Charging abnormity diagnosis and evaluation system applied to mobile charging vehicle

Abstract

The invention provides a charging abnormity diagnosis and evaluation system applied to a mobile charging vehicle. The charging abnormity diagnosis and evaluation system comprises the steps of collecting feedback working data of the mobile charging vehicle, constructing output feedback information corresponding to the mobile charging vehicle by utilizing the feedback working data, and establishing a plurality of actual charging characteristics of a charged vehicle according to the output feedback information, performing abnormality evaluation on each actual charging characteristic to obtain a charging evaluation report of the charged vehicle, performing troubleshooting according to the charging evaluation report, determining an abnormal problem corresponding to the charged vehicle and the mobile charging vehicle, calling a solution corresponding to each abnormal problem, and performing optimization to obtain a solution corresponding to each abnormal problem; a problem optimization scheme is generated and implemented, and by monitoring current and voltage when the target vehicle is charged by the mobile charging vehicle, abnormality assessment and judgment are carried out, so that effective implementation of the charging process is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile charging vehicles, and in particular to a charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle. Background Art

[0002] In recent years, electric vehicles have been vigorously promoted in various provinces and cities in my country due to their advantages of environmental protection, cleanliness and energy saving, and have gradually become popular. As an important component in the promotion of electric vehicles, electric vehicle charging facilities have also developed rapidly. At present, the internal structure of electric vehicle charging facilities has gradually become more refined, with more and more functions, and the intelligence is gradually increasing. For example, the latest technology mobile charging vehicle can reach the location of the electric vehicle when needed and provide fast charging, which is particularly suitable for scenarios where charging stations are insufficient or temporary. This type of vehicle is usually equipped with a large-capacity battery pack and a charging interface to support the charging needs of different types of electric vehicles. However, during the charging process, related faults still occur from time to time. The issue of electric vehicle charging safety will be a problem that must be faced and solved in the further promotion and development of electric vehicles in the future.

[0003] Therefore, the present invention provides a charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle. Summary of the invention

[0004] The present invention discloses a charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle. The system monitors the current and voltage when the mobile charging vehicle is charging a target vehicle, and performs abnormality evaluation and judgment to ensure the effective charging process.

[0005] The present invention provides a charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle, comprising:

[0006] A feedback collection module, used to collect feedback work data of the mobile charging vehicle, and use the feedback work data to construct output feedback information corresponding to the mobile charging vehicle;

[0007] A charging analysis module, used to establish a plurality of actual charging characteristics of the charged vehicle according to the output feedback information, and to perform abnormality evaluation on each of the actual charging characteristics to obtain a charging evaluation report of the charged vehicle;

[0008] An abnormality diagnosis module, used to troubleshoot problems according to the charging evaluation report and determine abnormal problems corresponding to the charged vehicle and the mobile charging vehicle;

[0009] The abnormality optimization module is used to retrieve and optimize the solution corresponding to each abnormal problem, generate and implement the problem optimization solution.

[0010] In one practicable manner,

[0011] Also includes:

[0012] A vehicle positioning module, used to construct a data screening rule according to the positioning condition, use the data screening rule to screen a number of position data in the feedback working data, and determine the position information of the corresponding mobile charging vehicle based on the position data;

[0013] Obtain a data expression of the data screening rule, and use the data expression to respectively obtain node parameters of each of the position information in the feedback working data;

[0014] The first node parameter in the feedback working data is regarded as a parent node, and a leaf node corresponding to each node parameter is established based on a logical relationship between each node parameter and the first node parameter;

[0015] Using the pre-mentioned parent node and leaf node to construct the DAG scheduling information of the corresponding mobile charging vehicle;

[0016] Determine the corresponding working position of the mobile charging vehicle at different times according to the DAG scheduling information, and construct the working trajectory of the mobile charging vehicle;

[0017] The working track corresponding to each mobile charging vehicle is obtained respectively, and a mobile charging vehicle track distribution map is constructed and displayed.

[0018] In one practicable manner,

[0019] The feedback collection module comprises:

[0020] A cycle division unit, used to respectively collect a plurality of pieces of real-time working information corresponding to each of the mobile charging vehicles, respectively obtain an information cycle corresponding to each piece of the real-time working information, and divide each piece of the real-time working information into a plurality of periodic sub-information;

[0021] A logic analysis unit, used to respectively obtain a bit stream corresponding to each of the periodic sub-information, perform a logic operation on the bit stream to obtain a plurality of logic values ​​corresponding to each of the real-time working information, locate each of the logic values ​​in the real-time working information, and obtain a working information value corresponding to each of the logic values;

[0022] An information encoding unit is used to construct a value distribution axis of the work information value in the real-time work information, perform period mining on the value distribution axis to generate a regular period corresponding to the real-time work information, and adjust each of the period sub-information according to the regular period to generate period encoding information corresponding to the real-time work information;

[0023] The feedback analysis unit is used to determine the coded feedback information generated by the corresponding mobile charging vehicle in each feedback cycle according to the periodic coding information, map the coded feedback information to a preset feedback list for information filling, and obtain the output feedback information corresponding to each mobile charging vehicle.

