An intelligent detection system and method for charging device based on dynamic load simulation

Abstract

The present 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 includes a load configuration module, a charging parameter acquisition module, a load fluctuation calculation module, an anomaly identification module, a central processing unit, etc. The load configuration module configures dynamic load parameters according to the type of charging device and generates load simulation rules; the charging parameter acquisition module obtains the operating parameter set according to the rules; the load fluctuation calculation module generates a load fluctuation curve; the anomaly identification module calculates the anomaly fluctuation index; and the central processing unit fuses and processes the parameters of each detection dimension to generate status evaluation parameters. In addition, the system is also provided with a fault tracing module and a storage module. The invention can simulate actual load changes, collect and analyze parameters in multiple dimensions, realize intelligent fault diagnosis and tracing, and can be widely used in the detection of various charging devices, improve detection accuracy, reliability and efficiency, and reduce detection costs.

Description

Technical Field

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

[0002] With the rapid development of industries such as electric vehicles and electric equipment, charging devices are becoming increasingly popular. Their performance and safety are directly related to the normal operation of the equipment and the safety of users. However, existing charging device detection technologies have many shortcomings and cannot meet the growing detection needs.

[0003] Traditional charging device testing mostly uses static load testing. This method can only simulate fixed load conditions and cannot truly reflect the complex load variations that charging devices face during actual use. For example, electric vehicles have vastly different charging requirements under different driving conditions, and the load on the charging device is subject to dynamic changes. Static load testing ignores this dynamic characteristic, resulting in test results that are out of sync with actual usage. It is unable to effectively detect potential problems that may arise when charging devices are under dynamic loads, such as excessive voltage fluctuations and unstable current, thus affecting the reliability assessment of the charging device.

[0004] Existing detection technologies focus on a limited range of parameters for collecting and analyzing charging device operating parameters. They often focus on a few key parameters, such as charging voltage and current, while overlooking numerous other parameters that can reflect the charging device's operational status. For example, parameters such as power factor and harmonic content during charging are crucial for fully understanding the performance and operational status of the charging device. The lack of comprehensive analysis of these parameters makes it difficult to accurately identify potential faults in the charging device. This can lead to unexpected failures in actual use, impacting normal operation of the device and even causing safety incidents.

[0005] Existing fault detection and diagnosis methods are not intelligent enough. When a charging device experiences an anomaly, it is impossible to quickly and accurately locate the cause and location of the fault. This often requires extensive manual troubleshooting and testing, which is not only time-consuming and labor-intensive, but also inefficient. For example, when a charging device experiences a charging interruption, existing detection methods may not be able to quickly determine whether it is caused by a problem with the charging port, a circuit fault, or some other cause. This greatly complicates maintenance work, prolongs equipment maintenance time, and reduces equipment efficiency.

[0006] Different types of charging devices vary in structure and performance, and existing detection systems lack adaptability to these diverse types. A single detection system is often limited to a specific type of charging device, and lacks the flexibility to adjust detection parameters and methods to meet diverse charging device testing needs. This, to a certain extent, limits the application scope of detection technology, increases testing costs for companies, and hinders the development of the charging device testing industry. Summary of the Invention

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

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

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

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

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

[0012] an abnormality identification module, configured to calculate an abnormal fluctuation index of the charging device using a preset fluctuation analysis method, and use the abnormal fluctuation index as a detection dimension parameter of the charging device;

[0013] A central processing unit is used 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 fuse all the detection dimension parameters to generate the status evaluation parameters of the charging device.

[0014] Preferably, the calculating of the abnormal fluctuation index of the charging device by a preset fluctuation analysis method includes:

[0015] The charging device and its associated preset reference device are used as dynamic analysis nodes, and parameter change patterns of the dynamic analysis nodes over multiple load cycles are obtained based on historical operating data;

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

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

[0018] Set the target analysis node to be any dynamic analysis node, and the target historical data to be any 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 independent total fluctuation amount in all historical data, as well as the ratio of the global fluctuation amount of all analysis nodes in the target historical data to the global total fluctuation amount in all historical data. If the independent fluctuation amount ratio exceeds the global total fluctuation amount ratio, it is determined that the target analysis node has abnormal fluctuation characteristics in the target historical data.

[0019] Preferably, the load simulation rules include a basic load type group and a variable load type group; the basic load type group contains multiple basic load modes and a fixed adjustment coefficient corresponding to each basic load mode; the variable load type group contains multiple variable load modes and a dynamic adjustment coefficient corresponding to each variable load mode.

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

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

[0022] Preferably, when the load association relationship is a direct load association, it is determined whether the charging device is directly connected to the load node. If a direct connection relationship exists, the load fluctuation curve is a 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 indirect load association, the load fluctuation curve is a dynamic weighted result of the fluctuation contribution values ​​of all associated nodes obtained by the charging device through multi-level load links.

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

[0025] The fault tracing module is used to set a target fault type, obtain a 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 a detection dimension parameter of the charging device.

[0026] Preferably, the fault tracing module obtains the feature matching degree by a preset correlation matching method, including:

[0027] Extracting a historical fault waveform set of the charging device from a fault model library, extracting a standard waveform set of the target fault type, and calculating a time domain coincidence degree between the historical fault waveform set and the standard waveform set as a first matching degree;

[0028] Comparing the parameter change trend of the charging device during the load cycle with the standard change trend of the target fault type, and obtaining a trend similarity between the two as a second matching degree;

[0029] The first matching degree, the second matching degree, or a combination thereof is calculated as the feature matching degree.

[0030] Preferably, the central processing unit generates the state evaluation parameters by linear weighting or nonlinear mapping for all the detection dimension parameters.

[0031] Preferably, it further comprises a storage module connected to the central processing unit, wherein the storage module is used to record the operating parameter set, load fluctuation curve, abnormal fluctuation index and status assessment parameters.

