Mobile charging equipment operation and maintenance management system and method based on data analysis

Abstract

The invention discloses a mobile charging equipment operation and maintenance management system and method based on data analysis, and relates to the technical field of robot operation and maintenance management. The method comprises the following steps: analyzing the number of events with used identifiers in a monitoring sub-region, analyzing the charging demand density based on the number of events with used identifiers, and dividing a high-demand sub-region; analyzing the weight of a road section between any two nodes, and constructing a graph structure based on the road section analysis weight; analyzing the emergency degree of each high-demand sub-region, and generating a service list based on the emergency degree; health assessment is carried out based on historical operation data of the mobile charging robot, and an early warning label is marked on the mobile charging robot based on a health assessment value and a preset health assessment value threshold value; and carrying out final transfer path set analysis based on the service list and the idle mobile charging robot, and carrying out transfer optimization in combination with the health assessment value of the mobile charging robot. The analysis accuracy is improved, and the overall operation efficiency of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of robot operation and maintenance management technology, specifically a mobile charging equipment operation and maintenance management system and method based on data analysis. Background Technology

[0002] Mobile charging robots are intelligent, mobile charging devices. Once an electric vehicle is parked, the robot, equipped with charging capabilities, can automatically move next to the vehicle and begin charging via an app or other system. As a temporary and rapid way to replenish power, mobile charging robots will complement existing fixed charging, battery swapping, and wireless charging methods in the future, building a comprehensive electric vehicle energy replenishment ecosystem.

[0003] The charging demand differs significantly between peak and off-peak hours, but existing methods rely on rigid thresholds, leading to missed detections in high-demand areas or excessive resource allocation to low-demand areas. In some areas, although demand is moderate, the actual service capacity is nearing exhaustion due to overall robot battery depletion. Traditional methods, lacking multi-dimensional data analysis capabilities, cannot proactively identify such hidden problems, resulting in longer user wait times and increased risk of service interruptions. Furthermore, current technology lacks dynamic tracking and emergency replacement mechanisms for robot states during task execution, resulting in weak overall system fault tolerance.

[0004] Therefore, this invention discloses a mobile charging device operation and maintenance management system and method based on data analysis to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a mobile charging device operation and maintenance management system and method based on data analysis to solve the problems raised in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a mobile charging device operation and maintenance management method based on data analysis, the method comprising the following steps:

[0007] S1: Obtain historical operation data of the mobile charging robot, analyze the number of events with usage tags in the monitoring sub-region, analyze the charging demand density based on the number of events with usage tags, and divide the high-demand sub-regions based on the charging demand density.

[0008] According to the above scheme, S1 includes the following:

[0009] S101: Acquire historical operating data of the mobile charging robot, including historical moving speed and historical position data of the mobile charging robot, as well as corresponding timestamps, remaining battery power, and remaining battery power reduction rate; divide the monitoring area into equal-distance monitoring sub-regions; analyze the monitoring sub-region index corresponding to the historical position of the mobile charging robot based on the historical position coordinates of the mobile charging robot and the minimum coordinate value of the monitoring area; the monitoring sub-region index corresponding to the historical position of the mobile charging robot is equal to the difference between the historical position coordinates of the mobile charging robot and the minimum coordinate value of the monitoring area divided by the length of the monitoring sub-region, rounded up to the nearest integer; mark the monitoring sub-region index corresponding to the historical position coordinates of the mobile charging robot.

[0010] If the rate of decrease of remaining power exceeds the corresponding threshold, the mobile charging robot is marked as being used. Within each sliding time window, the number of events with the "used" mark in the monitoring sub-region is counted, and the charging demand density is analyzed based on the number of events with the "used" mark. The charging demand density is equal to the quotient of the number of events with the "used" mark and the length of the time window. The demand density is updated based on the exponential smoothing method. The remaining power of mobile charging robots without the "used" mark in the monitoring sub-region is extracted, and the average remaining power is calculated.

[0011] S102: Extract the charging demand density corresponding to each monitoring sub-region within the monitoring area; and calculate the mean and standard deviation of the charging demand density of the monitoring area. Based on the mean and standard deviation of the charging demand density, dynamically analyze the intensity grading threshold. The intensity grading threshold includes a first intensity grading threshold and a second intensity grading threshold. The first intensity grading threshold is greater than the second intensity grading threshold. The intensity grading threshold is equal to the mean of the charging demand density plus the product of the standard deviation of the charging demand density and a proportionality coefficient. The proportionality coefficient is a preset constant.

[0012] The demand intensity level of each monitoring sub-region is determined based on the charging demand density and intensity grading threshold corresponding to the monitoring sub-region. If the charging demand density of the monitoring sub-region is greater than or equal to the first intensity grading threshold, the demand intensity level of the monitoring sub-region is level three. If the charging demand density of the monitoring sub-region is less than the first intensity grading threshold but greater than or equal to the second intensity grading threshold, the demand intensity level of the monitoring sub-region is level two. If the charging demand density of the monitoring sub-region is less than the second intensity grading threshold, the demand intensity level of the monitoring sub-region is level one. Monitoring sub-regions with a demand intensity level greater than or equal to level two and an average remaining power value less than the corresponding threshold are marked as high-demand sub-regions.

[0013] This invention combines robot usage status with simple location data to more accurately reflect user needs. It updates demand density using exponential smoothing to adapt to changing demand trends and avoid sudden disturbances. It uses statistical methods for dynamic grading to adapt to fluctuations in different regions and time periods, improving the accuracy of the analysis. It identifies the areas most in need of recharging, avoiding resource waste and improving the overall operating efficiency of the system.

[0014] S2: Obtain road topology information within the monitoring area, analyze the weight of road segments between any two nodes by combining the historical moving speed and remaining power of the mobile charging robot, and construct a graph structure based on the road segment analysis weight; analyze the urgency of each high-demand sub-region, and generate a service list based on the urgency.

