Charger fault real-time detection and safe cut-off method and system

Abstract

The invention relates to the technical field of power supply control, and discloses a charger fault real-time detection and safe cut-off method and system, and the method comprises the steps: recognizing the overlap ratio of a transient pulse and a high-frequency impact, and obtaining a coupling abnormality identifier when the overlap ratio exceeds a preset threshold value; the stability change trend of the switching frequency is analyzed, and the element aging degree is obtained in combination with the energy increase condition of the specific frequency band; constructing a fault risk assessment matrix of the charger, and carrying out comprehensive assessment on the current operation state to obtain a risk level; calling baseline parameters in a pre-stored security baseline database, and determining a first security threshold and a second security threshold; limiting the output power when the risk level reaches a first safety threshold; when the risk level reaches a second safety threshold value, the power switch is disconnected, and safe disconnection is achieved; according to the invention, the efficiency of charger fault real-time detection and safe cut-off can be improved.

Description

Technical Field

[0001] This invention relates to the field of power control technology, and in particular to a method and system for real-time detection and safe disconnection of charger faults. Background Technology

[0002] Existing charger fault detection technologies have limitations in time-domain signal analysis. They struggle to accurately identify the coupling characteristics between transient pulses and high-frequency impacts, and are slow to respond to abnormal states where the overlap exceeds a threshold. This results in a lack of timeliness and accuracy in generating coupling anomaly indicators, making it impossible to promptly capture potential fault causes during equipment operation. Furthermore, in the component aging assessment stage, existing technologies often rely solely on isolated indicators such as switching frequency or energy changes, without establishing a multi-dimensional collaborative analysis mechanism. This makes it difficult to comprehensively reflect the true state of component performance degradation, causing the aging assessment results to be disconnected from actual fault risks and failing to provide a reliable basis for subsequent safety protection.

[0003] Existing safety disconnection mechanisms lack a scientific risk classification system. Safety thresholds are mostly set based on fixed parameters without dynamic adjustment based on the charger's real-time operating mode, load status, and ambient temperature, resulting in insufficient threshold adaptability. When equipment malfunctions, existing technologies either fail to implement tiered protection measures according to the risk level, simply performing a disconnection operation that affects ease of use, or, due to delayed protection response, fail to disconnect the power switch in time when the risk level reaches the danger threshold, failing to effectively avoid safety hazards such as overload and short circuits. It is difficult to achieve a dynamic balance between safety protection and normal equipment use. Therefore, how to improve the efficiency of charger fault detection and safety disconnection has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for real-time detection and safe disconnection of charger faults to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for real-time fault detection and safety disconnection of a charger, comprising:

[0006] S1. In the time domain, the overlap between the transient pulse amplitude and the high-frequency impact amplitude of the charger is statistically analyzed. When the overlap exceeds a preset overlap threshold, the coupling abnormality indicator of the charger is obtained.

[0007] S2. Perform differential analysis on the timing data of the switching frequency in the charger to obtain the trend of the switching frequency change, and combine it with the energy growth of a specific frequency band in the charger to obtain the component aging degree of the charger.

[0008] S3. Construct a fault risk assessment matrix for the charger, using the anomaly type identified by the coupling anomaly as the row dimension and the aging degree of the component aging degree as the column dimension.

[0009] S4. Based on the fault risk assessment matrix, comprehensively assess the current operating status of the charger to obtain the risk level of the charger;

[0010] S5. Call the baseline parameters in the pre-stored safety baseline database, and determine the first safety threshold and the second safety threshold of the charger based on the baseline parameters;

[0011] S6. When the risk level reaches the first safety threshold, limit the output power of the charger; when the risk level reaches the second safety threshold, disconnect the power switch of the charger to achieve safe disconnection.

[0012] In a preferred embodiment, the step of statistically analyzing the overlap between the transient pulse amplitude and the high-frequency impulse amplitude of the charger in the time domain, and obtaining a coupling anomaly identifier for the charger when the overlap exceeds a preset overlap threshold, includes:

[0013] Transient pulse amplitude sequence and high-frequency impulse amplitude sequence are separated from the time-domain signal data of the charger;

[0014] Time-domain alignment is performed on the transient pulse amplitude sequence and the high-frequency impact amplitude sequence;

[0015] Calculate the overlap between the aligned transient pulse amplitude sequence and the aligned high-frequency impact amplitude sequence, wherein the formula for calculating the overlap is:

[0016] ;

[0017] in, Indicates the degree of overlap. Indicates the first Aligned transient pulse amplitude, Indicates the first after alignment A high-frequency impact amplitude, This represents the average value of the aligned transient pulse amplitude sequence. This represents the average value of the aligned high-frequency impact amplitude sequence. This indicates the number of valid event pairs within the current time window;

[0018] The overlap degree is compared with a preset overlap threshold to obtain the coupling abnormality identifier of the charger.

[0019] In a preferred embodiment, the step of performing differential analysis on the timing data of the switching frequency in the charger to obtain the trend of the switching frequency change, and combining this with the energy growth of a specific frequency band in the charger to obtain the component aging degree of the charger, includes:

[0020] performing multi-scale decomposition on the real-time switching frequency of the charger to obtain a performance evolution trend and an instantaneous fluctuation component of the charger;

[0021] analyzing the stability of the performance evolution trend and counting the frequency of abnormal events in the instantaneous fluctuation component that exceed the normal operation boundary of the charger in the same time window;

[0022] mapping the change process of the energy amplitude of the operating spectrum of the charger into an energy evolution trajectory of the charger;

[0023] performing multi-dimensional correlation analysis on the stability, the frequency of abnormal events, and the energy evolution trajectory to obtain a coordinated change mode of the charger;

[0024] quantifying the degradation degree of the element performance of the charger based on the significance of the coordinated change mode to obtain an element aging degree of the charger.

[0025] In a preferred embodiment, the fault risk assessment matrix of the charger is constructed with the abnormal types in the coupling abnormality identification as the row dimension and the aging degree of the element aging degree as the column dimension, including:

[0026] performing tensor synthesis on the abnormal types in the coupling abnormality identification to obtain an abnormal feature vector of the charger;

[0027] discretizing and encoding the aging degree level in the element aging degree to obtain an aging degree vector of the charger;

[0028] establishing a mapping relationship table between the abnormal feature vector and the aging degree vector according to the historical fault case library of the charger;

[0029] constructing the fault risk assessment matrix of the charger based on the mapping relationship table, with the abnormal feature vector as the horizontal vector and the aging degree vector as the vertical vector.

[0030] In a preferred embodiment, the current operating state of the charger is comprehensively evaluated according to the fault risk assessment matrix to obtain a risk level of the charger, including:

[0031] locating the position coordinates of the abnormal types and the aging degree in the fault risk assessment matrix as input parameters;

[0032] reading the reference risk indicators stored in the position coordinates and performing time dimension correction on the reference risk indicators to obtain a time domain risk indicator of the charger;

[0033] Adapting the time domain risk indicator to an environment according to a current load state and a working temperature environment of the charger to obtain an environmental risk indicator of the charger;

[0034] Performing multi-level evaluation on a trend consistency of the environmental risk indicator to obtain a risk level of the charger.

[0035] In a preferred embodiment, the multi-level evaluation on the trend consistency of the environmental risk indicator to obtain the risk level of the charger comprises:

[0036] Arranging the environmental risk indicators of consecutive periods in a time sequence to obtain a period risk evaluation sequence of the charger;

[0037] Extracting a historical risk evaluation sequence in the historical failure case library;

[0038] Analyzing a deviation degree of the period risk evaluation sequence and the historical risk evaluation sequence to obtain a risk trend consistency indicator of the charger, wherein a calculation formula of the risk trend consistency indicator is:

[0039] ;

[0040] wherein, represents the risk trend consistency indicator, represents a risk evaluation value of a current period, represents a risk evaluation value of a historical period, represents a preset time decay weight, represents a number of historical periods, represents an average value of the historical risk evaluation sequence;

[0041] Performing multi-level evaluation on the risk trend consistency indicator to obtain the risk level of the charger.

