Vehicle-mounted power output monitoring system powered by external load
By using multi-parameter anomaly analysis and fault tree reasoning, the problem of distinguishing between cascading anomalies and independent anomalies in vehicle power monitoring systems was solved, enabling accurate fault location and optimized processing priorities, thereby improving the stability and security of the system.
Patent Information
- Application Number
- CN202511243644.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-02
- Publication Date
- 2025-12-12
AI Technical Summary
Existing vehicle power monitoring systems cannot effectively distinguish between cascading anomalies and independent concurrent anomalies, making it difficult to pinpoint the root cause, and risk assessment and fault handling priorities are confused.
The system employs a load identification and analysis module, a normal monitoring and processing module, an anomaly analysis and processing module, and a comprehensive fault analysis module. Through multi-parameter anomaly analysis, combined with time series and fault tree reasoning, it identifies and distinguishes between cascading anomalies and independent anomalies, locates the root cause of the fault, and quantifies the scope and severity of the anomaly's impact.
It improves the accuracy of anomaly identification, reduces false positives, accurately locates fault points, optimizes fault handling priorities, and avoids resource waste.
Smart Images
Figure CN121124342A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of load monitoring technology, specifically to an on-board power output monitoring system for external load power supply. Background Technology
[0002] With the diversification of in-vehicle electronic devices, the types of loads for in-vehicle power output are becoming increasingly complex, covering inductive loads, resistive loads, etc., which significantly increases the requirements for power supply stability and safety.
[0003] Existing vehicle power monitoring systems have the following limitations: fixed threshold settings, no consideration of load type and environmental scenarios, single anomaly judgment, alarms are triggered only by a single parameter exceeding the threshold, and they are susceptible to sensor failure, electromagnetic interference and other factors, resulting in a large number of false judgments. The system lacks multi-parameter anomaly analysis capabilities, making it unable to distinguish between cascading anomalies and independent concurrent anomalies, making it difficult to pinpoint the root cause. Risk assessment is also crude, lacking quantitative grading of the impact range and severity of anomalies, leading to confusion in fault handling priorities. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides an on-board power output monitoring system powered by an external load, which solves the problems of insufficient multi-parameter anomaly analysis capabilities, inability to distinguish between cascading anomalies and independent concurrent anomalies, and difficulty in locating the root cause.
[0005] To achieve the above objectives, the present invention provides the following technical solution: an on-board power output monitoring system powered by an external load, comprising: The load identification and analysis module compares the output current, voltage, and wire harness temperature with the corresponding judgment thresholds. If any one of them fails to meet the standard, an abnormal signal is generated; if all of them meet the standard, a normal signal is generated. The normal monitoring and processing module is used to process normal analysis signals. It collects and verifies parameters multiple times according to a preset cycle. If all parameters are normal, it generates normal monitoring information; otherwise, it generates an abnormal signal. The anomaly analysis and processing module is used to process anomaly analysis signals, identify single or multiple types of anomalies, analyze single types of anomalies, verify whether sensors and communications are normal, and generate a fault signal if a continuous anomaly is confirmed, and transmit it to the fault comprehensive analysis module. For analysis of multiple types of situations, the correlation is determined by time series. If there is a correlation, a correlation abnormal signal is generated; otherwise, an independent abnormal signal is generated and transmitted to the fault comprehensive analysis module. The fault comprehensive analysis module processes fault analysis signals, determines the cause of the fault and judges its potential impact, and generates impact information. It processes related anomaly analysis signals, determines the fault range and generates fault causes by combining fault tree reverse reasoning. It processes independent anomaly analysis signals, scores the impact range and severity of each anomaly parameter, calculates the comprehensive risk level, and generates processing information according to priority.
[0006] As a further embodiment of the present invention, it also includes a real-time parameter acquisition module and a monitoring information output module; The real-time parameter acquisition module is used to acquire output voltage, output current and wire harness temperature through sensors and transmit them to the load identification and analysis module. The monitoring information output module is used to acquire impact information, fault causes, and handling information, and display them to the relevant management personnel.
