A charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle

Abstract

The application provides a charging abnormality diagnosis and evaluation system applied to a mobile charging vehicle, which comprises the following steps: collecting feedback working data of the mobile charging vehicle, constructing output feedback information corresponding to the mobile charging vehicle by using the feedback working data, establishing a plurality of actual charging characteristics of a charged vehicle according to the output feedback information, respectively performing abnormality evaluation on each actual charging characteristic to obtain a charging evaluation report of the charged vehicle, performing problem investigation according to the charging evaluation report, determining abnormal problems corresponding to the charged vehicle and the mobile charging vehicle, calling a solution corresponding to each abnormal problem and optimizing the solution, generating a problem optimization scheme and implementing the problem optimization scheme, and performing abnormality evaluation and judgment by monitoring the current and voltage of the mobile charging vehicle when the mobile charging vehicle charges a target vehicle, so that the charging process can be effectively performed.

Description

Technical Field

[0001] This invention relates to the field of mobile charging vehicle technology, and in particular to a charging anomaly diagnosis and assessment system for mobile charging vehicles. Background Technology

[0002] In recent years, electric vehicles have been vigorously promoted and gradually popularized in various provinces and cities across my country due to their environmental, clean, and energy-saving advantages. Electric vehicle charging infrastructure, as a crucial component of this promotion, has also developed rapidly. Currently, the internal structure of electric vehicle charging infrastructure is becoming increasingly sophisticated, with more and more functions and enhanced intelligence. For example, the latest technology, mobile charging vehicles, can reach the location of electric vehicles when needed to provide fast charging, making them particularly suitable for scenarios where charging stations are insufficient or for temporary needs. These vehicles are typically equipped with large-capacity battery packs and charging interfaces to support the charging needs of different types of electric vehicles. However, related malfunctions still occur frequently during charging. Electric vehicle charging safety will be a problem that must be addressed and resolved in the further promotion and development of electric vehicles in the future.

[0003] Therefore, the present invention provides a charging anomaly diagnosis and evaluation system for mobile charging vehicles. Summary of the Invention

[0004] This invention provides a charging anomaly diagnosis and assessment system for mobile charging vehicles. By monitoring the current and voltage of the mobile charging vehicle when charging a target vehicle, the system assesses and judges anomalies to ensure the effective operation of the charging process.

[0005] This invention provides a charging anomaly diagnosis and assessment system for mobile charging vehicles, comprising:

[0006] The feedback acquisition module is used to collect feedback working data of the mobile charging vehicle and to construct the corresponding output feedback information of the mobile charging vehicle using the feedback working data.

[0007] The charging analysis module is used to establish several actual charging characteristics of the vehicle being charged based on the output feedback information, and to perform anomaly assessment on each of the actual charging characteristics to obtain a charging evaluation report of the vehicle being charged.

[0008] The anomaly diagnosis module is used to troubleshoot problems based on the charging evaluation report and determine the anomalies corresponding to the vehicle being charged and the mobile charging vehicle.

[0009] The anomaly optimization module is used to retrieve the solution corresponding to each anomaly, optimize it, generate an optimization plan, and implement it.

[0010] In one feasible approach

[0011] Also includes:

[0012] The vehicle positioning module is used to construct data filtering rules based on positioning conditions, use the data filtering rules to filter several location data in the feedback working data, and determine the location information of the corresponding mobile charging vehicle based on the location data.

[0013] Obtain the data expression of the data filtering rule, and use the data expression to obtain the node parameters of each location information in the feedback working data;

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

[0015] The DAG scheduling information for the corresponding mobile charging vehicle is constructed using the parent node and leaf node mentioned earlier.

[0016] Based on the DAG scheduling information, determine the corresponding working position of the mobile charging vehicle at different times, and construct the working trajectory of the mobile charging vehicle;

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

[0018] In one feasible approach

[0019] The feedback acquisition module includes:

[0020] The period division unit is used to collect several real-time working information corresponding to each mobile charging vehicle, obtain the information period corresponding to each real-time working information, and divide each real-time working information into several period sub-information.