[0024] In one practicable manner,

[0025] The charging analysis module comprises:

[0026] A model analysis unit, configured to construct a charging work model corresponding to the mobile charging vehicle according to the feedback work data, run the charging work model to determine a plurality of lossless output features corresponding to the mobile charging vehicle, and add a corresponding feature label to each lossless output feature according to a feature attribute corresponding to each lossless input feature;

[0027] A deep analysis unit, configured to perform a deep analysis on the output feedback information based on the feature tag, obtain a plurality of independent features contained in the output feedback information, count the independent tags corresponding to each feature tag, and construct an actual charging feature of the charged vehicle;

[0028] A charging analysis unit, used for comparing the lossless output feature corresponding to the same feature label with the actual charging feature to obtain a plurality of feature differences, extracting the charging features whose feature differences are not 0 as abnormal charging features, and searching for the abnormal feature label corresponding to each abnormal charging feature respectively;

[0029] The charging evaluation unit is used to establish a reporting framework corresponding to the charged vehicle according to the abnormal dimension corresponding to the abnormal feature label, mark the feature difference corresponding to each abnormal feature label in the reporting framework, trace the abnormality of each abnormal dimension, and generate a charging evaluation report for the charged vehicle.

[0030] In one practicable manner,

[0031] The abnormality diagnosis module comprises:

[0032] A problem preliminary review unit, used to determine a plurality of first suspected problems of the mobile charging vehicle and a plurality of second suspected problems corresponding to the charged vehicle according to the charging evaluation report, and match each of the first suspected problems with different second suspected problems to obtain a plurality of problem groups;

[0033] A question screening unit is used to perform causal analysis on each of the question groups to obtain a target question group with a reasonable causal relationship, establish question data corresponding to each of the target question groups, input the question data into the output feedback information for a first matching, and obtain a first matching feature between each of the question groups and the output feedback information;

[0034] A deep screening unit, used to respectively establish a problem feature corresponding to each of the target problem groups, perform a second matching between the problem feature and each of the actual charging features, and obtain a second matching feature between each of the problem groups and each of the actual charging features;

[0035] A problem establishing unit is used to construct a matching degree corresponding to each of the problem groups according to the first matching feature and the second matching feature, determine the actual problem group corresponding to the charged vehicle and the mobile charging vehicle based on the matching degree, and determine the first abnormal problem corresponding to the mobile charging vehicle and the second abnormal problem corresponding to the charged vehicle in the actual problem group.

[0036] In one practicable manner,

[0037] The depth screening unit comprises:

[0038] The first screening subunit is used to construct the problem dimension of each target problem group according to the reasonable causal relationship corresponding to each target problem group, and to construct the problem feature corresponding to each target problem group in combination with the problem level corresponding to each target problem group;

[0039] A second screening subunit is used to search for the abnormal feature label contained in each of the actual charging features, construct a matching feature corresponding to the actual charging feature according to the abnormal feature label, match each of the problem features with different matching features, and obtain a plurality of matching nodes and non-matching nodes between each of the problem features and different matching features;

[0040] The third screening subunit is used to add a basic matching weight to each of the problem features according to the ratio of matching nodes and non-matching nodes corresponding to the same problem feature, and obtain the key nodes of each of the matching features respectively, and when the key nodes are matching nodes, add a deep matching weight to the corresponding problem feature, and when the key nodes are non-matching nodes, add a common matching weight to the corresponding problem feature;

[0041] The fourth screening subunit is used to establish a second matching feature between each of the question groups and each of the actual charging features based on the basic matching weight and the deep matching weight or the common matching weight corresponding to each of the question features.

[0042] In one practicable manner,

[0043] The abnormal optimization module comprises:

[0044] A solution matching unit, used to respectively find a solution corresponding to each of the abnormal problems, and respectively construct a first execution condition corresponding to the mobile charging vehicle and a second execution condition corresponding to the charged vehicle;

[0045] a device locating unit, configured to respectively determine a first execution device corresponding to each of the first execution conditions, and respectively determine a second execution device corresponding to each of the second execution conditions, and respectively obtain first device data corresponding to each of the first execution devices and second device data corresponding to each of the second execution devices;

[0046] a scheme optimization unit, configured to perform a first process optimization on the first execution condition based on the first device data to generate a corresponding first optimization condition, and perform a second process optimization on the second execution condition based on the second device data to generate a corresponding second optimization condition;

[0047] A solution execution unit is used to construct a problem optimization solution based on the first optimization condition and the second optimization condition, and control the mobile charging vehicle and the charged vehicle to implement the problem optimization solution.

