Automatic offline detection system for all-in-one controller

Abstract

The invention relates to the technical field of industrial automatic testing, in particular to an automatic offline detection system for an all-in-one controller. Comprising a hardware layer, a control layer and a data layer, the hardware layer is connected with the control layer through a data bus, the control layer is connected with the data layer through a data interface, the hardware layer is integrated with a CAN1939J protocol interface, a programmable load module, an EMI shielding unit and a temperature sensor, the control layer comprises a test logic engine and a coupling compensation engine based on a QT frame, and the coupling compensation engine is connected with the control layer. The data layer comprises an SQLite real-time test database, the EMI shielding unit is used for collecting electromagnetic interference intensity signals, the temperature sensor is used for collecting test environment temperature signals, the programmable load module is used for collecting dynamic load change rate signals, and the coupling compensation engine is used for receiving the signals and generating a correction instruction. The test logic engine controls the hardware layer to execute a test according to the correction instruction and transmits data to the data layer for storage; according to the invention, the test accuracy and the data traceability can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation testing, and in particular to an automatic offline detection system for an all-in-one controller. Background Art

[0002] In the field of off-line testing of all-in-one controllers, existing testing systems typically use a fixed architecture design: the hardware layer only integrates basic test interfaces and load modules, and does not set up dedicated acquisition units for interference factors such as electromagnetic interference and temperature fluctuations in the production line environment; the control layer test logic is executed based on preset parameters, and the test process cannot be dynamically adjusted according to real-time environmental changes; the data layer only stores test results and basic equipment parameters, and does not associate environmental parameters with test data. The above design has significant limitations in complex production line environments: electromagnetic interference generated by welding equipment and high-voltage cables in the production line will cause CAN message parsing deviations, ambient temperature fluctuations will aggravate load module output errors, and dynamic load changes during sudden start and stop of the controller will further amplify the above deviations. The existing system does not consider the coupling effects of the three, which makes it easy to misjudge or miss detections during the test process. At the same time, due to the lack of associated storage of environmental parameters and test data, when an abnormality occurs in the test, it is impossible to trace whether the abnormality is caused by environmental factors, making it difficult to accurately locate the root cause of the problem.

[0003] Based on the above problems, there is an urgent need for an all-in-one controller automatic offline detection system that can cope with the influence of electromagnetic interference, temperature fluctuations and dynamic load coupling, and improve test accuracy and data traceability. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings existing in the prior art, and to propose an all-in-one controller automatic offline detection system, which includes a hardware layer, a control layer and a data layer. The hardware layer and the control layer are connected through a data bus, and the control layer and the data layer are connected through a data interface. The hardware layer integrates a CAN1939J protocol interface, a programmable load module, an EMI shielding unit and a temperature sensor. The control layer includes a test logic engine and a coupling compensation engine based on the QT framework. The data layer includes an SQLite real-time test database. The EMI shielding unit is used to collect electromagnetic interference intensity signals, the temperature sensor is used to collect test environment temperature signals, the programmable load module is used to collect dynamic load change rate signals, and the coupling compensation engine is used to receive the above signals and generate correction instructions. The test logic engine controls the hardware layer to perform tests according to the correction instructions and transmits data to the data layer for storage.

[0005] Preferably, the CAN1939J protocol interface of the hardware layer includes a communication circuit adapted by Zhou Ligong's secondary development library. The communication circuit is connected to the control layer through a sub-thread. The sub-thread is used to independently execute CAN message reception and parsing operations to avoid occupying the main interface operating resources.

[0006] Further preferably, the test logic engine of the control layer includes a 17-step test process control module based on a state machine, which sequentially performs insulation detection, relay sequence test, load test, CAN message analysis and data archiving, and each step of the test receives the correction parameters output by the coupling compensation engine.

[0007] Further preferably, the SQLite real-time test database of the data layer includes a structured data storage area and a raw message storage area. The structured data storage area stores test results in CSV format, and the raw message storage area stores CAN raw messages in binary format. The two types of storage areas establish associated indexes through the SN code generated by the scanning gun.

[0008] Further preferably, when the coupling compensation engine performs CAN message parsing error correction, the corrected parsing error is calculated using the following formula:

[0009] ε CAN =k1·E α log2(T-T0+273)+k2·(T-T0) 3 +δ1;

[0010] Where: E is the electromagnetic interference intensity, T is the test environment temperature, T0 is the reference temperature, k1 is the EMI influence coefficient, k2 is the temperature influence coefficient, α is the EMI nonlinear index, δ1 is the basic analytical error, ε CAN is the corrected CAN message parsing error.

[0011] Further preferably, when the coupling compensation engine performs load output compensation, the load output correction value is calculated using the following formula:

[0012] ε Load =k3·ε CAN (dI / dt) β +k4·(T-T0)·(dI / dt) 0.5 +δ2;

[0013] Where: dI / dt is the dynamic load change rate, k3 is the analytical error coupling coefficient, k4 is the temperature-load coupling coefficient, β is the load mutation index, δ2 is the basic load error, ε Load is the load output correction value).

