Automatic diagnosis test system based on vehicle body domain controller

Abstract

The invention discloses an automatic diagnosis and test system based on a vehicle body domain controller, and relates to the technical field of vehicle body domain controller diagnosis and test, and the system is composed of a vehicle body domain controller, a controlled equipment monitoring module and a dynamic diagnosis module. The vehicle body domain controller sends a diagnosis command with a timestamp to the controlled equipment through the multiplexing communication interface; the controlled equipment monitoring module is responsible for monitoring response of controlled equipment and generating a state feedback signal containing performance indexes, and the performance indexes cover response delay, execution precision and an energy consumption coefficient; and the dynamic diagnosis module combines the performance indexes with time, constructs a space-time fusion coordinate system, records three-dimensional health track points, and constructs a diagnosis boundary curved surface. By calculating the mahalanobis distance, the system can judge the health state of the controlled equipment and write a result into a block chain evidence storage block for an operator to look up; the system integrates a plurality of technical means, and realizes comprehensive, accurate and real-time diagnosis of controlled equipment.

Description

Technical Field

[0001] This invention belongs to the field of vehicle body domain controller diagnostic testing technology, specifically, it relates to an automatic diagnostic testing system based on vehicle body domain controllers. Background Technology

[0002] The automotive industry is undergoing profound technological changes. In this process, the trend of vehicle connectivity and intelligence is becoming increasingly prominent. As a core component of the vehicle's electronic system, the body domain controller undertakes key control and coordination functions.

[0003] Existing vehicle diagnostic technologies suffer from the following shortcomings: Traditional systems typically rely on single-dimensional indicator assessments, lacking multi-dimensional dynamic fusion analysis of equipment health status. This results in incomplete and delayed diagnostic results. Furthermore, existing methods often use fixed thresholds to judge equipment status, failing to adapt to the degradation curves of equipment performance over time and under different operating conditions, leading to misjudgments or missed detections. Secondly, at the data processing level, historical health data is often stored in isolation and is difficult to trace, resulting in a lack of spatiotemporal continuity in the diagnostic process. This makes it impossible to construct the evolution trajectory of equipment performance, making it difficult to capture early signs of degradation. In addition, diagnostic results are usually stored in centralized databases, posing a risk of data tampering and affecting the credibility of maintenance decisions. Moreover, the determination of abnormal states is mostly based on instantaneous values ​​from a single test, without considering the trend of degradation under continuous monitoring.

[0004] To address the aforementioned issues, this invention proposes an automatic diagnostic testing system based on a vehicle body domain controller. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an automatic diagnostic testing system based on a vehicle body domain controller, which solves the problems of insufficient dynamic fusion of multi-dimensional indicators and low reliability of results in traditional vehicle body diagnostics.

[0006] The objective of this invention can be achieved through the following technical solutions: An automated diagnostic testing system based on a vehicle body domain controller includes the following components: The vehicle body domain controller is configured to send timestamped diagnostic command frames to several controlled devices interconnected with the vehicle body domain controller via a multiplexed communication interface. The controlled equipment monitoring module is configured to monitor the controlled equipment's response to diagnostic command frames and generate a status feedback signal containing the performance indicators of the controlled equipment. The dynamic diagnostic module, combined with performance indicators, constructs a spatiotemporal fusion coordinate system to record the three-dimensional health trajectory points associated with the corresponding controlled device's response to this diagnostic command frame; A diagnostic boundary surface is constructed. The health status of the controlled device is determined based on the three-dimensional health trajectory points associated with the controlled device and the Mahalanobis distance between the diagnostic boundary surfaces. The results are then written into a blockchain storage block for operators to review.

[0007] As a further aspect of the present invention, the diagnostic command frame sent by the vehicle domain controller includes a timestamp T and a diagnostic command; The diagnostic command is a preset diagnostic command; The timestamp T records the time when the vehicle domain controller sends a diagnostic command frame to the controlled device.

[0008] As a further aspect of the present invention, the controlled equipment monitoring module monitors the controlled equipment's response to diagnostic command frames in real time through several monitoring devices pre-built by the operator. The controlled equipment monitoring module also generates a status feedback signal that includes the performance indicators of the controlled equipment; The performance indicators include response latency (B), execution accuracy (A), and energy consumption coefficient (C). The response delay A is the time difference between the sending of the diagnostic command frame to the controlled device and the first execution of the diagnostic command. The execution accuracy is the absolute error B between the state of the controlled device after executing the diagnostic command and the standard state; The energy consumption coefficient C is the current integral of the controlled device during the entire process of executing diagnostic commands.

[0009] As a further aspect of the present invention, the specific method for constructing a spatiotemporal fusion coordinate system in the dynamic diagnostic module by combining performance indicators is as follows: Obtain performance metrics including response latency, execution accuracy, and energy consumption coefficient; A three-dimensional spatiotemporal fusion coordinate system is constructed with execution accuracy as the X-axis, response delay as the Y-axis, and energy consumption coefficient as the Z-axis.

