Intelligentization-based human resource scheduling configuration method and platform

Abstract

The invention provides an intelligentization-based human resource scheduling configuration method and platform, and the method comprises the steps: obtaining monitoring data of a monitoring device for a management region, and determining a dynamic scheduling region in the management region according to a monitoring frame corresponding to the monitoring data; performing region interference removal processing on the monitoring frame based on the scene restoration model to obtain an optimization frame corresponding to the dynamic scheduling region; according to the area layout information of the non-operation area in the optimization frame, determining a scheduling type corresponding to the dynamic scheduling area, the scheduling type including a special scheduling type and a cooperative scheduling type; and determining a personnel type corresponding to the dynamic scheduling area based on the scheduling type, determining a scheduling number corresponding to the personnel type according to the operation intensity index of the operation area in the optimization frame, and generating personnel scheduling data corresponding to the dynamic scheduling area based on the personnel type and the scheduling number.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to a method and platform for scheduling and configuring human resources based on intelligence. Background Art

[0002] The current sanitation management mainly involves scheduling sanitation workers and vehicles in advance, and then having the scheduled personnel clean fixed areas according to the configured time. However, this method cannot make intelligent arrangements for sanitation workers based on the actual situation of the cleaning area, and the scheduling efficiency is not high.

[0003] Therefore, how to automatically dispatch sanitation human resources based on the actual situation in the cleaning area and improve dispatch efficiency has become an urgent problem that needs to be solved today. Summary of the invention

[0004] The present invention provides a method and platform for human resource scheduling configuration based on intelligence, which can automatically schedule sanitation human resources in combination with the actual situation of the cleaning area, thereby improving the scheduling efficiency.

[0005] A first aspect of the present invention provides a method for intelligent human resource scheduling and configuration, comprising: Acquire monitoring data of the management area by the monitoring device, and determine the dynamic scheduling area in the management area according to the monitoring frame corresponding to the monitoring data; Performing regional interference removal processing on the monitoring frame based on the scene restoration model to obtain an optimized frame corresponding to the dynamic scheduling area; determining the scheduling type corresponding to the dynamic scheduling area according to the regional layout information of the non-operating area in the optimized frame, the scheduling type including a special scheduling type and a collaborative scheduling type; The personnel type corresponding to the dynamic scheduling area is determined based on the scheduling type, the scheduling quantity corresponding to the personnel type is determined according to the work intensity index of the work area in the optimization frame, and the personnel scheduling data corresponding to the dynamic scheduling area is generated based on the personnel type and the scheduling quantity.

[0006] Optionally, in a possible implementation manner of the first aspect, obtaining monitoring data of a management area by a monitoring device, and determining a dynamic scheduling area in the management area according to a monitoring frame corresponding to the monitoring data, includes: Arranging the monitoring frames in time order to obtain a monitoring sequence, and sequentially acquiring adjacent monitoring frames in the monitoring sequence as an analysis group; Extracting road markings in each monitoring frame in the analysis group, and dividing the road area in the monitoring frame according to the road markings to obtain a plurality of sub-road areas; Acquire vehicle profiles in each of the sub-road areas, determine the center point of each of the vehicle profiles as a positioning point, acquire the moving distance between the positioning points of the same vehicle profile in the analysis group, and determine the vehicle profile whose moving distance is greater than a preset threshold as a flow profile; The ratio of the flow contour to all vehicle contours in each sub-road area is statistically calculated to obtain a flow ratio. When the flow ratios of the corresponding sub-road areas in a preset number of analysis groups are all less than a flow ratio threshold, the corresponding sub-road area is determined to be a dynamic scheduling area.

[0007] Optionally, in a possible implementation manner of the first aspect, performing regional interference removal processing on the monitoring frame based on a scene restoration model to obtain an optimized frame corresponding to the dynamic scheduling area includes: Acquire the monitoring time of each monitoring frame based on the scene restoration model, determine the first monitoring frame as a processing frame, and take the monitoring time of the processing frame as a reference, and sequentially acquire multiple monitoring frames at preset intervals as processing frames; Arranging the processing frames from front to back according to the monitoring time of each processing frame to obtain a processing sequence, and extracting the target object contours located in the dynamic scheduling area in each processing frame in sequence based on the processing sequence, wherein the target object contours include at least a person contour and a vehicle contour; Obtain interference areas corresponding to the contours of each target object, and perform road completion on the dynamic scheduling area according to the interference areas to obtain an optimized frame.

[0008] Optionally, in a possible implementation manner of the first aspect, obtaining interference areas corresponding to the contours of each target object, and performing road filling on the dynamic scheduling area according to the interference areas to obtain an optimized frame includes: Generate an outer contour frame corresponding to each target object contour, and determine the area within the outer contour frame as an interference area; Acquire the adjacent processing frames as a processing group, determine the processing frames arranged at the rear in the processing group as subsequent frames, and the processing frames arranged at the front as preceding frames; Obtaining the same region corresponding to the interference region in the subsequent frame in the preceding frame, and when the same region in the preceding frame does not intersect with the remaining interference regions, extracting the image in the same region and replacing it into the corresponding interference region in the subsequent frame; Determine that the updated subsequent frame is a preceding frame in the next processing group, and repeat the step of updating the interference region corresponding to the subsequent frame in the processing group until no interference region exists in the updated subsequent frame, or stop the above updating step when all processing frames are updated; A processing frame without the interference area or a last processing frame in the processing sequence is obtained as an optimized frame.

[0009] Optionally, in a possible implementation manner of the first aspect, determining a scheduling type corresponding to the dynamic scheduling area according to the regional layout information of the non-operating area in the optimization frame, the scheduling type including a special scheduling type and a collaborative scheduling type, including: Obtaining interference areas in the optimization frame as non-operation areas, counting the total non-operation areas of the non-operation areas, and obtaining the non-operation ratio according to the ratio of the total non-operation area to the total area of ​​the dynamic scheduling area; Determine that the scheduling type of the dynamic scheduling area whose non-operation ratio is greater than or equal to the benchmark non-operation ratio is a special scheduling type, and add a manual label to the dynamic scheduling area; Obtaining the minimum distance between the non-operation areas in the dynamic scheduling area where the non-operation ratio is less than the benchmark non-operation ratio; Determine that the scheduling type of the dynamic call zone where the minimum distance of the area exists is less than or equal to the cleaning distance threshold is the collaborative scheduling type; The scheduling type of the dynamic scheduling area where the minimum distance of the non-operating area is less than or equal to the cleaning distance threshold is obtained as a special scheduling type, and an equipment label is added to the dynamic scheduling area. The regional layout information includes the non-operating proportion and the minimum distance of the non-operating area.

