Method, device and equipment for detecting high-temperature resistance of wire rod and storage medium

Abstract

The invention relates to the technical field of wire performance detection, and discloses a wire high temperature resistance detection method, device and equipment and a storage medium. The method comprises the following steps: arranging micro thermocouple array sensors on the surface, the bending part and the connecting point of an outer insulating layer of a to-be-measured wire rod to obtain a wire rod temperature monitoring network; placing the to-be-tested wire rod in a constant-temperature box to execute a high-temperature cyclic loading test, and collecting real-time temperature response data of the to-be-tested wire rod; carrying out temperature gradient analysis and heat distribution characteristic calculation to obtain a temperature change characteristic parameter set; executing temperature fluctuation compensation and thermal stress accumulation calculation to obtain thermal stress characteristic parameters; and carrying out aging characteristic analysis on the wire insulating material through the thermal stress attenuation network model to obtain wire predicted life and failure probability distribution of the to-be-tested wire in a high-temperature environment. The method can comprehensively capture the temperature change characteristics of each part of the wire rod, and achieves the precise prediction of the failure probability of the wire rod under different temperature conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of wire performance detection, and particularly relates to a method, device, equipment and storage medium for detecting the high-temperature resistance performance of wires. Background Art

[0002] Traditional methods for detecting the high-temperature resistance of wires mainly adopt accelerated aging experiments, placing the wires in a high-temperature environment for simple time-temperature extrapolation, lacking precise monitoring and analysis of the temperature distribution characteristics of the wires under actual working conditions, and it is difficult to accurately predict the actual service life and failure probability of the wires. Existing detection technologies usually ignore the temperature anomaly accumulation effects at key parts such as the bending points and connection points of the wires, and are unable to accurately capture the thermal stress characteristics of these areas, resulting in a large deviation between the detection results and the actual usage conditions.

[0003] Another key issue in the detection of the high-temperature resistance performance of wires is that the methods for collecting and analyzing temperature data are not precise enough. In the existing technology, the density of temperature sensor arrangement is insufficient and the sampling frequency is low, making it difficult to capture the small changes and transient response characteristics of the wires during the temperature cycle. At the same time, the temperature data processing algorithm is simple, only focusing on the average temperature and the highest temperature, ignoring key parameters such as temperature gradient and temperature uniformity, and it is impossible to establish a complete model of the thermal distribution characteristics of the wires. In addition, the existing detection methods lack an effective compensation mechanism for temperature fluctuations, resulting in the measurement results being greatly interfered by environmental factors and the data reliability being insufficient. Summary of the Invention

[0004] The main object of the present invention is to provide a method, device, equipment and storage medium for detecting the high-temperature resistance performance of wires, which can comprehensively capture the temperature change characteristics of each part of the wire and achieve accurate prediction of the failure probability of the wire under different temperature conditions.

[0005] To achieve the above object, the present invention provides a method for detecting the high-temperature resistance performance of wires, including the following steps: Arrange a micro-thermocouple array sensor on the outer insulation layer surface, bending points and connection points of the wire to be tested to obtain a wire temperature monitoring network; Place the wire to be tested in a constant temperature oven to perform a high-temperature cyclic loading test, and collect the real-time temperature response data of the wire to be tested during the temperature change process through the wire temperature monitoring network; Perform temperature gradient analysis and thermal distribution characteristic calculation on the real-time temperature response data to obtain a set of temperature change characteristic parameters; Perform temperature fluctuation compensation and thermal stress accumulation calculation on the set of temperature change characteristic parameters to obtain thermal stress characteristic parameters; Input the thermal stress characteristic parameters into the thermal stress attenuation network model for aging characteristic analysis of the wire insulation material, and obtain the predicted life and failure probability distribution of the wire under test in a high-temperature environment.

[0006] The present invention also provides a device for detecting the high-temperature resistance performance of a wire, including: An arrangement module, configured to arrange a micro-thermocouple array sensor on the outer insulation layer surface, bending points, and connection points of the wire under test to obtain a wire temperature monitoring network; A cyclic test module, configured to place the wire under test in a constant-temperature oven to perform a high-temperature cyclic loading test, and collect real-time temperature response data of the wire under test during the temperature change process through the wire temperature monitoring network; A gradient analysis module, configured to perform temperature gradient analysis and thermal distribution characteristic calculation on the real-time temperature response data to obtain a set of temperature change characteristic parameters; An accumulation calculation module, configured to perform temperature fluctuation compensation and thermal stress accumulation calculation on the set of temperature change characteristic parameters to obtain thermal stress characteristic parameters; An aging characteristic analysis module, configured to input the thermal stress characteristic parameters into the thermal stress attenuation network model for aging characteristic analysis of the wire insulation material, and obtain the predicted life and failure probability distribution of the wire under test in a high-temperature environment.

[0007] The present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0008] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0009] In summary, the technical solution provided by the present invention uses a three-dimensional spiral distributed micro-thermocouple array sensor at the key parts of the wire, realizing the acquisition of millisecond-level temperature fluctuation data. In particular, a higher-density measurement point arrangement is adopted at the bending and connection points of the wire, which can comprehensively capture the temperature change characteristics of each part of the wire, avoiding the evaluation deviation caused by the temperature monitoring blind area in the traditional method. By performing temperature gradient analysis and thermal distribution characteristic calculation on the real-time temperature response data, a thermal distribution characteristic model including multi-dimensional parameters such as temperature gradient distribution maps, thermal accumulation characteristic data, and temperature stability indicators is established, breaking through the limitation of only focusing on the average temperature in the traditional technology, and being able to accurately describe the temperature distribution characteristics of the wire in a complex thermal environment. An adaptive temperature gradient coupling algorithm is used to perform temperature fluctuation compensation and thermal stress accumulation calculation on the temperature change characteristic parameter set, effectively solving the problem of data instability caused by random temperature fluctuations in the traditional method, and improving the accuracy and reliability of the thermal characteristic evaluation of the wire. By performing piecewise linear fitting on the temperature-dependent aging rate function, a non-linear thermal response model of the wire is obtained, overcoming the problem of insufficient prediction accuracy of the traditional Arrhenius model in a wide temperature range, and being able to accurately describe the non-linear response characteristics of the wire in different temperature intervals. The thermal stress characteristic parameters are input into the thermal stress attenuation network model for aging characteristic analysis of the wire insulation material. By establishing a three-dimensional model of temperature-time-failure probability, accurate prediction of the failure probability of the wire under different temperature conditions is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a schematic diagram of the steps of a method for detecting the high-temperature resistance performance of a wire according to an embodiment of the present invention; Figure 2 is a structural block diagram of a device for detecting the high-temperature resistance performance of a wire according to an embodiment of the present invention; Figure 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.