[0014] Further preferably, when the coupling compensation engine performs fault response time adjustment, the response time parameter is calculated using the following formula:

[0015]

[0016] Where: k5 is the load error influence coefficient, k6 is the analytical error compensation coefficient, γ is the dynamic load attenuation coefficient, δ3 is the basic response delay, t resp It is the fault response time parameter.

[0017] Further preferably, the data layer also includes a data association analysis module, which establishes a mapping relationship between the electromagnetic interference intensity, ambient temperature, dynamic load change rate and test results according to timestamps, generates a multi-parameter association curve and stores it in the database.

[0018] Further preferably, the programmable load module of the hardware layer includes a 0-500A / 0-1000V output regulation circuit. After receiving the correction instruction from the control layer, the output regulation circuit adjusts the output voltage and current through the PWM signal to achieve load accuracy compensation.

[0019] Further preferably, the control layer also includes a fault location module, which receives CAN message parsing results and load test data, generates fault codes and location information in combination with response time parameters, and transmits the fault codes and location information to the data layer for archiving.

[0020] Technical effect: The present invention realizes real-time collection of electromagnetic interference, temperature and dynamic load by integrating EMI shielding unit, temperature sensor and programmable load module in hardware layer. The coupling compensation engine in control layer processes coupling influence and generates correction instructions. The SQLite database in data layer associates environment and test data. It solves the problems of test misjudgment, missed detection and difficulty in data tracing caused by the existing technology that does not consider the coupling influence of the three, thereby improving test accuracy and data traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a block diagram of an all-in-one controller automatic offline detection system for this application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] Traditional technical solutions have the following technical problems: the existing all-in-one controller offline detection system does not consider the coupled effects of electromagnetic interference, temperature fluctuations and dynamic load changes in complex production line environments. The hardware layer only integrates basic test interfaces and lacks targeted interference collection and compensation hardware; the control layer test logic is fixed and cannot dynamically adjust the test process according to real-time environmental parameters; the data layer only performs simple storage and is not associated with real-time interference parameters, resulting in CAN message parsing being susceptible to interference during the test, unstable load output accuracy, and test data being disconnected from environmental factors, making it difficult to trace the source of abnormal data.

[0024] Based on this, see Figure 1 This embodiment provides an all-in-one controller automatic offline detection system, including a hardware layer, a control layer and a data layer. The hardware layer and the control layer are connected through a data bus, and the control layer and the data layer are connected through a data interface. The hardware layer integrates a CAN1939J protocol interface, a programmable load module, an EMI shielding unit and a temperature sensor. The control layer includes a test logic engine and a coupling compensation engine based on the QT framework. The data layer includes an SQLite real-time test database. The EMI shielding unit is used to collect electromagnetic interference intensity signals, the temperature sensor is used to collect test environment temperature signals, the programmable load module is used to collect dynamic load change rate signals, and the coupling compensation engine is used to receive the above signals and generate correction instructions. The test logic engine controls the hardware layer to perform tests according to the correction instructions and transmits data to the data layer for storage.

[0025] This solution fills the gap in traditional systems' electromagnetic interference and temperature parameter collection by adding EMI shielding units and temperature sensors at the hardware level. Combined with the dynamic load collection of programmable load modules, it provides basic data for interference compensation. The control layer introduces a coupling compensation engine to break the limitations of traditional fixed logic and realize dynamic adjustment of the test process. The data layer uses an SQLite database to associate test data with environmental parameters to solve the problem of data isolation.

[0026] Specifically, the EMI shielding unit captures electromagnetic interference generated by production line welding equipment, high-voltage cables, etc. in real time, the temperature sensor tracks changes in ambient temperature, and the programmable load module records sudden changes in load during emergency start / stop of the controller. After the three data are transmitted to the coupling compensation engine, correction instructions adapted to the current environment are generated to ensure that the test logic engine can still stably control the hardware layer to execute tests in complex environments. At the same time, the data layer binds and stores environmental parameters with test results, providing a complete data chain for subsequent abnormality analysis.

[0027] The technical effects achieved by the above embodiments include: multi-parameter acquisition at the hardware layer realizes comprehensive perception of the test environment, avoiding test deviations caused by interference factors; the dynamic compensation mechanism of the control layer enables the test logic to adapt to different working conditions, ensuring the stability of the test process; the associative storage of the data layer allows test data to be traced back to specific environmental conditions, providing a more comprehensive basis for quality analysis.

[0028] Traditional technical solutions have the following technical problems: the data processing of the CAN protocol interface in the existing detection system mostly relies on the execution of the main program thread. When the CAN message transmission volume is large or the parsing task is complex, it is easy to occupy the main interface operating resources, resulting in main interface operation jams, response delays, and even untimely message reception or frame loss, affecting the continuity of the test process and the integrity of data collection. Especially in high-interference production line environments, the resource conflict between message parsing and main interface operations is more prominent.

[0029] Based on this, the CAN1939J protocol interface of the hardware layer includes a communication circuit adapted by Zhou Ligong's secondary development library. The communication circuit is connected to the control layer through a sub-thread. The sub-thread is used to independently execute CAN message reception and parsing operations to avoid occupying the main interface operating resources.