[0042] In a preferred embodiment, the calling of a baseline parameter in a pre-stored safety baseline database and the determination of a first safety threshold and a second safety threshold of the charger according to the baseline parameter comprises:

[0043] Extracting a historical operating modal spectrum corresponding to the charger in the pre-stored safety baseline database;

[0044] Performing analogy analysis on a current operating state of the charger and the historical operating modal spectrum to obtain a modal category of the charger;

[0045] Performing threshold mapping on the modal category to obtain a power limitation threshold of the charger;

[0046] ​Based on the intensity of the coupling anomaly identification, the power limit threshold is adjusted to obtain a first safety threshold of the charger.

[0047] In a preferred embodiment, the baseline parameters in the pre-stored safety baseline database are called, and the first safety threshold and the second safety threshold of the charger are determined according to the baseline parameters, including:

[0048] Extracting the failure features associated with the element aging degree in the pre-stored safety baseline database;

[0049] Co-analyzing the growth of the specific frequency band energy in the charger with the failure features to obtain the state features of the potential failure in the charger;

[0050] Quantifying the state features to obtain a state feature vector of the potential failure, and inputting the state feature vector into the failure risk assessment matrix to obtain the second safety threshold of the charger.

[0051] In a preferred embodiment, when the risk level reaches the first safety threshold, the output power of the charger is limited; when the risk level reaches the second safety threshold, the power switch connection of the charger is disconnected to achieve safety cutting, including:

[0052] Inputting the risk level into a pre-set safety response strategy library to obtain a hierarchical safety response strategy table of the charger;

[0053] Real-time detecting the change of the risk level, when the risk level exceeds the first safety threshold, querying the hierarchical safety response strategy table to obtain a primary safety response strategy of the charger;

[0054] According to the primary safety response strategy, limiting the output power of the charger, and continuously monitoring the risk change trend of the charger;

[0055] In the power limit mode, when it is monitored that the risk level continuously rises and reaches the second safety threshold, the hierarchical safety response strategy table is queried to obtain a final safety response strategy of the charger;

[0056] According to the primary safety response strategy, cutting off the switch connection of the charger to achieve safety protection of the charger.

[0057] In order to solve the above problems, the application also provides a charger failure real-time detection and safety cutting system, the system comprising:

[0058] An abnormal state identification module is configured to statistically determine the coincidence degree of the transient pulse amplitude and the high-frequency impact amplitude of the charger in the time domain, and obtain the coupling abnormality identification of the charger when the coincidence degree exceeds a preset coincidence threshold;

[0059] An element aging degree evaluation module is configured to perform differential analysis on the timing data of the switching frequency in the charger, obtain the variation trend of the switching frequency, and obtain the element aging degree of the charger in combination with the growth of the energy in a specific frequency band in the charger;

[0060] A fault risk matrix construction module is configured to construct a fault risk evaluation matrix of the charger by taking the abnormal type of the coupling abnormality identification as the row dimension and taking the aging degree of the element aging degree as the column dimension;

[0061] A risk level evaluation module is configured to comprehensively evaluate the current running state of the charger according to the fault risk evaluation matrix, and obtain the risk level of the charger;

[0062] A safety threshold determination module is configured to call baseline parameters in a pre-stored safety baseline database, and determine a first safety threshold and a second safety threshold of the charger according to the baseline parameters;

[0063] A charger control module is configured to limit the output power of the charger when the risk level reaches the first safety threshold, and disconnect the power switch connection of the charger when the risk level reaches the second safety threshold, so as to realize safe cutting.

[0064] Compared with the prior art, the present application has the following beneficial effects:

[0065] 1. The technical scheme of the present application accurately identifies the coincidence degree of the transient pulse and the high-frequency impact through time domain signal analysis, realizes quantitative evaluation of the element aging degree by combining multi-scale decomposition and multi-dimensional correlation analysis, quickly constructs a fault risk evaluation matrix and completes comprehensive judgment of the risk level, significantly improves the real-time performance and accuracy of charger fault detection, greatly improves the overall efficiency of fault identification and risk evaluation, and enables potential faults to be captured and quantified in a timely manner.

[0066] 2. The graded safety threshold is determined based on the pre-stored safety baseline database, and the output power limitation and power switch cutting are accurately controlled through the graded response strategy, which not only ensures the continuous availability of the charger in a low-risk scenario, but also quickly starts the safety cutting mechanism in a high-risk state, effectively enhances the safety protection capability during the operation of the charger, improves the safety and reliability during use, and reduces the safety hazards caused by faults. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1A flowchart of a charger fault real-time detection and safety cut-off method provided by an embodiment of the present application is shown in Fig.

[0068] Figure 2 A function module diagram of a charger fault real-time detection and safety cut-off system provided by an embodiment of the present application is shown in Fig.

[0069] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0070] It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

[0071] Embodiments of the present application provide a charger fault real-time detection and safety cut-off method. The execution subject of the charger fault real-time detection and safety cut-off method includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiments of the present application. In other words, the charger fault real-time detection and safety cut-off method can be executed by software or hardware installed in a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be a stand-alone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and big data and artificial intelligence platforms, etc. basic cloud computing services.

[0072] Referring to Figure 1 A flowchart of a charger fault real-time detection and safety cut-off method provided by an embodiment of the present application is shown in Fig. In this embodiment, the charger fault real-time detection and safety cut-off method includes:

[0073] S1, in the time domain, the coincidence degree of the transient pulse amplitude and the high-frequency impact amplitude of the charger is counted, and when the coincidence degree exceeds a preset coincidence threshold, a coupling abnormality identifier of the charger is obtained;

[0074] In the embodiment of the present application, the coincidence degree of the transient pulse amplitude and the high-frequency impact amplitude of the charger is counted in the time domain, and when the coincidence degree exceeds a preset coincidence threshold, a coupling abnormality identifier of the charger is obtained, including:

[0075] The transient pulse amplitude sequence and the high-frequency impact amplitude sequence are separated from the time domain signal data of the charger;

[0076] aligning the transient pulse amplitude sequence and the high-frequency impact amplitude sequence in time domain;

[0077] calculating a coincidence degree of the aligned transient pulse amplitude sequence and the aligned high-frequency impact amplitude sequence, wherein a calculation formula of the coincidence degree is:

[0078]

[0079] wherein, represents the coincidence degree, represents an i th aligned transient pulse amplitude, represents an i th aligned high-frequency impact amplitude, represents an average value of the aligned transient pulse amplitude sequence, represents an average value of the aligned high-frequency impact amplitude sequence, represents a number of valid event pairs in a current time window. comparing the coincidence degree with a preset coincidence threshold to obtain a coupling abnormality identification of the charger.

[0080] The continuous electric signal data generated during the operation of the charger is collected as the time domain signal data, and for separation of the transient pulse amplitude sequence, according to the characteristics of the transient pulse signal that the amplitude rises and falls sharply in a short time, a standard that the signal duration is between 1 microsecond and 10 microseconds and the amplitude exceeds 3 times the average amplitude during normal operation of the charger is set, all signal segments meeting the standard are selected from the time domain signal data, the peak value of each segment is recorded as the transient pulse amplitude, and the transient pulse amplitude sequence is formed by arranging the transient pulse amplitudes in the order of time.

[0081] For separation of the high-frequency impact amplitude sequence, the high-frequency electric signals with a frequency range between 1 MHz and 10 MHz are first selected through a special hardware filtering circuit, and then the mutation signal segments with an amplitude exceeding 2 times the average amplitude of the normal high-frequency working signal are identified in the high-frequency signals, the peak value of each mutation segment is recorded as the high-frequency impact amplitude, and the high-frequency impact amplitude sequence is formed by arranging the high-frequency impact amplitudes in the order of time.

[0082] ​​The time when the first valid signal appears after the charger starts is taken as the common time starting point, and a uniform time sampling interval of 0.1 microseconds is set. The time points corresponding to each amplitude in the transient pulse amplitude sequence and the high-frequency impulse amplitude sequence are checked respectively. For the time point of the missing data in the transient pulse amplitude sequence, the transient pulse amplitude corresponding to the time point is calculated according to the linear change rule based on the adjacent two existing transient pulse amplitudes before and after the time point, and the missing data is supplemented. For the time point of the missing data in the high-frequency impulse amplitude sequence, the same linear interpolation method is used to calculate the missing data based on the adjacent high-frequency impulse amplitudes before and after the time point, so that the lengths of the two sequences are completely consistent, and the amplitudes at each position correspond to the same time point, and the time domain alignment is completed.