[0007] As a further aspect of the present invention, the load identification and analysis module outputs current, voltage, and harness temperature and compares them with corresponding judgment thresholds. If the output current is greater than the overload threshold, or the temperature is greater than the temperature threshold, or the output voltage is less than the voltage threshold, it indicates that the load is abnormal and an abnormal analysis signal is generated and transmitted to the abnormal analysis processing module. If all three of the above meet the corresponding judgment thresholds, it indicates that the load is normal and a normal analysis signal is generated.
[0008] As a further aspect of the present invention, the normal monitoring and processing module processes the normal analysis signal in the following manner: The system continuously collects real-time parameters multiple times, with a time period t set by the operator. The collected real-time parameters are then compared with a judgment threshold. If all the collected real-time parameters are normal, normal monitoring information is generated. If any of the collected real-time parameters are abnormal, an abnormal analysis signal is generated.
[0009] As a further aspect of the present invention, the specific method by which the anomaly analysis processing module processes the anomaly analysis signal is as follows: Obtain the corresponding abnormal parameters from the real-time parameters, identify their abnormality types, and generate single-type parameters or multiple-type parameters; For single-type parameter analysis, if the sensor and communication are checked and no problems are found, it is confirmed as a real anomaly and a single analysis signal is generated. If any problem exists, it is judged as a false anomaly and re-collection and judgment are required.
[0010] As a further aspect of the present invention, the single analysis signal is processed to obtain the real abnormal parameters. The duration and trend of the abnormality are observed according to the period T set by the operator. If the abnormality recovers after exceeding the threshold for a short time, it is a transient abnormality and its characteristics are recorded. If the frequency is low and does not affect the load, normal monitoring information is generated. If the abnormality exceeds the threshold for a long time or fluctuates frequently, it is a continuous abnormality and a fault analysis signal is generated.
[0011] As a further aspect of the present invention, the specific method by which the anomaly analysis and processing module analyzes various types of situations is as follows: The authenticity of various types of abnormal parameters is verified, and the time series method is used to analyze the various types of abnormal parameters to determine their corresponding relationships. If a domino effect is caused by a root problem, it indicates that the various types of abnormal parameters are chain anomalies, and a related anomaly analysis signal is generated. If multiple unrelated abnormal parameters occur at the same time, it indicates that they are independent concurrent anomalies, and an independent anomaly analysis signal is generated.
[0012] As a further aspect of the present invention, the specific method by which the fault comprehensive analysis module processes the fault analysis signal is as follows: The corresponding fault cause is obtained based on the abnormal parameters. At the same time, the potential impact of the fault cause is judged to obtain the direct or indirect impact, and the impact information is generated and then transmitted to the monitoring information output module.
[0013] As a further aspect of the present invention, the specific method by which the fault comprehensive analysis module processes the associated anomaly analysis signal is as follows: Obtain various types of abnormal parameters and determine their fault range, such as component failure, module failure, and system failure. At the same time, combine the fault tree to perform reverse reasoning to obtain the cause of the failure.
[0014] As a further aspect of the present invention, the specific method by which the fault comprehensive analysis module processes independent anomaly analysis signals is as follows: Multiple types of abnormal parameters are obtained and labeled as i, where i = 1, 2, ..., j, and j represents the type of abnormal parameter. Then, the risk level of abnormal parameter i is judged from two aspects: the scope of impact and the severity. The scope of impact and the severity are scored and assigned respectively, and the weighted sum is calculated to obtain the comprehensive index corresponding to abnormal parameter i. At the same time, the comprehensive index is matched with the corresponding rating interval to obtain the risk level corresponding to abnormal parameter i. The parameters are then processed according to the risk level from large to small to generate priority processing information.
