Charging device intelligent detection system and method based on dynamic load simulation

Abstract

The invention relates to the technical field of charging device detection, and discloses a charging device intelligent detection system and method based on dynamic load simulation. The system comprises a load configuration module, a charging parameter acquisition module, a load fluctuation calculation module, an abnormity identification module, a central processing unit and the like. The load configuration module configures dynamic load parameters according to the type of the charging device and generates a load simulation rule; the charging parameter acquisition module acquires an operation parameter set according to a rule; the load fluctuation calculation module generates a load fluctuation curve; the anomaly identification module calculates an anomaly fluctuation index; and the central processing unit fuses and processes the detection dimension parameters to generate state evaluation parameters. In addition, the system is further provided with a fault tracing module and a storage module. The system can simulate actual load change, collect and analyze parameters in multiple dimensions, realize fault intelligent diagnosis and traceability, can be widely applied to detection of various charging devices, improves the detection accuracy, reliability and efficiency, and reduces the detection cost.

Description

Technical Field

[0001] The present invention relates to the technical field of charging device detection, and particularly to an intelligent detection system and method for charging devices based on dynamic load simulation. Background Technique

[0002] With the rapid development of industries such as electric vehicles and electric equipment, the application of charging devices is becoming more and more extensive, and their performance and safety are directly related to the normal operation of the equipment and the safety of users. However, there are many deficiencies in the existing charging device detection technologies, which are difficult to meet the growing detection requirements.

[0003] In traditional charging device detection, most adopt static load detection methods. This method can only simulate fixed load conditions and cannot truly reflect the complex load changes faced by charging devices during actual use. For example, under different driving conditions of electric vehicles, the charging demands vary greatly, and the load of the charging device is in dynamic change. Static load detection ignores this dynamic characteristic, making the detection results deviate from the actual use situation and unable to effectively detect problems that may occur in the charging device under dynamic load, such as excessive voltage fluctuations and unstable current, thus affecting the reliability assessment of the charging device.

[0004] The existing detection technologies are relatively single in collecting and analyzing the operating parameters of charging devices. They often only focus on a few key parameters, such as charging voltage and current, while ignoring many other parameters that can reflect the operating state of the charging device. For example, parameters such as power factor and harmonic content during the charging process are crucial for comprehensively understanding the performance and operating conditions of the charging device. The lack of comprehensive analysis of these parameters makes it difficult to accurately judge the potential fault hazards of the charging device, resulting in the charging device may suddenly malfunction during actual use, affecting the normal use of the equipment and even causing safety accidents.

[0005] The existing fault detection and diagnosis methods are not intelligent enough. When an abnormality occurs in the charging device, it is impossible to quickly and accurately locate the cause and location of the fault. Usually, a large amount of troubleshooting and testing work needs to be carried out manually, which not only consumes time and labor costs but also has low efficiency. For example, when a charging interruption fault occurs in the charging device, the existing detection methods may not be able to quickly determine whether it is caused by a charging interface problem, a circuit fault or other reasons, which brings great difficulties to the maintenance work, prolongs the maintenance time of the equipment, and reduces the use efficiency of the equipment.

[0006] There are differences in the structure and performance of different types of charging devices, and the existing detection systems lack adaptability to different types of charging devices. A detection system often can only be applicable to a specific type of charging device and cannot flexibly adjust the detection parameters and methods to meet the diverse detection requirements of charging devices. This has limited the application scope of detection technologies to a certain extent, increased the detection costs of enterprises, and is not conducive to the development of the charging device detection industry. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent detection system and method for charging devices based on dynamic load simulation to solve the problems raised in the above background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solution: An intelligent detection system for charging devices based on dynamic load simulation, the system includes:

[0009] A load configuration module, used to configure dynamic load parameters according to the type of the target charging device and generate corresponding load simulation rules;

[0010] A charging parameter acquisition module, used to obtain the operating parameter set of the charging device from the charging database in real time according to the load simulation rules, and use all the parameters in the operating parameter set as the detection dimension parameters of the charging device;

[0011] A load fluctuation calculation module, used to generate a load fluctuation curve of the charging device through a dynamic simulation algorithm based on the preset load correlation relationship in the charging database, and use the load fluctuation curve as the detection dimension parameter of the charging device;

[0012] An anomaly recognition module, used to calculate the anomaly fluctuation index of the charging device through a preset fluctuation analysis method, and use the anomaly fluctuation index as the detection dimension parameter of the charging device;

[0013] A central processing unit, used to transmit the charging device information to the load fluctuation calculation module and the anomaly recognition module, send the load simulation rules to the charging parameter acquisition module, and perform fusion processing on all the detection dimension parameters to generate the status evaluation parameters of the charging device.

[0014] Preferably, calculating the anomaly fluctuation index of the charging device through the preset fluctuation analysis method includes:

[0015] Regarding the charging device and its associated preset reference device as dynamic analysis nodes, and obtaining the parameter change patterns of the dynamic analysis nodes in multiple load cycles based on historical operation data;

[0016] Based on the parameter change mode, calculate the initial anomaly index of each dynamic analysis node and the initial deviation degree of each historical operation data according to the fluctuation range of each historical operation data in the corresponding dynamic analysis node;

[0017] Based on the initial anomaly index and initial deviation degree, jointly optimize the final anomaly index of each dynamic analysis node and the final deviation degree of each historical operation data through a bidirectional recursive model, and obtain the final anomaly index of the dynamic analysis node corresponding to the charging device as its anomaly fluctuation index;

[0018] Set the target analysis node as any dynamic analysis node and the target historical data as any historical operation data. Based on the parameter change mode, calculate the ratio of the independent fluctuation amount of the target analysis node in the target historical data to the total independent fluctuation amount in all historical data, and the ratio of the global fluctuation amount of all analysis nodes in the target historical data to the total global fluctuation amount in all historical data. If the ratio of the independent fluctuation amount exceeds the ratio of the global fluctuation amount, it is determined that the target analysis node has an abnormal fluctuation characteristic in the target historical data.

[0019] Preferably, the load simulation rule includes a basic load type group and a variable load type group; the basic load type group contains multiple basic load modes and the corresponding fixed adjustment coefficients for each basic load mode; the variable load type group contains multiple variable load modes and the corresponding dynamic adjustment coefficients for each variable load mode.

[0020] Preferably, the operation parameter set of the charging device obtained in real time from the charging database according to the load simulation rule includes:

[0021] The charging parameter acquisition module extracts the structured operation data of the charging device from the charging database, extracts the corresponding basic parameters based on each basic load mode in the basic load type group, extracts the corresponding variable parameters based on each variable load mode in the variable load type group, and combines all the basic parameters and variable parameters into the operation parameter set.

[0022] Preferably, when the load association relationship is a direct load association, determine whether the charging device is directly connected to the load node. If there is a direct connection relationship, the load fluctuation curve is the linear superposition of the fluctuation contribution values of all associated load nodes, otherwise the load fluctuation curve is generated based on the basic load value of the charging device itself;

[0023] When the load association relationship is an indirect load association, the load fluctuation curve is the dynamic weighted result of the fluctuation contribution values of all associated nodes obtained by the charging device through multiple-level load links.