[0083] The amplitudes in the aligned transient pulse amplitude sequence are added first, and then divided by the total number of the sequence to obtain the average amplitude of the aligned transient pulse amplitude sequence. The average amplitude of the aligned high-frequency impulse amplitude sequence is calculated by using the same method. The amplitudes at the corresponding positions in the two sequences are extracted one by one, each transient pulse amplitude is subtracted by the average amplitude of the transient pulse amplitude sequence to obtain the deviation of each transient pulse amplitude relative to the average value. Each high-frequency impulse amplitude is subtracted by the average amplitude of the high-frequency impulse amplitude sequence to obtain the deviation of each high-frequency impulse amplitude relative to the average value. The transient pulse deviation and the high-frequency impulse deviation corresponding to each position are multiplied, and all the multiplied results are accumulated to obtain a total difference value. The square of each transient pulse deviation is calculated, and the sum of all square values is obtained. The square of each high-frequency impulse deviation is calculated, and the sum of all square values is obtained. The square sum of the transient pulse deviation and the square sum of the high-frequency impulse deviation are multiplied, and the square root of the multiplied result is obtained. Finally, the total difference value obtained before is divided by the square root value, and the result is the coincidence degree of the two aligned sequences.

[0084] According to the design parameters of the charger, the safety operation standard and a large number of normal operation test data of the same type of charger, a preset coincidence threshold is determined, and the threshold is a fixed value of 0.7. The calculated coincidence degree is directly compared with the preset coincidence threshold. If the coincidence degree is greater than 0.7, it means that the correlation degree of the transient pulse and the high-frequency impulse exceeds the normal range, and the coupling abnormality identifier clearly marked as “there is coupling abnormality” is generated. If the coincidence degree is less than or equal to 0.7, it means that the correlation of the transient pulse and the high-frequency impulse is at a normal level, and the coupling abnormality identifier clearly marked as “coupling state is normal” is generated.

[0085] Relevant information is extracted from the time domain signal data of the charger, and a transient pulse amplitude sequence and a high-frequency impact amplitude sequence are separated; the separated transient pulse amplitude sequence and the high-frequency impact amplitude sequence are subjected to time domain alignment processing, so that the two sequences correspond to each other in the time dimension; the transient pulse amplitude at each corresponding position after alignment is the transient pulse amplitude at each position after alignment, and the high-frequency impact amplitude at each corresponding position after alignment is the high-frequency impact amplitude at each position after alignment; the sum of all values in the aligned transient pulse amplitude sequence is calculated, and then divided by the number of values in the sequence, to obtain the average value of the aligned transient pulse amplitude sequence; the sum of all values in the aligned high-frequency impact amplitude sequence is calculated, and then divided by the number of values in the sequence, to obtain the average value of the aligned high-frequency impact amplitude sequence; the number of pairs of data in the current set time window in which the transient pulse amplitude and the high-frequency impact amplitude can form an effective corresponding relationship is counted, and this number is the number of effective event pairs in the current time window.

[0086] By performing specific calculations on the aligned transient pulse amplitude sequence and the high-frequency impact amplitude sequence, the degree of correlation between the two sequences is quantified, and the fit of the two sequences in numerical changes is accurately reflected; the quantification result of this fit is directly used to determine whether the charger has a coupling anomaly, and when the quantification result exceeds a preset coincidence threshold, a coupling anomaly identifier of the charger is obtained.

[0087] When the synchronization of the aligned transient pulse amplitude and the high-frequency impact amplitude in numerical changes is stronger, for example, when one value increases, the other value also increases, and when one value decreases, the other value also decreases, and the amplitude ratio of the changes of the two tends to be consistent, then the result calculated to reflect the degree of coincidence will be closer to 1, indicating that the degree of coincidence of the two sequences is extremely high; when the synchronization of the aligned transient pulse amplitude and the high-frequency impact amplitude in numerical changes is weaker, for example, when one value increases, the other value decreases, or the amplitude ratio of the changes of the two has no regularity, then the result calculated to reflect the degree of coincidence will be closer to 0, indicating that the degree of coincidence of the two sequences is extremely low.

[0088] The beneficial effects are that the transient pulse amplitude sequence and the high-frequency impact amplitude sequence are accurately separated by clear signal screening criteria, time domain alignment is achieved by using a unified time starting point and linear interpolation, coincidence degree calculation is completed by step-by-step calculation of deviation, accumulation, square sum, and square root, and the coupling anomaly identifier is generated by comparison with a preset fixed threshold, the operation steps are specific and the logic is clear throughout the process, there is no black box processing link, ensuring the accuracy and timeliness of the generation of the coupling anomaly identifier, effectively solving the problem of difficult accurate identification of the coupling correlation characteristics of transient pulses and high-frequency impacts in the prior art, and providing reliable basic data support for subsequent charger fault risk assessment.

[0089] S2, differentiating the time sequence data of the switching frequency in the charger to obtain a change trend of the switching frequency, and combining a growth condition of the energy of the specific frequency band in the charger to obtain the element aging degree of the charger;

[0090] In the embodiment of the present application, the differentiating the time sequence data of the switching frequency in the charger to obtain a change trend of the switching frequency, and combining a growth condition of the energy of the specific frequency band in the charger to obtain the element aging degree of the charger, comprises:

[0091] performing multi-scale decomposition on the real-time switching frequency of the charger to obtain a performance evolution trend and an instantaneous fluctuation component of the charger;

[0092] In the same time window, analyzing the stability of the performance evolution trend, and counting a frequency of abnormal events in the instantaneous fluctuation component that exceed a normal operation boundary of the charger;

[0093] mapping a change process of the energy amplitude of the working frequency spectrum in the charger as an energy evolution track of the charger;

[0094] performing multi-dimensional correlation analysis on the stability, the frequency of abnormal events and the energy evolution track to obtain a cooperative change mode of the charger;

[0095] based on the significant degree of the cooperative change mode, quantitatively rating the element performance degradation degree of the charger to obtain the element aging degree.

[0096] Collecting continuous monitoring data of the switching frequency of the charger in the real-time running process, which are recorded at fixed time intervals and completely reflect the dynamic change of the switching frequency with time; according to the difference of time scales, the overall change trend of the switching frequency in a long time period is extracted, which can reflect the overall trend of the switching frequency in a period of time, that is, the performance evolution trend of the charger; at the same time, the rapid and small amplitude change part of the switching frequency in a short time period is extracted separately, and these rapid fluctuation parts in a short period are the instantaneous fluctuation components of the charger. The whole multi-scale decomposition process is based on the difference of time periods and the fast and slow characteristics of the switching frequency change, which ensures that the characteristics of the performance evolution trend and the instantaneous fluctuation component are clear and without overlap.

[0097] According to the working characteristics and data monitoring requirements of the charger, a fixed time window is set, for example, 10 minutes, which can effectively cover a complete fluctuation cycle of the switching frequency and facilitate data analysis; within the time window, the change of the performance evolution trend curve is observed, if the curve as a whole remains gentle, without obvious sudden rise or fall amplitude exceeding the design allowable range, it is determined that the performance evolution trend is stable, if the curve appears obvious large fluctuation, it is determined that the performance evolution trend is unstable; at the same time, according to the design specification of the charger, the normal operation boundary of the switching frequency is determined, that is, the maximum and minimum value range of the switching frequency allowed, each data point of the instantaneous fluctuation component in the time window is checked one by one, if a certain data point exceeds the normal operation boundary, it is recorded as an abnormal event, and the total number of all abnormal events in the time window is counted, that is, the abnormal event frequency of the instantaneous fluctuation component exceeding the normal operation boundary.

[0098] The working spectrum generated by the charger during operation is collected in real time by a spectrum analyzer, and the energy amplitude data under different frequency bands are obtained, which contains the energy distribution of the charger at each working frequency band; taking time as the horizontal axis and the energy amplitude of each frequency band collected at the corresponding time point as the vertical axis, the energy amplitude data of each frequency band corresponding to each time point is marked in the coordinate system to form a series of discrete data points; according to the time sequence, these discrete data points are connected by a smooth curve to form a curve that can reflect the change of the working spectrum energy amplitude at different time points, which is the energy evolution trajectory of the charger, which completely presents the change process of the working spectrum energy amplitude with time.

