Test method, device and equipment of multifunctional deconcentrator and storage medium

Abstract

The invention relates to the technical field of deconcentrator testing, and discloses a multifunctional deconcentrator testing method, device and equipment and a storage medium, and the method comprises the steps: dividing a multifunctional deconcentrator into a central processing area, a USB interface area, a card reader interface area, a charging circuit area and a lithium battery area, performing temperature data acquisition on the multifunctional deconcentrator in a working state to obtain multi-point temperature time sequence data; performing nonlinear thermal dynamic feature extraction on the multi-point temperature time sequence data to obtain a fusion feature vector, and performing two-dimensional temperature field reconstruction based on the fusion feature vector to obtain a target temperature distribution diagram; performing operation test on the multifunctional deconcentrator according to the target temperature distribution diagram to obtain a temperature response curve; according to the method, the problem that a traditional method is insufficient in hot spot area recognition is solved, accurate reconstruction of a whole surface temperature field is achieved, and the test accuracy of the multifunctional deconcentrator is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of splitter testing, and particularly to a testing method, device, equipment and storage medium for a multifunctional splitter. Background Art

[0002] As a device integrating USB data transmission, card reading and charging functions, multifunctional splitters are widely used in modern electronic systems. These splitters generate a large amount of heat during operation, and the thermal properties of their materials directly affect the stability, lifespan and safety of the devices. Therefore, it is particularly important to accurately test the thermal properties of the materials of multifunctional splitters. However, traditional thermal property testing methods often adopt a centralized measurement strategy, making it difficult to cope with the structural complexity and uneven thermal distribution of multifunctional splitters.

[0003] Currently, the thermal property testing of multifunctional splitters faces three major technical challenges: First, the testing cycle is long. Traditional methods require point-by-point measurement, which is time-consuming and laborious. Second, the thermal parameter variables are multi-dimensional. There are significant differences in the thermal characteristics of each functional area, and a single parameter is difficult to comprehensively evaluate the overall thermal performance. Third, the temperature distribution is uneven. Due to the integration of multiple functional modules inside the splitter, there are significant differences in the heat generation characteristics and heat dissipation requirements of each area, resulting in an extremely uneven surface temperature field distribution. Traditional centralized testing methods are difficult to accurately capture this thermal non-uniformity. These problems severely restrict the accuracy and efficiency of the thermal property evaluation of multifunctional splitter materials. Summary of the Invention

[0004] The present invention provides a testing method, device, equipment and storage medium for a multifunctional splitter. The present invention overcomes the problem of insufficient recognition of hot spots in traditional methods, realizes the precise reconstruction of the full surface temperature field, and improves the testing accuracy of multifunctional splitters.

[0005] In a first aspect, the present invention provides a testing method for a multifunctional splitter, and the testing method for the multifunctional splitter includes: Dividing the multifunctional splitter into a central processing area, a USB interface area, a card reader interface area, a charging circuit area and a lithium battery area, and collecting temperature data of the multifunctional splitter in a working state to obtain multi-point temperature time series data; Extracting non-linear thermal dynamic features from the multi-point temperature time series data to obtain a fusion feature vector, and reconstructing a two-dimensional temperature field based on the fusion feature vector to obtain a target temperature distribution map; Performing an operation test on the multifunctional splitter according to the target temperature distribution map to obtain a temperature response curve; Measuring the thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance and temperature response time of the multifunctional splitter based on the temperature response curve, and generating a comprehensive thermal performance evaluation result.

[0006] In a second aspect, the present invention provides a test device for a multifunctional splitter, and the test device for the multifunctional splitter includes: A data acquisition module, configured to divide the multifunctional splitter into a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, and collect temperature data of the multifunctional splitter in a working state to obtain multi-point temperature time series data; A temperature field reconstruction module, configured to extract non-linear thermal dynamic features from the multi-point temperature time series data to obtain a fusion feature vector, and perform two-dimensional temperature field reconstruction based on the fusion feature vector to obtain a target temperature distribution map; An operation test module, configured to perform an operation test on the multifunctional splitter according to the target temperature distribution map to obtain a temperature response curve; A generation module, configured to measure the thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time of the multifunctional splitter based on the temperature response curve, and generate a comprehensive thermal performance evaluation result.

[0007] In a third aspect of the present invention, there is provided a test device for a multifunctional splitter, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the test device for the multifunctional splitter executes the above-mentioned test method for the multifunctional splitter.

[0008] In a fourth aspect of the present invention, there is provided a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is made to execute the above-mentioned test method for the multifunctional splitter.

[0009] In the technical solution provided by the present invention, by dividing the multifunctional splitter into five key test areas: a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, precise testing for different functional areas is achieved, overcoming the problem of insufficient recognition of hot spots in traditional methods. According to the partition testing model, the optimized layout of the high-precision thermocouple array is carried out, making the thermocouple distribution match the hot spot distribution, effectively solving the limitation that traditional uniform sensor layout cannot accurately capture hot spots. By extracting non-linear thermal dynamic characteristics from multi-point temperature time-series data and fusing linear and non-linear characteristics, a more comprehensive description of thermal characteristics is obtained, breaking through the limitation of traditional single-characteristic extraction methods. The Kalman filter and multi-layer perceptron joint algorithm are used for two-dimensional temperature field reconstruction, and only a small number of sensors are required to achieve accurate reconstruction of the full-surface temperature field, solving the problem that traditional methods require a large number of sensors. Based on the reconstructed temperature distribution map, a hierarchical control strategy is created, realizing a hierarchical structure of global temperature trajectory planning, regional temperature regulation, and local precise control, overcoming the limitation that traditional PID control is difficult to handle multi-variable strongly coupled thermal systems. By comprehensively measuring key parameters such as thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time, and introducing a disturbance observer and an environmental temperature compensation unit, the influence of environmental temperature fluctuations on the test results is effectively eliminated, making the test process more stable and reliable.

[0010] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims, and drawings.

[0011] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of an embodiment of the test method for the multifunctional splitter in the embodiment of the present invention; Figure 2 It is a schematic diagram of an embodiment of the test device for the multifunctional splitter in the embodiment of the present invention; Figure 3 It is a schematic diagram of an embodiment of the test equipment for the multifunctional splitter in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0014] As used in the embodiments of the present invention, the terms "including" and "having", and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include other unlisted steps or units, or may optionally further include other steps or units inherent to these processes, methods, products, or devices.

[0015] For ease of understanding of this embodiment, a test method for a multifunctional splitter disclosed in the embodiments of the present invention will be introduced in detail first. As Figure 1 shown, this method includes the following steps: 101. Divide the multifunctional splitter into a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, and collect temperature data of the multifunctional splitter in the working state to obtain multi-point temperature time series data; It can be understood that the execution subject of the present invention can be a test device for the multifunctional splitter, or a terminal or a server. Specifically, it is not limited here. In the embodiments of the present invention, the server is taken as an example of the execution subject for illustration.

[0016] Specifically, conduct a three-dimensional structural scan and analysis of the multi-functional splitter. Use an industrial-grade three-dimensional modeling instrument or an X-ray tomography device to obtain its internal structure data, construct a structural distribution map, and display the spatial layout and connection relationships of core components such as the hub body, USB sockets, card reader sockets, charging positioning mechanisms, and lithium batteries. Based on this structural distribution map, use a high-resolution infrared thermal imager to perform thermal imaging scans on the multi-functional splitter under typical operating conditions (such as data transmission, charging, and full load). Through thermal pattern analysis, identify the hot spots and their temperature rise trends of different functional components during actual operation, and obtain the coordinate data of the regions with significant thermal characteristic responses, that is, the hot spot distribution data. According to the hot spot distribution data, combined with the structural distribution map, conduct a thermal performance regional division of the multi-functional splitter. This division meets requirements such as concentrated thermal characteristics, clear structural boundaries, and strong functional independence. Divide the entire device into five main test regions: the central processing region, USB interface region, card reader interface region, charging circuit region, and lithium battery region. Determine the boundary coordinates of each region in the three-dimensional space or two-dimensional expansion diagram through image processing algorithms or manual auxiliary annotation means to form standardized regional division parameters. On this basis, extract key parameters such as the thermophysical properties, structural material types, power density, and heat dissipation path complexity of each test region, generate a regional characteristic parameter table, and reflect the thermal response capabilities and structural differences of each region. According to the indicators such as thermal sensitivity, heating intensity, and heat propagation coupling degree recorded in the regional characteristic parameter table, use a normalization method to construct a weight distribution function and form a weight distribution matrix accordingly. Integrate the regional division parameters, regional characteristic parameter table, and weight distribution matrix through unified modeling to construct a structured partition test model. Arrange sensors according to the partition test model. Select high-sensitivity micro-thermocouples and use a silicone fixing agent with excellent thermal conductivity to ensure close fitting. Arrange a temperature sensor array at the predetermined coordinate points in each test region. After the multi-functional splitter enters the normal working state, use a high-speed data acquisition system to collect multi-point temperature time-series data in real time to form a multi-dimensional temperature response data set covering the entire thermal characteristic space.