[0021] The logic analysis unit is used to acquire the bit stream corresponding to each of the periodic sub-informations, perform logical operations on the bit streams to obtain several logical values ​​corresponding to each of the real-time working information, locate each of the logical values ​​in the real-time working information, and obtain the working information value corresponding to each logical value.

[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 periodic mining on the value distribution axis to generate a regular periodicity corresponding to the real-time work information, and adjust each periodic sub-information according to the regular periodicity to generate periodic 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 based on 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 feasible approach

[0025] The charging analysis module includes:

[0026] The model analysis unit is used to construct a charging operation model for the corresponding mobile charging vehicle based on the feedback working data, run the charging operation model to determine several lossless output features of the corresponding mobile charging vehicle, and add corresponding feature labels to each lossless output feature according to the feature attributes corresponding to each lossless input feature.

[0027] The deep analysis unit is used to perform deep analysis on the output feedback information based on the feature labels, obtain several independent features contained in the output feedback information, count the independent labels corresponding to each feature label, and construct the actual charging characteristics of the vehicle being charged.

[0028] The charging analysis unit is used to compare the lossless output feature and the actual charging feature corresponding to the same feature label to obtain several feature differences, extract the charging features with feature differences that are not 0 and record them as abnormal charging features, and find the abnormal feature label corresponding to each abnormal charging feature.

[0029] The charging evaluation unit is used to establish a report framework for the vehicle being charged based on the abnormal dimension corresponding to the abnormal feature label, mark the feature difference corresponding to each of the abnormal feature labels in the report framework, perform abnormal source tracing for each of the abnormal dimensions, and generate a charging evaluation report for the vehicle being charged.

[0030] In one feasible approach

[0031] The anomaly diagnosis module includes:

[0032] The problem preliminary review unit is used to determine several first suspected problems of the mobile charging vehicle and several second suspected problems of the vehicle being charged based on the charging evaluation report, and to match each first suspected problem with different second suspected problems to obtain several problem groups.

[0033] The problem filtering unit is used to perform causal analysis on each of the problem groups to obtain target problem groups with reasonable causal relationships, establish problem data corresponding to each target problem group, input the problem data into the output feedback information for first matching, and obtain the first matching feature between each of the problem groups and the output feedback information.

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

[0035] The problem identification unit is configured to construct a matching degree corresponding to each problem group based on the first matching feature and the second matching feature, determine the actual problem group corresponding to the vehicle being charged 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 vehicle being charged in the actual problem group.

[0036] In one feasible approach

[0037] The depth filtering unit includes:

[0038] The first screening subunit is used to construct the problem dimension of the 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] The second filtering subunit is used to find the abnormal feature tags contained in each of the actual charging features, construct matching features corresponding to the actual charging features based on the abnormal feature tags, match each of the problem features with different matching features, and obtain several matching nodes and non-matching nodes between each of the problem features and different matching features.

[0040] The third filtering subunit is used to add basic matching weights to each problem feature according to the ratio of matching nodes and non-matching nodes corresponding to the same problem feature, obtain the key nodes of each matching feature, add deep matching weights to the corresponding problem feature when the key node is a matching node, and add ordinary matching weights to the corresponding problem feature when the key node is a non-matching node.

[0041] The fourth screening subunit is used to establish a second matching feature between each problem group and each actual charging feature based on the basic matching weight and deep matching weight or ordinary matching weight corresponding to each problem feature.

[0042] In one feasible approach

[0043] The anomaly optimization module includes:

[0044] The solution matching unit is used to find the solution corresponding to each of the above-mentioned abnormal problems, and to construct the first execution condition corresponding to the mobile charging vehicle and the second execution condition corresponding to the vehicle being charged.

[0045] The device positioning unit is used to determine the first execution device corresponding to each first execution condition and the second execution device corresponding to each second execution condition, and to obtain the first device data corresponding to each first execution device and the second device data corresponding to each second execution device.