[0048] In one practicable manner,

[0049] Also includes:

[0050] An optimization supervision module, used to establish a first optimization target corresponding to the mobile charging vehicle and a second optimization target corresponding to the charged vehicle according to the problem optimization solution;

[0051] Obtaining a first real-time optimization result of the mobile charging vehicle and a second real-time optimization result of the charged vehicle;

[0052] When the first real-time optimization result is consistent with the first optimization target, and the second real-time optimization result is consistent with the second optimization target, it is determined that the mobile charging vehicle and the charged vehicle have completed optimization.

[0053] In one practicable manner,

[0054] Also includes:

[0055] The power monitoring module is used to monitor the real-time power of each of the charged vehicles, and when the real-time power is consistent with the specified power, the mobile charging vehicle is controlled to stop charging.

[0056] In one practicable manner,

[0057] Also includes:

[0058] The report analysis module is used to analyze each of the charging evaluation reports respectively, determine the high-frequency abnormal problems corresponding to each of the mobile charging vehicles, and generate a problem handling warning.

[0059] The achievable beneficial effects of the above technical solution are as follows: in order to conduct a safety assessment of the operation of the mobile charging vehicle, feedback work data of the mobile charging vehicle is collected when the mobile charging vehicle is working, thereby constructing the output feedback information of the mobile charging vehicle, and further using the output feedback information to construct the actual charging characteristics of the charged vehicle, thereby conducting an abnormality assessment on each actual charging characteristic, and generating a charging evaluation report for the charged vehicle. At this time, the generated charging evaluation report evaluates the charging power, charging current and charging voltage. The user can understand the charging status in real time according to the charging evaluation report, and then troubleshoot the problem according to the charging evaluation report, determine the abnormal problems of the charged vehicle and the mobile charging vehicle, and further optimize the solution to each abnormal problem in accordance with the actual situation, and construct a problem optimization solution. By implementing this solution, the abnormal problems of the charged vehicle and the mobile charging vehicle can be solved, thereby ensuring the charging efficiency and charging quality, and can also protect the charged vehicle and the mobile charging vehicle.

[0060] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0061] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0063] Figure 1 It is a schematic diagram of the composition of a charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle in an embodiment of the present invention;

[0064] Figure 2 The figure is a schematic diagram of the composition of an abnormality diagnosis module of a charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0066] Example 1

[0067] This embodiment provides a charging abnormality diagnosis and evaluation system for a mobile charging vehicle, such as Figure 1 As shown, including:

[0068] A feedback collection module, used to collect feedback work data of the mobile charging vehicle, and use the feedback work data to construct output feedback information corresponding to the mobile charging vehicle;

[0069] A charging analysis module, used to establish a plurality of actual charging characteristics of the charged vehicle according to the output feedback information, and to perform abnormality evaluation on each of the actual charging characteristics to obtain a charging evaluation report of the charged vehicle;

[0070] An abnormality diagnosis module, used to troubleshoot problems according to the charging evaluation report and determine abnormal problems corresponding to the charged vehicle and the mobile charging vehicle;

[0071] The abnormality optimization module is used to retrieve and optimize the solution corresponding to each abnormal problem, generate and implement the problem optimization solution.

[0072] In this example, the mobile charging vehicle is a vehicle specifically designed to provide charging services for electric vehicles;

[0073] In this example, the feedback working data refers to the data generated by the mobile charging vehicle during the working process;

[0074] In this example, the output feedback information is established based on the feedback work data of the mobile charging vehicle, and is used to represent the information generated by the mobile charging vehicle during the charging process;

[0075] In this example, when charging, one mobile charging vehicle charges one vehicle to be charged one by one;

[0076] In this example, the actual charging characteristics include actual current characteristics and actual voltage characteristics;

[0077] In this example, the abnormal problem refers to the problem of the charged vehicle and the mobile charging vehicle, including: abnormal connection, insufficient remaining power in the mobile charging vehicle, excessive battery loss in the charged vehicle, the charging environment does not meet the charging standards, and the connection is interrupted.

[0078] The working principle and beneficial effects of the above technical solution: In order to conduct a safety assessment of the operation of the mobile charging vehicle, its feedback work data is collected when the mobile charging vehicle is working, thereby constructing the output feedback information of the mobile charging vehicle, and further using the output feedback information to construct the actual charging characteristics of the charged vehicle, thereby performing an abnormal assessment on each actual charging characteristic, and generating a charging evaluation report for the charged vehicle. At this time, the generated charging evaluation report evaluates the charging power, charging current and charging voltage. Users can understand the charging status in real time according to the charging evaluation report, and then troubleshoot problems according to the charging evaluation report to determine abnormal problems of the charged vehicle and the mobile charging vehicle, and further optimize the solutions to each abnormal problem in accordance with the actual situation, and construct a problem optimization solution. By implementing this solution, abnormal problems of the charged vehicle and the mobile charging vehicle can be solved, thereby ensuring charging efficiency and charging quality, and can also protect the charged vehicle and the mobile charging vehicle.