[0099] The data of the three dimensions of performance evolution trend stability, abnormal event frequency and energy evolution trajectory are extracted respectively, among which the stability features include stable duration, fluctuation amplitude, etc., the abnormal event frequency features include frequency change rate, peak frequency, etc., and the energy evolution trajectory features include specific frequency band energy growth rate, peak energy occurrence time, etc.; the characteristic data of the three dimensions are placed in the same time coordinate system to observe the mutual relationship between different dimension characteristics, for example, when the performance evolution trend changes from stable to unstable, whether the abnormal event frequency rises at the same time, and whether the energy evolution trajectory of the specific frequency band presents an upward trend, if the characteristic changes of the three dimensions present obvious synchronous correlation relationship, such as "stability decreases-abnormal event frequency rises-specific frequency band energy rises", the correlation relationship is determined as the cooperative change mode of the charger.

[0100] A pre-established criterion for judging the significance of the coordinated change pattern is based on a large amount of charger component aging test data. For example, when the stability decrease exceeds 30%, the frequency of abnormal events increases by more than 50%, and the energy increase in a specific frequency band exceeds 40%, and the duration of the synchronous change of the three exceeds 20 minutes, the coordinated change pattern is judged to be highly significant. When the change of the above three indicators is between 10% and 30%, and the duration of the synchronous change is between 5 and 20 minutes, the coordinated change pattern is judged to be moderately significant. When the change of the above three indicators is less than 10%, and the duration of the synchronous change is less than 5 minutes, the coordinated change pattern is judged to be lowly significant. At the same time, a correspondence between significance and component performance degradation is established. High significance corresponds to severe component performance degradation, with a quantitative rating of A; moderate significance corresponds to moderate component performance degradation, with a quantitative rating of B; and low significance corresponds to slight component performance degradation, with a quantitative rating of C. Based on the significance of the coordinated change pattern obtained from actual analysis, the quantitative rating of component performance degradation is determined by referring to the correspondence. This quantitative rating is the component aging degree of the charger.

[0101] The beneficial effects of this implementation process are that it accurately separates the overall trend and instantaneous fluctuations of the switching frequency through multi-scale decomposition, achieves stability judgment and anomaly statistics by combining time window analysis, and then intuitively presents the changes in spectral energy through energy evolution trajectory. Finally, it obtains the coordinated change pattern through multi-dimensional correlation and completes quantitative rating. The entire process is based on clear physical characteristics and data correlation logic, ensuring that the aging degree of components is accurate and traceable, effectively avoiding the limitations of single index analysis, providing accurate component status basis for subsequent charger fault risk assessment, improving the comprehensiveness and reliability of fault detection, and providing clear guidance for preventive maintenance of chargers.

[0102] S3. Construct a fault risk assessment matrix for the charger, using the anomaly type identified by the coupling anomaly as the row dimension and the aging degree of the component aging degree as the column dimension.

[0103] In this embodiment of the invention, constructing the fault risk assessment matrix of the charger, using the anomaly type identified by the coupling anomaly as the row dimension and the aging degree of the component as the column dimension, includes:

[0104] The abnormal types in the coupling abnormality identifier are tensor synthesized to obtain the abnormal feature vector of the charger;

[0105] The aging degree level in the aging degree of the component is discretized and encoded to obtain the aging degree vector of the charger;

[0106] According to the historical failure case library of the charger, a mapping relationship table is established between the abnormal feature vector and the aging degree vector;

[0107] Based on the mapping relationship table, the abnormal feature vector is taken as a horizontal vector, and the aging degree vector is taken as a vertical vector to construct a failure risk assessment matrix of the charger.

[0108] All abnormal types contained in the charger coupling abnormality identification are collected, which are determined based on the circuit structure and coupling principle of the charger, specifically including three types of capacitive coupling abnormality, inductive coupling abnormality and electromagnetic coupling abnormality. Each type of abnormality is determined by early detection whether it exists or not. The existing abnormal type is recorded as 1, and the non-existing abnormal type is recorded as 0. According to the fixed order of "capacitive coupling abnormality-inductive coupling abnormality-electromagnetic coupling abnormality", the existing state of each type of abnormality is taken as an independent element to construct a three-dimensional data structure. Each element in the data structure directly corresponds to the state of one type of coupling abnormality. In this way, tensor synthesis is completed, and a three-dimensional data structure, i.e. the abnormal feature vector of the charger, is finally formed. The vector presents the type information of all coupling abnormalities of the charger in a complete and orderly manner.

[0109] The key element aging degree grades contained in the charger element aging degree are extracted. The key elements are determined according to the core function modules of the charger, specifically including three types of transformer, filter capacitor and power resistor. The aging degree grades of each element have been determined through early evaluation, which are A grade (severe degradation), B grade (general degradation) and C grade (slight degradation) respectively. The different aging degree grades are discretely coded. The coding rule is fixed as A grade corresponding to numerical value 3, B grade corresponding to numerical value 2 and C grade corresponding to numerical value 1. Then, according to the fixed order of "transformer-filter capacitor-power resistor", the coding numerical values of each key element are arranged in turn to form a one-dimensional data sequence. The data sequence is the aging degree vector of the charger, which accurately reflects the aging state of each key element.

[0110] The historical fault case library of the charger is called, and a large amount of complete data of the same type of charger when a fault occurs in the past is stored in the case library, including an abnormal feature vector, an aging degree vector and a corresponding fault risk level when each fault occurs. The fault risk level is divided into high risk, medium risk and low risk. The data in the case library is classified and counted. For each combination of the same abnormal feature vector and aging degree vector, the frequency of the corresponding fault risk level in the historical case is counted. The highest frequency risk level is determined as the standard risk level corresponding to the vector combination. For example, when the abnormal feature vector is [1, 0, 1], the vector represents that the capacitor coupling abnormality and the electromagnetic coupling abnormality exist and the inductance coupling abnormality does not exist, and the aging degree vector is [3, 2, 1], the vector represents that the transformer is seriously degraded, the filter capacitor is generally degraded and the power resistor is slightly degraded. The frequency of the high risk corresponding to the combination in the historical case accounts for 90%. The vector combination is associated with high risk. All vector combinations and their corresponding standard risk levels are arranged in table form, that is, the mapping relationship table between the abnormal feature vector and the aging degree vector.

[0111] The mapping relationship table constructed is used as the data basis to determine the row dimension and column dimension of the fault risk assessment matrix. All possible abnormal feature vectors are arranged in the order of “capacitor coupling abnormality-inductance coupling abnormality-electromagnetic coupling abnormality” according to the tensor synthesis in the early stage, as the horizontal vector of the matrix, that is, the row of the matrix. All possible aging degree vectors are arranged in the order of “transformer-filter capacitor-power resistor” according to the discretization coding in the early stage, as the vertical vector of the matrix, that is, the column of the matrix. Then, in each intersection cell of the row and the column of the matrix, the standard risk level associated with the corresponding abnormal feature vector and aging degree vector combination in the mapping relationship table is filled in, for example, the intersection cell of the row where the horizontal vector [1, 0, 1] is located and the column where the vertical vector [3, 2, 1] is located. The high risk corresponding to the combination in the mapping relationship table is filled in. In this way, the filling of all cells is completed, and the final table formed is the fault risk assessment matrix of the charger.

[0112] The implementation process has the beneficial effects that the abnormal feature vector is accurately constructed by the explicit tensor synthesis method, the aging degree vector is generated by the fixed coding rule, the objective mapping relationship table is established by combining the historical fault case library, and the fault risk assessment matrix is finally constructed in a fixed dimension and in an orderly manner. Each step has clear operation standards and data basis, ensuring the accuracy and objectivity of the fault risk assessment matrix, avoiding errors caused by subjective judgment, and clearly presenting the associated risk of coupling abnormalities and component aging in the matrix structure, providing an intuitive and reliable tool for subsequent rapid positioning of the current risk level of the charger, and effectively improving the efficiency and accuracy of the charger fault risk assessment.