[0046] The scheme optimization unit is used 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 to perform a second process optimization on the second execution condition based on the second device data to generate a corresponding second optimization condition;

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

[0048] In one feasible approach

[0049] Also includes:

[0050] The optimization supervision module is used to establish a first optimization objective for the mobile charging vehicle and a second optimization objective for the vehicle being charged based on the problem optimization scheme.

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

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

[0053] In one feasible approach

[0054] Also includes:

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

[0056] In one feasible approach

[0057] Also includes:

[0058] The report analysis module is used to analyze each of the charging evaluation reports, identify the high-frequency abnormal issues corresponding to each mobile charging vehicle, and generate problem handling warnings.

[0059] The 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 data is collected during the operation of the mobile charging vehicle to construct output feedback information. This output feedback information is then used to construct the actual charging characteristics of the vehicle being charged. Anomalies are assessed for each actual charging characteristic, generating a charging evaluation report for the vehicle being charged. This report evaluates the charging capacity, charging current, and charging voltage. Users can use the report to monitor the charging status in real time and troubleshoot problems, identifying anomalies in both the vehicle being charged and the mobile charging vehicle. Furthermore, solutions to each anomaly are optimized based on actual conditions, creating a problem optimization scheme. Implementing this scheme can resolve anomalies in both the vehicle being charged and the mobile charging vehicle, thereby ensuring charging efficiency and quality, and also protecting both the vehicle being charged and the mobile charging vehicle.

[0060] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0061] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0062] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

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

[0064] Figure 2 This is a schematic diagram of the composition of an anomaly diagnosis module in a charging anomaly diagnosis and evaluation system applied to a mobile charging vehicle, according to an embodiment of the present invention. Detailed Implementation

[0065] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0066] Example 1

[0067] This embodiment provides a charging anomaly diagnosis and assessment system for mobile charging vehicles, such as... Figure 1 As shown, it includes:

[0068] The feedback acquisition module is used to collect feedback working data of the mobile charging vehicle and to construct the corresponding output feedback information of the mobile charging vehicle using the feedback working data.

[0069] The charging analysis module is used to establish several actual charging characteristics of the vehicle being charged based on the output feedback information, and to perform anomaly assessment on each of the actual charging characteristics to obtain a charging evaluation report of the vehicle being charged.

[0070] The anomaly diagnosis module is used to troubleshoot problems based on the charging evaluation report and determine the anomalies corresponding to the vehicle being charged and the mobile charging vehicle.

[0071] The anomaly optimization module is used to retrieve the solution corresponding to each anomaly, optimize it, generate an optimization plan, and implement it.

[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 work data refers to the data generated by the mobile charging vehicle during its operation;

[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, during the charging process, one mobile charging vehicle corresponds to one vehicle being charged, providing one-to-one charging.

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

[0077] In this example, the abnormal issues refer to problems with the vehicle being charged and the mobile charging vehicle, including: connection problems, insufficient remaining power in the mobile charging vehicle, excessive battery wear on the vehicle being charged, charging environment not meeting charging standards, connection interruption, etc.

[0078] The working principle and beneficial effects of the above technical solution are as follows: To conduct a safety assessment of the mobile charging vehicle's operation, feedback data is collected during its operation to construct output feedback information. This output feedback information is then used to construct the actual charging characteristics of the vehicle being charged. Anomalies are assessed for each actual charging characteristic, generating a charging evaluation report for the vehicle being charged. This report evaluates the charging capacity, charging current, and charging voltage. Users can use the report to monitor the charging status in real time and troubleshoot problems, identifying anomalies in both the vehicle being charged and the mobile charging vehicle. Furthermore, solutions to each anomaly are optimized based on actual conditions, creating a problem optimization solution. Implementing this solution resolves anomalies in both the vehicle being charged and the mobile charging vehicle, ensuring charging efficiency and quality, and also protecting both the vehicle being charged and the mobile charging vehicle.

[0079] Example 2

[0080] Based on Embodiment 1, the charging anomaly diagnosis and assessment system applied to mobile charging vehicles further includes:

[0081] The vehicle positioning module is used to construct data filtering rules based on positioning conditions, use the data filtering rules to filter several location data in the feedback working data, and determine the location information of the corresponding mobile charging vehicle based on the location data.