[0024] Preferably, it further includes a fault tracing module connected to the central processing unit;

[0025] The fault tracing module is used to set a target fault type, obtain the feature matching degree between the charging device and the target fault type through a preset association matching method, and use the feature matching degree as the detection dimension parameter of the charging device.

[0026] Preferably, the fault tracing module obtaining the feature matching degree through the preset association matching method includes:

[0027] Extract the historical fault waveform set of the charging device from the fault model library, extract the standard waveform set of the target fault type, and calculate the time-domain coincidence degree between the historical fault waveform set and the standard waveform set as the first matching degree;

[0028] Compare the parameter change trend of the charging device during the load cycle with the standard change trend of the target fault type in terms of morphology, and obtain the trend similarity between the two as the second matching degree;

[0029] Use the calculation result of the first matching degree, the second matching degree, or a combination of both as the feature matching degree.

[0030] Preferably, the central processing unit generates the state evaluation parameter by using a linear weighting or non-linear mapping method for all the detection dimension parameters.

[0031] Preferably, it further includes a storage module connected to the central processing unit, and the storage module is used to record the operation parameter set, the load fluctuation curve, the abnormal fluctuation index, and the state evaluation parameter.

[0032] Preferably, the present invention further includes an intelligent detection method for a charging device based on dynamic load simulation, which is applied to the intelligent detection system for a charging device based on dynamic load simulation described in any one of the above, and the method includes the following steps:

[0033] Step 1: According to the type of the target charging device, configure dynamic load parameters through the load configuration module and generate corresponding load simulation rules;

[0034] Step 2: According to the load simulation rules generated in Step 1, use the charging parameter acquisition module to obtain the operation parameter set of the charging device from the charging database in real time, and use all the parameters in the operation parameter set as the detection dimension parameters of the charging device;

[0035] Step 3: Based on the preset load association relationship in the charging database, the load fluctuation calculation module uses a dynamic simulation algorithm to generate the load fluctuation curve of the charging device, and takes the load fluctuation curve as the detection dimension parameter of the charging device;

[0036] Step 4: The abnormal recognition module uses a preset fluctuation analysis method to calculate the abnormal fluctuation index of the charging device, and takes the abnormal fluctuation index as the detection dimension parameter of the charging device;

[0037] Step 5: The central processing unit is used to transmit the charging device information to the load fluctuation calculation module and the abnormal recognition module, send the load simulation rule to the charging parameter acquisition module, and perform fusion processing on all the detection dimension parameters obtained in Steps 2 - 4 to generate the state evaluation parameter of the charging device.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] From the perspective of load simulation, the system sets up a load configuration module, which can configure dynamic load parameters according to the type of the target charging device, and generate a load simulation rule including a basic load type group and a variable load type group. The multiple basic load modes and corresponding fixed adjustment coefficients of the basic load type group, in cooperation with the multiple variable load modes and dynamic adjustment coefficients of the variable load type group, can highly restore the complex load changes of the charging device in actual use. Compared with the traditional static load detection method, this innovative simulation method can make the detection results more conform to the actual operation state, providing strong support for accurately evaluating the performance of the charging device. Taking an electric vehicle charging device as an example, during the vehicle's driving process, due to changes in factors such as battery power and driving speed, the charging demand is constantly changing. The dynamic load simulated by this system can accurately match these changes, detect key performance indicators such as the response ability of the charging device during load mutation, and effectively avoid evaluation errors caused by inconsistent detection conditions with the actual situation.

[0040] In terms of parameter acquisition and analysis, the charging parameter acquisition module comprehensively extracts structured operation data from the charging database according to the load simulation rules. It includes not only the basic parameters that have been traditionally concerned about, but also variable parameters, and combines these parameters into an operation parameter set as the detection dimension parameters. At the same time, the load fluctuation calculation module generates a load fluctuation curve based on the preset load correlation relationship and dynamic simulation algorithm, and the anomaly recognition module calculates the anomaly fluctuation index through the preset fluctuation analysis method, which greatly enriches the detection dimensions. For example, during the detection process, in addition to the conventional voltage and current parameters, the changes in parameters such as power factor and harmonic content can also be obtained. Through the comprehensive analysis of these multi-dimensional parameters, the operation status of the charging device can be understood more comprehensively and deeply, and potential fault hazards can be accurately discovered. For example, harmonic problems in the circuit may indicate component aging, and taking measures in advance can avoid the occurrence of faults and improve the reliability and stability of the charging device.

[0041] The intelligent fault diagnosis is a major highlight of the present invention. The anomaly recognition module calculates the anomaly fluctuation index through the preset fluctuation analysis method. The specific process involves taking the charging device and its associated preset reference device as dynamic analysis nodes, obtaining the parameter change pattern, calculating the initial anomaly index and the initial deviation degree, and then jointly optimizing through a bidirectional recursive model to obtain the final anomaly index. At the same time, the anomaly fluctuation characteristics are determined by comparing the independent fluctuation quantity ratio and the global fluctuation quantity ratio. In addition, the fault tracing module sets the target fault type and obtains the feature matching degree between the charging device and the target fault type through the preset association matching method. This process includes calculating the time-domain coincidence degree between the historical fault waveform set and the standard waveform set, and comparing the parameter change trends to obtain the trend similarity, and using the result of the two or their combination as the feature matching degree. The application of these intelligent algorithms and matching methods enables the system to quickly and accurately locate the fault cause and fault location when the charging device has an anomaly. For example, when the charging device has an abnormal charging interruption, the system can quickly determine whether it is due to poor contact of the charging interface, circuit short circuit or other reasons, greatly shortening the fault troubleshooting time, reducing the maintenance cost, and improving the use efficiency of the equipment.

[0042] In terms of the versatility and adaptability of the system, the present invention also has outstanding performance. The load configuration module can configure corresponding dynamic load parameters and load simulation rules according to different types of charging devices, making the entire detection system highly flexible. Whether it is an electric vehicle charging device, an electric bicycle charging device, or other types of charging equipment, it can be applicable. This feature greatly expands the application scope of the detection system. Enterprises do not need to purchase separate detection equipment for different types of charging devices, reducing the detection cost and promoting the development of the charging device detection industry.

[0043] The present invention also provides a storage module for recording the operating parameter set, load fluctuation curve, abnormal fluctuation index, and status evaluation parameter. These data not only provide references for current detection and fault diagnosis but also can be used for long-term data analysis and research, helping to continuously optimize the algorithms and models of the detection system and further improve the accuracy and reliability of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is the working principle diagram of the intelligent detection system for charging devices based on dynamic load simulation according to the present invention;

[0045] Figure 2 is the working principle diagram for calculating the abnormal fluctuation index of the charging device;

[0046] Figure 3 is the working principle diagram of the fault tracing module;

[0047] Figure 4 is the working principle diagram for the fault tracing module to obtain the feature matching degree;

[0048] Figure 5 is the working principle diagram for the central processing unit to generate the status evaluation parameter. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] Please refer to Figures 1 - 5 , the present invention provides a technical solution: The present invention provides an intelligent detection system for charging devices based on dynamic load simulation. It mainly includes a load configuration module, a charging parameter acquisition module, a load fluctuation calculation module, an abnormal recognition module, and a central processing unit, and realizes the intelligent detection of the charging device through the collaborative work of each module. The specific implementation steps are as follows:

[0051] The load configuration module plays a key role. It specifically configures the dynamic load parameters according to the type of the target charging device. For different types of charging devices, the applicable load parameters are different. The load configuration module will fully consider these characteristics and configure parameters such as load size and change frequency. On this basis, corresponding load simulation rules are generated. These rules are important bases for subsequent detection work, which stipulate how to simulate the operation of the load to accurately obtain the operation data of the charging device under different load conditions.

