A charging module offline layered start and partition fault diagnosis test system

Abstract

This invention discloses an offline hierarchical startup and zone fault diagnosis test system for charging modules, belonging to the field of fault diagnosis technology. The offline docking module detaches the charging module from the main equipment and completes pre-test preparation; the digital twin module constructs a digital twin model containing equivalent models of multiple detection zones; the hierarchical startup module sequentially delays power-on according to the control low-voltage power supply, auxiliary power supply, and main power circuit, recording the power-on parameters; the zone diagnosis module collects electrical status data streams of each detection zone, deploys dynamic mapping points on the digital twin model, and generates a fault segment book; the fault determination module performs fault analysis based on the fault segment book, determining the fault propagation chain, root cause fault point, and complex fault evolution results; and the report generation module performs hierarchical power-off and generates a test report. This invention effectively achieves accurate fault location and complex deduction of charging modules through digital twin dynamic mapping and fault segment books.

Description

Technical Field

[0001] This invention relates to the field of fault diagnosis technology, and specifically to a charging module offline hierarchical startup and partition fault diagnosis test system. Background Technology

[0002] In existing technologies, offline testing of charging modules typically utilizes a general-purpose test bench. After the module is disconnected from the main equipment, the test system provides power and load to verify its power-on, load-bearing, and protection functions. In terms of fault diagnosis, threshold comparison is often used to compare the data collected independently from each functional area with preset standard values ​​to determine whether there are any abnormalities. However, existing technologies have certain shortcomings. First, the zonal diagnostics are isolated from each other, only comparing parameters of each zone independently. They cannot identify the coupling relationship between control, power, communication, and sampling zones, and it is difficult to distinguish between root cause faults and secondary faults. When an anomaly in one zone causes changes in parameters of adjacent zones, multiple faults are easily reported simultaneously, leading to misjudgments. At the same time, the comparison of fault data remains at the static comparison level, only comparing the difference between measured data and normal models. It is impossible to deduce the evolution path of the fault in time and space, and there is a lack of structured organization of fault data. Fault information is presented in the form of scattered alarms, making it difficult to achieve automatic extraction of cross-zone propagation chains and decoupling and locating multiple fault sources. This results in low diagnostic efficiency and insufficient location accuracy for complex coupled faults. Therefore, it is of great significance to develop an offline hierarchical startup and partition fault diagnosis test system for charging modules. Summary of the Invention

[0003] The purpose of this invention is to provide a charging module offline hierarchical startup and partition fault diagnosis test system to solve the problems in the background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a charging module offline hierarchical startup and partition fault diagnosis test system, comprising: Offline docking module: Used to disconnect the charging module from the main equipment and independently dock it with the test system. Through pre-test preparation, the charging module is put into the test state and serves as the charging module under test. Digital twin module: Connects to the offline docking module and constructs a digital twin model of the charging module under test based on its electrical data; Layered startup module: Connects to the offline docking module to construct a layered startup scheme, apply the layered startup scheme to the charging module under test, and record the power-on parameters layer by layer; Partition Diagnosis Module: Connected to the hierarchical startup module, after the charging module under test is started, the charging module under test is divided into multiple detection partitions, and the electrical status data streams of multiple detection partitions are collected in real time. By deploying dynamic mapping points on the digital twin model, a fault segment book is generated based on the electrical status data. Fault determination module: connected to the partition diagnosis module, used to receive electrical status data stream and fault segment list, perform fault analysis based on fault segment list, and generate fault analysis results; Report generation module: Connected to the fault determination module, it performs graded power outages on the charging module, ends the test, records the test process, and generates a test report.

[0005] In a preferred embodiment, the offline docking module includes: The offline state unit is used to disconnect the charging module from the logical link of the whole device, making the charging module independent of the whole device; The interface connection unit is used to independently connect the charging module to the test interface of the test system. The test interface includes a power sampling interface, a control interface, and a communication interface. The status self-test unit is used to perform pre-test self-tests to put the charging module into the test state. As the charging module under test, the pre-test self-test includes hardware connection detection, test system self-test, and sampling reference calibration.

[0006] In a preferred embodiment, the digital twin module includes: The data acquisition unit obtains the electrical data of the charging module under test based on the complete equipment manual. The electrical data includes rated parameters, circuit topology and historical operating data. The model building unit is used to build a digital twin model of the charging module under test based on electrical data. The digital twin model includes equivalent models of control partition, power partition, communication partition, and sampling partition.

[0007] In a preferred embodiment, the hierarchical startup module includes: The hierarchical scheme unit constructs the step-by-step delayed power-on sequence of the charging module under test based on the circuit hierarchy of the charging module under test; Configure a preset delay time, and output a power-on enable signal according to the preset delay time based on the step-by-step power-on sequence to start the charging module under test step by step; The power-on parameter acquisition unit is used to acquire and record the power-on parameters of each level during the power-on process, compare the power-on parameters of each level with the corresponding start-up threshold, determine whether the start-up of each level is normal, and stop the power-on of subsequent levels when any level starts abnormally.

[0008] In a preferred embodiment, the partition diagnostic module includes: The detection area configuration unit is used to divide the charging module under test into multiple detection zones, locate the areas with electrical connections between the detection zones as boundary nodes, and simultaneously mark the zone identifiers in the digital twin model; The detection zones include control zones, power zones, communication zones, and sampling zones. The parameter acquisition unit enters the normal testing process when all levels of the charging module under test start normally, and applies test data to the charging module under test. The test data-driven excitation causes the charging module under test to enter the test state, and the electrical status data stream of each test zone is collected synchronously. The electrical status data stream includes voltage data, current data, temperature data, communication message data and operating status parameters. The mapping point unit is used to set up a dynamic mapping mechanism on the digital twin model, generate fault segments based on the dynamic mapping mechanism, and generate a fault segment book based on multiple fault segments.

