Temperature detection method and device of electronic component, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a temperature detection method and device for an electronic component, equipment and a medium. The method comprises the following steps: acquiring historical temperature data of the electronic component, and constructing a temperature distribution model of the electronic component based on the historical temperature data; obtaining the average temperature of the electronic component in the temperature distribution model, and determining corresponding temperature detection frequencies of the electronic component under different temperature conditions based on the average temperature; obtaining material characteristics and real-time environment characteristics corresponding to the electronic component, and correcting the temperature detection frequency based on the material characteristics and the real-time environment characteristics to obtain a target temperature detection frequency; and performing temperature detection on the electronic component based on the target detection frequency to obtain a corresponding temperature detection result, and updating the temperature distribution model based on the temperature detection result. According to the embodiment of the invention, the rationality and accuracy of temperature detection of the electronic component are improved.

Description

Technical Field

[0001] The present application relates to the field of thermal management technology, and in particular to a temperature detection method and device for electronic components, an electronic device, and a computer-readable storage medium. Background Art

[0002] With the in-depth development of automotive electronics, the integration of chips and hardware inside the ECU is becoming increasingly higher, and power consumption is also increasing. High temperature environment will have a negative impact on the performance of chips and hardware, and even cause failures. Therefore, the efficiency and accuracy of the thermal management module are directly related to the reliability and service life of the ECU.

[0003] However, in existing electronic equipment thermal management systems, in order to prevent a chip such as SOC, MCU chip, etc. and some hardware such as camera screen from overheating and being damaged, it is usually necessary to monitor the temperature of each chip and hardware, and use the application layer software to periodically read all temperature data through the API interface at a certain frequency. When the temperature of a monitored chip or hardware is overheated, take corresponding measures. However, due to the significant differences in thermal conductivity between different materials, the temperature variation range and overheating range of each monitored object are different. The traditional fixed-frequency API call method cannot accurately and quickly reflect the actual temperature status of each component. Summary of the invention

[0004] To solve the above technical problems, embodiments of the present application provide a temperature detection method and device for electronic components, an electronic device, a computer-readable storage medium, and a computer program product.

[0005] According to one aspect of an embodiment of the present application, a temperature detection method for electronic components is provided, including: obtaining historical temperature data of the electronic components, and constructing a temperature distribution model of the electronic components based on the historical temperature data; obtaining the average temperature of the electronic components in the temperature distribution model, and determining the temperature detection frequency corresponding to the electronic components under different temperature conditions based on the average temperature; obtaining the material properties and real-time environmental characteristics corresponding to the electronic components, and correcting the temperature detection frequency based on the material properties and the real-time environmental characteristics to obtain a target temperature detection frequency; performing temperature detection on the electronic components based on the target detection frequency to obtain corresponding temperature detection results, and updating the temperature distribution model based on the temperature detection results.

[0006] According to one aspect of an embodiment of the present application, determining the temperature detection frequency of the electronic components under different temperature conditions based on the average temperature includes: determining the thermal conductivity corresponding to the material properties of the electronic components; determining the initial temperature detection frequency of the electronic components corresponding to the average temperature based on the thermal conductivity; and determining the temperature detection frequency of the electronic components under different temperature conditions based on the relationship between the average temperature and the real-time temperature of the electronic components.

[0007] According to one aspect of an embodiment of the present application, the temperature detection efficiency of the electronic components under different temperature conditions is determined based on the relationship between the average temperature and the real-time temperature of the electronic components, including: if the real-time temperature of the electronic components is greater than the average temperature, adjusting the initial temperature detection frequency based on a first adjustment rate coefficient to obtain a first target temperature detection frequency corresponding to the electronic components; if the real-time temperature of the electronic components is lower than the average temperature, adjusting the initial temperature detection frequency based on a second adjustment rate coefficient to obtain a second target temperature detection frequency corresponding to the electronic components, wherein the second adjustment rate coefficient is lower than the first adjustment rate coefficient.

[0008] According to one aspect of an embodiment of the present application, the method further includes: determining a temperature change difference of the electronic component within a preset time period based on the temperature distribution model; and determining the first adjustment rate coefficient and the second adjustment rate coefficient based on the temperature change difference.

[0009] According to one aspect of an embodiment of the present application, the method also includes: obtaining environmental characteristics corresponding to the electronic components at a current moment, the environmental characteristics including ambient temperature and ambient humidity; determining the first adjustment rate coefficient and the second adjustment rate coefficient based on the ambient temperature, the ambient humidity and the thermal conductivity.