[0030] This solution addresses the problem of main thread resource conflicts in traditional CAN communications by adopting a collaborative optimization approach of hardware and software: the CAN1939J protocol interface at the hardware layer ensures communication compatibility with the controller through a communication circuit adapted by Zhou Ligong's secondary development library; an independent sub-thread is designed at the software level to specifically handle the reception and parsing of CAN messages, separating the main interface thread from the data processing thread.

[0031] Specifically, after receiving CAN messages from the controller, the communication circuit transmits the data to a child thread, which processes the messages using pre-set parsing logic and feeds the results back to the control layer. This entire process does not occupy the main interface's CPU resources. This design avoids the resource competition between main interface operations and message parsing in traditional solutions. Even with high-frequency message transmission, such as the dense message flow during load testing, the main interface maintains smooth operation, while the child thread can focus on message parsing, reducing frame drops or parsing delays caused by insufficient resources.

[0032] The technical effects achieved by the above embodiments include: improving the real-time performance of CAN message reception and parsing, avoiding data loss caused by main interface operations occupying resources; ensuring the smoothness of main interface operations, and testers can set parameters or monitor processes while parsing data, thereby improving operational efficiency; the collaboration between communication circuits and sub-threads enables the system to remain stable in high-load communication scenarios, ensuring the integrity of test data.

[0033] Traditional technical solutions have the following technical problems: the process control of existing test systems mostly adopts a fixed-step execution mode. The parameters of the test steps cannot be adjusted according to real-time environmental changes after being preset. When encountering emergencies such as increased electromagnetic interference and sudden temperature rise, the fixed parameters may cause test items to be missed or misjudged; at the same time, the test process is disconnected from the environmental compensation mechanism. Even if environmental interference is detected, the execution parameters of the test steps cannot be corrected in real time, resulting in insufficient test coverage and difficulty in detecting hidden defects.

[0034] Based on this, the test logic engine of the control layer includes a 17-step test process control module based on a state machine. The test process control module performs insulation detection, relay sequence test, load test, CAN message analysis and data archiving in sequence, and each step of the test receives the correction parameters output by the coupling compensation engine.

[0035] This solution addresses the limitations of traditional fixed processes through the collaborative design of a state machine and coupling compensation. A 17-step test process control module based on the state machine breaks down the test process into sequential steps, such as insulation testing and relay testing, ensuring comprehensive test coverage. A key improvement lies in the introduction of correction parameters from the coupling compensation engine at each test step, enabling step execution parameters to dynamically adapt to environmental changes.

[0036] Specifically, during the insulation test step, the coupling compensation engine adjusts the detection threshold based on EMI intensity to avoid misjudgments of insulation resistance caused by electromagnetic interference. During relay sequence testing, the command transmission interval is adjusted based on temperature parameters to compensate for the impact of temperature on relay operation delay. During the load test phase, the load output parameters are corrected based on the dynamic load change rate to ensure that test conditions are consistent with actual operating conditions. This design eliminates the need for a "mechanical execution of fixed steps" test process and allows for flexible adjustments based on real-time environmental parameters, compensating for the poor environmental adaptability of traditional fixed processes.

[0037] The technical effects achieved by the above-mentioned embodiments include: enhanced adaptability of the test process, which can maintain test accuracy in complex environments such as electromagnetic interference and temperature fluctuations; 17 sequential steps ensure comprehensive test coverage, combined with dynamic correction parameters to avoid omissions of test items due to environmental interference; targeted corrections at each test step make hidden defects, such as poor contact of temperature-sensitive relays, easier to detect, thereby improving test reliability.

[0038] Traditional technical solutions have the following technical problems: the data storage of existing systems mostly uses a single format to store test results, and there is a lack of distinction between raw data and structured data, which makes it difficult to trace the details of the original message during subsequent analysis; at the same time, the test data is not associated with the controller's unique identifier. When quality problems occur, it is impossible to quickly locate the complete test records of the corresponding controller, and manual retrieval is required one by one, which is inefficient; in addition, the data storage is not designed with a classification architecture, and structured data is mixed with raw messages, which increases the difficulty of data management and calling.

[0039] Based on this, the SQLite real-time test database of the data layer includes a structured data storage area and a raw message storage area. The structured data storage area stores test results in CSV format, and the raw message storage area stores CAN raw messages in binary format. The two types of storage areas establish associated indexes through the SN code generated by the scanner.

[0040] This solution addresses the structural flaws of traditional data storage through a dual storage area design that associates and indexes SN codes. The structured data area stores test results, such as load test voltage / current values ​​and fault codes, in CSV format, facilitating quick query and statistical analysis. The raw message storage area preserves raw CAN messages in binary format, ensuring the original, unprocessed data and providing a basis for later in-depth analysis, such as troubleshooting message timing anomalies. The key is that the two storage areas are linked using the unique identifiers generated by the scanner and the code controller. Each SN code corresponds to a structured record and a set of raw messages.