[0011] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] In order to make the object, technical solution, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0013] Referring to Figure 1 , this embodiment provides a method for detecting the high-temperature resistance performance of a wire, including the following steps: S1, arranging a micro-thermocouple array sensor on the outer insulation layer surface, bending part, and connection point of the wire to be tested to obtain a wire temperature monitoring network; Among them, an ultra-thin thermistor micro-thermocouple array sensor is constructed with a platinum-rhodium alloy as the base material. A high-sensitivity platinum-rhodium alloy sheet with a thickness of about 0.03 mm is used. It is cut into standard-size units through microfabrication processes, and thermocouple pairs are formed by welding fine wires at the ends of each unit, so that each sensor has both high-temperature stability and can achieve fast thermal response in an extremely small volume. According to the preset layout rules, measurement points are planned for the wire under test. Measurement points are set on the surface of the outer insulation layer at a uniform interval of 10 mm. At the bending and connection points, considering the higher thermal stress concentration and temperature gradient changes in these areas, the measurement point interval is further reduced to 2 mm to improve the monitoring accuracy of local thermal anomalies, and a distribution map of the wire temperature monitoring points is generated. According to the above-mentioned point distribution map, the micro-thermocouple array sensors are installed on the surface of the wire under test in a three-dimensional spiral distribution manner, so that the sensors are arranged in a spiral and staggered pattern along the axial and circumferential directions of the wire, ensuring that regardless of whether the wire is in a straight, coiled or bent state, the sensors can fully cover key positions and capture heat transfer behaviors in different directions. During the sensor fixing process, an epoxy resin with high thermal conductivity and high adhesion is selected as the bonding medium to firmly fix each micro-thermocouple on the wire surface, while ensuring close thermal contact between the thermistor and the wire surface, minimizing the contact thermal resistance to the greatest extent and improving the temperature sensing accuracy. After the sensor layout is completed, the wire temperature monitoring array is uniformly connected to a high-precision analog-to-digital converter and a signal conditioning circuit. The analog-to-digital converter needs to have a resolution of more than 24 bits and a sampling rate of at least 100 Hz to ensure the capture of temperature changes at the millisecond level; while the signal conditioning circuit includes a low-noise amplifier, a filter and a zero calibration module, which are used to accurately amplify and standardize the weak voltage signal of the thermocouple, suppressing the influence of environmental noise on the signal quality. All micro-thermocouple array sensors are connected to the data acquisition module through a high-density shielded multi-core cable. The multi-core cable adopts a partitioned shielding design to physically isolate signals of different channels, and at the same time, a full-metal braided layer is set on the outer layer to effectively shield external electromagnetic interference, ensuring that the wire temperature monitoring network can still work stably in a complex electromagnetic environment, and obtaining a wire temperature monitoring network.

[0014] S2, Place the wire under test in an incubator to perform a high-temperature cyclic loading test, and collect the real-time temperature response data of the wire under test during the temperature change process through the wire temperature monitoring network; Specifically, set the temperature control parameters of the incubator, including the target temperature range, heating rate, cooling rate, holding time, and number of cycles, etc. The temperature control range is set from room temperature to 200 °C, and the temperature stability requirement is ±0.2 °C. Enable the air circulation system and temperature equalization device to ensure that the temperature field in the test space is uniform without significant gradient differences. Fix the wire under test on a specially designed test bracket inside the incubator according to its structural characteristics. This test bracket is made of high-temperature stable materials and can maintain its shape unchanged in the full temperature range. At the same time, the bracket layout needs to ensure that all key monitoring positions of the wire, including straight sections, bent sections, and connection endpoints, can be kept exposed to the uniform temperature field and will not cause stress concentration due to its own weight or thermal expansion. Start the cyclic loading test according to the preset high-temperature temperature cycle plan. This cycle plan is set to heat from room temperature to 70 °C and hold for 2 hours, then rise to 80 °C and continue to hold for 2 hours, then rise to 90 °C and maintain for 2 hours, and finally slowly cool back to room temperature. At the same time, it supports adjusting the maximum temperature to 120 °C or even higher according to actual application requirements. During the whole process of the wire experiencing temperature cyclic loading, through the wire temperature monitoring network, use a micro-thermocouple array to synchronously collect the temperature change data of each measurement point in real time. All collection processes are time-synchronized and sampled in units of 10 milliseconds to ensure the consistency of data between different measurement points, and obtain the original temperature response data. Perform two-point calibration on the original temperature response data to eliminate the systematic measurement deviation caused by sensor individual errors, wiring errors, and electronic drift. Select 0 °C (the temperature of ice-water mixture) and 100 °C (the temperature of boiling water) as the standard reference points for two-point calibration. Calculate the calibration coefficient by comparing the actual output value of the sensor with the standard value, and linearly correct the original data to obtain the calibrated temperature response data. Classify and organize the calibrated temperature response data according to the wire structure position. Classify the measurement point data on the surface of the outer insulation layer into the first measurement point group, classify the measurement point data at the bent places (including various different bending radius positions) into the second measurement point group, and classify the measurement point data at the connection points (such as welding areas, terminal connection areas) into the third measurement point group to obtain the classified and organized real-time temperature response data.

[0015] S3. Perform temperature gradient analysis and thermal distribution characteristic calculation on the real-time temperature response data to obtain a set of temperature change characteristic parameters; It should be noted that the data of the first measurement point group corresponding to the surface of the outer insulation layer are extracted from the real-time temperature response data, and the temperature gradient distribution map is formed by calculating the ratio of the temperature difference between every two adjacent measurement points to the actual distance between them, showing the temperature change rate of each area on the surface of the outer insulation layer under high-temperature cycling, so as to reveal the uniformity of surface heat diffusion and the potential risk of local hot spots. The data of the second measurement point group located at the bending part are extracted. Since the measurement points at these positions are arranged more densely, a more detailed analysis of the temperature change speed is carried out. By calculating the temperature rise rate of each measurement point in a short time, the phenomenon of heat accumulation in the bending area is captured, which helps to evaluate the thermal stability of the wire under complex deformation conditions. At the same time, the data of the third measurement point group at the connection point are obtained, and the temperature response at these positions is statistically analyzed, paying attention to the fluctuation range of the temperature in the relatively stable stage, so as to infer the temperature stability of the connection area. If the temperature fluctuation is small, it means that the connection point can maintain good thermal stability in a high-temperature environment; on the contrary, if the temperature fluctuation is large, it indicates that there are potential hidden dangers in the connection process. Based on the temperature gradient distribution map of the surface of the outer insulation layer, the overall surface temperature uniformity is calculated to evaluate the temperature difference between different positions. At the same time, the thermal response time constant is extracted according to the heat accumulation characteristic data at the bending part to measure the temperature response speed of the bending area, and combined with the temperature stability index of the connection point, data fusion is carried out according to the preset weight to establish a thermal distribution characteristic model. Using the finite difference heat conduction algorithm, based on the above thermal distribution characteristic model, the thermal diffusion coefficient of the wire in different regions is calculated, and the thermal diffusion ability of the surface of the outer insulation layer, the bending part and the connection point is obtained respectively by analyzing the temperature change trend of each node. At the same time, the thermal impedance characteristics of each region are deduced, that is, to measure the resistance encountered when heat is transferred at different positions. All the extracted temperature gradient distribution maps, heat accumulation characteristic data at the bending part, temperature stability indexes of the connection point, the comprehensively formed thermal distribution characteristic model and the thermal response conduction parameters of each region are integrated in multi-dimensional features, and summarized to form a temperature change characteristic parameter set.

