation evaluation results.

[0089] In a specific application scenario, the activity distribution map, operation and maintenance time data, travel time data, and saturation evaluation results can be integrated to generate a work saturation report. That is, these data and results are summarized and output in a structured report form. From data to conclusion, a complete link is formed, which not only helps managers quickly understand the work saturation of operation and maintenance personnel, but also discovers potential problems in the work process based on this, providing decision basis for subsequent optimization of scheduling and improvement of efficiency.

[0090] As a possible implementation, the work saturation report can also include statistical analysis results of the operation time consumption data and the travel time consumption data, including but not limited to the average value, the median, and the standard deviation. Based on the statistical analysis results, the data value can be further mined. For example, the average value of the operation time consumption can reflect the average processing time length of the routine task. If the single operation time consumption of a certain employee is much higher than the average value, there may be problems such as unskilled technology or complex equipment failure. The standard deviation can reflect the dispersion degree of the data. The larger the standard deviation, the more intense the fluctuation of the operation time consumption, which means that the work stability is poor. Through these statistical analyses, the manager can not only understand the overall work load, but also accurately locate the inefficient links and abnormal fluctuations, so as to optimize personnel scheduling and improve work processes, and realize the refinement and efficiency of operation management.

[0091] In summary, the technical scheme in the present application obtains daily report data containing operation action types, store addresses, and device states, dynamically calculates job time consumption using a pre-trained time consumption prediction model combined with action types and device states, generates an operation path based on a store address, fuses real-time road condition data through a map interface with historical travel data to calculate travel time consumption, and then adds job time consumption and travel time consumption to obtain total work time. Then, the work saturation level is divided into three levels of substandard, standard, and overload through double preset thresholds, and an activity distribution map marking abnormal events and a work saturation report containing multi-dimensional data are generated. The present scheme breaks through the limitation of traditional evaluation based on the number of actions, can realize accurate prediction of job time consumption, scientific quantification of travel time consumption, and dynamic classification of work load, effectively solves the problem of unfair evaluation caused by spatial distribution differences, path planning, and different job difficulties, and can provide managers with refined decision-making basis based on actual work time and spatio-temporal characteristics, avoid waste of human resources, and improve operation efficiency and evaluation fairness.

[0092] Further, as a specific implementation of the method shown in Figure 1 and Figure 2 The present embodiment provides an operation work saturation evaluation device. As shown in Figure 3 The device comprises an acquisition module 31, a prediction module 32, a calculation module 33, and an evaluation module 34.

[0093] The acquisition module 31 can be used to acquire daily report data of a target operation personnel in a single-day operation period, and the daily report data at least contains operation action types, store addresses, and device states corresponding to each job store;

[0094] The prediction module 32 can be used to predict operation time consumption of the target operation personnel in each job store according to the operation action types and the device states, and to determine travel time consumption between any adjacent job stores according to the store addresses;

[0095] The computing module 33 can be configured to accumulate the operation and maintenance time consumption and the travel time consumption to obtain total work time of the target operation and maintenance personnel in a single-day operation and maintenance period.

[0096] The evaluation module 34 can be configured to evaluate the operation and maintenance work saturation of the target operation and maintenance personnel according to the total work time to obtain a saturation evaluation result.

[0097] In some embodiments of the present application, when predicting the operation and maintenance time consumption of the target operation and maintenance personnel in each work shop according to the operation and maintenance action type and the equipment state, the prediction module 32 can be specifically configured to input the operation and maintenance action type and the equipment state corresponding to each work shop into a pre-trained time consumption prediction model to obtain the operation and maintenance time consumption of the target operation and maintenance personnel in the corresponding work shop.

[0098] Correspondingly, as shown in Figure 4 the device further includes a training module 35.