[0009] In a preferred embodiment, the step of setting a dynamic mapping mechanism on the digital twin model and generating fault fragments based on the dynamic mapping mechanism is as follows: Based on the electrical data of the charging module under test, the electrical data of the digital twin model is set as standard data, serving as the standard data twin model; The test data is applied to a standard data twin model to obtain the baseline data stream for each detection partition; A preset tolerance threshold is set, and the electrical status data stream of each detection zone is compared with the baseline data stream of each detection zone in real time to calculate the difference. When the difference at a certain location exceeds the tolerance threshold, the location is marked as a fault node, fault data is stored in the fault node, and mapping points are deployed. A mapping space is constructed based on the mapping points, and a subsequent electrical structure twin model of the fault node is generated in the mapping space, thus generating a mapping twin model. In the digital twin model, the fault data of the fault node is corrected to the corresponding data in the baseline data stream and then transmitted in the subsequent electrical structure twin model. In the mapping twin model, fault fragments are generated by parallel transmission of fault data, and multiple fault fragments are generated based on multiple mapping twin models; Multiple fault fragments are overlapped and concatenated at the boundary nodes according to the generation order of multiple fault fragments to generate a fault fragment book.

[0010] In a preferred embodiment, the partition diagnostic module includes: The fragment parsing unit parses the partition identifier, boundary node location, and timestamp carried by each faulty fragment; The path reachability determination unit is used to extract the circuit path between the corresponding mapping points of any two fault segments in the digital twin model; if there is at least one circuit path that makes the two mapping points electrically connected through the circuit path, then it is determined that the two have a path reachability relationship. The propagation relationship determination unit is used to compare the order of the timestamps of two fault segments that have a path reachability relationship, and extract the transmission characteristics of the fault data of the two segments on the circuit path. If the chronological order is consistent with the signal flow direction of the circuit path in the digital twin model, and the transmission characteristics meet the preset propagation conditions, then the two are determined to belong to the same fault propagation chain; otherwise, the two are determined to be independent faults. Among them, the preset propagation conditions include whether the amplitude attenuation of the fault data of the two fault segments at the junction node is within a preset range and whether the time interval matches the signal propagation delay of the circuit path in the digital twin model. The fault propagation chain construction unit is used to connect fault fragments belonging to the same fault propagation chain in sequence according to timestamps to form a cross-partition fault propagation chain. The starting point of the fault propagation chain is the fault fragment with the earliest timestamp in the fault propagation chain. The root cause fault location unit is used to locate the mapping point corresponding to the fault segment at the starting point of each fault propagation chain as the root cause fault point, and output the partition to which the mapping point belongs and the fault type. If multiple fault propagation chains exist, the system is determined to have multiple independent fault sources. The composite fault simulation unit is used to select fault segments with at least two different fault propagation chains, and superimpose their fault data on the data twin model according to their respective mapping point positions to perform composite fault simulation. After the complex fault simulation is completed, the mapping points in the digital twin model whose deviation exceeds the preset threshold are extracted, a list of affected mapping points is generated, and the partition to which each mapping point belongs is marked. Simultaneously, the action state of the protection circuit model in the digital twin model is extracted, and the protection action timing and protection action type are output as the result of the complex fault evolution.

[0011] In a preferred embodiment, the report generation module includes: The graded power-off control unit is used to, after fault determination, sequentially power off the charging module under test in the reverse order of the graded startup module, and record the parameter changes of each grade during the power-off process. The test report generation unit is used to summarize the power-on parameters, electrical status data streams of each test zone, zone identifiers and boundary node locations carried by each fault segment, fault propagation chain information, root cause fault location results, composite fault evolution results, and parameter changes to generate an offline test report.

[0012] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention constructs a digital twin model and deploys dynamic mapping points on it to compare the electrical status data streams of each detection zone of the charging module with the reference data stream in real time. Once the deviation exceeds the tolerance threshold, a mapping point is automatically generated at the corresponding circuit node and the fault data is stored. On this basis, the system uses the boundary node as a link to overlap and connect multiple fault segments in the order of generation to form a fault segment book, realizing the structured organization and spatial expression of fault information. Based on this mechanism, faults are no longer presented as isolated parameter alarms, but are recorded and associated in the form of segments with partition identifiers, location information and timestamps, which significantly improves the precision and automation level of fault diagnosis. 2. This invention solves the problem in existing partition diagnosis methods where remote faults are misjudged as independent faults due to intermediate nodes not triggering thresholds by determining path reachability and constructing fault propagation chains. Utilizing the circuit topology of a digital twin model, it determines whether an electrical path exists between the mapping points of any two fault segments. Combining propagation characteristics such as timestamp order, amplitude attenuation, and signal propagation delay, it automatically identifies fault segments belonging to the same fault propagation chain. Based on this mechanism, it effectively distinguishes between root cause faults and secondary faults, overcomes the limitations of traditional isolated partition diagnosis, and significantly improves the accuracy and efficiency of locating complex coupled faults. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1, please refer to Figure 1 As shown in this embodiment, a charging module offline hierarchical startup and partition fault diagnosis test system includes: Offline docking module: Used to disconnect the charging module from the main equipment and independently dock it with the test system. Through pre-test preparation, the charging module is put into the test state and serves as the charging module under test. Digital twin module: Connects to the offline docking module and constructs a digital twin model of the charging module under test based on its electrical data; Layered startup module: Connects to the offline docking module to construct a layered startup scheme, apply the layered startup scheme to the charging module under test, and record the power-on parameters layer by layer; Partition Diagnosis Module: Connected to the hierarchical startup module, after the charging module under test is started, the charging module under test is divided into multiple detection partitions, and the electrical status data streams of multiple detection partitions are collected in real time. By deploying dynamic mapping points on the digital twin model, a fault segment book is generated based on the electrical status data. Fault determination module: Connected to the partition diagnosis module, it is used to receive electrical status data streams and fault segment books, perform fault analysis based on the fault segment books, and generate fault analysis results; Report generation module: Connected to the fault determination module, it performs graded power outages on the charging module, ends the test, records the test process, and generates a test report; Furthermore, in existing technologies, offline testing of charging modules typically utilizes a general-purpose test bench. After the module is disconnected from the complete device, the test system provides power and load to verify its power-on, load-bearing, and protection functions. In terms of fault diagnosis, threshold comparison is often used to compare the data collected independently from each functional area with preset standard values ​​to determine whether there are any abnormalities. However, existing technologies have certain shortcomings. First, the zonal diagnostics are isolated from each other, only comparing parameters of each zone independently. They cannot identify the coupling relationship between control, power, communication, and sampling zones, and it is difficult to distinguish between root cause faults and secondary faults. When an anomaly in one zone causes changes in parameters of adjacent zones, multiple faults are easily reported simultaneously, leading to misjudgments. At the same time, the comparison of fault data remains at the static comparison level, only comparing the difference between measured data and normal models. It is impossible to deduce the evolution path of the fault in time and space, and there is a lack of structured organization of fault data. Fault information is presented in the form of scattered alarms, making it difficult to achieve automatic extraction of cross-zone propagation chains and decoupling and locating multiple fault sources. This results in low diagnostic efficiency and insufficient location accuracy for complex coupled faults. This invention constructs a digital twin model and deploys dynamic mapping points on it to compare the electrical status data streams of each detection zone of the charging module with the reference data stream in real time. Once the deviation exceeds the tolerance threshold, a mapping point is automatically generated at the corresponding circuit node and the fault data is stored. Based on this, the system uses the boundary node as a link to overlap and connect multiple fault segments in the order of generation to form a fault segment book, realizing the structured organization and spatial expression of fault information. Based on this mechanism, faults are no longer presented as isolated parameter alarms, but are recorded and associated in the form of segments with partition identifiers, location information, and timestamps, which significantly improves the precision and automation level of fault diagnosis. By determining path reachability and constructing fault propagation chains, this method solves the problem in existing partition diagnosis methods where remote faults are misjudged as independent faults due to intermediate nodes not triggering thresholds. Utilizing the circuit topology of a digital twin model, it determines whether there is an electrical path between the mapping points of any two fault segments. Combining propagation characteristics such as timestamp order, amplitude attenuation, and signal propagation delay, it automatically identifies fault segments belonging to the same fault propagation chain. Based on this mechanism, it effectively distinguishes between root cause faults and secondary faults, overcomes the limitations of traditional isolated partition diagnosis, and significantly improves the accuracy of locating and diagnosing complex coupled faults.