[0015] According to the above scheme, S2 includes the following:

[0016] S201: Obtain road topology information within the monitoring area and generate a graph structure G=(V,E), where the node set V contains all road intersections within the monitoring area, and the road intersection attributes include elevation data; the edge set E contains road segments between adjacent road intersections within the monitoring area, and the road segment attributes include segment length and road type; the road types include arterial roads, secondary arterial roads, and side roads; combine the historical moving speed and remaining battery power of the mobile charging robot to analyze the weight of the road segment between any two nodes, and denote the weight of the road segment between the i-th node and the j-th node as W. （i，j） :

[0017] ;

[0018] Among them, dist （i，j） This represents the length of the road segment between the i-th node and the j-th node; v a The historical average moving speed of the a-th mobile charging robot is represented by η (road_type); the road correction factor is represented by η (road_type), which is based on a preset road type; h i h represents the elevation data of the i-th node. j Represents the elevation data of the j-th node, ρ(h) i h j ) represents the slope energy consumption coefficient between the i-th node and the j-th node, ρ(h) i h j =1 + 0.05sgn(h) j -h i ); sgn() represents the sign function; E adenoted by k1, k2 represents the unit energy consumption of the a-th mobile charging robot; k1 represents the drag coefficient, k2 represents the basic energy consumption coefficient, α represents the time cost coefficient, and β represents the energy cost coefficient; the drag coefficient, basic energy consumption coefficient, time cost coefficient, and energy cost coefficient are all preset constants.

[0019] S202: Analyze the urgency of each high-demand sub-region, and denote the urgency of the k-th high-demand sub-region as U. k U k =λ k / [max(1,△T)×E kv ]; where λ k E represents the charging demand density of the k-th high-demand sub-region, ΔT represents the peak interval, which is equal to the time interval between the current moment and the start of the peak, and E kv This represents the average remaining power of the k-th high-demand sub-region; the high-demand sub-regions are sorted in descending order of urgency to generate a service list; if the peak interval is greater than the corresponding threshold, it is removed from the service list.

[0020] This invention takes into account slope, road type, energy consumption, and time cost, making it more consistent with the actual driving losses of mobile charging robots; it introduces a peak time factor to prioritize high-demand areas near peak hours, improving scheduling efficiency; and it optimizes path selection through a weighted model to reduce ineffective driving and lower robot energy consumption and time costs.

[0021] S3: Perform a health assessment based on the historical operating data of the mobile charging robot, and mark the mobile charging robot with a warning label based on the health assessment value and the preset health assessment value threshold.

[0022] According to the above scheme, S3 includes the following:

[0023] Motor current signals are extracted from the historical operating data of the mobile charging robot; DC component elimination processing is performed on the motor current signals, the maximum difference in motor current is calculated, and the maximum difference in motor current is divided by the mean motor current as the motor current characteristic; the mean internal resistance change rate and the mean vibration acceleration are analyzed from the historical operating data of the mobile charging robot; after normalizing the motor current characteristic, the mean internal resistance change rate, and the mean vibration acceleration, a weighted method is used to analyze the health assessment value of the mobile charging robot;

[0024] The mobile charging robot is labeled with warning tags based on health assessment values ​​and preset health assessment value thresholds. The warning tags include immediate maintenance, maintenance warning, and normal.

[0025] The preset health assessment thresholds include a first health assessment threshold and a second health assessment threshold; the first health assessment threshold is greater than the second health assessment threshold.

[0026] If the health assessment value of the mobile charging robot is greater than or equal to the first health assessment value threshold, it is marked as normal; if the health assessment value of the mobile charging robot is less than the first health assessment value threshold, and the health assessment value of the mobile charging robot is greater than or equal to the second health assessment value threshold, it is marked as a maintenance warning; if the health assessment value of the mobile charging robot is less than the second health assessment value threshold, it is marked as immediate maintenance.

[0027] This invention employs multi-dimensional data (current, internal resistance, vibration) cross-validation to improve the accuracy of fault detection; three-level labels clearly define the robot's status, avoiding over-maintenance or missed detections, and optimizing the allocation of operation and maintenance resources.

[0028] S4: Perform a set analysis of the final transfer path based on the service list and idle mobile charging robots, and optimize the transfer by combining the health assessment value of the mobile charging robots.

[0029] According to the above scheme, S4 includes the following:

[0030] S401: Obtain the number of mobile charging robots that need to be dispatched in each high-demand sub-region. The number of mobile charging robots that need to be dispatched in each high-demand sub-region is equal to the difference between the maximum number of events identified by the used identifier in the high-demand sub-region and the number of existing mobile charging robots.

[0031] Extract mobile charging robots that are not in high-demand sub-regions, have no usage identifiers, and whose warning tags are not set to immediate maintenance, and generate a set of mobile charging robots to be matched; based on the set of mobile charging robots to be matched and the service list, use Dijkstra's algorithm to analyze the optimal path from each mobile charging robot to each high-demand sub-region; generate an optimal path set based on the set of mobile charging robots to be matched, the service list, and the corresponding optimal paths; take the optimal path set with the minimum sum of road segment weights as the final transfer path set, and load the final transfer path onto the corresponding mobile charging robot;

[0032] S402: Extract the health assessment value sequence of each mobile charging robot in the final transfer path set; replace the warning label of the mobile charging robot whose health score is less than the corresponding threshold for N consecutive times and whose internal resistance change rate exceeds the threshold for a single time with "immediate maintenance", where N is a preset constant; remove the final transfer path with the warning label replaced with "immediate maintenance" from the final transfer path set, and for the high-demand sub-region corresponding to the removed mobile charging robot, rematch the mobile charging robot corresponding to the optimal path with the smallest sum of road segment weights.

[0033] This invention dispatches robots based on the actual gaps in high-demand sub-regions, avoiding blind scheduling; real-time linkage between health monitoring and path planning promptly eliminates faulty robots and reassigns tasks, ensuring service continuity; and optimal path selection based on a weighted model ensures the lowest total energy / time cost during the scheduling process, further improving operational efficiency.