[0017] In this embodiment, based on the partition test model, the regional characteristic parameter table therein is extracted to guide the matching process of the thermocouple specification parameters. The regional characteristic parameter table records key indicators such as the structural material, thermal conductivity characteristics, heat generation power density, operating temperature range, and thermal response speed of each functional area. Based on these parameters, thermocouple models with corresponding temperature measurement ranges, response speeds, accuracy levels, and size matching characteristics are preferentially selected. The thermocouple layout is optimized and planned according to the preset weight distribution matrix in the partition test model. The weight distribution matrix reflects the importance of each test area in the overall thermal performance evaluation. During the thermocouple layout process, sensor resources are allocated according to the weight level. For example, the weight of the central processing area is 0.3, and compared with the 0.1 of the card reader interface area, the number of thermocouples arranged in it should be more and the distribution should be denser, so as to improve the time and space resolution of temperature monitoring in key areas. Based on the above principles, a thermocouple layout diagram is generated with the help of a point layout optimization algorithm or an automatic layout tool, marking the installation position, number, connection method, and sampling channel mapping of the thermocouples in each test area. According to the generated thermocouple layout diagram, the multi-functional splitter is arranged in the standardized test environment, and four typical working conditions are applied respectively, namely the standard working state (ambient temperature 25°C, load 50%), the high-temperature working state (ambient temperature 40°C, load 50%), the high-load working state (ambient temperature 25°C, load 100%), and the composite extreme state (ambient temperature 40°C, load 100%). Under each working condition, the real-time temperature signals of the thermocouples arranged in each test area are continuously collected, and the data sampling frequency is kept above 10 Hz to ensure the acquisition of raw temperature data with high time resolution. The test time under each working condition should be long enough to capture the complete temperature rise, stabilization, and decline processes to obtain the full-cycle thermal response characteristics. A weighted processing mechanism of regional importance is introduced into the collected raw temperature data, different weight coefficients are assigned to the sampling data of different regions according to the weight distribution matrix, and methods such as weighted average or weighted interpolation are used to form unified multi-point temperature time series data.

[0018] 102. Extract non-linear thermal dynamic characteristics from the multi-point temperature time series data to obtain a fusion feature vector, and based on the fusion feature vector, reconstruct the two-dimensional temperature field to obtain the target temperature distribution map; Specifically, preprocess the multi-point temperature time series data. Through data synchronization, outlier removal, noise filtering, and normalization, the data at different sampling points and different time periods are uniformly transformed into a standardized temperature matrix with a consistent structure, where each row corresponds to the temperature change sequence of a sensor point, and each column represents the temperature field snapshot of all measurement points at a certain moment. Based on this standardized temperature matrix, the principal component analysis algorithm is introduced to solve the covariance matrix and perform eigenvalue decomposition on the entire temperature data set. By truncating the principal components, the first several principal components with a cumulative variance contribution rate of over 95% are retained, obtaining the reduced-dimensional temperature feature vector. A non-linear feature extraction model is constructed based on the reduced-dimensional temperature feature vector, and a multi-layer perceptron neural network is used as the non-linear mapper to map the input principal component vector to a high-dimensional non-linear feature space. This neural network consists of an input layer, multiple hidden layers, and an output layer. The hidden layers use non-linear activation functions (such as ReLU or Tanh), and during the training process, the weights are optimized through backpropagation and gradient descent methods, enabling the network to gradually capture the non-linear response characteristics of the material to temperature changes in different regions, obtaining the non-linear feature vector, which reflects the complex heat conduction and response patterns of the material under multi-point heat input conditions. At the same time, based on the standardized temperature matrix, linear modeling methods such as canonical correlation analysis are introduced. The temperature data of each test region and the reference thermal performance parameters (such as thermal conductivity, specific heat capacity, etc.) are respectively used to form the input and output variable sets. By calculating their linear correlation coefficients and canonical variable pairs, the linear response laws between the temperature changes in each region and the thermal parameters are extracted, forming the linear feature vector of the temperature field. Calculate the signal-to-noise ratio of the non-linear feature vector and the linear feature vector, and use the signal-to-noise ratio distribution ratio to reflect the information content of each vector, generating a set of adaptive weight coefficients. Weightedly fuse the linear and non-linear feature vectors according to the adaptive weight coefficients, and the weights are automatically adjusted by the signal-to-noise ratio, obtaining a fused feature vector containing a multi-dimensional information structure. Input the fused feature vector into the temperature field reconstruction model jointly composed of a Kalman filter and a multi-layer perceptron. This model uses the Kalman filter to perform dynamic estimation and smoothing processing on the temperature measurement data, updates the temperature state values of each key measurement point through the state transition matrix and the observation equation, and then inputs the filtered data into the trained MLP neural network. This network takes the temperature of the sensor points as the input, and the output is the complete temperature field information in a two-dimensional grid structure, obtaining the target temperature distribution map.

[0019] In this embodiment, the fused feature vector is analyzed to extract the key thermophysical parameters representing the thermal behavior of the material, including thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time, and a unified thermophysical parameter table is constructed based on this. Based on the thermophysical parameter table, the Kalman filtering algorithm is used to filter the temperature data at sparse points of the sensor. The Kalman filter filters the data collected at the sparse temperature sensor points in the multifunctional splitter by constructing a state transition equation and an observation model. Using the thermal diffusion characteristics and response time parameters contained in the fused feature vector, the filter suppresses high-frequency noise and occasional anomalies, and dynamically corrects the state in combination with the thermal conductivity and thermal resistance parameters to obtain the temperature estimate of the target point. This estimate is dynamically updated in a time series manner to reflect the evolution trend of the temperature field of the splitter under different thermal loads. The temperature estimate of the target point is mapped to the surface geometric coordinate system of the multifunctional splitter, and a one-to-one correspondence between the temperature estimation point and the actual spatial position is established according to the regional division coordinate information generated by the previous structure modeling. During the mapping process, to ensure the rationality and thermal continuity at the model boundary, boundary constraint processing is performed on the boundary temperatures of each test area. For example, the Dirichlet boundary condition is used to keep the boundary temperature constant, or the Neumann boundary condition is used to set the boundary heat flux density to form a discrete temperature grid with boundary constraints. To fill the blank areas of the temperature data between sensor points, a spatial interpolation algorithm based on a multi-layer perceptron (MLP) is used to complement the discrete temperature grid data. The MLP interpolation network learns the temperature relationship model between sensor points and maps it to non-measured points to complete the prediction of the temperature values at the grid gaps. To ensure the rationality of the prediction results in terms of physical meaning, physical constraints based on the heat conduction equation are introduced for optimization and calibration. For example, a constraint term is introduced to ensure that the temperature gradient change satisfies Fourier's law, or a thermal diffusion time scale constraint is imposed to avoid non-physical temperature mutations. Through the dual constraints of physical laws and the output results of the neural network, high-precision reconstruction of continuous temperature field data is achieved. Based on the reconstructed continuous temperature field data, the temperature distribution of each test area of the multifunctional splitter is analyzed, especially the positioning and quantitative description of the hot spot area. By setting criteria such as temperature thresholds, gradient boundaries, or temperature change rates, the exact coordinates and thermal response intensity of the hot spot positions are identified to form the target hot spot distribution information. The continuous temperature field data and the target hot spot distribution information are integrated in terms of data structure and processed graphically. Through graphic rendering techniques such as interpolation smoothing, color mapping, and boundary enhancement, a high-resolution, visual target temperature distribution map is generated.