[0032] Preferably, the present invention further includes a method for intelligent detection of a charging device based on dynamic load simulation, which is applied to any of the above-mentioned intelligent detection systems for charging devices based on dynamic load simulation, and the method includes the following steps:

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

[0034] Step 2: Based on the load simulation rule generated in step 1, a charging parameter acquisition module is used to obtain the operating parameter set of the charging device from the charging database in real time, and all parameters in the operating parameter set are used as detection dimension parameters of the charging device;

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

[0036] Step 4: Calculate an abnormal fluctuation index of the charging device using a preset fluctuation analysis method through an abnormality identification module, and use the abnormal fluctuation index as a detection dimension parameter of the charging device;

[0037] Step 5: Use the central processing unit 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 fuse all the detection dimension parameters obtained in steps 2-4 to generate the status evaluation parameters of the charging device.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] From the perspective of load simulation, the system has a load configuration module that can configure dynamic load parameters based on the type of target charging device, generating load simulation rules that include 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, combined with the multiple variable load modes and dynamic adjustment coefficients of the variable load type group, can closely replicate the complex load changes experienced by charging devices in actual use. Compared to traditional static load detection methods, this innovative simulation method can make the detection results more consistent with actual operating conditions, providing strong support for accurately evaluating the performance of charging devices. Taking electric vehicle charging devices as an example, during vehicle driving, the charging demand constantly changes due to changes in factors such as battery power and driving speed. The dynamic load simulated by this system can accurately match these changes, detecting key performance indicators such as the charging device's responsiveness to sudden load changes, effectively avoiding evaluation errors caused by detection conditions that do not match actual conditions.

[0040] In terms of parameter collection and analysis, the charging parameter collection module comprehensively extracts structured operating data from the charging database based on load simulation rules. This includes not only the traditionally focused basic parameters but also variable parameters, which are combined into an operating parameter set as detection dimension parameters. Furthermore, the load fluctuation calculation module generates load fluctuation curves based on preset load associations and dynamic simulation algorithms, and the anomaly identification module calculates abnormal fluctuation indicators using preset fluctuation analysis methods. These factors greatly enrich the detection dimensions. For example, during the detection process, in addition to conventional voltage and current parameters, changes in parameters such as power factor and harmonic content can also be obtained. Through comprehensive analysis of these multi-dimensional parameters, a more comprehensive and in-depth understanding of the operating status of the charging device can be achieved, accurately identifying potential fault hazards. For example, harmonic problems in the circuit may indicate component aging. Taking proactive measures can avoid failures and improve the reliability and stability of the charging device.

[0041] Intelligent fault diagnosis is a key feature of this invention. The anomaly identification module calculates an abnormal fluctuation index using a preset fluctuation analysis method. This process involves using the charging device and its associated preset reference device as dynamic analysis nodes, acquiring parameter change patterns, calculating initial anomaly indices and initial deviations, and then jointly optimizing the final anomaly index using a bidirectional recursive model. Furthermore, the abnormal fluctuation characteristics are determined by comparing the ratio of independent fluctuations to the ratio of global total fluctuations. Furthermore, the fault tracing module sets a target fault type and uses a preset correlation matching method to determine the characteristic matching degree between the charging device and the target fault type. This process involves calculating the time-domain overlap between a set of historical fault waveforms and a set of standard waveforms, and comparing parameter change trends to determine trend similarity. The results, or a combination of these, are used as the characteristic matching degree. The application of these intelligent algorithms and matching methods enables the system to quickly and accurately locate the cause and location of a charging device anomaly. For example, if a charging device experiences an abnormal charging interruption, the system can quickly determine whether the cause is poor charging port contact, a short circuit, or other causes. This significantly reduces troubleshooting time, reduces maintenance costs, and improves equipment efficiency.

[0042] The present invention also demonstrates outstanding system versatility and adaptability. The load configuration module can configure dynamic load parameters and load simulation rules based on different types of charging devices, making the entire detection system highly flexible. This applies to charging devices for electric vehicles, electric bicycles, and other types of charging equipment. This feature greatly expands the application scope of the detection system, eliminating the need for companies to purchase separate detection equipment for different types of charging devices, reducing detection costs and promoting the development of the charging device detection industry.

[0043] The present invention also includes a storage module for recording operating parameter sets, load fluctuation curves, abnormal fluctuation indicators, and status assessment parameters. This data not only provides a reference for current detection and fault diagnosis, but can also be used for long-term data analysis and research, helping to continuously optimize the detection system's algorithms and models, further improving detection accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0045] Figure 2 A diagram showing the working principle for calculating abnormal fluctuation indicators of charging devices;

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

[0047] Figure 4 A working diagram for obtaining feature matching for the fault tracing module;

[0048] Figure 5 A diagram showing the working principle of generating condition assessment parameters for the central processing unit. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] See also Figure 1-Figure 5 The present invention provides a technical solution: a charging device intelligent detection system based on dynamic load simulation. It mainly includes a load configuration module, a charging parameter acquisition module, a load fluctuation calculation module, an anomaly identification module, and a central processing unit. Through the coordinated operation of these modules, intelligent detection of charging devices is achieved. The specific implementation steps are as follows:

[0051] The load configuration module plays a key role. It configures dynamic load parameters based on the target charging device type. Different types of charging devices require different load parameters. The load configuration module considers these characteristics and configures parameters such as load size and load change frequency. Based on this, it generates corresponding load simulation rules. These rules are an important basis for subsequent testing, specifying how to simulate load operation to accurately obtain operating 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 obtains the charging device's operating parameter set in real time from the charging database. This operating parameter set covers key parameters of the charging device during operation, such as charging voltage, charging current, and charging power. These parameters are used as detection dimension parameters for the charging device, providing a rich data foundation for subsequent comprehensive analysis of the charging device's operating status.