[0034] Another aspect of this application is a mobile charging device operation and maintenance management system based on data analysis. The system is applied to the above-mentioned mobile charging device operation and maintenance management method based on data analysis. The system includes a charging demand analysis module, a road analysis and service list construction module, a health assessment module, and a demand matching module.

[0035] The charging demand analysis module is used to acquire historical operating data of the mobile charging robot, analyze the number of events with usage identifiers in the monitoring sub-region, analyze the charging demand density based on the number of events with usage identifiers, and divide high-demand sub-regions based on the charging demand density.

[0036] The road analysis and service list construction module is used to obtain road topology information within the monitoring area, combine the historical moving speed and remaining power of the mobile charging robot to analyze the weight of road segments between any two nodes, and construct a graph structure based on the road segment analysis weight; analyze the urgency of each high-demand sub-region, and generate a service list based on the urgency.

[0037] The health assessment module is used to perform health assessments based on the historical operating data of the mobile charging robot, and to mark the mobile charging robot with warning tags based on the health assessment value and a preset health assessment value threshold.

[0038] The demand matching module is used to perform final transfer path set analysis based on the service list and idle mobile charging robots, and to optimize the transfer by combining the health assessment value of the mobile charging robots.

[0039] According to the above scheme, the charging demand analysis module includes a charging demand density analysis unit and a high demand segmentation unit;

[0040] The charging demand density analysis unit is used to analyze the monitoring sub-region index corresponding to the historical location coordinate markers of the mobile charging robot. Within each sliding time window, it counts the number of events with usage markers in the monitoring sub-region and analyzes the charging demand density based on the number of events with usage markers. It updates the demand density based on the exponential smoothing method, extracts the remaining power of mobile charging robots without usage markers in the monitoring sub-region, and calculates the average remaining power.

[0041] The high-demand segmentation unit is used to extract the charging demand density corresponding to each monitoring sub-region within the monitoring area; calculate the mean and standard deviation of the charging demand density of the monitoring area; dynamically analyze the intensity grading threshold based on the mean and standard deviation of the charging demand density; determine the demand intensity level of each monitoring sub-region based on the charging demand density and intensity grading threshold corresponding to the monitoring sub-region; and mark the monitoring sub-regions with a demand intensity level greater than or equal to level two and a remaining average power less than the corresponding threshold as high-demand sub-regions.

[0042] According to the above scheme, the road analysis and service list construction module includes a graph construction unit and a service list construction unit;

[0043] The graph construction unit is used to acquire road topology information within the monitoring area, combine the historical moving speed and remaining power of the mobile charging robot to analyze the weight of road segments between any two nodes, and construct a graph structure based on the road segment analysis weight.

[0044] The service list construction unit analyzes the urgency of each high-demand sub-region, sorts the high-demand sub-regions in descending order of urgency, and generates a service list; if the peak interval is greater than the corresponding threshold, it is removed from the service list.

[0045] According to the above scheme, the health assessment module includes a health assessment unit and an early warning label marking unit;

[0046] The health assessment unit extracts motor current signals from the historical operating data of the mobile charging robot; performs DC component elimination processing on the motor current signals, calculates the maximum difference in motor current, and divides the maximum difference in motor current by the mean motor current to record the motor current characteristic; analyzes the mean internal resistance change rate and the mean vibration acceleration from the historical operating data of the mobile charging robot; after normalizing the motor current characteristic, the mean internal resistance change rate, and the mean vibration acceleration, a weighted method is used to analyze the health assessment value of the mobile charging robot;

[0047] The warning tag marking unit is used to mark the mobile charging robot with warning tags based on the health assessment value and a preset health assessment value threshold. The warning tags include immediate maintenance, maintenance warning, and normal.

[0048] According to the above scheme, the demand matching module includes a final transfer path analysis unit and a path optimization unit;

[0049] The final transfer path analysis unit is used to obtain the number of mobile charging robots that need to be dispatched in each high-demand sub-region, extract mobile charging robots that are not in the high-demand sub-region, have no usage identifier, and whose warning tag is not set to immediate maintenance, and generate a set of mobile charging robots to be matched; based on the set of mobile charging robots to be matched and the service list, the Dijkstra algorithm is used to analyze the optimal path from each mobile charging robot to each high-demand sub-region; an optimal path set is generated according to the set of mobile charging robots to be matched, the service list, and the corresponding optimal path; the optimal path set with the smallest sum of road segment weights is taken as the final transfer path set, and the final transfer path is loaded onto the corresponding mobile charging robot;

[0050] The path optimization unit is used to extract the health assessment value sequence of each mobile charging robot in the final transfer path set, remove the warning label from the final transfer path set and replace it with the final transfer path that needs to be maintained immediately, and for the high-demand sub-regions corresponding to the removed mobile charging robots, rematch the mobile charging robot corresponding to the optimal path with the smallest sum of road segment weights.

[0051] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention combines robot usage status rather than simple location data, more realistically reflecting user needs; the exponential smoothing method updates demand density, adapting to demand change trends and avoiding sudden disturbances; dynamic grading using statistical methods adapts to fluctuations in different regions and time periods, improving the accuracy of analysis; it identifies the areas most in need of recharging, avoiding resource waste and improving the overall operating efficiency of the system; this invention integrates slope, road type, energy consumption, and time cost, better reflecting the actual driving losses of mobile charging robots; it introduces a peak time factor, prioritizing high-demand areas near peak times, improving scheduling timeliness; it optimizes path selection through a weighted model, reducing ineffective driving and lowering robot energy consumption and time costs; this invention uses multi-dimensional data cross-validation to improve fault detection accuracy; three-level labels clearly define robot status, avoiding over-maintenance or missed detections, and optimizing the allocation of maintenance resources; this invention dispatches robots according to the actual gaps in high-demand sub-regions, avoiding blind scheduling; real-time linkage between health monitoring and path planning promptly eliminates faulty robots and reassigns tasks, ensuring service continuity; and optimal path selection based on a weighted model ensures the lowest total energy consumption / time cost during the scheduling process, further improving operating efficiency. Attached Figure Description

[0052] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0053] Figure 1 This is a flowchart illustrating the operation and maintenance management method for mobile charging devices based on data analysis according to the present invention.