[0020] 103. Conduct an operation test on the multifunctional splitter according to the target temperature distribution map to obtain a temperature response curve; Specifically, according to the target temperature distribution map, the control system is divided into a hierarchical control architecture including a global temperature trajectory planning layer, a regional temperature regulation layer, and a local precise control layer. The global layer is responsible for setting the overall temperature control direction and evolution path. The regional layer finely allocates power according to specific test partitions. The local layer is used for fine adjustment and disturbance compensation under small perturbations. According to the hierarchical control architecture, the heat conduction theory is used to dynamically model the heat system of the splitter. By splitting the entire heat system into two subsystems: one for describing the ideal heat conduction path, i.e., the nominal heat conduction system, and the other for reflecting the actual deviation caused by environmental fluctuations or structural inhomogeneity, i.e., the temperature deviation system. By modeling the behaviors of these two systems, a heat dynamic model with time evolution ability is established to predict the spatio-temporal change trend of temperature in each test area. Based on the heat system dynamic model, an optimal temperature trajectory is set for the global temperature trajectory planning layer. This layer combines the structural distribution, material properties, and workload requirements of each area to pre-plan an optimal temperature path that rises from the starting temperature to the stable operating temperature and then slowly drops back to the initial level. This trajectory takes into account the different tolerances of different areas to the temperature rise rate and the maximum temperature, aiming to make the entire test system perform a rhythmic and orderly heating and cooling process according to the plan. The regional temperature regulation layer executes the model predictive control algorithm based on the heat system model to dynamically evaluate the thermal inertia, heat capacity, and heat dissipation capacity of each area. On this basis, an optimal control problem including constraints such as the target trajectory, energy consumption, and regulation accuracy is solved by numerical optimization methods, and the heating power or cooling intensity required for each area in the current time period is output to obtain a set of regional parameters for control. In the local precise control layer, according to the control parameters generated by the regional layer, an adaptive PID controller is configured for each key test position. This adaptive controller monitors the temperature deviation amplitude in real time and automatically adjusts the response speed and regulation amplitude of the controller to ensure that the system remains stable in the face of sudden load changes or local environmental perturbations. To improve the robustness of the system in a non-constant temperature environment, a disturbance observer module is integrated into the local control logic to identify and estimate the change trend of the external environmental temperature, and the control signal is pre-compensated according to the identification result to enable the system to have feed-forward control ability and effectively suppress the error accumulation caused by external temperature disturbances. When the above three-layer control logic runs in coordination, the system applies the set power to each test area of the multifunctional splitter according to the control instructions, coordinates the cooling rhythm, and completes the whole process of temperature rise and fall during the entire test cycle. At the same time, the temperature change data is collected in real time through the thermocouple array arranged in each functional area. As time goes by, the temperature response of each measurement point gradually forms a complete curve, depicting the heat dynamic response trajectory of the device under the combined action of external heat excitation and internal regulation. These curves are summarized to generate a temperature response curve graph covering all functional areas.

[0021] 104. Measure the thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time of the multifunctional splitter based on the temperature response curve to generate a comprehensive thermal performance evaluation result.

[0022] Specifically, by extracting the temperature change slope at the initial stage of temperature rise and the steady-state temperature difference at thermal equilibrium, and combining with the heat source power data, calculate the thermal conductivity distribution information of each test area, and reveal the differences in heat transfer capabilities of different structural materials in the transverse and longitudinal directions. Based on the temperature modulation period data of the temperature response curve, analyze the temperature change rate of different areas under unit heat input, establish the relationship between heat input and temperature rise rate, and calculate the specific heat capacity distribution data of each area accordingly. As an important parameter reflecting the heat storage capacity of unit mass material, the higher the specific heat capacity value, the slower the area temperature rise, and vice versa, it is more sensitive. Combine the thermal conductivity distribution data with the specific heat capacity distribution data, and introduce the previously known material density parameter to construct a thermal diffusion characteristic analysis model, and obtain the thermal diffusivity distribution data of each test area, which reflects the thermal diffusion ability of the material and measures the propagation speed of thermal energy inside the material. Its level directly determines the system response speed and heat distribution uniformity. Analyze the part of the temperature response curve that reflects the thermal behavior of the material contact interface, especially the interface positions between different structural modules. By extracting the instantaneous temperature difference on both sides of the interface and the thermal steady-state information under the applied heat flux condition, estimate the temperature drop amplitude when the heat flux passes through the interface, and calculate the thermal contact resistance of each contact position. Thermal contact resistance is an important parameter for evaluating the internal thermal coupling performance and heat transfer continuity of the splitter, and its value directly affects the uniformity and steady-state efficiency of the overall temperature field. Process the step excitation section in the temperature response curve, extract the response time required for each area to reach a certain proportion of the final temperature from the application of the heat source, and obtain the distribution data of the temperature response time, which is used to characterize the reaction agility and thermal inertia degree of the system under thermal excitation. After completing the quantitative extraction of the above various thermal parameters, unify and integrate all distribution data, and introduce a thermal performance index calculation model. By weighting and calculating the thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time according to the set weights, obtain the thermal performance index values of each area. Subsequently, compare and evaluate these index values with the standard thresholds of reference materials to divide the thermal performance grades of materials in different areas. By visually marking the thermal performance grade results in the area and combining with the overall structural layout, output the comprehensive thermal performance evaluation result.

[0023] In the embodiments of the present invention, by dividing the multifunctional splitter into five key test areas: a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, accurate testing for different functional areas is achieved, overcoming the problem of insufficient recognition of hot spots in traditional methods. According to the partition testing model, an optimized layout of a high-precision thermocouple array is carried out, making the thermocouple distribution match the hot spot distribution, effectively solving the limitation that traditional uniform sensor layout cannot accurately capture hot spots. By extracting non-linear thermal dynamic characteristics from multi-point temperature time-series data and fusing linear and non-linear characteristics, a more comprehensive description of thermal characteristics is obtained, breaking through the limitation of traditional single-characteristic extraction methods. The Kalman filter and multi-layer perceptron joint algorithm are used for two-dimensional temperature field reconstruction, and only a small number of sensors are required to accurately reconstruct the full-surface temperature field, solving the problem that traditional methods require a large number of sensors. Based on the reconstructed temperature distribution map, a hierarchical control strategy is created, realizing a hierarchical structure of global temperature trajectory planning, regional temperature regulation, and local precise control, overcoming the limitation that traditional PID control is difficult to handle multi-variable strongly coupled thermal systems. By comprehensively measuring key parameters such as thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time, and introducing a disturbance observer and an environmental temperature compensation unit, the influence of environmental temperature fluctuations on test results is effectively eliminated, making the test process more stable and reliable.

[0024] In a specific embodiment, the process of executing step 101 may specifically include the following steps: Conduct a structural scan analysis of the multifunctional splitter to determine the structural distribution maps of the hub body, USB sockets, card reader sockets, charging positioning mechanism, and lithium battery of the multifunctional splitter; Based on the structural distribution map, perform a thermal imaging scan on the multifunctional splitter to identify the hot spot distribution data of the multifunctional splitter; According to the hot spot distribution data, divide the multifunctional splitter into five test areas including a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, and determine the boundary coordinates of each test area to obtain area division parameters; Generate a regional characteristic parameter table based on the area division parameters, and based on the regional characteristic parameter table, assign weight coefficients to each test area to obtain a weight distribution matrix; Integrate the area division parameters, the regional characteristic parameter table, and the weight distribution matrix to obtain a partition testing model; Arrange thermocouples in each test area according to the partition testing model, and collect temperature data of the multifunctional splitter under working conditions to obtain multi-point temperature time-series data.