[0053] The load fluctuation calculation module uses a dynamic simulation algorithm to generate a load fluctuation curve for the charging device based on the load associations preset in the charging database. Load associations reflect the interconnection between the charging device and other loads. The dynamic simulation algorithm can simulate the load fluctuations of the charging device under different load conditions. The generated load fluctuation curve also serves as a detection dimension parameter, providing an important reference for charging device detection from the perspective of load variation.

[0054] The anomaly identification module uses a preset fluctuation analysis method to calculate the abnormal fluctuation index of the charging device. This index is one of the key indicators for determining whether the charging device is operating normally. Through in-depth analysis of the fluctuations in the charging device's operating parameters, it can effectively identify abnormal fluctuations and thus discover potential fault hazards. The abnormal fluctuation index is also used as a detection dimension parameter.

[0055] Finally, the central processing unit (CPU) performs core coordination and processing. It transmits charging device information to the load fluctuation calculation module and the anomaly identification module, providing the necessary data support for their operations. It also sends load simulation rules to the charging parameter acquisition module, ensuring that the acquisition module accurately captures data according to the rules. Furthermore, the CPU integrates and analyzes all detection dimension parameters, generating evaluation parameters that accurately reflect the charging device status through comprehensive analysis, thereby enabling intelligent detection of the charging device's operating status.

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

[0057] Example 1:

[0058] The anomaly identification module is crucial to the entire detection system for promptly detecting abnormal operating conditions of the charging device. When calculating the abnormal fluctuation index, the dynamic analysis node must be identified. The charging device and its associated preset reference device are designated as dynamic analysis nodes. This is because the operating data of the reference device provides a reference for determining whether the charging device is functioning properly. Next, the parameter change patterns of these dynamic analysis nodes over multiple load cycles are obtained from historical operating data. This historical operating data records the operating parameter changes of the charging device and reference device under different load conditions in the past. By analyzing this data, the patterns and patterns of parameter changes can be summarized.

[0059] Based on the acquired parameter change patterns, the initial anomaly index and initial deviation are further calculated. This calculation is based on the fluctuation range of each historical operating data point in the corresponding dynamic analysis node. The fluctuation range reflects the severity of the parameter change. By analyzing the fluctuation range, we can preliminarily determine the degree of anomaly for each dynamic analysis node and obtain the initial anomaly index. At the same time, the degree of deviation of each historical operating data point from the normal range, i.e., the initial deviation, is calculated.

[0060] A bidirectional recursive model is used to jointly optimize the initial anomaly index and initial deviation. This model fully considers the contextual relationships between data. Through multiple iterative calculations, it adjusts and optimizes the initial anomaly index and deviation, resulting in a more accurate final anomaly index and deviation. The final anomaly index of the dynamic analysis node corresponding to the charging device is used as its abnormal fluctuation index, which more accurately reflects the abnormal fluctuation of the charging device.

[0061] In addition, it is necessary to determine the abnormal fluctuation characteristics. Set the target analysis node to any dynamic analysis node, and the target historical data to 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 independent total fluctuation amount in all historical data, as well as the ratio of the global fluctuation amount of all analysis nodes in the target historical data to the global total fluctuation amount in all historical data. When the independent fluctuation amount ratio exceeds the global total fluctuation amount ratio, 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 abnormal conditions of the charging device.

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

[0063] A dynamic analysis node is identified, and the EV charging device and its associated reference charging device of the same model, operating normally, are set as the dynamic analysis nodes. The parameter variation patterns of these two dynamic analysis nodes over multiple load cycles are obtained from historical operating data. For example, over the past 100 load cycles, the charging voltage and current of the charging device, as well as the corresponding parameters of the reference device, are analyzed. Through collation and analysis of this data, it was found that under normal load cycles, the charging voltage of the charging device typically fluctuates between 380V and 420V, and the current fluctuates between 10A and 20A. The voltage of the reference device fluctuates between 390V and 410V, and the current fluctuates between 12A and 18A. This represents their parameter variation patterns.

[0064] Based on this parameter variation pattern, the initial anomaly index and initial deviation are calculated. For example, assume that the charging voltage of the charger during one load cycle is 450V, exceeding the normal fluctuation range. The initial anomaly index is calculated based on the fluctuation range in the corresponding dynamic analysis node. Due to the significant deviation from the normal range, the initial anomaly index is set to a higher value. The initial deviation of the historical operating data (i.e., the charging voltage of 450V during this cycle) is also calculated. By comparing it with the upper limit of the normal range of 420V, the degree of deviation is calculated to obtain the initial deviation.

[0065] A bidirectional recursive model is used to jointly optimize the initial anomaly index and initial deviation. This model comprehensively considers the data correlations across multiple load cycles. For example, if the voltage of a charging device remains consistently high over several subsequent load cycles, the model will adjust the previously calculated initial anomaly index and initial deviation based on these consecutive anomalies. After multiple iterative calculations, a more accurate final anomaly index and final deviation are ultimately obtained. The final anomaly index of the dynamic analysis node corresponding to the charging device is used as its abnormal fluctuation indicator.

[0066] Determine the characteristics of abnormal fluctuations. Set the charging device currently being analyzed as the target analysis node, and the operating data for this load cycle as the target historical data. Calculate the ratio of the target analysis node's independent fluctuation in the target historical data to the independent total fluctuation in all historical data. Assuming that during this load cycle, the independent voltage fluctuation of the charging device is 30V (i.e., the difference between 450V and the normal range average of 400V), and the independent total fluctuation in all historical data is 1000V (obtained by summing the voltage fluctuations for all historical load cycles), the ratio is 30 ÷ 1000 = 0.03. Then, calculate the ratio of the global fluctuation of all analysis nodes (i.e., the charging device and the reference device) in the target historical data to the global total fluctuation in all historical data. Assuming that during this load cycle, the global fluctuation of all analysis nodes is 50V (combining the fluctuations of voltage, current, and other parameters of the charging device and the reference device), and the global total fluctuation in all historical data is 2000V, the ratio is 50 ÷ 2000 = 0.025. Since 0.03>0.025, that is, the ratio of independent fluctuations exceeds the ratio of global total fluctuations, it is determined that the target analysis node (that is, the current electric vehicle charging device) has abnormal fluctuation characteristics in the target historical data (that is, the current load cycle).