[0010] According to one aspect of an embodiment of the present application, the method of obtaining historical temperature data of electronic components and constructing a temperature distribution model of the electronic components based on the historical temperature data includes: obtaining historical temperature data and historical environmental data of the electronic components within a preset time period; calculating a normal distribution model corresponding to the temperature of the electronic components based on the historical temperature data, the normal distribution model including a temperature mean and a temperature standard deviation corresponding to the electronic components; and correcting the normal distribution model based on the historical environmental data to obtain a temperature distribution model corresponding to the electronic components.

[0011] According to one aspect of an embodiment of the present application, the method also includes: obtaining historical temperature data of the electronic components within a preset time period; extracting target historical temperature data corresponding to multiple different time points from the historical temperature data through a target random function; and calculating a normal distribution model corresponding to the temperature of the electronic components based on the target historical temperature data.

[0012] According to one aspect of an embodiment of the present application, a temperature detection device for electronic components is provided, the device comprising: a first acquisition module, used to acquire historical temperature data of the electronic components, and construct a temperature distribution model of the electronic components based on the historical temperature data; a second acquisition module, used to acquire the average temperature of the electronic components in the temperature distribution model, and determine the temperature detection frequency of the electronic components under different temperature conditions based on the average temperature; a third acquisition module, used to acquire the material properties corresponding to the electronic components and the environmental characteristics in which they are located, and correct the temperature detection frequency based on the material properties and the environmental characteristics to obtain a target temperature detection frequency; a detection module, used to perform temperature detection on the electronic components based on the target detection frequency to obtain corresponding temperature detection results, and update the temperature distribution model based on the temperature detection results.

[0013] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device implements the temperature detection method for electronic components as described above.

[0014] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer executes the temperature detection method of electronic components as described above.

[0015] According to one aspect of an embodiment of the present application, a computer program product is also provided, including a computer program, which implements the steps in the temperature detection method of electronic components as described above when executed by a processor.

[0016] In the technical solution provided in the embodiments of the present application, by acquiring the historical temperature data of electronic components and constructing the temperature distribution model of electronic components, it is possible to more accurately understand the temperature distribution of electronic components in different time periods and under different working conditions. This refined temperature management helps to timely discover potential overheating risks and avoid performance degradation or damage caused by excessive temperature. By correcting the temperature detection frequency by considering the material properties and real-time environmental characteristics of electronic components, the temperature detection strategy is more in line with actual working conditions. This dynamic adjustment capability enables efficient temperature monitoring to be maintained in the face of different working environments and conditions, and forms a closed-loop feedback through real-time temperature detection and the update of the temperature distribution model. By continuously acquiring new temperature data and updating the model, the temperature detection of electronic components is more accurate and effective.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0019] Figure 1 is a schematic diagram of an implementation environment for temperature detection of electronic components shown in an exemplary embodiment of the present application;

[0020] Figure 2 is a flow chart of a temperature detection method of an electronic component shown in an exemplary embodiment of the present application;

[0021] Figure 3 is a flow chart of a temperature detection method of an electronic component shown in another exemplary embodiment of the present application;

[0022] Figure 4 is a flow chart of a temperature detection method of an electronic component shown in another exemplary embodiment of the present application;

[0023] Figure 5 is a flow chart of a temperature detection method of an electronic component shown in another exemplary embodiment of the present application;

[0024] Figure 6 is a flow chart of a temperature detection method of an electronic component shown in another exemplary embodiment of the present application;

[0025] Figure 7 is a flow chart of a temperature detection method of an electronic component shown in another exemplary embodiment of the present application;

[0026] Figure 8 is a normal temperature distribution model of an electronic component shown in another exemplary embodiment of the present application;

[0027] Fig. 9 is a flow chart of a temperature detection method of an electronic component shown in another exemplary embodiment of the present application;

[0028] Fig.10 It is a brief flow chart of temperature detection of electronic components in an exemplary application scenario;

[0029] Fig.11 is a block diagram of a temperature detection device for electronic components shown in an exemplary embodiment of the present application;

[0030] Fig.12 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0031] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0034] The term "multiple" as used in this application refers to two or more than two. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0035] First of all, it should be noted that the thermal management modules of different chips and hardware in the ECU (Engine Control Unit) are crucial to ensure the stable operation and efficient performance of the ECU.