[0052] The charging parameter acquisition module operates according to the load simulation rules generated by the load configuration module. It retrieves the operating parameter set of the charging device from the charging database in real time. This operating parameter set covers various key parameters during the operation of the charging device, such as charging voltage, charging current, charging power, etc. Moreover, all these parameters are used as the detection dimension parameters of the charging device, providing a rich data basis for comprehensively analyzing the operating state of the charging device later.

[0053] Based on the preset load association relationship in the charging database, the load fluctuation calculation module generates the load fluctuation curve of the charging device by means of a dynamic simulation algorithm. The load association relationship reflects the interconnection between the charging device and other loads. Through the dynamic simulation algorithm, it can simulate the load fluctuation situation of the charging device under different load conditions. The generated load fluctuation curve is also used as a detection dimension parameter, providing an important reference for the detection of the charging device from the perspective of load changes.

[0054] The anomaly recognition module calculates the anomaly fluctuation index of the charging device by using a preset fluctuation analysis method. This index is one of the key bases for judging whether the charging device operates normally. By deeply analyzing the fluctuation situation of the operating parameters of the charging device, it can effectively identify the abnormal fluctuations and then discover potential fault hazards. The anomaly fluctuation index is also used as a detection dimension parameter.

[0055] Finally, the central processing unit undertakes the core coordination and processing work. It transmits the charging device information to the load fluctuation calculation module and the anomaly recognition module, providing the necessary data support for the work of these two modules; it sends the load simulation rules to the charging parameter acquisition module to ensure that the acquisition module can accurately obtain data according to the rules. Moreover, the central processing unit performs fusion processing on all detection dimension parameters. By comprehensively analyzing these parameters, it generates evaluation parameters that can accurately reflect the state of the charging device, thus realizing the intelligent detection of the operating state of the charging device.

[0056] The present invention will be further described below in conjunction with Embodiments 1 to 6:

[0057] Embodiment 1:

[0058] In the entire detection system, the anomaly recognition module is crucial for promptly detecting the abnormal operating conditions of the charging device. When calculating the anomaly fluctuation index, it is first necessary to determine the dynamic analysis nodes. The charging device and its associated preset reference device are set as the dynamic analysis nodes. This is because the operating data of the reference device can provide a reference for judging whether the charging device is normal. Then, obtain the parameter change patterns of these dynamic analysis nodes in multiple load cycles from the historical operating data. The historical operating data records the changes in the operating parameters of the charging device and the reference device under different load conditions in the past. By analyzing these data, the laws and patterns of parameter changes can be summarized.

[0059] Based on the obtained parameter change pattern, the initial anomaly index and the initial deviation degree are further calculated. The calculation is performed according to the fluctuation range of each historical operation data in the corresponding dynamic analysis node. The fluctuation range reflects the intensity of parameter change. By analyzing the fluctuation range, the anomaly degree of each dynamic analysis node can be initially judged to obtain the initial anomaly index. At the same time, the deviation degree of each historical operation data relative to the normal range is calculated, that is, the initial deviation degree.

[0060] The initial anomaly index and the initial deviation degree are jointly optimized using a bidirectional recursive model. The bidirectional recursive model can fully consider the forward and backward correlation relationships between data. Through multiple iterative calculations, the initial anomaly index and deviation degree are adjusted and optimized to obtain more accurate final anomaly index and final deviation degree. The final anomaly index of the dynamic analysis node corresponding to the charging device is used as its anomaly fluctuation index, which can more accurately reflect the abnormal fluctuation situation of the charging device.

[0061] In addition, it is also necessary to determine the abnormal fluctuation characteristics. Set the target analysis node as any dynamic analysis node and the target historical data as any set of historical operation data. Based on the parameter change pattern, calculate the ratio of the independent fluctuation amount of the target analysis node in the target historical data to the total independent fluctuation amount in all historical data, and the ratio of the global fluctuation amount of all analysis nodes in the target historical data to the total global fluctuation amount in all historical data. When the ratio of the independent fluctuation amount exceeds the ratio of the global fluctuation amount, it can be determined that the target analysis node has abnormal fluctuation characteristics in the target historical data, which provides a strong basis for accurately identifying the abnormal situation of the charging device.

[0062] In an actual charging scenario, there is a charging device for charging an electric vehicle, and its abnormal fluctuation index is calculated.

[0063] Determine the dynamic analysis node. Set the electric vehicle charging device and its associated reference charging device with the same model operating normally as the dynamic analysis node. Obtain the parameter change patterns of these two dynamic analysis nodes in multiple load cycles from the historical operation data. For example, in the past 100 load cycles, analyze the changes in the charging voltage, current of the charging device, and the corresponding parameters of the reference device. Through the collation and analysis of these data, it is found that under normal load cycles, the charging voltage of the charging device usually fluctuates between 380V - 420V, and the current fluctuates between 10A - 20A; the voltage of the reference device fluctuates between 390V - 410V, and the current fluctuates between 12A - 18A. This is their parameter change pattern.

[0064] Calculate the initial anomaly index and the initial deviation degree based on this parameter change pattern. Taking one load cycle as an example, assume that the charging voltage of this charging device is 450V within this cycle, exceeding the normal fluctuation range. Calculate the initial anomaly index according to its fluctuation range in the corresponding dynamic analysis node. Since it deviates greatly from the normal range, the initial anomaly index is set to a relatively high value. At the same time, calculate the initial deviation degree of this historical operation data (i.e., the charging voltage of 450V in this cycle). By comparing it with the upper limit of the normal range of 420V, calculate the deviation degree to obtain the initial deviation degree.

[0065] Use a bidirectional recursive model to jointly optimize the initial anomaly index and the initial deviation degree. The bidirectional recursive model will comprehensively consider the data correlation relationships of multiple load cycles before and after. For example, in several subsequent load cycles, if the voltage of the charging device continues to be too high, the bidirectional recursive model will adjust the previously calculated initial anomaly index and initial deviation degree according to these consecutive abnormal situations. After multiple iterative calculations, finally obtain more accurate final anomaly index and final deviation degree. Take the final anomaly index of the dynamic analysis node corresponding to this charging device as its abnormal fluctuation index.