[0079] Example 2

[0080] Based on Example 1, the charging abnormality diagnosis and evaluation system applied to the mobile charging vehicle further includes:

[0081] A vehicle positioning module, used to construct a data screening rule according to the positioning condition, use the data screening rule to screen a number of position data in the feedback working data, and determine the position information of the corresponding mobile charging vehicle based on the position data;

[0082] Obtain a data expression of the data screening rule, and use the data expression to respectively obtain node parameters of each of the position information in the feedback working data;

[0083] The first node parameter in the feedback working data is regarded as a parent node, and a leaf node corresponding to each node parameter is established based on a logical relationship between each node parameter and the first node parameter;

[0084] Using the pre-mentioned parent node and leaf node to construct the DAG scheduling information of the corresponding mobile charging vehicle;

[0085] Determine the corresponding working position of the mobile charging vehicle at different times according to the DAG scheduling information, and construct the working trajectory of the mobile charging vehicle;

[0086] The working track corresponding to each mobile charging vehicle is obtained respectively, and a mobile charging vehicle track distribution map is constructed and displayed.

[0087] In this example, the positioning condition represents the condition executed when locating the position of the mobile charging vehicle;

[0088] In this example, the data screening rule represents a rule for screening location data;

[0089] In this example, the location information represents the description information of the location where the mobile charging vehicle is working;

[0090] In this example, the data expression represents the uniform format of the data filtered by the data filtering rule, which helps the system understand how the data flows and transforms between nodes;

[0091] In this example, the node parameters represent the relationship between various types of data and position data in the feedback work data;

[0092] In this example, the first node parameter represents the first position data in the feedback working data;

[0093] In this example, the DAG scheduling information represents a method of expressing the dependency relationship between various location data using a directed acyclic graph.

[0094] The working principle and beneficial effects of the above technical solution: In order to achieve better control and keep track of the location of each mobile charging vehicle at any time, a data screening rule is first constructed according to the positioning conditions, and the corresponding location data in the feedback work data is screened using the rule to construct the location information of the mobile charging vehicle. The data expression of the data screening rule is further used to obtain the node parameters of each location information in the feedback work data, in which the parent node and leaf node are screened to construct the DAG scheduling information, and the corresponding working position of the mobile charging vehicle at different times is determined, thereby constructing the working trajectory of the mobile charging vehicle. This makes it convenient for managers to uniformly manage the mobile charging vehicles and effectively reduces the probability of losing the mobile charging vehicles.

[0095] Example 3

[0096] On the basis of Example 1, the charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle, the feedback collection module includes:

[0097] A cycle division unit, used to respectively collect a plurality of pieces of real-time working information corresponding to each of the mobile charging vehicles, respectively obtain an information cycle corresponding to each piece of the real-time working information, and divide each piece of the real-time working information into a plurality of periodic sub-information;

[0098] A logic analysis unit, used to respectively obtain a bit stream corresponding to each of the periodic sub-information, perform a logic operation on the bit stream to obtain a plurality of logic values ​​corresponding to each of the real-time working information, locate each of the logic values ​​in the real-time working information, and obtain a working information value corresponding to each of the logic values;

[0099] An information encoding unit is used to construct a value distribution axis of the work information value in the real-time work information, perform period mining on the value distribution axis to generate a regular period corresponding to the real-time work information, and adjust each of the period sub-information according to the regular period to generate period encoding information corresponding to the real-time work information;

[0100] The feedback analysis unit is used to determine the coded feedback information generated by the corresponding mobile charging vehicle in each feedback cycle according to the periodic coding information, map the coded feedback information to a preset feedback list for information filling, and obtain the output feedback information corresponding to each mobile charging vehicle.

[0101] In this example, the information cycle represents the generation cycle of real-time working information;

[0102] In this example, the period sub-information represents the sub-information obtained by dividing the real-time work information according to the period;

[0103] In this example, the bit stream refers to a stream of bits (0s and 1s) arranged in actual order, which is the encoding of the data;

[0104] In this example, the logic value represents the bit value of the real-time working information obtained through logic operation;

[0105] In this example, the value distribution axis represents a data axis indicating the distribution density of the work information value;

[0106] In this example, the work information value represents a data value corresponding to a logical value in the real-time work information;

[0107] In this example, the preset feedback list refers to a blank feedback list prepared in advance;

[0108] In this instance, cycle mining refers to the process of mining data cycles that exist on the value distribution axis;

[0109] In this example, the feedback cycle is consistent with the regular cycle in length, and the starting point of the feedback cycle is the time when the mobile charging vehicle starts working.

[0110] The working principle and beneficial effects of the above technical solution: In order to collect the output feedback information of the mobile charging vehicle in real time, several real-time working information of the mobile charging vehicle are first collected respectively, and then the real-time working information is divided into periodic sub-information according to the information cycle of each real-time working information, and further logical operations are performed on the bit stream of each periodic sub-information to determine its corresponding logical value and the working information value of the real-time working information, and then a value distribution axis is established, and the regular cycle of the real-time working information is determined by cycle mining, and the periodic sub-information is encoded using the regular cycle to obtain the periodic encoding information of the real-time working information, and then the encoded feedback information generated by the mobile charging vehicle in each feedback cycle is finally filled in according to the plan to obtain the output feedback information of the mobile charging vehicle. In this way, not only the feedback information of the mobile charging vehicle can be collected in real time, but also the data of the mobile charging vehicle can be preliminarily encoded and adjusted, which ensures the stability of the output feedback information and reduces the time for subsequent information sorting.

