Vehicle-mounted navigation system testing method and device

Abstract

The invention discloses a method and a device for testing a vehicle-mounted navigation system, and particularly relates to the technical field of navigation performance testing. A temperature gradient of a vehicle from low-temperature starting to high-temperature operation is simulated, and temperature change is applied to a hardware component of the vehicle-mounted navigation system for comprehensive testing: a Markov random field is utilized to analyze intensity distribution of a signal propagation path, disturbance intensity change near a metal part in a high-temperature environment is evaluated, and normal signal intensity is ensured; when the signal intensity is normal, the graph convolutional neural network is adopted to analyze the drift of the metal surface reflection position under the temperature gradient, hardware micro-delay response data is analyzed through the generalized linear hybrid model, the thermal fatigue accumulation trend of hardware is evaluated, and finally, the reflection position drift degree and the thermal fatigue accumulation trend are comprehensively analyzed. And judging the influence degree of the temperature gradient on the performance of the vehicle-mounted navigation system. According to the method, the performance change of the system in actual use can be accurately predicted, and the reliability and safety of the vehicle-mounted navigation system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation performance testing, and more specifically, to a vehicle-mounted navigation system testing method and device. Background Art

[0002] Before leaving the factory, the in-vehicle navigation system usually undergoes standardized temperature tests, mainly simulating static high and low temperature environments to verify the system's working stability and temperature adaptability. These tests mainly focus on the temperature resistance of the system hardware components and the accuracy of sensor data to ensure that the in-vehicle navigation system can operate normally under extreme temperature conditions.

[0003] In the prior art, the vehicle navigation system usually only simulates the impact of normal temperature changes on the system in the test environment before leaving the factory, without considering the temperature gradient changes that may occur during the actual operation of the vehicle. This will lead to insufficient adaptability of the vehicle navigation system to the temperature gradient in actual use, which in turn affects the stability of the vehicle navigation system, resulting in unstable performance and even system failure.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a vehicle navigation system testing method and device to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A vehicle navigation system testing method comprises the following steps: Simulate the temperature gradient from low-temperature startup to high-temperature operation of the vehicle in the test environment, and impose temperature changes on the hardware components of the vehicle navigation system; The intensity distribution of the signal propagation path is analyzed using the Markov random field to evaluate the change in instantaneous disturbance intensity near metal parts in a high-temperature environment and determine whether the signal intensity caused by temperature is normal. When the signal strength caused by temperature is normal, the reflection point position of the signal path under the influence of temperature gradient is analyzed through graph convolutional neural network to evaluate the drift degree of the reflection position of the metal surface when the temperature rises; A generalized linear mixed model is used to analyze the hardware micro-delay response data in different temperature ranges to evaluate the thermal fatigue accumulation trend of hardware components; A comprehensive analysis is conducted on the drift of the reflection position of the metal surface when the temperature rises and the thermal fatigue accumulation trend of the hardware components to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system.

[0007] In a preferred embodiment, the temperature gradient of the vehicle from low temperature startup to high temperature operation is simulated in the test environment, and the temperature change is applied to the hardware components of the vehicle navigation system, specifically: Configure temperature control equipment in the test environment and set the temperature range from low to high temperature; By programming the temperature control system, the temperature is gradually increased from the initial low temperature to the high temperature, and the temperature change rate is set to simulate the actual vehicle environment; Place the vehicle navigation system hardware components in a temperature-controlled area to ensure uniform heating; During the temperature control process, sensors are used to monitor the temperature of each component of the navigation system in real time.

[0008] In a preferred embodiment, the intensity distribution of the signal propagation path is analyzed using the Markov random field to evaluate the change in instantaneous disturbance intensity near the metal parts in the high temperature environment and determine whether the signal intensity caused by the temperature is normal, specifically: Use a signal strength detection device to record the initial signal strength of each path node in the vehicle navigation system; As the temperature gradually increases, the signal strength of each path node is collected at fixed time intervals, and the signal strength when the temperature changes is recorded; The initial signal strength and the signal strength during temperature change are combined to construct a spatial distribution model of the signal propagation path; Based on the spatial distribution model of signal propagation paths, the Markov random field is used to calculate the signal strength correlation and distribution of each path node, and analyze the strength fluctuation of the path nodes; Based on the analysis results of the intensity fluctuations of the path nodes, it is determined whether the signal intensity caused by temperature is normal.

[0009] In a preferred embodiment, based on the spatial distribution model of the signal propagation path, the Markov random field is applied to calculate the signal strength correlation and distribution of each path node, and the strength fluctuation of the path node is analyzed, specifically: Calculate the signal strength conditional probability, the expression is: ;in, is the signal strength conditional probability, It is The signal strength of each path node, It is The neighborhood signal strength set of path nodes, is the normalization constant, Indicates The set of neighboring nodes of a path node, Indicates path nodes and The association parameters between neighboring nodes, It is The signal strength of neighboring nodes, is the number of the path node, is the number of the neighboring node corresponding to the path node.