[0067] Example 2:

[0068] Load simulation rules are the key to the entire detection system simulating real load conditions, while charging parameter collection is an important link in obtaining charging device operating data.

[0069] Load simulation rules include basic load type groups and variable load type groups. The basic load type group consists of multiple basic load patterns and the fixed adjustment coefficient corresponding to each basic load pattern. Different charging devices have different load pattern requirements under basic operating conditions, 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 phase. The basic load pattern can be set according to this requirement, and the fixed adjustment coefficient is used to fine-tune the size of the basic load.

[0070] The variable load type group also contains multiple variable load modes and a corresponding dynamic adjustment coefficient for each variable load mode. During actual operation of a charging device, load conditions often change dynamically. The variable load type group is designed to simulate these dynamic load conditions. The dynamic adjustment coefficient adjusts in real time based on factors such as the charging device's operating stage and battery status, thereby more realistically simulating actual load changes.

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

[0072] Consider a charging device for electric bicycles. The basic load type group in the load simulation rules plays a crucial role for this device. This basic load type group contains multiple basic load modes and corresponding fixed adjustment coefficients. For example, when an electric bicycle's battery is low and charging is just beginning, a basic load mode can be set to simulate a stable, low-power charging state. In this basic load mode, the charger's output voltage 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 might be 1.00 for voltage and 1.05 for current. A voltage adjustment coefficient of 1.00 means that under normal conditions, the output voltage remains at the set 48V. A current adjustment coefficient of 1.05 means that the actual output current may be slightly adjusted from 1.5A. The adjusted current value is 1.5A × 1.05 = 1.575A, simulating the slight current fluctuations that may occur during actual charging.

[0073] The variable load type group is also essential. As the e-bike battery charge gradually increases and approaches full charge, the charger needs to adjust the load to prevent overcharging and battery damage. This is where the variable load type group comes into play. It includes multiple variable load modes and dynamic adjustment coefficients. For example, a variable load mode can be set to activate when the battery charge reaches 80%. In this mode, the charging voltage gradually decreases, and the current also decreases accordingly. Assuming the voltage is initially set to 54V and the current to 0.8A, as charging progresses, the dynamic adjustment coefficients are adjusted in real time. The dynamic adjustment coefficients may be correlated with changes in battery charge. For example, for every 1% increase in charge, the voltage dynamic adjustment coefficient decreases by 0.05, and the current dynamic adjustment coefficient decreases by 0.03. When the battery charge 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. This dynamic adjustment simulates the load changes that occur when the battery is nearly full.

[0074] Based on these load simulation rules, the charging parameter acquisition module extracts structured operating data from the charging database. For the basic load type group, the module accurately extracts basic parameters corresponding to the aforementioned basic load pattern from the database: a voltage of 48V and a current of 1.575A (values ​​adjusted by a fixed adjustment factor). For the variable load type group, the module extracts variable parameters corresponding to the corresponding variable load pattern based on the real-time changes in battery charge. For example, when the battery charge reaches 85%, the module extracts the adjusted voltage of 40.5V and current of 0.68A. Finally, all extracted basic and variable parameters are combined to form an operating parameter set. This operating parameter set comprehensively records the operating data of the e-bike charger at different charging stages and under different load modes. It provides rich and accurate data support for subsequent testing and analysis of the charger, helping to promptly detect potential abnormalities during charging and ensure charging safety and stability.

[0075] Example 3:

[0076] This embodiment focuses on the generation method of the load fluctuation curve. Depending on the 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 determine the connection between the charging device and the load node. If the charging device and the load node are directly connected, then the load fluctuation curve is a 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 their fluctuation contribution can be reflected by simple linear addition. For example, if there are multiple directly connected load nodes, the fluctuation of each node at different times will affect the load of the charging device. By superimposing these influences according to a linear relationship, the load fluctuation curve of the charging device can be obtained.

[0077] If the charging device isn't directly connected to the load node, the load fluctuation curve is generated based on the charging device's own base load. In this case, the charging device is primarily affected by its own base load, and its load fluctuation curve reflects how its base load changes 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 multi-level load links. In actual power systems, there are indirect connections between many charging devices and load nodes, which influence each other through multi-level load links. The fluctuations of these associated nodes have different degrees of influence on the load of the charging device, so it is necessary to use a dynamic weighting method to calculate. Dynamic weighting will adjust the weight of the fluctuation contribution value of each associated node in real time according to factors such as the distance and load characteristics of the associated nodes, so as to obtain a load fluctuation curve that is more in line with the actual situation. This method can more accurately simulate the load fluctuation of the charging device under indirect load association conditions, and provide more accurate data for subsequent detection and analysis.

[0079] Assume that multiple DC fast charging piles for electric vehicles are installed in a commercial parking lot, among which the charging pile numbered C001 is the research object. In the power supply system of this parking lot, there are clear load association relationships, which are divided into direct load association and indirect load association.

[0080] When the load association is direct, it's necessary to determine the connection between charging pile C001 and the load nodes. In this parking lot's power supply network, charging pile C001 is directly connected to two load nodes, L1 and L2. In this case, its load fluctuation curve is a linear superposition of the fluctuation contributions of all associated load nodes.