[0111] Example 4

[0112] On the basis of Example 1, the charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle, the charging analysis module includes:

[0113] A model analysis unit, configured to construct a charging work model corresponding to the mobile charging vehicle according to the feedback work data, run the charging work model to determine a plurality of lossless output features corresponding to the mobile charging vehicle, and add a corresponding feature label to each lossless output feature according to a feature attribute corresponding to each lossless input feature;

[0114] A deep analysis unit, configured to perform a deep analysis on the output feedback information based on the feature tag, obtain a plurality of independent features contained in the output feedback information, count the independent tags corresponding to each feature tag, and construct an actual charging feature of the charged vehicle;

[0115] A charging analysis unit, used for comparing the lossless output feature corresponding to the same feature label with the actual charging feature to obtain a plurality of feature differences, extracting the charging features whose feature differences are not 0 as abnormal charging features, and searching for the abnormal feature label corresponding to each abnormal charging feature respectively;

[0116] The charging evaluation unit is used to establish a reporting framework corresponding to the charged vehicle according to the abnormal dimension corresponding to the abnormal feature label, mark the feature difference corresponding to each abnormal feature label in the reporting framework, trace the abnormality of each abnormal dimension, and generate a charging evaluation report for the charged vehicle.

[0117] In this example, the lossless output characteristic refers to the output characteristic of the mobile charging vehicle without external interference and self-loss;

[0118] In this example, the characteristic tags include: power tag, current tag and voltage tag;

[0119] In this example, the independent features represent independent electrical quantity, independent current and independent voltage in the output feedback information;

[0120] In this example, the abnormal charging characteristics indicate a charging characteristic in which loss occurs;

[0121] In this example, the abnormal dimensions include: abnormal power, abnormal current, or abnormal voltage.

[0122] The working principle and beneficial effects of the above technical solution are as follows: by constructing a charging working model of a mobile charging vehicle to analyze its lossless output characteristics during operation, and adding corresponding feature labels to each lossless output feature respectively, and then finding the corresponding independent features in the output feedback information to construct the actual charging characteristics of the charged vehicle, and by comparing the feature difference between the lossless output characteristics and the actual charging characteristics under the same feature label to screen the abnormal charging characteristics, and by tracing the source to determine the abnormal position of the abnormal charging characteristics, a charging evaluation report is constructed. By constructing the charging evaluation report, a preliminary analysis of the charging status of the charged vehicle can be made and the abnormal position can be located. In addition, through the charging evaluation report, the faults of the charged vehicle and the mobile charging vehicle can be understood at any time, as well as the time when the fault occurred, which is beneficial to equipment maintenance.

[0123] Example 5

[0124] Based on Example 1, the charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle is as follows: Figure 2 As shown, the abnormality diagnosis module includes:

[0125] A problem preliminary review unit, used to determine a plurality of first suspected problems of the mobile charging vehicle and a plurality of second suspected problems corresponding to the charged vehicle according to the charging evaluation report, and match each of the first suspected problems with different second suspected problems to obtain a plurality of problem groups;

[0126] A question screening unit is used to perform causal analysis on each of the question groups to obtain a target question group with a reasonable causal relationship, establish question data corresponding to each of the target question groups, input the question data into the output feedback information for a first matching, and obtain a first matching feature between each of the question groups and the output feedback information;

[0127] A deep screening unit, used to respectively establish a problem feature corresponding to each of the target problem groups, perform a second matching between the problem feature and each of the actual charging features, and obtain a second matching feature between each of the problem groups and each of the actual charging features;

[0128] A problem establishing unit is used to construct a matching degree corresponding to each of the problem groups according to the first matching feature and the second matching feature, determine the actual problem group corresponding to the charged vehicle and the mobile charging vehicle based on the matching degree, and determine the first abnormal problem corresponding to the mobile charging vehicle and the second abnormal problem corresponding to the charged vehicle in the actual problem group.

[0129] In this example, the first suspected problem indicates a suspected problem with the mobile charging vehicle, and the second suspected problem indicates a suspected problem with the charged vehicle;

[0130] In this example, a problem group includes a first suspected problem and a second suspected problem;

[0131] In this example, the target problem group indicates that there is a reasonable causal relationship between the first suspected problem and the second suspected problem in the problem group;

[0132] In this example, the first matching feature indicates the matching degree between the question data and the output feedback information;

[0133] In this example, the first matching feature indicates the actual matching degree between the question set and the actual charging feature;

[0134] In this example, the first abnormal problem indicates a problem with the mobile charging vehicle, and the second abnormal problem indicates a problem with the charged vehicle.