[0054] Figure 2 This is a schematic diagram of the operation and maintenance management system for mobile charging devices based on data analysis, as described in this invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Please see Figure 1 This invention provides a technical solution: a mobile charging device operation and maintenance management method based on data analysis, which includes the following steps:

[0057] S1: Obtain historical operation data of the mobile charging robot, analyze the number of events with usage tags in the monitoring sub-region, analyze the charging demand density based on the number of events with usage tags, and divide the high-demand sub-regions based on the charging demand density.

[0058] S2: Obtain road topology information within the monitoring area, analyze the weight of road segments between any two nodes by combining the historical moving speed and remaining power of the mobile charging robot, and construct a graph structure based on the road segment analysis weight; analyze the urgency of each high-demand sub-region, and generate a service list based on the urgency.

[0059] S3: Perform a health assessment based on the historical operating data of the mobile charging robot, and mark the mobile charging robot with a warning label based on the health assessment value and the preset health assessment value threshold.

[0060] S4: Perform a set analysis of the final transfer path based on the service list and idle mobile charging robots, and optimize the transfer by combining the health assessment value of the mobile charging robots.

[0061] S1 includes the following:

[0062] S101: Acquire historical operating data of the mobile charging robot, including its historical moving speed and position data, as well as corresponding timestamps, remaining battery power, and rate of remaining battery power depletion; divide the monitoring area into equidistant monitoring sub-regions; analyze the monitoring sub-region index corresponding to the historical position of the mobile charging robot based on the historical position coordinates of the mobile charging robot and the minimum coordinate value of the monitoring area; the monitoring sub-region index corresponding to the historical position of the mobile charging robot is equal to the difference between the historical position coordinates of the mobile charging robot and the minimum coordinate value of the monitoring area divided by the length of the monitoring sub-region, rounded up to the nearest integer; mark the monitoring sub-region index corresponding to the historical position coordinates of the mobile charging robot.

[0063] Example 1: In this example, the coordinates of the mobile charging robot are (3, 4); the boundary coordinates of the monitoring area are (0, 0), (10, 0), (0, 10) and (10, 10); the monitoring sub-area is a square with a length of 2.

[0064] Therefore, horizontal index Vertical index Therefore, the mobile charging robot with coordinates (3, 4) belongs to the monitoring sub-area of ​​the second row and second column.

[0065] If the rate of decrease of remaining power exceeds the corresponding threshold, mark the mobile charging robot with a "used" identifier; within each sliding time window, count the number of events with the "used" identifier in the monitoring sub-region, analyze the charging demand density based on the number of events with the "used" identifier, and the charging demand density is equal to the quotient of the number of events with the "used" identifier and the length of the time window; update the demand density based on the exponential smoothing method; extract the remaining power of mobile charging robots without the "used" identifier in the monitoring sub-region, and calculate the average remaining power;

[0066] S102: Extract the charging demand density corresponding to each monitoring sub-region within the monitoring area; and calculate the mean and standard deviation of the charging demand density of the monitoring area. Based on the mean and standard deviation of the charging demand density, dynamically analyze the intensity grading threshold. The intensity grading threshold includes a first intensity grading threshold and a second intensity grading threshold. The first intensity grading threshold is greater than the second intensity grading threshold. The intensity grading threshold is equal to the mean of the charging demand density plus the product of the standard deviation of the charging demand density and the proportional coefficient. The proportional coefficient is a preset constant.

[0067] The demand intensity level of each monitoring sub-region is determined based on the charging demand density and intensity grading threshold corresponding to the monitoring sub-region. If the charging demand density of the monitoring sub-region is greater than or equal to the first intensity grading threshold, the demand intensity level of the monitoring sub-region is level three. If the charging demand density of the monitoring sub-region is less than the first intensity grading threshold but greater than or equal to the second intensity grading threshold, the demand intensity level of the monitoring sub-region is level two. If the charging demand density of the monitoring sub-region is less than the second intensity grading threshold, the demand intensity level of the monitoring sub-region is level one. Monitoring sub-regions with a demand intensity level greater than or equal to level two and an average remaining power value less than the corresponding threshold are marked as high-demand sub-regions.

[0068] S2 contains the following:

[0069] S201: Obtain road topology information within the monitoring area and generate a graph structure G=(V,E), where the node set V contains all road intersections within the monitoring area, and the road intersection attributes include elevation data; the edge set E contains road segments between adjacent road intersections within the monitoring area, and the road segment attributes include segment length and road type; road types include arterial roads, secondary arterial roads, and side roads; combine the historical moving speed and remaining battery power of the mobile charging robot to analyze the weight of the road segment between any two nodes, and denote the weight of the road segment between the i-th node and the j-th node as W. （i，j） :

[0070] ;

[0071] Among them, dist （i，j） This represents the length of the road segment between the i-th node and the j-th node; v a The historical average moving speed of the a-th mobile charging robot is represented by η(road_type); the road correction factor is represented by η(road_type), which is based on a preset road type; h i h represents the elevation data of the i-th node. j Represents the elevation data of the j-th node, ρ(h) i h j ) represents the slope energy consumption coefficient between the i-th node and the j-th node, ρ(h) i h j =1 + 0.05sgn(h) j -h i ); sgn() represents the sign function; E a denoted by k1, k2 represents the basic energy consumption coefficient, α represents the time cost coefficient, and β represents the energy cost coefficient; the drag coefficient, basic energy consumption coefficient, time cost coefficient, and energy cost coefficient are all preset constants.

[0072] S202: Analyze the urgency of each high-demand sub-region, and denote the urgency of the k-th high-demand sub-region as U. k U k =λ k / [max(1,△T)×E kv ]; where λ k E represents the charging demand density of the k-th high-demand sub-region, ΔT represents the peak interval, which is equal to the time interval between the current moment and the start of the peak, and E represents the peak interval. kv This represents the average remaining power of the k-th high-demand sub-region; the high-demand sub-regions are sorted in descending order of urgency to generate a service list; if the peak interval is greater than the corresponding threshold, it is removed from the service list.