[0017] In one embodiment, the offline docking module includes: The offline state unit is used to disconnect the charging module from the logical link of the whole device, making the charging module independent of the whole device; The interface connection unit is used to independently connect the charging module to the test interface of the test system. The test interface includes a power sampling interface, a control interface, and a communication interface. The status self-test unit is used to perform pre-test self-tests to put the charging module into the test state. As the charging module under test, the pre-test self-test includes hardware connection detection, test system self-test and sampling reference calibration. Furthermore, the offline state unit disconnects the control bus, communication bus, and power bus connections between the charging module and the complete device, completely isolating the charging module electrically from the complete device. At this point, the charging module no longer receives any control commands from the complete device, nor does it provide status feedback. This ensures that all excitation and response signals during subsequent testing are generated solely between the test system and the charging module, preventing interference from the complete device on the test results. After establishing the offline state, the interface connection unit physically connects the charging module's reserved power sampling interface, control interface, and communication interface to the corresponding test interfaces of the test system. The power sampling interface is used to connect the voltage and current signals from each sampling point inside the charging module to the test system. The control interface is used to transmit enable signals, pulse width modulation signals, and other control signals sent from the test system to the charging module. The communication interface is used to realize data message exchange between the test system and the charging module. After all interfaces are connected, the interface connection unit performs connectivity checks on each connection point to ensure there are no short circuits, open circuits, or poor contacts. After the interface connection is completed, the status self-test unit first performs hardware connection detection, sending test signals to each interface channel by channel and reading the return signals to verify the electrical connectivity and signal integrity of the power sampling channel, control channel, and communication channel. Then, it performs a test system self-test, performing self-diagnosis on the power module, acquisition module, and communication module inside the test system to ensure that each component of the test system is in normal working condition. Finally, it performs sampling reference calibration by injecting known standard voltage and standard current signals into the test system to calibrate the zero point and range of each sampling channel, eliminating the sampling deviation of the test system itself. After all three tests are passed, the status self-test unit marks the charging module as the charging module to be tested and puts it into the test state.

[0018] In one embodiment, the digital twin module includes: The data acquisition unit obtains the electrical data of the charging module under test based on the complete equipment manual. The electrical data includes rated parameters, circuit topology and historical operating data. The model building unit is used to build a digital twin model of the charging module under test based on electrical data. The digital twin model includes equivalent models of control partition, power partition, communication partition, and sampling partition. Furthermore, the data acquisition unit extracts the rated parameters, circuit topology, and historical operating data of the charging module under test by reading the technical specifications, circuit diagrams, and bill of materials of the charging module under test as recorded in the complete equipment manual. The rated parameters include the rated input voltage range, rated output voltage range, rated output current, rated power, switching frequency, various protection thresholds, and operating temperature range. The circuit topology includes the connection relationships and component parameters between the input rectifier circuit, power factor correction circuit, DC-DC converter circuit, output filter circuit, and auxiliary power supply circuit. The historical operating data includes the efficiency curves, temperature rise records, protection action records, and fault repair records of the charging module under test under different load conditions. After acquiring the above electrical data, the model building unit, according to the actual circuit topology of the charging module under test, builds equivalent models of control zones and power zones step by step in the digital twin environment. The system includes equivalent models for the control, communication, and sampling zones. The control zone equivalent model includes equivalent models for the control chip, pulse width modulation signal generation circuit, and feedback loop. The power zone equivalent model includes equivalent models for the switching transistor, transformer, inductor, capacitor, and drive circuit. The communication zone equivalent model includes equivalent models for the communication interface chip, transceiver, and communication protocol processing. The sampling zone equivalent model includes equivalent models for the voltage sampling circuit, current sampling circuit, temperature sampling circuit, and signal conditioning circuit. The equivalent models for each zone are electrically connected according to the signal flow and power supply dependence in the actual circuit. Boundary nodes and zone identifiers are synchronously marked at the connection points of the equivalent models for each zone, ensuring that the digital twin model is consistent with the charging module under test at the topology level.

