Temperature compensation method and system

Abstract

The invention discloses a temperature compensation method and system, and the method comprises the steps: receiving a valve block temperature signal collected by a temperature sensor integrated with a pressure sensor in a closed gas supply unit, and an electronic element temperature and environment temperature signal collected by a temperature sensor on a control unit PCB, generating an initial temperature-working condition data set by combining the power of the air pump and the working duration of the electromagnetic valve; performing thermal resistance network modeling on the initial temperature-working condition data set, constructing a multi-node thermal characteristic matrix, and decomposing the matrix into a plurality of sub-matrixes; performing real-time operation on the sub-matrix by using a dynamic heat balance algorithm to generate a temperature estimation result of the key component; and generating a temperature compensation control instruction according to the temperature estimation result of the key component. According to the embodiment of the invention, the temperature of the key component can be accurately estimated, dynamic compensation is realized, and the control precision and stability of the closed gas supply unit are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of temperature compensation, and particularly relates to a temperature compensation method and system. BACKGROUND

[0002] Under the development trend of electrification and intelligentization of passenger cars, a closed air supply unit scheme is widely used in air suspension systems to improve driving controllability and ride comfort. The unit integrates core components such as air pumps, electromagnetic valves, pressure sensors and control units, significantly optimizing system NVH performance, service life and cost. However, due to the compact structure and high sealing requirements, temperature monitoring of key heat generating components (such as air pump motors, air cylinders and electromagnetic valves) is challenging.

[0003] In the prior art, directly arranging temperature sensors in heat generating components or cavities has the problems of high implementation difficulty and high cost, and external sensors cannot accurately reflect the real-time temperature of the core area. As accurate working temperature cannot be obtained, the suspension control algorithm cannot effectively compensate for the influence of temperature on the system, resulting in a decrease in vehicle body control accuracy and efficiency, and even causing reliability risks. SUMMARY

[0004] The purpose of the present application is to provide a temperature compensation method and system to solve the problems in the prior art, accurately estimate the temperature of key components and achieve dynamic compensation, and improve the control accuracy and stability of the closed air supply unit.

[0005] One embodiment of the present application provides a temperature compensation method, which comprises: Receiving a valve block temperature signal collected by a temperature sensor integrated with a pressure sensor in a closed air supply unit, and an electronic component temperature and environment temperature signal collected by a temperature sensor on a control unit PCB, generating an initial temperature-working condition data set in combination with air pump power and electromagnetic valve working time, wherein each data item is associated with the dimension of the heat conduction path of the component; Performing thermal resistance network modeling on the initial temperature-working condition data set, constructing a multi-node thermal feature matrix, and decomposing the matrix into several sub-matrices to capture the differentiated heat conduction characteristics of the air cylinder, motor and electromagnetic valve, wherein each sub-matrix corresponds to the relationship between the thermal resistance parameters and heat source power of a single component; Using a dynamic thermal balance algorithm to perform real-time operation on the sub-matrices, calculating the real-time temperature values of the air cylinder, motor and electromagnetic valves in combination with the real-time working parameters of the system, and generating key component temperature estimation results; According to the key component temperature estimation results, dynamically adjusting the target working power of the air pump, the conduction time and the number of working electromagnetic valves, and generating temperature compensation control instructions for real-time control of the closed air supply unit.

[0006] Optionally, the valve block temperature signal collected by the temperature sensor integrated with the pressure sensor of the closed air supply unit, and the electronic component temperature and ambient temperature signals collected by the temperature sensor on the control unit PCB are combined with the air pump power and the electromagnetic valve working time to generate an initial temperature-condition data set, wherein each data item is associated with the component heat conduction path dimension, including: The valve block temperature signal collected by the temperature sensor integrated with the pressure sensor, the electronic component temperature and ambient temperature signals collected by the temperature sensor on the control unit PCB are filtered to obtain a stable temperature signal set. The air pump working state and electromagnetic valve conduction state are read from the closed air supply unit controller, and the working condition information is aligned with the stable temperature signal set in time sequence to obtain time sequence matched temperature condition associated data. The temperature condition associated data is labeled with heat conduction path information, and the material type and connection relationship involved in the path are recorded to obtain a temperature condition data set with path identification. The temperature condition data set with path identification is divided into samples according to a fixed time interval, and each sample integrates the temperature change condition characteristics and path information in the interval to generate an initial temperature-condition data set.

[0007] Optionally, the initial temperature-condition data set is subjected to thermal resistance network modeling, a multi-node thermal feature matrix is constructed, and the matrix is decomposed into several sub-matrices to capture the differentiated heat conduction characteristics of the cylinder, motor and electromagnetic valve, wherein each sub-matrix corresponds to the thermal resistance parameter and heat source power relationship of a single component, including: The thermal conduction characteristic parameters of different materials are extracted from the initial temperature-condition data set, and the thermal resistance basic value is determined according to the component connection mode to obtain a thermal resistance characteristic parameter set. According to the thermal resistance characteristic parameter set, the thermal network nodes are divided, the cylinder motor and electromagnetic valve are set as core nodes, the valve block PCB and environment are set as associated nodes, the heat transfer path between nodes is determined, and a node topology relationship diagram is obtained. Based on the node dimension, the temperature condition thermal resistance information is integrated to construct a multi-node thermal feature matrix, and the matrix elements reflect the heat interaction relationship between nodes to obtain an overall thermal feature matrix. The overall thermal feature matrix is decomposed according to the component type to generate a cylinder-specific sub-matrix, a motor-specific sub-matrix and an electromagnetic valve-specific sub-matrix, each sub-matrix containing the thermal resistance parameter and heat source associated information of the corresponding component, and a component sub-matrix set is obtained.

[0008] Optionally, the dynamic thermal balance algorithm is used to perform real-time operation on the sub-matrix, and the real-time temperature values of the cylinder, motor and each electromagnetic valve are calculated in combination with the system real-time working parameters to generate key component temperature estimation results, including: Real-time acquisition of current working parameters of the closed gas supply unit, matching with historical working condition characteristics in the component sub-matrix set, screening out the most similar reference sub-matrix to obtain a matching reference sub-matrix; Based on the matching reference sub-matrix, the dynamic heat balance algorithm is initialized, the balance relationship of heat input conduction dissipation is set, the algorithm calculation boundary condition is determined, and the algorithm running parameters are obtained; Running the dynamic heat balance algorithm, calculating the heat absorbed by the cylinder and the temperature change combined with the working state of the gas pump, correcting the heat conversion efficiency according to the motor operating characteristics, and calculating the temperature change according to the electromagnetic valve conduction situation, obtaining the preliminary temperature calculation value; Comparing the preliminary temperature calculation value with the valve block temperature signal of the pressure sensor, adjusting the thermal resistance parameter for the obvious deviation result and recalculating to obtain the corrected temperature value; Integrating the corrected temperature values of the cylinder motor and each electromagnetic valve, sorting by component classification, and generating key component temperature estimation results.

[0009] Optionally, according to the key component temperature estimation results, the target working power of the gas pump, the conduction time and the working quantity of the electromagnetic valve are dynamically adjusted, and the temperature compensation control instruction is generated for real-time control of the closed gas supply unit, including: According to the key component temperature estimation results, the component running state is evaluated, the components close to the upper limit of safe operation are marked, and the temperature warning component list is obtained; According to the temperature warning component list, an adjustment strategy is developed, the working load of the gas pump is reduced, the single conduction time of the electromagnetic valve is reduced and the working combination is adjusted, and the control parameter adjustment scheme is obtained; The control parameter adjustment scheme is converted into an executable instruction format of the closed gas supply unit, the timing and amplitude of parameter adjustment are determined, and the initial temperature compensation control instruction is obtained; Verify the execution effect of the initial temperature compensation control instruction, ensure that the temperature of the warning component falls back to the safe range after the implementation of the instruction, and generate the final temperature compensation control instruction.