[0036] Different chips and hardware have different thermal management requirements. For example: Central Processing Unit (CPU): As the core of ECU, the CPU's computing speed and processing power directly affect the overall performance of ECU. CPU generates a lot of heat when working, which needs to be dissipated in time through the thermal management module to ensure its stable operation. The thermal management module needs to accurately control the temperature of CPU to prevent performance degradation or damage caused by overheating; Power Management Chip: Responsible for providing stable power supply for other chips and hardware in ECU. Power management chip also generates a certain amount of heat when working. If the heat dissipation is poor, it will cause power fluctuation and affect the stability of ECU. Therefore, the thermal management module needs to ensure that the temperature of power management chip is within a reasonable range; Communication interface chip: Such as CAN bus interface chip, responsible for the communication between ECU and other vehicle systems. Communication interface chip also needs thermal management when working to ensure the stability and reliability of communication; Sensors and actuators: Although the heat generated by sensors and actuators themselves is relatively small, their performance is greatly affected by temperature. The thermal management module needs to ensure that sensors and actuators work within a suitable temperature range to improve their measurement accuracy and execution efficiency.

[0037] Figure 1 FIG. 1 is a schematic diagram of an implementation environment for temperature detection of electronic components during thermal management, shown in an exemplary embodiment of the present application. Figure 1As shown, in the thermal management process, for the temperature detection scheme of the electronic component 110, the historical temperature data of the electrical component can be obtained through the server 120, and a temperature distribution model of the electronic component can be constructed based on the historical temperature data. Then, the average temperature of the electronic component can be determined from the temperature distribution model, and the temperature detection frequency of the electronic component under different temperature conditions can be determined according to the average temperature. Furthermore, the server 120 obtains the material properties and real-time environmental characteristics corresponding to the electronic component, and corrects the temperature detection frequency of the electronic component based on the material properties and the real-time environmental characteristics to obtain a target temperature detection frequency. Then, the electronic component is temperature detected according to the target detection frequency to obtain a corresponding detection result, and the temperature distribution model can be further optimized according to the obtained detection result to make the calculated target temperature detection frequency more accurate.

[0038] in, Figure 1 The server 120 shown may be, for example, an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, and is not limited here. It may also be an intelligent terminal, such as a smart phone, a car computer, a tablet computer, a laptop computer, or a wearable device, or any other terminal device that supports thermal management.

[0039] With the in-depth development of automotive electronics, the integration of chips and hardware inside the ECU is becoming increasingly higher, and power consumption is also increasing. High temperature environment will have a negative impact on the performance of chips and hardware, and even cause failures. Therefore, the efficiency and accuracy of the thermal management module are directly related to the reliability and service life of the ECU.

[0040] However, the existing thermal management system collects the temperature of electronic components at a fixed frequency and cannot accurately and quickly reflect the actual temperature status of each component.

[0041] The above-mentioned problems are generally applicable in the general thermal management process. In order to solve these problems, the embodiments of the present application respectively propose a temperature detection method of an electronic component, a temperature detection device of an electronic component, an electronic device, a computer-readable storage medium and a computer program product, which will be described in detail below.

[0042] See also Figure 2 , Figure 2is a flow chart of a temperature detection method for an electronic component shown in an exemplary embodiment of the present application. The method can be applied to Figure 1 The implementation environment shown is specifically executed by the server 120 in the implementation environment. It should be understood that the method can also be applied to other exemplary implementation environments and be specifically executed by devices in other implementation environments. This embodiment does not limit the implementation environment to which the method is applicable.

[0043] like Figure 2 As shown, in an exemplary embodiment, the temperature detection of the electronic component includes at least steps S210 to S240, which are described in detail as follows:

[0044] Step S210, acquiring historical temperature data of electronic components, and constructing a temperature distribution model of the electronic components based on the historical temperature data.

[0045] Specifically, in order to build a normal temperature distribution model for different chips and hardware in the intelligent driving ECU, it is necessary to collect sufficient historical temperature data. The core of data collection is to ensure that the model can accurately reflect the actual temperature distribution of each chip and hardware. Data sources: Equipment monitoring data: The most direct data source is the monitoring data of electronic components in actual applications. These data are usually collected in real time by the temperature sensors of the equipment and stored in the database or data warehouse; Experimental data: In a laboratory environment, a large amount of experimental data can be obtained by simulating the working conditions of electronic components under different temperature conditions. These data are crucial to understanding the performance of electronic components at different temperatures; Public data sets: For example, some research institutions or companies may disclose their experimental data or monitoring data, which can be used as a supplement to building models. Select a suitable model based on the characteristics and needs of the data. Common models include linear regression, nonlinear regression, neural network, support vector machine, etc. For the temperature distribution model of electronic components, it may be necessary to consider the change of temperature over time and space, so it may be necessary to adopt a spatiotemporal model or a dynamic model.

[0046] Step S220, obtaining the average temperature of the electronic components in the temperature distribution model, and determining the temperature detection frequency corresponding to the electronic components under different temperature conditions based on the average temperature.