[0010] As a further aspect of the present invention, the specific method for recording the three-dimensional health trajectory points associated with the corresponding controlled device's response to this diagnostic command frame in the dynamic diagnostic module is as follows: S51. Extract the status feedback signal associated with the controlled device's response diagnostic command frame; S52. Extract the performance indicators associated with the controlled equipment based on the status feedback signal, including response delay B, execution accuracy A, and energy consumption coefficient C; S53. Combine the response delay B, execution accuracy A, and energy consumption coefficient C in the corresponding order of the X-axis, Y-axis, and Z-axis, and denote them as three-dimensional coordinates (A, B, C). S54. Extract the timestamp T from the diagnostic command frame sent by the vehicle domain controller to the controlled device, and bind the timestamp T to the three-dimensional coordinates (A,B,C) in time and space to generate a three-dimensional health trajectory point P(A,B,C|T) with time stamp. The timestamp T is independent of the position of the three-dimensional health trajectory point P(A,B,C|T) in the spatiotemporal fusion coordinate system; S55. Based on the three-dimensional coordinates (A,B,C) of the three-dimensional health trajectory point P(A,B,C|T), map the three-dimensional health trajectory point P(A,B,C|T) in the spatiotemporal fusion coordinate system to determine the position of the three-dimensional health trajectory point P(A,B,C|T) in the spatiotemporal fusion coordinate system.

[0011] As a further aspect of the present invention, the specific method for constructing the diagnostic boundary surface in the dynamic diagnostic module is as follows: Retrieve normal, similar devices from a cloud database; Extract several three-dimensional health trajectory points associated with several normal devices of the same type, and record the total number as j; The determined j three-dimensional health trajectory points are summarized and denoted as the historical health trajectory point set; Copy the spatiotemporal fusion coordinate system to obtain spatiotemporal fusion coordinate system _1; Obtain all three-dimensional health trajectory points from the historical health trajectory point set, and extract the average execution accuracy, average response latency, and average energy consumption coefficient from all three-dimensional health trajectory points, denoted as A0, B0, and C0 respectively; Combine A0, B0 and C0 to construct a three-dimensional health trajectory point P0(A0,B0,C0), and plot P0(A0,B0,C0) in the spatiotemporal fusion coordinate system _1; Connect the origin of the spatiotemporal fusion coordinate system _1 with P0(A0,B0,C0) using a short line, and denote this short line as R; With the origin of the spatiotemporal fusion coordinate system _1 as the center and the short line R as the radius, construct an eighth sphere in the spatiotemporal fusion coordinate system _1, and denote the diagnostic boundary surface SS; The diagnostic boundary surface SS does not contain points on the plane containing any two of the X-axis, Y-axis and Z-axis in the spatiotemporal fusion coordinate system _1; The diagnostic boundary surface SS is automatically updated every preset update cycle.

[0012] As a further aspect of the present invention, the specific method by which the dynamic diagnostic module determines the health status of the controlled device and writes it into the blockchain evidence storage block is as follows: Obtain the three-dimensional health trajectory point P(A,B,C|T) associated with the controlled device's response to this diagnostic command frame; Extract the detection cycle TJZ and detection time interval JG preset by the operator; Based on the detection time interval JG, determine the total number of detections m within a detection cycle TJZ; Within any detection cycle TJZ, the vehicle domain controller continuously sends m diagnostic command frames to the controlled device, and the time interval between two adjacent diagnostic command frames is a detection time interval JG. Take the three-dimensional health trajectory points associated with m diagnostic command frames, sort them in chronological order, and obtain the three-dimensional health trajectory point sequence P1, P2, ..., Pm; All three-dimensional health trajectory points in the three-dimensional health trajectory point sequence P1, P2, ..., Pm are sequentially mapped in the spatiotemporal fusion coordinate system _1, and the Mahalanobis distance between each three-dimensional health trajectory point and the diagnostic boundary surface SS is calculated. Determine the inner and outer Mahalanobis distance thresholds, assess the health status of all three-dimensional health trajectory points in the three-dimensional health trajectory point sequence P1, P2, ..., Pm, and determine the health status of the controlled device; The health status of the controlled device is written into the blockchain's evidence storage block.

[0013] As a further aspect of the present invention, the dynamic diagnostic module determines the inner Mahalanobis distance threshold and the outer Mahalanobis distance threshold, assesses the health status of the corresponding three-dimensional health trajectory points, and the specific method for determining the health status of the controlled device is as follows: Copy the spatiotemporal fusion coordinate system to obtain spatiotemporal fusion coordinate system _2; Obtain the set of historical health trajectory points, and map all three-dimensional health trajectory points in the set of historical health trajectory points into the spatiotemporal fusion coordinate system _2; The minimum convex hull surface SS1 and the maximum convex hull surface SS2 of all three-dimensional health trajectory points in the historical health trajectory point set in the spatiotemporal fusion coordinate system _2 are determined by the convex hull algorithm. The minimum convex hull surface SS1 is located between the diagnostic boundary surface SS and the origin; The maximum convex hull surface SS2 is located outside the diagnostic boundary surface SS; Calculate the average distance between the minimum convex hull surface SS1 and the diagnostic boundary surface SS, and denote the inner Mahalanobis distance threshold D1; Calculate the average distance between the maximum convex hull surface SS2 and the diagnostic boundary surface SS, and denote it as the Mahalanobis distance threshold D2; Determine the Mahalanobis distances between all three-dimensional health trajectory points in the sequence P1, P2, ..., Pm associated with the controlled device and the diagnostic boundary surface SS, and record them as the Mahalanobis distance sequence d1, d2, ..., dm according to the sorting order of the three-dimensional health trajectory point sequence P1, P2, ..., Pm; If the three-dimensional health trajectory point is located between the diagnostic boundary surface SS and the origin, then the Mahalanobis distance of the three-dimensional health trajectory point is compared with the inner Mahalanobis distance threshold D1. If the Mahalanobis distance does not exceed the inner Mahalanobis distance threshold D1, the three-dimensional health trajectory points are considered healthy. If the Mahalanobis distance exceeds the inner Mahalanobis distance threshold D1, the three-dimensional healthy trajectory point is considered unhealthy. If the three-dimensional health trajectory point is located outside the diagnostic boundary surface SS, then the Mahalanobis distance of the three-dimensional health trajectory point is compared with the external Mahalanobis distance threshold D2; If the Mahalanobis distance does not exceed the inner Mahalanobis distance threshold D2, the three-dimensional health trajectory points are considered healthy. If the Mahalanobis distance exceeds the inner Mahalanobis distance threshold D2, the three-dimensional healthy trajectory point is considered unhealthy. Traverse and evaluate m Mahalanobis distances in the Mahalanobis distance sequence d1, d2, ..., dm. If there are o consecutive Mahalanobis distances associated with three-dimensional health trajectory points whose health status is unhealthy, then the health status of the controlled device is determined to be unhealthy, where o is a preset value. Conversely, the controlled device is deemed to be in a healthy state.