[0082] Obtain the data expression of the data filtering rule, and use the data expression to obtain the node parameters of each location information in the feedback working data;

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

[0084] The DAG scheduling information for the corresponding mobile charging vehicle is constructed using the parent node and leaf node mentioned earlier.

[0085] Based on the DAG scheduling information, determine the corresponding working position of the mobile charging vehicle at different times, and construct the working trajectory of the mobile charging vehicle;

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

[0087] In this example, the location conditions refer to the conditions executed when locating the mobile charging vehicle;

[0088] In this example, the data filtering rules represent the rules for filtering location data;

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

[0090] In this example, the data expression represents the uniform format of the data filtered using data filtering rules, helping the system understand how the data flows and transforms between various nodes;

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

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

[0093] In this example, the DAG scheduling information representation uses a directed acyclic graph to express the dependencies between data at various locations.

[0094] The working principle and beneficial effects of the above technical solution are as follows: To achieve better control and know the location of each mobile charging vehicle at any time, a data filtering rule is first constructed based on the positioning conditions. This rule is then used to filter the corresponding location data in the feedback work data, thereby constructing the location information of the mobile charging vehicle. Furthermore, the data expression of the data filtering rule is used to obtain the node parameters of each location information in the feedback work data. Parent nodes and leaf nodes are then filtered to construct DAG scheduling information, determining the working position of the mobile charging vehicle at different times, thus constructing the working trajectory of the mobile charging vehicle. This facilitates unified management of the mobile charging vehicles by the administrator and effectively reduces the probability of mobile charging vehicles being lost.

[0095] Example 3

[0096] Based on Embodiment 1, the feedback acquisition module of the charging anomaly diagnosis and assessment system applied to mobile charging vehicles includes:

[0097] The period division unit is used to collect several real-time working information corresponding to each mobile charging vehicle, obtain the information period corresponding to each real-time working information, and divide each real-time working information into several period sub-information.

[0098] The logic analysis unit is used to acquire the bit stream corresponding to each of the periodic sub-informations, perform logical operations on the bit streams to obtain several logical values ​​corresponding to each of the real-time working information, locate each of the logical values ​​in the real-time working information, and obtain the working information value corresponding to each logical value.

[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 periodic mining on the value distribution axis to generate a regular periodicity corresponding to the real-time work information, and adjust each periodic sub-information according to the regular periodicity to generate periodic 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 based on 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 refers to the generation cycle of real-time work information;

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

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

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

[0105] In this example, the value distribution axis represents a data axis that indicates the density of the distribution of working information values;

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

[0107] In this example, the preset feedback list refers to a pre-defined, blank feedback list;

[0108] In this example, periodic mining refers to the process of mining data periods that exist on the value distribution axis.

[0109] In this example, the feedback cycle has the same cycle length as the regular cycle, and the starting point of the feedback cycle is the moment when the mobile charging vehicle starts working.

[0110] The working principle and beneficial effects of the above technical solution are as follows: 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. Then, according to the information period of each real-time working information, the real-time working information is divided into a number of periodic sub-information. 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. Then, a value distribution axis is established, and the regular period of the real-time working information is determined by period mining. The regular period is used to encode the periodic sub-information to obtain the periodic encoded information of the real-time working information. Then, the encoded feedback information generated by the mobile charging vehicle in each feedback period is filled in according to the plan to obtain the output feedback information of the mobile charging vehicle. In this way, not only can the feedback information of the mobile charging vehicle be collected in real time, but the data of the mobile charging vehicle can also be initially encoded and adjusted, ensuring the stability of the output feedback information and reducing the time for subsequent information sorting.

[0111] Example 4

[0112] Based on Embodiment 1, the charging anomaly diagnosis and assessment system applied to mobile charging vehicles, wherein the charging analysis module includes:

[0113] The model analysis unit is used to construct a charging operation model for the corresponding mobile charging vehicle based on the feedback working data, run the charging operation model to determine several lossless output features of the corresponding mobile charging vehicle, and add corresponding feature labels to each lossless output feature according to the feature attributes corresponding to each lossless input feature.