[0019] In one embodiment, the hierarchical startup module includes: The hierarchical scheme unit constructs the step-by-step delayed power-on sequence of the charging module under test based on the circuit hierarchy of the charging module under test; Configure a preset delay time, and output a power-on enable signal according to the preset delay time based on the step-by-step power-on sequence to start the charging module under test step by step; The power-on parameter acquisition unit is used to acquire and record the power-on parameters of each level during the power-on process, compare the power-on parameters of each level with the corresponding start-up threshold, determine whether the start-up of each level is normal, and stop the power-on of subsequent levels when any level starts abnormally. Furthermore, the hierarchical scheme unit first reads the circuit hierarchy of the charging module under test (DUT). Based on the power supply dependencies and signal transmission sequence among the control low-voltage power supply, auxiliary power supply, and main power circuit in the DUT, it constructs a step-by-step delayed power-on sequence for the DUT. The control low-voltage power supply level is designated as the first level, the auxiliary power supply level as the second level, and the main power circuit level as the third level. The startup of each level is contingent upon the normal operation of the preceding level. After determining the step-by-step delayed power-on sequence, the hierarchical scheme unit configures preset delay times between adjacent levels. Specifically, a first preset delay time is configured between the control low-voltage power supply level and the auxiliary power supply level, and a second preset delay time is configured between the auxiliary power supply level and the main power circuit level. A second preset delay time is configured. The first and second preset delay times can be adjusted according to the specifications and actual startup characteristics of the charging module under test. Based on the step-by-step power-on sequence and preset delay times, a power-on enable signal is output. First, a control low-voltage power supply enable signal is output to the control low-voltage power supply level to establish a stable control supply voltage. After the first preset delay time, an auxiliary power supply enable signal is output to the auxiliary power supply level to establish auxiliary supply voltages for each circuit. After the second preset delay time, a main power circuit enable signal is output to the main power circuit level to start the main power circuit, thereby achieving a smooth and safe startup of the charging module. Power-on parameter acquisition... During the power-on process of each level, the unit synchronously collects and records the power-on parameters of that level. During the power-on process of the low-voltage control power supply level, it collects the output voltage, output current, ripple voltage, and control chip operating status signal of the low-voltage control power supply. The collected power-on parameters of the low-voltage control power supply level are compared with the corresponding start-up threshold. If the output voltage of the low-voltage control power supply is within the start-up threshold range, the output current does not exceed the upper limit of the start-up threshold, and the control chip operating status signal is normal, then the low-voltage control power supply level is judged to have started normally, and entry into the auxiliary power supply level is allowed. During the power-on process of the auxiliary power supply level, it collects the output voltages of each auxiliary power supply, the power supply voltage of the drive circuit, and the auxiliary power supply protection voltage. The auxiliary power supply level is started normally, and the power-on parameters of the auxiliary power supply level are compared with the corresponding start-up threshold. If the output voltage of each circuit is within the allowable range, the power supply voltage of the drive circuit is normal, and the protection circuit is not triggered, then the auxiliary power supply level is judged to start normally and is allowed to enter the main power circuit level. During the power-on process of the main power circuit level, the input current, bus voltage, output voltage and output current of the main power circuit are collected. The power-on parameters of the main power circuit level are compared with the corresponding start-up threshold. If the input current does not exceed the inrush current threshold, the bus voltage is established normally, and the output voltage and output current meet the expected range, then the main power circuit level is judged to start normally and the layered start-up process is completed.During the comparison of power-on parameters at any level, if any power-on parameter exceeds the corresponding start-up threshold range or an abnormal state signal occurs, the power-on parameter acquisition unit immediately determines that the level has a start-up abnormality, stops outputting power-on enable signals to subsequent levels, and records the power-on parameters and abnormality type at the time of the abnormality.

[0020] In one embodiment, the partition diagnostic module includes: The detection area configuration unit is used to divide the charging module under test into multiple detection zones, locate the areas with electrical connections between the detection zones as boundary nodes, and simultaneously mark the zone identifiers in the digital twin model; The detection zones include control zones, power zones, communication zones, and sampling zones. The parameter acquisition unit enters the normal testing process when all levels of the charging module under test start normally, and applies test data to the charging module under test. The test data-driven excitation causes the charging module under test to enter the test state, and the electrical status data stream of each test zone is collected synchronously. The electrical status data stream includes voltage data, current data, temperature data, communication message data and operating status parameters. Mapping point unit is used to set up a dynamic mapping mechanism on the digital twin model, generate fault segments based on the dynamic mapping mechanism, and generate a fault segment book based on multiple fault segments; Furthermore, after the charging module under test (DUT) has started normally at all levels, the detection area configuration unit divides the DUT into control, power, communication, and sampling zones based on the functional attributes of each circuit module. The control zone corresponds to the control chip and its peripheral circuits; the power zone corresponds to the main power conversion link and its drive circuits; the communication zone corresponds to the communication interface chip and its transceiver circuits; and the sampling zone corresponds to the voltage sampling circuit, current sampling circuit, temperature sampling circuit, and signal conditioning circuit. After completing the detection zone division, the detection area configuration unit analyzes the areas in the DUT where there is an electrical connection between any two detection zones. Circuit nodes or signal paths belonging to at least two detection zones are located as boundary nodes, such as the pulse width modulation signal path between the control and power zones, the sampling resistor connection point between the power and sampling zones, and the communication bus interface between the control and communication zones. The corresponding zone identifiers and boundary node location information are simultaneously marked in the digital twin model. After the detection area configuration unit completes the detection zone division and boundary node location, the parameter acquisition unit enters the normal testing process, applying test data to the DUT. The test includes input voltage, output load current, ambient temperature, and communication commands under different load conditions. The test data excites the charging module under test (DUT) to enter the test state. During the test state, the parameter acquisition unit synchronously acquires electrical status data streams from each detection zone. The electrical status data stream for the control zone includes the control chip supply voltage, pulse width modulation signal duty cycle, feedback loop error signal, and control chip temperature. The electrical status data stream for the power zone includes the drain current of the switching transistor, transformer primary voltage, output inductor current, bus voltage, and power device temperature. The electrical status data stream for the communication zone... The electrical status data stream includes communication message data, communication interface chip power supply voltage, transceiver status register data, and communication bit error rate. The electrical status data stream of the sampling partition includes voltage sampling output value, current sampling output value, temperature sampling output value, and signal conditioning circuit output value. All electrical status data streams are timestamped to align the data of different detection partitions in time. While the parameter acquisition unit synchronously acquires the electrical status data streams of each detection partition, the mapping point unit sets up a dynamic mapping mechanism on the digital twin model, generates fault segments based on the dynamic mapping mechanism, and generates a fault segment book through multiple fault segments.