[0081] Assume that the load fluctuation curve of charging pile C001 is , the fluctuation contribution of load node L1 is , the fluctuation contribution of load node L2 is , then the calculation formula of the load fluctuation curve is: .in, The final load fluctuation curve of charging pile C001 is obtained after comprehensively considering the fluctuation influence of all directly related load nodes; Represents the contribution of load node L1 to the load fluctuation of charging pile C001. It is affected by the load changes of L1 itself. For example, when other devices connected to L1 are turned on or off, their power will change, which in turn affects the load contribution to charging pile C001. Indicates the contribution of load node L2 to the load fluctuation of charging pile C001, which is related to the operating status of L2.

[0082] For example, at a certain moment, the load node L1 increases its power demand due to the connected equipment, and its fluctuation contribution value becomes 3 (the 3 here is obtained based on the quantification of the load fluctuation contribution, assuming it is measured in a certain power fluctuation unit), and the load node L2 has a fluctuation contribution value of becomes -1 (the negative sign indicates that its influence on the total load fluctuation is opposite to the positive direction), then according to the formula, the load fluctuation curve of charging pile C001 at this time .

[0083] If charging pile C001 doesn't have a direct connection to the load node, and instead connects to the other load nodes via an intermediate transformer, its load fluctuation curve is generated based on its base load value. Assuming the base load value of charging pile C001 is 10 during a stable charging phase (also measured in specific power fluctuation units), its load fluctuation curve will fluctuate within a small range around this base load value of 10, for example, between 9.5 and 10.5.

[0084] When the load association is indirect, charging pile C001 is connected to multiple associated nodes via multi-level load links. In this parking lot's power supply network, charging pile C001 is connected to three other associated nodes, N1, N2, and N3, via two levels of load links. In this case, the load fluctuation curve is a dynamically weighted result of the fluctuation contributions of all associated nodes, obtained through the multi-level load links.

[0085] When calculating the dynamic weighted results, it is necessary to adjust the weight of the fluctuation contribution value 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 , the weight is ; The fluctuation contribution value of the associated node N2 is , the weight is ; The fluctuation contribution value of the associated node N3 is , the weight is Since N1 is closer to the C001 charging pile and has a greater impact on its load, the weight Set to 0.5; N2 distance is moderate and the influence is relatively small, weight Set to 0.3; N3 is farther away and has the least influence, and the weight Set to 0.2. At a certain moment, the fluctuation contribution of N1 =4, the fluctuation contribution of N2 2, N3's fluctuation contribution value is 1, then according to the dynamic weighted calculation, the load fluctuation curve of charging pile C001 is: This dynamic weighting method can more accurately simulate the load fluctuation of charging piles under indirect load correlation conditions, providing more realistic data support for subsequent accurate detection of the operating status of charging piles.

[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 fault occurs in the charging device.

[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 faults and short circuit faults. The fault tracing module will set the target fault type to be detected based on actual needs.

[0089] After the target fault type is determined, a set of historical charging device fault waveforms is extracted from the fault model library. This library stores waveform data from past charging device faults, documenting changes in the charging device's operating state when the fault occurred. Simultaneously, a set of standard waveforms for the target fault type is extracted. This set of standard waveforms, developed through extensive research and practical experience, represents the typical waveform characteristics of that fault type.

[0090] The time-domain overlap between the historical fault waveform set and the standard waveform set is calculated as the first matching degree. This reflects the degree of similarity between the historical fault waveform and the standard waveform along the time axis. This is calculated by comparing the waveform shapes and amplitudes at different moments. This metric provides a preliminary assessment of the similarity between the charging device's historical faults and the target fault type.

[0091] The parameter change trends of the charging device during the load cycle are compared with the standard change trends of the target fault type, and the trend similarity between the two is obtained as the second matching degree. The parameter change trends reflect the changes in the key parameters of the charging device during operation. Comparing them with the standard change trends of the target fault type can determine the similarity of the fault from another perspective. For example, in the case of an overcharging fault, the change trends of the charging voltage and current have certain characteristics. By comparing the actual parameter change trends with these, the trend similarity can be obtained.

[0092] The first matching degree, the second matching degree, or a combination of the two is calculated as the characteristic matching degree. Based on the size of the characteristic matching degree, the degree of match between the charging device and the target fault type can be determined, thereby tracing the fault source and providing strong support for rapid fault location and resolution.

[0093] For example, consider a common mobile phone fast-charging device. Suppose the target fault type is set to "overvoltage" during fault detection. If the output voltage of a mobile phone fast-charging device is too high while charging, it can cause irreversible damage to the phone's battery and even pose safety risks. Therefore, detecting overvoltage faults is crucial.

[0094] First, a collection of historical fault waveforms for the charger is extracted from the fault model library. This library is built by collecting and organizing waveform data for various fault conditions for this type of charger. For example, the waveform data for a previous overvoltage fault is fully recorded. These waveforms may reveal information such as the moment of the sudden voltage increase, the magnitude of the increase, and the duration of the increase.

[0095] A set of standard waveforms for the target fault type, "overvoltage fault," was extracted. These waveforms represent typical waveform characteristics, derived from analyzing and summarizing numerous overvoltage fault cases. For example, the standard waveform shows that when an overvoltage fault occurs, the voltage rapidly rises above the normal charging voltage range (assuming the normal voltage range for a mobile phone fast charger is 4.5V-5.5V, the standard waveform for an overvoltage fault shows a rapid rise to over 6V). The voltage remains high for a period of time, followed by voltage fluctuations or sudden drops.

[0096] The time-domain overlap between the historical fault waveform set and the standard waveform set is calculated as the first degree of matching. This can be achieved using a specialized waveform comparison algorithm, such as comparing the historical fault waveforms and the standard waveforms point by point on the time axis. The more points within a certain period of time at which the voltage values ​​of the historical fault waveforms are similar to those of the standard overvoltage fault waveform within a certain error range, the higher the time-domain overlap. Assuming that calculations show that the voltage value error between a certain historical fault waveform and the standard waveform is within 0.5V at 80% of the time points within a 5-second period, it can be preliminarily concluded that the time-domain overlap between the historical fault waveform and the standard waveform is 80%.