[0016] S4. Perform temperature fluctuation compensation and thermal stress accumulation calculation on the temperature change characteristic parameter set to obtain thermal stress characteristic parameters; Specifically, temperature random fluctuation compensation is performed on the temperature gradient distribution map, heat accumulation characteristic data, and temperature stability index in the temperature change characteristic parameter set. By means of weighted moving average or other smoothing processing methods, the random high-frequency temperature fluctuations caused by environmental disturbances, sampling noise, or local material non-uniformity are eliminated, and the temperature characteristic data after fluctuation compensation is obtained. Based on the compensated temperature characteristic data, an Arrhenius equation model that conforms to the thermal aging characteristics of the material is established, and based on this mathematical basis, the aging rate of the wire insulation material under different temperature conditions is deduced. By correlating the high-temperature experimental conditions with the actual use environment, a temperature-dependent aging rate function that describes the aging behavior of the insulation material is constructed. This function can reflect the degree of acceleration of material degradation under different thermal stress actions. The aging rate function is subjected to piecewise linear fitting processing, dividing the entire temperature range into several sub-intervals, and linear fitting is performed separately within each interval to form a non-linear thermal response model that is more in line with the actual complex material response characteristics. This model can effectively capture the dynamic characteristics when the thermal aging mechanism changes in different temperature segments of the material, such as the phenomenon of a sharp increase in the aging rate in some high-temperature segments. Based on the thermal distribution characteristic model, the heat conduction paths between the surface of the outer insulation layer, the bending points, and the connection points are deduced. By analyzing the diffusion process of heat in different structural units, the overall heat conduction characteristic curve of the wire is formed, describing the migration law of heat flow inside the material, and revealing which positions are prone to become heat accumulation and stress concentration points. The bending radius correction process is performed on the heat accumulation characteristic data at the bending points. Considering that in the actual coiled or bent state, the local heat conduction characteristics of the wire will be significantly different due to the curvature change, a correction coefficient is introduced to correct the original heat accumulation data, and the heat stress concentration distribution data more in line with the actual working conditions is obtained. Multiply the temperature-dependent aging rate function by the heat conduction characteristic curve to form the material aging evolution trend under the combined action of the temperature field and the heat flow field. At the same time, the obtained result and the heat stress concentration distribution data corrected by the bending radius are weighted and superimposed according to the set weight. Through this superimposition process, the influence of the local stress peak region is incorporated into the overall aging process model, and thus the thermal stress characteristic parameters are obtained.

[0017] S5. Input the thermal stress characteristic parameters into the thermal stress attenuation network model for analyzing the aging characteristics of the wire insulation material, and obtain the predicted life and failure probability distribution of the wire under test in a high-temperature environment.

[0018] Among them, vector encoding processing is performed on the thermal stress characteristic parameters. According to the importance and physical relevance of each thermodynamic characteristic parameter during the overall aging process, these data are reorganized and arranged to form a set of high-dimensional wire thermal characteristic feature vectors. The wire thermal characteristic feature vectors are input into the thermal stress analysis layer of the thermal stress attenuation network model. This analysis layer adopts a long short-term memory network structure. Through its internal memory units and gating mechanisms, it effectively captures the complex temperature-dependent relationship of the thermal stress characteristic parameters during the time evolution process, and models the short-term fluctuations and long-term trends respectively, so as to accurately predict the performance change trajectory of the wire under test in a high-temperature environment at different time scales, and output a series of predicted performance indicators representing the material state at different times. The predicted performance indicators that continuously change in the time dimension are input into the life prediction layer of the thermal stress attenuation network. The life prediction layer internally integrates a life modeling mechanism based on the Weibull distribution. This mechanism calculates the characteristic life parameter and the shape parameter by fitting the distribution characteristics of the predicted performance indicators. The characteristic life parameter describes the time length required for the wire to reach a certain failure probability under a given stress level, while the shape parameter characterizes the speed and mode of the change of the failure probability with time. These two parameters jointly define the basic probability structure of the wire aging failure. Based on the characteristic life parameter and the shape parameter, a wire reliability function is constructed. This function reflects the probability level that the wire still maintains its complete function after experiencing different working times. At the same time, by calculating the failure time probability density of the reliability function, the probability distribution of the wire failure at different time nodes is obtained. Combining the cumulative damage theory, the failure probability distribution is converted by the acceleration factor, that is, according to the difference in the aging rate of the material under high-temperature accelerated test conditions and actual use conditions, the failure probability data in the accelerated environment are converted into the prediction results in the standard use environment through a conversion method. During the acceleration factor conversion process, multiple factors such as the thermal stress level, temperature fluctuation amplitude, and temperature sensitivity of the aging rate are comprehensively considered to ensure the physical rationality and mathematical accuracy of the conversion process. The converted failure probability curve and the characteristic life data are integrated to form the final predicted life model of the wire under actual high-temperature environmental conditions. The output content includes the predicted service life value, and provides the time prediction range at different failure probability levels, such as the time required to reach 10%, 50%, and 90% failure probabilities.

[0019] Substitute the characteristic life parameters and shape parameters into the reliability basic equation to construct a preliminary wire reliability function. To adapt to the actual situation of material property changes under different temperature conditions, a temperature correction term is introduced into the reliability basic equation. This correction term is dynamically adjusted according to the thermal sensitivity characteristics of the wire insulation material, so as to ensure that the reliability function truly reflects the influence of temperature on the material life and guarantee the accuracy and consistency of the prediction results under different thermal environments. Perform the first-order differentiation of the wire reliability function with respect to the time dimension to obtain the failure rate function, which reflects the conditional probability of failure per unit time of the wire at any moment. Therefore, based on the failure rate function, the failure probability density function is derived to form the failure time distribution curve of the wire to be tested at each temperature point, depicting the probability change law of the failure event over time under different thermal load levels. Perform piecewise interval integration on the failure time distribution curve of the wire to be tested at each temperature point to obtain the cumulative failure probability function, that is, the probability change trajectory of the cumulative failure of the wire over time, reflecting the overall failure evolution process of the wire. Introduce the cumulative failure probability function into the Miner linear cumulative damage model, linearly superimpose the damage effects at different temperature points using the Miner model, and at the same time combine the equivalent life principle. By weighing the time proportion and damage contribution degree corresponding to each temperature segment, calculate the comprehensive failure probability under the composite temperature condition to obtain the comprehensive failure probability matrix. This matrix corresponds to different temperature conditions in the horizontal dimension and different time nodes in the vertical dimension. Perform temperature-time bivariate interpolation calculation based on the comprehensive failure probability matrix. By filling the area not directly covered by the test between temperature and time, establish a continuous three-dimensional model of temperature-time-failure probability. Based on this three-dimensional model, quickly extract the corresponding wire life prediction value under any given temperature condition, and at the same time invert the maximum allowable use time according to the preset failure probability threshold to obtain the failure probability distribution of the wire to be tested under different temperature conditions.

[0020] In one example, a micro-thermocouple array sensor is arranged on the outer insulation layer surface, bending points, and connection points of the wire to be tested to obtain a wire temperature monitoring network, including: Construct a micro-thermocouple array sensor of platinum-rhodium alloy ultra-thin thermistor; According to the preset layout rules, plan the measurement points for the wire to be tested. Arrange a measurement point every 10 mm on the outer insulation layer surface of the wire to be tested, and arrange a measurement point every 2 mm at the bending points and connection points of the wire to be tested to obtain a wire temperature monitoring point distribution map; Arrange the micro-thermocouple array sensor in a three-dimensional spiral distribution according to the wire temperature monitoring point distribution map, and fix the micro-thermocouple array sensor and the surface of the wire to be tested with high thermal conductivity epoxy resin to obtain a wire temperature monitoring array; The wire temperature monitoring array is connected to a high-precision analog-to-digital converter and a signal conditioning circuit, and the wire temperature monitoring array is connected to the data acquisition module through a shielded multi-core cable to obtain a wire temperature monitoring network.