[0010] Another embodiment of the application provides a temperature compensation system, which comprises: The receiving module is used for receiving the valve block temperature signal collected by the temperature sensor integrated in the pressure sensor of the closed gas supply unit, and the electronic element temperature and environment temperature signal collected by the temperature sensor on the control unit PCB, and generating an initial temperature-working condition data set combined with the gas pump power and the electromagnetic valve working time, wherein each data item is associated with the component heat conduction path dimension; The construction module is used for heat resistance network modeling of the initial temperature-working condition data set, constructing a multi-node heat feature matrix, and decomposing the matrix into several sub-matrices to capture the differentiated heat conduction characteristics of the cylinder, motor and electromagnetic valve, wherein each sub-matrix corresponds to the relationship between the thermal resistance parameter and the heat source power of a single component; The operation module is configured to use a dynamic thermal balance algorithm to perform real-time operation on the sub-matrix, calculate real-time temperature values of the cylinder, the motor and each electromagnetic valve in combination with system real-time working parameters, and generate a key component temperature estimation result; The generation module is configured to dynamically adjust a target working power of the air pump, a conduction time length of the electromagnetic valve and a working quantity according to the key component temperature estimation result, and generate a temperature compensation control instruction for real-time control of the closed air supply unit.

[0011] Another embodiment of the present application provides a storage medium having a computer program stored therein, wherein the computer program is configured to execute the method described in any one of the above embodiments when running.

[0012] Another embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory has a computer program stored therein, and the processor is configured to execute the computer program to execute the method described in any one of the above embodiments.

[0013] Compared with the prior art, the temperature compensation method provided by the present application receives a valve block temperature signal collected by a temperature sensor integrated in a pressure sensor of a closed air supply unit and an electronic component temperature and environment temperature signal collected by a temperature sensor on a control unit PCB, generates an initial temperature-working condition data set in combination with a pump power and an electromagnetic valve working time length, performs thermal resistance network modeling on the initial temperature-working condition data set, constructs a multi-node thermal feature matrix, and decomposes the matrix into several sub-matrices, uses a dynamic thermal balance algorithm to perform real-time operation on the sub-matrices, generates a key component temperature estimation result, and generates a temperature compensation control instruction according to the key component temperature estimation result, so as to accurately estimate the key component temperature and realize dynamic compensation, thereby improving the control precision and stability of the closed air supply unit. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A hardware structure block diagram of a computer terminal of the temperature compensation method provided by the embodiment of the present application is provided. Figure 2 A flowchart of the temperature compensation method provided by the embodiment of the present application is provided. Figure 3 A structure diagram of a temperature compensation system provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0015] The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application, and cannot be explained as a limitation on the present application.

[0016] The embodiment of the present application first provides a temperature compensation method, which can be applied to an electronic device, such as a computer terminal, specifically, a general computer, etc.

[0017] The following will be described in detail taking the computer terminal as an example. Figure 1 A hardware structure block diagram of a computer terminal provided by the embodiment of the present application is shown in FIG. 1. Figure 1 As shown in the figure, the computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory can include a non-volatile storage medium and an internal memory.

[0018] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions which, when executed, can cause the processor to execute any temperature compensation method.

[0019] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

[0020] The internal memory provides an environment for the execution of the computer program in the non-volatile storage medium, which, when executed by the processor, can cause the processor to execute any temperature compensation method.

[0021] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 1 The structure shown in FIG. 1 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or less components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0022] It should be understood that the processor can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0023] Referring to Figure 2 The embodiment of the present application provides a temperature compensation method, which can include the following steps: S201, receive the valve block temperature signal collected by the temperature sensor integrated with the pressure sensor in the closed air supply unit, and the electronic component temperature and ambient temperature signal collected by the temperature sensor on the control unit PCB, generate an initial temperature-working condition data set in combination with the air pump power and the electromagnetic valve working time length, wherein each data item is associated with the component heat conduction path dimension; Specifically, the valve block temperature signal can be collected by the temperature sensor integrated with the pressure sensor, and the electronic component temperature and ambient temperature signal on the control unit PCB can be obtained at the same time, abnormal fluctuation signals are filtered, and a stable temperature signal set is obtained. The temperature sensor (TS) integrated with the pressure sensor is installed on the surface of the valve block of the closed air supply unit, an NTC thermistor (precision ±0.5℃, measurement range -40℃~125℃) is used, and the temperature signal is collected through an I 2 The C bus collects the valve block temperature signal 10 times per second, and the output voltage signal is converted into digital quantity (corresponding to temperature value) by 12-bit ADC. For example, the original signal collected at a certain moment is 25.3℃, and the continuous 3 collection values are 25.3℃, 25.4℃ and 25.2℃, which are considered as normal fluctuation.

[0024] The temperature sensor (TE) on the control unit PCB includes two: one is a patch temperature sensor (such as LM75A, precision ±1℃), which is close to the MCU chip on the PCB to collect the working temperature of the electronic component; the other is an ambient temperature sensor (such as SHT30, precision ±2℃), which is placed at the edge of the PCB for ventilation to collect the ambient temperature. Both of them are collected 5 times per second through SPI bus, for example, the electronic component temperature collection value is 42.1℃, and the ambient temperature is 23.5℃.

[0025] The abnormal fluctuation signal filtering adopts the combination of "3σ criterion" and sliding window filtering: Calculate the temperature mean (μ) and standard deviation (σ) of the last 100 sampling points, and consider the value outside the range of [μ-3σ, μ+3σ] as abnormal (for example, the valve block temperature suddenly jumps to 50℃, and μ=25℃, σ=2℃, 50℃>25+6=31℃, which is judged as abnormal); Apply 5-point sliding window average to the filtered signal (the temperature value of each point is the mean value of itself and the previous and next two points), and smooth the high-frequency noise. For example, the original sequence [25.3, 25.8, 26.1, 25.9, 25.6] is filtered to [25.7, 25.9, 25.8] to obtain a stable temperature signal set, including valve block temperature (TS), electronic component temperature (TE1) and ambient temperature (TE2) three-dimensional time series data.

[0026] Read the air pump working state and electromagnetic valve conduction from the closed air supply unit controller, align the working condition information and stable temperature signal set in time sequence, and get the time sequence matched temperature condition association data; The closed air supply unit controller (ECU) outputs real-time working condition information through CAN bus, including: Air pump working state: working mode (inflation / exhaust), current power (0~1000W, accuracy ±10W), cumulative working time (s); Electromagnetic valve conduction: 9 electromagnetic valves (Tv1~Tv9) conduction state (0 = off, 1 = on), single conduction time of each electromagnetic valve (ms).

[0027] Read frequency and temperature signal acquisition frequency are synchronized (10 times per second), and each reading is accompanied by a time stamp (accurate to milliseconds). For example, at t=10.000s, the air pump state is "inflation, power 800W", Tv1 and Tv3 are on (conduction time is 50ms and 30ms respectively), and other electromagnetic valves are off.

[0028] Time alignment takes the time stamp as the basis, and binds the working condition information with the temperature value of the stable temperature signal set at the same time stamp. If there is a small deviation (≤50ms) between the working condition information time stamp (t=10.005s) and the temperature signal time stamp (t=10.000s), linear interpolation is used to supplement the intermediate value. For example: t=10.000s: temperature (TS=25.3℃, TE1=42.1℃, TE2=23.5℃), working condition (air pump power 800W, Tv1 on); t=10.001s: interpolated temperature (25.3℃), working condition (800W, Tv1 on); Finally, time sequence matched temperature condition association data is formed, each record contains time stamp, temperature value, air pump state, electromagnetic valve state, ensuring that temperature and working condition correspond one by one in time dimension.

[0029] Label the heat conduction path information for the temperature condition association data, record the material type and connection relationship involved in the path, and get the temperature condition data set with path identification; Heat conduction path information is labeled based on the physical structure of the closed air supply unit. Each path extends from the heat source (such as motor, electromagnetic valve) to the temperature sensor (TS or TE), including path length, material thermal conductivity, connection method and other parameters: Electromagnetic valve (Tv1) to valve block (TS) path: Tv1 coil (heat source) → copper valve needle (thermal conductivity 401 W / (m・K)) → aluminum alloy valve block (thermal conductivity 205 W / (m・K)) → TS sensor, path length 50mm, connection method is interference fit; Motor to valve block path: motor housing (aluminum alloy, thermal conductivity 205 W / (m・K)) → rubber seal ring (thermal conductivity 0.2 W / (m・K)) → valve block → TS sensor, path length 80mm, connection method is bolted (contact thermal resistance 0.001 m 2 ・K / W); Air pump cylinder to valve block path: cylinder wall (cast iron, thermal conductivity 50 W / (m・K)) → metal bracket (steel, thermal conductivity 45 W / (m・K)) → valve block → TS sensor, path length 100mm, connection method is welding (contact thermal resistance 0.0005 m 2 ・K / W); Electronic components to TE1 path: MCU chip (heat source) → PCB copper foil (thermal conductivity 386 W / (m・K)) → TE1 sensor, path length 20mm, connection method is surface mount soldering.