[0114] The deep analysis unit is used to perform deep analysis on the output feedback information based on the feature labels, obtain several independent features contained in the output feedback information, count the independent labels corresponding to each feature label, and construct the actual charging characteristics of the vehicle being charged.

[0115] The charging analysis unit is used to compare the lossless output feature and the actual charging feature corresponding to the same feature label to obtain several feature differences, extract the charging features with feature differences that are not 0 and record them as abnormal charging features, and find the abnormal feature label corresponding to each abnormal charging feature.

[0116] The charging evaluation unit is used to establish a report framework for the vehicle being charged based on the abnormal dimension corresponding to the abnormal feature label, mark the feature difference corresponding to each of the abnormal feature labels in the report framework, perform abnormal source tracing for each of the abnormal dimensions, and generate a charging evaluation report for the vehicle being charged.

[0117] In this example, the lossless output characteristic refers to the output characteristic of the mobile charging vehicle without external interference and its own losses.

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

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

[0120] In this example, abnormal charging characteristics indicate charging characteristics that result in losses;

[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 the mobile charging vehicle, the lossless output characteristics during operation are analyzed, and corresponding feature labels are added to each lossless output characteristic. Then, the corresponding independent features are searched in the output feedback information to construct the actual charging characteristics of the vehicle being charged. Abnormal charging characteristics are screened by comparing the feature difference between the lossless output characteristics and the actual charging characteristics under the same feature label. The abnormal location of the abnormal charging characteristics is determined by tracing the source, thereby constructing a charging evaluation report. By constructing the charging evaluation report, the charging status of the vehicle being charged can be preliminarily analyzed, the location of its abnormality can be located, and the charging evaluation report can be used to understand the fault location of the vehicle being charged and the mobile charging vehicle 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 anomaly diagnosis and assessment system applied to mobile charging vehicles, such as... Figure 2 As shown, the anomaly diagnosis module includes:

[0125] The problem preliminary review unit is used to determine several first suspected problems of the mobile charging vehicle and several second suspected problems of the vehicle being charged based on the charging evaluation report, and to match each first suspected problem with different second suspected problems to obtain several problem groups.

[0126] The problem filtering unit is used to perform causal analysis on each of the problem groups to obtain target problem groups with reasonable causal relationships, establish problem data corresponding to each target problem group, input the problem data into the output feedback information for first matching, and obtain the first matching feature between each of the problem groups and the output feedback information.

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

[0128] The problem identification unit is configured to construct a matching degree corresponding to each problem group based on the first matching feature and the second matching feature, determine the actual problem group corresponding to the vehicle being charged 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 vehicle being charged in the actual problem group.

[0129] In this example, the first suspected problem refers to the problem that the mobile charging vehicle is suspected of exhibiting, and the second suspected problem refers to the problem that the vehicle being charged is suspected of exhibiting.

[0130] In this example, a problem group contains 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 and second suspected problems in the problem group;

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

[0133] In this example, the first matching feature represents the actual degree of matching between the problem group and the actual charging feature;

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

[0135] The working principle and beneficial effects of the above technical solution are as follows: To further analyze the abnormal problems of the mobile charging vehicle and the vehicle being charged, the suspected problems of the mobile charging vehicle and the vehicle being charged are first determined based on the charging evaluation report. Then, a problem group is constructed, and target problem groups with reasonable causal relationships are extracted by performing causal analysis on the problem groups. 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. The matching degree of each problem group is constructed through the results of the two matching processes, thereby determining the actual problems of the vehicle being charged and the mobile charging vehicle. In this way, not only can the problems existing in the vehicle being charged and the mobile charging vehicle be quickly identified, but the fault problems of the vehicle being charged and the mobile charging vehicle are also initially matched during the problem identification process, effectively reducing the error in locating fault problems.