[0021] In this example, a micro thermocouple array sensor of platinum-rhodium alloy ultra-thin thermistor is constructed. High-purity platinum-rhodium alloy is selected as the thermosensitive material. Taking advantage of its high-temperature stability, extremely low thermal resistance change rate and long-term durability, the platinum-rhodium alloy film is made into an ultra-thin structure with a thickness of about 0.03 mm through micro-nano processing technology, and combined with micro-electrode welding technology, it is prepared into a small-sized micro-thermocouple array unit with high response speed and high sensitivity. Differential design is used inside each unit to improve the ability to capture small temperature changes and effectively suppress the introduction of environmental noise. At the same time, a flexible support film substrate is used in the overall structure, so that the entire array can be firmly attached to the surface of the curved wire, and adapt to thermal expansion and deformation under high-temperature cycle conditions to maintain stable mechanical and electrical properties. According to the preset layout rules, the measurement points of the wire to be tested are planned, and a measurement point is arranged every 10 mm on the surface of the outer insulation layer of the wire in the length direction. While ensuring the overall temperature field coverage, the reasonable balance between the total number of sensors and the complexity of the system is controlled. In the bending and connection points of the wire, due to the local heat accumulation, material stress concentration and potential failure risks in these areas, monitoring is carried out with a higher density, and the distance between the measurement points is reduced to 2 mm, so as to achieve high-precision capture of temperature changes in key areas. After point planning, the distribution map of the wire temperature monitoring points is obtained, the position and number of each sensor are marked, and the area category to which each measurement point belongs is recorded according to the structural characteristics of the wire. According to the established point distribution map, the micro-thermocouple array sensors are fixed to the surface of the wire to be tested in turn in a three-dimensional spiral distributed manner. In the process of three-dimensional spiral layout, ensure that the spacing of each spiral circle is adapted to the diameter of the wire, and at the same time, the sensor winding angle is encrypted in the area with a smaller bending radius to ensure the continuity and comprehensiveness of the capture of thermal field changes. In order to ensure good thermal contact between the sensor and the wire surface and minimize thermal contact resistance, high thermal conductivity epoxy resin is used for fixing. The epoxy resin has good high temperature resistance (temperature resistance of at least 200 degrees Celsius), high thermal conductivity (greater than 1 watt per meter Kelvin) and excellent adhesion. When fixing, micro pressure is applied evenly to avoid bubble inclusion or local voids, to achieve a complete and dense thermal connection interface, while maintaining the flatness of the sensor body to avoid the breakage or peeling of the thermistor due to local stress concentration. After the sensor array is fixed, the electrical connection design of the wire temperature monitoring array is carried out, and each micro-thermocouple array unit is connected to the signal conditioning circuit through a high-precision analog-to-digital converter. The analog-to-digital converter is required to have a resolution of at least 24 bits and high-speed sampling capability to achieve millisecond-level temperature change capture, and the signal conditioning circuit includes a low-noise amplifier, a bandpass filter and a temperature reference calibration module to stably amplify the weak thermoelectric signal, filter noise and compensate for baseline drift, to ensure that each temperature data has sufficient dynamic range and signal-to-noise ratio.All sensor output signals shall be uniformly aggregated through a shielded multi-core cable. The shield layer adopts a double-layer metal braided structure, and the internal signal lines adopt a grouped and layered design to effectively isolate the crosstalk between different sensing channels. At the same time, the overall cable has performance indicators of high temperature resistance, bending resistance, and electromagnetic interference resistance, ensuring stable and reliable data transmission under actual high-temperature environments and mechanical disturbances. Connect the integrated wire temperature monitoring array to the data acquisition module through a shielded multi-core cable. The data acquisition module internally integrates a high-speed data cache, an intelligent data packet distribution, and a time synchronization module, which can receive, cache, and synchronously process the temperature data from all measurement points in real time, and at the same time support docking with the upper computer software or database system to obtain a wire temperature monitoring network.

[0022] In one example, place the wire to be measured in an incubator to perform a high-temperature cyclic loading test, and collect the real-time temperature response data of the wire to be measured during the temperature change process through the wire temperature monitoring network, including: Set the temperature control parameters of the incubator and fix the wire to be measured on the test bracket in the incubator; Perform a cyclic loading test on the wire to be measured according to the preset high-temperature temperature cycle scheme, and perform real-time temperature acquisition on the wire to be measured through the wire temperature monitoring network to obtain the original temperature response data; Perform two-point calibration processing on the original temperature response data to obtain the calibrated temperature response data, and classify and organize the calibrated temperature response data according to the first measurement point group on the outer insulation layer surface, the second measurement point group at the bending point, and the third measurement point group at the connection point to obtain the real-time temperature response data.

[0023] In this example, the temperature control parameters of the thermostat are set. According to the application environment and temperature resistance requirements of the wire under test, the basic control range of the thermostat is set. For example, the temperature control range is set between room temperature and two hundred degrees Celsius, and the temperature fluctuation accuracy is required to be controlled within plus or minus zero point two degrees Celsius to ensure the consistency and stability of the temperature conditions at each stage during the test. At the same time, parameters are formulated for the heating rate, cooling rate, and temperature holding time respectively. The heating and cooling rates are set between one degree Celsius per minute and five degrees Celsius per minute, and the holding time is set according to the thermal response characteristics and aging mechanism characteristics of the material to fully simulate the thermal load accumulation experienced by the material during actual use. The wire under test is fixed on the test bracket inside the thermostat. The test bracket is made of a high-strength and high-temperature-resistant alloy material, and its design takes into account both mechanical stability and uniform heat convection, avoiding local temperature field distortion caused by excessive heat capacity of the bracket or structural occlusion. During the fixing process, it is reasonably arranged according to the layout characteristics of the wire itself, keeping the wire in a natural unfolded state. At the same time, micro grippers are set in the bending and connection point areas to assist in fixing, preventing displacement due to thermal expansion and contraction or air flow disturbance during the heating or cooling process, ensuring that the temperature monitoring array is always in the designed position throughout the test cycle, and guaranteeing the consistency and reliability of data acquisition. After the wire is fixed and the temperature field uniformity in the thermostat is confirmed to be good, the cyclic loading test is started according to the preset high-temperature temperature cycle scheme. The temperature cycle scheme includes multiple stages. For example, it is heated from room temperature to seventy degrees Celsius and held for two hours, then raised to eighty degrees Celsius and held for two hours, raised to ninety degrees Celsius and held for two hours, and finally slowly cooled back to room temperature. The entire cycle process is set to be executed once or continuously multiple times according to the test requirements. If long-term aging is simulated, the number of temperature cycles is set to more than ten thousand times. During the cyclic loading process, temperature data is collected in real time throughout the whole process through the preset wire temperature monitoring network. The data acquisition module records at a high speed according to a fixed sampling frequency, set at a time interval of ten milliseconds to one hundred milliseconds, to ensure capturing the dynamic details and mutation points of temperature changes. At the same time, each sensor measurement point data is timestamped to ensure the time synchronization of data at different positions in subsequent analysis. The original temperature response data is subjected to two-point calibration processing. Two standard temperature points of zero degrees Celsius and one hundred degrees Celsius are selected as references, and the actual outputs of each measurement point are tested and recorded in ice-water mixture and boiling water respectively. The correction coefficient is calculated based on the deviation between the theoretical standard value and the actual output value, and the calibration relationship formula for each measurement point is generated by using the method of linear interpolation or linear regression. The collected original temperature response data is corrected point by point according to their respective corresponding calibration relationship formulas to obtain the calibrated temperature response data. The calibrated temperature response data is systematically classified and sorted according to the wire structure position.The measured point data on the surface of the outer insulation layer are classified into the first group of measured points, and this group of data is used to evaluate the uniformity of the overall surface temperature distribution of the wire and the thermal response characteristics of the outer insulation material; the measured point data located at the bending points are classified into the second group of measured points, which is used to analyze the heat accumulation behavior of the wire in the local deformation area and the temperature anomalies caused by potential stress concentration; the measured point data at the connection points are classified into the third group of measured points, focusing on the temperature rise rate, thermal stability in areas such as connection terminals and solder joints, and the risk of local overheating due to material interface differences. During the classification and sorting process, the time series integrity of the original data is maintained, and at the same time, the physical space positions corresponding to each group of data and the corresponding acquisition time periods are marked to obtain real-time temperature response data.