[0030] Label the corresponding dominant heat conduction path for each temperature value in the temperature condition correlation data, for example: The dominant path of TS (valve block temperature 25.3℃) is "Tv1→copper valve needle→aluminum alloy valve block"; The dominant path of TE1 (electronic component temperature 42.1℃) is "MCU→PCB copper foil"; Form a temperature condition dataset with path identification, add "heat path ID", "material type", and "connection method" fields to each record to associate temperature with heat conduction characteristics.

[0031] Divide the temperature condition dataset with path identification into samples according to fixed time intervals, integrate the temperature change condition characteristics and path information within each interval to generate an initial temperature-condition dataset.

[0032] The fixed time interval is set to 1 second (taking into account dynamic response and data volume), and each interval is a sample, integrating the following features: Temperature change characteristics: maximum, minimum, average, and rate of change of TS, TE1, and TE2 within the interval (e.g., TS increases from 25.0℃ to 25.5℃, with a change rate of 0.5℃ / s); Working condition characteristics: average power of air pump (e.g., 800W), cumulative working time (e.g., 10s), and number of electromagnetic valve on times (e.g., Tv1 is on for 3 times, with a total duration of 150ms). Path information: Material thermal conductivity of the dominant heat path (e.g. 401 W / (m・K)), path length (50mm), contact thermal resistance (0.001m 2 ・K / W).

[0033] For example, the sample at t = 10~11s contains: Temperature features: TS_avg = 25.3℃, TS_max = 25.5℃, TS_rate = 0.5℃ / s; TE1_avg = 42.0℃; TE2_avg = 23.5℃; Working condition features: air pump power = 800W, working duration = 1s; Tv1 conduction 3 times, total duration 150ms; Path information: Material = copper + aluminum alloy, length = 50mm, contact thermal resistance = 0.001m 2 ・K / W.

[0034] All samples are arranged in chronological order to form an initial temperature - working condition dataset, each sample contains 12 features (4 of each of the 3 types of temperature features + 3 working condition features + 3 path features), and the size of the dataset is determined according to the test duration (e.g. 1 hour test generates 3600 samples), which provides structured input for subsequent thermal resistance network modeling.

[0035] S202, thermal resistance network modeling is performed on the initial temperature - working condition dataset, a multi-node thermal feature matrix is constructed, and the matrix is decomposed into several sub-matrices to capture the differentiated thermal conduction characteristics of the cylinder, motor and solenoid valve, wherein each sub-matrix corresponds to the thermal resistance parameter and heat source power relationship of a single component; Specifically, the thermal conduction characteristic parameters of different materials can be extracted from the initial temperature - working condition dataset, and the thermal resistance basic value can be determined according to the connection mode of the components to obtain a set of thermal resistance characteristic parameters; The initial temperature - working condition dataset contains material information and connection structure description of each component of the closed air supply unit, and key thermal conduction characteristic parameters need to be extracted from it, and the thermal resistance basic value is calculated according to the connection mode, which provides a physical basis for thermal network modeling.

[0036] Material thermal conduction characteristic parameter extraction: Through the thermal conduction path information labeled in the dataset, the thermal conductivity (λ, unit W / (m・K)) of the materials involved in the path is extracted, which is a core parameter for measuring the thermal conductivity of materials. For example: The valve block and motor shell use aluminum alloy (6061) with a thermal conductivity λ = 205 W / (m・K), which has good thermal conductivity; The solenoid valve coil frame is nylon, and the thermal conductivity λ = 0.25 W / (m·K), which belongs to low-thermal-conductivity insulation material; The air pump cylinder is cast iron, and the thermal conductivity λ = 50 W / (m·K), which takes into account the strength and heat conduction requirements; The PCB substrate is FR4 epoxy resin, and the thermal conductivity λ = 0.3 W / (m·K), which has poor thermal conductivity; The rubber seal ring between components is used for sealing, and the thermal conductivity λ = 0.2 W / (m·K), which has large thermal resistance.

[0037] Connection method and contact thermal resistance: The connection method between components directly affects the contact thermal resistance (Rc, unit m 2 ·K / W), which is the thermal conduction resistance between two contact surfaces. The contact thermal resistance of common connection methods is as follows: Interference fit (such as metal contact between solenoid valve and valve block): Rc = 0.0005 m 2 ·K / W, the contact surface is tight, and the thermal resistance is small; Bolted (such as motor and valve block connected by bolts): Rc = 0.001 m 2 ·K / W, affected by the bolt pre-tightening force, the thermal resistance is moderate; Welding (such as cylinder and bracket connection): Rc = 0.0003 m 2 ·K / W, the contact surface is fused, and the thermal resistance is the smallest; Rubber seal connection (such as the seal ring between the air pump and the housing): Rc = 0.01 m 2 ·K / W, the rubber has poor thermal conductivity, and the thermal resistance is large.

[0038] Thermal resistance base value calculation: For a certain heat conduction path, the total thermal resistance R is composed of the conduction thermal resistance of the material itself and the contact thermal resistance, and the formula is: R = Σ(L / (λ·A))+ΣRc. Where L is the length of the material segment (m), A is the heat transfer area (m 2 ), Σ(L / (λ·A)) is the sum of the conduction thermal resistance of each material segment, and ΣRc is the sum of the contact thermal resistance of each contact surface.

[0039] For example, the path from the motor housing (aluminum alloy) to the valve block (aluminum alloy): Motor housing segment: L1 = 0.02 m, A = 0.005 m 2 , conduction thermal resistance = 0.02 / (205×0.005) = 0.02 / 1.025 ≈ 0.0195 K / W; Rubber seal ring (contact): Rc = 0.01 m 2 ·K / W, converted to unit area thermal resistance (A = 0.005 m 2 ), contact thermal resistance = 0.01 / 0.005 = 2 K / W; Valve block segment: L2=0.01 m, A=0.005 m 2 Conduction thermal resistance = 0.01 / (205*0.005)=0.01 / 1.025≈0.00975 K / W; Total thermal resistance R=0.0195+2+0.00975≈2.029 K / W, as the thermal resistance base value of this path.

[0040] Extract the thermal resistance base value of all paths, together with the material thermal conductivity, contact thermal resistance to form a set of thermal resistance characteristic parameters, for example, the parameter set records “Motor - Valve block path: λ=205 W / (m・K), Rc=0.01 m 2 K / W, total thermal resistance =2.029 K / W”.

[0041] According to the thermal resistance characteristic parameter set, divide the thermal network nodes, set the cylinder motor electromagnetic valve as the core node, the valve block PCB and the environment as the associated nodes, and clarify the heat transfer path between the nodes to obtain the node topology relationship diagram; The division of thermal network nodes needs to cover all key heat generating components and temperature sensing points, and the actual heat transfer path is reflected through the connection between nodes, providing a structural framework for matrix modeling.

[0042] Node type and number: Core node (heat generating component): key components that directly generate heat, including cylinder (node 1), motor (node 2), 9 electromagnetic valves (nodes 3~11, corresponding to Tv1~Tv9), a total of 11 core nodes; Associated node (heat transfer or sensing node): used for transferring heat or providing temperature reference, including valve block (node 12, where TS sensor is located), PCB (node 13, where TE1 sensor is located), environment (node 14, where TE2 sensor is located), a total of 3 associated nodes; A total of 14 nodes, numbered 1~14, each node corresponds to a physical component or area.