[0047] Specifically, after obtaining the temperature distribution model of the electronic components, the average temperature in the model can be further calculated, and the temperature detection frequency of the electronic components under different temperature conditions can be determined based on this average temperature. Different temperature intervals (such as normal temperature interval, warning temperature interval, dangerous temperature interval, etc.) are divided based on the operating temperature range and temperature distribution model of the electronic components. Different temperature detection frequency standards are set according to the importance and risk level of each temperature interval. Generally speaking, the higher the temperature and the greater the risk, the higher the detection frequency should be. A temperature detection frequency table is formulated to clarify the frequency of temperature detection in each temperature interval. This table can be a simple threshold table or a dynamic frequency table that changes continuously according to the temperature.

[0048] Step S230, obtaining material properties and real-time environmental characteristics corresponding to the electronic components, and correcting the temperature detection frequency based on the material properties and the real-time environmental characteristics to obtain a target temperature detection frequency.

[0049] Specifically, the material properties of electronic components usually include conductivity, insulation, thermal stability and corrosion resistance. These properties are crucial to the performance and stability of electronic components. Conductivity:

[0050] Materials with strong conductivity (such as copper, aluminum, etc.) are often used to make metal wires and electrodes in electronic components. The conductivity of the material may affect its heating when current passes through it, and thus affect the setting of the temperature detection frequency. Insulation: Insulating materials (such as plastics, silicone, rubber, etc.) are used to prevent short circuits between electronic components. The thermal conductivity of insulating materials may also affect the temperature distribution and detection frequency of electronic components. Thermal stability: Materials with strong thermal stability (such as organic polymer materials, ceramic materials, high-temperature alloys, etc.) can maintain stable performance in high-temperature environments. For electronic components working in high-temperature environments, the thermal stability of their materials will directly affect the setting of the temperature detection frequency. Corrosion resistance: Materials with strong corrosion resistance can resist the erosion of corrosive agents and extend the service life of electronic components. The corrosion resistance of materials may indirectly affect the heat dissipation performance and temperature detection requirements of electronic components. Real-time environmental characteristics include factors that may affect the temperature of electronic components, such as ambient temperature, humidity, air flow velocity, and electromagnetic radiation. The ambient temperature will directly affect the operating temperature and heat dissipation of electronic components, and it is necessary to monitor them in real time through equipment such as temperature sensors. In addition, excessive humidity may cause electronic components to become damp or corroded, affecting performance, which can be monitored through humidity sensors. Air flow velocity affects the heat dissipation effect of electronic components and can be monitored through an anemometer or air flow sensor.

[0051] Based on the material properties and real-time environmental characteristics of electronic components, the temperature detection frequency can be modified as follows: Influence of material properties: For electronic components made of materials with strong conductivity and poor thermal stability, the temperature detection frequency should be increased to prevent overheating damage. For electronic components with good insulation, the influence of their insulation layer on temperature distribution can be considered during temperature detection. When the ambient temperature is high, the temperature detection frequency should be increased to ensure that the electronic components will not overheat. At the same time, when the humidity is too high, in addition to increasing the temperature detection frequency, attention should also be paid to moisture-proof measures for electronic components. When the air flow speed is slow, the heat dissipation effect of electronic components may not be good, and the temperature detection frequency also needs to be increased. When the electromagnetic radiation is strong, in addition to paying attention to the electromagnetic compatibility of electronic components, its interference with the temperature detection equipment should also be considered. According to the monitoring results of the material properties and real-time environmental characteristics of electronic components, their influence on the temperature detection frequency is comprehensively evaluated, and the performance and stability of electronic components under different detection frequencies are verified through experiments or simulations. According to the verification results, the temperature detection frequency is adjusted to the target value to ensure that the electronic components operate in a safe and reliable state.

[0052] Step S240 , performing temperature detection on the electronic components based on the target detection frequency to obtain corresponding temperature detection results, and updating the temperature distribution model based on the temperature detection results.

[0053] Specifically, develop a detailed temperature detection plan based on the previously determined target detection frequency. This includes determining the time point of detection, the detection equipment and methods used, etc. According to the detection plan, use temperature detection equipment (such as temperature sensors, infrared thermometers, etc.) to perform temperature detection on electronic components at specified time points. Ensure the accuracy and reliability of the detection equipment to reduce measurement errors. Record the detected temperature data, including detection time, detection location, temperature value, etc. These data will be used for subsequent analysis and model updates, and then based on the new temperature detection results, evaluate whether the current temperature distribution model is still applicable. If there is a large deviation between the model and the new data, it may need to be updated.