[0024] In one example, temperature gradient analysis and thermal distribution characteristic calculation are performed on the real-time temperature response data to obtain a set of temperature change characteristic parameters, including: Obtain the first temperature response data corresponding to the first group of measured points on the surface of the outer insulation layer in the real-time temperature response data, and calculate the ratio of the temperature difference between adjacent measured points to the distance between the measured points based on the first temperature response data to obtain the temperature gradient distribution map of the surface of the outer insulation layer; Obtain the second temperature response data corresponding to the second group of measured points at the bending points in the real-time temperature response data, and perform an analysis of the temperature rise rate at dense points on the second temperature response data to obtain the heat accumulation characteristic data at the bending points; Obtain the third temperature response data corresponding to the third group of measured points at the connection points in the real-time temperature response data, and perform a statistical analysis of temperature fluctuations on the third temperature response data to obtain the temperature stability index at the connection points; Based on the temperature gradient distribution map, calculate the uniformity of the surface of the outer insulation layer, extract the time constant from the heat accumulation characteristic data at the same time, and perform weighted fusion in combination with the temperature stability index to obtain the thermal distribution characteristic model of the wire under test; Use the finite difference heat conduction algorithm to calculate the thermal diffusion coefficient of the thermal distribution characteristic model, and analyze the thermal impedance characteristics of the surface of the outer insulation layer, the bending points, and the connection points respectively to obtain the thermal response conduction parameters of the wire under test; Integrate the temperature gradient distribution map, heat accumulation characteristic data, temperature stability index, thermal distribution characteristic model, and thermal response conduction parameters in multiple dimensions to obtain a set of temperature change characteristic parameters.

[0025] In this example, the data of the first measurement point group on the surface of the outer insulation layer is extracted from the real-time temperature response data as the object of preliminary processing. Based on the data of these measurement points, the temperature difference between each pair of adjacent measurement points is calculated in sequence. At the same time, combined with the actual physical distance between each pair of measurement points, the ratio operation is performed between the temperature difference value and the corresponding distance to obtain a continuous temperature gradient data sequence. By plotting each temperature gradient value according to the spatial position into a two-dimensional or three-dimensional distribution map, a temperature gradient distribution map of the outer insulation layer surface is generated, revealing the differences in temperature change rates in different regions and reflecting the uniformity and local anomalies of the surface thermal field distribution. The second measurement point group at the bending part is extracted from the real-time temperature response data, and the data of these densely distributed measurement points in this part are analyzed. The temperature change rate of each measurement point is tracked with a short time step. By continuously calculating the temperature rise rate in the time series, it is identified whether there is an obvious heat accumulation phenomenon in the bending area, that is, the characteristic that the local temperature continuously rises over time instead of quickly reaching a steady state, forming heat accumulation characteristic data reflecting the heat accumulation behavior in the bending area. The real-time temperature response data of the third measurement point group at the connection point is obtained and statistically analyzed, mainly focusing on the temperature fluctuation characteristics. By calculating statistical quantities such as the standard deviation, range, and coefficient of variation of the temperature in the stable stage, the temperature stability index at the connection point is extracted, reflecting the connection process quality, the thermal contact effect of the material interface, and the thermal conduction consistency. Based on the temperature gradient distribution map, the temperature uniformity of the outer insulation layer surface is calculated. Based on the relationship between the highest temperature, the lowest temperature, and the average temperature in the whole region, the degree of consistency of the temperature distribution is evaluated. At the same time, the time constant is extracted from the heat accumulation characteristic data at the bending part. By analyzing the time required to reach the stable state in the temperature change curve, the heat response speed of the bending area is measured. Combined with the temperature stability index of the connection point, the three types of characteristics are comprehensively processed by using the method of weighted fusion. The weight setting is determined according to the proportion of the importance of each region in the overall thermal stability, generating a thermal distribution characteristic model of the wire under test, describing the thermal behavior differences and evolution trends of different structural units in the high-temperature cyclic environment. Based on the above thermal distribution characteristic model, the finite difference heat conduction algorithm is used for numerical solution. By using the methods of spatial discretization and time discretization, the process of temperature change over time is simulated at each discrete node, and by analyzing the heat transfer law between different regions, the thermal diffusion coefficients of the outer insulation layer surface, the bending part, and the connection point are respectively derived, so as to calculate the corresponding thermal impedance characteristics. By comparing the thermal impedance values of each region, it is clear which regions have greater obstacles in the heat conduction process and which regions are sensitive parts of heat accumulation and aging. The temperature gradient distribution map, the heat accumulation characteristic data at the bending part, the temperature stability index at the connection point, the comprehensively formed thermal distribution characteristic model, and the derived thermal response conduction parameters are integrated into multi-dimensional characteristics to form a temperature change characteristic parameter set.

[0026] In one example, temperature fluctuation compensation and thermal stress accumulation calculation are performed on the temperature change characteristic parameter set to obtain thermal stress characteristic parameters, including: Perform temperature random fluctuation compensation on the temperature gradient distribution diagram, heat accumulation characteristic data, and temperature stability index in the temperature change characteristic parameter set to obtain the temperature characteristic data after fluctuation compensation; Based on the temperature characteristic data after fluctuation compensation, construct an Arrhenius equation model, and calculate the aging rate of the wire insulation material under different temperature conditions according to the Arrhenius equation model to obtain a temperature-dependent aging rate function; Perform piecewise linear fitting on the temperature-dependent aging rate function to obtain a non-linear thermal response model of the wire to be tested; Based on the heat distribution characteristic model, calculate the heat conduction path between the surface, bending points, and connection points of the outer insulation layer to obtain the heat conduction characteristic curve of the wire to be tested; Perform bending radius correction on the heat accumulation characteristic data at the bending points to obtain the heat stress concentration distribution data of the wire in the bent state; Multiply the temperature-dependent aging rate function by the heat conduction characteristic curve, and perform weighted superposition calculation with the heat stress concentration distribution data to obtain the thermal stress characteristic parameters.