[0136] Example 6

[0137] Based on Example 5, the deep screening unit of the charging anomaly diagnosis and assessment system applied to mobile charging vehicles includes:

[0138] The first screening subunit is used to construct the problem dimension of the 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] The second filtering subunit is used to find the abnormal feature tags contained in each of the actual charging features, construct matching features corresponding to the actual charging features based on the abnormal feature tags, match each of the problem features with different matching features, and obtain several matching nodes and non-matching nodes between each of the problem features and different matching features.

[0140] The third filtering subunit is used to add basic matching weights to each problem feature according to the ratio of matching nodes and non-matching nodes corresponding to the same problem feature, obtain the key nodes of each matching feature, add deep matching weights to the corresponding problem feature when the key node is a matching node, and add ordinary matching weights to the corresponding problem feature when the key node is a non-matching node.

[0141] The fourth screening subunit is used to establish a second matching feature between each problem group and each actual charging feature based on the basic matching weight and deep matching weight or ordinary matching weight corresponding to each problem feature.

[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 them with its corresponding problem level, problem features of each target problem group are constructed. Then, corresponding abnormal feature labels are searched in the actual charging features to construct matching features of the actual charging features, thereby performing matching work. Matching nodes and non-matching nodes are determined. Based on the ratio of matching nodes to non-matching nodes, basic matching weights are added to the corresponding problem features. Furthermore, based on the location of key nodes, corresponding deepened matching weights or ordinary matching weights are added to the problem features, thus obtaining the second matching features between each problem group and the actual charging features. In this way, different levels of weights can be added to different target problem groups, and a corresponding preliminary analysis of the target problem groups is performed based on the construction of matching features.

[0143] Example 7

[0144] Based on Embodiment 1, the charging anomaly diagnosis and evaluation system applied to mobile charging vehicles, wherein the anomaly optimization module includes:

[0145] The solution matching unit is used to find the solution corresponding to each of the above-mentioned abnormal problems, and to construct the first execution condition corresponding to the mobile charging vehicle and the second execution condition corresponding to the vehicle being charged.

[0146] The device positioning unit is used to determine the first execution device corresponding to each first execution condition and the second execution device corresponding to each second execution condition, and to obtain the first device data corresponding to each first execution device and the second device data corresponding to each second execution device.

[0147] The scheme optimization unit is used 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 to perform a second process optimization on the second execution condition based on the second device data to generate a corresponding second optimization condition;

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

[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 condition of the vehicle being charged, the devices that execute different conditions are identified. Based on the device data, the execution conditions are optimized, and a problem optimization solution is finally constructed. By implementing the problem optimization solution, the faults of the mobile charging vehicle and the vehicle being charged are eliminated.

[0150] Example 8

[0151] Based on Embodiment 7, the charging anomaly diagnosis and assessment system applied to mobile charging vehicles further includes:

[0152] The optimization supervision module is used to establish a first optimization objective for the mobile charging vehicle and a second optimization objective for the vehicle being charged based on the problem optimization scheme.

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

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

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

[0156] Example 9

[0157] Based on Embodiment 1, the charging anomaly diagnosis and assessment system applied to mobile charging vehicles further includes:

[0158] The power monitoring module is used to monitor the real-time power of each of the vehicles being charged. 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 level is 100.

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

[0161] Example 10

[0162] Based on Embodiment 1, the charging anomaly diagnosis and assessment system applied to mobile charging vehicles further includes:

[0163] The report analysis module is used to analyze each of the charging evaluation reports, identify the high-frequency abnormal issues corresponding to each mobile charging vehicle, and generate problem handling warnings.

[0164] The working principle and beneficial effects of the above technical solution are as follows: It provides early warning for high-frequency abnormalities in mobile charging vehicles, reminding relevant personnel to handle the problems in a timely manner and reducing the frequency of failures.