[0015] This invention provides an on-board power output monitoring system powered by an external load. Compared with the prior art, it has the following advantages: This invention solves the problem of misjudging the impact of fixed thresholds on the start-up of inductive loads and the influence of ambient temperature by combining a basic threshold, load type correction, and scenario compensation threshold system. It adapts to diverse load scenarios, introduces historical sensor data verification, communication log analysis, and continuous periodic monitoring to effectively eliminate false anomalies and significantly improve the accuracy of identifying true anomalies. Through time series analysis and fault tree reasoning, it accurately distinguishes between cascading anomalies and independent anomalies and locates the core fault point, reducing blind troubleshooting. It quantifies and scores the impact range and severity of anomalies, determines the risk level and processing priority through comprehensive indicators, avoids resource waste, and prioritizes the resolution of high-risk faults. Attached Figure Description
[0016] Figure 1 This is a system block diagram of the present invention. Detailed Implementation
[0017] 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, and 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.
[0018] Example 1 Please see Figure 1 This application provides an on-board power output monitoring system powered by an external load, including a real-time parameter acquisition module, a load identification and analysis module, a normal monitoring and processing module, an anomaly analysis and processing module, a fault comprehensive analysis module, and a monitoring information output module, and in combination with... Figure 1 It can be seen that the information between the above functional modules is transmitted in one direction only.
[0019] The real-time parameter acquisition module is used to collect the output voltage, output current, and wiring harness temperature during the vehicle's power output process through sensors. It performs moving average filtering on the current and voltage signals to remove invalid data that is obviously outside the physical range, and transmits the collected real-time parameters to the load identification and analysis module.
[0020] The load identification and analysis module compares the acquired real-time parameters with the judgment thresholds. The judgment thresholds are specifically obtained by combining the base threshold, the load type correction value, and the scene compensation value. The specific base threshold is set according to the characteristics of the vehicle power supply, such as a base voltage threshold of 10.5V, a base current threshold of 10A, and a base temperature threshold of 65℃ for a 12V system. The load type correction value includes inductive loads and resistive loads. When an inductive load starts, it is allowed to have twice the current threshold. There is no correction for resistive loads. The scene compensation is specifically for the compensation of the wiring harness temperature. In low temperature environments (<-10℃), the temperature threshold is increased by 5℃, and in high temperature environments (>35℃), it is decreased by 5℃. If the output current is greater than the overload threshold, or the temperature is greater than the temperature threshold, or the output voltage is less than the voltage threshold, it indicates an abnormal load and generates an abnormal analysis signal, which is then transmitted to the abnormal analysis processing module. If all three conditions are met, it indicates a normal load and generates a normal analysis signal, which is then transmitted to the normal monitoring processing module.
[0021] The normal monitoring and processing module processes the acquired normal analysis signals, with a time period of t. The value of time t is set by the operator, and the specific time period t is determined according to different load types. For stable loads, such as vehicle LED lights, the verification period is t=30 seconds; for intermittent loads, such as vehicle refrigerators, t=10 seconds; and for loads in the initial startup phase, such as a newly started air conditioner, t=5 seconds. Real-time parameters are continuously collected multiple times, generally three times, and the collected real-time parameters are compared with a judgment threshold. If all the collected real-time parameters are normal, it indicates that there is no false judgment, and normal monitoring information is generated and transmitted to the monitoring information output module. If the collected real-time parameters are abnormal, it indicates that there is a false judgment, and an abnormal analysis signal is generated and transmitted to the abnormal analysis and processing module.
[0022] The monitoring information output module is used to display the acquired normal monitoring information to the corresponding management personnel.