[0054] In this embodiment, by acquiring the historical temperature data of electronic components and constructing a temperature distribution model of electronic components, it is possible to more accurately understand the temperature distribution of electronic components in different time periods and under different working conditions. This refined temperature management helps to promptly detect potential overheating risks and avoid performance degradation or damage caused by excessive temperature. The temperature detection frequency is corrected by considering the material properties and real-time environmental characteristics of the electronic components, so that the temperature detection strategy is more in line with actual working conditions. This dynamic adjustment capability enables the system to maintain efficient temperature monitoring effects when facing different working environments and conditions, and the real-time temperature detection and the update of the temperature distribution model form a closed-loop feedback system. By continuously acquiring new temperature data and updating the model, the temperature detection of electronic components is more accurate and effective.

[0055] Further, based on the above embodiments, please refer to Figure 3 In one of the exemplary embodiments provided in the present application, the specific implementation process of determining the temperature detection frequency of the electronic components under different temperature conditions based on the average temperature may also include steps S310 to S330, which are described in detail as follows:

[0056] Step S310, determining the thermal conductivity corresponding to the material characteristics of the electronic component;

[0057] Step S320, determining the initial temperature detection frequency corresponding to the electronic component at the average temperature based on the thermal conductivity;

[0058] Step S330, based on the relationship between the average temperature and the real-time temperature of the electronic component, determine the temperature detection frequency of the electronic component under different temperature conditions.

[0059] Specifically, first of all, it is necessary to clarify the material properties of electronic components, especially their thermal conductivity. Thermal conductivity is a physical quantity that measures the thermal conductivity of a material, which determines how fast heat is transferred inside the material. For electronic components, the level of thermal conductivity directly affects their heat dissipation performance and temperature stability.

[0060] In step S310, the thermal conductivity corresponding to the material characteristics of the electronic components needs to be determined, which is usually obtained by consulting relevant material manuals or experimental measurements. The higher the thermal conductivity, the faster the heat is transferred inside the material, and the better the heat dissipation performance of the electronic components.

[0061] In step S320, the initial temperature detection frequency corresponding to the electronic components at the average temperature can be determined based on the thermal conductivity. The setting of the initial temperature detection frequency should take into account the temperature variation range of the electronic components under normal working conditions and the influence of thermal conductivity on the temperature transfer speed. The temperature of electronic components with high thermal conductivity may change relatively quickly, so a higher initial temperature detection frequency needs to be set to ensure timely monitoring of temperature changes.

[0062] In step S330, based on the relationship between the average temperature and the real-time temperature of the electronic components, the temperature detection frequency of the electronic components under different temperature conditions is determined. This usually involves an in-depth analysis of the temperature characteristics of the electronic components. For example, when the real-time temperature of the electronic components is much higher than the average temperature, it may mean that the electronic components are experiencing severe heat accumulation or poor heat dissipation. At this time, the temperature detection frequency should be increased to more closely monitor the temperature changes. On the contrary, when the real-time temperature is close to or slightly lower than the average temperature, the temperature detection frequency can be appropriately reduced to save resources.

[0063] In summary, determining the temperature detection frequency of electronic components requires comprehensive consideration of their material properties (especially thermal conductivity), average temperature, and the relationship between real-time temperature and average temperature. By scientifically and reasonably setting the temperature detection frequency, it can ensure that electronic components can maintain a stable temperature state under various working conditions, thereby extending their service life and improving system reliability.

[0064] In this embodiment, by setting a suitable initial temperature detection frequency according to the thermal conductivity, potential overheating problems can be discovered and handled in time, preventing components from failing due to excessive temperature. By dynamically adjusting the temperature detection frequency according to the deviation between the real-time temperature and the average temperature, the number of detections can be increased when the temperature fluctuates greatly, ensuring the stable operation of components under extreme conditions. By real-time monitoring and adjusting the temperature detection frequency, temperature anomalies can be discovered and handled in time, improving the reliability and service life of electronic components.

[0065] Further, based on the above embodiments, please refer to Figure 4 In one of the exemplary embodiments provided in the present application, the specific implementation process of determining the temperature detection efficiency of the electronic components under different temperature conditions based on the relationship between the average temperature and the real-time temperature of the electronic components may also include step S410 and step S420, which are described in detail as follows:

[0066] Step S410, if the real-time temperature of the electronic component is greater than the average temperature, adjusting the initial temperature detection frequency based on the first adjustment rate coefficient to obtain a first target temperature detection frequency corresponding to the electronic component;

[0067] Step S420: If the real-time temperature of the electronic component is lower than the average temperature, adjust the initial temperature detection frequency based on the second adjustment rate coefficient to obtain the second target temperature detection frequency corresponding to the electronic component, where the second adjustment rate coefficient is less than the first adjustment rate coefficient.