[0014] The beneficial effects of this invention are: This invention establishes a timing benchmark for subsequent performance index analysis by using a timestamp T to mark the sending time of diagnostic commands, effectively eliminating the data drift problem caused by clock asynchrony in traditional systems and ensuring the accuracy of response delay calculation. Furthermore, the ability for operators to pre-set diagnostic commands gives the system high flexibility, allowing for customized test logic for different controlled devices or fault scenarios. Secondly, it innovatively collects three heterogeneous performance indicators—response delay, execution accuracy, and energy consumption coefficient—simultaneously, overcoming the limitations of traditional single-dimensional diagnostics. This invention achieves multi-dimensional dynamic visualization modeling of equipment health status by constructing a three-dimensional spatiotemporal fusion coordinate system and generating health trajectory points with time stamps. It is based on mapping three types of heterogeneous performance indicators into a unified mathematical space (three-dimensional spatiotemporal fusion coordinate system): using execution precision as the X-axis to quantify action accuracy, response delay as the Y-axis to characterize timeliness, and energy consumption coefficient as the Z-axis to characterize energy efficiency status, forming a "spatial fingerprint" of the equipment's comprehensive performance, so that the equipment health status is transformed from abstract data into a quantifiable spatial trajectory. This invention generates three-dimensional health trajectory points by spatiotemporally binding timestamp T, preserving the spatial correlation of three types of heterogeneous performance indicators while maintaining the continuity of the time series. This spatiotemporal separation mechanism (timestamp independent of coordinate position) avoids the interference of the time dimension on the spatial model, paving the way for subsequent accurate diagnosis based on spatial distance. This invention achieves intelligent diagnosis and reliable evidence storage of equipment health status through dynamic diagnostic boundary surface construction and a dual-threshold Mahalanobis distance determination mechanism. Based on historical data in the cloud, the diagnostic boundary surface SS is defined by the mean point and the origin, which not only conforms to the equipment performance distribution characteristics but also avoids coordinate plane interference. Secondly, it innovatively uses the minimum / maximum convex hull surface to automatically generate inner and outer dual Mahalanobis distance thresholds, forming a dynamic diagnostic interval to adapt to the equipment performance degradation law and avoid misjudgment caused by a single threshold. Finally, combined with the continuous non-health point determination logic, it effectively captures the cumulative effect of intermittent faults and improves the fault detection rate. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a schematic diagram of the system described in this invention; Figure 2 This is a flowchart illustrating the method described in Embodiment 3 of the present invention; Figure 3 This is a flowchart illustrating the method described in Embodiment 4 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 Automatic diagnostic testing system based on vehicle body domain controller, such as Figure 1 As shown, this system includes the following: This system mainly includes a vehicle domain controller, a controlled equipment monitoring module, a dynamic diagnostic module, and a cloud database for storing data.

[0019] The vehicle body domain controller serves as the central hub of the entire system, broadcasting diagnostic command frames containing timestamp T to multiple controlled devices (such as window motors, headlight controllers, etc.) through multiplexed communication interfaces (such as CANFD or Ethernet). The diagnostic command frame includes a timestamp T and a diagnostic command, which is customized by the operator based on the actual controlled equipment. The timestamp T precisely records the transmission time of the diagnostic command frame, so as to distinguish it from other diagnostic command frames and eliminate the influence of system clock deviation.

[0020] The controlled equipment monitoring module described in this system uses pre-set hardware sensors (such as current / voltage sensors) and software monitoring units to capture in real time the status feedback signal generated by the controlled equipment in response to diagnostic command frames, which contains the real-time performance indicators of the controlled equipment. The performance indicators include response latency (B), execution accuracy (A), and energy consumption coefficient (C), which quantify the device status from three orthogonal dimensions of time, accuracy, and energy consumption, avoiding the one-sidedness of a single indicator. The response delay A is the time difference between the sending of the diagnostic command frame to the controlled device and the first execution of the diagnostic command. The execution accuracy is the absolute error B between the state of the controlled device after executing the diagnostic command and the standard state. For example, if the target opening degree of the car window is 80% and the actual opening degree is 78%, then A = 2%. The energy consumption coefficient C is the current integral of the controlled device during the entire process of executing diagnostic commands, which quantifies energy consumption efficiency.