[0023] Example 2 As a second embodiment of the present invention, it is implemented based on the first embodiment, and the difference from the first embodiment is as follows: The anomaly analysis and processing module processes the acquired anomaly analysis signals, obtains the corresponding anomaly parameters in the real-time parameters, identifies the anomaly types, generates single-type parameters or multiple-type parameters, and analyzes different situations separately. For the analysis of a single type of parameter, the corresponding abnormal parameters are obtained and their authenticity is verified. This includes sensor fault diagnosis and communication interference diagnosis. For sensor fault diagnosis, the stability of the sensor's historical data is checked. If there are no records of sudden changes in sensor values in the historical data, the sensor fault is ruled out. For communication interference diagnosis, the data transmission log is checked to determine if there is a communication interruption, verification error, or electromagnetic interference. If none of these exist, communication interference is ruled out. Combining the results of sensor fault diagnosis and communication interference diagnosis, if neither exists, the abnormal parameter is considered a true anomaly, and a single analysis signal is generated. Conversely, if any set of interference exists, the abnormal parameter is considered a false anomaly. For false anomalies, real-time parameters are re-acquired and re-evaluated. For a single analysis signal, the abnormal parameters corresponding to the actual anomaly are obtained, with time T as the period, and the specific value of time T is set by the operator. The duration and trend of the anomaly within the time period T are judged. If the parameter exceeds the threshold for a short time and then returns to normal, it is mostly due to load fluctuations or dynamic system adjustments, which is indicated as a transient anomaly. The transient characteristics are recorded, including the peak value and duration. If the frequency is low and does not affect the load operation, it can be regarded as normal and normal monitoring information is generated. Conversely, if the abnormal parameter exceeds the threshold for a long time or fluctuates frequently, it is a continuous anomaly, and a fault analysis signal is generated and then transmitted to the fault comprehensive analysis module. For the analysis of multiple types of abnormal conditions, the same verification process is used for single-parameter abnormal conditions. The corresponding multiple types of abnormal parameters are obtained, and then the time series method is used to analyze the multiple types of abnormal parameters to determine their corresponding relationships. If a domino effect is caused by a root cause problem, it indicates that the multiple types of abnormal parameters are chained anomalies, and a related anomaly analysis signal is generated. If multiple unrelated abnormal parameters occur at the same time, it indicates that they are independent concurrent anomalies, and an independent anomaly analysis signal is generated. Both are transmitted to the fault comprehensive analysis module.
[0024] The fault comprehensive analysis module processes the acquired fault analysis signals, associated anomaly analysis signals, and independent anomaly analysis signals, and the specific processing methods are as follows: The fault analysis signal is processed to obtain the corresponding fault cause based on the abnormal parameters. At the same time, the potential impact of the fault cause is judged to obtain the direct or indirect impact. Specifically, for the direct impact, it is determined whether it will cause load damage or safety hazards, such as excessive voltage burning out the car refrigerator or excessive temperature causing a fire. For the indirect impact, it is determined whether it indicates the aging of other components, such as current fluctuations being a precursor to contactor failure. Impact information is generated and then transmitted to the monitoring information output module. The associated anomaly analysis signals are analyzed to obtain various types of anomaly parameters and determine their fault range, such as component failure, module failure and system failure. At the same time, the fault tree is combined to perform reverse reasoning to obtain the cause of the failure and transmit it to the monitoring information output module. Component-level failures: The anomalies are concentrated in a single core component, while other parameter anomalies are derivative effects, such as inverter power transistor damage, output voltage fluctuations, current pulses, and inverter temperature rise. Module-level faults: The anomaly involves multiple related modules, such as the power module and the load module, but does not spread to the entire vehicle system. For example, the battery is depleted, the generator is overloaded, the output voltage is low, and all load currents are abnormal, spreading in sequence. System-level faults: The anomalies cover the entire vehicle's power chain and involve core control units, such as CAN bus communication failures, simultaneous distortion of voltage / current / temperature data, and malfunctions of protection mechanisms.
[0025] Using the most severe abnormal parameter as the top event, we trace back layer by layer by combining time series and physical logic: Top event: such as the system automatically cutting off the output; Intermediate events: Overcurrent protection triggered, current exceeding limit, load terminal short circuit; The problem is a short circuit inside the external electric cooker.
[0026] In the reasoning process, key evidence weights are introduced. For example, if the timestamp of the current anomaly is earlier than that of the temperature anomaly and the current change rate is greater than 5A / ms, then the short circuit evidence weight is added.