[0135] The working principle and beneficial effects of the above technical solution: In order to further analyze the abnormal problems of the mobile charging vehicle and the charged vehicle, the suspected problems of the mobile charging vehicle and the charged vehicle are first determined according to the charging evaluation report, and then a problem group is constructed. The target problem group with a reasonable causal relationship is extracted by performing causal analysis on the problem group, and then the problem data corresponding to the target problem group is matched with the output feedback information, and the problem characteristics of the target problem group are matched with each actual charging characteristic. After two matching results, the matching degree of each problem group is constructed, thereby determining the actual problems of the charged vehicle and the mobile charging vehicle. In this way, not only can the problems existing in the charged vehicle and the mobile charging vehicle be quickly determined, but also the fault problems of the charged vehicle and the mobile charging vehicle are preliminarily matched in the process of determining the problem, effectively reducing the work errors in locating the fault problems.

[0136] Example 6

[0137] On the basis of Example 5, the charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle, the depth screening unit includes:

[0138] The first screening subunit is used to construct the problem dimension of each target problem group according to the reasonable causal relationship corresponding to each target problem group, and to construct the problem feature corresponding to each target problem group in combination with the problem level corresponding to each target problem group;

[0139] A second screening subunit is used to search for the abnormal feature label contained in each of the actual charging features, construct a matching feature corresponding to the actual charging feature according to the abnormal feature label, match each of the problem features with different matching features, and obtain a plurality of matching nodes and non-matching nodes between each of the problem features and different matching features;

[0140] The third screening subunit is used to add a basic matching weight to each of the problem features according to the ratio of matching nodes and non-matching nodes corresponding to the same problem feature, and obtain the key nodes of each of the matching features respectively, and when the key nodes are matching nodes, add a deep matching weight to the corresponding problem feature, and when the key nodes are non-matching nodes, add a common matching weight to the corresponding problem feature;

[0141] The fourth screening subunit is used to establish a second matching feature between each of the question groups and each of the actual charging features based on the basic matching weight and the deep matching weight or the common matching weight corresponding to each of the question features.

[0142] The working principle and beneficial effects of the above technical solution are as follows: by analyzing the problem dimensions of the target problem group and combining their corresponding problem levels to construct the problem features of each target problem group, and then searching the corresponding abnormal feature labels in the actual charging features to construct the matching features of the actual charging features for matching, the matching nodes and non-matching nodes are determined, and basic matching weights are added to the corresponding problem features according to the ratio of matching nodes to non-matching nodes. Further, combined with the location of the key nodes, corresponding deepened matching weights or ordinary matching weights are added to the problem features, and then the second matching features between each problem group and the actual charging features are obtained. In this way, different degrees of weights can be added to different target problem groups, and the target problem groups are subjected to corresponding preliminary analysis based on the construction of matching features.

[0143] Example 7

[0144] On the basis of Example 1, the charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle, the abnormality optimization module includes:

[0145] A solution matching unit, used to respectively find a solution corresponding to each of the abnormal problems, and respectively construct a first execution condition corresponding to the mobile charging vehicle and a second execution condition corresponding to the charged vehicle;

[0146] a device locating unit, configured to respectively determine a first execution device corresponding to each of the first execution conditions, and respectively determine a second execution device corresponding to each of the second execution conditions, and respectively obtain first device data corresponding to each of the first execution devices and second device data corresponding to each of the second execution devices;

[0147] a scheme optimization unit, configured to perform a first process optimization on the first execution condition based on the first device data to generate a corresponding first optimization condition, and perform a second process optimization on the second execution condition based on the second device data to generate a corresponding second optimization condition;

[0148] A solution execution unit is used to construct a problem optimization solution based on the first optimization condition and the second optimization condition, and control the mobile charging vehicle and the charged vehicle to implement the problem optimization solution.

[0149] The working principle and beneficial effects of the above technical solution are as follows: by finding solutions to each abnormal problem and determining the first execution condition of the mobile charging vehicle and the second execution addition of the charged vehicle, the devices for executing different conditions are determined, thereby optimizing the execution conditions according to the device data, and finally constructing a problem optimization solution, and eliminating the faults of the mobile charging vehicle and the charged vehicle by implementing the problem optimization solution.

[0150] Example 8

[0151] Based on Example 7, the charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle further includes:

[0152] An optimization supervision module, used to establish a first optimization target corresponding to the mobile charging vehicle and a second optimization target corresponding to the charged vehicle according to the problem optimization solution;

[0153] Obtaining a first real-time optimization result of the mobile charging vehicle and a second real-time optimization result of the charged vehicle;

[0154] When the first real-time optimization result is consistent with the first optimization target, and the second real-time optimization result is consistent with the second optimization target, it is determined that the mobile charging vehicle and the charged vehicle have completed optimization.

[0155] The working principle and beneficial effects of the above technical solution: In order to further improve the diagnosis work, corresponding supervision work is carried out during the optimization of the mobile charging vehicle and the charged vehicle according to the problem optimization solution to ensure the smooth progress of the optimization work.