[0010] In a preferred embodiment, the reflection point position of the signal path under the influence of the temperature gradient is analyzed by a graph convolutional neural network to evaluate the drift degree of the reflection position of the metal surface when the temperature rises, specifically: Using the node data of the signal path in the vehicle navigation system and the reflection point position of the metal parts, a spatial graph model of the signal path and the reflection point position is established, and the initial position of each node and the corresponding reflection relationship are recorded; As the temperature gradually increases, the position of the reflection point of the signal path is collected in real time, and the coordinate changes of the reflection point are recorded at fixed time intervals; The initial spatial graph model is combined with the reflection point position data collected during temperature changes to construct a signal path graph sequence that changes with temperature to mark the reflection position coordinates of each node at different temperatures. The constructed signal path diagram that changes with temperature is input into the graph convolutional neural network, and the average drift distance of the reflection point position on the metal surface is calculated to evaluate the drift degree of the reflection position on the metal surface when the temperature rises.

[0011] In a preferred embodiment, a generalized linear mixed model is used to analyze the hardware micro-delay response data in different temperature ranges to evaluate the thermal fatigue accumulation trend of the hardware components, specifically: During the gradual temperature increase process, the initial micro-delay response of the hardware components in each temperature range is monitored and recorded in real time to establish an initial response benchmark for each temperature range. Record the micro-delay response of hardware components in a dynamic acquisition manner, and organize the micro-delay response of hardware components in layers according to temperature range and time sequence; According to the data fluctuation characteristics of the temperature range, the random effect variables in the generalized linear mixed model are selected to define the temperature sensitivity factor; The micro-delay responses of the hierarchical hardware components are input into the generalized linear mixed model. Based on the temperature sensitivity factor, the nonlinear change of the micro-delay response with the increase of temperature is analyzed, and the trend index of thermal fatigue accumulation is calculated.

[0012] In a preferred embodiment, the drift degree of the reflection position of the metal surface when the temperature rises and the thermal fatigue accumulation trend of the hardware components are comprehensively analyzed to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system, specifically: The average drift distance and the thermal fatigue trend index are normalized, and weight coefficients are assigned to the normalized average drift distance and the thermal fatigue trend index, respectively, to calculate the temperature gradient performance influence coefficient; Compare the temperature gradient performance impact coefficient with the temperature gradient performance impact threshold to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system: When the temperature gradient performance impact coefficient is greater than the temperature gradient performance impact threshold, it is determined that the temperature gradient has too much impact on the performance of the vehicle navigation system; when the temperature gradient performance impact coefficient is less than or equal to the temperature gradient performance impact threshold, it is determined that the temperature gradient has not too much impact on the performance of the vehicle navigation system.

[0013] On the other hand, the present invention provides a vehicle navigation system test device, including a temperature gradient simulation module, a propagation intensity analysis module, a reflection drift analysis module, a hardware delay analysis module and a performance impact assessment module; Temperature gradient simulation module: simulates the temperature gradient of the vehicle from low-temperature startup to high-temperature operation in the test environment, and applies temperature changes to the hardware components of the vehicle navigation system; Propagation intensity analysis module: Uses Markov random fields to analyze the intensity distribution of the signal propagation path, evaluates the instantaneous disturbance intensity changes near metal parts in a high-temperature environment, and determines whether the signal intensity caused by temperature is normal; Reflection drift analysis module: When the signal strength caused by temperature is normal, the reflection point position of the signal path under the influence of temperature gradient is analyzed through graph convolutional neural network to evaluate the drift degree of the reflection position of the metal surface when the temperature rises; Hardware delay analysis module: When the signal strength caused by temperature is normal, a generalized linear mixed model is used to analyze the hardware micro-delay response data in different temperature ranges to evaluate the thermal fatigue accumulation trend of hardware components; Performance Impact Assessment Module: Comprehensively analyzes the drift of the metal surface reflection position when the temperature rises and the thermal fatigue accumulation trend of the hardware components to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system.

[0014] The technical effects and advantages of the vehicle navigation system testing method and device of the present invention are as follows: 1. The present invention provides a vehicle navigation system testing method, which comprehensively evaluates the impact of temperature changes that the system may encounter in the actual working environment on the performance by simulating the temperature gradient of the vehicle from low-temperature startup to high-temperature operation. Unlike the prior art that only relies on static temperature testing, the present invention can more accurately reflect the temperature fluctuations that the vehicle navigation system may encounter during actual driving, especially changes in signal propagation, drift of metal surface reflection position, and hardware response in high-temperature environments by dynamically simulating temperature gradients. This method can efficiently analyze and predict the comprehensive impact of temperature changes on the navigation system by introducing advanced analysis methods such as Markov random fields and graph convolutional neural networks, thereby ensuring the reliability of the system in complex environments.