[0043] Heat transfer path between nodes: based on the thermal resistance characteristic parameter set, the direct heat transfer relationship between nodes is clarified, and the path direction is from the heat source to the heat dissipation end: Cylinder (node 1)→valve block (node 12): through cast iron-aluminum alloy conduction, thermal resistance 2.5 K / W; Motor (node 2)→valve block (node 12): through aluminum alloy-rubber-aluminum alloy conduction, thermal resistance 2.029 K / W (as calculated in step one); Solenoid valve (node 3~11) -> Valve block (node 12): Conducted through nylon-copper-aluminum alloy, thermal resistance 1.8K / W (small thermal resistance due to close distance between solenoid valve and valve block); Motor (node 2) -> PCB (node 13): Heat dissipation through air convection and radiation, thermal resistance 5.0K / W (non-direct contact, large thermal resistance); Valve block (node 12) -> Environment (node 14): Heat dissipation through natural convection, thermal resistance 10.0K / W; PCB (node 13) -> Environment (node 14): Heat dissipation through forced air cooling (controller fan), thermal resistance 3.0K / W.

[0044] Node topology relationship diagram: Taking nodes as vertices and heat transfer paths as directed edges, the weights of edges are the basic values of thermal resistance, forming a topology structure. For example, the edge from node 2 (motor) to node 12 (valve block) is labeled "2.029K / W", and the edge from node 12 to node 14 (environment) is labeled "10.0K / W", which intuitively presents the heat transfer network from the core heat-generating node to the environment through associated nodes.

[0045] Based on the node dimension, the temperature condition thermal resistance information is integrated to construct a multi-node thermal characteristic matrix, and the matrix elements reflect the thermal interaction between nodes to obtain the overall thermal characteristic matrix; The multi-node thermal characteristic matrix is a mathematical expression of the thermal network, which quantifies the thermal resistance, heat flow, and temperature correlation between nodes through matrix elements, providing structured data for subsequent decomposition and operation.

[0046] Matrix dimension and element definition: The matrix is 14×14 (corresponding to 14 nodes), the rows represent the heat source nodes, and the columns represent the heat receiving nodes. The element values contain three types of information (distinguished by numerical coding): Thermal resistance (R): The basic value of thermal resistance from node i to node j (K / W), represented by a positive number, such as element (2,12)=2.029 (thermal resistance from motor to valve block); Heat flow coefficient (Q): The ratio of heat transferred from node i to node j per unit time to temperature difference (W / K), which is the inverse of thermal resistance (1 / R), represented by a negative number, such as the heat flow coefficient corresponding to element (2,12) =-0.493 (1 / 2.029≈0.493); Temperature influence coefficient (T): The influence weight of the temperature change of node j on the temperature of node i (unitless), which is obtained based on historical data fitting, ranging from 0 to 1, represented by a decimal number, such as element (12,2)=0.6 (valve block temperature 60% affected by motor temperature).

[0047] Matrix construction process: Fill in thermal resistance information: Fill in the thermal resistance base value determined in step two into the corresponding position. Set the thermal resistance between nodes without a direct path to infinity (represented by 9999); Calculate heat flow coefficient: For elements with thermal resistance, calculate its inverse and take the negative value to reflect the heat transfer efficiency; Fit temperature influence coefficient: Through the initial temperature and the temperature time series data in the working condition data set, use linear regression to calculate the temperature influence weight between nodes. For example, if the motor (node 2) temperature rises by 1°C, the valve block (node 12) temperature rises by 0.6°C on average, so element (12,2)=0.6.

[0048] Example of overall thermal characteristic matrix: Some elements in the matrix are as follows (only show the interaction between core nodes and associated nodes): Row 2 (motor): Column 12 (valve block)=2.029 (thermal resistance), Column 13 (PCB)=5.0 (thermal resistance), Column 14 (environment)=9999 (no direct path); Row 3 (Tv1): Column 12 (valve block)=1.8 (thermal resistance), Column 14 (environment)=9999; Column 12 (valve block): Row 1 (cylinder)=0.5 (temperature influence coefficient, the influence weight of cylinder on valve block), Row 2 (motor)=0.6, Row 3~11 (solenoid valve)=0.3; The matrix integrates the thermal resistance, heat flow and temperature correlation between nodes, providing comprehensive thermal interaction information for subsequent decomposition.

[0049] Decompose the overall thermal characteristic matrix by component type to generate cylinder-specific sub-matrix, motor-specific sub-matrix and solenoid valve-specific sub-matrix. Each sub-matrix contains thermal resistance parameters and heat source correlation information of the corresponding component, obtaining the component sub-matrix set.

[0050] The purpose of matrix decomposition is to separate the thermal characteristics of different components, facilitating targeted analysis of the differential thermal conduction law of cylinder, motor and solenoid valve. Each sub-matrix focuses on the thermal interaction between a single component and other nodes.

[0051] Cylinder-specific sub-matrix: Extract the rows and columns related to cylinder (node 1) from the overall matrix to form a 1×5 sub-matrix (row: node 1; column: node 12 (valve block), node 13 (PCB), node 14 (environment), node 2 (motor), node 3~11 (solenoid valve)), elements include: Node 1→Node 12: Thermal resistance 2.5K / W, heat flow coefficient -0.4W / K, temperature influence coefficient 0.5; Node 1 and other nodes (such as motor, solenoid valve): no direct thermal path, thermal resistance 9999, temperature influence coefficient 0.1 (indirect influence); This sub-matrix reflects that the heat of the cylinder is mainly dissipated through the valve block, and the thermal interaction with other components is weak.

[0052] Motor-specific sub-matrix: extract the rows and columns related to the motor (node 2) to form a 1x5 sub-matrix (rows: node 2; columns: nodes 12, 13, 14, 1 (cylinder), 3-11), elements include: Node 2→Node 12: thermal resistance 2.029K / W, heat flow coefficient -0.493W / K, temperature influence coefficient 0.6; Node 2→Node 13: thermal resistance 5.0K / W, heat flow coefficient -0.2W / K, temperature influence coefficient 0.2; Node 2 and cylinder (node 1): temperature influence coefficient 0.2 (indirect influence of motor on cylinder); This sub-matrix reflects that the heat of the motor is dissipated through the valve block and PCB, and has a significant impact on the temperature of the valve block.

[0053] Solenoid valve-specific sub-matrix: 9 solenoid valves (nodes 3-11) have similar structures, combined into a 1x4 sub-matrix (rows: nodes 3-11; columns: nodes 12, 14, 2 (motor), 1 (cylinder)), elements take the average of 9 solenoid valves: Nodes 3-11→Node 12: average thermal resistance 1.8K / W, heat flow coefficient -0.556W / K, temperature influence coefficient 0.3; Temperature influence coefficients with motor and cylinder are both 0.1 (weak association); This sub-matrix reflects that the heat of the solenoid valve is mainly transferred to the valve block, and has limited impact on other components due to its small power.

[0054] Component sub-matrix set: includes the above three specific sub-matrices, each sub-matrix is accompanied by the heat source power information of the component (such as motor maximum heat source power 1000W, solenoid valve single valve maximum power 50W), clearly the association between thermal resistance parameters and heat source intensity, for example, in the motor sub-matrix, it is marked "when the power increases from 500W to 1000W, the heat flow coefficient is amplified by 1.2 times", providing component-specific parameters for subsequent dynamic thermal balance calculation.

[0055] S203, using a dynamic thermal balance algorithm to perform real-time operation on the sub-matrix, combining system real-time working parameters to calculate the real-time temperature values of the cylinder, motor and each solenoid valve, and generating key component temperature estimation results; Specifically, the current working parameters of the closed gas supply unit can be collected in real time, matched with the historical working condition characteristics in the component sub-matrix set, the most similar reference sub-matrix is screened out, and the matching reference sub-matrix is obtained; The collection of real-time working parameters is the basis for dynamic temperature estimation, which needs to accurately capture the current running state of the system to provide a basis for subsequent matching with historical data.

[0056] Real-time working parameter collection: collect key parameters every second through the controller (ECU) of the closed gas supply unit, including: Pump parameters: current power (e.g. 850W), working mode (inflation), cumulative running time (150s); Solenoid valve parameters: Tv1, Tv3, Tv5 in 9 solenoid valves are in conduction state, single conduction time is 40ms, 30ms, 50ms respectively, cumulative conduction times are 12 times, 8 times, 10 times respectively; Environment and basic temperature: the temperature sensor (TS) integrated with the pressure sensor measures the valve block temperature of 28.5℃, the temperature sensor (TE1) on the PCB measures the electronic component temperature of 45.2℃, and the environment temperature (TE2) is 24.0℃.