[0099] When training the time consumption prediction model, the training module 35 can be specifically configured to: take a preset sliding window as a data collection period to obtain historical operation and maintenance data of a sample operation and maintenance personnel, the historical operation and maintenance data including historical operation and maintenance action types, historical equipment states, and actual time consumption of the sample operation and maintenance personnel when performing historical operation and maintenance tasks, the actual time consumption being a time difference between a check-in time and a log uploading time; take the historical operation and maintenance action type and the historical equipment state corresponding to each actual time consumption as input data, and take the actual time consumption as a training label; iteratively train the time consumption prediction model until a loss function of the time consumption prediction model is less than a preset threshold, and determine that the training of the time consumption prediction model is completed.

[0100] In some embodiments of the present application, when determining the travel time consumption between any adjacent work shops according to the shop address, the prediction module 32 can be specifically configured to generate an operation and maintenance work path of the target operation and maintenance personnel in a single-day operation and maintenance period based on the shop address, and determine a path distance between any adjacent work shops on the operation and maintenance work path; predict a first travel time consumption under each path distance by using a preset map interface; calculate a second travel time consumption under each path distance according to cumulative travel data of the target operation and maintenance personnel at a historical time; weight and sum the first travel time consumption and the second travel time consumption corresponding to the same adjacent work shop according to a preset weighting weight to obtain a third travel time consumption; and determine any one of the first travel time consumption, the second travel time consumption, and the third travel time consumption as the travel time consumption between the corresponding adjacent work shops.

[0101] In some embodiments of the present application, when predicting the first travel time consumption under each path distance by using the preset map interface, the prediction module 32 can be specifically configured to obtain, by calling the preset map interface, a real-time road condition time consumption prediction value of each preset travel mode for the path distance between any adjacent work stores in the current time period; arrange the plurality of real-time road condition time consumption prediction values corresponding to the plurality of preset travel modes in descending order, remove the maximum real-time road condition time consumption prediction value, and calculate the arithmetic mean of the remaining real-time road condition time consumption prediction values as the first travel time consumption between the corresponding adjacent work stores.

[0102] In some embodiments of the present application, the cumulative travel data includes cumulative travel distance and cumulative travel time consumption, and when calculating the second travel time consumption under each path distance according to the cumulative travel data of the target operation and maintenance personnel at the historical time, the prediction module 32 can be specifically configured to calculate the average travel speed of the target operation and maintenance personnel based on the cumulative travel distance and the cumulative travel time consumption; and calculate the second travel time consumption under each path distance based on the average travel speed.

[0103] In some embodiments of the present application, when evaluating the operation and maintenance work saturation of the target operation and maintenance personnel according to the total working time to obtain the saturation evaluation result, the evaluation module 34 can be specifically configured to compare the total working time with a first preset time threshold and a second preset time threshold respectively, the first preset time threshold being smaller than the second preset time threshold; if the total working time is less than or equal to the first preset time threshold, it is judged that the saturation evaluation result corresponding to the target operation and maintenance personnel is substandard; if the total working time is greater than the first preset time threshold and less than the second preset time threshold, it is judged that the saturation evaluation result corresponding to the target operation and maintenance personnel is up to standard; and if the total working time is greater than or equal to the second preset time threshold, it is judged that the saturation evaluation result corresponding to the target operation and maintenance personnel is overloaded.

[0104] In some embodiments of the present application, as shown in Figure 4 the device further comprises a generation module 36 and an output module 37;

[0105] The generation module 36 can be configured to generate an activity distribution map of the target operation and maintenance personnel according to the store address, the operation and maintenance time consumption of the target operation and maintenance personnel at each work store, and the travel time consumption between any adjacent work stores, and mark abnormal events in the activity distribution map;

[0106] The output module 37 can be configured to output a work saturation report containing the activity distribution map, the operation and maintenance time consumption of the target operation and maintenance personnel at each work store, the travel time consumption between any adjacent work stores, and the saturation evaluation result.

[0107] It should be noted that other corresponding descriptions of the various functional units involved in the evaluation device for operation and maintenance work saturation provided in the present embodiment can be referred toFigure 1 and Figure 2 in the corresponding description, which will not be repeated here.