[0073] S3 includes the following:

[0074] Motor current signals are extracted from the historical operating data of the mobile charging robot; DC component elimination processing is performed on the motor current signals, the maximum difference in motor current is calculated, and the maximum difference in motor current is divided by the mean motor current as the motor current characteristic; the mean internal resistance change rate and the mean vibration acceleration are analyzed from the historical operating data of the mobile charging robot; after normalizing the motor current characteristic, the mean internal resistance change rate, and the mean vibration acceleration, a weighted method is used to analyze the health assessment value of the mobile charging robot;

[0075] The mobile charging robot is labeled with warning tags based on health assessment values ​​and preset health assessment value thresholds. The warning tags include immediate maintenance, maintenance warning, and normal.

[0076] S4 includes the following:

[0077] S401: Obtain the number of mobile charging robots that need to be dispatched in each high-demand sub-region. The number of mobile charging robots that need to be dispatched in a high-demand sub-region is equal to the difference between the maximum number of events of the used identifier in the high-demand sub-region and the number of existing mobile charging robots.

[0078] Extract mobile charging robots that are not in high-demand sub-regions, have no usage identifiers, and whose warning tags are not set to immediate maintenance, and generate a set of mobile charging robots to be matched; based on the set of mobile charging robots to be matched and the service list, use Dijkstra's algorithm to analyze the optimal path from each mobile charging robot to each high-demand sub-region; generate an optimal path set based on the set of mobile charging robots to be matched, the service list, and the corresponding optimal paths; take the optimal path set with the minimum sum of road segment weights as the final transfer path set, and load the final transfer path onto the corresponding mobile charging robot;

[0079] Example 2: In this example, there is a set of mobile charging robots to be matched {robot 1, robot 2, robot 3}, and a service list {(high demand sub-region 1, U1=10, 2 mobile charging robots need to be dispatched), (high demand sub-region 2, U2=7, 1 mobile charging robot needs to be dispatched)};

[0080] Dijkstra's algorithm was used to analyze the optimal paths from each mobile charging robot to each high-demand sub-region. The total weight of the road segments on the optimal path from robot 1 to high-demand sub-region 1 was 5, the total weight of the road segments on the optimal path from robot 2 to high-demand sub-region 1 was 7, and the total weight of the road segments on the optimal path from robot 3 to high-demand sub-region 1 was 10.

[0081] The total weight of the road segments on the optimal path from Robot 1 to the high-demand sub-region 2 is 3; the total weight of the road segments on the optimal path from Robot 2 to the high-demand sub-region 1 is 8; and the total weight of the road segments on the optimal path from Robot 3 to the high-demand sub-region 2 is 9.

[0082] The optimal path set 1 is {(Robot 1, high-demand sub-region 1), (Robot 2, high-demand sub-region 1), (Robot 3, high-demand sub-region 2)}; the optimal path set 2 is {(Robot 1, high-demand sub-region 1), (Robot 3, high-demand sub-region 1), (Robot 2, high-demand sub-region 2)}; the optimal path set 3 is {(Robot 3, high-demand sub-region 1), (Robot 2, high-demand sub-region 1), (Robot 1, high-demand sub-region 2)}.

[0083] Since the urgency of high-demand sub-region 1 is higher than that of high-demand sub-region 2, high-demand sub-region 1 is matched first; for high-demand sub-region 1, the sum of the road segment weights of the optimal paths corresponding to robot 1 and robot 2 is the smallest.

[0084] Therefore, in this embodiment, the final set of transfer paths is {(Robot 1, high-demand sub-region 1), (Robot 2, high-demand sub-region 1), (Robot 3, high-demand sub-region 2)};

[0085] Example 3: In this example, there is a set of mobile charging robots to be matched {robot 1, robot 2, robot 3}, and a service list {(high demand sub-region 1, U1=10, 2 mobile charging robots need to be dispatched), (high demand sub-region 2, U2=10, 1 mobile charging robot needs to be dispatched)};

[0086] Dijkstra's algorithm was used to analyze the optimal paths from each mobile charging robot to each high-demand sub-region. The total weight of the road segments on the optimal path from robot 1 to high-demand sub-region 1 was 5, the total weight of the road segments on the optimal path from robot 2 to high-demand sub-region 1 was 7, and the total weight of the road segments on the optimal path from robot 3 to high-demand sub-region 1 was 10.

[0087] The total weight of the road segments on the optimal path from Robot 1 to the high-demand sub-region 2 is 3; the total weight of the road segments on the optimal path from Robot 2 to the high-demand sub-region 1 is 8; and the total weight of the road segments on the optimal path from Robot 3 to the high-demand sub-region 2 is 9.

[0088] The optimal path set 1 is {(Robot 1, high-demand sub-region 1), (Robot 2, high-demand sub-region 1), (Robot 3, high-demand sub-region 2)}; the optimal path set 2 is {(Robot 1, high-demand sub-region 1), (Robot 3, high-demand sub-region 1), (Robot 2, high-demand sub-region 2)}; the optimal path set 3 is {(Robot 3, high-demand sub-region 1), (Robot 2, high-demand sub-region 1), (Robot 1, high-demand sub-region 2)}.

[0089] In this embodiment, since the urgency of high-demand sub-region 1 is equal to that of high-demand sub-region 2, the sum of the road segment weights of each optimal path set is calculated.

[0090] Among them, the total sum of road segments in the optimal path set 1 is 5 + 7 + 9 = 21;

[0091] The total number of road segments in the optimal path set 2 is 5 + 10 + 8 = 23;

[0092] The total number of road segments in the optimal path set 3 is 10 + 7 + 3 = 20;

[0093] Therefore, in this embodiment, the final set of transfer paths is {(Robot 3, high-demand sub-region 1), (Robot 2, high-demand sub-region 1), (Robot 1, high-demand sub-region 2)};

[0094] S402: Extract the health assessment value sequence of each mobile charging robot in the final transfer path set; replace the warning label of the mobile charging robot whose health score is less than the corresponding threshold for N consecutive times and whose internal resistance change rate exceeds the threshold for a single time with "immediate maintenance", where N is a preset constant; remove the final transfer path with the warning label replaced with "immediate maintenance" from the final transfer path set, and for the high-demand sub-region corresponding to the removed mobile charging robot, rematch the mobile charging robot corresponding to the optimal path with the smallest sum of road segment weights.