[0015] 2. The present invention also uses a generalized linear mixed model to analyze the hardware micro-delay response and evaluate the thermal fatigue accumulation trend of the system hardware components in different temperature ranges. By comprehensively analyzing the drift degree of the reflection point position of the signal path and the thermal fatigue accumulation trend of the hardware, the degree of influence of the temperature gradient on the system performance can be accurately determined. This comprehensive evaluation can not only effectively predict the possible performance degradation of the system, but also provide a basis for the design and optimization of the vehicle navigation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a vehicle navigation system testing method of the present invention; Figure 2 The present invention is a structural schematic diagram of a vehicle navigation system testing device. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0018] Figure 1 The present invention provides a vehicle navigation system testing method, which comprises the following steps: The temperature gradient from low-temperature startup to high-temperature operation of the vehicle is simulated in the test environment, and temperature changes are imposed on the hardware components of the vehicle navigation system.

[0019] The Markov random field is used to analyze the intensity distribution of the signal propagation path, evaluate the instantaneous disturbance intensity changes near metal parts in a high-temperature environment, and determine whether the signal intensity caused by temperature is normal.

[0020] When the signal intensity caused by temperature is normal, the graph convolutional neural network is used to analyze the reflection point position of the signal path under the influence of temperature gradient, and the drift degree of the reflection position on the metal surface when the temperature rises is evaluated. The generalized linear mixed model is used to analyze the hardware micro-delay response data in different temperature ranges to evaluate the thermal fatigue accumulation trend of hardware components.

[0021] A comprehensive analysis is conducted on the drift of the reflection position of the metal surface when the temperature rises and the thermal fatigue accumulation trend of the hardware components to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system.

[0022] In the test environment, the temperature gradient from low-temperature startup to high-temperature operation of the vehicle is simulated, and the temperature changes are imposed on the hardware components of the vehicle navigation system, specifically: Configure the temperature control equipment in the test environment and set the temperature range from low temperature to high temperature: In a controlled test environment, select temperature control equipment with precision adjustment function to ensure that the temperature control accuracy meets the requirements. Set the starting temperature and target temperature of the temperature control equipment. The starting temperature is set to the low temperature environment of the vehicle cold start (such as -20°C), and the target temperature is set to the high temperature environment after the vehicle has been running for a long time (such as 70°C). By setting the temperature range of the temperature control equipment, the basic conditions are provided for the subsequent simulation of the temperature gradient.

[0023] Through the temperature control system programming, the temperature is gradually increased from the initial low temperature to the high temperature, and the temperature change rate is set to simulate the actual vehicle environment: Connect the temperature control device to the control system and program the gradual temperature change rate so that the temperature rises from low temperature to high temperature at a uniform rate. The temperature change rate is set according to the temperature change law in the actual use environment of the vehicle to simulate the process of cold start to high temperature operation of the vehicle. This rate programming process is completed through the automatic adjustment module in the temperature control system, so that the temperature change conforms to the preset gradual gradient increase, ensuring the stability and consistency of the system during the test.

[0024] Place the vehicle navigation system hardware components in a temperature-controlled area to ensure even heating: Arrange the core hardware components of the vehicle navigation system, including the global positioning system receiver, inertial sensor, etc., in the designated temperature control area of ​​the temperature control equipment. Fix the hardware components in the center of the temperature-receiving area to ensure that all components can evenly receive the set temperature changes. Ensure the stability of the hardware component layout to avoid temperature errors that may be caused by component position deviation during the test and ensure the accuracy of the test.

[0025] During the temperature control process, sensors are used to monitor the temperature of each component of the navigation system in real time: During the temperature change process, high-precision temperature sensors are placed near the key components of the navigation system to record the temperature changes of the hardware components in real time. The data collected by the sensors are recorded at fixed time intervals to form a series of real-time data of temperature changes. The collected temperature data is stored in the data recording system to ensure that the subsequent analysis of the impact of temperature on each hardware component has accurate data support. At the same time, the temperature monitoring system sets an abnormal alarm threshold. If the temperature deviates from the set change rate, an alarm will be automatically triggered to ensure the temperature accuracy of the test process.

[0026] The intensity distribution of the signal propagation path is analyzed using the Markov random field to evaluate the change in instantaneous disturbance intensity near metal parts in a high-temperature environment and determine whether the signal intensity caused by temperature is normal. Specifically: Use a signal strength detection device to record the initial signal strength of each path node in the vehicle navigation system: Before the test begins, the vehicle navigation system is placed in a controlled test environment, and a signal strength detection device is used to collect the initial signal strength of each path node in the system, especially for path nodes near metal parts. The initial signal strength is recorded as baseline data for subsequent comparative analysis.

[0027] Among them, path nodes refer to specific locations on the signal propagation path of the vehicle navigation system, which are used to monitor changes in signal strength. Each path node represents a sampling point of the signal at a specific location, and special attention is paid to the location of these nodes near key components (such as metal parts) in the navigation system in order to observe the impact of temperature changes on the signal.