[0068] Specifically, define the conditions and coefficients: Given the real-time temperature (Treal) and average temperature (Tavg) of the electronic component. Define two adjustment rate coefficients: the first adjustment rate coefficient (k1) and the second adjustment rate coefficient (k2), where k2 < k1. These two coefficients are used to adjust the initial temperature detection frequency (finit) according to the deviation between the real-time temperature and the average temperature. Determine the relationship between the real-time temperature and the average temperature: If Treal > Tavg: This means that the real-time temperature of the electronic component is higher than its average temperature, and there may be a risk of overheating or poor heat dissipation. Therefore, it is necessary to increase the initial temperature detection frequency based on the first adjustment rate coefficient k1 to monitor the temperature more frequently and ensure the safe operation of the electronic component. The adjusted first target temperature detection frequency (ftarget1) can be expressed as: ftarget1 = finit × (1 + k1), where (1 + k1) represents the increase ratio of the frequency, which is determined by the first adjustment rate coefficient. If Treal < Tavg: This indicates that the real-time temperature of the electronic component is lower than its average temperature, and it may be in a relatively stable operating state or have good heat dissipation. In this case, the temperature detection frequency can be appropriately reduced to save resources. The adjusted second target temperature detection frequency (ftarget2) can be expressed as: ftarget2 = finit × (1 - k2), where (1 - k2) represents the reduction ratio of the frequency, which is determined by the second adjustment rate coefficient, and since k2 < k1, the reduction ratio will be less than the increase ratio to maintain sensitivity to temperature changes.

[0069] Exemplarily, according to the comparison result of the real-time temperature and the average temperature, select the corresponding adjustment rate coefficient, calculate the target temperature detection frequency, and apply the calculated target temperature detection frequency to the temperature monitoring system of the electronic component to ensure that the system can perform temperature detection at the new frequency. Regularly evaluate and adjust the temperature detection frequency to adapt to the changes in the working state and environmental conditions of the electronic component.

[0070] In this embodiment, when the real-time temperature of the electronic component is higher than the average temperature, the temperature detection frequency is rapidly increased through the higher first adjustment rate coefficient, which can quickly capture the temperature change trend, timely take necessary heat dissipation measures or adjust the workload to prevent the component from overheating and damage. When the real-time temperature is lower than the average temperature, the smaller second adjustment rate coefficient is used to reduce the temperature detection frequency, which can not only maintain the monitoring of temperature changes but also avoid over-detection when the temperature is stable, improving the overall efficiency.

[0071] Further, based on the above embodiments, please refer to Figure 5 In one of the exemplary embodiments provided in the present application, the specific implementation process of the temperature detection method of the electronic component may further include step S510 and step S520, which are described in detail as follows:

[0072] Step S510, determining the temperature change difference of the electronic component within a preset time period based on the temperature distribution model;

[0073] Step S520: determining a first adjustment rate coefficient and a second adjustment rate coefficient based on the temperature change difference.

[0074] Specifically, determine the temperature change difference within a preset time: First, use the temperature distribution model to predict the temperature change of electronic components within a preset time (for example, one day, one hour, etc.). This usually involves time integration or iterative calculation of the model to simulate the evolution of temperature over time. Calculate the temperature prediction values ​​at the beginning and end of the preset time, recorded as Tstart and Tend. The temperature change difference ΔT can be calculated by the following formula: ΔT = |Tend-Tstart| Here, the absolute value ensures that a positive temperature change is obtained regardless of whether the temperature rises or falls.

[0075] Determine the adjustment rate coefficient based on the temperature change difference: The first adjustment rate coefficient k1: When ΔT is large, it means that the electronic components have experienced significant temperature changes within a preset period of time, and more frequent temperature detection may be required to ensure safety. Therefore, k1 can be set to be proportional to ΔT or determined by an increasing function (such as logarithm, exponential function, etc.) to ensure that more detection frequencies are increased when the temperature changes greatly. The second adjustment rate coefficient k2: When ΔT is small, it means that the temperature of the electronic components is relatively stable, and the frequency of temperature detection can be appropriately reduced to save resources. k2 can be set to a constant smaller than k1, or determined by a decreasing function (but it usually does not need to be too complicated, because the magnitude of the reduction in detection frequency is usually smaller than the magnitude of the increase). Example calculation (simplified version): Assume that there is a simple linear relationship to determine k1 and k2, where kbase is the base adjustment rate and α is the adjustment factor (α>1):

[0076] k1=kbase·α·ΔTmaxΔT (when ΔT>ΔTthreshold)

[0077] k2=kbase(when ΔT≤ΔTthreshold)

[0078] Here, ΔTmax is the preset maximum temperature change threshold, and ΔTthreshold is the temperature difference threshold for distinguishing large changes from small changes. When ΔT exceeds ΔTthreshold, k1 is used; otherwise, k2 (i.e., the basic adjustment rate) is used.