[0021] In one embodiment, the step of setting a dynamic mapping mechanism on the digital twin model and generating fault fragments based on the dynamic mapping mechanism is as follows: Based on the electrical data of the charging module under test, the electrical data of the digital twin model is set as standard data, serving as the standard data twin model; The test data is applied to a standard data twin model to obtain the baseline data stream for each detection partition; A preset tolerance threshold is set, and the electrical status data stream of each detection zone is compared with the baseline data stream of each detection zone in real time to calculate the difference. When the difference at a certain location exceeds the tolerance threshold, the location is marked as a fault node, fault data is stored in the fault node, and mapping points are deployed. A mapping space is constructed based on the mapping points, and a subsequent electrical structure twin model of the fault node is generated in the mapping space, thus generating a mapping twin model. In the digital twin model, the fault data of the fault node is corrected to the corresponding data in the baseline data stream and then transmitted in the subsequent electrical structure twin model. In the mapping twin model, fault fragments are generated by parallel transmission of fault data, and multiple fault fragments are generated based on multiple mapping twin models; Multiple fault fragments are overlapped and concatenated at the boundary nodes according to the generation order of multiple fault fragments to generate a fault fragment book. Furthermore, based on the electrical data of the charging module under test (DUT), the digital twin module standardizes the voltage, current, temperature, and status parameters of each circuit node in the digital twin model into standard data. This standard data originates from the rated parameters of the DUT and typical values ​​under normal operating conditions from historical operating data. A standard data twin model is then generated. Test data is applied to this standard data twin model, and simulation is performed using it to obtain the reference data stream for each detection zone at its corresponding position on the standard data twin model. This reference data stream reflects the expected electrical data values ​​that each detection zone should present under fault-free conditions. A preset tolerance threshold is also included, which is determined based on the requirements of each detection zone. The accuracy requirements and noise characteristics of different electrical parameters are set separately. The electrical status data streams of each detection zone are compared with the reference data streams of each detection zone in real time to calculate the difference. The difference is the absolute deviation between the parameter values ​​in the electrical status data stream and the corresponding parameter values ​​in the reference data stream. When the difference at a certain position exceeds the tolerance threshold corresponding to that position, the position is marked as a fault node. The electrical status data stream segment that causes the difference to exceed the limit is stored as fault data in the fault node. A mapping point is deployed at the corresponding circuit node in the digital twin model. The mapping point carries the partition identifier, circuit node coordinates and timestamp information of the fault node as the anchor mark of the fault in the digital twin model. After the mapping points are deployed, the mapping point unit constructs a mapping space based on the mapping points. The mapping space is a simulation space independently created outside the digital twin model. The mapping space shares the same circuit topology and component parameters as the digital twin model. The mapping point unit generates the subsequent electrical structure twin model of the fault node in the mapping space. The subsequent electrical structure twin model includes all circuit nodes and connections extending downstream from the fault node along the signal flow and power flow, thereby generating the mapping twin model. This mapping twin model is only used to carry the independent evolution process of the fault data. Simultaneously with the generation of the mapping twin model, the mapping point unit corrects the fault data of the fault node in the digital twin model to the corresponding data in the reference data stream. According to the standard data, the fault data is replaced with standard data so that the digital twin model maintains the standard data state in subsequent simulations. The corrected standard data continues to be transmitted along the electrical structure after the fault node in the digital twin model, ensuring that the digital twin model does not deviate from the normal operating baseline due to the continued existence of fault data. In parallel, the mapping point unit transmits and evolves the fault data along the electrical structure after the fault node in the mapping twin model, generating the fault segment corresponding to the fault node. The fault segment is a data set generated by the independent evolution of fault data in the mapping twin model, including voltage deviation data, current deviation data, temperature deviation data, and state parameter change data caused by the fault data at subsequent circuit nodes.When the charging module under test encounters multiple fault nodes during testing, the mapping point unit constructs its own mapping space and mapping twin model based on the mapping points corresponding to each fault node. Each mapping twin model independently generates a fault segment, resulting in multiple fault segments. Each fault segment carries its corresponding partition identifier, boundary node position, and timestamp information during generation. The mapping point unit uses the boundary nodes as the connection basis and overlaps and concatenates multiple fault segments according to their generation order to generate a fault segment book. Specifically, when two adjacent fault segments have fault data records at at least one boundary node, the two fault segments are aligned and connected at that boundary node, creating a multi-layered concatenated structure with boundary nodes as connection points and the generation order as the arrangement direction.