[0021] The dynamic diagnostic module described in this system is the core module of this system. First, a three-dimensional spatiotemporal fusion coordinate system is constructed with execution accuracy as the X-axis, response delay as the Y-axis, and energy consumption coefficient as the Z-axis. Next, the performance indicators associated with the controlled equipment are mapped to three-dimensional health trajectory points. If there are continuous three-dimensional health trajectory points in the order of X-axis, Y-axis, and Z-axis, it indicates the trajectory of performance change of the controlled equipment. Next, a diagnostic boundary surface is determined in the spatiotemporal fusion coordinate system, the diagnostic boundary surface being determined based on historical data from several normal controlled devices; The Mahalanobis distance between the three-dimensional health trajectory point of the controlled device to be tested and the diagnostic boundary surface is calculated and compared with the preset Mahalanobis distance threshold to determine the health status (healthy / unhealthy) of the three-dimensional health trajectory point of the controlled device. At the same time, it is written into the blockchain storage block (which has the characteristic of being immutable) so that the operator can review it later.

[0022] The cloud database described in this system is a pre-built database by operators to store various analytical data and calculation results generated by the system, as well as historical data associated with various controlled devices. It also integrates blockchain technology and has tamper-proof characteristics.

[0023] This embodiment introduces an automatic diagnostic testing system based on a vehicle domain controller. Using the vehicle domain controller as a central hub, it broadcasts timestamped diagnostic command frames to multiple controlled devices via a multiplexed communication interface. The controlled device monitoring module captures the status feedback signals generated by the controlled devices in real time, quantifying the device status using three orthogonal dimensions: response latency, execution accuracy, and energy consumption coefficient. The dynamic diagnostic module constructs a three-dimensional spatiotemporal fusion coordinate system, mapping performance indicators to three-dimensional health trajectory points, determining the diagnostic boundary surface, calculating the Mahalanobis distance from the three-dimensional health trajectory points to the surface, comparing it with a threshold, judging the health status of the controlled devices, and simultaneously writing the data into a blockchain storage block. The purpose of this embodiment is to achieve real-time and accurate diagnosis of vehicle-controlled devices. Through multi-dimensional performance indicators and dynamic diagnostic algorithms, it accurately assesses device status, promptly detects anomalies, and utilizes blockchain to ensure the immutability of diagnostic data, facilitating later review, thereby improving the reliability and maintainability of the vehicle system.

[0024] Example 2 This embodiment, based on Embodiment 1, discloses a method for determining the three-dimensional health trajectory points of a controlled device in a constructed spatiotemporal fusion coordinate system, specifically including the following: As described in Example 1, it can be seen that a three-dimensional spatiotemporal fusion coordinate system is constructed by using execution accuracy as the X-axis of the spatiotemporal fusion coordinate system, response delay as the Y-axis of the spatiotemporal fusion coordinate system, and energy consumption coefficient as the Z-axis of the spatiotemporal fusion coordinate system. Next, the position of the three-dimensional health trajectory points associated with the status feedback signal of the controlled device in response to this diagnostic command frame needs to be determined in the spatiotemporal fusion coordinate system. The status feedback signal includes the performance indicators of the controlled device, namely response delay B, execution accuracy A, and energy consumption coefficient C.

[0025] Then, the response delay B, execution accuracy A, and energy consumption coefficient C, determined by the combination of the X-axis, Y-axis, and Z-axis, are used to obtain a three-dimensional coordinate system, denoted as (A,B,C). The three-dimensional coordinate system (A,B,C) summarizes the performance of the controlled device in the three core dimensions of response, accuracy, and energy consumption, and represents a snapshot of the performance of the controlled device at the moment of execution of this specific diagnostic command. Next, according to the content described in Example 1, the timestamp T in the diagnostic command frame sent by the vehicle domain controller to the controlled device is determined, and the determined timestamp T is spatiotemporally bound to the three-dimensional coordinates (A,B,C) to form a three-dimensional health trajectory point with a time stamp, which is denoted as P(A,B,C|T). The timestamp T is independent of the position of the three-dimensional health trajectory point in the three-dimensional spatial coordinate system (A,B,C). The timestamp T is independent temporal information. In this step, a time dimension is introduced to record when the performance snapshot of the controlled device occurs. Secondly, by distinguishing between spatial attributes (A, B, C represent the current performance state) and time attributes (T represents the time when this state occurred), the use of time itself as a spatial coordinate axis is avoided, which would cause the space to be stretched and distorted by time, making it difficult to analyze the clustering and change trends of the performance state itself. That is, time information is only used for sorting and associating historical states.