[0097] The parameter change trends of the charging device during the load cycle are compared with the standard change trends of the target fault type, and the trend similarity between the two is calculated as the second matching degree. During the mobile phone charging process, parameters such as the voltage and current of the charging device vary with the charging stage. During normal charging, the voltage remains stable within a certain range, while the current gradually decreases as the battery charge increases. In contrast, the standard change trend for an overvoltage fault is a rapid voltage increase. During the analysis, the voltage curve of the charging device during the load cycle is observed. If, during a certain period of time, the voltage is initially stable at around 5V, then suddenly rises to 6.5V within 1 second and then remains at a high voltage, this trend is highly similar to the standard overvoltage fault trend. By comparing characteristics such as the slope, the time of the rise or fall, and the magnitude of the change, a specific algorithm is used to calculate the trend similarity. Assume that the trend similarity is 75%.

[0098] The first matching degree, the second matching degree, or a combination of the two is calculated as the characteristic matching degree. If the two are simply added and averaged, the characteristic matching degree is (80% + 75%) ÷ 2 = 77.5%. By setting a threshold (assuming the threshold is 70%), when the characteristic matching degree exceeds the threshold, it can be determined that the charging device has a high degree of match with the target fault type "overvoltage fault" and is likely to have an overvoltage fault. This provides maintenance personnel with a strong basis for quickly locating and resolving charging device faults, improving the efficiency and accuracy of troubleshooting and ensuring the safety and stability of the mobile phone charging process.

[0099] Example 5:

[0100] This example focuses on how the central processing unit (CPU) processes detection dimension parameters and the role of the storage module. The CPU is responsible for data integration and output within the entire detection system, while the storage module stores critical data, ensuring stable system operation and subsequent analysis.

[0101] The central processing unit fuses all detection dimension parameters to generate status assessment parameters, using methods such as linear weighting or nonlinear mapping. Linear weighting is a relatively simple and direct processing method, which assigns corresponding weights to each detection dimension parameter based on its importance to the charging device status assessment. For example, for the operational safety of the charging device, the abnormal fluctuation index may be more important than other parameters, so it can be assigned a larger weight. All detection dimension parameters are multiplied by their respective weights and then added together to obtain the status assessment parameter. This method is simple to calculate and can quickly obtain an assessment parameter that roughly reflects the status of the charging device.

[0102] Nonlinear mapping is suitable for situations where complex relationships exist between detection dimension parameters. Because charging device operation is influenced by a variety of factors, nonlinear correlations may exist between detection dimension parameters. Nonlinear mapping builds a complex mathematical model, such as a neural network model, and processes the detection dimension parameters. The model automatically learns the complex relationships between the parameters and outputs an evaluation parameter that more accurately reflects the charging device status.

[0103] The storage module, connected to the central processing unit, is responsible for recording the operating parameter set, load fluctuation curve, abnormal fluctuation index, and status assessment parameters. The operating parameter set contains various real-time data from the charging device during operation, which can be used for subsequent data analysis and troubleshooting. The load fluctuation curve records the dynamic changes in the charging device's load and is valuable for studying the charging device's performance under different load conditions. The abnormal fluctuation index reflects abnormal conditions during the charging device's operation. Storing these indicators facilitates subsequent in-depth analysis of these abnormalities. The status assessment parameters are the final output of the entire detection system. The storage of status assessment takes the charging of new energy vehicles at public charging stations as an example. In this scenario, the central processing unit is responsible for fusing the detection dimension parameters to generate the status assessment parameters. The detection dimension parameters include the operating parameter set acquired by the charging parameter acquisition module, such as real-time voltage, current, and power during charging; the load fluctuation curve generated by the load fluctuation calculation module, which reflects the time-varying load changes during charging; and the abnormal fluctuation index calculated by the abnormality identification module, which is used to determine whether abnormal fluctuations exist during charging.

[0104] Assume that operating parameters are centralized, with real-time voltage at 380V, current at 50A, and power at 19kW. The load fluctuation curve shows two small load fluctuations in the past 10 minutes, and the abnormal fluctuation indicator indicates that abnormal fluctuations in the current charging process are relatively low. The central processing unit uses a linear weighting method to generate state assessment parameters. Based on expert experience and extensive data analysis, the weights for voltage, current, and power were determined to be 0.3, 0.2, 0.2, 0.2 for load fluctuation curve characteristics (e.g., fluctuation amplitude, frequency, and other factors), and 0.1 for the abnormal fluctuation indicator.

[0105] According to the linear weighted formula, the state assessment parameter = 380 × 0.3 + 50 × 0.2 + 19 × 0.2 + load fluctuation curve comprehensive score × 0.2 + abnormal fluctuation index score × 0.1. Assuming the load fluctuation curve comprehensive score, based on its fluctuation amplitude and frequency, is 80 points (out of 100), and the abnormal fluctuation index score is 90 points (out of 100), the state assessment 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 state assessment parameter can intuitively reflect the current operating status of the charging pile. Using a preset threshold range (for example, 100-150 is the normal range), it can be determined that the charging pile is currently in a slightly abnormal state and may require further inspection.

[0106] If a nonlinear mapping approach is used, the central processing unit can utilize a neural network model for processing. First, the detection dimension parameters, such as the operating parameter set, load fluctuation curve data, and abnormal fluctuation indicators, are encoded and converted into an input format acceptable to the neural network. Suppose a neural network model is constructed consisting of an input layer, two hidden layers, and an output layer. The number of input layer nodes is determined by the number of detection dimension parameters. For example, if there are five main parameter categories, the input layer would have five nodes. The number of hidden layer nodes is determined based on experience and experimentation. Assume that the first hidden layer has 10 nodes, the second hidden layer has 8 nodes, and the output layer has one node to output the state assessment parameters.