[0022] In one embodiment, the partition diagnostic module includes: The fragment parsing unit parses the partition identifier, boundary node location, and timestamp carried by each faulty fragment; The path reachability determination unit is used to extract the circuit path between the corresponding mapping points of any two fault segments in the digital twin model; if there is at least one circuit path that makes the two mapping points electrically connected through the circuit path, then it is determined that the two have a path reachability relationship. The propagation relationship determination unit is used to compare the order of the timestamps of two fault segments that have a path reachability relationship, and extract the transmission characteristics of the fault data of the two segments on the circuit path. If the chronological order is consistent with the signal flow direction of the circuit path in the digital twin model, and the transmission characteristics meet the preset propagation conditions, then the two are determined to belong to the same fault propagation chain; otherwise, the two are determined to be independent faults. Among them, the preset propagation conditions include whether the amplitude attenuation of the fault data of the two fault segments at the junction node is within a preset range and whether the time interval matches the signal propagation delay of the circuit path in the digital twin model. The fault propagation chain construction unit is used to connect fault fragments belonging to the same fault propagation chain in sequence according to timestamps to form a cross-partition fault propagation chain. The starting point of the fault propagation chain is the fault fragment with the earliest timestamp in the fault propagation chain. The root cause fault location unit is used to locate the mapping point corresponding to the fault segment at the starting point of each fault propagation chain as the root cause fault point, and output the partition to which the mapping point belongs and the fault type. If multiple fault propagation chains exist, the system is determined to have multiple independent fault sources. The composite fault simulation unit is used to select fault segments with at least two different fault propagation chains, and superimpose their fault data on the data twin model according to their respective mapping point positions to perform composite fault simulation. After the complex fault simulation is completed, the mapping points in the digital twin model whose deviation exceeds the preset threshold are extracted, a list of affected mapping points is generated, and the partition to which each mapping point belongs is marked. Simultaneously, the action state of the protection circuit model in the digital twin model is extracted, and the protection action timing and protection action type are output as the result of the composite fault evolution. Furthermore, the fault segment parsing unit first reads the fault segment list generated by the partition diagnosis module, and parses the partition identifier, boundary node position, and timestamp carried by each fault segment in the fault segment list. The partition identifier is used to determine the detection partition to which the fault segment belongs, the boundary node position is used to determine the specific circuit node coordinates of the fault segment at the boundary of the detection partition, and the timestamp is used to determine the chronological order of the fault segment generation. The parsed information is organized into a fault segment information list according to the order of the fault segments for subsequent use by other units. After obtaining the fault segment information list, the path reachability determination unit performs path reachability analysis on any two fault segments in the list. The path reachability determination unit then performs path reachability analysis on any two fault segments in the digital twin model. In the model, the mapping points corresponding to the two fault segments are located respectively. Based on the circuit topology in the digital twin model, all circuit paths between the two mapping points are extracted. The circuit path is the set of conductive paths connecting the two mapping points in the digital twin model, including a single conductive path directly connecting the two mapping points and multiple conductive paths connecting the two mapping points through intermediate circuit nodes. If there is at least one circuit path that electrically connects the two mapping points, it is determined that the two fault segments have a path reachability relationship. If there is no circuit path that electrically connects the two mapping points, it is determined that the two fault segments do not have a path reachability relationship. The propagation relationship determination unit determines the path reachability relationship for... The propagation relationship analysis is performed on two fault segments. The propagation relationship determination unit compares the order of the timestamps of the two fault segments, determining that the fault segment with the earlier timestamp is the preceding fault segment and the fault segment with the later timestamp is the following fault segment. Then, the propagation characteristics of the preceding and following fault segments in the circuit path between them are extracted. These characteristics include the attenuation degree between the fault data amplitude of the preceding fault segment at the boundary node and the fault data amplitude of the following fault segment at the corresponding boundary node, as well as the time interval between the timestamps of the preceding and following fault segments. The propagation relationship determination unit compares the extracted propagation characteristics with preset propagation conditions, including amplitude attenuation. If the magnitude of the fault is within a preset range and the time interval matches the signal propagation delay of the circuit path in the digital twin model, and if the time interval between the timestamp of the preceding fault segment and the timestamp of the following fault segment is consistent with or substantially consistent with the signal propagation delay of the circuit path in the digital twin model, and the magnitude attenuation of the preceding fault segment and the following fault segment at the junction node falls within a preset range set according to the impedance characteristics of the circuit path, then the two fault segments are determined to belong to the same fault propagation chain. If the time sequence is inconsistent with the signal flow direction of the circuit path in the digital twin model, or the magnitude attenuation exceeds the preset range, or the time interval does not match the signal propagation delay significantly, then the two fault segments are determined to be independent faults.After the propagation relationship determination unit completes the determination of all fault fragment pairs with path reachability, the fault propagation chain construction unit concatenates fault fragments belonging to the same fault propagation chain in chronological order according to their timestamps to form a cross-partition fault propagation chain. The starting point of the cross-partition fault propagation chain is the fault fragment with the earliest timestamp in the chain, and the ending point is the fault fragment with the latest timestamp. If multiple fault fragments in the same fault propagation chain have the same timestamp, they are concatenated in chronological order according to their arrangement in the fault fragment register. The root cause fault location unit reads all cross-partition fault propagation chain outputs from the fault propagation chain construction unit. The fault propagation chain is defined, and the starting fault segment of each cross-regional fault propagation chain is located. The root source fault location unit locates the mapping point corresponding to the starting fault segment as the root source fault point and outputs the region to which the mapping point belongs and the fault type. The fault type is determined based on the electrical characteristics of the fault data at the root source fault point, including overcurrent fault, overvoltage fault, undervoltage fault, overtemperature fault, communication abnormality fault, and control abnormality fault. If the fault propagation chain construction unit outputs multiple cross-regional fault propagation chains, the root source fault location unit determines that the charging module under test has multiple independent fault sources and outputs the root source fault point, its region, and fault type corresponding to each independent fault source. After the root cause fault location unit completes the root cause fault location, if at least two cross-partition fault propagation chains exist, the composite fault simulation unit selects at least two fault segments from different cross-partition fault propagation chains. The fault data of each selected fault segment is then superimposed and injected into the digital twin model according to its corresponding mapping point position. Superposition injection involves simultaneously writing the fault data of each fault segment into the node data at the corresponding mapping point in the digital twin model. Subsequently, a composite fault simulation is run on the digital twin model. During the composite fault simulation, the fault data at each mapping point propagates downstream along the circuit topology of the digital twin model and superimposes with each other. After the composite fault simulation is completed, the results are submitted... The system extracts mapping points from the digital twin model whose deviations exceed a preset threshold, generates a list of affected mapping points, and labels the partition to which each mapping point belongs. Simultaneously, it extracts the operational status of the protection circuit model within the digital twin model, outputting the protection action sequence and type. Protection action types include overcurrent protection, overvoltage protection, undervoltage protection, overtemperature protection, and communication interruption protection. The protection action sequence represents the trigger order and timing of each protection action on the composite fault simulation timeline. The composite fault inference unit outputs the list of affected mapping points, the partition labeling information for each mapping point, the protection action sequence, and the protection action type as the composite fault evolution result.