[0041] Specifically, at the start of a test, the controller's SN code is scanned, and the system automatically creates a storage directory named after the SN code. Structured data and raw messages are stored in corresponding subdirectories, linked through an index table. This design ensures a one-to-one correspondence between test data and controllers. When tracing the test process of a specific controller, simply enter the SN code to retrieve the complete data, eliminating the need for manual screening.

[0042] The technical effects achieved by the above embodiments include: structural enhancement of data storage, classified management of structured data and original messages, which facilitates data retrieval in different scenarios; SN code association index realizes precise binding of test data and controller, greatly improving quality traceability efficiency; the complete preservation of original messages provides the original basis for complex fault analysis, and structured data facilitates the rapid generation of test reports, taking into account both analysis depth and operational efficiency.

[0043] Traditional technical solutions have the following technical problems: existing CAN message parsing only considers basic hardware errors and does not incorporate the coupling effects of electromagnetic interference (EMI) and temperature. In the production line environment, EMI generated by welding equipment and high-voltage cables will cause message edge jitter, and temperature fluctuations will aggravate the timing offset of the hardware interface. The combined effect of the two causes the parsing error to grow nonlinearly. At the same time, traditional parsing does not establish an error quantification model and cannot accurately correct the errors caused by interference, resulting in unstable message parsing accuracy and affecting the accuracy of fault diagnosis.

[0044] Based on this, when the coupling compensation engine performs CAN message parsing error correction, the corrected parsing error is calculated using the following formula:

[0045] ε CAN =k1·E α log2(T-T0+273)+k2·(T-T0) 3 +δ1;

[0046] Where E is the electromagnetic interference intensity, T is the test environment temperature, T0 is the reference temperature, k1 is the EMI influence coefficient, k2 is the temperature influence coefficient, α is the EMI nonlinear index, δ1 is the basic analytical error, ε CAN is the corrected CAN message parsing error.

[0047] This solution incorporates the coupling effect of EMI and temperature into analytical error correction through a quantitative formula, solving the non-quantitative defect of traditional analysis. α The term reflects the nonlinear effect of EMI. The higher the EMI intensity, the faster the error grows. The log2(T-T0+273) term reflects the effect of temperature on hardware timing. The more the temperature deviates from the benchmark, the greater the error. The quadratic term (T-T0) 3 The nonlinear effect of temperature is further enhanced, and the basic error δ1 ensures the analytical benchmark when there is no interference.

[0048] The above formula is the core calculation model used by the coupling compensation engine to correct CAN message parsing errors. Its various components are designed to closely integrate the patented hardware layer's parameter acquisition capabilities with the control layer's dynamic compensation requirements, as follows:

[0049] From the parameter definition, E represents the electromagnetic interference intensity, which is collected in real time by the EMI shielding unit of the hardware layer, and directly reflects the electromagnetic interference level generated by welding equipment, high-voltage cables, etc. in the production line; T is the test environment temperature, which is collected by the temperature sensor of the hardware layer and is used to quantify the degree to which the ambient temperature deviates from the baseline value; T0 is set to 25°C, which is the reference temperature for normal production line testing and is used to calculate the temperature deviation; k1 is the EMI influence coefficient, which is determined by the hardware characteristics of the CAN1939J protocol interface of the hardware layer and quantifies the basic influence weight of electromagnetic interference on message parsing; k2 is the temperature influence coefficient, which associates the component characteristics of the temperature sensor and the CAN interface, and reflects the degree of influence of temperature changes on hardware timing; α is the EMI nonlinearity index. Since the impact of electromagnetic interference on CAN signals grows nonlinearly, the stronger the interference, the faster the parsing error grows. This index is used to fit this characteristic; δ1 is the basic parsing error, which is the inherent error of the CAN interface when there is no electromagnetic interference and the temperature is stable, ensuring the parsing benchmark in an interference-free scenario.

[0050] From the formula structure, the first term k1·E α log2(T-T0+273) is the coupling effect of electromagnetic interference and temperature, reflecting the amplification effect of temperature fluctuation on the analytical error when electromagnetic interference increases.

[0051] For example, in a high temperature environment, the signal jitter caused by EMI will be more obvious. This term quantifies this synergistic effect by combining a logarithmic function with an exponential function. The second term k2·(T-T0) 3 This is a temperature-independent effect term used to capture the nonlinear effect of large temperature deviations from the baseline, such as high temperatures in the summer or low temperatures in the winter on the CAN interface hardware timing. The greater the temperature deviation, the more significant the error growth.

[0052] The third term δ1 is the basic error term, which ensures that the formula can still output a reasonable benchmark error value when there is no interference and the temperature is stable.

[0053] The core function of this formula is to convert the real-time environmental parameters collected by the hardware layer into quantifiable analytical error correction values. The coupling compensation engine calculates ε through this formula. CAN Afterwards, correction instructions are sent to the test logic engine of the control layer, such as adjusting the width of the CAN message receiving window, to solve the problem of unstable parsing errors caused by the failure to consider the influence of electromagnetic interference and temperature coupling in traditional technologies, and ultimately achieve the key technical indicator of 0.1ms CAN message parsing accuracy in the patent.