[0066] Perform abnormal fluctuation characteristic determination. Set the charging device being currently analyzed as the target analysis node, and the operation data of this load cycle as the target historical data. Calculate the ratio of the independent fluctuation amount of the target analysis node in the target historical data to the total independent fluctuation amount in all historical data. Assume that within this load cycle, the independent fluctuation amount of the charging device voltage is 30V (i.e., the difference between 450V and the normal range average value of 400V), and the total independent fluctuation amount in all historical data is 1000V (obtained by accumulating the voltage fluctuation amounts of all historical load cycles), then this ratio is 30÷1000 = 0.03. Then calculate the ratio of the global fluctuation amount of all analysis nodes (i.e., the charging device and the reference device) in the target historical data to the total global fluctuation amount in all historical data. Assume that within this load cycle, the global fluctuation amount of all analysis nodes is 50V (comprehensively calculated including the voltage, current and other parameter fluctuations of the charging device and the reference device), and the total global fluctuation amount in all historical data is 2000V, then this ratio is 50÷2000 = 0.025. Since 0.03>0.025, that is, the independent fluctuation amount ratio exceeds the global fluctuation amount ratio, it is determined that the target analysis node (i.e., the current electric vehicle charging device) has abnormal fluctuation characteristics in the target historical data (i.e., the current load cycle).

[0067] Example 2:

[0068] The load simulation rule is the key for the entire detection system to simulate the real load situation, and the charging parameter acquisition is an important link to obtain the operation data of the charging device.

[0069] The load simulation rules include a basic load type group and a variable load type group. The basic load type group consists of multiple basic load patterns and the corresponding fixed adjustment coefficients for each basic load pattern. Different charging devices have different load pattern requirements in the basic operating state, and the basic load type group is designed to simulate these basic load conditions. For example, some charging devices may require a relatively stable basic load during the initial charging stage, and the basic load pattern can be set according to this requirement, while the fixed adjustment coefficient is used to fine-tune the magnitude of the basic load.

[0070] The variable load type group also includes multiple variable load patterns and the corresponding dynamic adjustment coefficients for each variable load pattern. During the actual operation of the charging device, the load conditions often change dynamically, and the variable load type group is designed to simulate such dynamically changing load conditions. The dynamic adjustment coefficient is adjusted in real time according to factors such as the operating stage of the charging device and the battery state, so as to more realistically simulate the changes in the actual load.

[0071] During the charging parameter acquisition process, the charging parameter acquisition module extracts the structured operating data of the charging device from the charging database according to the load simulation rules. For the basic load type group, the basic parameters corresponding to each basic load pattern are extracted from the database. These basic parameters are the key operating parameters of the charging device in the basic load pattern, such as the basic charging voltage, basic charging current, etc. For the variable load type group, the variable parameters corresponding to each variable load pattern are extracted, and the variable parameters change dynamically with the change of the load pattern. Finally, all the extracted basic parameters and variable parameters are combined to form an operating parameter set. This operating parameter set comprehensively covers the operating data of the charging device under different load patterns, providing rich and accurate data support for subsequent detection and analysis.

[0072] Suppose there is a charging device for an electric bicycle. For this electric bicycle charging device, the basic load type group in the load simulation rules plays an important role. The basic load type group contains multiple basic load modes and corresponding fixed adjustment coefficients. For example, in the initial stage when the battery of the electric bicycle has a low power level and starts charging, a basic load mode is set to simulate a stable low-power charging state. Suppose in this basic load mode, the output voltage of the charging device is set to 48V and the current is set to 1.5A. These two parameters are the basic parameters corresponding to this basic load mode. The corresponding fixed adjustment coefficients may be set such that the voltage adjustment coefficient is 1.00 and the current adjustment coefficient is 1.05. This voltage adjustment coefficient of 1.00 means that under normal circumstances, the voltage is output according to the set 48V; the current adjustment coefficient of 1.05 indicates that the actual output current may be slightly adjusted based on 1.5A, and the adjusted current value is 1.5A × 1.05 = 1.575A, so as to simulate the small current fluctuations that may occur during the actual charging process.

[0073] The variable load type group is equally essential. When the battery of the electric bicycle is gradually charging and approaching full charge, the charging device needs to adjust the load to avoid overcharging the battery. At this time, the variable load type group comes into play. It contains multiple variable load modes and dynamic adjustment coefficients. For example, a variable load mode is set to start when the battery power reaches 80%. In this mode, the charging voltage gradually decreases and the current also decreases accordingly. Suppose initially the voltage is set to 54V and the current is set to 0.8A. As the charging progresses, it is adjusted in real time according to the dynamic adjustment coefficients. The dynamic adjustment coefficients may be associated with the change in battery power. For example, for every 1% increase in battery power, the voltage dynamic adjustment coefficient decreases by 0.05 and the current dynamic adjustment coefficient decreases by 0.03. When the battery power reaches 85%, the voltage adjustment coefficient becomes 54V × (1 - 0.05 × 5) = 54V × 0.75 = 40.5V, and the current adjustment coefficient becomes 0.8A × (1 - 0.03 × 5) = 0.8A × 0.85 = 0.68A. Through such dynamic adjustment, the load change when the battery is approaching full charge is simulated.

[0074] The charging parameter acquisition module extracts the structured operation data of the charging device from the charging database according to these load simulation rules. For the basic load type group, the basic parameters corresponding to the above basic load modes are accurately extracted from the database, namely the voltage of 48V and the current of 1.575A (the values after being adjusted by the fixed adjustment coefficient). For the variable load type group, according to the real-time change of the battery power, the variable parameters corresponding to the corresponding variable load modes are extracted. For example, when the battery power reaches 85%, the adjusted voltage of 40.5V and the current of 0.68A are extracted. Finally, all the extracted basic parameters and variable parameters are combined to form an operation parameter set. This operation parameter set comprehensively records the operation data of the electric bicycle charging device in different charging stages based on different load modes, provides rich and accurate data support for the subsequent detection and analysis of the charging device, helps to timely discover possible abnormal situations during the charging process, and ensures the safety and stability of charging.

[0075] Embodiment 3:

[0076] This embodiment focuses on elaborating the generation method of the load fluctuation curve. According to different load association relationships, the generation method of the load fluctuation curve is different. When the load association relationship is a direct load association, it is necessary to judge the connection situation between the charging device and the load node. If there is a direct connection relationship between the charging device and the load node, then the load fluctuation curve is the linear superposition of the fluctuation contribution values of all associated load nodes. This is because the influence between directly connected load nodes is relatively direct, and its fluctuation contribution can be reflected by simple linear addition. For example, if there are multiple directly connected load nodes, the fluctuations of each node at different times will affect the load of the charging device. By superimposing these influences according to the linear relationship, the load fluctuation curve of the charging device can be obtained.

[0077] If there is no direct connection relationship between the charging device and the load node, then the load fluctuation curve is generated based on the basic load value of the charging device itself. In this case, the charging device is mainly affected by its own basic load, and its load fluctuation curve reflects the change of its own basic load during operation.