[0025] Specifically, conduct a structural scanning analysis on the multifunctional splitter to determine the spatial arrangement and connection methods of its internal and external key components. Use a high-resolution three-dimensional structure imaging system such as an industrial-grade CT scanner or a structured light scanning device to obtain complete structural information including the hub body, USB sockets, card reader sockets, charging positioning mechanisms, and lithium batteries. Generate a three-dimensional structure distribution map of the multifunctional splitter through multi-view reconstruction and image registration techniques. In this distribution map, all functional modules, their physical boundaries, packaging materials, assembly positions, and internal interconnection methods are reflected. Through image annotation and semantic segmentation algorithms, identify the hub body and its sub-structures, and extract the range values of each functional component in the coordinate space to form a structure recognition data set. On the basis of completing the structural scanning and establishing a high-precision structural model, perform a thermal imaging scanning operation to identify the true thermal distribution state of the multifunctional splitter in the working state. Power on the splitter and run typical functional modules, such as simultaneously performing USB data transmission, charging, card reading, etc., to make the device enter the actual load operation mode, and use a high-sensitivity infrared thermal imager to collect thermal images of its surface and exposed structures. The collection process is carried out in multiple angles and continuously to ensure coverage of different structural surfaces and temporal changes, and obtain a temperature distribution map through a temperature color-coded image. Combine the previously obtained structure distribution map to achieve image alignment, align the thermal imaging map with the structure coordinate system, analyze the temperature rise intensity and temperature fluctuation characteristics of different structural components under different working conditions, and extract the temperature peak concentration areas, slow thermal diffusion areas, and potential overheating risk areas, thereby forming a hot spot distribution data map. Based on the spatial fusion of the hot spot distribution data and the structural information, divide the functional thermal zones of the splitter, and divide it into five main test areas, namely the central processing area, USB interface area, card reader interface area, charging circuit area, and lithium battery area. The division is carried out by combining the dynamic combination strategy of thermal feature significance and physical function boundaries. Each area sets different boundary sizes according to its thermal response intensity, load density, and functional complexity, and extracts its coordinate range in the structural model through image analysis and spatial projection methods to form a regional division parameter table in a unified format. Taking this regional division parameter as the core, generate a regional characteristic parameter table. In this table, key parameters such as the typical operating temperature range, maximum power consumption, current load capacity, thermal inertia index, packaging material type, heat dissipation path complexity, and temperature measurement point distribution density of each test area are recorded. These parameters are jointly constructed from the structural material database, previous thermal imaging data, product manual indicators, and experimental verification data to form a comprehensive parameter table covering the three aspects of thermophysics, structural mechanics, and electronic electricity. Based on this parameter table, quantitatively evaluate the role degree of different areas in the entire thermal system, and establish a weight distribution model accordingly. This model comprehensively normalizes the influence of thermal conductivity, heat capacity, temperature response speed, and heat dissipation efficiency through the multi-index weighting method to obtain the corresponding weight coefficients for each area and construct a weight distribution matrix.Integrate the region division parameters, the region characteristic parameter table, and the weight distribution matrix to generate a partition test model. This model includes spatial division boundaries and physical parameter data, and embeds control information such as the number of sensors arranged in each region, the data sampling frequency, the importance ranking, and the weight distribution of the computing resources participating in the temperature characteristic modeling, which is regarded as a partition temperature test guidance template integrating multiple parameters. Based on this model, enter the actual sensor layout link. Select the thermocouple model that matches the working temperature and response characteristics of each region. In the central processing area and power consumption-intensive areas, select micro-thermocouples with shorter response time, smaller diameter, and higher measurement accuracy. In areas with slow temperature rise and higher stability requirements, select high-stability thermocouples with stable structure and small drift. The specific layout positions and quantities of the thermocouples are executed according to the layout strategy in the partition test model to ensure that the thermal information collection in each region covers the hot spot center and the boundary transition region to obtain as complete a temperature change curve as possible. After the sensors are installed, connect the multifunctional splitter to the standard working environment, apply typical test loads respectively, including various working conditions such as static operation, high-frequency data transmission, high-speed charging, long-time reading and writing, and combined load impact, and start the data acquisition system to record the temperature time series. The data sampling system has the capabilities of high-frequency sampling and multi-channel parallel processing, and at the same time combines the temperature compensation algorithm to eliminate the influence of environmental disturbances. Store the real-time temperature collected at the thermocouple layout points in sequence, and record the temperature value at each sampling point at each time step to form a set of multi-point temperature time series data.

[0026] In a specific embodiment, the process of performing the steps of arranging thermocouples in each test region according to the partition test model and collecting temperature data of the multifunctional splitter in the working state to obtain multi-point temperature time series data may specifically include the following steps: Match the thermocouple specification parameters according to the region characteristic parameter table in the partition test model; Based on the weight distribution matrix of the partition test model and the thermocouple specification parameters, plan the thermocouple layout for each test region to obtain a thermocouple layout diagram; According to the thermocouple layout diagram, place the multifunctional splitter in the standard working state, high-temperature working state, high-load working state, and composite extreme working state, and continuously collect the temperature of each test region to obtain the original temperature data; Perform weighted processing on the original temperature data according to the importance of the region to obtain multi-point temperature time series data.

[0027] Specifically, the specification parameters of the thermocouple are matched according to the regional characteristic parameter table in the partition test model. For example, in the central processing area, since it integrates the main control chip and the signal processing circuit, along with high-frequency switching current and rapid temperature rise behavior, the temperature change rate is fast and the transient response is strong. It is preferred to select a micro thermocouple with high sensitivity, fast response ability and high temperature measurement accuracy. The probe diameter is controlled below 0.3 mm, the response time should be less than 0.5 s, and it has a measurement accuracy of ±0.05 °C. At the same time, the temperature resistance performance covers its working upper limit. In the lithium battery area, the temperature rise is relatively slow, but due to the extremely high requirement for temperature stability, especially the need to monitor small temperature difference changes to judge the stability of the battery cells, a thermocouple model with good long-term stability and low drift coefficient is selected, and its installation structure is required to have anti-oxidation and anti-electrolysis properties to adapt to the complex chemical environment around the battery. In the USB interface area and the card reader interface area, since they are mainly involved in data transmission and weak current control, the heat distribution has the characteristics of local concentration and periodic fluctuation. A thermocouple type with medium response speed but strong structural adaptability is selected to meet the layout requirements under the socket structure limitations. In the charging circuit area, a thermocouple with good insulation and medium-high response speed is selected to monitor the stability of the power management chip and the voltage conversion module. Based on the weight distribution matrix of the partition test model and the thermocouple specification parameters, the thermocouple layout plan is carried out for each test area. This matrix sets the importance coefficient of each area in the overall thermal performance evaluation, and clarifies the resource weight that each area should occupy in data acquisition through numerical mapping. On this basis, combined with the matched thermocouple model library and structural model, a three-dimensional point layout algorithm is used to simulate the thermocouple layout, while maintaining the integrity of the regional thermal response, and optimizing the use efficiency of the sensors as much as possible. For the central processing area and the USB interface area with higher weights, the number of layout points is increased, and a high-density layout strategy is adopted to cover all the heat-generating core areas and the boundary transition zones. In the charging circuit area and the lithium battery area with medium weights, a layout plan with medium point density and structural redundancy is adopted to ensure that effective data can still be obtained in case of unexpected thermal shock. In the card reader interface area with the lowest weight, a low-density and key point layout method is adopted, and only symmetric points or typical working path points are laid out to realize the monitoring of the heat diffusion path. Through simulation optimization and layout verification, a thermocouple layout diagram is output. This diagram marks the specific installation position, installation direction, number identification, wiring direction and data channel mapping relationship of the thermocouple in each test area, ensuring the precise one-to-one correspondence between the physical position and the data stream in subsequent data acquisition.Prepare the equipment according to the thermocouple layout diagram, install the multi-functional splitter in the thermal test platform, and collect temperature data under four typical working conditions, namely the standard working state (ambient temperature is normal temperature, system load is 50%), high-temperature working state (ambient temperature rises to 40 degrees Celsius, load remains 50%), high-load working state (ambient temperature is normal temperature, system load is increased to 100%), and combined extreme working state (ambient temperature rises to 40 degrees Celsius, load is also increased to 100%). Under each working condition, ensure that the thermocouple is stably connected to the acquisition system, the sampling frequency is stable above 10 Hz, and a sufficient sampling period length is maintained to fully cover the heating stage, stable stage, and cooling process of each thermal system. After the acquisition is completed, the original temperature data covering all thermocouple points under the four working conditions are obtained. These data have a high degree of time series and spatial distribution differences. When entering the subsequent processing stage, perform regional importance weighting processing in combination with the aforementioned weight distribution matrix. Apply the weight factor of the corresponding region to each thermocouple sampling sequence to make the data better reflect the thermal influence degree in the actual engineering significance during the global calculation and analysis process. During the weighting process, perform interpolation smoothing, outlier removal, and time series alignment on the data in combination with the regional heat transfer characteristics, and finally obtain a set of stable, continuous, and reasonably weighted multi-point temperature time series data sets.