[0028] As the temperature gradually increases, the signal strength of each path node is collected at fixed time intervals, and the signal strength when the temperature changes is recorded: In the process of simulating the vehicle starting from low temperature and gradually rising to high temperature, the signal strength of each path node is collected at fixed time intervals. Special attention is paid to the path nodes near metal parts, and the signal strength of these nodes is collected as they change with temperature. The data collected each time is used to construct a signal strength set that changes with temperature, which serves as the basis for subsequent Markov random field analysis.

[0029] The initial signal strength and the signal strength during temperature change are combined to construct a spatial distribution model of the signal propagation path: The spatial distribution model of the signal propagation path is expressed as: ;in, , and Respectively represent the position coordinates of the path nodes in three-dimensional space; is the initial signal strength, which indicates the signal strength of the path node in the initial state; is the signal strength when the temperature changes, indicating the path node at time The signal strength during the temperature change.

[0030] Each path node , and Coordinates and as well as Correspondingly, it provides a basis for subsequent analysis.

[0031] Based on the spatial distribution model of the signal propagation path, the Markov random field is used to calculate the signal strength correlation and distribution of each path node, and the strength fluctuation of the path node is analyzed: Markov random fields can analyze the correlation in space and describe the characteristics of signal strength distribution by defining the relationship between path nodes.

[0032] Calculate the signal strength conditional probability, the expression is: ;in, is the signal strength conditional probability, which means In the neighborhood of a path node The conditional probability of It is The signal strength of each path node; It is The neighborhood signal strength set of path nodes; is a normalization constant used to ensure the standardization of probability distribution; represents the exponential function; Indicates The set of neighboring nodes of a path node; Indicates path nodes and The association parameter between neighboring nodes is used to measure the path nodes and The correlation of signal strength between neighboring nodes; It is The signal strength of neighboring nodes; is the number of the path node, is the number of the neighboring node corresponding to the path node.

[0033] Through Markov random field analysis, the signal intensity fluctuation of path nodes under high temperature conditions can be obtained, and the intensity changes of signal path nodes near metal parts can be identified.

[0034] The smaller the signal strength conditional probability, the greater the strength fluctuation of the path node.

[0035] Based on the analysis results of the intensity fluctuations of the path nodes, determine whether the signal intensity caused by temperature is normal: The signal strength conditional probability of each path node is summarized to analyze whether the strength of the entire signal propagation path is abnormally affected by temperature changes.

[0036] The signal strength conditional probabilities of all path nodes are compared with the preset probability thresholds, and the path nodes whose signal strength conditional probabilities are less than the preset probability thresholds are marked as abnormal path nodes.

[0037] The ratio of abnormal path nodes to the total number of path nodes is marked as the abnormal node ratio.

[0038] When the abnormal node ratio is greater than a preset ratio, the signal strength caused by temperature is determined to be abnormal; when the abnormal node ratio is less than or equal to the preset ratio, the signal strength caused by temperature is determined to be normal.

[0039] Among them, the preset probability threshold is used to measure whether the signal strength of the path node is normal. The threshold is set based on the system's signal strength distribution at standard temperature and historical test data to ensure that the conditional probability of signal strength of all nodes in a normal temperature environment is mostly higher than the threshold. If the conditional probability of signal strength of the node is lower than this threshold, it indicates that the signal strength has fluctuated abnormally under the influence of temperature changes. The preset ratio is used to evaluate the proportion of abnormal nodes in the entire signal propagation path. This ratio is set based on the test results of the vehicle navigation system under different temperature environments, and represents the range of abnormal nodes that the system can tolerate under specific conditions. If the proportion of abnormal nodes exceeds the preset ratio, it means that there is a large abnormality in the signal strength distribution caused by temperature changes, which may affect the overall performance of the navigation system.

[0040] The graph convolutional neural network is used to analyze the reflection point position of the signal path under the influence of temperature gradient, and to evaluate the drift degree of the reflection position on the metal surface when the temperature rises. Specifically: Using the node data of the signal path in the vehicle navigation system and the reflection point position of the metal parts, a spatial graph model of the signal path and the reflection point position is established, and the initial position of each node and the corresponding reflection relationship are recorded: A signal path consists of multiple nodes, each of which represents a position of the signal in space.

[0041] Record the initial position of each node and the corresponding reflection relationship, which is the path of the signal reflected from the node to the next node.

[0042] When constructing a spatial graph model, the nodes are considered as vertices in the graph, and the signal transmission relationships between nodes are considered as edges in the graph. The reflection point positions of metal parts are taken as special nodes, and their initial coordinates and reflection characteristics are recorded. The spatial graph model can be expressed as: ;in, It is a spatial graph model; It is a node set, including all signal path nodes and metal component reflection points; It is a set of edges, representing the signal transmission relationship between nodes.

[0043] As the temperature gradually increases, the reflection point position of the signal path is collected in real time, and the coordinate changes of the reflection point are recorded at fixed time intervals: In a controlled temperature environment, the temperature is gradually increased from the initial temperature to the target high temperature to simulate the temperature change during vehicle operation. During this process, a high-precision position monitoring device is used to collect the position of the reflection point of the signal path in real time.