[0023] In one embodiment, the report generation module includes: The graded power-off control unit is used to, after fault determination, sequentially power off the charging module under test in the reverse order of the graded startup module, and record the parameter changes of each grade during the power-off process. The test report generation unit is used to summarize the power-on parameters, electrical status data streams of each test zone, zone identifiers and boundary node locations carried by each fault segment, fault propagation chain information, root cause fault location results, composite fault evolution results, and parameter changes to generate an offline test report. Furthermore, after the fault determination module completes the root cause fault location and complex fault deduction, the tiered power-off control unit performs a tiered power-off operation on the charging module under test in the reverse order of the tiered startup module's power-on sequence. Specifically, it performs a power-off operation on the main power circuit level of the charging module under test, outputting a main power circuit shutdown signal to the main power circuit level, causing the switching transistors in the main power circuit to stop switching, and the output current in the main power circuit to begin to decay. Simultaneously, the tiered power-off control unit collects and records the bus voltage drop curve, the output current decay process, and voltage change data at key nodes of the main power circuit during the main power circuit power-off process. After the main power circuit level power-off is completed and a preset power-off delay has elapsed, the tiered power-off control unit performs a power-off operation on the auxiliary power supply level of the charging module under test, outputting an auxiliary power supply shutdown signal to the auxiliary power supply level, causing the auxiliary power supply to shut down. The power supply stops supplying power to each drive circuit and sampling circuit. Simultaneously, the order of voltage drops in each auxiliary power supply, the disappearance process of the power supply voltage of the drive circuit, and the status changes of the auxiliary power supply protection circuit are collected and recorded during the auxiliary power supply power failure. After the auxiliary power supply level power failure is completed and a preset power failure delay has elapsed, the graded power failure control unit performs a power failure operation on the control low-voltage power supply level of the charging module under test. It outputs a control low-voltage power supply shutdown signal to the control low-voltage power supply level, causing the control low-voltage power supply to stop supplying power to the control chip. Simultaneously, the decrease process of the control chip power supply voltage, the disappearance sequence of the control chip working status signal, and the voltage change data of each node in the control partition are collected and recorded during the control low-voltage power failure. The graded power failure control unit outputs all parameter change data recorded during the power failure of the main power circuit level, the auxiliary power supply level, and the control low-voltage power supply level as the power failure process record.After the graded power-off control unit completes the step-by-step power-off, the test report generation unit summarizes the power-on parameters recorded by the graded startup module, the electrical status data streams of each detection zone collected by the zone diagnosis module, the zone identifiers and boundary node positions carried by each fault zone in the fault zone register, the fault propagation chain information, the root cause fault location results, the composite fault evolution results, and the parameter changes of each level recorded by the graded power-off control unit during the power-off process, and generates an offline test report. The offline test report is organized in the order of the test process as follows: the graded startup conclusion section, the zone operation status summary section, the fault diagnosis conclusion section, the composite fault deduction result section, and the power-off process record section. The graded startup conclusion section lists the control low-voltage power supply level, the auxiliary power supply level, and the main power supply level. The document presents a comparison of measured values ​​of power-on parameters at each power circuit level with the start-up threshold, along with conclusions regarding the normality of start-up at each level. The zone operation status summary lists the key parameter statistics and abnormal fluctuations of the electrical status data streams for each control zone, power zone, communication zone, and sampling zone. The fault diagnosis conclusions list the zone to which the root cause fault point belongs, the fault type, and the number of independent fault sources corresponding to the starting fault segment of each fault propagation chain. The composite fault deduction results list the affected mapping points, the zone labeling of each mapping point, the protection action sequence, and the protection action type. The power outage process record lists the changes in voltage, current, and status parameters at the main power circuit level, auxiliary power supply level, and control low-voltage power supply level during the power outage process.

[0024] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A charging module offline hierarchical startup and partition fault diagnosis test system, characterized in that, include: Offline docking module: Used to disconnect the charging module from the main equipment and independently dock it with the test system. Through pre-test preparation, the charging module is put into the test state and serves as the charging module under test. Digital twin module: Connects to the offline docking module and constructs a digital twin model of the charging module under test based on its electrical data; Layered startup module: Connects to the offline docking module to construct a layered startup scheme, apply the layered startup scheme to the charging module under test, and record the power-on parameters layer by layer; Partition Diagnosis Module: Connected to the hierarchical startup module, after the charging module under test is started, the charging module under test is divided into multiple detection partitions, and the electrical status data streams of multiple detection partitions are collected in real time. By deploying dynamic mapping points on the digital twin model, a fault segment book is generated based on the electrical status data streams. Fault determination module: connected to the partition diagnosis module, used to receive electrical status data stream and fault segment list, perform fault analysis based on fault segment list, and generate fault analysis results; Report generation module: Connected to the fault determination module, it performs graded power outages on the charging module, ends the test, records the test process, and generates a test report.

2. The charging module offline layered startup and partition fault diagnosis test system according to claim 1, characterized in that, The offline docking module includes: The offline state unit is used to disconnect the charging module from the logical link of the whole device, making the charging module independent of the whole device; The interface connection unit is used to independently connect the charging module to the test interface of the test system. The test interface includes a power sampling interface, a control interface, and a communication interface. The status self-test unit is used to perform pre-test self-tests to put the charging module into the test state. As the charging module under test, the pre-test self-test includes hardware connection detection, test system self-test, and sampling reference calibration.