[0054] Specifically, the EMI shielding unit collects E in real time, the temperature sensor collects T, and the coupling compensation engine substitutes the parameters into the formula to calculate the corrected ε CAN , and adjust the message parsing threshold accordingly, such as extending the receiving window to compensate for the jitter caused by EMI.

[0055] This design eliminates the reliance on empirical values ​​for parsing error correction, and instead relies on quantitative calculations of real-time parameters, ensuring consistent parsing accuracy across diverse environments. The technical benefits achieved by the aforementioned embodiments include: enhanced anti-interference capabilities for CAN message parsing, maintaining stable accuracy in environments with EMI and temperature fluctuations; the quantifiable nature of parsing errors provides a more reliable basis for parameter adjustments during subsequent load testing and fault diagnosis, avoiding test misjudgments due to parsing bias; and the associated storage of original messages and corrected errors provides data support for analyzing the relationship between environmental interference, parsing errors, and test results.

[0056] Traditional technical solutions have the following technical problems: In existing load tests, the load output accuracy only considers basic hardware errors, and does not associate the CAN message parsing error, temperature fluctuations and the coupling effects of dynamic load change rate. When the CAN message is parsed and delayed due to interference, the execution of the load control instructions will produce deviations. At the same time, temperature changes will cause the parameters of the load module components to drift, and dynamic load mutations, such as the current shock when the controller is suddenly started, will further amplify the output error. The combination of these three factors causes the load test results to deviate greatly from the actual working conditions, making it difficult to detect hidden defects caused by insufficient load adaptability.

[0057] Based on this, when the coupling compensation engine performs load output compensation, the load output correction value is calculated using the following formula:

[0058] ε Load =k3·ε CAN (dI / dt) β +k4·(T-T0)·(dI / dt) 0.5 +δ2;

[0059] Where dI / dt is the dynamic load change rate, k3 is the analytical error coupling coefficient, k4 is the temperature-load coupling coefficient, β is the load mutation index, δ2 is the basic load error, ε Load Output correction value for the load.

[0060] This formula is the calculation model used by the coupling compensation engine to correct the output error of the programmable load module. Its design inherits the above analytical error correction results and combines the dynamic load characteristics. The details are as follows:

[0061] From the parameter definition, ε CAN is the CAN message parsing error calculated above, reflecting the impact of errors in the preceding communication link on load control; dI / dt is the dynamic load change rate, collected by the programmable load module at the hardware layer, reflecting the current change speed during the controller's sudden start and stop; k3 is the parsing error coupling coefficient, which quantifies the weight of the impact of CAN parsing delay on load output;

[0062] k4 is the temperature-load coupling coefficient, which relates the synergistic effect of temperature deviation and load change; β is the load mutation index. The more drastic the dynamic load change, the more significant the impact of analytical error on load output. This index is used to fit this nonlinear relationship; δ2 is the basic load error, which is the inherent output deviation of the load module under ideal working conditions.

[0063] From the formula structure, the first term k3·ε CAN (dI / dt) β is the CAN parsing error-dynamic load coupling term, for example, when the CAN message parsing delay ε CAN When the controller starts quickly, the dI / dt increases, and the delay in the load module receiving the control command will cause the output deviation to expand. This term quantifies this synergistic effect through a multiplicative relationship.

[0064] The second term k4·(T-T0)·(dI / dt) 0.5 It is the temperature and dynamic load coupling term, which is used to capture the impact of temperature deviation from the reference, such as high temperature causing parameter drift of load module components and dynamic load changes on output accuracy. The larger the temperature deviation and the faster the load change, the more obvious the deviation. The third term δ2 is the basic error term, which ensures the output reference under ideal working conditions.

[0065] The core function of this formula is to integrate the communication link error, ambient temperature and dynamic load characteristics into the load output correction value. The coupling compensation engine will ε Load After being sent to the programmable load module, the module compensates for the deviation by adjusting the internal PWM output duty cycle. This solves the problem in traditional technology that the load output only relies on fixed parameters and does not consider the coupling of multiple factors, and achieves high-precision output under the load test range of 0-500A / 0-1000.

[0066] This solution incorporates the influence of multi-factor coupling into load output compensation through a quantitative formula, solving the non-correlation defect of traditional load testing.

[0067] In the formula, k3·ε CAN (dI / dt) β The term reflects the synergistic effect of CAN resolution error and dynamic load (the longer the resolution delay and the faster the load change, the larger the correction amount), k4·(T-T0)·(dI / dt) 0.5 The term reflects the impact of temperature deviation from the reference value on the load output (temperature deviation and load change rate act together), and the basic error δ2 ensures the output reference when there is no interference. Specifically, the coupling compensation engine receives the CAN analysis error (ε CAN ), temperature (T) and dynamic load change rate (dI / dt), substitute them into the formula to calculate ε LoadThe correction value is sent to the programmable load module, which adjusts the module's internal PWM output duty cycle to compensate for output deviations. This design allows load output to no longer be executed with fixed parameters, but to dynamically adapt to real-time interference and changing operating conditions.