[0044] Set a fixed time interval and record the position of the reflection point at each time point.

[0045] The initial spatial graph model is combined with the reflection point position data collected during temperature changes to construct a signal path graph sequence that changes with temperature to mark the reflection position coordinates of each node at different temperatures: In the signal path diagram at each moment, the spatial coordinates of each node in the node set are updated to the coordinates collected at that time. In this way, the signal path diagram reflects the dynamic changes of the signal path and the reflection point position during the temperature change process.

[0046] The constructed signal path graph that changes with temperature is input into the graph convolutional neural network to evaluate the degree of drift of the reflection position on the metal surface when the temperature rises: The signal path graph sequence is input into the Graph Convolutional Network (GCN) to analyze the impact of temperature changes on the signal path. GCN can effectively process graph structure data and capture the complex relationship between node features and topological structures.

[0047] Input signal path graph sequence, each signal path graph contains node feature matrix and adjacency matrix.

[0048] The node feature matrix represents the feature vector of each node, including spatial coordinates and temperature. The adjacency matrix represents the connection relationship between nodes.

[0049] Through multiple layers of graph convolutional layers, node features are nonlinearly transformed.

[0050] No. Layer in time The node feature matrix is ​​expressed as , and its calculation formula is: ;in, Indicates Layer in time The node feature matrix of is the adjacency matrix, is the weight matrix, is the activation function, is the layer index in the graph convolutional neural network.

[0051] The weight matrix is ​​the graph convolutional network The learnable parameter matrix of the layer is continuously adjusted during the network training process to capture the association relationship between the signal path nodes and the reflection point location nodes.

[0052] The activation function usually adopts a nonlinear function, such as the rectified linear unit (ReLU) function. The activation function is used to introduce nonlinearity to enhance the expressive power of the model.

[0053] The output of the last layer contains the high-level feature representation of each node. These features are aggregated to calculate the overall drift.

[0054] During the temperature rise process, the average drift distance of the reflection point position on the metal surface is calculated. The calculation formula for the average drift distance is: ;in, is the average drift distance, which is used to quantify the overall drift of the position of the reflection point on the metal surface during the temperature rise process; is the total number of reflection points; Is the index of the reflection point, ranging from 1 to ; Indicates Reflection points at time of Coordinate location; Indicates The reflection point at the initial moment Coordinate location; Indicates Reflection points at time of Coordinate location; Indicates The reflection point at the initial moment Coordinate location; Indicates Reflection points at time of Coordinate location; Indicates The reflection point at the initial moment Coordinate location.

[0055] Because the reflection point drift reflects the impact of temperature changes on the metal surface, especially the thermal expansion or contraction of metal materials. When the temperature rises, the metal surface will undergo a slight physical deformation, causing the position of the signal reflection point to change. The average drift distance refers to the average displacement of the reflection point on the metal surface during the temperature change process. It is an important indicator for evaluating the impact of temperature changes on the reflection point.

[0056] Therefore, the larger the average drift distance, the greater the drift of the reflection position on the metal surface when the temperature rises.

[0057] A generalized linear mixed model is used to analyze the hardware micro-delay response data in different temperature ranges to evaluate the thermal fatigue accumulation trend of hardware components. Specifically: During the gradual temperature increase process, the initial micro-delay response of the hardware components in each temperature range is monitored and recorded in real time, and an initial response benchmark is established for each temperature range: Specifically, key indicators of hardware components (such as signal transmission time, instruction execution delay, etc.) are selected as the monitoring objects of micro-latency. At the initial stage of each temperature range, precision latency detection equipment is used to record the micro-latency response of hardware components to ensure that benchmark data can be accurately obtained at different temperatures.

[0058] Assume that the temperature range is divided into multiple fixed intervals, using variables Indicates that each represents the temperature range, where In each temperature range, record the corresponding initial micro-delay response value and set it as the benchmark response value of the temperature range. The benchmark response value is recorded as , is the temperature range number.

[0059] The micro-delay response of hardware components is recorded in a dynamic acquisition mode, and the micro-delay response of hardware components is layered and sorted according to temperature range and time sequence: As the temperature gradually increases, the micro-delay response data of the hardware components in different temperature ranges is continuously collected in a dynamic manner. In order to ensure the accuracy of the collected data, a fixed time interval is set for data recording. Assume that at time The micro-delay response collected at every moment is , Continuously updated as temperature changes.

[0060] All collected The data are layered and sorted according to the temperature range and time sequence for subsequent model analysis. The micro-delay response data collected in the temperature range is set as , then the recorded data set can be expressed as: .

[0061] According to the data fluctuation characteristics of the temperature range, the random effect variables in the generalized linear mixed model are selected to define the temperature sensitivity factor: According to the hierarchical micro-latency data, the micro-latency fluctuations within and between different temperature intervals are observed. In order to capture the impact trend of temperature on the micro-latency response of hardware components, random effect variables and temperature sensitivity factors are introduced into the generalized linear mixed model.