[0010] Optionally, in a possible implementation manner of the first aspect, determining the personnel type corresponding to the dynamic scheduling area based on the scheduling type, and determining the scheduling quantity corresponding to the personnel type according to the operation intensity index of the operation area in the optimization frame includes: Determine that the dispatch type is a special dispatch type, and the personnel type corresponding to the manual label is a first-class personnel type, and the first-class personnel type is a personnel type that is not equipped with cleaning equipment; The dispatch type is obtained as a special dispatch type, and the personnel type corresponding to the equipment tag is a second-class personnel type, and the second-class personnel type is the personnel type for configuring the cleaning equipment; Determine that the personnel type whose scheduling type is the collaborative scheduling type is a first-class personnel type and a second-class personnel type; Based on the special dispatch type, a single matching strategy is called to obtain the work intensity index corresponding to the work area in the optimization frame, and the dispatch quantity corresponding to the personnel type is determined according to the work intensity index; or, According to the collaborative scheduling type, a comprehensive matching strategy is called to obtain the work intensity index corresponding to the work area in the optimization frame, and the scheduling quantity corresponding to the personnel type is determined according to the work intensity index.

[0011] Optionally, in a possible implementation of the first aspect, a single matching strategy is retrieved based on the special dispatch type to obtain a work intensity index corresponding to the work area in the optimization frame, and a dispatch quantity corresponding to the personnel type is determined according to the work intensity index, including: Acquire the area other than the non-operation area in the dynamic scheduling area in the optimization frame as the operation area, and determine the pixel points in the operation area whose pixel values ​​are not located in the road pixel interval as abnormal pixel points; Counting the number of abnormal pixels and the total number of pixels in the operation area, and obtaining a first contamination coefficient according to a ratio of the number of abnormal pixels to the total number of pixels; Obtain a first coefficient difference between the first pollution coefficient and a benchmark pollution coefficient, obtain an intensity adjustment coefficient based on a ratio of the first coefficient difference to the benchmark difference, and obtain an operation intensity index based on the product of the intensity adjustment coefficient and the benchmark intensity index; retrieve a quantity matching table corresponding to the personnel type, traverse the quantity matching table according to the operation intensity index, and determine that the standard intensity interval corresponding to the operation intensity index in the quantity matching table is the target intensity interval; Determine the standard matching quantity corresponding to the target intensity interval as the scheduling quantity corresponding to the personnel type.

[0012] Optionally, in a possible implementation manner of the first aspect, calling a comprehensive matching strategy according to the collaborative scheduling type to obtain a work intensity index corresponding to the work area in the optimization frame, and determining a scheduling quantity corresponding to the personnel type according to the work intensity index, includes: Acquire a non-operating area in the dynamic scheduling area whose minimum distance is less than or equal to a cleaning distance threshold as a neighboring area group, generate a comprehensive outer frame corresponding to the contours of each target object in the neighboring area group, and determine an area other than the non-operating area in the comprehensive outer frame as a first operating area; Determine an area in the dynamic scheduling area other than the area corresponding to the integrated outer frame and the non-operating area as a second operating area, wherein the operating area includes a first operating area and a second operating area; Obtaining a first adjustment coefficient according to a ratio of the total operation area of ​​each of the first operation areas to a type of benchmark operation area, and obtaining an operation intensity index corresponding to the type of personnel according to the product of the first adjustment coefficient and the benchmark intensity index; Counting pixel points in the second operation area whose pixel values ​​are not located in the road pixel interval as abnormal pixel points, and obtaining a second pollution coefficient according to the ratio between the total number of the abnormal pixel points and the total number of all pixel points in the second operation area; Obtaining a second coefficient difference between the second pollution coefficient and the benchmark pollution coefficient, obtaining a second adjustment coefficient according to a ratio of the second coefficient difference to the benchmark difference, and obtaining an operation intensity index of the second type of personnel based on the product of the second adjustment coefficient and the benchmark intensity index; Retrieve a quantity matching table corresponding to the corresponding personnel type, traverse the quantity matching table according to the operation intensity index, and determine that the standard intensity interval corresponding to the operation intensity index in the quantity matching table is the target intensity interval; Determine the standard matching quantity corresponding to the target intensity interval as the scheduling quantity corresponding to the corresponding personnel type.

[0013] Optionally, in a possible implementation manner of the first aspect, generating personnel scheduling data corresponding to the dynamic scheduling area based on the personnel type and the scheduling quantity includes: Acquire cleaning personnel in an idle state from among a plurality of cleaning personnel corresponding to each of the personnel types as personnel to be assigned, and determine the interval distance between the personnel to be assigned and each management area that meets the scheduling type of the management area; According to the interval distance, the number of personnel to be assigned is selected from small to large as the target personnel corresponding to the corresponding management area; the scheduling distribution map is retrieved, the scheduling distribution map includes the road segments corresponding to each of the management areas, and the clean matching table corresponding to the corresponding road segment is updated according to the personnel identity of each of the target personnel, and the personnel scheduling data is obtained and sent to the management end.

[0014] The second aspect of the present invention provides a human resource scheduling and configuration platform based on intelligence, comprising: A division module, used for obtaining monitoring data of a management area by a monitoring device, and determining a dynamic scheduling area in the management area according to a monitoring frame corresponding to the monitoring data; An optimization module, configured to perform regional interference removal processing on the monitoring frame based on a scene restoration model to obtain an optimized frame corresponding to the dynamic scheduling area; A classification module, used to determine the scheduling type corresponding to the dynamic scheduling area according to the regional layout information of the non-operating area in the optimization frame, wherein the scheduling type includes a special scheduling type and a collaborative scheduling type; A scheduling module is used to determine the personnel type corresponding to the dynamic scheduling area based on the scheduling type, determine the scheduling quantity corresponding to the personnel type according to the work intensity index of the work area in the optimization frame, and generate personnel scheduling data corresponding to the dynamic scheduling area based on the personnel type and the scheduling quantity.

[0015] The beneficial effects of the present invention are as follows: The present invention can automatically dispatch sanitation human resources in combination with the actual situation of the cleaning area, thereby improving dispatch efficiency. The present invention determines the pollution status of the road area based on monitoring data, and then matches the corresponding number and type of cleaning personnel to perform cleaning operations in combination with the pollution status, thereby achieving rapid matching of dispatch personnel, improving dispatch efficiency, and performing targeted personnel dispatch in combination with the actual situation of the road area.

[0016] Before performing pollution analysis, the present invention first performs interference removal processing on the monitoring data to obtain an optimized frame after interference removal, thereby reducing the interference caused by dynamically moving objects in the road area and improving the accuracy of pollution analysis.

[0017] The present invention will configure corresponding personnel types for different roads based on the obstacle distribution conditions, and match the corresponding number of dispatchers based on the pollution conditions in the road area, so that dispatch resources can be reasonably allocated. The present invention can also realize automatic and rapid matching of dispatchers, thereby improving dispatch efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a method for intelligent human resource scheduling configuration provided by an embodiment of the present invention; Figure 2 is a schematic diagram of a processing group provided by an embodiment of the present invention; Figure 3 is a schematic diagram of a non-operating area provided by an embodiment of the present invention; Figure 4 It is a structural diagram of an intelligent human resource scheduling and configuration platform provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 , is a flow chart of a method for intelligent human resource scheduling and configuration provided by an embodiment of the present invention, Figure 1The execution subject of the method shown may be a software and / or hardware device. The execution subject of the present application may include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, the user equipment may include but is not limited to computers, smart phones, personal digital assistants (Personal Digital Assistant, referred to as: PDA) and the electronic devices mentioned above. The network equipment may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers, wherein cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. It includes steps S1 to S4, as follows: S1, obtaining monitoring data of a management area by a monitoring device, and determining a dynamic scheduling area in the management area according to a monitoring frame corresponding to the monitoring data.