[0156] Example 9

[0157] Based on Example 1, the charging abnormality diagnosis and evaluation system applied to the mobile charging vehicle further includes:

[0158] The power monitoring module is used to monitor the real-time power of each of the charged vehicles, and when the real-time power is consistent with the specified power, the mobile charging vehicle is controlled to stop charging.

[0159] In this example, the specified power is 100.

[0160] The working principle and beneficial effects of the above technical solution are as follows: when the charged vehicle completes charging, the mobile charging vehicle is controlled to stop charging.

[0161] Example 10

[0162] Based on Example 1, the charging abnormality diagnosis and evaluation system applied to the mobile charging vehicle further includes:

[0163] The report analysis module is used to analyze each of the charging evaluation reports respectively, determine the high-frequency abnormal problems corresponding to each of the mobile charging vehicles, and generate a problem handling warning.

[0164] The working principle and beneficial effects of the above technical solution are: to issue early warnings for high-frequency abnormal problems of mobile charging vehicles, remind relevant personnel to deal with problems in a timely manner, and reduce the frequency of failures.

[0165] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle, characterized in that: include: A feedback collection module, used to collect feedback work data of the mobile charging vehicle, and use the feedback work data to construct output feedback information corresponding to the mobile charging vehicle; A charging analysis module, used to establish a plurality of actual charging characteristics of the charged vehicle according to the output feedback information, and to perform abnormality evaluation on each of the actual charging characteristics to obtain a charging evaluation report of the charged vehicle; An abnormality diagnosis module, used to troubleshoot problems according to the charging evaluation report and determine abnormal problems corresponding to the charged vehicle and the mobile charging vehicle; The abnormality optimization module is used to retrieve and optimize the solution corresponding to each abnormal problem, generate and implement the problem optimization solution.

2. A charging abnormality diagnosis and evaluation system for a mobile charging vehicle as claimed in claim 1, characterized in that: Also includes: A vehicle positioning module, used to construct a data screening rule according to the positioning condition, use the data screening rule to screen a number of position data in the feedback working data, and determine the position information of the corresponding mobile charging vehicle based on the position data; Obtain a data expression of the data screening rule, and use the data expression to respectively obtain node parameters of each of the position information in the feedback working data; The first node parameter in the feedback working data is regarded as a parent node, and a leaf node corresponding to each node parameter is established based on a logical relationship between each node parameter and the first node parameter; Using the pre-mentioned parent node and leaf node to construct the DAG scheduling information of the corresponding mobile charging vehicle; Determine the corresponding working position of the mobile charging vehicle at different times according to the DAG scheduling information, and construct the working trajectory of the mobile charging vehicle; The working track corresponding to each mobile charging vehicle is obtained respectively, and a mobile charging vehicle track distribution map is constructed and displayed.

3. A charging abnormality diagnosis and evaluation system for a mobile charging vehicle as claimed in claim 1, characterized in that: The feedback collection module comprises: A cycle division unit, used to respectively collect a plurality of pieces of real-time working information corresponding to each of the mobile charging vehicles, respectively obtain an information cycle corresponding to each piece of the real-time working information, and divide each piece of the real-time working information into a plurality of periodic sub-information; A logic analysis unit, used to respectively obtain a bit stream corresponding to each of the periodic sub-information, perform a logic operation on the bit stream to obtain a plurality of logic values ​​corresponding to each of the real-time working information, locate each of the logic values ​​in the real-time working information, and obtain a working information value corresponding to each of the logic values; An information encoding unit is used to construct a value distribution axis of the work information value in the real-time work information, perform period mining on the value distribution axis to generate a regular period corresponding to the real-time work information, and adjust each of the period sub-information according to the regular period to generate period encoding information corresponding to the real-time work information; The feedback analysis unit is used to determine the coded feedback information generated by the corresponding mobile charging vehicle in each feedback cycle according to the periodic coding information, map the coded feedback information to a preset feedback list for information filling, and obtain the output feedback information corresponding to each mobile charging vehicle.

4. A charging abnormality diagnosis and evaluation system for a mobile charging vehicle as claimed in claim 1, characterized in that: The charging analysis module comprises: A model analysis unit, configured to construct a charging work model corresponding to the mobile charging vehicle according to the feedback work data, run the charging work model to determine a plurality of lossless output features corresponding to the mobile charging vehicle, and add a corresponding feature label to each lossless output feature according to a feature attribute corresponding to each lossless input feature; A deep analysis unit, configured to perform a deep analysis on the output feedback information based on the feature tag, obtain a plurality of independent features contained in the output feedback information, count the independent tags corresponding to each feature tag, and construct an actual charging feature of the charged vehicle; A charging analysis unit, used for comparing the lossless output feature corresponding to the same feature label with the actual charging feature to obtain a plurality of feature differences, extracting the charging features whose feature differences are not 0 as abnormal charging features, and searching for the abnormal feature label corresponding to each abnormal charging feature respectively; The charging evaluation unit is used to establish a reporting framework corresponding to the charged vehicle according to the abnormal dimension corresponding to the abnormal feature label, mark the feature difference corresponding to each abnormal feature label in the reporting framework, trace the abnormality of each abnormal dimension, and generate a charging evaluation report for the charged vehicle.