[0108] Based on the above method as shown in Figure 1 and Figure 2 , accordingly, the embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to implement the above-mentioned evaluation method of operation and maintenance work saturation as shown in Figure 1 and Figure 2 .

[0109] Based on such understanding, the technical scheme of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.), including a plurality of instructions for causing an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute the method of each implementation scenario of the present application.

[0110] Based on the above method as shown in Figure 1 and Figure 2 , and Figure 3 and Figure 4 the virtual device embodiment, in order to achieve the above-mentioned purpose, the embodiment of the present application also provides an electronic device, which can be a personal computer, a tablet computer, a server, or other network devices, etc., the device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to realize the above-mentioned evaluation method of operation and maintenance work saturation as shown in Figure 1 and Figure 2 .

[0111] Optionally, the above-mentioned entity device can also include a user interface, a network interface, a camera, a radio frequency (Radio Frequency, RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface can include a display screen (Display), an input unit such as a keyboard (Keyboard), etc. The optional user interface can also include a USB interface, a card reader interface, etc. The network interface can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0112] Those skilled in the art can understand that the above-mentioned entity device structure provided by the embodiment does not constitute a limitation on the entity device, and can include more or fewer components, or combine certain components, or different component arrangements.

[0113] The storage medium can further include an operating system and a network communication module. The operating system is a program for managing hardware and software resources of the information processing entity device, and supports the running of information processing programs and other software and / or programs. The network communication module is used to realize communication between components in the storage medium and communication with other hardware and software in the information processing entity device.

[0114] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software with a necessary general hardware platform, or by hardware.

[0115] The embodiment of the present application obtains daily report data containing operation and maintenance action type, store address and device state, dynamically calculates job time consumption by using a pre-trained time consumption prediction model combined with action type and device state, generates an operation and maintenance path based on the store address, calculates travel time consumption by fusing real-time road condition data and historical travel data through a map interface, adds job time consumption and travel time consumption to obtain total working time, and then divides the working time into three saturation levels of substandard, standard and overload through double preset thresholds, generates an activity distribution map of marked abnormal events and a work saturation report containing multi-dimensional data. The present scheme breaks through the limitation of traditional evaluation based on action quantity, can realize accurate prediction of job time consumption, scientific quantification of travel time consumption and dynamic classification of work load, effectively solves the problem of unfair evaluation caused by spatial distribution difference, path planning and different job difficulty, provides fine decision basis for managers based on actual working time and space-time characteristics, avoids waste of human resources and improves operation and maintenance efficiency and evaluation fairness.

[0116] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the drawings are not necessarily essential for implementing the present application. Those skilled in the art can understand that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the description of the implementation scenario, or can be changed and located in one or more devices different from the implementation scenario. The modules of the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0117] The above application serial number is only for description, and does not represent the advantages and disadvantages of the implementation scenario. The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present application.

Claims

1. A method for assessing the saturation of operation and maintenance workload, characterized in that, include: Obtain daily data of the target maintenance personnel within a single day's maintenance cycle. The daily data shall include at least the maintenance action type, store address, and equipment status for each work store. Based on the type of maintenance action and the status of the equipment, predict the maintenance time of the target maintenance personnel in each of the work shops, and determine the travel time between any two adjacent work shops based on the shop address; By summing the maintenance time and the travel time, the total working time of the target maintenance personnel within the daily maintenance cycle is obtained. The saturation of the target maintenance personnel is assessed based on the total working time, and the saturation assessment result is obtained.

2. The method according to claim 1, characterized in that, The step of predicting the maintenance time of the target maintenance personnel in each of the work sites based on the maintenance action type and the equipment status includes: Input the operation and maintenance action type and equipment status corresponding to each operation store into the pre-trained time prediction model to obtain the operation and maintenance time of the target operation and maintenance personnel in the corresponding operation store; The training process of the time consumption prediction model includes: Using a preset sliding window as the data collection cycle, historical maintenance data of sample maintenance personnel is obtained. The historical maintenance data includes historical maintenance action types, historical equipment status, and the actual time consumed by the sample maintenance personnel when performing historical maintenance tasks. The actual time consumed is the time difference between the time of clocking in at the store and the time of log upload. The historical operation and maintenance action type and historical device status corresponding to each actual time consumption are used as input data, and the actual time consumption is used as training label. The time consumption prediction model is iteratively trained until the loss function of the time consumption prediction model is less than a preset threshold, and the training of the time consumption prediction model is determined to be complete.