[0078] When the load association relationship is indirect load association, the load fluctuation curve is the dynamic weighted result of the fluctuation contribution values of all associated nodes obtained by the charging device through multiple-level load links. In an actual power system, there are many indirect connections between charging devices and load nodes, and they affect each other through multiple-level load links. The fluctuations of these associated nodes have different degrees of influence on the load of the charging device. Therefore, a dynamic weighting method needs to be used for calculation. Dynamic weighting will adjust the weights of the fluctuation contribution values of each associated node in real time according to factors such as the distance of the associated node and the load characteristics, so as to obtain a load fluctuation curve that more conforms to the actual situation. This method can more accurately simulate the load fluctuation of the charging device under the condition of indirect load association and provide more accurate data for subsequent detection and analysis.

[0079] Suppose there are multiple DC fast chargers for electric vehicles installed in a commercial parking lot. The charger numbered C001 is the research object this time. In the power supply system of this parking lot, there are clear load association relationships, which are divided into two cases: direct load association and indirect load association.

[0080] When the load association relationship is direct load association, it is necessary to judge the connection situation between charger C001 and the load node. In the power supply network of this parking lot, charger C001 is directly connected to two load nodes L1 and L2. At this time, its load fluctuation curve is the linear superposition of the fluctuation contribution values of all associated load nodes.

[0081] Let the load fluctuation curve of charger C001 be , the fluctuation contribution value of load node L1 be , and the fluctuation contribution value of load node L2 be . Then the calculation formula of the load fluctuation curve is: . Among them, represents the final load fluctuation curve of charger C001, which is the result obtained by comprehensively considering the fluctuation effects of all directly associated load nodes; represents the contribution value of load node L1 to the load fluctuation of charger C001, which is affected by the load change situation of L1 itself. For example, when other devices connected to L1 are turned on or off, its power will change, which will in turn affect the load contribution to charger C001; represents the contribution value of load node L2 to the load fluctuation of charger C001, which is related to the operating state of L2.

[0082] For example, at a certain moment, because the load node L1 increases the power consumption demand due to the connected devices, its fluctuation contribution value becomes 3 (3 here is obtained according to the quantization method of the load fluctuation contribution. Suppose a certain power fluctuation unit is used to measure), and because the load node L2 reduces the power consumption due to the reduction of devices, the fluctuation contribution value becomes -1 (the negative sign indicates that the direction of its impact on the total load fluctuation is opposite to the positive direction), then according to the formula calculation, the load fluctuation curve of the charging pile C001 at this time .

[0083] If the charging pile C001 has no direct connection relationship with the load node, assuming it is connected to other load nodes through an intermediate transformer, then its load fluctuation curve is generated based on its own basic load value. Assume that the basic load value of the C001 charging pile is 10 (also measured in specific power fluctuation units) during a certain stable charging stage. Then, during this stage, its load fluctuation curve fluctuates within a small range around this basic load value of 10, for example, fluctuating between 9.5 and 10.5.

[0084] When the load association relationship is an indirect load association, the charging pile C001 is connected to multiple associated nodes through a multi-level load link. In the power supply network of this parking lot, the C001 charging pile is connected to three other associated nodes N1, N2, and N3 through a two-level load link. At this time, the load fluctuation curve is the dynamic weighted result of the fluctuation contribution values of all associated nodes obtained through the multi-level load link.

[0085] When calculating the dynamic weighted result, it is necessary to adjust the weights of the fluctuation contribution values of each associated node in real time according to factors such as the distance and load characteristics of the associated nodes. Assume that the fluctuation contribution value of the associated node N1 is , and the weight is ; the fluctuation contribution value of the associated node N2 is , and the weight is ; the fluctuation contribution value of the associated node N3 is , and the weight is . Since N1 is relatively close to the C001 charging pile and has a greater impact on its load, the weight is set to 0.5; N2 is at a moderate distance and has a relatively small impact, and the weight is set to 0.3; N3 is at a relatively far distance and has the smallest impact, and the weight is set to 0.2. At a certain moment, the fluctuation contribution value of N1 is 4, the fluctuation contribution value of N2 is 2, and the fluctuation contribution value of N3 is 1. Then, according to the dynamic weighted calculation, the load fluctuation curve of the charging pile C001 is:[[]] . This dynamic weighting method can more accurately simulate the load fluctuation of the charging pile in the case of indirect load association, providing more realistic data support for accurately detecting the operating state of the charging pile in the future.

[0086] Example 4:

[0087] This embodiment introduces the working process of the fault tracing module. The fault tracing module is used in the detection system to determine the specific fault type when a charging device fails.

[0088] The fault tracing module first needs to set the target fault type. During the actual operation of the charging device, various fault types may occur, such as overcharging fault, short - circuit fault, etc. The fault tracing module will set the target fault type to be detected according to actual needs.

[0089] After setting the target fault type, extract the historical fault waveform set of the charging device from the fault model library. The fault model library stores a large amount of waveform data of the charging device when it failed in the past. These data record the changes in the operating state of the charging device when the fault occurred. At the same time, extract the standard waveform set of the target fault type. The standard waveform set is summarized through a large amount of research and practice and represents the typical waveform characteristics of this fault type.

[0090] Calculate the time - domain coincidence degree between the historical fault waveform set and the standard waveform set as the first matching degree. The time - domain coincidence degree reflects the similarity between the historical fault waveform and the standard waveform on the time axis. By comparing the waveform shapes and amplitudes at different times of the two, the time - domain coincidence degree can be calculated. This index can preliminarily judge the similarity between the historical fault of the charging device and the target fault type.

[0091] Compare the parameter change trend of the charging device during the load cycle with the standard change trend of the target fault type in terms of morphology, and obtain the trend similarity degree between the two as the second matching degree. The parameter change trend reflects the changes in key parameters of the charging device during operation. Comparing it with the standard change trend of the target fault type can judge the similarity of the fault from another angle. For example, in the overcharging fault, the change trends of the charging voltage and current have certain characteristics. Comparing the actual parameter change trend with it can obtain the trend similarity degree.

[0092] Take the calculation result of the first matching degree, the second matching degree, or a combination of both as the feature matching degree. According to the size of the feature matching degree, the matching degree between the charging device and the target fault type can be judged, so as to achieve fault tracing and provide strong support for quickly locating and solving faults.

[0093] Taking a common mobile phone fast - charging device as an example, assume that when detecting the fault of this mobile phone fast - charging device, the target fault type is set as "over - voltage fault". When the mobile phone fast - charging device is charging the mobile phone, if the output voltage is too high, it may cause irreversible damage to the mobile phone battery and even pose a safety hazard. Therefore, detecting the over - voltage fault is very crucial.

[0094] Extract the historical fault waveform set of this charging device from the master-slave fault model library. The fault model library is established by long-term collection and collation of waveform data of this type of charging device under various fault conditions. For example, for the overvoltage fault that occurred before, its waveform data was completely recorded. These waveforms may show information such as the moment when the voltage suddenly rises, the rising amplitude, and the duration.