[0021] When scheduling sanitation human resources based on the actual situation of the cleaning area, this solution will determine the pollution status of the road area based on monitoring data, and then match the corresponding number and type of cleaning personnel to perform cleaning operations based on the pollution status.

[0022] It is understandable that for some road areas, such as roads with large areas without complex obstacles, sanitation vehicles can be used to quickly clean them, while for some road areas, such as roads with complex obstacles, it may be inconvenient to use sanitation vehicles for cleaning operations. In this case, the corresponding areas can be cleaned manually. Therefore, this solution includes personnel types with sanitation vehicles and personnel types without sanitation vehicles to adapt to different conditions on different roads. And because the pollution status of different road areas may also be different, the number of personnel configured for different road areas may also be different.

[0023] In practical applications, the monitoring device may be a camera, the monitoring data may be a monitoring video, and the management area may be a road area where cleaning personnel are dispatched.

[0024] It is understandable that the road areas captured by the monitoring data may include road areas such as motor vehicle lanes, non-motor vehicle lanes, and sidewalks. Due to the large traffic volume on motor vehicle lanes, in order to reduce the impact on traffic, this plan mainly dispatches personnel to other road areas except motor vehicle lanes. The dynamic dispatch area refers to other road areas within the management area except motor vehicle lanes.

[0025] In some embodiments, the dynamic scheduling area in the management area may be determined by the following steps: S11, arranging the monitoring frames in time order to obtain a monitoring sequence, and sequentially acquiring adjacent monitoring frames in the monitoring sequence as an analysis group.

[0026] In practical applications, motor vehicle lanes and other road areas are generally separated by dividing lines, and vehicles in motor vehicle lanes are generally in motion. Therefore, multiple road areas can be divided by determining the dividing lines in the monitoring data, and other areas except motor vehicle lanes can be determined for scheduling analysis based on the vehicle movement conditions in each road.

[0027] Specifically, we can first obtain a monitoring sequence in which each monitoring frame is arranged in chronological order, divide the road area by the dividing lines in each monitoring frame in the monitoring sequence, and determine the vehicle movement in each divided road area according to the change of vehicles in adjacent monitoring frames. It is worth mentioning that the position and posture of the monitoring equipment when shooting the monitoring data is unchanged.

[0028] S12, extracting road markings in each monitoring frame in the analysis group, and dividing the road area in the monitoring frame according to the road markings to obtain a plurality of sub-road areas.

[0029] Road markings can be the dividing line between motor vehicle lanes and other road areas. For example, they can be thicker, darker white solid lines or yellow solid lines. The specific settings can be made according to the actual situation of the road. The extracted road markings are generally located on one side or both sides of the road. The road area can be divided by road markings to obtain multiple sub-road areas.

[0030] S13, obtaining the vehicle contours in each of the sub-road areas, determining the center point of each of the vehicle contours as a positioning point, obtaining the moving distance between the positioning points of the same vehicle contour in the analysis group, and determining the vehicle contour with a moving distance greater than a preset threshold as a flow contour.

[0031] It is understandable that, since the monitoring equipment is fixed, the multiple areas divided in each monitoring frame are consistent, so the vehicle movement situation in the corresponding area can be obtained by determining the vehicle changes at different times in the same area.

[0032] Specifically, the vehicle contours in each sub-road area can be extracted, and then the displacement distance between the same vehicle contour in the same sub-road area in adjacent monitoring frames at different times can be determined, that is, the distance between the position points of the center point of the vehicle contour at different times, and whether the corresponding vehicle is moving can be determined by the displacement distance.

[0033] Among them, the moving distance refers to the displacement change distance of the corresponding vehicle at different times, and the preset threshold refers to the threshold for determining the displacement change of the vehicle. If the moving distance is greater than the threshold, it means that the corresponding vehicle is in motion, and its corresponding contour can be used as the flow contour, that is, the vehicle contour in motion.

[0034] S14, calculating the ratio of the flow contour to all vehicle contours in each sub-road area to obtain a flow ratio. When the flow ratios of the corresponding sub-road areas in a preset number of analysis groups are all less than a flow ratio threshold, the corresponding sub-road area is determined to be a dynamic scheduling area.

[0035] The flow ratio refers to the proportion of vehicles in motion, and the flow ratio threshold refers to the threshold of the proportion of vehicles used to judge the motion status. If it is greater than or equal to the threshold, it means that most of the vehicles in the corresponding area are in motion. If it is less than the threshold, it means that the vehicles in the corresponding area may be stationary, or there may be no vehicles in the corresponding area.

[0036] Therefore, when the flow ratios of the corresponding sub-road areas in a preset number of analysis groups are all less than the flow ratio threshold, it means that there are no vehicles in the sub-road area for a certain period of time, or the vehicles in the sub-road area are in a stationary state for a certain period of time, which may be vehicles parked on the roadside. The area may not be a motor vehicle lane, so it can be used as a dynamic scheduling area.

[0037] In actual applications, the preset number can be set corresponding to the judgment time. For example, when the judgment time is five minutes, the total number of frames corresponding to five minutes can be determined. Since the analysis group contains two monitoring frames, it can be determined that half of the total number of frames is the preset number. By judging the corresponding number of analysis groups, the vehicle conditions in the corresponding area within the corresponding time period can be obtained, thereby determining the area for the final personnel dispatch.

[0038] Through the above method, the area where personnel scheduling is performed can be automatically analyzed and acquired, thereby improving the efficiency of personnel scheduling.

[0039] S2, performing regional interference removal processing on the monitoring frame based on the scene restoration model to obtain an optimized frame corresponding to the dynamic scheduling area.

[0040] In actual applications, there may be dynamic objects passing through the road area, such as people or vehicles. In this case, when analyzing the road area in the monitoring frame, the pollution situation in the corresponding area may not be identified due to regional occlusion, reducing the accuracy of pollution analysis. Therefore, in order to improve the accuracy of pollution analysis and to be able to reasonably dispatch personnel based on the pollution analysis results, before conducting pollution analysis, the monitoring frame can be de-interferenceed to obtain the optimized frame after de-interference for subsequent analysis and processing.

[0041] Specifically, the area where the dynamic object passes can be filled in according to the road conditions in the monitoring frames at different times to obtain the optimized frame after data optimization. Among them, the scene restoration model refers to the model for filling and restoring the road area.

[0042] Based on the above embodiment, the specific implementation of step S2 may be: S21, based on the scene restoration model, the monitoring time of each monitoring frame is obtained, the first monitoring frame is determined as a processing frame, and multiple monitoring frames are sequentially obtained as processing frames at preset intervals based on the monitoring time of the processing frame.

[0043] It is understandable that the monitoring frames at different times can capture the situations in the road area at different times. Therefore, the areas with human or vehicle outlines in the monitoring frames corresponding to different times may be different. Therefore, the image of the road area can be completed and restored through different monitoring frames to reduce the interference caused by dynamically moving objects such as people and vehicles in the road area.