[0113] S4. Based on the fault risk assessment matrix, comprehensively assess the current operating status of the charger to obtain the risk level of the charger;

[0114] In this embodiment of the invention, the step of comprehensively assessing the current operating status of the charger based on the fault risk assessment matrix to obtain the risk level of the charger includes:

[0115] Using the anomaly type and the degree of aging as input parameters, the coordinates of the input parameters in the fault risk assessment matrix are located.

[0116] The baseline risk index stored in the location coordinates is read, and the baseline risk index is corrected in the time dimension to obtain the time domain risk index of the charger.

[0117] Based on the current load state and operating temperature environment of the charger, the time-domain risk index is adjusted for environmental adaptability to obtain the environmental risk index of the charger.

[0118] The risk level of the charger is obtained by conducting a multi-level assessment of the trend consistency of the environmental risk indicators.

[0119] The multi-level assessment of the trend consistency of the environmental risk indicators to obtain the risk level of the charger includes:

[0120] The environmental risk indicators are arranged in chronological order to obtain the periodic risk assessment sequence of the charger.

[0121] Extract historical risk assessment sequences from the historical failure case library;

[0122] By analyzing the deviation between the cyclical risk assessment sequence and the historical risk assessment sequence, a risk trend consistency index for the charger is obtained. The calculation formula for the risk trend consistency index is as follows:

[0123] ;

[0124] in, This indicates the consistency index of the aforementioned risk trends. This represents the risk assessment value for the current period. Indicates the first Risk assessment values ​​for each historical period, This indicates the preset time decay weight. Indicates the number of historical cycles. This represents the average value of the historical risk assessment sequence;

[0125] The risk level of the charger is obtained by performing a multi-level assessment on the risk trend consistency index.

[0126] First, the horizontal vector in the fault risk assessment matrix is compared with the arrangement order, which is consistent with the arrangement order of the abnormal feature vector when the matrix is constructed in the early stage. The abnormal feature vector corresponding to the abnormal type of the current charger is compared with each abnormal feature vector in the matrix row by row, and the row number where the completely matched abnormal feature vector is located is found. Then, the vertical vector in the matrix is compared with the arrangement order, which is consistent with the arrangement order of the aging degree vector when the matrix is constructed in the early stage. The aging degree vector corresponding to the aging degree of the current charger is compared with each aging degree vector in the matrix column by column, and the column number where the completely matched aging degree vector is located is found. The row number determined is used as the horizontal coordinate, and the column number is used as the vertical coordinate. The numerical pair formed by the combination of the two is the position coordinate of the input parameter in the fault risk assessment matrix.

[0127] The specific risk value corresponding to the position coordinate is read from the fault risk assessment matrix, which is the reference risk indicator. Then, the time interval between the current evaluation time and the last update time of the fault risk assessment matrix data is determined. The change rule of the reference risk indicator under the same time interval in the historical fault case library is referred to, for example, historical data shows that the reference risk indicator will rise by 5% due to natural aging of components every 10 days. If the current time interval is 20 days, the reference risk indicator is corrected according to the rule of rising by 5% every 10 days, that is, the reference risk indicator is multiplied by 1.1, which is the result of 1 plus 5% multiplied by 2. In this way, the time dimension is corrected, and the risk value obtained after correction is the time domain risk indicator of the charger.

[0128] First, the actual load power of the charger is detected, and the load rate is calculated by dividing the actual load power by the rated load power. If the load rate is greater than 100%, it is determined to be a high load state. If the load rate is between 80% and 100%, it is determined to be a normal load state. If the load rate is less than 80%, it is determined to be a low load state. At the same time, the working temperature inside the charger is obtained through the temperature sensor. If the temperature is greater than 45℃, it is determined to be a high temperature environment. If the temperature is between 25℃ and 45℃, it is determined to be a normal temperature environment. If the temperature is less than 25℃, it is determined to be a low temperature environment. According to the preset load influence coefficient and temperature influence coefficient, the load influence coefficient corresponding to the high load state is 1.2, the load influence coefficient corresponding to the normal load state is 1.0, and the load influence coefficient corresponding to the low load state is 0.8. The temperature influence coefficient corresponding to the high temperature environment is 1.3, the temperature influence coefficient corresponding to the normal temperature environment is 1.0, and the temperature influence coefficient corresponding to the low temperature environment is 0.9. The time domain risk indicator is multiplied by the load influence coefficient corresponding to the current load state and the temperature influence coefficient corresponding to the current temperature environment, respectively. The result obtained is the environmental risk indicator of the charger.

[0129] Pre-set the length of each evaluation period, combined with the general speed of charger failure development, set each hour as an evaluation period, continuously collect environmental risk indicator data in the last 24 evaluation periods, starting from the earliest evaluation period, i.e. 24 hours ago, record the environmental risk indicator values corresponding to each period in turn, and then arrange these values in order from early to late to form an ordered sequence containing 24 data points, which is the periodic risk assessment sequence of the charger.

[0130] From the historical failure case library of the charger, filter the historical cases matching the current charger, the filtering criteria include the same charger model, similar use time and error not more than three months, and similar early abnormal records such as the same existence of capacitive coupling abnormality. From each historical case filtered out, extract the risk assessment data of the case in the same number of evaluation periods as the current period before failure, i.e. extract the risk assessment data of the case 24 hours before failure, arrange the historical risk assessment data in order according to time sequence to form an ordered sequence, which is the historical risk assessment sequence.

[0131] Correspond the periodic risk assessment sequence and the extracted historical risk assessment sequence according to the same period position, i.e. the first data of the periodic risk assessment sequence corresponds to the first data of the historical risk assessment sequence, the second data corresponds to the second data, and so on until all period positions are matched; calculate the difference absolute value of the data of the two sequences at each corresponding period position, add all the difference absolute values to get the total difference value, and then divide the total difference value by the total number of evaluation periods, i.e. 24, to get the average difference value, which is the risk trend consistency index of the charger. The smaller the average difference value, the smaller the deviation degree of the two sequences, and the higher the trend consistency.

[0132] Pre-set multi-level evaluation criteria for the risk trend consistency index, which is determined based on a large number of historical cases, specifically, when the risk trend consistency index is less than 5, it is determined as high trend consistency, when the index is between 5 and 10, it is determined as medium trend consistency, and when the index is greater than 10, it is determined as low trend consistency; at the same time, establish the corresponding relationship between trend consistency and risk level, high trend consistency corresponds to high risk, medium trend consistency corresponds to medium risk, and low trend consistency corresponds to low risk; compare the currently calculated risk trend consistency index with the pre-set criteria, and determine the final charger risk level according to the corresponding trend consistency level.

[0133] The risk assessment value of the current period comes from the environmental risk indicator adjusted according to the fault risk assessment matrix combined with the current load state and working temperature environment.

[0134] The risk assessment value of each historical period is from the historical risk assessment sequence extracted from the historical failure case library.

[0135] The preset time decay weight is a weight value set in advance to reflect the influence degree of different historical periods on the current assessment.

[0136] The number of historical periods is determined by the number of periods contained in the historical risk assessment sequence extracted from the historical failure case library.

[0137] The average value of the historical risk assessment sequence is calculated by multiplying the risk assessment value of each historical period by the corresponding time decay weight, adding all the products to obtain a sum, and finally dividing the sum by the sum of all time decay weights.

[0138] The core of this calculation is to quantify the consistency degree of the current risk change trend and the historical risk change trend, which is specifically achieved by analyzing the deviation degree of the risk assessment value of the current period and the historical risk assessment sequence.

[0139] In the calculation, the historical risk assessment values are first weighted and averaged by the time decay weight to obtain the weighted average result of the historical risk assessment values, and then the absolute difference between the risk assessment value of the current period and the weighted average result is calculated as the numerator.

[0140] The calculation process of the denominator is to first find the difference between the risk assessment value of each historical period and the average value of the historical risk assessment sequence, square each difference, multiply it by the corresponding time decay weight, add all the products to obtain a sum, divide the sum by the sum of all time decay weights, and then take the square root of the result.

[0141] The value obtained by dividing the numerator by the denominator is the risk trend consistency index, which directly reflects the fit of the current and historical risk change trends.