[0027] The independent anomaly analysis signal is analyzed to obtain various types of anomaly parameters, which are labeled as i, where i = 1, 2, ..., j, and j represents the type of anomaly parameter, such as output current, output voltage, or harness temperature. Then, the risk level of the anomaly parameter i is judged from two aspects: the scope of influence and the severity. The scope of influence and the severity are scored and assigned, and a weighted sum is calculated to obtain the comprehensive index corresponding to the anomaly parameter i. At the same time, the comprehensive index is matched with the corresponding rating interval to obtain the risk level corresponding to the anomaly parameter i. The parameters are then processed according to the risk level from large to small to generate priority processing information, which is then transmitted to the monitoring information output module.
[0028] The score is assigned based on the scope of impact. At the local level, if it only involves its monitoring point, such as a temperature sensor malfunction, it is assigned 1 point. At the extended level, if it affects a load, such as a dashcam voltage malfunction, it is assigned 3 points. At the system level, if it affects the power supply of the whole vehicle, such as a main circuit current malfunction, it is assigned 5 points.
[0029] The severity of the disease is scored by combining the duration and deviation. Minor: lasting less than 5 minutes and deviating less than 10%, is scored as 1 point; Moderate: lasting 5-30 minutes or deviating 10%-30%, is scored as 3 points; Severe: lasting more than 30 minutes or deviating more than 30%, is scored as 5 points; Fatal: directly related to safety risks is scored as 7 points.
[0030] Next, the comprehensive index is calculated according to the formula: Comprehensive Index = Impact Range Assignment × Weight 1 + Severity Assignment × Weight 2. The specific values of Weight 1 and Weight 2 are set by the operator. If the comprehensive index is > 8 points, the corresponding risk level is Level 1. If the comprehensive index is 5-8 points, the corresponding risk level is Level 2. If the comprehensive index is < 5 points, the corresponding risk level is Level 3.
[0031] The monitoring information output module is used to display the acquired impact information, fault causes, and priority handling information to the relevant management personnel.
[0032] Example 3 As a third embodiment of the present invention, the focus is on combining the implementation processes of the first and second embodiments.
[0033] The data in the above formulas are all calculated using numerical values, without substituting the units of the parameters. In addition, the contents not described in detail in this specification are all prior art known to those skilled in the art.
[0034] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. A vehicle-mounted power output monitoring system powered by an external load, characterized in that, include: The load identification and analysis module compares the output current, voltage, and wire harness temperature with the corresponding judgment thresholds. If any one of them fails to meet the standard, an abnormal signal is generated; if all of them meet the standard, a normal signal is generated. The normal monitoring and processing module is used to process normal analysis signals. It collects and verifies parameters multiple times according to a preset cycle. If all parameters are normal, it generates normal monitoring information; otherwise, it generates an abnormal signal. The anomaly analysis and processing module is used to process anomaly analysis signals, identify single or multiple types of anomalies, analyze single types of anomalies, verify whether sensors and communications are normal, and generate a fault signal if a continuous anomaly is confirmed, and transmit it to the fault comprehensive analysis module. For analysis of multiple types of situations, the correlation is determined by time series. If there is a correlation, a correlation abnormal signal is generated; otherwise, an independent abnormal signal is generated and transmitted to the fault comprehensive analysis module. The fault comprehensive analysis module processes fault analysis signals, determines the cause of the fault and judges its potential impact, and generates impact information. It processes related anomaly analysis signals, determines the fault range and generates fault causes by combining fault tree reverse reasoning. It processes independent anomaly analysis signals, scores the impact range and severity of each anomaly parameter, calculates the comprehensive risk level, and generates processing information according to priority.
2. The vehicle-mounted power output monitoring system for external load power supply according to claim 1, characterized in that, It also includes a real-time parameter acquisition module and a monitoring information output module; The real-time parameter acquisition module is used to acquire output voltage, output current and wire harness temperature through sensors and transmit them to the load identification and analysis module. The monitoring information output module is used to acquire impact information, fault causes, and handling information, and display them to the relevant management personnel.