[0044] The preset time may be set in advance, for example, 2 minutes. Starting from the first monitoring frame, corresponding monitoring frames are selected as processing frames at intervals of preset time, which can reduce the amount of data processing and improve the efficiency of data analysis. The processing frame refers to the video frame that undergoes interference removal processing.

[0045] S22, arranging each processing frame from front to back according to the monitoring time of each processing frame to obtain a processing sequence, and extracting the target object contours located in the dynamic scheduling area in each processing frame in turn based on the processing sequence, and the target object contours include at least personnel contours and vehicle contours.

[0046] In order to be able to fill in the areas where dynamic objects are occluded by the areas without dynamic object occlusion in each monitoring frame, the processing frames can be arranged in chronological order to obtain a processing sequence, and then the contour area of ​​dynamic objects such as people or vehicles is determined based on the target object contours in each processing frame in the processing sequence, so that the corresponding area filling processing can be performed subsequently based on the area corresponding to the target object contour.

[0047] S23, obtaining interference areas corresponding to the contours of each target object, and completing roads in the dynamic scheduling area according to the interference areas to obtain an optimized frame.

[0048] The interference area refers to the area corresponding to a person or a vehicle. In some embodiments, the interference area can be determined by the following steps, and the road filling of the dynamic scheduling area is performed according to the interference area to obtain an optimized frame: S231, generating an outer contour frame corresponding to each target object contour, and determining the area within the outer contour frame as an interference area.

[0049] The outer contour frame refers to a rectangular outer frame corresponding to the target contour object, which is the minimum outer rectangular frame that can surround the target contour, and the area within the rectangular outer frame can correspond to the interference area corresponding to the target contour.

[0050] S232, acquiring adjacent processing frames as a processing group, determining that the processing frames arranged at the rear in the processing group are post-order frames, and the processing frames arranged at the front are pre-order frames.

[0051] The subsequent frame is a processing frame at a later time, the preceding frame is a processing frame at an earlier time, and each processing group includes two processing frames at successive times.

[0052] S233, obtaining the same region in the previous frame corresponding to the interference region in the subsequent frame, and when the same region in the previous frame does not intersect with the rest of the interference regions, extracting the image in the same region and replacing it into the corresponding interference region in the subsequent frame.

[0053] See also Figure 2 , is a schematic diagram of a processing group provided by an embodiment of the present invention, from Figure 2 It can be seen that the positions of the interference areas in the preceding frame and the succeeding frame in the processing group are different, and there is no interference in the interference area of ​​the succeeding frame in the preceding frame. Therefore, the corresponding area in the preceding frame is replaced with the area with interference in the succeeding frame, and the interference area in the succeeding frame can be filled. Moreover, since the two image frames in the same processing group are close in time, the pollution conditions in the road area may not change much in a short time. Therefore, the replaced and updated succeeding frame can restore and display the road conditions at the corresponding moment to a certain extent.

[0054] Among them, the same area refers to the area in the previous frame corresponding to the interference area in the subsequent frame. If the same area in the previous frame does not intersect with the remaining interference areas in the previous frame, it means that there is no interference in the same area. Therefore, the image in the same area can be replaced with the corresponding interference area in the subsequent frame, so as to achieve the completion of the interference area in the subsequent frame.

[0055] S234, determining that the updated subsequent frame is the preceding frame in the next group of the processing group, repeating the step of updating the interference area corresponding to the subsequent frame in the processing group until there is no interference area in the updated subsequent frame, or stopping the above updating step when all the processing frames are updated.

[0056] It is understandable that in order to obtain a more accurate road image, the corresponding subsequent frames may be padded in chronological order, so that the time of the image data finally obtained after the interference is removed may be the latest.

[0057] When there is no interference area in the updated subsequent frame, it means that there is no interference in the subsequent frame at this time, so the updating step of the subsequent frame can be stopped, or the updating step of the subsequent frame can be stopped when the processing frame is updated.

[0058] S235, obtaining a processing frame without the interference area or the last processing frame in the processing sequence as an optimized frame.

[0059] There is no interference area, which means that the corresponding processing frame is not blocked by interference objects. Using the processed frame as an optimized frame for subsequent analysis can improve the accuracy of the analysis.

[0060] In actual applications, there may be situations where vehicles are parked on the side of the road. In this case, the corresponding occlusion area may always exist in the processed frame. Therefore, the last processed frame can be obtained as the optimized frame, and then the corresponding type of personnel scheduling can be performed in combination with the occlusion area in the image, so that targeted personnel scheduling can be performed based on the actual situation in the area.

[0061] In the above manner, the image data can be de-interferenceed by filling in the image area, thereby improving the accuracy of image data analysis.

[0062] S3, determining the scheduling type corresponding to the dynamic scheduling area according to the regional layout information of the non-operating area in the optimization frame, wherein the scheduling type includes a special scheduling type and a collaborative scheduling type.

[0063] In actual applications, the layout of different road areas may be different. For example, there may be more obstacles in some areas, such as many parked vehicles, and there may be no obstacles in some areas. According to the layout of different areas, this solution will match the corresponding scheduling type, so as to configure different types of personnel for cleaning in different areas.

[0064] Specifically, the corresponding dispatch type can be determined based on the distribution of the non-operating area, i.e., the area corresponding to the obstacle, in the road area. The special dispatch type refers to the type of dispatch for one type of personnel, and the coordinated dispatch type refers to the type of dispatch for multiple types of personnel.

[0065] Step S3 may include steps S31 to S35, which are specifically as follows: S31, obtaining interference areas in the optimization frame as non-operation areas, counting the total non-operation areas of the non-operation areas, and obtaining the non-operation ratio according to the ratio of the total non-operation area to the total area of ​​the dynamic scheduling area.

[0066] See also Figure 3 , is a schematic diagram of a non-operating area provided by an embodiment of the present invention. Figure 3 As shown, there may be multiple vehicles parked on the roadside, resulting in multiple interference areas in the optimization frame. In this case, the corresponding interference area can be used as a non-operating area, and the area ratio of the non-operating area in the dynamic dispatching area is obtained based on the ratio of the total area of ​​all non-operating areas to the total area of ​​the dynamic dispatching area, that is, the non-operating ratio mentioned above. The scheduling type corresponding to the dynamic dispatching area is subsequently determined based on this area ratio.

[0067] S32, determining that the scheduling type of the dynamic scheduling area whose non-operation ratio is greater than or equal to the benchmark non-operation ratio is a special scheduling type, and adding a manual label to the dynamic scheduling area.

[0068] The benchmark non-operation ratio refers to the area ratio of the non-operation area under normal conditions set in advance. If it is greater than or equal to this ratio, it means that there may be more interference areas in the dynamic scheduling area. For example, multiple vehicles may be parked. In this case, there may be more obstacles in the dynamic scheduling area during cleaning, and the flexibility required for cleaning may be higher. The flexibility during manual cleaning will be higher than the flexibility during cleaning by sanitation vehicles. Therefore, the scheduling type of the dynamic scheduling area can be determined as the special scheduling type, and a manual label is added to it, that is, a label corresponding to the type of personnel who are not configured with sanitation vehicles, so that the type of personnel who are not configured with sanitation vehicles can be configured for it in the future.