[0026] Next, based on the determined three-dimensional health trajectory point P(A,B,C|T), the three-dimensional health trajectory point P(A,B,C|T) is mapped in the constructed spatiotemporal fusion coordinate system with the scale on the X-axis as A, the scale on the Y-axis as B, and the scale on the Z-axis as C. After mapping, the position of this three-dimensional health trajectory point P(A,B,C|T) in the spatiotemporal fusion coordinate system can be determined. By mapping abstract data points to an intuitive spatial model, it is easier to understand the position of the device's current performance status within the overall performance space. In subsequent operations, as time progresses and multiple diagnoses are performed (generating multiple 3D health trajectory points), these 3D health trajectory points are connected in chronological order to form the health trajectory of the controlled device in the 3D performance space. This clearly shows the trend and drift path of the controlled device's performance status over time, and enables the comparison of the status points of different devices, the same device at different times, or at the same time in the same 3D space.

[0027] Example 3 This embodiment discloses a method for constructing diagnostic boundary surfaces based on Embodiments 1 and 2, such as... Figure 2 As shown, it specifically includes the following: First, as can be seen from the content described in Example 1, the cloud database stores various historical data associated with several controlled devices. Several devices that are of the same type and normal as the controlled devices described in this solution are obtained from the cloud database and are recorded as normal devices of the same type. The purpose of obtaining several normal devices of the same type is to establish a reliable, group-based health benchmark, which reflects the expected performance distribution range of this type of device under healthy conditions.

[0028] Next, obtain the data source associated with the health benchmark, namely the historical performance data of several normal devices of the same type and several three-dimensional health trajectory points constructed from the historical performance data, and count the total number of several three-dimensional health trajectory points, denoted as j; Then, the determined j three-dimensional health trajectory points are summarized in no particular order, and the summaries are recorded as the historical health trajectory point set. Obtain the spatiotemporal fusion coordinate system, clear the spatiotemporal fusion coordinate system, keep only the spatiotemporal fusion coordinate system itself, copy the spatiotemporal fusion coordinate system to obtain a new spatiotemporal fusion coordinate system, and denote it as spatiotemporal fusion coordinate system_1.

[0029] Next, all three-dimensional health trajectory points in the historical health trajectory point set are extracted, and the historical performance data (response latency, execution accuracy, and energy consumption coefficient) in the three-dimensional health trajectory points are averaged to determine the average execution accuracy, average response latency, and average energy consumption coefficient. The average execution accuracy, average response latency, and average energy consumption coefficient are denoted as A0, B0, and C0, respectively. A three-dimensional health trajectory point is formed by the average execution accuracy A0, the average response delay B0, and the average energy consumption coefficient C0, denoted as P0(A0,B0,C0). The three-dimensional health trajectory point P0(A0,B0,C0) represents the average expected level of accuracy, delay, and energy consumption of similar devices.

[0030] The three-dimensional health trajectory point P0(A0,B0,C0) is mapped in the constructed spatiotemporal fusion coordinate system _1 based on the average execution accuracy A0, the average response delay B0, and the average energy consumption coefficient C0.

[0031] Then, the origin of the spatiotemporal fusion coordinate system _1 is determined, and a short line is used to connect the origin and the three-dimensional health trajectory point P0(A0,B0,C0) plotted in the spatiotemporal fusion coordinate system _1, and this short line is denoted as R; Next, with the origin of the spatiotemporal fusion coordinate system _1 as the center and the determined short line R as the radius, an eighth sphere is constructed in the spatiotemporal fusion coordinate system _1, and this sphere is denoted as the diagnostic boundary surface SS. It is important to note that the diagnostic boundary surface SS does not include any two of the X-axis, Y-axis and Z-axis in the spatiotemporal fusion coordinate system _1. In other words, the diagnostic boundary surface SS is strictly located within the spatiotemporal fusion coordinate system _1 to avoid boundary ambiguity caused by intersection with the coordinate plane. As described in Example 1, the response latency, execution accuracy, and energy consumption coefficient are as follows: The time difference between the sending of the diagnostic command frame to the controlled device and the first execution of the diagnostic command. The absolute error between the state of the controlled device after executing the diagnostic command and the standard state; The current integral throughout the entire process of the controlled device executing diagnostic commands; Therefore, the smaller the time difference, the smaller the absolute error, and the smaller the current integral, the closer the controlled device is to the ideal healthy state. In the spatiotemporal fusion coordinate system _1, the smaller the time difference, the smaller the absolute error, and the smaller the current integral, the closer it is to the origin. Therefore, the part between the diagnostic boundary surface SS and the origin is regarded as the healthy region, while the part outside the diagnostic boundary surface SS is regarded as the unhealthy region. It is important to note that response latency, execution accuracy, and energy consumption coefficient must all be positive numbers, conforming to the definitions of actual physical quantities; In summary: if any three-dimensional health trajectory point is located between the diagnostic boundary surface SS and the origin, and is closer to the origin, it indicates that the health status of the controlled device corresponding to this three-dimensional health trajectory point is better; if the three-dimensional health trajectory point is outside the diagnostic boundary surface SS, and is farther away from the origin, it indicates that the health status of the controlled device corresponding to this three-dimensional health trajectory point is worse.

[0032] The diagnostic boundary surface SS is not static and needs to be updated periodically. According to the update cycle preset by the operator, a periodic automatic update operation is performed, that is, a new diagnostic boundary surface is constructed by obtaining the performance history data of the same type of equipment from the segment database and covering the original diagnostic boundary surface.