[0062] Assume that the temperature range is a fixed effect, expressed as , and the response changes of hardware components at different temperatures are taken as random effects, expressed as , used to describe the impact of time fluctuations with temperature on micro-delay. Introducing temperature sensitivity factor , which is used to quantify the sensitivity of the temperature range to the micro-delay response.

[0063] The generalized linear mixed model can be expressed as: ;in, It is Temperature intervals in time The micro-delay response, is the error term (used to represent random error), For the The representative temperature value (e.g. average temperature) of a temperature interval.

[0064] In this model, the temperature sensitivity factor corresponds to the thermal fatigue sensitivity of each temperature interval, which helps to capture the nonlinear cumulative effect of temperature on micro-delay.

[0065] The micro-delay responses of the hierarchically organized hardware components are input into the generalized linear mixed model. Based on the temperature sensitivity factor, the nonlinear change of the micro-delay response with the increase of temperature is analyzed, and the trend index of thermal fatigue accumulation is calculated: The data set to be organized The data were input into the generalized linear mixed model for data fitting. The influence of each temperature range on micro-delay is quantified, and the nonlinear change of micro-delay response with temperature increase is analyzed to evaluate the accumulation of thermal fatigue.

[0066] Temperature sensitivity factor obtained by model fitting The influence of different temperature ranges on micro-delay response can be described. In order to evaluate the overall trend of thermal fatigue accumulation, the thermal fatigue trend index is calculated, and its expression is as follows: ;in, is the thermal fatigue tendency index.

[0067] The thermal fatigue trend index is obtained by multiplying the temperature sensitivity factor and the representative temperature value and summing them up. This indicates the degree of influence of temperature changes (especially high temperature) on the micro-delay response of hardware components. As the temperature rises or the amplitude of the change increases, the impact of temperature on hardware components will also increase, leading to the accumulation of thermal fatigue.

[0068] Thermal cycles and temperature changes that hardware components are subjected to during long-term operation can cause material fatigue, especially when the temperature changes are large. As the thermal fatigue trend index increases, the degree to which the component is affected by temperature also increases, resulting in more significant changes in micro-delay responses, threatening the long-term stability and reliability of the hardware, and accumulated thermal fatigue may cause premature aging or failure of the component.

[0069] Therefore, the larger the thermal fatigue trend index is, the more obvious and serious the thermal fatigue accumulation trend of the hardware component is.

[0070] Comprehensively analyze the drift of the metal surface reflection position when the temperature rises and the thermal fatigue accumulation trend of the hardware components to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system. Specifically: The average drift distance and thermal fatigue trend index are normalized, and weight coefficients are assigned to the normalized average drift distance and thermal fatigue trend index respectively, and the temperature gradient performance influence coefficient is calculated.

[0071] Among them, the temperature gradient performance influence coefficient can be calculated by the following method: ;in, is the temperature gradient performance influence coefficient, and is the normalized average drift distance and thermal fatigue tendency index, and are the weight coefficients of the average drift distance and thermal fatigue tendency index, and Both are greater than 0.

[0072] The larger the temperature gradient performance impact coefficient is, the more serious the impact of the temperature gradient on the performance of the vehicle navigation system is.

[0073] Compare the temperature gradient performance impact coefficient with the temperature gradient performance impact threshold to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system: When the temperature gradient performance impact coefficient is greater than the temperature gradient performance impact threshold, it is determined that the temperature gradient has too much impact on the performance of the vehicle navigation system, which indicates that the temperature gradient has too much impact on the performance of the vehicle navigation system, and the system may face serious performance degradation or failure risks. In this case, the following measures should be considered: Enhanced heat dissipation design: Increase the heat dissipation capacity of hardware components to ensure stable operation of the system in high temperature environments.

[0074] Optimize hardware materials: Use high temperature resistant and thermal fatigue resistant materials to reduce the degree to which hardware is affected by temperature changes.

[0075] Real-time temperature monitoring and adjustment: Introduce a temperature monitoring system to dynamically adjust the system's operating status and power output based on real-time temperature data.

[0076] Increase redundancy design: Add redundancy to key components of the navigation system to ensure that the system can continue to operate stably when some components are damaged.

[0077] When the temperature gradient performance impact coefficient is less than or equal to the temperature gradient performance impact threshold, it is determined that the temperature gradient has not had a significant impact on the performance of the vehicle navigation system, which means that the impact of the temperature gradient on the vehicle navigation system is within an acceptable range and the system performance can operate stably.