3. The vehicle-mounted power output monitoring system for external load power supply according to claim 1, characterized in that, The load identification and analysis module outputs current, voltage, and harness temperature, which are compared with corresponding judgment thresholds. If the output current is greater than the overload threshold, or the temperature is greater than the temperature threshold, or the output voltage is less than the voltage threshold, it indicates that the load is abnormal and an abnormal analysis signal is generated and transmitted to the abnormal analysis processing module. If all three conditions are met, it indicates that the load is normal and a normal analysis signal is generated.
4. The vehicle-mounted power output monitoring system for external load power supply according to claim 1, characterized in that, The normal monitoring and processing module processes normal analysis signals in the following way: The system continuously collects real-time parameters multiple times, with a time period t set by the operator. The collected real-time parameters are then compared with a judgment threshold. If all the collected real-time parameters are normal, normal monitoring information is generated. If any of the collected real-time parameters are abnormal, an abnormal analysis signal is generated.
5. The vehicle-mounted power output monitoring system for external load power supply according to claim 1, characterized in that, The specific method by which the anomaly analysis processing module processes anomaly analysis signals is as follows: Obtain the corresponding abnormal parameters from the real-time parameters, identify their abnormality types, and generate single-type parameters or multiple-type parameters; For single-type parameter analysis, if the sensor and communication are checked and no problems are found, it is confirmed as a real anomaly and a single analysis signal is generated. If any problem exists, it is judged as a false anomaly and re-collection and judgment are required.
6. The vehicle-mounted power output monitoring system for external load power supply according to claim 5, characterized in that, The process involves processing a single analysis signal to obtain real abnormal parameters. The duration and trend of the abnormality are observed according to the period T set by the operator. If the abnormality recovers after a short period of exceeding the threshold, it is a transient abnormality, and its characteristics are recorded. If the frequency is low and does not affect the load, normal monitoring information is generated. If the abnormality exceeds the threshold for a long period of time or fluctuates frequently, it is a continuous abnormality, and a fault analysis signal is generated.
7. The vehicle-mounted power output monitoring system for external load power supply according to claim 1, characterized in that, The specific methods by which the anomaly analysis and processing module analyzes various types of situations are as follows: The authenticity of various types of abnormal parameters is verified, and the time series method is used to analyze the various types of abnormal parameters to determine their corresponding relationships. If a domino effect is caused by a root problem, it indicates that the various types of abnormal parameters are chain anomalies, and a related anomaly analysis signal is generated. If multiple unrelated abnormal parameters occur at the same time, it indicates that they are independent concurrent anomalies, and an independent anomaly analysis signal is generated.
8. The vehicle-mounted power output monitoring system for external load power supply according to claim 1, characterized in that, The specific method by which the fault comprehensive analysis module processes fault analysis signals is as follows: The corresponding fault cause is obtained based on the abnormal parameters. At the same time, the potential impact of the fault cause is judged to obtain the direct or indirect impact, and the impact information is generated and then transmitted to the monitoring information output module.
9. The vehicle-mounted power output monitoring system for external load power supply according to claim 1, characterized in that, The specific method by which the fault comprehensive analysis module processes the associated anomaly analysis signals is as follows: Obtain various types of abnormal parameters and determine their fault range, such as component failure, module failure, and system failure. At the same time, combine the fault tree to perform reverse reasoning to obtain the cause of the failure.
10. The vehicle-mounted power output monitoring system for external load power supply according to claim 1, characterized in that, The specific method by which the fault comprehensive analysis module processes independent anomaly analysis signals is as follows: Multiple types of abnormal parameters are obtained and labeled as i, where i = 1, 2, ..., j, and j represents the type of abnormal parameter. Then, the risk level of abnormal parameter i is judged from two aspects: the scope of impact and the severity. The scope of impact and the severity are scored and assigned respectively, and the weighted sum is calculated to obtain the comprehensive index corresponding to abnormal parameter i. At the same time, the comprehensive index is matched with the corresponding rating interval to obtain the risk level corresponding to abnormal parameter i. The parameters are then processed according to the risk level from large to small to generate priority processing information.