[0095] Please see Figure 2The present invention provides a technical solution: a mobile charging equipment operation and maintenance management system based on data analysis, which includes a charging demand analysis module, a road analysis and service list construction module, a health assessment module and a demand matching module;

[0096] The charging demand analysis module is used to acquire historical operating data of the mobile charging robot, analyze the number of events with usage tags in the monitored sub-region, analyze the charging demand density based on the number of events with usage tags, and divide the high-demand sub-regions based on the charging demand density.

[0097] The road analysis and service list construction module is used to obtain road topology information within the monitoring area, combine the historical moving speed and remaining power of the mobile charging robot to analyze the weight of road segments between any two nodes, and construct a graph structure based on the road segment analysis weight; analyze the urgency of each high-demand sub-region, and generate a service list based on the urgency.

[0098] The health assessment module is used to perform health assessments based on the historical operating data of the mobile charging robot, and to mark the mobile charging robot with warning labels based on the health assessment value and the preset health assessment value threshold.

[0099] The demand matching module is used to perform a set analysis of the final transfer path based on the service list and idle mobile charging robots, and to optimize the transfer by combining the health assessment value of the mobile charging robots.

[0100] The charging demand analysis module includes a charging demand density analysis unit and a high demand segmentation unit;

[0101] The charging demand density analysis unit is used to analyze the monitoring sub-region index corresponding to the historical location coordinates of the mobile charging robot. Within each sliding time window, it counts the number of events with usage tags in the monitoring sub-region and analyzes the charging demand density based on the number of events with usage tags. It updates the demand density based on the exponential smoothing method, extracts the remaining power of mobile charging robots without usage tags in the monitoring sub-region, and calculates the average remaining power.

[0102] The high-demand segmentation unit is used to extract the charging demand density corresponding to each monitoring sub-region within the monitoring area; calculate the mean and standard deviation of the charging demand density of the monitoring area; dynamically analyze the intensity grading threshold based on the mean and standard deviation of the charging demand density; determine the demand intensity level of each monitoring sub-region based on the charging demand density and intensity grading threshold corresponding to the monitoring sub-region; and mark the monitoring sub-regions with a demand intensity level greater than or equal to level two and a remaining average power less than the corresponding threshold as high-demand sub-regions.

[0103] The road analysis and service list construction module includes a graph construction unit and a service list construction unit;

[0104] The graph construction unit is used to obtain road topology information within the monitoring area, combine the historical moving speed and remaining power of the mobile charging robot to analyze the weight of road segments between any two nodes, and construct a graph structure based on the road segment analysis weight.

[0105] The service list building unit analyzes the urgency of each high-demand sub-region, sorts the high-demand sub-regions in descending order of urgency, and generates a service list; if the peak interval is greater than the corresponding threshold, it is removed from the service list.

[0106] The health assessment module includes a health assessment unit and an early warning label marking unit;

[0107] The health assessment unit extracts motor current signals from the historical operating data of the mobile charging robot; it performs DC component elimination processing on the motor current signals, calculates the maximum difference in motor current, and divides the maximum difference in motor current by the mean motor current to record the motor current characteristic; it analyzes the mean internal resistance change rate and the mean vibration acceleration from the historical operating data of the mobile charging robot; after normalizing the motor current characteristic, the mean internal resistance change rate, and the mean vibration acceleration, it uses a weighted method to analyze the health assessment value of the mobile charging robot.

[0108] The warning tag marking unit is used to mark the mobile charging robot with warning tags based on the health assessment value and the preset health assessment value threshold. The warning tags include immediate maintenance, maintenance warning and normal.

[0109] The demand matching module includes a final transfer path analysis unit and a path optimization unit;

[0110] The final transfer path analysis unit is used to obtain the number of mobile charging robots that need to be dispatched in each high-demand sub-region, extract mobile charging robots that are not in high-demand sub-regions, have no usage identifier, and whose warning tag is not set to immediate maintenance, and generate a set of mobile charging robots to be matched; based on the set of mobile charging robots to be matched and the service list, the Dijkstra algorithm is used to analyze the optimal path from each mobile charging robot to each high-demand sub-region; an optimal path set is generated according to the set of mobile charging robots to be matched, the service list, and the corresponding optimal path; the optimal path set with the minimum sum of road segment weights is taken as the final transfer path set, and the final transfer path is loaded onto the corresponding mobile charging robot;

[0111] The path optimization unit is used to extract the health assessment value sequence of each mobile charging robot in the final transfer path set, remove the warning label from the final transfer path set and replace it with the final transfer path that needs to be maintained immediately, and for the high-demand sub-regions corresponding to the removed mobile charging robots, rematch the mobile charging robot corresponding to the optimal path with the smallest sum of road segment weights.

[0112] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or robot that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or robot.

[0113] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A data analysis-based operation and maintenance management method for mobile charging equipment, characterized in that, The method includes the following steps: S1: Obtain historical operation data of the mobile charging robot, analyze the number of events with usage tags in the monitoring sub-region, analyze the charging demand density based on the number of events with usage tags, and divide the high-demand sub-regions based on the charging demand density. S2: Obtain road topology information within the monitoring area, analyze the weight of road segments between any two nodes by combining the historical moving speed and remaining power of the mobile charging robot, and construct a graph structure based on the road segment analysis weight; analyze the urgency of each high-demand sub-region, and generate a service list based on the urgency. S3: Perform a health assessment based on the historical operating data of the mobile charging robot, and mark the mobile charging robot with a warning label based on the health assessment value and the preset health assessment value threshold. S4: Perform a set analysis of the final transfer path based on the service list and idle mobile charging robots, and optimize the transfer by combining the health assessment value of the mobile charging robots.