3. The charging module offline layered startup and partition fault diagnosis test system according to claim 1, characterized in that, The digital twin module includes: The data acquisition unit obtains the electrical data of the charging module under test based on the complete equipment manual. The electrical data includes rated parameters, circuit topology and historical operating data. The model building unit is used to build a digital twin model of the charging module under test based on electrical data. The digital twin model includes equivalent models of control partition, power partition, communication partition, and sampling partition.

4. The charging module offline layered startup and partition fault diagnosis test system according to claim 1, characterized in that, The hierarchical startup module includes: The hierarchical scheme unit constructs the step-by-step delayed power-on sequence of the charging module under test based on the circuit hierarchy of the charging module under test; Configure a preset delay time, and output a power-on enable signal according to the preset delay time based on the step-by-step power-on sequence to start the charging module under test step by step; The power-on parameter acquisition unit is used to acquire and record the power-on parameters of each level during the power-on process, compare the power-on parameters of each level with the corresponding start-up threshold, determine whether the start-up of each level is normal, and stop the power-on of subsequent levels when any level starts abnormally.

5. The charging module offline layered startup and partition fault diagnosis test system according to claim 4, characterized in that, The partition diagnostic module includes: The detection area configuration unit is used to divide the charging module under test into multiple detection zones, locate the areas with electrical connections between the detection zones as boundary nodes, and simultaneously mark the zone identifiers in the digital twin model; The detection zones include control zones, power zones, communication zones, and sampling zones. The parameter acquisition unit enters the normal testing process when all levels of the charging module under test start normally, and applies test data to the charging module under test. The test data-driven excitation causes the charging module under test to enter the test state, and the electrical status data stream of each test zone is collected synchronously. The electrical status data stream includes voltage data, current data, temperature data, communication message data and operating status parameters. The mapping point unit is used to set up a dynamic mapping mechanism on the digital twin model, generate fault segments based on the dynamic mapping mechanism, and generate a fault segment book based on multiple fault segments.

6. The offline layered startup and partitioned fault diagnosis test system for a charging module according to claim 5, characterized in that, The steps for setting a dynamic mapping mechanism on the digital twin model and generating fault fragments based on the dynamic mapping mechanism are as follows: Based on the electrical data of the charging module under test, the electrical data of the digital twin model is set as standard data, serving as the standard data twin model; The test data is applied to a standard data twin model to obtain the baseline data stream for each detection partition; A preset tolerance threshold is set, and the electrical status data stream of each detection zone is compared with the baseline data stream of each detection zone in real time to calculate the difference. When the difference at a certain location exceeds the tolerance threshold, the location is marked as a fault node, fault data is stored in the fault node, and mapping points are deployed. A mapping space is constructed based on the mapping points, and a subsequent electrical structure twin model of the fault node is generated in the mapping space, thus generating a mapping twin model. In the digital twin model, the fault data of the fault node is corrected to the corresponding data in the baseline data stream and then transmitted in the subsequent electrical structure twin model. In the mapping twin model, fault fragments are generated by parallel transmission of fault data, and multiple fault fragments are generated based on multiple mapping twin models; Multiple fault fragments are overlapped and concatenated at the boundary nodes according to the generation order of multiple fault fragments to generate a fault fragment book.

7. The charging module offline layered startup and partition fault diagnosis test system according to claim 6, characterized in that, The partition diagnostic module includes: The fragment parsing unit parses the partition identifier, boundary node location, and timestamp carried by each faulty fragment; The path reachability determination unit is used to extract the circuit path between the corresponding mapping points of any two fault segments in the digital twin model; if there is at least one circuit path that makes the two mapping points electrically connected through the circuit path, then it is determined that the two have a path reachability relationship. The propagation relationship determination unit is used to compare the order of the timestamps of two fault segments that have a path reachability relationship, and extract the transmission characteristics of the fault data of the two segments on the circuit path. If the chronological order is consistent with the signal flow direction of the circuit path in the digital twin model, and the transmission characteristics meet the preset propagation conditions, then the two are determined to belong to the same fault propagation chain; otherwise, the two are determined to be independent faults. Among them, the preset propagation conditions include whether the amplitude attenuation of the fault data of the two fault segments at the junction node is within a preset range and whether the time interval matches the signal propagation delay of the circuit path in the digital twin model. The fault propagation chain construction unit is used to connect fault fragments belonging to the same fault propagation chain in sequence according to timestamps to form a cross-partition fault propagation chain. The starting point of the fault propagation chain is the fault fragment with the earliest timestamp in the fault propagation chain. The root cause fault location unit is used to locate the mapping point corresponding to the fault segment at the starting point of each fault propagation chain as the root cause fault point, and output the partition to which the mapping point belongs and the fault type. If multiple fault propagation chains exist, the system is determined to have multiple independent fault sources. The composite fault simulation unit is used to select fault segments with at least two different fault propagation chains, and superimpose their fault data on the data twin model according to their respective mapping point positions to perform composite fault simulation. After the complex fault simulation is completed, the mapping points in the digital twin model whose deviation exceeds the preset threshold are extracted, a list of affected mapping points is generated, and the partition to which each mapping point belongs is marked. Simultaneously, the action state of the protection circuit model in the digital twin model is extracted, and the protection action timing and protection action type are output as the result of the complex fault evolution.

8. The offline layered startup and partitioned fault diagnosis test system for a charging module according to claim 7, characterized in that, The report generation module includes: The graded power-off control unit is used to, after fault determination, sequentially power off the charging module under test in the reverse order of the graded startup module, and record the parameter changes of each grade during the power-off process. The test report generation unit is used to summarize the power-on parameters, electrical status data streams of each test zone, zone identifiers and boundary node locations carried by each fault segment, fault propagation chain information, root cause fault location results, composite fault evolution results, and parameter changes to generate an offline test report.

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