[0027] In this example, temperature random fluctuation compensation is performed on the temperature gradient distribution map, heat accumulation characteristic data, and temperature stability index in the temperature change characteristic parameter set. The weighted moving average method or the smoothing filtering technique based on a sliding window is used to suppress the random high-frequency temperature fluctuations caused by environmental disturbances, electronic noise, or instantaneous local material effects, and the temperature characteristic data after fluctuation compensation is obtained. An Arrhenius equation model is constructed based on the compensated temperature characteristic data to describe the relationship between the material aging rate and temperature. By extracting the corresponding thermal response characteristic parameters at multiple temperature points, such as the thermal diffusion change rate, the thermal stress accumulation rate, or the temperature stability deterioration rate, and combining the exponential influence law of temperature on the reaction rate expressed by the Arrhenius equation, the aging rate of the wire insulation material under different temperature conditions is deduced, forming a temperature-dependent aging rate function, revealing the basic dynamic characteristics of the material deterioration process in a high-temperature environment, and at the same time providing the key input parameters required for life prediction and reliability analysis. The temperature-dependent aging rate function is subjected to piecewise linear fitting. Since the material exhibits different aging mechanisms in different temperature ranges, for example, physical aging is dominant in the low-temperature section and chemical degradation is dominant in the high-temperature section, the temperature range is divided into several small sections, and linear fitting is performed within each section respectively to capture the local characteristics of the aging rate change in different temperature sections, forming a non-linear thermal response model. Based on the heat distribution characteristic model, the heat conduction paths between the outer insulation layer surface, the bending points, and the connection points of the wire under test are calculated. By analyzing the thermal diffusion direction, the local thermal resistance change, and the heat flux density distribution, the dominant path of heat transfer between regions and the corresponding heat conduction intensity are determined, and a heat conduction characteristic curve is formed to describe the migration law of heat between different structural units, and the heat flow bottleneck positions and potential high-temperature accumulation regions are identified. On the basis of completing the overall heat conduction characteristic analysis, special bending radius correction processing is performed on the heat accumulation characteristic data at the bending points. Since when the wire is in a bent state, the stress distribution, the thermal diffusion path, and the local heat accumulation behavior between the internal layers of the material will all change significantly due to the curvature change, a correction coefficient related to the actual bending radius is introduced to re-adjust the original heat accumulation data, and the heat stress concentration distribution data reflecting the true coiled state is obtained. The temperature-dependent aging rate function and the heat conduction characteristic curve are multiplied point by point to combine the temperature sensitivity of the material and the structural heat diffusion characteristics, forming a preliminary data set reflecting the local thermal stress evolution trend; the above-mentioned preliminary data and the heat stress concentration distribution data in the bent state are weighted and superimposed according to the set weights, and the weight distribution is determined according to the relative importance of each region in the overall life contribution. For example, the bending region is given a higher weight due to its stress concentration effect, and the surface region is appropriately weighted according to the actual heat load level. Finally, the thermal stress characteristic parameters of the wire under test are comprehensively formed.

[0028] In one example, the thermal stress characteristic parameters are input into the thermal stress attenuation network model for the analysis of the aging characteristics of the wire insulation material, and the predicted life and failure probability distribution of the wire to be tested in a high-temperature environment are obtained, including: Perform vector encoding on the thermal stress characteristic parameters to obtain the wire thermal characteristic feature vector; Input the wire thermal characteristic feature vector into the thermal stress analysis layer of the thermal stress attenuation network model, and capture the temperature dependence through the long short-term memory network in the thermal stress analysis layer to obtain the predicted performance indicators of the wire to be tested at different time points; Input the predicted performance indicators of the wire to be tested at different time points into the life prediction layer of the thermal stress attenuation network model, and calculate the characteristic life parameter and shape parameter of the life prediction layer through the Weibull distribution model in the life prediction layer; Construct a wire reliability function based on the characteristic life parameter and shape parameter, and perform a failure time probability density calculation on the wire reliability function to obtain the failure probability distribution of the wire to be tested under different temperature conditions; Combine the cumulative damage theory to perform an acceleration factor conversion on the failure probability distribution, obtain the prediction results under the accelerated test conditions, and convert the prediction results under the accelerated test conditions into data under the actual use temperature conditions to obtain the predicted life of the wire to be tested in a high-temperature environment.

[0029] In this example, the thermal stress characteristic parameters are standardized and vector-encoded. The temperature gradient characteristics, heat accumulation rate, thermal diffusivity, local thermal impedance, aging rate change curve, bending stress concentration correction data, and other relevant thermodynamics behavior characteristics are uniformly sorted into a group of high-dimensional wire thermal characteristic vectors in a set order. At the same time, during the encoding process, the characteristics with different dimensions are normalized to ensure that each characteristic can participate in model learning with the same weight during the subsequent neural network input, improving the training convergence speed and prediction accuracy. The wire thermal characteristic vectors are input into the thermal stress analysis layer of the thermal stress attenuation network model. The core of this analysis layer uses a long short-term memory network as the basic structure. Through its internal memory units and gating mechanisms, it effectively captures the dynamic correlation characteristics of the input feature vectors changing over time, and extracts the complex dependence relationship between temperature, time, and material property degradation. The long short-term memory network continuously updates the state of the memory units, records the cumulative impact of historical temperature changes on the current material state, and adjusts the prediction trend in a timely manner according to short-term input changes, outputting a set of predicted performance indicators covering different time points, reflecting the thermal aging evolution trajectory of the wire under specific temperature cycles and thermal stress. The predicted performance indicators are input into the life prediction layer of the thermal stress attenuation network model. Inside this layer, a life analysis framework is constructed based on the Weibull distribution model. By fitting the time change trend of the predicted performance indicators, the characteristic life parameter and shape parameter required by the life prediction layer are derived. The characteristic life parameter is used to characterize the time required for the wire to reach the preset failure probability, while the shape parameter describes the acceleration degree and distribution characteristics of the failure probability increasing with time. These two parameters jointly determine the shape and position of the wire life distribution curve. A wire reliability function is constructed based on the characteristic life parameter and shape parameter. This function describes the probability level that the wire still maintains normal function and has not failed at any moment. By taking the first-order derivative of the reliability function along the time dimension, the failure rate function is obtained, and through the derivation and operation of the failure rate function, the failure time probability density function is obtained. This function describes the probability density distribution of the wire failure event at different time nodes, thereby obtaining the failure probability distribution curve of the wire to be tested under different temperature conditions. In order to extend the failure probability distribution results obtained under accelerated test conditions to the actual use environment, the accelerated factor conversion of the failure probability distribution is carried out in combination with the cumulative damage theory. Based on the difference in the aging rate of the material in different temperature environments, the accelerated aging test data is mapped to the target use temperature conditions through the accelerated factor. The conversion process comprehensively considers the multiple effects of temperature on the material aging mechanism, reaction rate, and failure mode, ensuring that the converted data has physical rationality and engineering application significance. After completing the accelerated factor conversion, the life prediction result under the accelerated test conditions is obtained, and this result is converted into the reliability data under the actual use temperature conditions.Obtain the predicted life data of the wire to be tested in a high-temperature environment, including the average life value, and provide the expected service time intervals corresponding to different failure probability levels (such as 10%, 50%, 90%).