[0033] The purpose of this embodiment is to collect historical performance data of normal devices of the same type, construct a three-dimensional health trajectory point set, and process these data through mathematical models such as a spatiotemporal fusion coordinate system to determine the diagnostic boundary surface SS. This diagnostic boundary surface divides the spatiotemporal fusion coordinate system into healthy and unhealthy regions. The closer the three-dimensional health trajectory point of the device is to the origin, the healthier it is. This achieves a quantitative assessment of the health status of the device.

[0034] Example 4 This embodiment further discloses a method for determining the health status of a controlled device, based on embodiment 3. Figure 3 As shown, it specifically includes the following: First, obtain the three-dimensional health trajectory point P(A,B,C|T) associated with the controlled device's response to this diagnostic command frame, and determine the method for extracting the three-dimensional health trajectory point P(A,B,C|T) from the above embodiments; Next, extract the detection cycle TJZ and the detection time interval JG preset by the operator. The diagnostic rhythm is controlled by the detection cycle TJZ and the detection time interval JG. The total number of tests that need to be performed within a detection cycle TJZ is determined and denoted as m. For example, if the existing detection cycle TJZ is 24 hours and the detection interval JG is once every 10 minutes, then a total of 144 detections are required within one detection cycle TJZ. In other words, within this detection cycle TJZ, the controlled device needs to respond to 144 diagnostic command frames.

[0035] Next, the performance indicators obtained from the controlled device's response to m diagnostic command frames are processed according to the method for determining the three-dimensional health trajectory point P(A,B,C|T), resulting in m three-dimensional health trajectory points with time stamps associated with the m performance indicators.

[0036] Next, the m three-dimensional health trajectory points with time stamps are sorted according to the timeline (chronological order), and the sorted result is recorded as the three-dimensional health trajectory point sequence, represented as: P1, P2, ..., Pm; The purpose of this step is to preserve the temporal relationship of performance changes in order to analyze degradation trends.

[0037] Next, all three-dimensional health trajectory points in the sequence P1, P2, ..., Pm are sequentially mapped onto the constructed spatiotemporal fusion coordinate system _1, and the Mahalanobis distance between each three-dimensional health trajectory point and the diagnostic boundary surface SS is calculated, finally obtaining m Mahalanobis distances.

[0038] Next, the m obtained Mahalanobis distances are compared with the inner Mahalanobis distance threshold and the outer Mahalanobis distance threshold in turn. The health status of the corresponding three-dimensional health trajectory points is evaluated based on the comparison results. The health status of the controlled device is further determined based on the health status of the three-dimensional health trajectory points, and the health status of the controlled device is written into the blockchain storage block.

[0039] It should be added here that the method for determining the health status of the controlled equipment by assessing the health status of the corresponding 3D health trajectory points based on the inner and outer Mahalanobis distance thresholds also includes the following steps: First, obtain the spatiotemporal fusion coordinate system and clear it. Then, copy the spatiotemporal fusion coordinate system to obtain spatiotemporal fusion coordinate system_2. Next, obtain the set of historical health trajectory points described in Example 3, and then sequentially map all three-dimensional health trajectory points in the set of historical health trajectory points into the constructed spatiotemporal fusion coordinate system _2; The double-layer convex hull boundary is generated by using the convex hull algorithm (the convex hull algorithm is an existing technology and will not be described in detail in this solution). That is, two convex hull surfaces are generated, namely the minimum convex hull surface SS1 and the maximum convex hull surface SS2, which are composed of three-dimensional health trajectory points in the set of historical health trajectory points in the spatiotemporal fusion coordinate system_2. Furthermore, the minimum convex hull surface SS1 is located between the diagnostic boundary surface SS and the origin, while the maximum convex hull surface SS2 is located outside the diagnostic boundary surface SS (because the diagnostic boundary surface SS is composed of the average of all three-dimensional health trajectory points in the historical health trajectory point set). Then, the average distance between the minimum convex hull surface SS1 and the diagnostic boundary surface SS is calculated, and the calculated average distance is denoted as the inner Mahalanobis distance threshold D1. Calculate the average distance between the maximum convex hull surface SS2 and the diagnostic boundary surface SS, and record the calculated average distance as the external Mahalanobis distance threshold D2.

[0040] Determine the Mahalanobis distance between all three-dimensional health trajectory points in the sequence P1, P2, ..., Pm associated with the controlled device and the diagnostic boundary surface SS, and record the Mahalanobis distance sequence according to the sorting order of the three-dimensional health trajectory point sequence P1, P2, ..., Pm, denoted as: d1, d2, ..., dm; Next, the m Mahalanobis distances in the Mahalanobis distance sequence d1, d2, ..., dm need to be determined based on the following criteria: if any three-dimensional health trajectory point is located between the diagnostic boundary surface SS and the origin, the Mahalanobis distance of this three-dimensional health trajectory point is compared with the inner Mahalanobis distance threshold D1. If the Mahalanobis distance does not exceed the inner Mahalanobis distance threshold D1, the three-dimensional health trajectory point is considered healthy; if the Mahalanobis distance exceeds the inner Mahalanobis distance threshold D1, the three-dimensional health trajectory point is considered unhealthy. If any three-dimensional health trajectory point is located outside the diagnostic boundary surface SS, the Mahalanobis distance of the three-dimensional health trajectory point is compared with the outer Mahalanobis distance threshold D2. If the Mahalanobis distance does not exceed the inner Mahalanobis distance threshold D2, the three-dimensional health trajectory point is considered healthy. If the Mahalanobis distance exceeds the inner Mahalanobis distance threshold D2, the three-dimensional health trajectory point is considered unhealthy. Continue traversing the Mahalanobis distance sequence d1, d2, ..., dm, and successively determine the inner Mahalanobis distance threshold or the outer Mahalanobis distance threshold for each of the m Mahalanobis distances; If, during the determination process, there are o consecutive three-dimensional health trajectory points associated with Mahalanobis distance whose health status is all unhealthy, then the health status of the controlled device is determined to be unhealthy. If there are no consecutive O Mahalanobis distances associated with the three-dimensional health trajectory points and all of them are unhealthy, then the health status of the controlled device is determined to be healthy, where O is an integer preset by the operator based on the actual situation.