[0069] S33, obtaining the minimum distance between the non-operation areas in the dynamic scheduling area where the non-operation ratio is less than the benchmark non-operation ratio.

[0070] If the non-operating ratio is smaller than the benchmark non-operating ratio, it means that there may be fewer non-operating areas in the dynamic scheduling area. In this case, the corresponding scheduling type can be determined based on the positional relationship between the non-operating areas in the dynamic scheduling area.

[0071] For example Figure 3 As shown in the figure, the distance between the two non-operating areas arranged up and down on the left may be relatively close, and the sanitation vehicle may not be able to enter the area between the two non-operating areas for cleaning. The corresponding area needs to be cleaned manually, while the remaining areas can be cleaned by sanitation vehicles. In this case, a collaborative scheduling type that can simultaneously schedule multiple types of personnel can be configured for the dynamic scheduling area. If there is no non-operating area in the dynamic dispatching area or the distances between the non-operating areas are relatively large, a special scheduling type corresponding to the personnel type of sanitation vehicles can be configured for it, and sanitation vehicles can be dispatched to quickly clean it.

[0072] In some embodiments, when obtaining the minimum regional distance between non-operating areas, two adjacent regional lines of each non-operating area may be obtained, and then the minimum regional distance, ie, the minimum spacing distance between non-operating areas, is determined by the distance between the two regional lines.

[0073] S34, determining that the scheduling type of the dynamic call area where the minimum distance of the area is less than or equal to the cleaning distance threshold is a collaborative scheduling type.

[0074] It can be understood that if the minimum distance between non-operating areas is less than or equal to the cleaning distance threshold, it means that there may be non-operating areas with very close intervals in the dynamic scheduling area. In this case, it can be determined as a collaborative scheduling type.

[0075] Among them, the cleaning distance threshold refers to the cleaning interval distance corresponding to the sanitation vehicle. If it is less than the threshold, it means that the sanitation vehicle may not be able to clean the area between the corresponding non-operating areas. Therefore, various types of personnel can be dispatched together during scheduling, so that cleaning can be more targeted.

[0076] S35, obtaining the scheduling type of the dynamic scheduling area where there is no area minimum distance less than or equal to the cleaning distance threshold as a special scheduling type, and adding an equipment label to the dynamic scheduling area, the area layout information includes the non-operation ratio and the area minimum distance corresponding to the non-operation area.

[0077] If there is no minimum distance between non-operating areas that is less than or equal to the cleaning distance threshold, it means that the interval distances between non-operating areas in the dynamic scheduling area may be far, or there is no non-operating area in the dynamic scheduling area. In this case, there are relatively few obstacles in the dynamic scheduling area, and sanitation vehicles can be dispatched to quickly clean the corresponding areas. Therefore, it can be determined that it is a special scheduling type corresponding to the personnel type with sanitation vehicles, and the equipment label is the label corresponding to the personnel type with sanitation vehicles.

[0078] Through the above method, corresponding personnel types can be configured for different road areas based on their actual conditions, so that targeted cleaning operations can be carried out on the road areas.

[0079] S4, determining the personnel type corresponding to the dynamic scheduling area based on the scheduling type, determining the scheduling quantity corresponding to the personnel type according to the work intensity index of the work area in the optimization frame, and generating personnel scheduling data corresponding to the dynamic scheduling area based on the personnel type and the scheduling quantity.

[0080] Among them, the work intensity index refers to the cleaning intensity index corresponding to the area where cleaning operations are performed in the dynamic scheduling area. The work intensity of the corresponding road area can be obtained through the index, so that the corresponding number of cleaning personnel can be matched to the corresponding road area in combination with the index and the personnel type.

[0081] On the basis of the above embodiment, the specific implementation method of step S4 of "determining the type of personnel corresponding to the dynamic scheduling area based on the scheduling type, and determining the scheduling quantity corresponding to the type of personnel according to the operation intensity index of the operation area in the optimization frame" may be: S41, determining that the dispatch type is a special dispatch type, and the personnel type corresponding to the manual label is a first-class personnel type, and the first-class personnel type is a personnel type that is not equipped with cleaning equipment.

[0082] In practical applications, the cleaning equipment may be a sanitation vehicle, and there is a correspondence between the dispatch type and the personnel type. Therefore, after determining the dispatch type corresponding to the corresponding area, the corresponding personnel type may be determined accordingly.

[0083] S42, obtaining that the scheduling type is a special scheduling type and the personnel type corresponding to the equipment tag is a second-class personnel type, and the second-class personnel type is the personnel type for configuring the cleaning equipment.

[0084] When determining the personnel type corresponding to the special dispatch type, the corresponding determination can be made based on the label corresponding to the special dispatch type to obtain the personnel type specifically corresponding to the corresponding road area.

[0085] S43, determining that the personnel types whose scheduling type is the collaborative scheduling type are type I personnel and type II personnel.

[0086] The collaborative scheduling type includes two types of personnel, so it can correspond to type one personnel and type two personnel.

[0087] S44, based on the special dispatch type, a single matching strategy is retrieved to obtain the work intensity index corresponding to the work area in the optimization frame, and the dispatch quantity corresponding to the personnel type is determined according to the work intensity index.

[0088] Since the special dispatch type only corresponds to one type of personnel, when determining the dispatch quantity corresponding to the corresponding personnel type, a single matching strategy can be called for corresponding determination. The single matching strategy refers to a strategy that matches the dispatch quantity for a single personnel type based on the actual situation of the road area.

[0089] In some embodiments, step S44 includes steps S441 to S445: S441, obtaining an area other than the non-operation area in the dynamic scheduling area in the optimization frame as an operation area, and determining that pixel points in the operation area whose pixel values ​​are not located in the road pixel interval are abnormal pixels.

[0090] When cleaning personnel are cleaning the dynamic scheduling area, they may not be able to clean the non-operating area due to obstacles, so they can clean the remaining area and use the area that can be cleaned as the operating area.

[0091] It is understandable that the pixel points in the work area whose pixel values ​​are not within the road pixel interval may correspond to pollutants, so the corresponding pixel points can be regarded as abnormal pixels. The road pixel interval refers to the pixel value interval corresponding to the road under normal conditions.

[0092] S442, counting the number of abnormal pixels and the total number of pixels in the operation area, and obtaining a first contamination coefficient according to a ratio of the number of abnormal pixels to the total number of pixels.

[0093] Among them, the first pollution coefficient is the numerical value corresponding to the proportion of the polluted area in the working area. The more anomalies there are, the larger the polluted area in the working area may be, and the higher its proportion in the working area may be.

[0094] S443, obtaining a first coefficient difference between the first pollution coefficient and a benchmark pollution coefficient, obtaining an intensity adjustment coefficient according to a ratio of the first coefficient difference to the benchmark difference, and obtaining an operation intensity index based on the product of the intensity adjustment coefficient and a benchmark intensity index.