[0068] The technical effects achieved by the above embodiments include: the load output accuracy remains stable under complex working conditions, avoiding test deviations caused by the coupling of multiple factors; the load test can more realistically simulate the load environment of the controller during actual operation, and hidden defects, such as poor dynamic load adaptation at high temperatures, are more easily detected; the associated storage of load correction values ​​and original parameters provides data support for analyzing the relationship between interference, load and controller response, thereby improving the reference value of test data.

[0069] Traditional technical solutions have the following technical problems: existing test data storage only records test results and basic equipment parameters, and does not associate real-time environmental parameters such as electromagnetic interference intensity, ambient temperature, and dynamic load change rate with test data. When an abnormality occurs in the test, it is impossible to trace whether the abnormality is caused by environmental factors, and the only option is to check the equipment itself, which increases the difficulty of fault analysis; at the same time, the data lacks multi-dimensional correlation analysis capabilities, making it difficult to discover potential patterns such as communication anomalies that a certain type of controller is prone to under specific electromagnetic interference intensity, which is not conducive to the optimization of production line test parameters.

[0070] Based on this, the data layer also includes a data association analysis module, which establishes a mapping relationship between the electromagnetic interference intensity, ambient temperature, dynamic load change rate and test results according to timestamps, generates a multi-parameter association curve and stores it in the database.

[0071] This solution addresses the correlation gaps in traditional data storage through a data association analysis module, enabling deep binding of environmental parameters and test results. After receiving the electromagnetic interference intensity (E), ambient temperature (T), and dynamic load change rate (dI / dt) transmitted from the hardware layer and the test results output by the control layer, the data association analysis module establishes a four-dimensional mapping relationship based on timestamps. For example, it binds the CAN message parsing error and load output value at a specific moment to the E, T, and dI / dt values ​​at the corresponding moment. Curve fitting is then used to generate an "electromagnetic interference-parsing error-load output" correlation curve.

[0072] Specifically, when a CAN communication anomaly occurs during testing, the correlation curve can be used to quickly view the electromagnetic interference intensity at the time of the anomaly, determining whether it was caused by strong interference. When a batch of controllers frequently exhibits deviations during load testing, the dynamic load change rate and temperature in the correlation curve can be analyzed to optimize load parameters for subsequent tests. This design upgrades test data from "isolated records" to "correlated datasets," providing a multi-dimensional basis for anomaly analysis.

[0073] The technical effects achieved by the above-mentioned embodiments include: improving the efficiency of tracing test anomalies, and being able to quickly distinguish between anomalies caused by defects in the equipment itself and those caused by environmental interference; discovering potential environment-equipment response patterns through multi-parameter correlation curves, providing data support for optimizing production line test parameters; and the long-term accumulation of correlation data can form an "environmental adaptability database" for controllers of different models, providing a reference for the formulation of test standards for new controllers.

[0074] The traditional technical solution has the following technical problems: the output adjustment of existing programmable load modules mostly adopts fixed parameter settings and cannot receive real-time compensation instructions for dynamic adjustment. When the coupling compensation engine generates a load output correction value, the load module cannot adjust the output according to the correction value, resulting in the load test still being performed according to the preset parameters. The correction value cannot actually take effect on the test process, affecting the load test accuracy; at the same time, the output range of the load module is limited, and it is difficult to cover the wide range test requirements of 0-500A / 0-1000V of the new energy controller, which limits the applicability of the test.

[0075] Based on this, the programmable load module of the hardware layer includes a 0-500A / 0-1000V output regulation circuit. After receiving the correction instruction of the control layer, the output regulation circuit adjusts the output voltage and current through the PWM signal to achieve load accuracy compensation. This solution solves the fixed output defect of the traditional load module through the output regulation circuit and PWM regulation mechanism, while meeting the wide range test requirements. The output regulation circuit of the programmable load module includes a power drive unit, a PWM control unit and a feedback sampling unit. When receiving the load output correction value (ε Load ), the PWM control unit converts the correction value into a PWM signal duty cycle adjustment instruction. For example, when the correction value shows that the current needs to be increased by 5A, the PWM duty cycle is adjusted from 30% to 35%, amplified by the power drive unit and output to the controller under test; the feedback sampling unit collects the output voltage and current in real time, and forms a closed-loop adjustment after comparing them with the target value.

[0076] At the same time, the output range of 0-500A / 0-1000V can cover the full operating condition test of the new energy controller from low voltage and low current standby to high voltage and high current operation. Specifically, during the load test stage, when the coupling compensation engine generates a load output correction instruction due to temperature increase, the regulation circuit can respond quickly and adjust the output to ensure that the load applied to the controller is consistent with the actual working condition. The technical effects achieved by the above embodiments include: the load output can be adjusted in real time following the compensation instruction, eliminating the output deviation caused by environmental factors, and improving the accuracy of the load test; the wide-range output covers the full operating condition test requirements of the controller, and the test of different working modes can be completed without replacing the load module; the closed-loop regulation mechanism ensures the stability of the load output, avoids test errors caused by power supply fluctuations, and improves the reliability of the test results.