[0041] At this point, the health status assessment of the controlled equipment is complete.

[0042] All data in the formulas described above are numerical calculations performed with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0043] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0044] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. An automatic diagnostic testing system based on a vehicle body domain controller, characterized in that, This system includes the following: The vehicle body domain controller is configured to send timestamped diagnostic command frames to several controlled devices interconnected with the vehicle body domain controller via a multiplexed communication interface. The controlled equipment monitoring module is configured to monitor the controlled equipment's response to diagnostic command frames and generate a status feedback signal containing the performance indicators of the controlled equipment. The dynamic diagnostic module, combined with performance indicators, constructs a spatiotemporal fusion coordinate system to record the three-dimensional health trajectory points associated with the corresponding controlled device's response to this diagnostic command frame; A diagnostic boundary surface is constructed. The health status of the controlled device is determined based on the three-dimensional health trajectory points associated with the controlled device and the Mahalanobis distance between the diagnostic boundary surfaces. The results are then written into a blockchain storage block for operators to review.

2. The automatic diagnostic testing system based on the vehicle body domain controller according to claim 1, characterized in that, The diagnostic command frame sent by the vehicle domain controller includes a timestamp T and a diagnostic command. The diagnostic command is a preset diagnostic command; The timestamp T records the time when the vehicle domain controller sends a diagnostic command frame to the controlled device.

3. The automatic diagnostic testing system based on the vehicle body domain controller according to claim 1, characterized in that, The controlled equipment monitoring module monitors the controlled equipment's response to diagnostic command frames in real time through several monitoring devices pre-built by the operator. The controlled equipment monitoring module also generates a status feedback signal that includes the performance indicators of the controlled equipment; The performance indicators include response latency (B), execution accuracy (A), and energy consumption coefficient (C). The response delay A is the time difference between the sending of the diagnostic command frame to the controlled device and the first execution of the diagnostic command. The execution accuracy is the absolute error B between the state of the controlled device after executing the diagnostic command and the standard state; The energy consumption coefficient C is the current integral of the controlled device during the entire process of executing diagnostic commands.

4. The automatic diagnostic testing system based on the vehicle body domain controller according to claim 1, characterized in that, In the dynamic diagnostic module, the specific method for constructing a spatiotemporal fusion coordinate system based on performance indicators is as follows: Obtain performance metrics including response latency, execution accuracy, and energy consumption coefficient; A three-dimensional spatiotemporal fusion coordinate system is constructed with execution accuracy as the X-axis, response delay as the Y-axis, and energy consumption coefficient as the Z-axis.

5. The automatic diagnostic testing system based on the vehicle body domain controller according to claim 4, characterized in that, In the dynamic diagnostic module, the specific method for recording the three-dimensional health trajectory points associated with the corresponding controlled device's response to this diagnostic command frame is as follows: S51. Extract the status feedback signal associated with the controlled device's response diagnostic command frame; S52. Extract the performance indicators associated with the controlled equipment based on the status feedback signal, including response delay B, execution accuracy A, and energy consumption coefficient C; S53. Combine the response delay B, execution accuracy A, and energy consumption coefficient C in the corresponding order of the X-axis, Y-axis, and Z-axis, and denote them as three-dimensional coordinates (A, B, C). S54. Extract the timestamp T from the diagnostic command frame sent by the vehicle domain controller to the controlled device, and bind the timestamp T to the three-dimensional coordinates (A,B,C) in time and space to generate a three-dimensional health trajectory point P(A,B,C|T) with time stamp. The timestamp T is independent of the position of the three-dimensional health trajectory point P(A,B,C|T) in the spatiotemporal fusion coordinate system; S55. Based on the three-dimensional coordinates (A,B,C) of the three-dimensional health trajectory point P(A,B,C|T), map the three-dimensional health trajectory point P(A,B,C|T) in the spatiotemporal fusion coordinate system to determine the position of the three-dimensional health trajectory point P(A,B,C|T) in the spatiotemporal fusion coordinate system.