[0079] The calculated k1 and k2 are used to adjust the initial temperature detection frequency according to the relationship between the real-time temperature and the average temperature of the electronic component to obtain the first target temperature detection frequency and the second target temperature detection frequency.

[0080] Among them, kbase, α, ΔTmax and ΔTthreshold in the above calculation need to be selected and adjusted according to the specific application scenario and characteristics of electronic components.

[0081] Optionally, in some feasible embodiments, we introduce a humidity parameter h based on the previous formula to adjust the k(x,h) value so that it depends not only on temperature but also on humidity. For example, the humidity feature in the environmental feature is introduced into the detection frequency.

[0082] When the real-time temperature of electronic components is greater than the average temperature value, it can be expressed as:

[0083] T(x,h)=10+k(x,h)×(70-x)

[0084] Among them, k(x,h) can be defined as:

[0085] k(x,h)=[0.1+α×(70-x)]×(1+(ch))

[0086] Where c is the current humidity and h is the set standard humidity (which can be adjusted dynamically depending on the environment).

[0087] When the real-time temperature of electronic components is less than the average temperature value, it can be expressed as:

[0088] T(x,h)=10-k(x,h)×(70-x)

[0089] Among them, k(x,h) can be defined as:

[0090] k(x,h)=[0.1+α×(70-x)]×(1+(ch))

[0091] Where c is the current humidity and h is the set standard humidity (which can be adjusted dynamically depending on the environment).

[0092] Optionally, in some feasible embodiments, the temperature of the chip and hardware is monitored in real time, the temperature normal distribution model is dynamically updated, and the detection frequency is adjusted according to the latest distribution model. At the same time, the detection results are fed back to the thermal management module to decide whether to take further cooling measures. For example, if the temperature of electronic components is too high, it may cause abnormal operation of the system or even damage to the equipment, and self-overtemperature protection is required. A passive high-temperature overheating protection strategy is given below. Specific treatment measures can be evaluated and graded according to actual conditions. High temperature fault level 1 (85℃-100℃ specific temperature threshold to be fed back by thermal simulation TBD): overtemperature triggers hardware protection and turns off the algorithm peripheral expansion function; high temperature fault level 2 (100℃-110℃ specific temperature threshold to be fed back by thermal simulation TBD): overtemperature triggers SOC power off, and only MCU works; high temperature fault level 3 (temperature>110℃ specific temperature threshold to be fed back by thermal simulation TBD): function inhibition, DTC fault code writing.

[0093] In this embodiment, by predicting the temperature change difference of the electronic components within a preset time period through the temperature distribution model, the temperature change trend of the components can be understood more accurately. Based on this prediction, the set first and second adjustment rate coefficients can more accurately reflect the actual temperature change, thereby improving the accuracy of temperature management.

[0094] Further, based on the above embodiments, please refer to Figure 6 In one of the exemplary embodiments provided in the present application, the specific implementation process of the temperature detection method of the electronic component may further include step S610 and step S620, which are described in detail as follows:

[0095] Step S610, obtaining environmental characteristics corresponding to the electronic components at the current moment, the environmental characteristics including environmental temperature and environmental humidity;

[0096] Step S620: determining a first adjustment rate coefficient and a second adjustment rate coefficient based on the ambient temperature, ambient humidity, and thermal conductivity.

[0097] Specifically, obtain environmental characteristics: use sensors or data interfaces to obtain the current ambient temperature (Tenv) and ambient humidity (RH). Consider the influence of thermal conductivity: the thermal conductivity (λ) of electronic components is known, which determines the efficiency of heat transfer in the material. The higher the thermal conductivity, the faster the heat transfer, which may mean that more frequent temperature detection is required to capture rapid temperature changes. Define the calculation logic of the adjustment rate coefficient: The first adjustment rate coefficient k1: When the ambient temperature is high or the ambient humidity is low (both of which may aggravate the heat dissipation problem of electronic components), the temperature detection frequency should be increased. Therefore, k1 can be a function of ambient temperature, ambient humidity and thermal conductivity, and increases as the "unfavorable" degree of these factors increases. The second adjustment rate coefficient k2: When the ambient temperature is low or the ambient humidity is high (both of which are conducive to the heat dissipation of electronic components), the temperature detection frequency can be appropriately reduced. Therefore, k2 should be less than k1, and decrease as these "favorable" conditions increase, but generally will not decrease below zero.