[0095] The baseline pollution coefficient refers to the pollution coefficient value under normal conditions, which can be set in advance. The baseline difference refers to the pollution coefficient difference under normal conditions. The baseline difference can be offset by the first coefficient difference between the first pollution coefficient and the baseline pollution coefficient, thereby obtaining an intensity adjustment coefficient that can display the degree of pollution in the corresponding road area. The operation intensity index can be obtained by offsetting the baseline intensity index through the intensity adjustment coefficient. The larger the intensity adjustment coefficient, the more serious the pollution situation in the corresponding area may be, and therefore the corresponding operation intensity index may also be larger. The baseline intensity index refers to the intensity index under normal conditions. The baseline difference and the baseline intensity index can be set accordingly.

[0096] S444, retrieve the quantity matching table corresponding to the personnel type, traverse the quantity matching table according to the work intensity index, and determine that the standard intensity interval corresponding to the work intensity index in the quantity matching table is the target intensity interval.

[0097] It is understandable that since the personnel types corresponding to different road areas may be different, and the work intensities adapted to different personnel types may also be different, it is possible to pre-configure a quantity matching table corresponding to different personnel types, and use the quantity matching table to determine the dispatch quantity corresponding to different intensities.

[0098] The quantity matching table refers to a table that corresponds to the corresponding personnel type and determines the dispatch quantity by the work intensity value. The table includes multiple standard intensity intervals, that is, intervals corresponding to the work intensity. Different standard intensity intervals correspond to different dispatch quantities. In actual applications, the larger the interval value corresponding to the standard intensity interval, the more corresponding dispatch quantities can be set.

[0099] S445, determining that the standard matching quantity corresponding to the target intensity interval is the scheduling quantity corresponding to the personnel type.

[0100] The standard matching quantity refers to the dispatch quantity corresponding to the corresponding standard strength range. After determining the target strength range, the corresponding standard matching quantity can be used as the final scheduling quantity.

[0101] S45, or, according to the collaborative scheduling type, calling the comprehensive matching strategy to obtain the work intensity index corresponding to the work area in the optimization frame, and determining the scheduling quantity corresponding to the personnel type according to the work intensity index.

[0102] Since the collaborative dispatch type corresponds to multiple types of personnel, when determining the dispatch quantity, the comprehensive matching strategy can be called up for corresponding determination. The comprehensive matching strategy refers to a strategy for matching the dispatch quantity of multiple types of personnel based on the actual situation of the road area.

[0103] In some embodiments, the scheduling quantity corresponding to step S45 may be determined by the following steps: S451, obtaining the non-operating area in the dynamic scheduling area whose minimum distance is less than or equal to the cleaning distance threshold as a neighboring area group, generating a comprehensive outer frame corresponding to the contours of each target object in the neighboring area group, and determining the area except the non-operating area in the comprehensive outer frame as the first operating area.

[0104] It is understandable that the non-operating area in the dynamic scheduling area where the minimum distance between the areas is less than or equal to the cleaning distance threshold may be a non-operating area with a relatively close distance. The cleaning area between such areas may be relatively narrow and require manual cleaning, which may correspond to a type of personnel. Therefore, when determining the scheduling quantity corresponding to a type of personnel corresponding to the collaborative scheduling type, a corresponding determination can be made based on the first operating area. Among them, the first operating area refers to the cleaning area between obstacle areas with relatively close locations.

[0105] The comprehensive outer frame refers to the rectangular outer frame corresponding to the contours of all targets in the adjacent area group. The rectangular outer frame is the minimum rectangular outer frame that can contain the contours of all targets. The area within the rectangular outer frame except the operating area is the cleaning area, that is, the above-mentioned first operating area.

[0106] S452: Determine an area in the dynamic scheduling area other than the area corresponding to the integrated outer frame and the non-operating area as a second operating area, wherein the operating area includes a first operating area and a second operating area.

[0107] The second operating area refers to other clean areas except the areas corresponding to obstacles that are close to the operating area and the non-operating area. There may be no obstacles in the second operating area, or the obstacles are arranged more dispersedly. It can correspond to the second type of personnel, so sanitation vehicles can be dispatched to clean it, thereby improving the efficiency of cleaning.

[0108] S453, obtaining a first adjustment coefficient according to the ratio of the total operation area of ​​each of the first operation zones to a type of benchmark operation area, and obtaining an operation intensity index corresponding to the type of personnel based on the product of the first adjustment coefficient and a benchmark intensity index.

[0109] Since the first working area is blocked by obstacles, it may not be possible to accurately identify the pollution degree in the first working area. Therefore, when determining the working intensity index corresponding to the first working area, it can be determined accordingly in combination with its corresponding cleaning area.

[0110] Among them, a type of reference operation area refers to a pre-configured standard cleaning area between obstacle areas that are close in distance. Through the ratio between the total operation area of ​​the first operation area and the type of reference operation area, the offset ratio between the cleaning area corresponding to the obstacle that is close in distance in the corresponding road area and the standard cleaning area in this case can be obtained, so that the reference intensity index can be offset by obtaining the coefficient to obtain the operation intensity index corresponding to a type of personnel type. In practical applications, a type of reference operation area can be set corresponding to the reference intensity index, so that the reference intensity index can be offset and adjusted by the total operation area.

[0111] S454, counting pixel points in the second operation area whose pixel values ​​are not located in the road pixel interval as abnormal pixel points, and obtaining a second pollution coefficient according to the ratio between the total number of the abnormal pixel points and the total number of all pixel points in the second operation area.

[0112] When determining the operation intensity index corresponding to the second operation area, it can be determined accordingly based on the pollution level in the second operation area. The second pollution coefficient refers to the proportion of the polluted area in the second operation area.

[0113] S455, obtaining a second coefficient difference between the second pollution coefficient and a benchmark pollution coefficient, obtaining a second adjustment coefficient according to a ratio of the second coefficient difference to the benchmark difference, and obtaining the work intensity index of the second type of personnel based on the product of the second adjustment coefficient and the benchmark intensity index.

[0114] The above method of obtaining the work intensity index of the two types of personnel is similar to the method in step S443 (obtaining the first coefficient difference between the first pollution coefficient and the benchmark pollution coefficient, obtaining the intensity adjustment coefficient according to the ratio of the first coefficient difference and the benchmark difference, and obtaining the work intensity index based on the product of the intensity adjustment coefficient and the benchmark intensity index.), which will not be repeated here.

[0115] S456, retrieve the quantity matching table corresponding to the corresponding personnel type, traverse the quantity matching table according to the work intensity index, and determine that the standard intensity interval corresponding to the work intensity index in the quantity matching table is the target intensity interval.

[0116] Similarly, since different types of personnel have different corresponding work intensity indexes, the number of dispatches may also be different. The corresponding number of dispatches can be determined through the quantity matching table corresponding to the corresponding personnel type.

[0117] S457, determining that the standard matching quantity corresponding to the target intensity interval is the scheduling quantity corresponding to the corresponding personnel type.

[0118] Since the collaborative scheduling type corresponds to a variety of personnel types, the above method can reasonably allocate the dispatch quantities corresponding to different personnel types and reasonably allocate personnel scheduling in advance.