[0165] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A charging abnormality diagnosis evaluation system applied to a mobile charging vehicle, characterized by, The method comprises the following steps: a feedback collection module is used to collect feedback working data of the mobile charging vehicle, and output feedback information corresponding to the mobile charging vehicle is constructed by using the feedback working data; wherein the feedback working data represents data generated by the mobile charging vehicle during the working process; a charging analysis module is used to establish a plurality of actual charging characteristics of the charged vehicle according to the output feedback information, to perform abnormal evaluation on each actual charging characteristic respectively, and to obtain a charging evaluation report of the charged vehicle; wherein the output feedback information represents information generated by the mobile charging vehicle during the charging process, and the actual charging characteristics include actual current characteristics and actual voltage characteristics; an abnormal diagnosis module is used to perform problem investigation according to the charging evaluation report, and to determine abnormal problems corresponding to the charged vehicle and the mobile charging vehicle; an abnormal optimization module is used to call a solution corresponding to each abnormal problem and to perform optimization, to generate a problem optimization scheme, and to implement the problem optimization scheme; the abnormal diagnosis module comprises: a problem preliminary review unit is 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, to perform problem matching on each first suspected problem and a different second suspected problem respectively, and to obtain a plurality of problem groups; a problem screening unit is used to perform causal analysis on each problem group respectively, to obtain a target problem group having a reasonable causal relationship, to establish problem data corresponding to each target problem group respectively, to input the problem data into the output feedback information for first matching, and to obtain first matching characteristics between each problem group and the output feedback information; a deep screening unit is used to establish problem characteristics corresponding to each target problem group respectively, to perform second matching on the problem characteristics and each actual charging characteristic, and to obtain second matching characteristics between each problem group and each actual charging characteristic; a problem establishment unit is used to construct a matching degree corresponding to each problem group according to the first matching characteristics and the second matching characteristics, to determine an actual problem group corresponding to the charged vehicle and the mobile charging vehicle based on the matching degree, to determine a first abnormal problem corresponding to the mobile charging vehicle and a second abnormal problem corresponding to the charged vehicle in the actual problem group; the deep screening unit comprises: a first screening subunit is used to construct a problem dimension of the corresponding target problem group according to a reasonable causal relationship corresponding to each target problem group, and to construct a problem characteristic corresponding to each target problem group in combination with a problem level corresponding to each target problem group; a second screening subunit is used to find an abnormal characteristic label contained in each actual charging characteristic respectively, to construct a matching characteristic of the corresponding actual charging characteristic according to the abnormal characteristic label, to perform matching on each problem characteristic and a different matching characteristic, and to obtain a plurality of matching nodes and non-matching nodes between each problem characteristic and the different matching characteristics. The third screening subunit is configured to add a basic matching weight to each problem feature according to a ratio of matching nodes and non-matching nodes corresponding to the same problem feature, acquire a key node of each matching feature, add a deep matching weight to the corresponding problem feature when the key node is a matching node, and add an ordinary matching weight to the corresponding problem feature when the key node is a non-matching node. The fourth screening subunit is configured to establish a second matching feature between each problem group and each actual charging feature based on the basic matching weight and the deep matching weight or the ordinary matching weight corresponding to each problem feature.

2. The charging abnormality diagnosis evaluation system for a mobile charging vehicle according to claim 1, wherein Further comprising: The vehicle positioning module is configured to construct a data screening rule according to a positioning condition, screen a plurality of position data from the feedback working data by using the data screening rule, and determine position information of the corresponding mobile charging vehicle based on the position data. A data expression of the data screening rule is acquired, and a node parameter of each position information in the feedback working data is acquired by using the data expression. 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. The DAG scheduling information of the corresponding mobile charging vehicle is constructed by using the parent node and the leaf node. The working position of the corresponding mobile charging vehicle at different times is determined according to the DAG scheduling information, and the working track of the mobile charging vehicle is constructed. The working track corresponding to each mobile charging vehicle is acquired, and a mobile charging vehicle track distribution map is constructed and displayed.