[0028] In a specific embodiment, the process of executing step 102 may specifically include the following steps: Preprocess the multi-point temperature time series data to obtain a standardized temperature matrix, and perform principal component analysis on the standardized temperature matrix to obtain a dimensionality-reduced temperature feature vector; Extract the non-linear features of the material thermal response according to the dimensionality-reduced temperature feature vector to obtain a non-linear feature vector; Based on the standardized temperature matrix, calculate the linear correlation between the temperature data of each test area and the reference thermal performance parameters, and extract the linear change law of the temperature field to obtain a linear feature vector; Calculate the adaptive weight coefficient according to the signal-to-noise ratio of the non-linear feature vector and the linear feature vector, and perform weighted fusion according to the adaptive weight coefficient to obtain a fused feature vector; Input the fused feature vector into the joint algorithm of Kalman filter and multi-layer perceptron for two-dimensional temperature field reconstruction to obtain the target temperature distribution map.

[0029] Specifically, preprocess the multi-point temperature time-series data. The original data is sourced from the continuous measurements of thermocouples in each test area of the multi-functional splitter. It has a high time dimension and spatial dimension. Perform unified time-axis alignment processing on it, eliminate time errors caused by communication delays or sampling drifts, and interpolate and complete the data of each point with a unified time step, so that the data of all sampling points has correspondence at the same time node. Subsequently, perform noise smoothing and outlier processing, including operations such as low-pass filtering, median filtering, and boundary outlier correction, to obtain a preliminary cleaned data matrix without obvious jumps and drifts. After the data cleaning is completed, perform a standardization operation, zero the mean and normalize the standard deviation of the temperature sequence of each sensor measurement point, so that the temperature values between different measurement points are comparable on the numerical scale, and avoid the influence of the original dimensional differences caused by physical position, heat capacity differences, or different workloads on the overall modeling accuracy. After the preprocessing is completed, a standardized temperature matrix is obtained. The rows of this matrix represent the measurement point numbers, and the columns represent the temperature values at time nodes. Based on the standardized temperature matrix, perform principal component analysis, construct a covariance matrix and perform eigen-decomposition operations on the entire temperature matrix, and identify the principal component vectors corresponding to the dominant change trends in the data. Determine the number of principal components to be retained by calculating and accumulating the principal component contribution rates, and select the first few items required for the cumulative contribution rate to reach more than 95% to achieve the data dimensionality reduction operation and obtain the dimensionality-reduced temperature feature vectors. Extract the non-linear features of the material's thermal response according to the dimensionality-reduced temperature feature vectors. Use a multi-layer perceptron neural network as the feature mapper. Take the principal component temperature vector as the input, and through the mapping ability of the non-linear activation function and the deep neural structure set in the multi-layer perceptron, automatically extract the non-linear response behaviors of different material or structural regions to temperature perturbations from the temperature changes, including complex thermal effects such as temperature acceleration, hysteresis, and saturation. These non-linear features reflect the non-linear thermodynamics mode caused by factors such as material phase change, electromagnetic interference, and local heat capacity change in the actual heat conduction process, and the output of its feature vector is the non-linear feature vector. At the same time, based on the original standardized temperature matrix, by constructing a regression model between the temperature data of each region and the known reference thermal performance parameters, identify its linear correlation relationship, that is, calculate the correlation coefficient between the time series of each temperature measurement point and the reference values (such as thermal conductivity, specific heat capacity, diffusion coefficient, etc.), and perform canonical correlation vector analysis, so as to identify the variable combination reflecting the linear heat transfer law, that is, the part describing the proportional change or linear superposition trend with time in the temperature field, and obtain the linear feature vector, which is used to represent the linear mapping relationship between the temperatures of each part in the temperature field and the material's thermal performance. Calculate the adaptive weight coefficient according to the signal-to-noise ratio of the non-linear feature vector and the linear feature vector, calculate the ratio of the information content of the non-linear feature and the linear feature in the extracted data dimension to the redundant noise respectively, and normalize this ratio to the weight coefficient, which is used to allocate the proportion of the two types of features in the fusion vector. Perform weighted fusion according to the adaptive weight coefficient to obtain the fusion feature vector.The fused feature vector is input into the joint algorithm of Kalman filter and multi-layer perceptron for two-dimensional temperature field reconstruction. The Kalman filter module performs state estimation on the temperatures of each key measurement point, and through recursive calculation using the state transition matrix and the observation model, eliminates the interference of errors and system noise based on the original measurement data, and outputs a temperature estimation result that is smoother and closer to the true value. These temperature estimation values are sent as input data into the multi-layer perceptron neural network, which has been trained with a large amount of two-dimensional thermal field data and can interpolate and expand the limited measurement point information to the two-dimensional temperature field grid points. Inside the network, each hidden layer completes the encoding and decoding operations of the spatial heat diffusion pattern, and finally outputs a structured two-dimensional temperature field, that is, the target temperature distribution map.

[0030] In a specific embodiment, the process of performing the step of inputting the fused feature vector into the joint algorithm of Kalman filter and multi-layer perceptron for two-dimensional temperature field reconstruction to obtain the target temperature distribution map may specifically include the following steps: Analyze the fused feature vector and extract the thermal property parameter table containing information on thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time parameters; Based on the thermal property parameter table, use the Kalman filter algorithm to filter the temperature data at the sparse sensor points to obtain the target point temperature estimation values; Map the target point temperature estimation values to the surface coordinate system of the multi-functional splitter, and perform boundary temperature constraint processing at the boundary of the test area to obtain the discrete temperature grid; Use the multi-layer perceptron spatial interpolation algorithm to calculate the temperature values at the grid gaps for the discrete temperature grid data, and perform constraint optimization based on the physical heat transfer law to obtain the continuous temperature field data; Analyze the temperature distribution of each test area based on the continuous temperature field data to obtain the target hot spot distribution information; Integrate the continuous temperature field data and the target hot spot distribution information to generate the target temperature distribution map.

[0031] Specifically, the fused feature vector is structurally analyzed, and five core thermophysical property indicators representing the thermal behavior of materials and structures are extracted through inverse mapping with the trained thermal parameter recognition model, namely thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time. The extraction of these indicators not only combines the correlation characteristics between temperature changes and spatial structures but also refers to the predefined parameter space mapping relationship to form a thermophysical property parameter table with a clear structure and controllable numerical values. Based on the thermophysical property parameter table, the Kalman filter algorithm is used to filter the temperature data at sparse sensor points, predict the system state at the next moment using the current known state and observation values, and gradually approximate the estimated value to the true temperature field by dynamically correcting the error covariance matrix. During the implementation process, thermal conductivity and thermal diffusivity are used as the basic parameters in the state transition function, temperature response time is used as the state change rate constraint term, thermal contact resistance is used to adjust the thermal connection strength between different regions, and specific heat capacity is combined to model the thermal inertia of the system to construct a state equation set that highly conforms to the heat conduction behavior. This state model is continuously iteratively updated to form a set of target point temperature estimation results based on inference calculation. The above temperature estimation values are mapped to the surface coordinate system of the multifunctional splitter, and according to the previously established structural geometry model, each estimated point is corresponding and matched with its true physical coordinates on the device surface. Through the coordinate mapping relationship, the temperature estimation data is accurately arranged on the two-dimensional or three-dimensional surface space of the structural model, and boundary temperature constraint processing is implemented according to the boundary contour of each test region on the model. Temperature constraint conditions are preset at the grid boundary positions to avoid abnormal results that deviate from the actual situation during numerical interpolation or neural network backpropagation. The boundary conditions are set as fixed temperature values based on actual measurement data and approximate constant gradient conditions based on heat flow conservation, so as to ensure strict thermal boundary closure during the entire temperature reconstruction process. Through this step, a discrete temperature grid with boundary constraints composed of temperature estimation points is obtained. To achieve the derivation from discrete data to a continuous thermal field, a spatial interpolation model composed of a multi-layer perceptron is introduced. This model uses the spatial coordinates and temperature values of discrete temperature grid points as training samples, encodes and non-linearly maps the input features through multiple hidden layers in the neural network, and then outputs the temperature estimates at unobserved positions within the entire grid region. Since the neural network has a high non-linear fitting ability, it can effectively capture the change trend of local temperature gradients, the aggregation behavior of hot spot regions, and the attenuation law of boundary temperatures. To improve the reconstruction accuracy and prevent physical distortion in the network output, heat transfer physical constraints are introduced during the training and inference processes. These constraints are constructed based on the heat diffusion law and the principle of energy conservation, and physical terms such as temperature gradient smoothing constraints, local heat flow continuity constraints, and material interface response consistency are added to the loss function, so as to obtain a set of continuous temperature field data that meets physical rationality and is consistent with the discrete estimates at the output stage.Analyze the temperature distribution of each test area based on the continuous temperature field data. The analysis content includes the average temperature level, the position of the maximum temperature rise, the temperature gradient distribution, and the identification of hot spots, etc. Among them, the identification of hot spots needs to combine spatial gradient analysis, local extreme value detection and statistical clustering methods to accurately extract over-temperature points or rapidly heating areas on the known temperature field, and mark their physical positions, durations and heating rates, forming a target hot spot distribution information report. Integrate the structure and image process the reconstructed continuous temperature field data and the extracted target hot spot distribution information, and generate a target temperature distribution map through visual rendering technology.