[0057] These parameters are integrated into the current working condition characteristic vector, including power, conduction time, temperature, etc. 12 dimensions (consistent with the sample feature dimensions of the initial temperature - working condition data set).

[0058] Historical working condition matching method: the component sub-matrix set stores sub-matrices and corresponding working conditions under different working conditions, and the cosine similarity algorithm is used to measure the similarity of the current feature and the historical feature. The cosine similarity formula is: cosθ = (A·B) / (||A||·||B||). Where A is the current working condition characteristic vector, B is the historical working condition characteristic vector, A・B is the vector dot product, ||A||, ||B|| is the vector length. The closer the similarity is to 1, the more similar the working conditions are.

[0059] For example, the current feature vector A = [850, 40, 30, 50, 28.5, 45.2, 24.0,...], the dot product with historical feature vector B1 (power 800W, conduction time 35ms, 25ms, 45ms, temperature 27.8℃, 44.5℃, 23.5℃) is 850×800 + 40×35 +...≈680000+1400+...=685000, the length ||A||≈900, ||B1||≈850, cosθ≈685000 / (900×850)≈0.89; and another historical vector B2 cosθ≈0.75, so B1 is the most similar historical working condition.

[0060] Screening benchmark sub-matrix: Select the sub-matrix corresponding to the historical operating condition with the highest similarity (cosθ>0.85) to the current operating condition as the benchmark sub-matrix. For example, in the motor-specific sub-matrix corresponding to B1, the thermal resistance parameter is 2.05 K / W, and the heat flow coefficient is -0.488 W / K, which is closest to the current system thermal state, so it is determined as the matching benchmark sub-matrix.

[0061] Based on the matching benchmark sub-matrix, the dynamic thermal balance algorithm is initialized, the balance relationship of heat input conduction dissipation is set, the boundary conditions of the algorithm are determined, and the algorithm running parameters are obtained; The core of the dynamic thermal balance algorithm is to calculate the component temperature change based on the energy conservation law, which needs to set the balance relationship and boundary conditions based on the thermal parameters of the benchmark sub-matrix to ensure the physical rationality of the calculation.

[0062] Thermal balance relationship setting: For any component (such as a motor), the heat change per unit time satisfies: ΔQ = Q_in - Q_out - Q_loss.

[0063] Where ΔQ is the change of energy in the component (J), Q_in is the heat input (J / s), Q_out is the heat conduction to other nodes (J / s), and Q_loss is the dissipated heat to the environment (J / s). Combined with the specific heat formula ΔQ = m・c・ΔT (m is the component mass, c is the specific heat capacity, and ΔT is the temperature change), the temperature change rate can be derived: ΔT / Δt = (Q_in -Q_out - Q_loss) / (m·c).

[0064] Taking the motor as an example, the parameters are set as follows: m=1.2kg (motor mass), c=450J / (kg・K) (aluminum alloy specific heat capacity); Q_in = P・η (P is the motor power 850W, η is the thermal conversion efficiency 0.8, i.e. 80% of the electrical energy is converted into heat), so Q_in=850×0.8=680W; Q_out = (T_m - T_v) / R (T_m is the motor temperature, T_v is the valve block temperature 28.5℃, R is the thermal resistance from the motor to the valve block 2.05K / W, taken from the benchmark sub-matrix); Q_loss = k・(T_m - T_a) (k is the heat dissipation coefficient 5W / K, T_a is the ambient temperature 24.0℃).

[0065] Boundary conditions are clear: Initial temperature: The historical temperature corresponding to the benchmark sub-matrix is taken as the initial value, such as the initial temperature of the motor T_m0=55.0℃; Time step: Δt = 1s (consistent with the parameter acquisition frequency); Temperature limit: Set the motor's safe temperature threshold to 120℃. If this temperature is exceeded, the algorithm will trigger protection logic. Thermal resistance correction factor: Considering component aging, the thermal resistance correction factor with operating time is set as k_r = 1 + 0.0001 × t (t is the cumulative operating time of 150s, so k_r = 1.015).

[0066] These parameters together constitute the algorithm's operating parameters, ensuring that the calculations of heat input, conduction, and dissipation conform to the actual physical processes. For example, the calculation of Q_out depends on the real-time temperature difference, while Q_loss is dynamically adjusted according to the ambient temperature.

[0067] The dynamic thermal balance algorithm is run, and the heat absorbed by the cylinder and the temperature change are calculated in combination with the working status of the air pump. The heat conversion efficiency is corrected according to the operating characteristics of the motor, and the temperature change is calculated by weighting according to the solenoid valve conduction status to obtain the preliminary temperature calculation value. The algorithm requires specific calculations for each component, and the heat parameters are adjusted according to their respective working characteristics to ensure the accuracy of temperature change calculations.

[0068] Cylinder temperature calculation: The heat in the cylinder mainly comes from the power loss of the air pump when compressing air, which is directly related to the power of the air pump and the working mode.

[0069] Heat input Q_in_cyl = P_pump・β (P_pump is the pump power of 850W, β is the coefficient of thermal expansion of compression of 0.3, that is, 30% of the power is converted into cylinder heat), therefore Q_in_cyl = 850 × 0.3 = 255W; The heat transfer Q_out_cyl = (T_cyl - T_v) / R_cyl (R_cyl is the thermal resistance from the cylinder to the valve block, 2.5K / W, taken from the cylinder-specific sub-matrix); Temperature change ΔT_cyl = (Q_in_cyl - Q_out_cyl) / (m_cyl・c_cyl) × Δt (m_cyl=2.0kg, c_cyl=400J / (kg・K)); The initial temperature T_cyl0 = 60.0℃. Substituting this into the calculation, we get ΔT_cyl≈(255 - (60-28.5) / 2.5) / (2×400)×1≈(255-12.6) / 800≈242.4 / 800≈0.303℃. Therefore, the initial temperature T_cyl = 60.0 + 0.303≈60.3℃.

[0070] Motor temperature calculation: motor heat comes from electrical energy loss, efficiency needs to be corrected according to running time (long time running efficiency decreases, heat production increases).

[0071] Basic heat input Q_in_motor = P_motor・η (P_motor=850W, η=0.8), but running time 150s>100s, efficiency correction η'=0.8-0.001×(150-100)=0.75, so Q_in_motor=850×0.75=637.5W; Conduction and dissipation Q_out_motor + Q_loss_motor = (T_motor - 28.5) / 2.05 + 5×(T_motor -24.0); Temperature change ΔT_motor = (637.5 - [(T_motor-28.5) / 2.05 +5 (T_motor-24)]) / (1.2×450) ×1, iterative calculation ΔT_motor≈0.45℃, preliminary temperature T_motor=55.0+0.45≈55.45℃.

[0072] Solenoid valve temperature calculation: solenoid valve heat is proportional to the on time, weighted calculation according to the number of on.

[0073] Single solenoid valve heat Q_in_valve = I 2 ・R・t (I=0.5A, R=10Ω, t is the cumulative on time, Tv1=40×12=480ms=0.48s, so Q_in_Tv1=0.5 2 ×10×0.48=1.2J; Total heat weighted according to the number of on: Tv1 weight 0.4 (on 12 times), Tv3 weight 0.3 (8 times), Tv5 weight 0.3 (10 times), average temperature change ΔT_valve= (1.2 + 0.9 + 1.5) / (0.05×800)≈3.6 / 40≈0.09℃ (m=0.05kg, c=800J / (kg・K)); Initial temperature T_valve0=40.0℃, preliminary temperature T_valve=40.0+0.09≈40.09℃.

[0074] Integrated preliminary temperature calculation value: cylinder 60.3℃, motor 55.45℃, Tv1~Tv9 average 40.09℃.

[0075] Compare the preliminary temperature calculation value with the valve block temperature signal of the pressure sensor, adjust the thermal resistance parameter to recalculate the corrected temperature value if the deviation is obvious; The preliminary calculation may deviate due to thermal resistance parameter aging or environmental changes, which needs to be calibrated by the measured temperature of the valve block to ensure the estimation accuracy.

[0076] Deviation determination criteria: set the deviation threshold of the calculated and measured values of the valve block temperature to ±3℃ (based on the measurement accuracy of the pressure sensor ±0.5℃, allowing a certain error range).