3. The charging abnormality diagnosis evaluation system for a mobile charging vehicle according to claim 1, wherein The feedback collection module comprises: The cycle division unit is configured to acquire a plurality of real-time working information corresponding to each mobile charging vehicle, acquire an information cycle corresponding to each real-time working information, and divide each real-time working information into a plurality of cycle sub-information. The logical analysis unit is configured to acquire a bit stream corresponding to each cycle sub-information, perform logical operation on the bit stream to obtain a plurality of logic values corresponding to each real-time working information, locate each logic value in the real-time working information, and obtain a working information value corresponding to each logic value. The information coding unit is configured to construct a value distribution axis of the working information value in the real-time working information, perform cycle mining on the value distribution axis, generate a regular cycle of the corresponding real-time working information, adjust each cycle sub-information according to the regular cycle, and generate cycle coding information of the corresponding real-time working information. The feedback analysis unit is configured to determine coding feedback information generated by the corresponding mobile charging vehicle in each feedback cycle according to the cycle coding information, map the coding feedback information to a preset feedback list for information filling, and obtain output feedback information corresponding to each mobile charging vehicle.

4. The charging abnormality diagnosis evaluation system for a mobile charging vehicle according to claim 1, wherein The charging analysis module comprises: The model analysis unit is configured to construct a charging operation model of the corresponding mobile charging vehicle according to the feedback operation data, run the charging operation model to determine a plurality of lossless output characteristics of the corresponding mobile charging vehicle, and add a corresponding feature label to each of the lossless output characteristics according to a feature attribute corresponding to each of the lossless output characteristics. The lossless output characteristics represent output characteristics of the mobile charging vehicle in the absence of external interference and self-loss, and the feature label includes a power label, a current label, and a voltage label. The deep analysis unit is configured to perform deep analysis on the output feedback information based on the feature label to obtain a plurality of independent characteristics contained in the output feedback information, count independent labels corresponding to each of the feature labels, and construct actual charging characteristics of the charged vehicle. The independent characteristics represent independent power, independent current, and independent voltage in the output feedback information. The charging analysis unit is configured to compare the lossless output characteristics and the actual charging characteristics corresponding to the same feature label to obtain a plurality of feature differences, extract charging characteristics with a feature difference of 0 as abnormal charging characteristics, and find an abnormal feature label corresponding to each of the abnormal charging characteristics. The charging evaluation unit is configured to establish a report framework of the charged vehicle according to an abnormal dimension corresponding to the abnormal feature label, mark a feature difference corresponding to each of the abnormal feature labels in the report framework, perform abnormal tracing on each of the abnormal dimensions, and generate a charging evaluation report of the charged vehicle. The abnormal dimension includes power abnormality, current abnormality, or voltage abnormality.

5. The charging abnormality diagnosis evaluation system for a mobile charging vehicle according to claim 1, wherein The abnormal optimization module includes: The scheme matching unit is configured to find a solution corresponding to each of the abnormal problems, construct a first execution condition corresponding to the mobile charging vehicle and a second execution condition corresponding to the charged vehicle, respectively. The device positioning unit is configured to determine a first execution device corresponding to each of the first execution conditions and a second execution device corresponding to each of the second execution conditions, respectively, and acquire first device data corresponding to each of the first execution devices and second device data corresponding to each of the second execution devices. The scheme optimization unit is configured to perform first process optimization on the first execution condition based on the first device data to generate a corresponding first optimized condition, and perform second process optimization on the second execution condition based on the second device data to generate a corresponding second optimized condition. The scheme execution unit is configured to construct a problem optimization scheme based on the first optimized condition and the second optimized condition, and control the mobile charging vehicle and the charged vehicle to implement the problem optimization scheme.

6. The charging abnormality diagnosis evaluation system for a mobile charging vehicle according to claim 5, wherein The optimization supervision module is configured 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 scheme. The first real-time optimization result of the mobile charging vehicle and the second real-time optimization result of the charged vehicle are acquired. ​ 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 complete optimization.

7. The charging abnormality diagnosis evaluation system for a mobile charging vehicle according to claim 1, wherein Further comprising: An electric quantity supervision module is configured to supervise real-time electric quantity of each charged vehicle, and control the mobile charging vehicle to stop charging work when the real-time electric quantity is consistent with a specified electric quantity.

8. The charging abnormality diagnosis evaluation system for a mobile charging vehicle according to claim 1, wherein Further comprising: A report analysis module is configured to analyze each charging evaluation report respectively, determine high-frequency abnormal problems corresponding to each mobile charging vehicle, and generate a problem processing warning.

Images