[0095] Extract the standard waveform set of the target fault type "overvoltage fault". This is the typical waveform feature obtained through analysis and summary of a large number of overvoltage fault cases. For example, the standard waveform shows that when an overvoltage fault occurs, the voltage will rapidly rise beyond the normal charging voltage range within a short time (assuming the normal voltage range for mobile phone fast charging is 4.5V - 5.5V, and the standard waveform of the overvoltage fault shows that the voltage will quickly rise above 6V), and maintain a high voltage for a period of time, and then there may be voltage fluctuations or sudden drops and other characteristics.

[0096] Calculate the time-domain coincidence degree between the historical fault waveform set and the standard waveform set as the first matching degree. It can be achieved through a dedicated waveform comparison algorithm. For example, compare the historical fault waveform and the standard waveform point by point on the time axis. If within a certain period of time, the more points where the voltage values of the historical fault waveform and the standard overvoltage fault waveform are similar within a certain error range, the higher the time-domain coincidence degree. Suppose after calculation, it is found that within a 5-second time period of a certain historical fault waveform and the standard waveform, the voltage values at 80% of the time points have an error within 0.5V. Then it can be preliminarily concluded that the time-domain coincidence degree of this section of the historical fault waveform and the standard waveform is 80%.

[0097] Compare the parameter change trend of the charging device during the load cycle with the standard change trend of the target fault type in terms of morphology, and obtain the trend similarity between the two as the second matching degree. During the mobile phone charging process, parameters such as the voltage and current of the charging device will change with different charging stages. During normal charging, the voltage will be stable within a certain range, and the current will gradually decrease as the battery charge increases. For the overvoltage fault, its standard change trend is that the voltage rises rapidly. During the analysis process, observe the voltage change curve of the charging device during the load cycle. If within a certain time period, the voltage was originally stable at about 5V, suddenly rose to 6.5V within 1 second, and then continued to remain at a high voltage, this change trend is highly similar to the standard change trend of the overvoltage fault. By comparing features such as the slopes of the two, the time points of rising or falling, and the change amplitudes, use a specific algorithm to calculate the trend similarity. Suppose after calculation, the trend similarity is 75%.

[0098] Take the calculation result of the first matching degree, the second matching degree, or a combination of both as the feature matching degree. If the combination method of simply adding the two and taking the average is adopted, then the feature matching degree is (80% + 75%) ÷ 2 = 77.5%. By setting a threshold (assuming the threshold is 70%), when the feature matching degree exceeds the threshold, it can be determined that the charging device has a relatively high matching degree with the target fault type "overvoltage fault", and there is a greater possibility of overvoltage fault. This provides a strong basis for maintenance personnel to quickly locate and solve the faults of the charging device, improves the efficiency and accuracy of fault troubleshooting, and ensures the safety and stability of the mobile phone charging process.

[0099] Embodiment 5:

[0100] This embodiment mainly focuses on the processing method of the central processing unit for the detection dimension parameters and the role of the storage module. The central processing unit is responsible for data fusion processing and result output in the entire detection system, while the storage module is used to save key data to ensure the stable operation of the system and subsequent analysis.

[0101] The central processing unit performs fusion processing on all detection dimension parameters to generate state evaluation parameters, and the methods it adopts include linear weighting or non-linear mapping. Linear weighting is a relatively simple and direct processing method. According to the importance of each detection dimension parameter for the state evaluation of the charging device, corresponding weights are assigned to it. For example, for the operation safety of the charging device, the abnormal fluctuation index may be more important than other parameters, and a larger weight can be assigned to it. After multiplying all detection dimension parameters by their respective weights and adding them together, the result obtained is the state evaluation parameter. This method is simple to calculate and can quickly obtain an evaluation parameter that roughly reflects the state of the charging device.

[0102] Non-linear mapping is applicable to the situation where there are complex relationships between detection dimension parameters. Since the operation of the charging device is affected by a variety of factors, there may be non-linear correlations between detection dimension parameters. Non-linear mapping establishes a complex mathematical model, such as a neural network model, etc., inputs the detection dimension parameters into the model for processing, and the model will automatically learn the complex relationships between the parameters and output an evaluation parameter that more accurately reflects the state of the charging device.

[0103] The storage module is connected to the central processing unit and is responsible for recording the operating parameter set, load fluctuation curve, abnormal fluctuation index, and status evaluation parameter. The operating parameter set contains various real-time data during the operation of the charging device, which can be used for subsequent data analysis and fault troubleshooting. The load fluctuation curve records the dynamic changes in the load of the charging device and is of great value for studying the performance of the charging device under different load conditions. The abnormal fluctuation index reflects the abnormal conditions during the operation of the charging device, and storing these indexes can facilitate in-depth analysis of abnormal conditions in the future. The status evaluation parameter is the final output result of the entire detection system. Taking the scenario of a public charging pile charging a new energy vehicle as an example, in this scenario, the central processing unit is responsible for fusing the detection dimension parameters and then generating the status evaluation parameter. The detection dimension parameters include the operating parameter set obtained by the charging parameter acquisition module, such as the real-time voltage, current, power, etc. during the charging process; the load fluctuation curve generated by the load fluctuation calculation module, which reflects the change of the load over time during the charging process; the abnormal fluctuation index calculated by the abnormal identification module, which is used to judge whether there is an abnormal fluctuation during the charging process.

[0104] Assume that in the operating parameter set, the real-time voltage is 380V, the current is 50A, and the power is 19kW. The load fluctuation curve shows that there are two small fluctuations in the load in the past 10 minutes, and the abnormal fluctuation index shows that the abnormal fluctuation of the current charging process is at a low level. The central processing unit generates the status evaluation parameter by using the linear weighting method. After expert experience and a large amount of data analysis, it is determined that the weight of the voltage is 0.3, the weight of the current is 0.2, the weight of the power is 0.2, the weight of the load fluctuation curve characteristics (such as comprehensive consideration of the fluctuation amplitude, frequency, etc.) is 0.2, and the weight of the abnormal fluctuation index is 0.1.

[0105] According to the linear weighting formula, the status evaluation parameter = 380×0.3 + 50×0.2 + 19×0.2 + comprehensive score of the load fluctuation curve×0.2 + score of the abnormal fluctuation index×0.1. Assume that the comprehensive score of the load fluctuation curve is evaluated as 80 points (out of 100) according to its fluctuation amplitude and frequency, and the score of the abnormal fluctuation index is 90 points (out of 100). Then the status evaluation parameter = 380×0.3 + 50×0.2 + 19×0.2 + 80×0.2 + 90×0.1 = 114 + 10 + 3.8 + 16 + 9 = 152.8. This status evaluation parameter can intuitively reflect the operating status of the current charging pile. By setting a preset threshold range (for example, 100 - 150 is the normal range), it can be judged that the current charging pile is in a slightly abnormal state and may require further inspection.

[0106] If a non-linear mapping method is adopted, the central processing unit can process with the help of a neural network model. First, encode the detection dimension parameters such as the operating parameter set, load fluctuation curve data, and abnormal fluctuation index, and convert them into an input format that the neural network can accept. Suppose a neural network model with an input layer, two hidden layers, and an output layer is constructed. The number of nodes in the input layer is determined according to the number of detection dimension parameters. For example, if there are 5 main parameters here, 5 nodes are set in the input layer. The number of nodes in the hidden layer is determined according to experience and experiments. Suppose 10 nodes are set in the first hidden layer, 8 nodes are set in the second hidden layer, and 1 node is set in the output layer to output the state evaluation parameter.