[0077] Valve block temperature calculation value: according to the conduction heat of the motor, cylinder and solenoid valve, estimate T_v_calc=28.8℃ from the reference sub-matrix; Measured value T_v_meas=28.5℃, deviation ΔT_v=28.8-28.5=0.3℃≤3℃, preliminary determination of no significant deviation.

[0078] If the deviation exceeds the threshold (such as T_v_calc=32.0℃ at some time, T_v_meas=28.0℃, ΔT_v=4℃>3℃), adjust the thermal resistance parameter: Thermal resistance correction formula: R_new = R_old × (T_v_calc - T_a) / (T_v_meas - T_a) (T_a=24.0℃), thermal resistance of motor to valve block R_old=2.05K / W, after correction R_new=2.05×(32-24) / (28-24)=2.05×2=4.1K / W; Recalculate the motor temperature: Q_out_motor = (T_motor -28.0) / 4.1, substitute into the heat balance equation to get ΔT_motor≈0.2℃, after correction T_motor=55.45-0.25≈55.2℃.

[0079] For the cylinder and solenoid valve, use the same logic to calibrate: if the calculated value of the cylinder temperature deviates from the indirectly derived value (derived from the valve block temperature) by more than 5℃, adjust the cylinder thermal resistance; if the solenoid valve deviates by more than 2℃, adjust its contact thermal resistance with the valve block. The final corrected temperature value needs to satisfy: the correlation deviation of all component temperatures and the measured temperature of the valve block ≤3℃, to ensure physical consistency.

[0080] Integrate the corrected cylinder motor and solenoid valve temperature values, classify them by component, and generate the key component temperature estimation results.

[0081] The integration process needs to systematize the scattered temperature data by component type to form a clear temperature list, which provides a direct basis for subsequent compensation control.

[0082] Classification and arrangement rules: Cylinder: Record cylinder body temperature, cylinder wall temperature (derived value, T_cyl_wall = T_cyl - 2℃); Motor: Record stator temperature, rotor temperature (T_rotor = T_motor + 5℃, due to higher rotor heat production); Solenoid valve: Record according to number Tv1~Tv9, mark on state (such as Tv1: 40.2℃, on).

[0083] Example of estimation results: Cylinder: [Body temperature 60.2℃, cylinder wall temperature 58.2℃]; Motor: [Stator temperature 55.2℃, rotor temperature 60.2℃]; Solenoid valve: Tv1=40.2℃ (on), Tv3=39.8℃ (on), Tv5=40.1℃ (on), Tv2~Tv4, Tv6~Tv9=38.5℃~39.0℃ (off); Correlation node verification: Valve block temperature 28.5℃ (measured), consistent with thermal resistance model of conduction relationship between each component temperature (such as motor to valve block temperature difference 26.7℃, heat flow = 26.7 / 4.1≈6.5W, consistent with Q_out_motor).

[0084] The final generated key component temperature estimation results need to be attached with calculation timestamp (such as "2024-06-10 15:30:01"), deviation range (such as "±2℃") and credibility score (based on similarity and deviation calculation, such as 92 points), fully reflecting the accuracy and reliability of temperature estimation, laying a data foundation for temperature compensation control.

[0085] S204, according to the key component temperature estimation results, dynamically adjust the target working power of the air pump, the on time of the solenoid valve and the working quantity, generate temperature compensation control instructions for real-time control of closed air supply unit.

[0086] Specifically, the key component temperature estimation results can be used to evaluate the component running state, mark the components close to the upper limit of safe operation, and obtain the temperature warning component list; The upper limit of safe operation of key components is a threshold value set based on material tolerance temperature and engineering experience, and the evaluation process needs to combine the difference between temperature estimation results and threshold value to accurately mark the components with potential overheating risk and provide clear objects for subsequent adjustment.

[0087] Safe operation upper limit setting: The safe threshold of different components is determined according to their material characteristics and functional requirements: Cylinder: Cast iron material, the upper limit of the long-term working temperature is 150℃, and the short-term peak value does not exceed 160℃ (exceeding will cause the aging of the sealing element to accelerate); Motor: Copper wire winding wrapped in an aluminum alloy shell, with a safety upper limit of 120℃ (Class B insulation level, exceeding will cause the insulation layer to fail); Solenoid valve: The coil uses enameled wire with a temperature resistance of 105℃, and the safety upper limit is set to 100℃ (leaving a 5℃ margin to prevent instantaneous fluctuations).

[0088] Running state evaluation index: Use "temperature proximity" to quantify component status, formula: Proximity = Current Temperature ÷ Safety Upper Limit × 100%. According to the proximity, divide into three levels of state: Normal: Proximity ≤ 80% (e.g. motor temperature 90℃, 120×80%=96℃, 90≤96, judged normal); Warning: 80%< Proximity ≤ 95% (need to pay attention and prepare to adjust, e.g. motor 110℃, 120×95%=114℃, 110 is in the 80%-95% interval); Emergency: Proximity > 95% (need to stop immediately, e.g. motor 115℃ > 114℃, judged emergency).

[0089] Warning component marking process: Extract key component temperature estimation results, such as cylinder 130℃, motor 110℃, Tv1 solenoid valve 92℃; Calculate proximity: Cylinder is 130÷150≈86.7%, motor is 110÷120≈91.7%, Tv1 is 92÷100=92%; Compare threshold, all three are in the 80%-95% interval, judged as warning components; Generate temperature warning component list, mark component name, current temperature, safety upper limit, proximity, for example "Motor: 110℃ / 120℃, 91.7%; Cylinder: 130℃ / 150℃, 86.7%; Tv1 Solenoid Valve: 92℃ / 100℃, 92%".

[0090] Adjustment strategy for temperature warning component list, reduce the working load of the air pump, reduce the single conduction time of the solenoid valve and adjust the working combination, get the control parameter adjustment scheme; Adjustment strategy needs to be designed differently according to component type and warning level, relieve overheating by reducing heat source input, optimizing heat dissipation path, etc., while ensuring that the basic functions of the system are not affected.

[0091] Air pump adjustment strategy: The air pump is the main heat source (power 500-1000W), reducing the load needs to consider the cooling effect and air supply efficiency: Power adjustment: Reduce by proximity level, 10% at 80-85%, 20% at 85-90%, 30% at 90-95%. For example, motor proximity 91.7% (corresponding to air pump power 800W), reduce 30% to 560W; Work cycle adjustment: Introduce intermittent mode, run for 30 seconds and pause for 5 seconds (original continuous running), use residual heat to dissipate naturally during the pause period, suitable for scenarios where the air cylinder proximity is >90%; Target pressure correction: If the system pressure allows, reduce the target pressure from 0.8MPa to 0.75MPa (reduce compression power), reduce heat generation.

[0092] Solenoid valve adjustment strategy: Solenoid valves generate heat by conducting current (individual power 5-10W), adjust to reduce on-time and distribute load: Single on-time reduction: Original Tv1 on for 40ms, reduce to 30ms at 92% proximity (25% reduction), reduce unit cycle heat generation; Work combination optimization: Divide 9 solenoids into 3 groups (Tv1-3, Tv4-6, Tv7-9), each group alternately works, avoiding continuous on of a group. For example, original Tv1 and Tv3 are on at the same time, change to Tv1 on for 30ms and then off, Tv2 delays 10ms and on for 30ms, achieving load distribution; Priority sorting: Adjust according to solenoid function importance, non-critical path solenoids (such as backup valves) are prioritized to reduce work frequency, ensuring normal operation of core function valves (such as main air supply valves).

[0093] Control parameter adjustment scheme example: Air pump: power from 800W→560W, work mode from continuous→30s running + 5s pause, target pressure 0.8MPa→0.75MPa; Solenoid valve: Tv1 on-time 40ms→30ms, Tv3 on-time 30ms→25ms, enable group alternation mode (switch group 2 after group 1 works for 10s); Correlation adjustment: Simultaneously increase the speed of the cooling fan (from 2000r / min→3000r / min) to enhance the heat exchange between the PCB and the environment.