[0142] When the risk assessment value of the current period is closer to the weighted average result of the historical risk assessment values, the numerator value is smaller, the risk trend consistency index value is smaller, and the current risk change trend is more consistent with the historical risk change trend.

[0143] When the risk assessment value of the current period deviates from the weighted average result of the historical risk assessment values, the numerator value is larger, the risk trend consistency index value is larger, and the current risk change trend is more different from the historical risk change trend.

[0144] When the dispersion degree of the historical risk assessment values is smaller, i.e., the risk assessment values of each historical period are closer to the average value of the historical risk assessment sequence, the denominator value is smaller, and under the condition that the numerator is unchanged, the risk trend consistency index value is larger.

[0145] The greater the dispersion degree between the historical risk assessment values, that is, the greater the difference between the risk assessment values of each historical period and the average value of the historical risk assessment sequence, the greater the denominator value, and in the case of an unchanged numerator, the smaller the risk trend consistency index value.

[0146] The beneficial effect is that the implementation process ensures the accuracy of the reference risk index by accurately positioning the coordinates in the fault risk assessment matrix, combines time dimension correction to make up for the problem of insufficient timeliness of matrix data, adjusts the risk assessment based on the current load and temperature environment to make it fit the actual working condition, and realizes trend consistency judgment through deviation analysis of the periodic risk sequence and the historical sequence, finally completes multi-level risk assessment, and each link is supported by clear operation logic and data basis throughout the process, effectively avoiding the evaluation deviation caused by single factor or static data, significantly improving the accuracy and reliability of the comprehensive evaluation of the current running state of the charger, and providing a scientific decision basis for subsequent targeted power limitation or safety shutdown measures.

[0147] S5, calling a baseline parameter in a pre-stored safety baseline database, and determining a first safety threshold and a second safety threshold of the charger according to the baseline parameter;

[0148] In the embodiment of the application, the calling of the baseline parameter in the pre-stored safety baseline database and the determination of the first safety threshold and the second safety threshold of the charger according to the baseline parameter include:

[0149] extracting a historical operating modal spectrum corresponding to the charger in the pre-stored safety baseline database;

[0150] comparative analysis of the current running state of the charger and the historical operating modal spectrum to obtain a modal category of the charger;

[0151] threshold mapping of the modal category to obtain a power limitation threshold of the charger;

[0152] based on the intensity of the coupling anomaly identification, offset adjustment of the power limitation threshold to obtain a first safety threshold of the charger.

[0153] The calling of the baseline parameter in the pre-stored safety baseline database and the determination of the first safety threshold and the second safety threshold of the charger according to the baseline parameter include:

[0154] extracting a failure feature associated with the element aging degree in the pre-stored safety baseline database;

[0155] cooperative analysis of the growth of the specific frequency band energy in the charger and the failure feature to obtain a state feature of a potential failure in the charger;

[0156] Quantize the state features to obtain a state feature vector of the potential fault, and input the state feature vector into the fault risk assessment matrix to obtain a second safety threshold of the charger.

[0157] First, the model, specification parameters and production batch information of the current charger are determined, and these information is searched in the pre-stored safety baseline database to filter out historical data consistent with the model of the current charger, completely matched in specification parameters and similar in production batch. The electrical signal modal information of the charger under different operating conditions is extracted from these historical data, which includes the typical distribution characteristics of parameters such as switching frequency, voltage amplitude and current fluctuation under each condition. The complete modal set formed by integrating these information is the historical operating modal spectrum corresponding to the current charger.

[0158] The actual operating parameters of the current charger are collected, including real-time load rate, internal working temperature, switching frequency stability range and voltage output fluctuation value. These actual operating parameters are compared with the parameter ranges corresponding to each modal in the historical operating modal spectrum one by one. For example, if the current load rate is 85%, the working temperature is 32℃, and the switching frequency fluctuation is within ±2%, and these parameters all fall within the parameter interval of "moderate load-normal temperature stable modal" in the historical operating modal spectrum, it is determined that the modal category of the current charger is "moderate load-normal temperature stable modal".

[0159] A fixed mapping relationship table between each modal category and power limit threshold has been pre-established in the pre-stored safety baseline database. This mapping relationship table is formulated based on a large number of safety running test data and fault simulation experiment results of similar chargers. For example, the power limit threshold corresponding to "low load-low temperature modal" is 100% of the rated output power, the power limit threshold corresponding to "moderate load-normal temperature stable modal" is 90% of the rated output power, and the power limit threshold corresponding to "high load-high temperature modal" is 80% of the rated output power. According to the determined modal category of the charger, the corresponding power limit value is found in the mapping relationship table, which is the power limit threshold of the charger.

[0160] The electrical signal amplitude corresponding to the coupling abnormality identifier is collected by the signal detection device. The strength level of the coupling abnormality identifier is determined according to the ratio of the signal amplitude to the coupling signal amplitude when the charger is working normally. The ratio between 1.2 and 1.5 is mild abnormality, the ratio between 1.5 and 2.0 is moderate abnormality, and the ratio above 2.0 is severe abnormality. At the same time, threshold adjustment coefficients corresponding to different strength levels are preset. The adjustment coefficient corresponding to mild abnormality is 0.9, the adjustment coefficient corresponding to moderate abnormality is 0.8, and the adjustment coefficient corresponding to severe abnormality is 0.7. The power limit threshold obtained before is multiplied by the adjustment coefficient corresponding to the strength level of the current coupling abnormality identifier, and the result calculated is the first safety threshold of the charger.

[0161] According to the aging degree of the elements of the current charger, the aging level of each key element is determined, and the aging level is used as a retrieval condition to query the pre-stored safety baseline database, and the fault features associated with the aging level of each key element are extracted, for example, when the aging level of the transformer is A level, the associated fault features include "continuous increase of energy in a specific frequency band" and "no-load loss value exceeds 30% of the rated value", and when the aging level of the filter capacitor is B level, the associated fault features include "ripple voltage increase by more than 20%" and "accelerated energy fluctuation frequency in a specific frequency band". These associated fault features are integrated to form a complete fault feature set.

[0162] The energy change of the charger in a specific frequency band is monitored in real time by a spectrum analyzer, and the growth rate, duration and fluctuation amplitude of the energy in the frequency band per unit time are recorded. These actual monitoring data are compared with the extracted fault features one by one, for example, if the fault feature is "continuous increase of energy in a specific frequency band", it is judged whether the current energy growth rate exceeds the preset critical rate in the fault feature, and if the fault feature is "accelerated energy fluctuation frequency", it is judged whether the current fluctuation frequency reaches the warning frequency in the fault feature. Based on the comprehensive comparison results, the specific form of the potential fault of the current charger is determined, for example, "energy in the 150-250 kHz frequency band increases at a rate of 8% / week and the no-load loss is close to the critical value", which is the state feature of the potential fault in the charger.

[0163] The state feature of the potential fault is quantitatively processed, and a quantitative rule is set for each feature dimension, for example, in the "energy growth rate" dimension, growth rate ≤ 3% / week is recorded as 1, 3%-6% / week is recorded as 2, and > 6% / week is recorded as 3; in the "no-load loss proximity" dimension, loss value ≤ 20% of the rated value is recorded as 1, 20%-30% is recorded as 2, and > 30% is recorded as 3. According to the set rule, each dimension of the state feature is converted into a specific numerical value, and the ordered sequence formed by arranging these numerical values in a fixed order is the state feature vector of the potential fault. The state feature vector is input into the fault risk assessment matrix constructed before, the position in the matrix that completely matches the vector is found, and the risk threshold stored in the position is read, which is the second safety threshold of the charger.

[0164] The beneficial effect is that the implementation process can accurately extract the historical operation modal spectrum and element aging associated fault characteristics matched with the charger from the pre-stored safety baseline database, combine the analog analysis of the current operation state, modal threshold mapping and coupled abnormal intensity adjustment, ensure that the first safety threshold can be matched with the actual working condition to realize reasonable power limitation, and through the cooperative analysis of the energy growth of the specific frequency band and the fault characteristics, the state characteristic quantization and the matrix input, the second safety threshold can accurately reflect the potential fault risk, the overall process provides accurate and reliable threshold basis for subsequent graded safety control of the charger, effectively balances the equipment operation efficiency and safety protection effect, and avoids the problems of excessive protection or insufficient protection caused by unreasonable threshold setting.