[0030] In one example, a wire reliability function is constructed based on the characteristic life parameter and the shape parameter, and the failure time probability density calculation is performed on the wire reliability function to obtain the failure probability distribution of the wire to be tested under different temperature conditions, including: Substitute the characteristic life parameter and the shape parameter into the basic reliability equation and introduce a temperature correction term to obtain the wire reliability function; Perform the first-order differentiation of the wire reliability function in the time dimension to obtain the failure rate function, and calculate the failure probability density function according to the failure rate function to obtain the failure time distribution curve of the wire to be tested at each temperature point; Perform piecewise interval integration on the failure time distribution curve of the wire to be tested at each temperature point to obtain the cumulative failure probability function; Substitute the cumulative failure probability function into the Miner linear cumulative damage model and calculate the comprehensive failure probability under the composite temperature condition through the equivalent life principle to obtain the comprehensive failure probability matrix; Perform temperature-time bivariate interpolation calculation based on the comprehensive failure probability matrix, establish a three-dimensional temperature-time-failure probability model, and extract the corresponding relationship between temperature and wire life based on the three-dimensional temperature-time-failure probability model to obtain the failure probability distribution of the wire to be tested under different temperature conditions.

[0031] In this example, the characteristic life parameter and the shape parameter are substituted into the reliability basic equation, and a temperature correction term is introduced in the process to consider the influence of different temperature environments on the material aging behavior. The temperature correction term is dynamically adjusted according to the material thermosensitivity characteristics, the change law of the acceleration factor, and the experimental statistical results, so that the constructed wire reliability function can truly and comprehensively reflect the reliability evolution law of the wire under test under different temperature loads. The first-order differential of the wire reliability function with respect to the time dimension is taken to obtain the corresponding failure rate function. The failure rate function reveals the conditional probability of failure per unit time of the wire under test at any moment, and reflects the trend of acceleration or deceleration of material property degradation over time. Based on the failure rate function, through further mathematical derivation, the failure probability density function is calculated, that is, the relative frequency density of the occurrence of failure events at each time point is described, and the failure time distribution curve of the wire under test at each temperature point is obtained. The failure time distribution curve of the wire under test at each temperature point is integrated in segmented intervals. By integrating the failure probability density function in a specific time interval, the cumulative failure probability function of the corresponding interval is obtained. This function describes the probability evolution process of the wire accumulating to the failure state at different operating times. The cumulative failure probability function is substituted into the Miner linear cumulative damage model. By linearly superimposing the cumulative failure probabilities under different temperature conditions according to the Miner theory, and combining the temperature change trajectory in actual applications, the damage contributions of each stage are renormalized according to the equivalent life principle, and the comprehensive failure probability under the composite temperature condition is calculated. This step can effectively solve the problem that the damage rates of each stage of the material are different in the multi-temperature cycle environment, so that the obtained failure prediction results have both multi-condition adaptability and the simplicity and practicability of theoretical derivation. Through the above calculations, a comprehensive failure probability matrix is obtained, where the row direction corresponds to different temperature points, the column direction corresponds to different usage time nodes, and the value of each matrix element represents the probability level of the wire accumulating failure under the given temperature and usage time conditions. Temperature-time bivariate interpolation calculation is performed based on the comprehensive failure probability matrix. By selecting an interpolation algorithm, such as bilinear interpolation, cubic spline interpolation, or interpolation method based on radial basis function, a continuous and smooth three-dimensional model of temperature-time-failure probability is constructed on the basis of the original discrete data of the matrix. Based on the three-dimensional model of temperature-time-failure probability, data extraction and mapping are carried out. The corresponding failure probability curves are extracted for different temperature conditions, and then the corresponding relationship diagram between temperature and wire life is drawn. By analyzing this corresponding relationship diagram, a non-linear trend that the wire life decreases rapidly with the increase of the environmental temperature is obtained, and at the same time, the acceleration point of material property degradation in a specific temperature range is identified.

[0032] Referring to Figure 2 , this embodiment provides a wire high-temperature performance detection device, including: Arrangement module 1, which is used to arrange a micro-thermocouple array sensor on the surface of the outer insulating layer, at the bending points and connection points of the wire to be tested, so as to obtain a wire temperature monitoring network; Circular test module 2, which is used to place the wire to be tested in a constant temperature box to perform a high-temperature cyclic loading test, and collect the real-time temperature response data of the wire to be tested during the temperature change process through the wire temperature monitoring network; Gradient analysis module 3, which is used to perform temperature gradient analysis and thermal distribution characteristic calculation on the real-time temperature response data to obtain a set of temperature change characteristic parameters; Accumulation calculation module 4, which is used to perform temperature fluctuation compensation and thermal stress accumulation calculation on the set of temperature change characteristic parameters to obtain thermal stress characteristic parameters; Aging characteristic analysis module 5, which is used to input the thermal stress characteristic parameters into a thermal stress attenuation network model for analyzing the aging characteristics of the wire insulating material, so as to obtain the predicted life and failure probability distribution of the wire to be tested in a high-temperature environment.

[0033] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to that described in the above method embodiment, and details will not be elaborated here.

[0034] Refer to Figure 3 , in the embodiment of the present invention, a computer device is further provided. This computer device can be a server, and its internal structure can be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of this computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0035] Those skilled in the art can understand that Figure 3 the structure shown in

[0036] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0037] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0038] It should be noted that in this article, the terms "including", "comprising", or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, device, article, or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, device, article, or method including the element.

[0039] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall equally be included in the patent protection scope of the present invention.

Claims

1. A method for detecting the high-temperature resistance performance of wire materials, characterized in that, Including: Arranging a micro-thermocouple array sensor on the surface of the outer insulating layer, bending points and connection points of the wire under test to obtain a wire temperature monitoring network; Placing the wire under test in an incubator to perform a high-temperature cyclic loading test, and collecting real-time temperature response data of the wire under test during the temperature change process through the wire temperature monitoring network; Performing temperature gradient analysis and thermal distribution characteristic calculation on the real-time temperature response data to obtain a temperature change characteristic parameter set; Performing temperature fluctuation compensation and thermal stress accumulation calculation on the temperature change characteristic parameter set to obtain thermal stress characteristic parameters; Inputting the thermal stress characteristic parameters into a thermal stress attenuation network model for aging characteristic analysis of the wire insulating material to obtain the predicted life and failure probability distribution of the wire under test in a high-temperature environment.

2. The wire high-temperature resistance performance detection method according to claim 1, characterized in that, The arranging a micro-thermocouple array sensor on the surface of the outer insulating layer, bending points and connection points of the wire under test to obtain a wire temperature monitoring network includes: Constructing a micro-thermocouple array sensor of a platinum-rhodium alloy ultra-thin thermistor; Performing measuring point planning on the wire under test according to a preset layout rule, arranging a measuring point every 10 mm on the surface of the outer insulating layer of the wire under test, and arranging a measuring point every 2 mm at the bending points and connection points of the wire under test to obtain a wire temperature monitoring point distribution map; Arranging the micro-thermocouple array sensor in a three-dimensional spiral distribution according to the wire temperature monitoring point distribution map, and fixing the micro-thermocouple array sensor and the surface of the wire under test with high thermal conductivity epoxy resin to obtain a wire temperature monitoring array; Connecting the wire temperature monitoring array to a high-precision analog-to-digital converter and a signal conditioning circuit, and connecting the wire temperature monitoring array to a data acquisition module through a shielded multi-core cable to obtain a wire temperature monitoring network.