[0032] In a specific embodiment, the process of executing step 103 may specifically include the following steps: Divide the control system into a hierarchical control architecture including a global temperature trajectory planning layer, a regional temperature regulation layer, and a local precise control layer according to the target temperature distribution map; According to the hierarchical control architecture, dynamically decompose the thermal system of the multifunctional splitter, decompose the heat conduction system into a nominal heat conduction system and a temperature deviation system, and establish a heat conduction equation to obtain a dynamic model of the thermal system; Based on the dynamic model of the thermal system, set an optimal temperature trajectory for the global temperature trajectory planning layer, so that each test area performs heating and cooling according to a predetermined time-temperature relationship, and obtain a global temperature control curve; Execute a model predictive control algorithm based on the regional temperature regulation layer, and solve a quadratic programming problem to calculate the heating and cooling powers required for each test area, and obtain regional control parameters; Based on the regional control parameters, configure an adaptive PID controller in the local precise control layer, dynamically adjust the control parameters according to the temperature deviation, and set an interference observer to compensate for the influence of environmental temperature fluctuations to obtain local control instructions; According to the local control instructions, apply a constant power to each test area of the multifunctional splitter and perform a cooling test to obtain a temperature response curve.

[0033] Specifically, according to the temperature gradient distribution, hotspot concentration degree, thermal coupling intensity and time evolution trend of each functional area reflected in the target temperature distribution map, the entire control system is divided into three mutually supportive control levels, namely the global temperature trajectory planning layer, the regional temperature regulation layer and the local precise control layer. Among them, the global temperature trajectory planning layer formulates a unified temperature control strategy covering all functional areas to ensure that the thermal response process follows a predetermined time series and the overall thermal evolution path; the regional temperature regulation layer configures local strategies for the thermal inertia, power demand and regulation delay of different test areas, and dynamically calculates the power control amount; while the local precise control layer focuses on rapid response and error correction under micro disturbance conditions to ensure that the actual temperature closely follows the predetermined target value. According to the hierarchical control architecture, a dynamic model of the thermal system of the multi-functional splitter is established. The entire heat conduction behavior is systematically decomposed into two parts: the nominal heat conduction system and the temperature deviation system. Among them, the nominal heat conduction system is used to describe the heat transfer behavior of each test area under ideal conditions according to the designed material parameters and structural conditions, reflecting the basic directionality and steady-state characteristics of heat diffusion; while the temperature deviation system is used to characterize the deviation trend between the actual operating temperature and the ideal temperature caused by factors such as environmental disturbances, material non-uniformity, power fluctuations or poor contact interfaces, manifested as local heating delay, hotspot migration or abnormal heat distribution. On this basis, a dynamic equation of the thermal system covering changes in time, space, power and boundary conditions can be constructed through the heat conduction theory, and it is transformed into a state equation set that can be discretely solved, enabling the control system to predict future temperature changes based on this model and implement early intervention. Based on the above thermal system dynamic model, the global temperature trajectory planning layer designs an optimal temperature trajectory during the temperature control process. This trajectory fully combines the thermal stability points, response speeds, structural temperature resistance limits and functional operation cycles of each test area, and uses the thermal control time-temperature curve method to clarify the heating rate, heat preservation time and cooling control points of each area in different time periods. The trajectory setting should ensure synchronous heating and orderly heat dissipation in each area, and avoid abnormal temperature coupling caused by local overheating or reverse heat conduction at the cold end. When setting the temperature control trajectory, comprehensively consider the thermal inertia and boundary thermal resistance of the entire system to form a temperature evolution curve that conforms to the actual operating ability of the system and is convenient for tracking control. The regional temperature regulation layer implements a model predictive control mechanism in combination with the thermal system dynamic model and the global temperature control curve. This mechanism establishes a time rolling window prediction model, predicts the temperature change trend in the future period based on the current regional state, historical temperature changes and control actions, and constructs a quadratic programming problem by setting the target temperature trajectory and system constraint conditions, with minimizing the temperature deviation and control energy consumption as the objective function, and solves the optimal heating or cooling power in the current regulation cycle. This power distribution depends on the current temperature difference and is related to the regional heat capacity, heat diffusion speed and peripheral thermal coupling intensity. During the solution process, the thermal parameters are continuously updated to ensure that the control strategy has real-time adaptability.In this way, each test area obtains a set of forward-looking control parameters, which can guide the local power execution module to apply heat or direct heat dissipation. Based on the above regional adjustment output results, the local precise control layer refines the execution strategy. An adaptive PID controller is used to perform closed-loop adjustment on the temperature error, and the PID parameters are dynamically adjusted according to the temperature deviation in each sampling period. The adjustment principle is based on the gain adjustment model, where the proportional coefficient increases with the increase in the error amplitude to improve the response speed; the integral coefficient weakens in the small error stage to avoid integral saturation; the differential coefficient is dynamically weighted according to the temperature fluctuation frequency to improve the anti-disturbance ability. At the same time, a disturbance observer is configured in this control layer to estimate in real time the non-systematic errors caused by external temperature fluctuations, wind speed changes, or power supply fluctuations, and perform feed-forward compensation on the control command through the observer output value, so that the local controller has feedback ability and can respond to the disturbance trend in advance, improving the system robustness and temperature stability. After all local controllers complete parameter update and disturbance compensation, they output specific control commands to guide the power execution unit to apply a constant heating power to the corresponding test area and control the heat dissipation module to perform stable cooling. In the actual execution stage, the thermocouple array synchronously starts the data acquisition device to record the complete response process of the temperature values of each area changing with time at the set sampling frequency. This response process covers the heating response stage at the initial stage of temperature rise, the heat stability maintenance stage in the middle, and the cooling transition stage at the later stage, and reflects the dynamic adjustment effect of the control system under different disturbance conditions. By sorting out and curve fitting the continuously recorded temperature data, the temperature response curve of each test area is obtained, which reflects the heat response speed, steady-state maintenance ability, and cooling recovery characteristics of the multifunctional splitter under the combined action of external heat excitation and internal control strategy.

[0034] In a specific embodiment, the process of performing step 104 may specifically include the following steps: Analyze the slope of the temperature rise curve and the steady-state temperature difference in the constant power heating stage according to the temperature response curve, and calculate the thermal conductivity distribution data of each test area; Based on the analysis of the relationship between the temperature change rate and the heat input according to the temperature modulation cycle data of the temperature response curve, and calculate the specific heat capacity distribution data of each test area; According to the thermal conductivity distribution data and the specific heat capacity distribution data, combined with the density parameters of each material of the multifunctional splitter, calculate the thermal diffusivity distribution data of each test area; According to the interface temperature data of the temperature response curve, analyze the ratio of the temperature difference and heat flow on both sides of the material contact interface, and calculate the thermal contact resistance distribution data of each contact interface; Based on the step response data of the temperature response curve, measure the temperature response time distribution data of each test area; Calculate the thermal performance index and compare the threshold values for the thermal conductivity distribution data, specific heat capacity distribution data, thermal diffusivity distribution data, thermal contact resistance distribution data, and temperature response time distribution data to generate a comprehensive thermal performance evaluation result.