[0165] S6、When the risk level reaches the first safety threshold, the output power of the charger is limited, and when the risk level reaches the second safety threshold, the power switch connection of the charger is disconnected to realize safe cutting off.

[0166] In the embodiment of the application, when the risk level reaches the first safety threshold, the output power of the charger is limited, and when the risk level reaches the second safety threshold, the power switch connection of the charger is disconnected to realize safe cutting off, including:

[0167] The risk level is input into a preset safety response strategy library to obtain a graded safety response strategy table of the charger;

[0168] The change of the risk level is detected in real time, when the risk level exceeds the first safety threshold, the graded safety response strategy table is queried to obtain a primary safety response strategy of the charger;

[0169] According to the primary safety response strategy, the output power of the charger is limited, and the risk change trend of the charger is continuously monitored;

[0170] In the power limitation mode, when it is monitored that the risk level continuously rises and reaches the second safety threshold, the graded safety response strategy table is queried to obtain a final safety response strategy of the charger;

[0171] According to the primary safety response strategy, the switch connection of the charger is cut off to realize the safety protection of the charger.

[0172] A preset security response strategy library is pre-constructed, which stores the association rules of different risk level intervals of the charger and corresponding security response measures. The rules are formulated based on the charger security operation standards and historical fault handling experience. For example, the risk level between the first security threshold and the second security threshold corresponds to the primary security response measure, and the risk level exceeding the second security threshold corresponds to the ultimate security response measure. Each measure includes specific operation parameters such as power limit ratio and shutdown delay time. The real-time risk level of the current charger is input into the strategy library, and the system automatically matches the corresponding response measure according to the interval of the risk level. All matched response measures are arranged in a table in the order of "risk level from low to high". The table clearly marks the response strategy type corresponding to each risk level interval, i.e. primary or ultimate, specific operation content and execution parameters. This table is the hierarchical security response strategy table of the charger.

[0173] The risk monitoring module inside the charger acquires real-time risk level data at a fixed cycle, i.e. once every 1 second. The risk level value collected each time is compared with the preset first security threshold in real time. If the risk level value collected at a certain time is greater than the first security threshold, it is determined that the risk level exceeds the first security threshold. At this time, the system calls the hierarchical security response strategy table and finds the corresponding primary security response measure in the table according to the specific magnitude of the current risk level exceeding the first security threshold, such as a slight excess of 5% or less, a moderate excess of 5%-10%. For example, a slight excess corresponds to the measure of "limiting output power by 10%", and a moderate excess corresponds to the measure of "limiting output power by 20%". The specific measure found is the primary security response strategy of the charger.

[0174] According to the power limit ratio specified in the primary security response strategy, the control unit of the charger sends a control signal to the internal power regulation module. By adjusting the duty cycle of the PWM signal, i.e. the pulse width modulation signal, in the power regulation module, the output power limit is achieved. For example, if the original PWM signal duty cycle is 50%, which corresponds to the rated output power, and the primary strategy is to limit 10% power, the duty cycle is adjusted to 45%, so that the output power is reduced by the same proportion. At the same time, the risk monitoring module shortens the collection cycle to a frequency of once every 0.5 seconds, continuously acquires risk level data and records it as a risk change curve. By comparing the risk level values collected at adjacent times, the risk change trend is determined to be continuously rising, slowly falling or remaining stable, ensuring real-time mastery of risk dynamics.

[0175] In the power limiting mode, if the risk level values collected by the risk monitoring module for three consecutive times are all higher than the previous collection value, and the interval between each collection is 0.5 seconds, and the latest collected risk level value reaches the preset second safety threshold, it is determined that the risk level is continuously rising and reaches the second safety threshold; at this time, the system queries the hierarchical safety response strategy table again, extracts the corresponding ultimate safety response measure in the table according to the condition of "risk level reaching the second safety threshold", and the measure is marked with the operation instruction of "immediately disconnecting the power switch connection" and the maximum delay time of executing the instruction, such as 0.1 seconds, and the extracted measure is the ultimate safety response strategy of the charger.

[0176] According to the operation instruction in the ultimate safety response strategy, the control unit of the charger sends a cut-off signal to the driving circuit of the power switch, and the driving circuit receives the signal and immediately cuts off the power switch, such as the gate driving voltage of the MOS tube switch, so that the power switch is quickly switched from the on state to the off state, thereby disconnecting the circuit connection between the input power supply and the output end of the charger; at the same time, the control unit records the time of this power switch cut-off, the risk level value triggering the cut-off and the current operating parameters such as load power and working temperature, and stores the recorded information into the fault log of the charger, thereby completing the safety protection of the charger.

[0177] The beneficial effect is that the implementation process realizes accurate matching of risk level and response measure through the preset strategy library, ensures the timeliness of hierarchical response in combination with real-time risk monitoring, and maintains the basic running function of the charger as much as possible under the premise of ensuring safety through the power limitation of the primary strategy, avoids use interruption caused by excessive protection, and blocks the expansion of faults before the risk is out of control through the timely cut-off of the ultimate strategy, while the complete operation record facilitates subsequent fault troubleshooting, and the overall process takes into account safety and practicality, thereby effectively improving the reliability and intelligent level of charger fault handling.

[0178] As shown in Figure 2 FIG. 1 is a functional module diagram of a charger fault real-time detection and safety cut-off system according to an embodiment of the present application.

[0179] The charger fault real-time detection and safety cut-off system 100 can be installed in an electronic device. According to the realized functions, the charger fault real-time detection and safety cut-off system 100 can include an abnormal state identification module 101, an element aging degree evaluation module 102, a fault risk matrix construction module 103, a risk level evaluation module 104, a safety threshold determination module 105, and a charger control module 106. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

[0180] In the embodiment, the functions of the modules / units are as follows:

[0181] The abnormal state identification module 101 is configured to statistically determine coincidence degrees of transient pulse amplitudes and high-frequency impact amplitudes of the charger in the time domain, and obtain a coupling abnormality identification of the charger when the coincidence degrees exceed a preset coincidence threshold value.

[0182] The element aging degree evaluation module 102 is configured to perform differential analysis on timing data of a switching frequency in the charger, obtain a variation trend of the switching frequency, and obtain an element aging degree of the charger in combination with a growth condition of a specific frequency band energy in the charger.

[0183] The fault risk matrix construction module 103 is configured to construct a fault risk evaluation matrix of the charger by taking an abnormal type of the coupling abnormality identification as a row dimension and taking an aging degree of the element aging degree as a column dimension.

[0184] The risk level evaluation module 104 is configured to comprehensively evaluate a current running state of the charger according to the fault risk evaluation matrix, and obtain a risk level of the charger.

[0185] The safety threshold determination module 105 is configured to call baseline parameters in a pre-stored safety baseline database, and determine a first safety threshold and a second safety threshold of the charger according to the baseline parameters.

[0186] The charger control module 106 is configured to limit an output power of the charger when the risk level reaches the first safety threshold, and disconnect a power switch connection of the charger when the risk level reaches the second safety threshold, so as to achieve safe cutting.

[0187] In several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, for example, the division of the modules is only a logical function division, and another division mode can be used in actual implementation.

[0188] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the embodiment.

[0189] In addition, each function module in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware, or in the form of hardware plus software function module.

[0190] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0191] Embodiments of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology and application system for using digital computers or computer-controlled machines to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain optimal results.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for real-time fault detection and safety disconnection of a charger, characterized in that, The method includes: S1. In the time domain, the overlap between the transient pulse amplitude and the high-frequency impact amplitude of the charger is statistically analyzed. When the overlap exceeds a preset overlap threshold, the coupling abnormality indicator of the charger is obtained. S2. Perform differential analysis on the timing data of the switching frequency in the charger to obtain the trend of the switching frequency change, and combine it with the energy growth of a specific frequency band in the charger to obtain the component aging degree of the charger. S3. Construct a fault risk assessment matrix for the charger, using the anomaly type identified by the coupling anomaly as the row dimension and the aging degree of the component aging degree as the column dimension. S4. Based on the fault risk assessment matrix, comprehensively assess the current operating status of the charger to obtain the risk level of the charger; S5. Call the baseline parameters in the pre-stored safety baseline database, and determine the first safety threshold and the second safety threshold of the charger based on the baseline parameters; S6. When the risk level reaches the first safety threshold, limit the output power of the charger; when the risk level reaches the second safety threshold, disconnect the power switch of the charger to achieve safe disconnection.