2. The mobile charging equipment operation and maintenance management method based on data analysis according to claim 1, characterized in that: S1 includes the following: S101: Acquire historical operating data of the mobile charging robot, including historical moving speed and historical position data of the mobile charging robot, as well as corresponding timestamps, remaining battery power, and remaining battery power reduction rate; divide the monitoring area into equal-distance monitoring sub-regions; analyze the monitoring sub-region index corresponding to the historical position of the mobile charging robot based on the historical position coordinates of the mobile charging robot and the minimum coordinate value of the monitoring area; the monitoring sub-region index corresponding to the historical position of the mobile charging robot is equal to the difference between the historical position coordinates of the mobile charging robot and the minimum coordinate value of the monitoring area divided by the length of the monitoring sub-region, rounded up to the nearest integer; mark the monitoring sub-region index corresponding to the historical position coordinates of the mobile charging robot. If the rate of decrease of remaining power exceeds the corresponding threshold, the mobile charging robot is marked as being used. Within each sliding time window, the number of events with the "used" mark in the monitoring sub-region is counted, and the charging demand density is analyzed based on the number of events with the "used" mark. The charging demand density is equal to the quotient of the number of events with the "used" mark and the length of the time window. The demand density is updated based on the exponential smoothing method. The remaining power of mobile charging robots without the "used" mark in the monitoring sub-region is extracted, and the average remaining power is calculated. S102: Extract the charging demand density corresponding to each monitoring sub-region within the monitoring area; and calculate the mean and standard deviation of the charging demand density of the monitoring area. Based on the mean and standard deviation of the charging demand density, dynamically analyze the intensity grading threshold. The intensity grading threshold includes a first intensity grading threshold and a second intensity grading threshold. The first intensity grading threshold is greater than the second intensity grading threshold. The intensity grading threshold is equal to the mean of the charging demand density plus the product of the standard deviation of the charging demand density and a proportionality coefficient. The proportionality coefficient is a preset constant. The demand intensity level of each monitoring sub-region is determined based on the charging demand density and intensity grading threshold corresponding to the monitoring sub-region. If the charging demand density of the monitoring sub-region is greater than or equal to the first intensity grading threshold, the demand intensity level of the monitoring sub-region is level three. If the charging demand density of the monitoring sub-region is less than the first intensity grading threshold but greater than or equal to the second intensity grading threshold, the demand intensity level of the monitoring sub-region is level two. If the charging demand density of the monitoring sub-region is less than the second intensity grading threshold, the demand intensity level of the monitoring sub-region is level one. Monitoring sub-regions with a demand intensity level greater than or equal to level two and an average remaining power consumption less than the corresponding threshold are marked as high-demand sub-regions.

3. The mobile charging equipment operation and maintenance management method based on data analysis according to claim 2, characterized in that: S2 contains the following: S201: Obtain road topology information within the monitoring area and generate a graph structure G=(V,E), where the node set V contains all road intersections within the monitoring area, and the road intersection attributes include elevation data; the edge set E contains road segments between adjacent road intersections within the monitoring area, and the road segment attributes include segment length and road type; the road types include arterial roads, secondary arterial roads, and side roads; combine the historical moving speed and remaining battery power of the mobile charging robot to analyze the weight of the road segment between any two nodes, and denote the weight of the road segment between the i-th node and the j-th node as W. （i，j） : ； Among them, dist （i，j） This represents the length of the road segment between the i-th node and the j-th node; v a The historical average moving speed of the a-th mobile charging robot is represented by η (road_type); the road correction factor is represented by η (road_type), which is based on a preset road type; h i h represents the elevation data of the i-th node. j Represents the elevation data of the j-th node, ρ(h) i h j E represents the slope energy consumption coefficient between the i-th node and the j-th node; a denoted by k1, k2 represents the unit energy consumption of the a-th mobile charging robot; k1 represents the drag coefficient, k2 represents the basic energy consumption coefficient, α represents the time cost coefficient, and β represents the energy cost coefficient; the drag coefficient, basic energy consumption coefficient, time cost coefficient, and energy cost coefficient are all preset constants. S202: Analyze the urgency of each high-demand sub-region, and denote the urgency of the k-th high-demand sub-region as U. k U k =λ k / [max(1,△T)×E kv ]; where λ k E represents the charging demand density of the k-th high-demand sub-region, ΔT represents the peak interval, which is equal to the time interval between the current moment and the start of the peak, and E kv This represents the average remaining power of the k-th high-demand sub-region; the high-demand sub-regions are sorted in descending order of urgency to generate a service list; if the peak interval is greater than the corresponding threshold, it is removed from the service list.

4. The mobile charging equipment operation and maintenance management method based on data analysis according to claim 3, characterized in that: S3 includes the following: Extract motor current signals from historical operating data of the mobile charging robot; The DC component of the motor current signal is eliminated, the maximum difference in motor current is calculated, and the maximum difference in motor current is divided by the mean motor current to be recorded as the motor current characteristic. The mean internal resistance change rate and the mean vibration acceleration are analyzed from the historical operating data of the mobile charging robot. After normalizing the motor current characteristic, the mean internal resistance change rate, and the mean vibration acceleration, the health assessment value of the mobile charging robot is analyzed using a weighted method. The mobile charging robot is labeled with warning tags based on health assessment values ​​and preset health assessment value thresholds. The warning tags include immediate maintenance, maintenance warning, and normal.