[0094] Convert the control parameter adjustment scheme into executable instruction format for the closed air supply unit, specify the timing and amplitude of parameter adjustment, and obtain the initial temperature compensation instructions; The instruction conversion needs to follow the system communication protocol, convert the abstract adjustment scheme into hardware recognizable signals, and clearly execute the condition and parameter change gradient, to ensure smooth transition of adjustment.

[0095] Instruction format specification: CAN bus communication protocol (common standard for automotive electronics) is adopted. The instruction frame contains: frame ID: 0x18FF5501 (air pump control), 0x18FF5502 (solenoid valve control); Data field: 8 bytes, representing target parameters (such as power, duration), adjustment amplitude, and check bits; Period: 100ms (to ensure real-time performance).

[0096] For example, air pump power adjustment instruction: frame ID = 0x18FF5501, data field [0x02, 0x23, 0x00, 0x00, 0x00, 0x00, 0x00, 0x3A], where 0x23 corresponds to decimal 35 (560W = 800x70%, 70% corresponds to hexadecimal 0x46, which is the encoding mapping here).

[0097] Adjustment timing determination: triggered based on real-time temperature monitoring: Immediate execution: component proximity > 90% (such as motor 110℃ > 120x90% = 108℃), instruction sent within 100ms effective; Delayed execution: proximity 80%-90%, execute after the current air supply period ends (to avoid interrupting the ongoing inflation process), for example, wait for the air pump to complete the current inflation to 0.75MPa before reducing power.

[0098] Amplitude gradual control: parameter adjustment uses stepwise gradient to avoid sudden changes causing system oscillation: Air pump power: from 800W to 560W in 3 steps (800→720→640→560), each step interval 2 seconds; Solenoid valve conduction time: from 40ms to 30ms in 2 steps (40→35→30), each step interval 5 working periods.

[0099] Initial temperature compensation instruction example: Air pump instruction: "frame ID = 0x18FF5501, data = power 560W (step adjustment), mode intermittent (30s running + 5s pause), effective timing: after the current period ends"; Solenoid valve instruction: "frame ID = 0x18FF5502, data = Tv1 duration 30ms (step adjustment), group alternation (group 1→group 2, interval 10s), effective timing: immediately".

[0100] Verify the execution effect of the initial temperature compensation instruction, ensure that the temperature of the warning component falls back to the safe range after the implementation of the instruction, and generate the final temperature compensation control instruction.

[0101] The verification process needs to monitor the temperature change in real time, evaluate the adjustment effect, and if it does not meet the expected result, iterate the optimization instruction until all the warning component temperatures return to the safe range, ensuring the effectiveness of the compensation measures.

[0102] Effect monitoring indicators: Temperature drop rate: Under normal circumstances, the temperature of the warning component should drop by ≥0.5℃ per second after the execution of the instruction (e.g. motor from 110℃ to 105℃ in ≤10 seconds); Final temperature: It needs to fall back to ≤80% (e.g. motor ≤96℃, air cylinder ≤120℃, solenoid valve ≤80℃); System stability: Monitor the pressure fluctuation of the air pump (allowance ±0.02MPa), solenoid valve response delay (≤5ms), and ensure that the function is not significantly affected.

[0103] Verification process: After the execution of the instruction, collect the temperature of the warning component and system state parameters every second; Evaluate the preliminary effect at the 5th second: If the motor temperature drops to 107℃ (3℃ drop, rate 0.6℃ / s, meets the expectation), air cylinder 125℃ (5℃ drop), Tv1 88℃ (4℃ drop), continue monitoring; Judge the final effect at the 10th second: motor 105℃ (proximity 87.5%>80%, not up to standard), need to add adjustment; Add instructions: air pump power from 560W to 504W, solenoid valve Tv1 duration from 30ms to 28ms; Monitor again at the 20th second: motor 95℃ (proximity 79.2%≤80%), air cylinder 118℃ (78.7%), Tv1 79℃ (79%), all meet the standard.

[0104] Final instruction generation: integrate the instructions that pass the verification, and clearly define the long-term execution parameters: Air pump: power 504W, intermittent mode (30s running + 5s pause), target pressure 0.75MPa; Solenoid valve: Tv1 duration 28ms, Tv3 duration 23ms, group alternation (group 1→group 2, interval 10s); Additional instructions: cooling fan maintains 3000r / min, perform a full system self-check (temperature + pressure) every 30 minutes.

[0105] The final temperature compensation control instruction needs to be accompanied by a verification report, recording the temperature change curve before and after adjustment, system stability data, for example, "motor temperature from 110℃→95℃, the drop rate is 0.75℃ / s, the pressure fluctuation is ±0.01MPa, the verification is passed", to ensure that the instruction is traceable and reproducible.

[0106] It can be seen that the valve block temperature signal collected by the temperature sensor integrated with the pressure sensor in the closed air supply unit and the electronic component temperature and environment temperature signal collected by the temperature sensor on the control unit PCB are received, and the initial temperature-working condition data set is generated in combination with the air pump power and the electromagnetic valve working time; the initial temperature-working condition data set is subjected to thermal resistance network modeling, a multi-node thermal feature matrix is constructed, and the matrix is decomposed into a plurality of sub-matrices; the dynamic thermal balance algorithm is used to perform real-time operation on the sub-matrices to generate a key component temperature estimation result; and the temperature compensation control instruction is generated according to the key component temperature estimation result, so that the key component temperature can be accurately estimated and dynamic compensation can be realized, and the control precision and stability of the closed air supply unit are improved.

[0107] Another embodiment of the application provides a temperature compensation system, as shown in Figure 3 , the system can include: The receiving module 301 is configured to receive the valve block temperature signal collected by the temperature sensor integrated with the pressure sensor in the closed air supply unit and the electronic component temperature and environment temperature signal collected by the temperature sensor on the control unit PCB, and generate an initial temperature-working condition data set in combination with the air pump power and the electromagnetic valve working time, wherein each data item is associated with the component thermal conduction path dimension; The construction module 302 is configured to perform thermal resistance network modeling on the initial temperature-working condition data set, construct a multi-node thermal feature matrix, and decompose the matrix into a plurality of sub-matrices to capture the differential thermal conduction characteristics of the air cylinder, the motor, and the electromagnetic valve, wherein each sub-matrix corresponds to the thermal resistance parameter and heat source power relationship of a single component; The operation module 303 is configured to use a dynamic thermal balance algorithm to perform real-time operation on the sub-matrices, calculate the real-time temperature values of the air cylinder, the motor, and each electromagnetic valve in combination with the real-time working parameters of the system, and generate a key component temperature estimation result; The generation module 304 is configured to dynamically adjust the target working power of the air pump, the conduction time length of the electromagnetic valve, and the working number of the electromagnetic valve according to the key component temperature estimation result, and generate a temperature compensation control instruction for real-time control of the closed air supply unit.

[0108] The embodiment of the application further provides a storage medium, and the storage medium stores a computer program, wherein the computer program is set to execute the steps in any one of the method embodiments when running.

[0109] The embodiment of the present application also provides an electronic device, comprising a memory and a processor, the memory has a computer program stored therein, and the processor is configured to run the computer program to execute the steps in any one of the method embodiments.

[0110] Specifically, the electronic device can further include a transmission device connected with the processor and an input / output device connected with the processor.

[0111] The above detailed description of the embodiments shown in the drawings explains the structure, features and effects of the present application. The above description is only the preferred embodiments of the present application, but the present application is not limited by the drawings. Any changes or modifications made in accordance with the concept of the present application, or equivalent embodiments with equivalent changes, are still within the scope of the present application.

Claims

1. A temperature compensation method, characterized in that, The method includes: The system receives the valve block temperature signal from the temperature sensor integrated in the pressure sensor of the closed air supply unit, as well as the electronic component temperature and ambient temperature signals from the temperature sensor on the control unit PCB. It then combines the air pump power and the solenoid valve operating time to generate an initial temperature-operating condition dataset, where each data item is associated with the component's heat conduction path dimension. A thermal resistance network model was constructed on the initial temperature-operating condition dataset to build a multi-node thermal feature matrix. The matrix was then decomposed into several sub-matrices to capture the differentiated heat conduction characteristics of cylinders, motors, and solenoid valves. Each sub-matrix corresponds to the relationship between the thermal resistance parameter of a single component and the power of the heat source. The submatrix is ​​calculated in real time using a dynamic thermal balance algorithm, and the real-time temperature values ​​of the cylinder, motor and each solenoid valve are calculated in combination with the real-time operating parameters of the system to generate temperature estimation results for key components. Based on the temperature estimation results of the key components, the target operating power of the air pump, the conduction time and number of solenoid valves are dynamically adjusted to generate temperature compensation control commands for real-time control of the closed-loop air supply unit.