[0077] Traditional technical solutions have the following technical problems: existing fault location is based solely on the comparison of fault codes in test data with preset thresholds, without incorporating fault response time parameters. When the fault is caused by environmental factors, such as transient communication anomalies caused by strong electromagnetic interference and defects in the equipment itself, it is impossible to distinguish the fault type and environmental interference can be easily misjudged as a device defect. At the same time, fault location information is not archived synchronously with the test data, making it impossible to view the complete test scenario when the fault occurred during later tracing, affecting fault reproduction and analysis.

[0078] Based on this, the control layer also includes a fault location module. The fault location module receives the CAN message analysis results and load test data, combines the response time parameters, generates fault codes and location information, and transmits the fault codes and location information to the data layer for archiving. This solution solves the single-basis defect of traditional fault location by combining the fault location module with the response time parameters, and achieves accurate positioning and information archiving. After receiving the CAN message analysis results, such as message loss, error frame and load test data, such as output voltage deviation and current anomaly, the fault location module calls the fault response time parameter t resp For example, when CAN message loss is detected, if t resp If the normal range is <0.5ms, it is judged as a device communication interface defect; if t resp If the duration is extended by >1ms due to strong electromagnetic interference, it is considered a transient anomaly caused by environmental interference.

[0079] The generated fault codes, such as "0x01" representing a communication interface defect and "0x02" representing environmental interference and positioning information, such as the time of fault occurrence and the corresponding test steps, will be transmitted to the data layer in real time and stored in conjunction with the controller's SN code.

[0080] Specifically, when tracing back the fault records of a controller later, the fault location information can be called up through the data layer to view the fault type, occurrence scenario, and response time parameters, quickly distinguishing between equipment defects and environmental interference. The technical effects achieved by the above embodiments include: improved fault location accuracy, avoiding the misjudgment of environmental interference as equipment defects and reducing ineffective troubleshooting; synchronous archiving of fault information and test data, providing a complete scenario basis for fault reproduction and analysis; and the classification and generation of different types of fault codes, which facilitates the optimization of production lines for equipment defects and environmental interference respectively, improving the targetedness of production line testing.

[0081] Traditional technical solutions have the following technical problems: the existing fault response time is set to a fixed value, which does not take into account the coupled effects of CAN message parsing errors, load output errors and dynamic load change rates. When the CAN parsing delay is long or the load mutation is drastic, the fixed response time cannot capture the fault signal in time, resulting in missed fault detection; at the same time, the response time is disconnected from the sampling frequency of the fault detection algorithm. Even if the response time is detected to be extended, the sampling frequency cannot be adjusted, affecting the timeliness of fault location.

[0082] Based on this, when the coupling compensation engine performs fault response time adjustment, the response time parameter is calculated using the following formula:

[0083] Where k5 is the load error influence coefficient, k6 is the analytical error compensation coefficient, γ is the dynamic load attenuation coefficient, δ3 is the basic response delay, t resp It is the fault response time parameter.

[0084] This solution incorporates the multi-factor coupling effect into the fault response time adjustment through a quantitative formula, solving the fixed defect of traditional response time. In the formula, the numerator k5·ε Load ·e γ·dI / dt It reflects the synergistic influence of load output error and dynamic load. The larger the load error and the faster the change, the longer the response time requirement. Through the reverse adjustment of the CAN analysis error, the smaller the analysis error, the larger the denominator, the shorter the response time, and the basic response delay δ3 ensures the response benchmark when there is no interference.

[0085] The above formula is the calculation model used by the coupling compensation engine to optimize the fault location response time. Its design associates the previous analytical error with the load error correction result to achieve dynamic adaptation of fault detection. The details are as follows: From the parameter definition, ε Load is the load output correction value calculated in the above embodiment, reflecting the actual output deviation of the load module; ε CAN is the CAN message parsing error in the above embodiment; dI / dt is the dynamic load change rate; k5 is the load error influence coefficient, which quantifies the weight of the load output deviation on the fault response requirement; k6 is the parsing error compensation coefficient, which is used to balance the impact of CAN parsing accuracy on response time; γ is the dynamic load attenuation coefficient. As the more drastic the dynamic load change, the shorter the duration of the fault signal, requiring a faster response. This coefficient is used to fit this attenuation characteristic; δ3 is the basic response delay, which is the inherent response benchmark of the fault detection module.

[0086] From the formula structure, the numerator k5·ε Load ·e γ·dI / dt Reflects the synergistic effects of load error and dynamic load.

[0087] When the load output deviation is large, ε Load Large; and the dynamic load changes quickly. When dI / dt is large, the fault signal is more likely to be masked, and the response time needs to be extended to ensure capture; the denominator This reflects the compensation effect of CAN resolution accuracy. When the CAN resolution error is small, ε CAN hours, the denominator increases and the overall response time is shortened because there is no need to wait too long when communication is reliable; the third term δ3 is the basic delay term, which ensures the response benchmark when there is no interference.

[0088] The core function of this formula is to convert the error and dynamic working condition of the previous link into a quantifiable response time parameter. The coupling compensation engine converts t resp After being sent to the fault location module, the module adjusts the sampling frequency, such as t resp The sampling rate is increased to 1MHz during extended operation to meet adaptation requirements, solving the problem of fixed fault response time and inability to adapt to complex working conditions in traditional technologies.