[0078] The setting of weight coefficients should be determined according to the actual working environment of the system and the sensitivity to the influence of different factors. Generally speaking, the weight coefficients of average drift distance and thermal fatigue trend index should be evaluated according to their actual influence on the performance of the vehicle navigation system. For example, if the performance of the system is more susceptible to hardware fatigue, a higher weight should be given to the thermal fatigue trend index; if the reflection position drift has a greater impact on the navigation accuracy, a higher weight should be given to the average drift distance. The setting of these weight coefficients needs to be optimized through experimental data and historical performance analysis to ensure the accuracy of system evaluation and operability in practical applications.

[0079] The setting of the temperature gradient performance impact threshold usually relies on the stability testing and evaluation of the vehicle navigation system under various temperature gradient conditions. The threshold should be determined based on historical data, system tolerance and industry standards. When setting the threshold, it is necessary to consider the normal operating range of the system, that is, within a specific temperature change range, the system can maintain stable performance without significant performance degradation or failure. When the temperature gradient performance impact coefficient exceeds this threshold, it means that the system performance is unacceptably affected and appropriate remedial measures need to be taken. The setting of the threshold needs to be optimized after comprehensively considering the hardware temperature resistance, temperature change rate and system application scenarios to ensure the long-term stability and safety of the system.

[0080] Example 2 The difference between Embodiment 2 of the present invention and Embodiment 1 is that this embodiment introduces a vehicle navigation system testing device.

[0081] Figure 2A structural schematic diagram of a vehicle navigation system test device of the present invention is given, which includes a temperature gradient simulation module, a propagation intensity analysis module, a reflection drift analysis module, a hardware delay analysis module and a performance impact evaluation module.

[0082] Temperature gradient simulation module: simulates the temperature gradient of the vehicle from low-temperature startup to high-temperature operation in the test environment, and imposes temperature changes on the hardware components of the vehicle navigation system.

[0083] Propagation intensity analysis module: Uses Markov random fields to analyze the intensity distribution of the signal propagation path, evaluates the instantaneous disturbance intensity changes near metal parts in a high-temperature environment, and determines whether the signal intensity caused by temperature is normal.

[0084] Reflection drift analysis module: When the signal strength caused by temperature is normal, the reflection point position of the signal path under the influence of temperature gradient is analyzed through graph convolutional neural network to evaluate the drift degree of the reflection position on the metal surface when the temperature rises.

[0085] Hardware delay analysis module: When the signal strength caused by temperature is normal, a generalized linear mixed model is used to analyze the hardware micro-delay response data in different temperature ranges to evaluate the thermal fatigue accumulation trend of hardware components.

[0086] Performance Impact Assessment Module: Comprehensively analyzes the drift of the metal surface reflection position when the temperature rises and the thermal fatigue accumulation trend of the hardware components to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system.

[0087] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0088] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0089] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0091] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0092] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0094] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0095] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0096] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle navigation system testing method, characterized in that: The steps include: Simulate the temperature gradient from low-temperature startup to high-temperature operation of the vehicle in the test environment, and impose temperature changes on the hardware components of the vehicle navigation system; The intensity distribution of the signal propagation path is analyzed using the Markov random field to evaluate the change in instantaneous disturbance intensity near metal parts in a high-temperature environment and determine whether the signal intensity caused by temperature is normal. When the signal strength caused by temperature is normal, the reflection point position of the signal path under the influence of temperature gradient is analyzed through graph convolutional neural network to evaluate the drift degree of the reflection position of the metal surface when the temperature rises; A generalized linear mixed model is used to analyze the hardware micro-delay response data in different temperature ranges to evaluate the thermal fatigue accumulation trend of hardware components; A comprehensive analysis is conducted on the drift of the reflection position of the metal surface when the temperature rises and the thermal fatigue accumulation trend of the hardware components to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system.

2. A vehicle navigation system testing method according to claim 1, characterized in that: In the test environment, the temperature gradient from low-temperature startup to high-temperature operation of the vehicle is simulated, and the temperature changes are imposed on the hardware components of the vehicle navigation system, specifically: Configure temperature control equipment in the test environment and set the temperature range from low to high temperature; By programming the temperature control system, the temperature is gradually increased from the initial low temperature to the high temperature, and the temperature change rate is set to simulate the actual vehicle environment; Place the vehicle navigation system hardware components in a temperature-controlled area to ensure uniform heating; During the temperature control process, sensors are used to monitor the temperature of each component of the navigation system in real time.

3. The vehicle navigation system testing method according to claim 1, characterized in that: The intensity distribution of the signal propagation path is analyzed using the Markov random field to evaluate the change in instantaneous disturbance intensity near metal parts in a high-temperature environment and determine whether the signal intensity caused by temperature is normal. Specifically: Use a signal strength detection device to record the initial signal strength of each path node in the vehicle navigation system; As the temperature gradually increases, the signal strength of each path node is collected at fixed time intervals, and the signal strength when the temperature changes is recorded; The initial signal strength and the signal strength during temperature change are combined to construct a spatial distribution model of the signal propagation path; Based on the spatial distribution model of signal propagation paths, the Markov random field is used to calculate the signal strength correlation and distribution of each path node, and analyze the strength fluctuation of the path nodes; Based on the analysis results of the intensity fluctuations of the path nodes, it is determined whether the signal intensity caused by temperature is normal.