2. The method for real-time fault detection and safety disconnection of a charger as described in claim 1, characterized in that, In the time domain, the overlap between the transient pulse amplitude and the high-frequency impulse amplitude of the charger is statistically analyzed. When the overlap exceeds a preset overlap threshold, a coupling anomaly indicator of the charger is obtained, including: Transient pulse amplitude sequence and high-frequency impulse amplitude sequence are separated from the time-domain signal data of the charger; Time-domain alignment is performed on the transient pulse amplitude sequence and the high-frequency impact amplitude sequence; Calculate the overlap between the aligned transient pulse amplitude sequence and the aligned high-frequency impact amplitude sequence, wherein the formula for calculating the overlap is: ; in, Indicates the degree of overlap. Indicates the first Aligned transient pulse amplitude, Indicates the first after alignment A high-frequency impact amplitude, This represents the average value of the aligned transient pulse amplitude sequence. This represents the average value of the aligned high-frequency impact amplitude sequence. This indicates the number of valid event pairs within the current time window; The overlap degree is compared with a preset overlap threshold to obtain the coupling abnormality identifier of the charger.

3. The method for real-time fault detection and safety disconnection of a charger as described in claim 1, characterized in that, The differential analysis of the timing data of the switching frequency in the charger yields the trend of the switching frequency variation, and combined with the energy growth of a specific frequency band in the charger, the component aging degree of the charger is determined, including: The real-time switching frequency of the charger is decomposed into multiple scales to obtain the performance evolution trend and instantaneous fluctuation components of the charger. Within the same time window, the stability of the performance evolution trend is analyzed, and the frequency of abnormal events exceeding the normal operating boundary of the charger in the instantaneous fluctuation component is counted. The process of changing the working spectrum energy amplitude in the charger is mapped to the energy evolution trajectory of the charger; A multi-dimensional correlation analysis was performed on the stability, the frequency of abnormal events, and the energy evolution trajectory to obtain the cooperative change mode of the charger; Based on the significance of the aforementioned collaborative change pattern, the degree of component performance degradation in the charger is quantitatively rated to obtain the component aging degree.

4. The method for real-time fault detection and safety disconnection of a charger as described in claim 1, characterized in that, The fault risk assessment matrix for the charger is constructed using the anomaly type identified by the coupling anomaly as the row dimension and the aging degree of the component as the column dimension, including: The abnormal types in the coupling abnormality identifier are tensor synthesized to obtain the abnormal feature vector of the charger; The aging degree level in the aging degree of the component is discretized and encoded to obtain the aging degree vector of the charger; Based on the historical fault case library of the charger, establish a mapping relationship table between the abnormal feature vector and the aging degree vector; Based on the mapping table, with the abnormal feature vector as the horizontal axis and the aging degree vector as the vertical axis, a fault risk assessment matrix for the charger is constructed.

5. The method for real-time fault detection and safety disconnection of a charger as described in claim 1, characterized in that, The step of comprehensively assessing the current operating status of the charger based on the fault risk assessment matrix to obtain the risk level of the charger includes: Using the anomaly type and the degree of aging as input parameters, the coordinates of the input parameters in the fault risk assessment matrix are located. The baseline risk index stored in the location coordinates is read, and the baseline risk index is corrected in the time dimension to obtain the time domain risk index of the charger. Based on the current load state and operating temperature environment of the charger, the time-domain risk index is adjusted for environmental adaptability to obtain the environmental risk index of the charger. The risk level of the charger is obtained by conducting a multi-level assessment of the trend consistency of the environmental risk indicators.

6. The method for real-time fault detection and safety disconnection of a charger as described in claim 5, characterized in that, The multi-level assessment of the trend consistency of the environmental risk indicators to obtain the risk level of the charger includes: The environmental risk indicators are arranged in chronological order to obtain the periodic risk assessment sequence of the charger. Extract historical risk assessment sequences from the historical failure case library; By analyzing the deviation between the cyclical risk assessment sequence and the historical risk assessment sequence, a risk trend consistency index for the charger is obtained. The calculation formula for the risk trend consistency index is as follows: ； in, This indicates the consistency index of the aforementioned risk trends. This represents the risk assessment value for the current period. Indicates the first Risk assessment values ​​for each historical period, This indicates the preset time decay weight. Indicates the number of historical cycles. This represents the average value of the historical risk assessment sequence; The risk level of the charger is obtained by performing a multi-level assessment on the risk trend consistency index.

7. The method for real-time fault detection and safety disconnection of a charger as described in claim 1, characterized in that, The step of calling the baseline parameters from the pre-stored security baseline database and determining the first and second security thresholds of the charger based on the baseline parameters includes: Extract the historical operating mode spectrum corresponding to the charger from the pre-stored safety baseline database; By comparing the current operating state of the charger with the historical operating mode spectrum, the mode category of the charger can be obtained. A threshold mapping is performed on the modal categories to obtain the power limiting threshold of the charger; Based on the strength of the coupling anomaly identifier, the power limit threshold is offset and adjusted to obtain the first safety threshold of the charger.

8. The method for real-time fault detection and safety disconnection of a charger as described in claim 1, characterized in that, The step of calling the baseline parameters from the pre-stored security baseline database and determining the first and second security thresholds of the charger based on the baseline parameters includes: Extract fault features associated with the aging degree of the component from the pre-stored security baseline database; By co-analyzing the energy growth in a specific frequency band of the charger with the fault characteristics, the state characteristics of potential faults in the charger can be obtained. The state characteristics are quantified to obtain the state feature vector of the potential fault, and the state feature vector is input into the fault risk assessment matrix to obtain the second safety threshold of the charger.

9. The method for real-time fault detection and safety disconnection of a charger as described in claim 1, characterized in that, When the risk level reaches the first safety threshold, the output power of the charger is limited; When the risk level reaches the second safety threshold, the power switch connection of the charger is disconnected to achieve a safe disconnection, including: The risk level is input into a preset safety response strategy library to obtain the graded safety response strategy table of the charger; The risk level is monitored in real time. When the risk level exceeds the first safety threshold, the graded safety response strategy table is queried to obtain the primary safety response strategy of the charger. According to the primary safety response strategy, the output power of the charger is limited, and the risk change trend of the charger is continuously monitored; In power limiting mode, when the risk level is detected to be rising continuously and reaching the second safety threshold, the graded safety response strategy table is queried to obtain the ultimate safety response strategy of the charger. According to the primary safety response strategy, the switch connection of the charger is disconnected to achieve safety protection for the charger.

10. A charger fault real-time detection and safety disconnection system, used to implement the charger fault real-time detection and safety disconnection method according to claim 1, the system comprising: An abnormal state identification module is used to statistically analyze the overlap between the transient pulse amplitude and the high-frequency impact amplitude of the charger in the time domain. When the overlap exceeds a preset overlap threshold, a coupling abnormality identification of the charger is obtained. The component aging assessment module is used to perform differential analysis on the timing data of the switching frequency in the charger to obtain the changing trend of the switching frequency, and combine it with the energy growth of a specific frequency band in the charger to obtain the component aging degree of the charger. The fault risk matrix construction module is used to construct the fault risk assessment matrix of the charger with the anomaly type of the coupling anomaly identifier as the row dimension and the aging degree of the component aging degree as the column dimension. The risk level assessment module is used to comprehensively assess the current operating status of the charger based on the fault risk assessment matrix to obtain the risk level of the charger. The safety threshold determination module is used to call the baseline parameters in the pre-stored safety baseline database and determine the first safety threshold and the second safety threshold of the charger based on the baseline parameters. The charger control module is used to limit the output power of the charger when the risk level reaches the first safety threshold, and to disconnect the power switch of the charger when the risk level reaches the second safety threshold, thereby achieving a safe disconnection.

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