5. The charging abnormality diagnosis and evaluation system for a mobile charging vehicle according to claim 1, characterized in that: The abnormality diagnosis module comprises: A problem preliminary review unit, used to determine a plurality of first suspected problems of the mobile charging vehicle and a plurality of second suspected problems corresponding to the charged vehicle according to the charging evaluation report, and match each of the first suspected problems with different second suspected problems to obtain a plurality of problem groups; A question screening unit is used to perform causal analysis on each of the question groups to obtain a target question group with a reasonable causal relationship, establish question data corresponding to each of the target question groups, input the question data into the output feedback information for a first matching, and obtain a first matching feature between each of the question groups and the output feedback information; A deep screening unit, used to respectively establish a problem feature corresponding to each of the target problem groups, perform a second matching between the problem feature and each of the actual charging features, and obtain a second matching feature between each of the problem groups and each of the actual charging features; A problem establishing unit is used to construct a matching degree corresponding to each of the problem groups according to the first matching feature and the second matching feature, determine the actual problem group corresponding to the charged vehicle and the mobile charging vehicle based on the matching degree, and determine the first abnormal problem corresponding to the mobile charging vehicle and the second abnormal problem corresponding to the charged vehicle in the actual problem group.

6. A charging abnormality diagnosis and evaluation system for a mobile charging vehicle as claimed in claim 5, characterized in that: The depth screening unit comprises: The first screening subunit is used to construct the problem dimension of each target problem group according to the reasonable causal relationship corresponding to each target problem group, and to construct the problem feature corresponding to each target problem group in combination with the problem level corresponding to each target problem group; A second screening subunit is used to search for the abnormal feature label contained in each of the actual charging features, construct a matching feature corresponding to the actual charging feature according to the abnormal feature label, match each of the problem features with different matching features, and obtain a plurality of matching nodes and non-matching nodes between each of the problem features and different matching features; The third screening subunit is used to add a basic matching weight to each of the problem features according to the ratio of matching nodes and non-matching nodes corresponding to the same problem feature, and obtain the key nodes of each of the matching features respectively, and when the key nodes are matching nodes, add a deep matching weight to the corresponding problem feature, and when the key nodes are non-matching nodes, add a common matching weight to the corresponding problem feature; The fourth screening subunit is used to establish a second matching feature between each of the question groups and each of the actual charging features based on the basic matching weight and the deep matching weight or the common matching weight corresponding to each of the question features.

7. A charging abnormality diagnosis and evaluation system for a mobile charging vehicle as claimed in claim 1, characterized in that: The abnormal optimization module comprises: A solution matching unit, used to respectively find a solution corresponding to each of the abnormal problems, and respectively construct a first execution condition corresponding to the mobile charging vehicle and a second execution condition corresponding to the charged vehicle; a device locating unit, configured to respectively determine a first execution device corresponding to each of the first execution conditions, and respectively determine a second execution device corresponding to each of the second execution conditions, and respectively obtain first device data corresponding to each of the first execution devices and second device data corresponding to each of the second execution devices; a scheme optimization unit, configured to perform a first process optimization on the first execution condition based on the first device data to generate a corresponding first optimization condition, and perform a second process optimization on the second execution condition based on the second device data to generate a corresponding second optimization condition; A solution execution unit is used to construct a problem optimization solution based on the first optimization condition and the second optimization condition, and control the mobile charging vehicle and the charged vehicle to implement the problem optimization solution.

8. A charging abnormality diagnosis and evaluation system for a mobile charging vehicle as claimed in claim 7, characterized in that: Also includes: An optimization supervision module, used to establish a first optimization target corresponding to the mobile charging vehicle and a second optimization target corresponding to the charged vehicle according to the problem optimization solution; Obtaining a first real-time optimization result of the mobile charging vehicle and a second real-time optimization result of the charged vehicle; When the first real-time optimization result is consistent with the first optimization target, and the second real-time optimization result is consistent with the second optimization target, it is determined that the mobile charging vehicle and the charged vehicle have completed optimization.

9. A charging abnormality diagnosis and evaluation system for a mobile charging vehicle as claimed in claim 1, characterized in that: Also includes: The power monitoring module is used to monitor the real-time power of each charged vehicle, and when the real-time power is consistent with the specified power, the mobile charging vehicle is controlled to stop charging.

10. A charging abnormality diagnosis and evaluation system for a mobile charging vehicle as claimed in claim 1, characterized in that: Also includes: The report analysis module is used to analyze each of the charging evaluation reports respectively, determine the high-frequency abnormal problems corresponding to each of the mobile charging vehicles, and generate a problem handling warning.

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