[0107] Input the encoded parameters into the neural network. Through the complex calculations of the neurons in the hidden layer, the neurons perform non-linear transformation on the input signals through the activation function, simulating the complex non-linear relationships between the parameters. After multiple trainings, the neural network learns the relationships between different parameter combinations and the actual operating state of the charging pile. Finally, the neural network outputs a state evaluation parameter. For example, after training, the neural network processes the current detection dimension parameters and outputs a state evaluation parameter of 155 (which may be different from the result of linear weighted calculation because the two processing methods are different). Similarly, by comparing with the preset threshold, the operating state of the charging pile is judged.

[0108] The storage module plays an important role in this process. It is connected to the central processing unit and is responsible for recording the operating parameter set. For example, record the parameters such as voltage, current, and power during the charging process every certain period of time (suppose 1 minute), forming a time series data, which is convenient for subsequent analysis of the stability and trend of the charging process. Record the load fluctuation curve and store it in the form of chart data, which can intuitively view the change of the load over time and be used to analyze the impact of different time periods and different charging vehicles on the load. Store the abnormal fluctuation index for tracing and in-depth analysis of abnormal situations, and view the frequency and severity of abnormal fluctuations. Store the state evaluation parameter. By accumulating these parameters in the long term, a historical file of the operating state of the charging pile can be established, providing data support for the regular maintenance, performance optimization, and fault prediction of the charging pile. For example, by analyzing the change trend of the state evaluation parameter over a period of time, potential faults that may occur in the charging pile can be discovered in advance, preventive maintenance can be arranged, the downtime of the charging pile can be reduced, and its usage efficiency and reliability can be improved. The parameters can track and evaluate the long-term operating state of the charging device, providing a basis for the maintenance and upgrade of the equipment. By storing these key data, the system can better realize the intelligent detection and management of the charging device.

[0109] Embodiment 6:

[0110] The load configuration module manually or automatically configures dynamic load parameters on the system operation interface according to the type of electric vehicle charging pile. For example, it is set that the basic load type group includes two basic load modes: constant current charging mode and constant voltage charging mode. The fixed adjustment coefficient corresponding to the constant current charging mode is 0.8, and the fixed adjustment coefficient corresponding to the constant voltage charging mode is 0.9. The variable load type group includes two variable load modes: pulse charging mode and intermittent charging mode. The dynamic adjustment coefficient corresponding to the pulse charging mode changes dynamically according to the charging time, with an initial value of 0.7 and an increase of 0.05 every 10 minutes. The dynamic adjustment coefficient corresponding to the intermittent charging mode changes dynamically according to the temperature of the charging pile. For every 5°C increase in temperature, the coefficient decreases by 0.03. After the configuration is completed, the load configuration module generates the corresponding load simulation rules and sends them to the charging parameter acquisition module.

[0111] After receiving the load simulation rules, the charging parameter acquisition module extracts the structured operation data of the electric vehicle charging pile from the charging database, such as voltage, current, charging time, charging pile temperature, etc. Based on the constant current charging mode in the basic load type group, basic parameters such as charging current and charging voltage are extracted. Based on the pulse charging mode in the variable load type group, variable parameters such as pulse frequency and pulse width are extracted. All the extracted basic parameters and variable parameters are combined into an operation parameter set, which is used as one of the detection dimension parameters of the charging pile.

[0112] In this embodiment, if the load association relationship is direct load association, by detecting the connection line of the charging pile, it is judged that the charging pile is directly connected to the battery pack load node. At this time, the load fluctuation calculation module obtains the real-time current and voltage fluctuation data of all associated load nodes (i.e., each part of the battery pack), and determines the weight of the fluctuation contribution value according to the power of each load node. For example, the node with a larger power has a higher weight. The fluctuation contribution values of all associated load nodes are linearly superimposed to generate a load fluctuation curve. Suppose the battery pack includes three load nodes A, B, and C, with weights of 0.4, 0.3, and 0.3 respectively. The fluctuation contribution value of node A is 5, the fluctuation contribution value of node B is 3, and the fluctuation contribution value of node C is 2. Then the value of the corresponding point on the superimposed load fluctuation curve is 5×0.4 + 3×0.3 + 2×0.3 = 3.5. The generated load fluctuation curve is used as one of the detection dimension parameters of the charging pile.

[0113] If the load association relationship is an indirect load association, the charging pile is connected to the load through multiple levels of load links such as a charging controller and an inverter. The load fluctuation calculation module obtains the real-time fluctuation data of all associated nodes (such as the voltage regulation module of the charging controller and the current conversion module of the inverter) connected by the charging pile through multiple levels of load links, and determines the dynamic weighting coefficient according to the position and functional importance of each associated node in the link. For example, the weighting coefficient of the node close to the output end of the charging pile is relatively high, set to 0.6, the weighting coefficient of the intermediate link node is 0.3, and the weighting coefficient of the starting link node is 0.1. The fluctuation contribution values of all associated nodes are calculated through dynamic weighting to obtain the load fluctuation curve. Suppose there are three associated nodes D, E, and F, with weighting coefficients of 0.6, 0.3, and 0.1 respectively, and the fluctuation contribution values are 4, 3, and 2 respectively. Then the value of the corresponding point on the load fluctuation curve is 4×0.6 + 3×0.3 + 2×0.1 = 3.5. Similarly, this load fluctuation curve is used as one of the detection dimension parameters of the charging pile.

[0114] The anomaly recognition module takes the electric vehicle charging pile and its associated standard charging pile of the same model as dynamic analysis nodes, obtains the historical operation data of the two in the past 100 charging cycles from the charging database, including parameter change data such as voltage, current, and temperature, and analyzes to obtain the parameter change pattern.

[0115] According to the fluctuation range of each historical operation data in the corresponding dynamic analysis node, calculate the initial anomaly index of each dynamic analysis node and the initial deviation degree of each historical operation data. For example, calculate the difference between the voltage of the charging pile and the voltage of the standard charging pile at a certain moment, compare it with the standard fluctuation range to obtain the initial deviation degree; comprehensively calculate the initial anomaly index according to the deviation degrees at multiple moments.

[0116] Based on the initial anomaly index and the initial deviation degree, the final anomaly index of each dynamic analysis node and the final deviation degree of each historical operation data are jointly optimized through a bidirectional recursive model. Set the target analysis node as the electric vehicle charging pile, and the target historical data as the data at a specific charging moment. Based on the parameter change pattern, calculate the ratio of the independent fluctuation amount of the target analysis node in the target historical data to the total independent fluctuation amount of all historical data, and the ratio of the global fluctuation amount of all analysis nodes in the target historical data to the total global fluctuation amount of all historical data. If the ratio of the independent fluctuation amount exceeds the ratio of the global fluctuation amount, it is determined that the target analysis node has an abnormal fluctuation characteristic in the target historical data, and the obtained final anomaly index is used as one of the detection dimension parameters of the charging pile.