3. The method according to claim 1, characterized in that, The step of determining the travel time between any two adjacent work locations based on the store address includes: Based on the store address, generate the operation and maintenance work path of the target operation and maintenance personnel within the daily operation and maintenance cycle, and determine the path distance between any adjacent operation stores on the operation and maintenance work path; The first travel time for each of the aforementioned path segments is estimated using a preset map interface. Based on the cumulative travel data of the target maintenance personnel at historical moments, calculate the second travel time for each of the aforementioned path distances; The first and second travel times corresponding to the same adjacent shops are weighted and summed according to a preset weighting weight to obtain the third travel time. Any one of the first travel time, the second travel time, and the third travel time is determined as the travel time between the corresponding adjacent operating stores.

4. The method according to claim 3, characterized in that, The step of estimating the first travel time for each path segment using a preset map interface includes: By calling the preset map interface, the real-time traffic time prediction value for each preset travel mode in the current time period is obtained for the path distance between any adjacent business stores. Multiple real-time traffic time predictions for various preset travel modes are sorted in descending order. The arithmetic mean of the remaining real-time traffic time predictions is calculated after removing the maximum real-time traffic time prediction, and this is used as the first travel time between adjacent shops.

5. The method according to claim 3, characterized in that, The cumulative travel data includes cumulative travel distance and cumulative travel time. Based on the cumulative travel data of the target maintenance personnel at historical moments, the second travel time for each segment of the path distance is calculated, including: Based on the cumulative travel distance and the cumulative travel time, calculate the average travel speed of the target maintenance personnel; The second travel time for each segment of the path is calculated based on the average travel speed.

6. The method according to claim 1, characterized in that, The workload saturation of the target maintenance personnel is assessed based on the total working time, and the saturation assessment results are obtained, including: The total working time is compared with a first preset time threshold and a second preset time threshold, respectively, and the first preset time threshold is less than the second preset time threshold; If the total working time is less than or equal to the first preset time threshold, then the saturation assessment result corresponding to the target maintenance personnel is determined to be substandard. If the total working time is greater than the first preset time threshold and less than the second preset time threshold, then the saturation assessment result corresponding to the target maintenance personnel is determined to be satisfactory. If the total working time is greater than or equal to the second preset time threshold, then the saturation assessment result corresponding to the target maintenance personnel is determined to be overloaded.

7. The method according to claim 1, characterized in that, The method further includes: Based on the store address, the maintenance time of the target maintenance personnel in each of the operation stores, and the travel time between any adjacent operation stores, an activity distribution map of the target maintenance personnel is generated, and abnormal events are marked in the activity distribution map. The output includes the activity distribution map, the operation and maintenance time of the target operation and maintenance personnel in each of the operation stores, the travel time between any adjacent operation stores, and the work saturation report of the saturation assessment results.

8. A device for evaluating the saturation of operation and maintenance work, characterized in that, include: The acquisition module is used to acquire the daily data of the target maintenance personnel within a single day's maintenance cycle. The daily data includes at least the maintenance action type, store address, and equipment status for each work store. The prediction module is used to predict the maintenance time of the target maintenance personnel in each of the work shops based on the maintenance action type and the equipment status, and to determine the travel time between any adjacent work shops based on the shop address. The calculation module is used to accumulate the operation and maintenance time and the travel time to obtain the total working time of the target operation and maintenance personnel within the single-day operation and maintenance cycle; The evaluation module is used to evaluate the work saturation of the target maintenance personnel based on the total working time, and obtain the saturation evaluation result.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

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