3. The method for detecting the high-temperature resistance performance of a wire according to claim 1, wherein The placing the wire under test in an incubator to perform a high-temperature cyclic loading test, and collecting real-time temperature response data of the wire under test during the temperature change process through the wire temperature monitoring network includes: Setting the temperature control parameters of the incubator, and fixing the wire under test on a test bracket in the incubator; Performing a cyclic loading test on the wire under test according to a preset high-temperature temperature cycle scheme, and performing real-time temperature acquisition on the wire under test through the wire temperature monitoring network to obtain original temperature response data; Performing two-point calibration processing on the original temperature response data to obtain calibrated temperature response data, and classifying and sorting the calibrated temperature response data according to a first measuring point group on the surface of the outer insulating layer, a second measuring point group at the bending points, and a third measuring point group at the connection points to obtain real-time temperature response data.

4. The method for detecting the high temperature resistance performance of the wire according to claim 3, characterized in that, The performing temperature gradient analysis and thermal distribution characteristic calculation on the real-time temperature response data to obtain a temperature change characteristic parameter set includes: Obtaining first temperature response data corresponding to the first measuring point group on the surface of the outer insulating layer in the real-time temperature response data, and calculating the ratio of the temperature difference between adjacent measuring points to the distance between the measuring points based on the first temperature response data to obtain a temperature gradient distribution map on the surface of the outer insulating layer; Obtain the second temperature response data corresponding to the second measurement point group at the bent part in the real-time temperature response data, and perform intensive point temperature rise rate analysis on the second temperature response data to obtain the heat accumulation characteristic data at the bent part; Obtain the third temperature response data corresponding to the third measurement point group at the connection point in the real-time temperature response data, and perform temperature fluctuation statistical analysis on the third temperature response data to obtain the temperature stability index at the connection point; Based on the temperature gradient distribution map, calculate the uniformity of the outer insulation layer surface, extract the time constant from the heat accumulation characteristic data at the same time, and perform weight fusion in combination with the temperature stability index to obtain the heat distribution characteristic model of the wire under test; Use the finite difference heat conduction algorithm to calculate the thermal diffusion coefficient of the heat distribution characteristic model, and analyze the thermal impedance characteristics of the outer insulation layer surface, the bent part and the connection point respectively to obtain the thermal response conduction parameters of the wire under test; Perform multi-dimensional feature integration on the temperature gradient distribution map, the heat accumulation characteristic data, the temperature stability index, the heat distribution characteristic model and the thermal response conduction parameters to obtain a temperature change characteristic parameter set.

5. The method for detecting the high-temperature resistance performance of a wire according to claim 1, wherein Performing temperature fluctuation compensation and thermal stress accumulation calculation on the temperature change characteristic parameter set to obtain thermal stress characteristic parameters, including: Perform temperature random fluctuation compensation on the temperature gradient distribution map, the heat accumulation characteristic data and the temperature stability index in the temperature change characteristic parameter set to obtain temperature characteristic data after fluctuation compensation; Based on the temperature characteristic data after fluctuation compensation, construct an Arrhenius equation model, and calculate the aging rate of the wire insulation material under different temperature conditions according to the Arrhenius equation model to obtain a temperature-dependent aging rate function; Perform piecewise linear fitting on the temperature-dependent aging rate function to obtain the non-linear thermal response model of the wire under test; Based on the heat distribution characteristic model, calculate the heat conduction path between the outer insulation layer surface, the bent part and the connection point to obtain the heat conduction characteristic curve of the wire under test; Correct the heat accumulation characteristic data at the bent part for the bending radius to obtain the heat stress concentration distribution data in the bent state of the wire; Multiply the temperature-dependent aging rate function by the heat conduction characteristic curve, and perform weighted superposition calculation with the heat stress concentration distribution data to obtain thermal stress characteristic parameters.

6. The method for detecting the high temperature resistance performance of the wire according to claim 1, characterized in that, Inputting the thermal stress characteristic parameters into a thermal stress attenuation network model for aging characteristic analysis of the wire insulation material to obtain the predicted life and failure probability distribution of the wire under test in a high-temperature environment, including: Perform vector encoding on the thermal stress characteristic parameters to obtain a wire thermal characteristic feature vector; Input the wire thermal characteristic feature vector into the thermal stress analysis layer of the thermal stress attenuation network model, and capture the temperature-dependent relationship through the long short-term memory network in the thermal stress analysis layer to obtain the predicted performance index of the wire under test at different time points; Input the predicted performance indicators of the wire to be tested at different time points into the life prediction layer of the thermal stress attenuation network model, and calculate the characteristic life parameter and shape parameter of the life prediction layer through the Weibull distribution model in the life prediction layer; Construct a wire reliability function based on the characteristic life parameter and the shape parameter, and perform a failure time probability density calculation on the wire reliability function to obtain the failure probability distribution of the wire to be tested under different temperature conditions; Combine the cumulative damage theory to perform an acceleration factor conversion on the failure probability distribution, obtain the prediction result under the accelerated test condition, and convert the prediction result under the accelerated test condition into data under the actual use temperature condition to obtain the predicted life of the wire to be tested in a high-temperature environment.

7. The wire high-temperature resistance performance detection method according to claim 6, wherein The constructing a wire reliability function based on the characteristic life parameter and the shape parameter, and performing a failure time probability density calculation on the wire reliability function to obtain the failure probability distribution of the wire to be tested under different temperature conditions includes: Substitute the characteristic life parameter and the shape parameter into the reliability basic equation, and introduce a temperature correction term to obtain a wire reliability function; Perform a first-order differentiation on the wire reliability function in the time dimension to obtain a failure rate function, and calculate a failure probability density function according to the failure rate function to obtain the failure time distribution curve of the wire to be tested at each temperature point; Perform a piecewise interval integration on the failure time distribution curve of the wire to be tested at each temperature point to obtain a cumulative failure probability function; Substitute the cumulative failure probability function into the Miner linear cumulative damage model, and calculate the comprehensive failure probability under the composite temperature condition through the equivalent life principle to obtain a comprehensive failure probability matrix; Perform a temperature-time bivariate interpolation calculation based on the comprehensive failure probability matrix, establish a three-dimensional temperature-time-failure probability model, and extract the corresponding relationship between temperature and wire life based on the three-dimensional temperature-time-failure probability model to obtain the failure probability distribution of the wire to be tested under different temperature conditions.

8. A device for detecting the high-temperature resistance performance of a wire, characterized in that, For implementing the steps of the wire high-temperature resistance performance detection method described in any one of claims 1 to 7, the wire high-temperature resistance performance detection device includes: An arrangement module, configured to arrange a micro-thermocouple array sensor on the outer insulation layer surface, bending points, and connection points of the wire to be tested to obtain a wire temperature monitoring network; A cyclic test module, configured to place the wire to be tested in a constant temperature box to perform a high-temperature cyclic loading test, and collect real-time temperature response data of the wire to be tested during the temperature change process through the wire temperature monitoring network; A gradient analysis module, configured to perform temperature gradient analysis and thermal distribution characteristic calculation on the real-time temperature response data to obtain a set of temperature change characteristic parameters; An accumulation calculation module, configured to perform temperature fluctuation compensation and thermal stress accumulation calculation on the set of temperature change characteristic parameters to obtain thermal stress characteristic parameters; The aging characteristic analysis module is used to input the thermal stress characteristic parameters into the thermal stress attenuation network model for analyzing the aging characteristics of the wire insulation material, and obtain the predicted life and failure probability distribution of the wire under test in a high-temperature environment.

9. A computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, characterized in that, When the processor executes the computer program, the steps of the wire high-temperature resistance performance detection method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the wire high-temperature resistance performance detection method according to any one of claims 1 to 7 are implemented.

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