[0035] Specifically, analyze the temperature rise part in the constant power heating stage of the temperature response curve. This stage is characterized by a rapid temperature rise section, whose slope reflects the temperature growth rate per unit time, and the final stable temperature difference value reflects the balance relationship between the heat input and heat dissipation of the system under steady-state conditions. By extracting the temperature rise slope at the initial stage of heating for each test area, and combining the known heating power and heat input time, estimate the heat flux distribution during the heat transfer process, and calculate the thermal conductivity of each area based on the ratio between the heat input and the temperature difference, forming the thermal conductivity distribution data. While obtaining the thermal conductivity, combine the data in the temperature modulation period part of the temperature response curve to inversely calculate the specific heat capacity. The modulated heating test stage includes the introduction of periodic thermal disturbances, such as small-amplitude positive and negative power adjustments, and observe the phase difference and amplitude change of the temperature response to the periodic input. By analyzing the ratio between the temperature change rate and the input energy when the temperature changes with the periodic power input, and combining the known heating power, modulation period, and steady-state temperature rise degree of the system, calculate the temperature change amplitude caused by the absorption of unit heat in each test area, and thus inversely deduce the specific heat capacity of this area. The specific heat capacity is a physical quantity that reflects the temperature rise caused by a unit mass of material absorbing heat, determines the thermal inertia of the material in this area, and helps to predict the temperature response speed and thermal load stability of the system. By repeating the above calculation process for all functional areas, form the specific heat capacity distribution data with spatial resolution. Combine the thermal conductivity distribution data and the specific heat capacity distribution data, and introduce the density parameter of the material used in the multi-functional splitter. Through the established regional structure-material mapping relationship, extract the density data of the materials used in each area from the database, and conduct a combined analysis with the thermal conductivity and specific heat capacity to calculate the thermal diffusivity distribution data of each test area. Conduct a special analysis on the temperature jump data related to the structural interface in the temperature response curve. Especially in the contact areas between different materials or different functional modules, there is an obvious thermal resistance phenomenon in the temperature transfer process. This phenomenon is manifested as inconsistent temperatures on different sides under continuous heating conditions in the curve, or delayed asymmetry in the temperature response. By extracting the temperature data at these interfaces, and combining the spatial information in the sensor layout diagram, calculate the average temperature difference between both sides of the material contact interface in the steady-state or dynamic process, and deduce its thermal resistance value based on the heat flux input value to obtain the thermal contact resistance distribution data. This type of data directly reflects the quality and efficiency of the interface between structures during the thermal coupling process. For the evaluation of the response speed, focus on the step excitation section in the temperature response curve. By applying an obvious power change, such as suddenly increasing the heating power or cutting off the power source, at a certain stable temperature level, observe the time required for the temperature response to reach the new steady-state value or a specific percentage of it, and measure the temperature response time of this area. The temperature response time is a comprehensive manifestation of the thermal inertia of the system, which is affected by both the specific heat of the material and the thermal diffusion path, heat capacity distribution, and dynamic behavior of the controller.By repeatedly performing step excitation experiments and extracting the average response time, the regional temperature response time distribution data is established to identify the areas that are prone to lag in the control system regulation and the sensitive modules that require priority response. Normalize and fuse the five types of data: thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time to construct a thermal performance index evaluation model. The model sets weights according to functional requirements. For example, in a control system that emphasizes fast response, the weights of thermal diffusivity and temperature response time are increased, while in a module that focuses on heat dissipation capacity, the contributions of thermal conductivity and thermal contact resistance are highlighted. After calculating the thermal performance index, compare the obtained results with the set threshold for classification, and classify them according to the level of the thermal performance index. For example, an area with a thermal performance index higher than 1.2 is a high-quality thermal performance area, an area between 1.0 and 1.2 is a qualified area, and an area lower than 1.0 is an area to be optimized or an unqualified area. Finally, generate a comprehensive thermal performance evaluation result with thermal performance level labels.

[0036] The test method of the multifunctional splitter in the embodiment of the present invention is described above. Next, the test device of the multifunctional splitter in the embodiment of the present invention will be described. Please refer to Figure 2 , an embodiment of the test device of the multifunctional splitter in the embodiment of the present invention includes: A data acquisition module 201, configured to divide the multifunctional splitter into a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, and collect temperature data of the multifunctional splitter in the working state to obtain multi-point temperature time series data; A temperature field reconstruction module 202, configured to extract non-linear thermal dynamic features from the multi-point temperature time series data to obtain a fusion feature vector, and perform two-dimensional temperature field reconstruction based on the fusion feature vector to obtain a target temperature distribution map; An operation test module 203, configured to perform an operation test on the multifunctional splitter according to the target temperature distribution map to obtain a temperature response curve; A generation module 204, configured to measure the thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time of the multifunctional splitter based on the temperature response curve, and generate a comprehensive thermal performance evaluation result.

[0037] Through the collaborative cooperation of the above-mentioned various components, by dividing the multifunctional splitter into five key test areas: the central processing area, the USB interface area, the card reader interface area, the charging circuit area, and the lithium battery area, precise testing for different functional areas is achieved, overcoming the problem of insufficient identification of hot spots in traditional methods. According to the partition testing model, the optimized layout of the high-precision thermocouple array is carried out, making the thermocouple distribution match the hot spot distribution, effectively solving the limitation that traditional uniform sensor layout cannot accurately capture hot spots. By extracting the nonlinear thermal dynamic characteristics from the multi-point temperature time-series data, fusing linear and nonlinear characteristics, a more comprehensive description of thermal characteristics is obtained, breaking through the limitation of traditional single-characteristic extraction methods. The Kalman filter and multi-layer perceptron joint algorithm are used for two-dimensional temperature field reconstruction, and only a small number of sensors are required to achieve accurate reconstruction of the full-surface temperature field, solving the problem that traditional methods require a large number of sensors. Based on the reconstructed temperature distribution map, a hierarchical control strategy is created, realizing a hierarchical structure of global temperature trajectory planning, regional temperature regulation, and local precise control, overcoming the limitation that traditional PID control is difficult to handle multi-variable strongly coupled thermal systems. By comprehensively measuring key parameters such as thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time, and introducing a disturbance observer and an environmental temperature compensation unit, the influence of environmental temperature fluctuations on test results is effectively eliminated, making the test process more stable and reliable.

[0038] Above Figure 2 The test device of the multifunctional splitter in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Next, the test equipment of the multifunctional splitter in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0039] Figure 3 It is a schematic structural diagram of a test equipment of a multifunctional splitter provided by an embodiment of the present invention. The test equipment 300 of the multifunctional splitter may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 for storing application programs 333 or data 332 (for example, one or more mass storage device ends). Among them, the memory 320 and the storage media 330 may be transient storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the test equipment 300 of the multifunctional splitter. Further, the processor 310 may be set to communicate with the storage media 330 and execute a series of instruction operations in the storage media 330 on the test equipment 300 of the multifunctional splitter to implement the steps of the above-mentioned test method of the multifunctional splitter.

[0040] The test device 300 for the multi-functional splitter may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that Figure 3 The structure of the test device for the multi-functional splitter shown does not constitute a limitation on the test device for the multi-functional splitter provided by the present invention. It may include more or fewer components than those shown, or combine certain components, or have different component arrangements.

[0041] The present invention also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is made to execute the steps of the test method for the multi-functional splitter.

[0042] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0043] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0044] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A test method for a multifunctional wire splitter, characterized in that, Including: Dividing the multi-functional splitter into a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, and collecting temperature data of the multi-functional splitter in a working state to obtain multi-point temperature time-series data; Performing non-linear thermal dynamic feature extraction on the multi-point temperature time-series data to obtain a fusion feature vector, and reconstructing a two-dimensional temperature field based on the fusion feature vector to obtain a target temperature distribution map; Performing an operation test on the multi-functional splitter according to the target temperature distribution map to obtain a temperature response curve; Measuring the thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time of the multi-functional splitter based on the temperature response curve, and generating a comprehensive thermal performance evaluation result.