[0119] Based on the above embodiment, the specific implementation method of "generating the personnel dispatching data corresponding to the dynamic dispatching area based on the personnel type and the dispatching quantity" in step S4 can be: S46, obtaining cleaning personnel in an idle state among a plurality of cleaning personnel corresponding to each of the personnel types as personnel to be assigned, and determining the interval distance between the personnel to be assigned that meets the scheduling type of the management area and each management area.

[0120] After determining the dispatch quantity corresponding to different types of personnel, the corresponding personnel can be determined for dispatch based on the personnel type and dispatch quantity. Specifically, the idle personnel among the multiple cleaning personnel corresponding to each type of personnel can be determined as personnel waiting for assignment, and then the distance between each person and the area eligible for dispatch can be determined, and then the corresponding personnel dispatch can be carried out based on the distance.

[0121] For example, if the scheduling type corresponding to management area 1 is a type of personnel, the distance between the cleaning personnel of type one among the personnel to be assigned and management area 1 can be determined, and the cleaning personnel specifically corresponding to management area 1 can be determined through the distance.

[0122] S47, selecting a scheduling number of personnel to be assigned from small to large according to the interval distance as target personnel corresponding to the corresponding management area.

[0123] It is understandable that the smaller the interval distance is, the closer the distance between the corresponding personnel and the corresponding management area may be. In order to enable the cleaning staff to quickly go to the corresponding area for cleaning, the number of people to be scheduled can be selected in order from small to large according to the interval distance as the target personnel of the management area, that is, the cleaning staff corresponding to the management area.

[0124] S48, retrieve a dispatch distribution map, the dispatch distribution map includes road segments corresponding to each management area, update the clean matching table corresponding to the corresponding road segment according to the identity of each target person, obtain the personnel dispatch data and send it to the management end.

[0125] The dispatch distribution map refers to a distribution display map containing roads corresponding to each management area. The road segment corresponding to each management area can be pre-configured. In order to enable the management end to intuitively view the dispatch personnel corresponding to each management area, the clean matching table of the corresponding road segment can be updated based on the personnel identity of each target personnel. The clean matching table refers to the matching table corresponding to the dispatch personnel identity.

[0126] Through the above method, the dispatching personnel of each management area can be quickly matched to improve the efficiency of personnel dispatching.

[0127] See also Figure 4 , is a schematic diagram of a structure of an intelligent human resource scheduling configuration platform provided by an embodiment of the present invention, the intelligent human resource scheduling configuration platform includes: A division module, used for obtaining monitoring data of a management area by a monitoring device, and determining a dynamic scheduling area in the management area according to a monitoring frame corresponding to the monitoring data; An optimization module, configured to perform regional interference removal processing on the monitoring frame based on a scene restoration model to obtain an optimized frame corresponding to the dynamic scheduling area; A classification module, used to determine the scheduling type corresponding to the dynamic scheduling area according to the regional layout information of the non-operating area in the optimization frame, wherein the scheduling type includes a special scheduling type and a collaborative scheduling type; A scheduling module is used to determine the personnel type corresponding to the dynamic scheduling area based on the scheduling type, determine the scheduling quantity corresponding to the personnel type according to the work intensity index of the work area in the optimization frame, and generate personnel scheduling data corresponding to the dynamic scheduling area based on the personnel type and the scheduling quantity.

[0128] Figure 4 The apparatus of the embodiment shown can be used to perform Figure 1 The implementation principles and technical effects of the steps in the method embodiment shown are similar and will not be repeated here.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for scheduling and configuring human resources based on intelligence, characterized in that: include: Acquire monitoring data of the management area by the monitoring device, and determine the dynamic scheduling area in the management area according to the monitoring frame corresponding to the monitoring data; Performing regional interference removal processing on the monitoring frame based on a scene restoration model to obtain an optimized frame corresponding to the dynamic scheduling area; Determine the scheduling type corresponding to the dynamic scheduling area according to the regional layout information of the non-operating area in the optimization frame, wherein the scheduling type includes a special scheduling type and a collaborative scheduling type; The personnel type corresponding to the dynamic scheduling area is determined based on the scheduling type, the scheduling quantity corresponding to the personnel type is determined according to the work intensity index of the work area in the optimization frame, and the personnel scheduling data corresponding to the dynamic scheduling area is generated based on the personnel type and the scheduling quantity.

2. The method according to claim 1, characterized in that: Acquiring monitoring data of a management area from a monitoring device, and determining a dynamic scheduling area in the management area according to a monitoring frame corresponding to the monitoring data, including: Arranging the monitoring frames in time order to obtain a monitoring sequence, and sequentially acquiring adjacent monitoring frames in the monitoring sequence as an analysis group; Extracting road markings in each monitoring frame in the analysis group, and dividing the road area in the monitoring frame according to the road markings to obtain a plurality of sub-road areas; Acquire vehicle profiles in each of the sub-road areas, determine the center point of each of the vehicle profiles as a positioning point, acquire the moving distance between the positioning points of the same vehicle profile in the analysis group, and determine the vehicle profile whose moving distance is greater than a preset threshold as a flow profile; The ratio of the flow contour to all vehicle contours in each sub-road area is statistically calculated to obtain a flow ratio. When the flow ratios of the corresponding sub-road areas in a preset number of analysis groups are all less than a flow ratio threshold, the corresponding sub-road area is determined to be a dynamic scheduling area.

3. The method according to claim 1, characterized in that Performing regional interference removal processing on the monitoring frame based on the scene restoration model to obtain an optimized frame corresponding to the dynamic scheduling area includes: Acquire the monitoring time of each monitoring frame based on the scene restoration model, determine the first monitoring frame as a processing frame, and take the monitoring time of the processing frame as a reference, and sequentially acquire multiple monitoring frames at preset intervals as processing frames; Arranging the processing frames from front to back according to the monitoring time of each processing frame to obtain a processing sequence, and extracting the target object contours located in the dynamic scheduling area in each processing frame in sequence based on the processing sequence, wherein the target object contours include at least a person contour and a vehicle contour; Obtain interference areas corresponding to the contours of each target object, and perform road completion on the dynamic scheduling area according to the interference areas to obtain an optimized frame.

4. The method according to claim 3, characterized in that: Obtaining interference areas corresponding to the contours of each target object, and completing the roads in the dynamic scheduling area according to the interference areas to obtain an optimized frame, including: Generate an outer contour frame corresponding to each target object contour, and determine the area within the outer contour frame as an interference area; Acquire the adjacent processing frames as a processing group, determine the processing frames arranged at the rear in the processing group as subsequent frames, and the processing frames arranged at the front as preceding frames; Obtaining the same region in the preceding frame corresponding to the interference region in the succeeding frame, and when the same region in the preceding frame does not intersect with the remaining interference regions, extracting the image in the same region and replacing it into the corresponding interference region in the succeeding frame; Determine that the updated subsequent frame is a preceding frame in the next processing group, and repeat the step of updating the interference region corresponding to the subsequent frame in the processing group until no interference region exists in the updated subsequent frame, or stop the above updating step when all processing frames are updated; A processing frame without the interference area or a last processing frame in the processing sequence is obtained as an optimized frame.