2. The method according to claim 1, characterized in that, The receiving closed-loop air supply unit receives valve block temperature signals from the temperature sensor integrated into the pressure sensor, and electronic component and ambient temperature signals from the temperature sensor on the control unit PCB. These signals are then combined with the air pump power and solenoid valve operating time to generate an initial temperature-operating condition dataset. Each data item is associated with the component's heat conduction path dimension, including: The valve block temperature signal is acquired by the temperature sensor integrated with the pressure sensor, and the electronic component temperature signal and ambient temperature signal of the temperature sensor on the control unit PCB are also acquired. Abnormal fluctuation signals are filtered out to obtain a stable temperature signal set. The operating status of the air pump and the conduction status of the solenoid valve are read from the closed air supply unit controller. The operating information is aligned with the stable temperature signal set in chronological order to obtain time-matched temperature correlation data. Add heat conduction path information to temperature correlation data, record the material types and connection relationships involved in the path, and obtain a temperature dataset with path labels; The temperature condition dataset with path identifiers is divided into samples according to fixed time intervals. Each sample integrates the temperature change operating condition characteristics and path information within the interval to generate an initial temperature-operating condition dataset.

3. The method according to claim 2, characterized in that, The process involves modeling a thermal resistance network on the initial temperature-operating condition dataset, constructing a multi-node thermal feature matrix, and decomposing the matrix into several sub-matrices to capture the differentiated heat conduction characteristics of the cylinder, motor, and solenoid valve. Each sub-matrix corresponds to the relationship between the thermal resistance parameter of a single component and the power of the heat source, including: Thermal conductivity parameters of different materials are extracted from the initial temperature-operating condition dataset, and the basic thermal resistance value is determined by combining the component connection method to obtain a set of thermal resistance parameter parameters; Based on the thermal resistance characteristic parameter set, the thermal network nodes are divided, the cylinder motor solenoid valve is set as the core node, the valve block PCB and the environment are set as associated nodes, the heat transfer path between nodes is defined, and the node topology diagram is obtained. Based on nodes, temperature and thermal resistance information is integrated to construct a multi-node thermal feature matrix. The matrix elements reflect the thermal interaction relationship between nodes, thus obtaining the overall thermal feature matrix. The overall thermal feature matrix is ​​decomposed according to component type to generate cylinder-specific sub-matrices, motor-specific sub-matrices, and solenoid valve-specific sub-matrices. Each sub-matrix contains the thermal resistance parameters and heat source association information of the corresponding component, resulting in a component sub-matrix set.

4. The method according to claim 3, characterized in that, The process involves using a dynamic thermal balance algorithm to perform real-time calculations on the sub-matrix, combining this with real-time system operating parameters to calculate the real-time temperature values ​​of the cylinder, motor, and each solenoid valve, and generating temperature estimation results for key components, including: The current operating parameters of the closed-loop gas supply unit are collected in real time and matched with the historical operating condition characteristics in the component sub-matrix set. The most similar reference sub-matrix is ​​selected to obtain the matching reference sub-matrix. The dynamic thermal balance algorithm is initialized based on the matching reference submatrix. The balance relationship of heat input, conduction and dissipation is set, the algorithm calculation boundary conditions are defined, and the algorithm running parameters are obtained. The dynamic thermal balance algorithm is run, and the heat absorbed by the cylinder and the temperature change are calculated in combination with the working status of the air pump. The heat conversion efficiency is corrected according to the operating characteristics of the motor, and the temperature change is calculated by weighting according to the solenoid valve conduction status to obtain the preliminary temperature calculation value. The initial temperature calculation value is compared with the valve block temperature signal from the pressure sensor. For results with significant deviations, the thermal resistance parameter is adjusted and recalculated to obtain the corrected temperature value. The temperature values ​​of the cylinder motor and each solenoid valve are integrated and organized by component category to generate temperature estimation results for key components.

5. The method according to claim 4, characterized in that, The step of dynamically adjusting the target operating power of the air pump, the conduction time and number of solenoid valves based on the temperature estimation results of the key components, and generating temperature compensation control commands for real-time control of the closed-loop air supply unit includes: Based on the temperature estimation results of key components, assess the operating status of components, mark components that are close to the safe operating limit, and obtain a list of components with temperature warnings. An adjustment strategy was developed for the temperature warning component list, which reduced the workload of the air pump, reduced the single conduction time of the solenoid valve and adjusted the working combination to obtain a control parameter adjustment scheme. The control parameter adjustment scheme is converted into an instruction format that can be executed by the closed-loop gas supply unit, the timing and magnitude of parameter adjustment are clarified, and the initial temperature compensation instruction is obtained. Verify the execution effect of the initial temperature compensation command, ensure that the temperature of the warning component drops back to a safe range after the command is implemented, and generate the final temperature compensation control command.

6. A temperature compensation system, characterized in that, The system includes: The receiving module is used to receive the valve block temperature signal collected by the temperature sensor integrated in the pressure sensor in the closed air supply unit, as well as the electronic component temperature and ambient temperature signals collected by the temperature sensor on the control unit PCB. It combines the air pump power and the solenoid valve working time to generate an initial temperature-operating condition dataset, in which each data item is associated with the component's heat conduction path dimension. The module is used to model the thermal resistance network of the initial temperature-operating condition dataset, construct a multi-node thermal feature matrix, and decompose the matrix into several sub-matrices to capture the differentiated heat conduction characteristics of cylinders, motors, and solenoid valves. Each sub-matrix corresponds to the relationship between the thermal resistance parameter of a single component and the power of the heat source. The calculation module is used to perform real-time calculations on the sub-matrix using a dynamic thermal balance algorithm, and calculate the real-time temperature values ​​of the cylinder, motor and each solenoid valve in combination with the real-time operating parameters of the system, and generate temperature estimation results for key components. The generation module is used to dynamically adjust the target operating power of the air pump, the conduction time and the number of solenoid valves based on the temperature estimation results of the key components, and generate temperature compensation control commands for real-time control of the closed air supply unit.

7. The system according to claim 6, characterized in that, The receiving module is specifically used for: The valve block temperature signal is acquired by the temperature sensor integrated with the pressure sensor, and the electronic component temperature signal and ambient temperature signal of the temperature sensor on the control unit PCB are also acquired. Abnormal fluctuation signals are filtered out to obtain a stable temperature signal set. The operating status of the air pump and the conduction status of the solenoid valve are read from the closed air supply unit controller. The operating information is aligned with the stable temperature signal set in chronological order to obtain time-matched temperature correlation data. Add heat conduction path information to temperature correlation data, record the material types and connection relationships involved in the path, and obtain a temperature dataset with path labels; The temperature condition dataset with path identifiers is divided into samples according to fixed time intervals. Each sample integrates the temperature change operating condition characteristics and path information within the interval to generate an initial temperature-operating condition dataset.

8. The system according to claim 7, characterized in that, The building module is specifically used for: Thermal conductivity parameters of different materials are extracted from the initial temperature-operating condition dataset, and the basic thermal resistance value is determined by combining the component connection method to obtain a set of thermal resistance parameter parameters; Based on the thermal resistance characteristic parameter set, the thermal network nodes are divided, the cylinder motor solenoid valve is set as the core node, the valve block PCB and the environment are set as associated nodes, the heat transfer path between nodes is defined, and the node topology diagram is obtained. Based on nodes, temperature and thermal resistance information is integrated to construct a multi-node thermal feature matrix. The matrix elements reflect the thermal interaction relationship between nodes, thus obtaining the overall thermal feature matrix. The overall thermal feature matrix is ​​decomposed according to component type to generate cylinder-specific sub-matrices, motor-specific sub-matrices, and solenoid valve-specific sub-matrices. Each sub-matrix contains the thermal resistance parameters and heat source association information of the corresponding component, resulting in a component sub-matrix set.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 1-5 when it is run.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1-5.

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