2. The test method of the multifunctional wire splitter according to claim 1, characterized in that The step of dividing the multi-functional splitter into a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, and collecting temperature data of the multi-functional splitter in a working state to obtain multi-point temperature time-series data includes: Performing a structural scan analysis on the multi-functional splitter to determine the structural distribution maps of the hub body, USB sockets, card reader sockets, charging positioning mechanisms, and lithium batteries of the multi-functional splitter; Based on the structural distribution maps, performing a thermal imaging scan on the multi-functional splitter to identify the hot spot distribution data of the multi-functional splitter; According to the hot spot distribution data, dividing the multi-functional splitter into five test areas including a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, and determining the boundary coordinates of each test area to obtain area division parameters; Generating an area characteristic parameter table based on the area division parameters, and based on the area characteristic parameter table, assigning weight coefficients to each test area to obtain a weight distribution matrix; Integrating the area division parameters, the area characteristic parameter table, and the weight distribution matrix to obtain a partition test model; Arranging thermocouples in each test area according to the partition test model, and collecting temperature data of the multi-functional splitter in a working state to obtain multi-point temperature time-series data.

3. The test method of the multifunctional wire splitter according to claim 2, characterized in that, The step of arranging thermocouples in each test area according to the partition test model, and collecting temperature data of the multi-functional splitter in a working state to obtain multi-point temperature time-series data includes: Matching the thermocouple specification parameters according to the area characteristic parameter table in the partition test model; Based on the weight distribution matrix of the partition test model and the thermocouple specification parameters, performing a thermocouple layout plan for each test area to obtain a thermocouple layout diagram; According to the thermocouple layout diagram, placing the multi-functional splitter in a standard working state, a high-temperature working state, a high-load working state, and a composite extreme working state, and continuously collecting temperatures of each test area to obtain raw temperature data; Performing a weighted processing of the regional importance on the raw temperature data to obtain multi-point temperature time-series data.

4. The test method of the multifunctional wire splitter according to claim 1, characterized in that The step of performing non-linear thermal dynamic feature extraction on the multi-point temperature time-series data to obtain a fusion feature vector, and reconstructing a two-dimensional temperature field based on the fusion feature vector to obtain a target temperature distribution map includes: Preprocess the multi-point temperature time-series data to obtain a standardized temperature matrix, and perform principal component analysis on the standardized temperature matrix to obtain a dimensionality-reduced temperature feature vector; Extract the non-linear features of the material's thermal response based on the dimensionality-reduced temperature feature vector to obtain a non-linear feature vector; Based on the standardized temperature matrix, calculate the linear correlation between the temperature data of each test area and the reference thermal performance parameters, and extract the linear change law of the temperature field to obtain a linear feature vector; Calculate the adaptive weight coefficient according to the signal-to-noise ratio of the non-linear feature vector and the linear feature vector, and perform weighted fusion according to the adaptive weight coefficient to obtain a fused feature vector; Input the fused feature vector into a joint algorithm of Kalman filtering and multi-layer perceptron for two-dimensional temperature field reconstruction to obtain a target temperature distribution map.

5. The test method of the multifunctional wire splitter according to claim 4, characterized in that The step of inputting the fused feature vector into a joint algorithm of Kalman filtering and multi-layer perceptron for two-dimensional temperature field reconstruction to obtain a target temperature distribution map includes: Analyze the fused feature vector to extract a table of thermal property parameters containing information on thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time parameters; Based on the table of thermal property parameters, use the Kalman filtering algorithm to filter the temperature data at sparse sensor points to obtain the estimated temperature value of the target point; Map the estimated temperature value of the target point to the surface coordinate system of the multi-functional splitter, and perform boundary temperature constraint processing at the boundary of the test area to obtain a discrete temperature grid; Use the multi-layer perceptron space interpolation algorithm to calculate the temperature values at the grid gaps of the discrete temperature grid data, and perform constraint optimization based on the physical heat transfer law to obtain continuous temperature field data; Analyze the temperature distribution of each test area based on the continuous temperature field data to obtain target hot spot distribution information; Integrate the continuous temperature field data and the target hot spot distribution information to generate a target temperature distribution map.

6. The test method of the multifunctional wire splitter according to claim 1, characterized in that, The step of performing an operation test on the multi-functional splitter according to the target temperature distribution map to obtain a temperature response curve includes: Divide the control system into a hierarchical control architecture including a global temperature trajectory planning layer, a regional temperature regulation layer, and a local precise control layer according to the target temperature distribution map; According to the hierarchical control architecture, dynamically decompose the thermal system of the multi-functional splitter, decompose the heat conduction system into a nominal heat conduction system and a temperature deviation system, and establish a heat conduction equation to obtain a dynamic model of the thermal system; Based on the dynamic model of the thermal system, set an optimal temperature trajectory for the global temperature trajectory planning layer, so that each test area performs heating and cooling according to a predetermined time-temperature relationship to obtain a global temperature control curve; Based on the regional temperature regulation layer, execute a model predictive control algorithm and solve a quadratic programming problem to calculate the heating and cooling powers required for each test area to obtain regional control parameters; Based on the regional control parameters, configure an adaptive PID controller in the local precise control layer, dynamically adjust the control parameters according to the temperature deviation, and set an interference observer to compensate for the influence of environmental temperature fluctuations to obtain local control instructions; Apply a constant power to each test area of the multifunctional splitter according to the local control instruction and conduct a cooling test to obtain a temperature response curve.

7. The test method of the multifunctional wire splitter according to claim 1, characterized in that, Measure the thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time of the multifunctional splitter based on the temperature response curve, and generate a comprehensive thermal performance evaluation result, including: Analyze the slope of the temperature rise curve and the steady-state temperature difference in the constant power heating stage according to the temperature response curve, and calculate the thermal conductivity distribution data of each test area; Analyze the relationship between the temperature change rate and the heat input based on the temperature modulation period data of the temperature response curve, and calculate the specific heat capacity distribution data of each test area; Calculate the thermal diffusivity distribution data of each test area according to the thermal conductivity distribution data and the specific heat capacity distribution data, in combination with the density parameters of each material of the multifunctional splitter; Analyze the ratio of the temperature difference between both sides of the material contact interface to the heat flux according to the interface temperature data of the temperature response curve, and calculate the thermal contact resistance distribution data of each contact interface; Measure the temperature response time distribution data of each test area based on the step response data of the temperature response curve; Perform thermal performance index calculation and threshold comparison on the thermal conductivity distribution data, the specific heat capacity distribution data, the thermal diffusivity distribution data, the thermal contact resistance distribution data, and the temperature response time distribution data to generate a comprehensive thermal performance evaluation result.

8. A test device for a multifunctional wire splitter, characterized in that, A test device for the multifunctional splitter for executing the test method of the multifunctional splitter according to any one of claims 1-7, the test device of the multifunctional splitter includes: A data acquisition module, configured to divide the multifunctional splitter into a central processing area, a USB interface area, a card reader interface area, a charging circuit area, and a lithium battery area, and collect temperature data of the multifunctional splitter in the working state to obtain multi-point temperature time-series data; A temperature field reconstruction module, configured to extract non-linear thermal dynamic features from the multi-point temperature time-series data to obtain a fusion feature vector, and perform two-dimensional temperature field reconstruction based on the fusion feature vector to obtain a target temperature distribution map; An operation test module, configured to perform an operation test on the multifunctional splitter according to the target temperature distribution map to obtain a temperature response curve; A generation module, configured to measure the thermal conductivity, specific heat capacity, thermal diffusivity, thermal contact resistance, and temperature response time of the multifunctional splitter based on the temperature response curve, and generate a comprehensive thermal performance evaluation result.

9. A test device for a multifunctional wire splitter, characterized in that The test equipment of the multifunctional splitter includes: a memory and at least one processor, and instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the test equipment of the multifunctional splitter executes the test method of the multifunctional splitter according to any one of claims 1-7.

10. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instructions are executed by the processor, the test method of the multifunctional splitter according to any one of claims 1-7 is implemented.

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