5. The method according to claim 4, characterized in that According to the regional layout information of the non-operating area in the optimization frame, the scheduling type corresponding to the dynamic scheduling area is determined, and the scheduling type includes a special scheduling type and a collaborative scheduling type, including: Obtaining interference areas in the optimization frame as non-operation areas, counting the total non-operation areas of the non-operation areas, and obtaining the non-operation ratio according to the ratio of the total non-operation area to the total area of ​​the dynamic scheduling area; Determine that the scheduling type of the dynamic scheduling area whose non-operation ratio is greater than or equal to the benchmark non-operation ratio is a special scheduling type, and add a manual label to the dynamic scheduling area; Obtaining the minimum distance between the non-operation areas in the dynamic scheduling area where the non-operation ratio is less than the benchmark non-operation ratio; Determine that the scheduling type of the dynamic call zone where the minimum distance of the area is less than or equal to the cleaning distance threshold is the collaborative scheduling type; The scheduling type of the dynamic scheduling area where the minimum distance of the non-operating area is less than or equal to the cleaning distance threshold is obtained as a special scheduling type, and an equipment label is added to the dynamic scheduling area. The regional layout information includes the non-operating proportion and the minimum distance of the non-operating area.

6. The method according to claim 5, characterized in that Determining the type of personnel corresponding to the dynamic scheduling area based on the scheduling type, and determining the scheduling quantity corresponding to the type of personnel according to the operation intensity index of the operation area in the optimization frame, including: Determine that the dispatch type is a special dispatch type, and the personnel type corresponding to the manual label is a first-class personnel type, and the first-class personnel type is a personnel type that is not equipped with cleaning equipment; The dispatch type is obtained as a special dispatch type, and the personnel type corresponding to the equipment tag is a second-class personnel type, and the second-class personnel type is the personnel type for configuring the cleaning equipment; Determine that the personnel type whose scheduling type is the collaborative scheduling type is a first-class personnel type and a second-class personnel type; Based on the special dispatch type, a single matching strategy is called to obtain the work intensity index corresponding to the work area in the optimization frame, and the dispatch quantity corresponding to the personnel type is determined according to the work intensity index; or, According to the collaborative scheduling type, a comprehensive matching strategy is called to obtain the work intensity index corresponding to the work area in the optimization frame, and the scheduling quantity corresponding to the personnel type is determined according to the work intensity index.

7. The method according to claim 6, characterized in that Based on the special dispatch type, a single matching strategy is retrieved to obtain the work intensity index corresponding to the work area in the optimization frame, and the dispatch quantity corresponding to the personnel type is determined according to the work intensity index, including: Acquire the area other than the non-operation area in the dynamic scheduling area in the optimization frame as the operation area, and determine the pixel points in the operation area whose pixel values ​​are not located in the road pixel interval as abnormal pixel points; Counting the number of abnormal pixels and the total number of pixels in the operation area, and obtaining a first contamination coefficient according to a ratio of the number of abnormal pixels to the total number of pixels; Obtain a first coefficient difference between the first pollution coefficient and a benchmark pollution coefficient, obtain an intensity adjustment coefficient based on a ratio of the first coefficient difference to the benchmark difference, and obtain an operation intensity index based on the product of the intensity adjustment coefficient and the benchmark intensity index; retrieve a quantity matching table corresponding to the personnel type, traverse the quantity matching table according to the operation intensity index, and determine that the standard intensity interval corresponding to the operation intensity index in the quantity matching table is the target intensity interval; Determine the standard matching quantity corresponding to the target intensity interval as the scheduling quantity corresponding to the personnel type.

8. The method according to claim 6, characterized in that According to the collaborative scheduling type, a comprehensive matching strategy is retrieved to obtain the operation intensity index corresponding to the operation area in the optimization frame, and according to the operation intensity index, the scheduling quantity corresponding to the personnel type is determined, including: Acquire a non-operating area in the dynamic scheduling area whose minimum distance is less than or equal to a cleaning distance threshold as a neighboring area group, generate a comprehensive outer frame corresponding to the contours of each target object in the neighboring area group, and determine an area other than the non-operating area in the comprehensive outer frame as a first operating area; Determine an area in the dynamic scheduling area other than the area corresponding to the integrated outer frame and the non-operating area as a second operating area, wherein the operating area includes a first operating area and a second operating area; Obtaining a first adjustment coefficient according to a ratio of the total operation area of ​​each of the first operation areas to a type of benchmark operation area, and obtaining an operation intensity index corresponding to the type of personnel according to the product of the first adjustment coefficient and the benchmark intensity index; Counting pixel points in the second operation area whose pixel values ​​are not located in the road pixel interval as abnormal pixel points, and obtaining a second pollution coefficient according to the ratio between the total number of the abnormal pixel points and the total number of all pixel points in the second operation area; Obtaining a second coefficient difference between the second pollution coefficient and the benchmark pollution coefficient, obtaining a second adjustment coefficient according to a ratio of the second coefficient difference to the benchmark difference, and obtaining an operation intensity index of the second type of personnel based on the product of the second adjustment coefficient and the benchmark intensity index; Retrieve a quantity matching table corresponding to the corresponding personnel type, traverse the quantity matching table according to the operation intensity index, and determine that the standard intensity interval corresponding to the operation intensity index in the quantity matching table is the target intensity interval; Determine the standard matching quantity corresponding to the target intensity interval as the scheduling quantity corresponding to the corresponding personnel type.

9. The method according to claim 7 or 8, characterized in that: Generating personnel dispatch data corresponding to the dynamic dispatch area based on the personnel type and the dispatch quantity includes: Acquire cleaning personnel in an idle state from among a plurality of cleaning personnel corresponding to each of the personnel types as personnel to be assigned, and determine the interval distance between the personnel to be assigned and each management area that meets the scheduling type of the management area; According to the interval distance, the number of personnel to be assigned is selected from small to large as the target personnel corresponding to the corresponding management area; the scheduling distribution map is retrieved, the scheduling distribution map includes the road segments corresponding to each of the management areas, and the clean matching table corresponding to the corresponding road segment is updated according to the personnel identity of each of the target personnel, and the personnel scheduling data is obtained and sent to the management end.

10. An intelligent human resource scheduling and configuration platform, characterized in that: include: A division module, used for obtaining monitoring data of a management area by a monitoring device, and determining a dynamic scheduling area in the management area according to a monitoring frame corresponding to the monitoring data; An optimization module, configured to perform regional interference removal processing on the monitoring frame based on a scene restoration model to obtain an optimized frame corresponding to the dynamic scheduling area; A classification module, used to determine the scheduling type corresponding to the dynamic scheduling area according to the regional layout information of the non-operating area in the optimization frame, wherein the scheduling type includes a special scheduling type and a collaborative scheduling type; A scheduling module is used to determine the personnel type corresponding to the dynamic scheduling area based on the scheduling type, determine the scheduling quantity corresponding to the personnel type according to the work intensity index of the work area in the optimization frame, and generate personnel scheduling data corresponding to the dynamic scheduling area based on the personnel type and the scheduling quantity.

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