6. The automatic diagnostic testing system based on the vehicle body domain controller according to claim 5, characterized in that, In the dynamic diagnostic module, the specific method for constructing the diagnostic boundary surface is as follows: Retrieve normal, similar devices from a cloud database; Extract several three-dimensional health trajectory points associated with several normal devices of the same type, and record the total number as j; The determined j three-dimensional health trajectory points are summarized and denoted as the historical health trajectory point set; Copy the spatiotemporal fusion coordinate system to obtain spatiotemporal fusion coordinate system _1; Obtain all three-dimensional health trajectory points from the historical health trajectory point set, and extract the average execution accuracy, average response latency, and average energy consumption coefficient from all three-dimensional health trajectory points, denoted as A0, B0, and C0 respectively; Combine A0, B0 and C0 to construct a three-dimensional health trajectory point P0(A0,B0,C0), and plot P0(A0,B0,C0) in the spatiotemporal fusion coordinate system _1; Connect the origin of the spatiotemporal fusion coordinate system _1 with P0(A0,B0,C0) using a short line, and denote this short line as R; With the origin of the spatiotemporal fusion coordinate system _1 as the center and the short line R as the radius, construct an eighth sphere in the spatiotemporal fusion coordinate system _1, and denote the diagnostic boundary surface SS; The diagnostic boundary surface SS does not contain points on the plane containing any two of the X-axis, Y-axis and Z-axis in the spatiotemporal fusion coordinate system _1; The diagnostic boundary surface SS is automatically updated every preset update cycle.

7. The automatic diagnostic testing system based on the vehicle body domain controller according to claim 6, characterized in that, The dynamic diagnostic module determines the health status of the controlled device and writes it into the blockchain evidence block in the following specific way: Obtain the three-dimensional health trajectory point P(A,B,C|T) associated with the controlled device's response to this diagnostic command frame; Extract the detection cycle TJZ and detection time interval JG preset by the operator; Based on the detection time interval JG, determine the total number of detections m within a detection cycle TJZ; Within any detection cycle TJZ, the vehicle domain controller continuously sends m diagnostic command frames to the controlled device, and the time interval between two adjacent diagnostic command frames is a detection time interval JG. Take the three-dimensional health trajectory points associated with m diagnostic command frames, sort them in chronological order, and obtain the three-dimensional health trajectory point sequence P1, P2, ..., Pm; All three-dimensional health trajectory points in the three-dimensional health trajectory point sequence P1, P2, ..., Pm are sequentially mapped in the spatiotemporal fusion coordinate system _1, and the Mahalanobis distance between each three-dimensional health trajectory point and the diagnostic boundary surface SS is calculated. Determine the inner and outer Mahalanobis distance thresholds, assess the health status of all three-dimensional health trajectory points in the three-dimensional health trajectory point sequence P1, P2, ..., Pm, and determine the health status of the controlled device; The health status of the controlled device is written into the blockchain's evidence storage block.

8. The automatic diagnostic testing system based on the vehicle body domain controller according to claim 7, characterized in that, In the dynamic diagnostic module, the inner and outer Mahalanobis distance thresholds are determined, and the health status of the corresponding three-dimensional health trajectory points is assessed. The specific method for determining the health status of the controlled device is as follows: Copy the spatiotemporal fusion coordinate system to obtain spatiotemporal fusion coordinate system _2; Obtain the set of historical health trajectory points, and map all three-dimensional health trajectory points in the set of historical health trajectory points into the spatiotemporal fusion coordinate system _2; The minimum convex hull surface SS1 and the maximum convex hull surface SS2 of all three-dimensional health trajectory points in the historical health trajectory point set in the spatiotemporal fusion coordinate system _2 are determined by the convex hull algorithm. The minimum convex hull surface SS1 is located between the diagnostic boundary surface SS and the origin; The maximum convex hull surface SS2 is located outside the diagnostic boundary surface SS; Calculate the average distance between the minimum convex hull surface SS1 and the diagnostic boundary surface SS, and denote the inner Mahalanobis distance threshold D1; Calculate the average distance between the maximum convex hull surface SS2 and the diagnostic boundary surface SS, and denote it as the Mahalanobis distance threshold D2; Determine the Mahalanobis distances between all three-dimensional health trajectory points in the sequence P1, P2, ..., Pm associated with the controlled device and the diagnostic boundary surface SS, and record them as the Mahalanobis distance sequence d1, d2, ..., dm according to the sorting order of the three-dimensional health trajectory point sequence P1, P2, ..., Pm; If the three-dimensional health trajectory point is located between the diagnostic boundary surface SS and the origin, then the Mahalanobis distance of the three-dimensional health trajectory point is compared with the inner Mahalanobis distance threshold D1. If the Mahalanobis distance does not exceed the inner Mahalanobis distance threshold D1, the three-dimensional health trajectory points are considered healthy. If the Mahalanobis distance exceeds the inner Mahalanobis distance threshold D1, the three-dimensional healthy trajectory point is considered unhealthy. If the three-dimensional health trajectory point is located outside the diagnostic boundary surface SS, then the Mahalanobis distance of the three-dimensional health trajectory point is compared with the external Mahalanobis distance threshold D2; If the Mahalanobis distance does not exceed the inner Mahalanobis distance threshold D2, the three-dimensional health trajectory points are considered healthy. If the Mahalanobis distance exceeds the inner Mahalanobis distance threshold D2, the three-dimensional healthy trajectory point is considered unhealthy. Traverse and evaluate m Mahalanobis distances in the Mahalanobis distance sequence d1, d2, ..., dm. If there are o consecutive Mahalanobis distances associated with three-dimensional health trajectory points whose health status is unhealthy, then the health status of the controlled device is determined to be unhealthy, where o is a preset value. Conversely, the controlled device is deemed to be in a healthy state.

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