5. The mobile charging equipment operation and maintenance management method based on data analysis according to claim 4, characterized in that: S4 includes the following: S401: Obtain the number of mobile charging robots that need to be dispatched in each high-demand sub-region. The number of mobile charging robots that need to be dispatched in each high-demand sub-region is equal to the difference between the maximum number of events identified by the used identifier in the high-demand sub-region and the number of existing mobile charging robots. Extract mobile charging robots that are not in high-demand sub-regions, have no usage identifiers, and whose warning tags are not set to immediate maintenance, and generate a set of mobile charging robots to be matched; based on the set of mobile charging robots to be matched and the service list, use Dijkstra's algorithm to analyze the optimal path from each mobile charging robot to each high-demand sub-region; generate an optimal path set based on the set of mobile charging robots to be matched, the service list, and the corresponding optimal paths; take the optimal path set with the minimum sum of road segment weights as the final transfer path set, and load the final transfer path onto the corresponding mobile charging robot; S402: Extract the health assessment value sequence of each mobile charging robot in the final transfer path set; replace the warning label of the mobile charging robot whose health score is less than the corresponding threshold for N consecutive times and whose internal resistance change rate exceeds the threshold for a single time with "immediate maintenance", where N is a preset constant; remove the final transfer path with the warning label replaced with "immediate maintenance" from the final transfer path set, and for the high-demand sub-region corresponding to the removed mobile charging robot, rematch the mobile charging robot corresponding to the optimal path with the smallest sum of road segment weights.

6. A mobile charging device operation and maintenance management system based on data analysis, wherein the system is applied to the mobile charging device operation and maintenance management method based on data analysis as described in any one of claims 1-5, characterized in that, The system includes a charging demand analysis module, a road analysis and service list construction module, a health assessment module, and a demand matching module; The charging demand analysis module is used to acquire historical operating data of the mobile charging robot, analyze the number of events with usage identifiers in the monitoring sub-region, analyze the charging demand density based on the number of events with usage identifiers, and divide high-demand sub-regions based on the charging demand density. The road analysis and service list construction module is used to obtain road topology information within the monitoring area, combine the historical moving speed and remaining power of the mobile charging robot to analyze the weight of road segments between any two nodes, and construct a graph structure based on the road segment analysis weight; analyze the urgency of each high-demand sub-region, and generate a service list based on the urgency. The health assessment module is used to perform health assessments based on the historical operating data of the mobile charging robot, and to mark the mobile charging robot with warning tags based on the health assessment value and a preset health assessment value threshold. The demand matching module is used to perform final transfer path set analysis based on the service list and idle mobile charging robots, and to optimize the transfer by combining the health assessment value of the mobile charging robots.

7. The mobile charging equipment operation and maintenance management system based on data analysis according to claim 6, characterized in that: The charging demand analysis module includes a charging demand density analysis unit and a high demand segmentation unit. The charging demand density analysis unit is used to analyze the monitoring sub-region index corresponding to the historical location coordinate markers of the mobile charging robot. Within each sliding time window, it counts the number of events with usage identifiers within the monitoring sub-region and analyzes the charging demand density based on the number of events with usage identifiers. The demand density is updated based on the exponential smoothing method; the remaining power of mobile charging robots that are not marked as being used within the monitoring sub-region is extracted, and the average remaining power is calculated; The high-demand segmentation unit is used to extract the charging demand density corresponding to each monitoring sub-region within the monitoring area; and to calculate the mean and standard deviation of the charging demand density of the monitoring area, dynamically analyze the intensity grading threshold based on the mean and standard deviation of the charging demand density, and determine the demand intensity level of each monitoring sub-region based on the charging demand density and intensity grading threshold corresponding to the monitoring sub-region. Monitoring sub-regions with a demand intensity level greater than or equal to level two and an average remaining power consumption less than the corresponding threshold are marked as high-demand sub-regions.

8. The mobile charging equipment operation and maintenance management system based on data analysis according to claim 6, characterized in that: The road analysis and service list construction module includes a graph construction unit and a service list construction unit; The graph construction unit is used to acquire road topology information within the monitoring area, combine the historical moving speed and remaining power of the mobile charging robot to analyze the weight of road segments between any two nodes, and construct a graph structure based on the road segment analysis weight. The service list construction unit analyzes the urgency of each high-demand sub-region, sorts the high-demand sub-regions in descending order of urgency, and generates a service list; if the peak interval is greater than the corresponding threshold, it is removed from the service list.

9. The mobile charging equipment operation and maintenance management system based on data analysis according to claim 6, characterized in that: The health assessment module includes a health assessment unit and an early warning label marking unit; The health assessment unit extracts motor current signals from the historical operating data of the mobile charging robot; The DC component of the motor current signal is eliminated, the maximum difference in motor current is calculated, and the maximum difference in motor current is divided by the mean motor current to be recorded as the motor current characteristic. The mean internal resistance change rate and the mean vibration acceleration are analyzed from the historical operating data of the mobile charging robot. After normalizing the motor current characteristic, the mean internal resistance change rate, and the mean vibration acceleration, the health assessment value of the mobile charging robot is analyzed using a weighted method. The warning tag marking unit is used to mark the mobile charging robot with warning tags based on the health assessment value and a preset health assessment value threshold. The warning tags include immediate maintenance, maintenance warning, and normal.

10. The mobile charging equipment operation and maintenance management system based on data analysis according to claim 6, characterized in that: The demand matching module includes a final transfer path analysis unit and a path optimization unit; The final transfer path analysis unit is used to obtain the number of mobile charging robots that need to be dispatched in each high-demand sub-region, extract mobile charging robots that are not in the high-demand sub-region, have no usage identifier, and whose warning tag is not set to immediate maintenance, and generate a set of mobile charging robots to be matched; based on the set of mobile charging robots to be matched and the service list, the Dijkstra algorithm is used to analyze the optimal path from each mobile charging robot to each high-demand sub-region; an optimal path set is generated according to the set of mobile charging robots to be matched, the service list, and the corresponding optimal path; the optimal path set with the smallest sum of road segment weights is taken as the final transfer path set, and the final transfer path is loaded onto the corresponding mobile charging robot; The path optimization unit is used to extract the health assessment value sequence of each mobile charging robot in the final transfer path set, remove the warning label from the final transfer path set and replace it with the final transfer path that needs to be maintained immediately, and for the high-demand sub-regions corresponding to the removed mobile charging robots, rematch the mobile charging robot corresponding to the optimal path with the smallest sum of road segment weights.

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