4. A vehicle navigation system testing method according to claim 3, characterized in that: Based on the spatial distribution model of the signal propagation path, the Markov random field is used to calculate the signal strength correlation and distribution of each path node, and the strength fluctuation of the path node is analyzed. Specifically: Calculate the signal strength conditional probability, the expression is: ;in, is the signal strength conditional probability, It is The signal strength of each path node, It is The neighborhood signal strength set of path nodes, is the normalization constant, Indicates The set of neighboring nodes of a path node, Indicates path nodes and The association parameters between neighboring nodes are It is The signal strength of neighboring nodes, is the number of the path node, is the number of the neighboring node corresponding to the path node.

5. The vehicle navigation system testing method according to claim 1, characterized in that: The graph convolutional neural network is used to analyze the reflection point position of the signal path under the influence of temperature gradient, and to evaluate the drift degree of the reflection position on the metal surface when the temperature rises. Specifically: Using the node data of the signal path in the vehicle navigation system and the reflection point position of the metal parts, a spatial graph model of the signal path and the reflection point position is established, and the initial position of each node and the corresponding reflection relationship are recorded; As the temperature gradually increases, the position of the reflection point of the signal path is collected in real time, and the coordinate changes of the reflection point are recorded at fixed time intervals; The initial spatial graph model is combined with the reflection point position data collected during temperature changes to construct a signal path graph sequence that changes with temperature to mark the reflection position coordinates of each node at different temperatures. The constructed signal path diagram that changes with temperature is input into the graph convolutional neural network, and the average drift distance of the reflection point position on the metal surface is calculated to evaluate the drift degree of the reflection position on the metal surface when the temperature rises.

6. The vehicle navigation system testing method according to claim 1, characterized in that: A generalized linear mixed model is used to analyze the hardware micro-delay response data in different temperature ranges to evaluate the thermal fatigue accumulation trend of hardware components. Specifically: During the gradual temperature increase process, the initial micro-delay response of the hardware components in each temperature range is monitored and recorded in real time to establish an initial response benchmark for each temperature range. Record the micro-delay response of hardware components in a dynamic acquisition manner, and organize the micro-delay response of hardware components in layers according to temperature range and time sequence; According to the data fluctuation characteristics of the temperature range, the random effect variables in the generalized linear mixed model are selected to define the temperature sensitivity factor; The micro-delay responses of the hierarchical hardware components are input into the generalized linear mixed model. Based on the temperature sensitivity factor, the nonlinear change of the micro-delay response with the increase of temperature is analyzed, and the trend index of thermal fatigue accumulation is calculated.

7. The vehicle navigation system testing method according to claim 1, characterized in that: Comprehensively analyze the drift of the metal surface reflection position when the temperature rises and the thermal fatigue accumulation trend of the hardware components to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system. Specifically: The average drift distance and the thermal fatigue trend index are normalized, and weight coefficients are assigned to the normalized average drift distance and the thermal fatigue trend index, respectively, to calculate the temperature gradient performance influence coefficient; Compare the temperature gradient performance impact coefficient with the temperature gradient performance impact threshold to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system: When the temperature gradient performance impact coefficient is greater than the temperature gradient performance impact threshold, it is determined that the temperature gradient has too great an impact on the performance of the vehicle navigation system; When the temperature gradient performance impact coefficient is less than or equal to the temperature gradient performance impact threshold, it is determined that the temperature gradient does not have a significant impact on the performance of the vehicle navigation system.

8. A vehicle navigation system testing device, used to implement a vehicle navigation system testing method according to any one of claims 1 to 7, characterized in that: It includes temperature gradient simulation module, propagation intensity analysis module, reflection drift analysis module, hardware delay analysis module and performance impact assessment module; Temperature gradient simulation module: simulates the temperature gradient of the vehicle from low-temperature startup to high-temperature operation in the test environment, and applies temperature changes to the hardware components of the vehicle navigation system; Propagation intensity analysis module: Uses Markov random fields to analyze the intensity distribution of the signal propagation path, evaluates the instantaneous disturbance intensity changes near metal parts in a high-temperature environment, and determines whether the signal intensity caused by temperature is normal; Reflection drift analysis module: When the signal strength caused by temperature is normal, the reflection point position of the signal path under the influence of temperature gradient is analyzed through graph convolutional neural network to evaluate the drift degree of the reflection position of the metal surface when the temperature rises; Hardware delay analysis module: When the signal strength caused by temperature is normal, a generalized linear mixed model is used to analyze the hardware micro-delay response data in different temperature ranges to evaluate the thermal fatigue accumulation trend of hardware components; Performance Impact Assessment Module: Comprehensively analyzes the drift of the metal surface reflection position when the temperature rises and the thermal fatigue accumulation trend of the hardware components to determine whether the temperature gradient has too great an impact on the performance of the vehicle navigation system.

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