[0107] The encoded parameters are input into the neural network. After complex calculations by neurons in the hidden layer, the neurons perform nonlinear transformations on the input signals using activation functions, simulating the complex nonlinear relationships between the parameters. After repeated training, the neural network learns the relationship between different parameter combinations and the actual operating status of the charging pile. Ultimately, the neural network outputs a status assessment parameter. For example, after processing the current detection dimension parameters, the trained neural network outputs a status assessment parameter of 155 (which may differ from the result of a linear weighted calculation due to different processing methods). Similarly, the operating status of the charging pile is determined by comparing it with a preset threshold.

[0108] The storage module plays a crucial role in this process. Connected to the central processing unit, it records operating parameter sets, such as voltage, current, and power, at regular intervals (for example, one minute) during the charging process. This generates time-series data, facilitating subsequent analysis of charging stability and trends. Load fluctuation curves are recorded and stored as charts, allowing for intuitive visualization of load changes over time and analysis of the impact of different charging vehicles and time periods. Abnormal fluctuation indicators are stored to facilitate tracing and in-depth analysis of abnormalities, identifying their frequency and severity. Status assessment parameters are stored and accumulated over time to create a historical archive of the charging pile's operating status, providing data support for regular maintenance, performance optimization, and fault prediction. For example, by analyzing the changing trends of status assessment parameters over time, potential charging pile failures can be identified in advance, allowing preventive maintenance to be scheduled, reducing downtime and improving efficiency and reliability. Parameters can track and evaluate the long-term operating status of the charging device, providing a basis for equipment maintenance and upgrades. By storing this critical data, the system can better implement intelligent detection and management of the charging device.

[0109] Example 6:

[0110] The load configuration module manually or automatically configures dynamic load parameters on the system interface based on the type of electric vehicle charging station. For example, the basic load type group is set to include two basic load modes: constant current charging mode and constant voltage charging mode. The fixed adjustment coefficient for constant current charging mode is 0.8, and the fixed adjustment coefficient for 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 for pulse charging mode changes dynamically based on charging time, with an initial value of 0.7 and an increase of 0.05 every 10 minutes. The dynamic adjustment coefficient for intermittent charging mode changes dynamically based on charging station temperature, with the coefficient decreasing by 0.03 for every 5°C increase in temperature. After 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 structured operating data from the charging database, such as voltage, current, charging time, and charging pile temperature. Based on the constant current charging mode in the basic load type group, it extracts basic parameters such as charging current and charging voltage. Based on the pulse charging mode in the variable load type group, it extracts variable parameters such as pulse frequency and pulse width. All extracted basic and variable parameters are combined into an operating parameter set, which serves as one of the charging pile detection dimension parameters.

[0112] In this embodiment, if the load association is direct, the charging pile's connection lines are detected to determine whether the charging pile is directly connected to the battery pack's load node. In this case, the load fluctuation calculation module obtains real-time current and voltage fluctuation data for all associated load nodes (i.e., each component of the battery pack) and determines a fluctuation contribution weight based on the power of each load node, with higher weights assigned to nodes with higher power. The fluctuation contributions of all associated load nodes are linearly superimposed to generate a load fluctuation curve. Assume that the battery pack contains three load nodes, A, B, and C, with weights of 0.4, 0.3, and 0.3, respectively. The fluctuation contribution of node A is 5, the fluctuation contribution of node B is 3, and the fluctuation contribution of node C is 2. The corresponding value of the superimposed load fluctuation curve is 5 × 0.4 + 3 × 0.3 + 2 × 0.3 = 3.5. The resulting load fluctuation curve is used as one of the detection dimension parameters for the charging pile.

[0113] If the load association is indirect, the charging pile is connected to the load through a multi-stage load link, including a charging controller and a current converter. The load fluctuation calculation module obtains real-time load fluctuation data for all nodes connected to the charging pile through the multi-stage load link (such as the voltage regulation module of the charging controller and the current conversion module of the current converter). It determines a dynamic weighting coefficient based on the position and functional importance of each node in the link. For example, nodes near the output of the charging pile have a higher weighting coefficient of 0.6, while intermediate link nodes have a weighting coefficient of 0.3 and the initial link node has a weighting coefficient of 0.1. By dynamically weighting the fluctuation contribution of all associated nodes, a load fluctuation curve is obtained. Assuming there are three associated nodes, D, E, and F, with weighting coefficients of 0.6, 0.3, and 0.1, respectively, and fluctuation contributions of 4, 3, and 2, respectively, the value of the corresponding point on the load fluctuation curve is 4 × 0.6 + 3 × 0.3 + 2 × 0.1 = 3.5. This load fluctuation curve is also used as one of the detection dimension parameters of the charging pile.

[0114] The anomaly identification module uses the electric vehicle charging pile and its associated standard charging pile of the same model as dynamic analysis nodes, obtains the historical operating 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 the parameter change pattern.

[0115] Based on the fluctuation range of each historical operating data point in the corresponding dynamic analysis node, the initial abnormality index for each dynamic analysis node and the initial deviation of each historical operating data point are calculated. For example, the difference between the charging pile voltage at a certain moment and the standard charging pile voltage is calculated and compared with the standard fluctuation range to obtain the initial deviation. The initial abnormality index is then calculated based on the deviations at multiple moments.

[0116] Based on the initial anomaly index and initial deviation, a bidirectional recursive model is used to jointly optimize the final anomaly index of each dynamic analysis node and the final deviation of each historical operating data. The target analysis node is set as an electric vehicle charging pile, and the target historical data is data from a specific charging moment. Based on the parameter change pattern, the ratio of the independent fluctuation of the target analysis node in the target historical data to the independent total fluctuation in all historical data is calculated, as well as the ratio of the global fluctuation of all analysis nodes in the target historical data to the global total fluctuation in all historical data. If the independent fluctuation ratio exceeds the global total fluctuation ratio, it is determined that the target analysis node has abnormal fluctuation characteristics in the target historical data, and the final anomaly index is used as one of the detection dimension parameters for the charging pile.