[0089] Specifically, the coupling compensation engine receives the load output error ε Load , CAN analysis error ε CAN And the dynamic load change rate dI / dt, substitute into the formula to calculate t resp , and sends the parameter to the fault location module, which adjusts the sampling frequency of fault detection accordingly, for example, t resp When extended to 1ms, the sampling frequency is increased from 500kHz to 1MHz, ensuring that sufficient fault signals are captured within the response time. The technical effects achieved by the above embodiments include: the fault response time can dynamically adapt to the real-time working conditions, avoiding fault omissions caused by fixed time settings; the response time and sampling frequency are adjusted in a coordinated manner to improve the response speed while ensuring detection accuracy; the response time parameters are synchronously stored with the fault data, providing a basis for analyzing the relationship between faults, responses, and the environment, and facilitating the continuous optimization of the fault detection algorithm.

[0090] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An all-in-one controller automatic offline detection system, characterized in that: It includes a hardware layer, a control layer and a data layer. The hardware layer and the control layer are connected through a data bus, and the control layer and the data layer are connected through a data interface. The hardware layer integrates a CAN1939J protocol interface, a programmable load module, an EMI shielding unit and a temperature sensor. The control layer includes a test logic engine and a coupling compensation engine based on the QT framework. The data layer includes an SQLite real-time test database. The EMI shielding unit is used to collect electromagnetic interference intensity signals, the temperature sensor is used to collect test environment temperature signals, the programmable load module is used to collect dynamic load change rate signals, and the coupling compensation engine is used to receive the above signals and generate correction instructions. The test logic engine controls the hardware layer to execute tests according to the correction instructions and transmits data to the data layer for storage.

2. The all-in-one controller automatic offline detection system according to claim 1, characterized in that: The CAN1939J protocol interface of the hardware layer includes a communication circuit adapted by Zhou Ligong's secondary development library. The communication circuit is connected to the control layer through a sub-thread. The sub-thread is used to independently execute CAN message reception and parsing operations to avoid occupying the main interface operating resources.

3. The all-in-one controller automatic offline detection system according to claim 1, characterized in that: The test logic engine of the control layer includes a 17-step test process control module based on a state machine. The test process control module sequentially performs insulation detection, relay sequence test, load test, CAN message analysis and data archiving, and each step of the test receives the correction parameters output by the coupling compensation engine.

4. The all-in-one controller automatic offline detection system according to claim 1, characterized in that: The SQLite real-time test database of the data layer includes a structured data storage area and a raw message storage area. The structured data storage area stores test results in CSV format, and the raw message storage area stores CAN raw messages in binary format. The two types of storage areas establish associated indexes through the SN code generated by the scanner.

5. The all-in-one controller automatic offline detection system according to claim 1, characterized in that: When the coupling compensation engine performs CAN message parsing error correction, the corrected parsing error is calculated using the following formula: e CAN =k1·E α ·log2(T-T0+273)+k2·(T-T0) 3 +δ1; Where: E is the electromagnetic interference intensity, T is the test environment temperature, T0 is the reference temperature, k1 is the EMI influence coefficient, k2 is the temperature influence coefficient, α is the EMI nonlinear index, δ1 is the basic analytical error, ε CAN is the corrected CAN message parsing error.

6. The all-in-one controller automatic offline detection system according to claim 5, characterized in that: When the coupling compensation engine performs load output compensation, the load output correction value is calculated using the following formula: ε Load =k3·ε CAN ·(dI / dt) β +k4·(T-T0)·(dI / dt) 0.5 +δ2; Where: dI / dt is the dynamic load change rate, k3 is the analytical error coupling coefficient, k4 is the temperature-load coupling coefficient, β is the load mutation index, δ2 is the basic load error, ε Load It is the load output correction value (unit: A / V).

7. The all-in-one controller automatic offline detection system according to claim 6, characterized in that: When the coupling compensation engine performs fault response time adjustment, the response time parameter is calculated using the following formula: Where: k5 is the load error influence coefficient, k6 is the analytical error compensation coefficient, γ is the dynamic load attenuation coefficient, δ3 is the basic response delay, t resp It is the fault response time parameter.

8. The all-in-one controller automatic offline detection system according to claim 1, characterized in that: The data layer also includes a data association analysis module, which establishes a mapping relationship between the electromagnetic interference intensity, ambient temperature, dynamic load change rate and test results according to timestamps, generates a multi-parameter association curve and stores it in a database.

9. The all-in-one controller automatic offline detection system according to claim 1, characterized in that: The programmable load module of the hardware layer includes a 0-500A / 0-1000V output regulation circuit. After receiving the correction instruction from the control layer, the output regulation circuit adjusts the output voltage and current through the PWM signal to achieve load accuracy compensation.

10. The all-in-one controller automatic offline detection system according to claim 7, characterized in that: The control layer also includes a fault location module, which receives CAN message analysis results and load test data, generates fault codes and location information in combination with response time parameters, and transmits the fault codes and location information to the data layer for archiving.

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