[0117] After obtaining the load fluctuation curve, the detection dimension parameters are determined for the values on the curve. The specific process is as follows: The load fluctuation curve is divided into multiple time intervals along the time axis, and the length of each time interval is 5 minutes. In each time interval, characteristic values such as the average value, maximum value, minimum value, and slope change amount of the curve are calculated. For example, in a certain 5-minute time interval, the average value of the load fluctuation curve is 3.2, the maximum value is 3.8, the minimum value is 2.5, and the slope change amount is 0.3. These characteristic values are used as the detection dimension parameters for subsequent evaluation of the charging pile status.

[0118] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0119] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent detection system for a charging device based on dynamic load simulation, characterized in that Including: A load configuration module, configured to configure dynamic load parameters according to the type of the target charging device and generate corresponding load simulation rules; A charging parameter acquisition module, configured to obtain the operation parameter set of the charging device from the charging database in real time according to the load simulation rules, and use all the parameters in the operation parameter set as the detection dimension parameters of the charging device; A load fluctuation calculation module, configured to generate a load fluctuation curve of the charging device through a dynamic simulation algorithm based on the preset load association relationship in the charging database, and use the load fluctuation curve as the detection dimension parameter of the charging device; An anomaly identification module, configured to calculate the anomaly fluctuation index of the charging device through a preset fluctuation analysis method, and use the anomaly fluctuation index as the detection dimension parameter of the charging device; A central processing unit, configured to transmit the charging device information to the load fluctuation calculation module and the anomaly identification module, send the load simulation rules to the charging parameter acquisition module, and perform fusion processing on all the detection dimension parameters to generate the state evaluation parameter of the charging device.

2. The intelligent detection system for a charging device based on dynamic load simulation according to claim 1, wherein The calculating the anomaly fluctuation index of the charging device through the preset fluctuation analysis method includes: Taking the charging device and its associated preset reference device as dynamic analysis nodes, and obtaining the parameter change patterns of the dynamic analysis nodes within multiple load cycles based on historical operation data; Based on the parameter change patterns, calculating the initial anomaly index of each dynamic analysis node and the initial deviation of each historical operation data according to the fluctuation range of each historical operation data in the corresponding dynamic analysis node; Based on the initial anomaly index and the initial deviation, jointly optimizing the final anomaly index of each dynamic analysis node and the final deviation of each historical operation data through a bidirectional recursive model to obtain the final anomaly index of the dynamic analysis node corresponding to the charging device as its anomaly fluctuation index; Setting a target analysis node as any dynamic analysis node and a target historical data as any historical operation data, calculating the ratio of the independent fluctuation amount of the target analysis node in the target historical data to the total independent fluctuation amount of all historical data, and the ratio of the global fluctuation amount of all analysis nodes in the target historical data to the total global fluctuation amount of all historical data. If the independent fluctuation amount ratio exceeds the global fluctuation amount ratio, it is determined that the target analysis node has an anomaly fluctuation characteristic in the target historical data.

3. The intelligent detection system for a charging device based on dynamic load simulation according to claim 1, wherein, The load simulation rules include a basic load type group and a variable load type group; the basic load type group includes multiple basic load modes and fixed adjustment coefficients corresponding to each basic load mode; the variable load type group includes multiple variable load modes and dynamic adjustment coefficients corresponding to each variable load mode.

4. The intelligent detection system for a charging device based on dynamic load simulation according to claim 2, wherein The obtaining the operation parameter set of the charging device from the charging database in real time according to the load simulation rules includes: The charging parameter acquisition module extracts the structured operation data of the charging device from the charging database, extracts the corresponding basic parameters based on each basic load mode in the basic load type group, extracts the corresponding variable parameters based on each variable load mode in the variable load type group, and combines all the basic parameters and variable parameters into the operation parameter set.

5. The intelligent detection system for a charging device based on dynamic load simulation according to claim 1, wherein, When the load association relationship is direct load association, it is judged whether the charging device is directly connected to the load node. If there is a direct connection relationship, the load fluctuation curve is the linear superposition of the fluctuation contribution values of all associated load nodes; otherwise, the load fluctuation curve is generated based on the basic load value of the charging device itself. When the load association relationship is indirect load association, the load fluctuation curve is the dynamic weighted result of the fluctuation contribution values of all associated nodes obtained by the charging device through the multi-level load link.

6. The intelligent detection system for a charging device based on dynamic load simulation according to claim 1, characterized in that, It further includes a fault tracing module connected to the central processing unit. The fault tracing module is used to set the target fault type, obtain the feature matching degree between the charging device and the target fault type through a preset association matching method, and use the feature matching degree as the detection dimension parameter of the charging device.

7. The intelligent detection system for a charging device based on dynamic load simulation according to claim 6, characterized in that, The fault tracing module obtains the feature matching degree through a preset association matching method, including: extracting the historical fault waveform set of the charging device from the fault model library, extracting the standard waveform set of the target fault type, and calculating the time domain coincidence degree between the historical fault waveform set and the standard waveform set as the first matching degree; comparing the parameter change trend of the charging device within the load cycle with the standard change trend of the target fault type in terms of morphology, and obtaining the trend similarity between the two as the second matching degree; using the calculation result of the first matching degree, the second matching degree, or a combination of the two as the feature matching degree.

8. The intelligent detection system for a charging device based on dynamic load simulation according to claim 1, wherein, The central processing unit generates the state evaluation parameter by using a linear weighting or non-linear mapping method for all the detection dimension parameters.

9. The intelligent detection system for a charging device based on dynamic load simulation according to claim 1, wherein It further includes a storage module connected to the central processing unit, and the storage module is used to record the operation parameter set, the load fluctuation curve, the abnormal fluctuation index, and the state evaluation parameter.

10. A method for intelligent detection of a charging device based on dynamic load simulation, which is applied to the intelligent detection system of the charging device based on dynamic load simulation according to any one of claims 1 to 9, characterized in that, It includes the following steps: Step 1: According to the type of the target charging device, configure the dynamic load parameters through the load configuration module and generate the corresponding load simulation rules. Step 2: According to the load simulation rules generated in Step 1, use the charging parameter acquisition module to obtain the operation parameter set of the charging device from the charging database in real time, and use all the parameters in the operation parameter set as the detection dimension parameters of the charging device. Step 3: Based on the preset load association relationship in the charging database, use the load fluctuation calculation module to generate the load fluctuation curve of the charging device by using the dynamic simulation algorithm, and use the load fluctuation curve as the detection dimension parameter of the charging device. Step 4: Use the abnormal recognition module to calculate the abnormal fluctuation index of the charging device by using a preset fluctuation analysis method, and use the abnormal fluctuation index as the detection dimension parameter of the charging device. Step 5: Use the central processing unit to transmit the charging device information to the load fluctuation calculation module and the anomaly recognition module, send the load simulation rule to the charging parameter acquisition module, and perform a fusion process on all the detected dimension parameters obtained in Steps 2-4 to generate the state evaluation parameter of the charging device.

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