[0117] After obtaining the load fluctuation curve, the detection dimension parameters are determined based on 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, with each time interval being 5 minutes long. Within each time interval, the curve's characteristic values, such as the average value, maximum value, minimum value, and slope change, are calculated. For example, within a 5-minute time interval, the load fluctuation curve has an average value of 3.2, a maximum value of 3.8, a minimum value of 2.5, and a slope change of 0.3. These characteristic values ​​are used as detection dimension parameters for subsequent evaluation of the charging pile status.

[0118] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0119] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent detection system for charging devices based on dynamic load simulation, characterized in that: include: A load configuration module, configured to configure dynamic load parameters according to the type of target charging device and generate corresponding load simulation rules; a charging parameter acquisition module, configured to obtain an operating parameter set of the charging device from a charging database in real time according to the load simulation rule, and use all parameters in the operating parameter set as 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 a load association relationship preset in the charging database, and use the load fluctuation curve as a detection dimension parameter of the charging device; an abnormality identification module, configured to calculate an abnormal fluctuation index of the charging device using a preset fluctuation analysis method, and use the abnormal fluctuation index as a 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 fuse all the detection dimension parameters to generate the charging device status assessment parameters; Calculating the abnormal fluctuation index of the charging device by using a preset fluctuation analysis method includes: The charging device and its associated preset reference device are used as dynamic analysis nodes, and parameter change patterns of the dynamic analysis nodes over multiple load cycles are obtained based on historical operating data; Based on the parameter change pattern, according to the fluctuation range of each of the historical operation data in the corresponding dynamic analysis node, calculate the initial abnormal index of each of the dynamic analysis nodes and the initial deviation of each of the historical operation data; Based on the initial abnormality index and the initial deviation, a final abnormality index of each dynamic analysis node and a final deviation of each historical operation data are jointly optimized through a bidirectional recursive model to obtain a final abnormality index of the dynamic analysis node corresponding to the charging device as its abnormal fluctuation index; Set the target analysis node to be any dynamic analysis node, and the target historical data to be any 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 independent total fluctuation amount in all historical data, as well as the ratio of the global fluctuation amount of all analysis nodes in the target historical data to the global total fluctuation amount in all historical data. If the independent fluctuation amount ratio exceeds the global total fluctuation amount ratio, it is determined that the target analysis node has abnormal fluctuation characteristics in the target historical data.

2. The intelligent detection system for charging devices based on dynamic load simulation according to claim 1, characterized in that: 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 a fixed adjustment coefficient corresponding to each basic load mode; the variable load type group includes multiple variable load modes and a dynamic adjustment coefficient corresponding to each variable load mode.

3. The intelligent detection system for charging devices based on dynamic load simulation according to claim 2, characterized in that: The step of acquiring the operating parameter set of the charging device in real time from the charging database according to the load simulation rule comprises: The charging parameter acquisition module extracts the structured operating data of the charging device from the charging database, extracts corresponding basic parameters based on each basic load mode in the basic load type group, extracts corresponding variable parameters based on each variable load mode in the variable load type group, and merges all the basic parameters and variable parameters into the operating parameter set.

4. The intelligent detection system for charging devices based on dynamic load simulation according to claim 1, characterized in that: When the load association relationship is direct load association, determining whether the charging device is directly connected to the load node; if so, the load fluctuation curve is a 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 a dynamic weighted result of the fluctuation contribution values ​​of all associated nodes obtained by the charging device through multi-level load links.

5. The intelligent detection system for charging devices based on dynamic load simulation according to claim 1, characterized in that: Also included is a fault tracing module connected to the central processing unit; The fault tracing module is used to set a target fault type, obtain a 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 a detection dimension parameter of the charging device.

6. The intelligent detection system for charging devices based on dynamic load simulation according to claim 5, characterized in that: The fault tracing module obtains the feature matching degree by a preset correlation matching method, including: Extracting a historical fault waveform set of the charging device from a fault model library, extracting a standard waveform set of the target fault type, and calculating a time domain coincidence degree between the historical fault waveform set and the standard waveform set as a first matching degree; Comparing the parameter change trend of the charging device during the load cycle with the standard change trend of the target fault type, and obtaining a trend similarity between the two as a second matching degree; The first matching degree, the second matching degree, or a combination thereof is calculated as the feature matching degree.

7. The intelligent detection system for charging devices based on dynamic load simulation according to claim 1, characterized in that: The central processing unit generates the state evaluation parameters by linear weighting or nonlinear mapping for all the detection dimension parameters.

8. The intelligent detection system for charging devices based on dynamic load simulation according to claim 1, characterized in that: It also includes a storage module connected to the central processing unit, and the storage module is used to record the operating parameter set, load fluctuation curve, abnormal fluctuation index and status evaluation parameters.

9. A method for intelligent detection of a charging device based on dynamic load simulation, applied to the intelligent detection system for a charging device based on dynamic load simulation according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Configure dynamic load parameters through the load configuration module according to the type of target charging device and generate corresponding load simulation rules; Step 2: Based on the load simulation rule generated in step 1, a charging parameter acquisition module is used to obtain the operating parameter set of the charging device from the charging database in real time, and all parameters in the operating parameter set are used as detection dimension parameters of the charging device; Step 3: Based on the load association relationship preset in the charging database, a load fluctuation calculation module uses a dynamic simulation algorithm to generate a load fluctuation curve of the charging device, and uses the load fluctuation curve as a detection dimension parameter of the charging device; Step 4: Calculate an abnormal fluctuation index of the charging device using a preset fluctuation analysis method through an abnormality identification module, and use the abnormal fluctuation index as a 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 identification module, send the load simulation rules to the charging parameter acquisition module, and fuse all the detection dimension parameters obtained in steps 2-4 to generate the status evaluation parameters of the charging device.

Images