[0098] In this embodiment, the adjustment rate coefficient determined based on ambient temperature, humidity and thermal conductivity can more reasonably set the frequency of temperature detection. In an environment with large temperature changes, the system will speed up the detection frequency to ensure that potential problems are discovered and handled in a timely manner; in an environment with relatively stable temperature, the detection frequency will be reduced to reduce unnecessary resource consumption.

[0099] Further, based on the above embodiment, please refer to Figure 7 In one of the exemplary embodiments provided in the present application, the specific implementation process of obtaining the historical temperature data of the electronic components and constructing the temperature distribution model of the electronic components based on the historical temperature data may also include steps S710 to S730, which are described in detail as follows:

[0100] Step S710, obtaining historical temperature data and historical environmental data of electronic components within a preset time period;

[0101] Step S720, calculating a normal distribution model corresponding to the temperature of the electronic component based on the historical temperature data, where the normal distribution model includes a temperature mean and a temperature standard deviation corresponding to the electronic component;

[0102] Step S730, correcting the normal distribution model based on the historical environmental data to obtain a temperature distribution model corresponding to the electronic components.

[0103] Specifically, the temperature data of electronic components within a preset time period (such as one week, one month, etc.) is obtained from the temperature monitoring system. These data should contain enough sample points to accurately reflect the temperature distribution of electronic components. Historical environmental data: Environmental data within the same time period is obtained at the same time, including factors such as ambient temperature and ambient humidity that may affect the temperature of electronic components. Calculate the temperature mean (μ): Use historical temperature data to calculate the average temperature of electronic components. This can be obtained by adding all temperature values ​​and then dividing by the total number of temperature values. Calculate the temperature standard deviation (σ): The standard deviation is a statistic that measures the degree of dispersion of data distribution. Calculate the average of the squares of the differences between all temperature values ​​and the mean, and then take the square root to get the standard deviation. This value reflects the fluctuation of the temperature of electronic components around the mean. Construct a normal distribution model: Based on the calculated mean and standard deviation, you can construct a normal distribution model such as Figure 8 The normal distribution model shown is used to describe the temperature distribution of electronic components. The normal distribution model is usually expressed as N(μ,σ2).

[0104] In this embodiment, by collecting and analyzing the historical temperature data of electronic components within a preset time, a normal distribution model of the component temperature can be constructed. The normal distribution model can describe the natural fluctuation and distribution of component temperature, providing a solid foundation for subsequent temperature prediction. Further correction of the normal distribution model in combination with historical environmental data (such as ambient temperature, humidity, etc.) can more accurately reflect the temperature behavior of components under different environmental conditions. This correction makes the temperature distribution model closer to the actual situation and improves the accuracy of the prediction.

[0105] Further, based on the above embodiments, please refer to Fig. 9 In one of the exemplary embodiments provided in the present application, the specific implementation process of the temperature detection method of the above electronic component may also include steps S910 to S930, which are described in detail as follows:

[0106] Step S910, obtaining historical temperature data of electronic components within a preset time period;

[0107] Step S920, extracting target historical temperature data corresponding to multiple different time points from the historical temperature data through a target random function;

[0108] Step S930: Calculate a normal distribution model corresponding to the temperature of the electronic component based on the target historical temperature data.

[0109] Specifically, in order to build a normal temperature distribution model for different chips and hardware in the intelligent driving ECU, it is necessary to collect sufficient historical temperature data. The core of data collection is to ensure that the model can accurately reflect the actual temperature distribution of each chip and hardware. Therefore, the following data collection strategies are given: Temperature data of the previous day: When optimizing temperature management, first collect and use the temperature data of the previous day. These data best reflect the current system status and environmental impact, and provide the latest temperature characteristics for the model. Randomly sample historical data from the previous week: In order to increase the robustness and generalization ability of the model, historical temperature data from the past week is introduced into the temperature model. The specific approach is to randomly sample several copies of the data from the previous week to ensure that the model can not only reflect short-term temperature changes, but also take into account temperature fluctuations under different working conditions.

[0110] Exemplarily, first, a reasonable preset time period (such as one week, one month, etc.) needs to be set, and the temperature data of all target electronic components (such as chips, sensors, etc.) within the time period is obtained from the monitoring system of the intelligent driving ECU. These data should contain timestamps and corresponding temperature values ​​for subsequent time series analysis and processing. Before extracting the target historical temperature data, necessary data preprocessing steps are performed to ensure the accuracy and reliability of the data: Data cleaning: remove outliers (such as extreme temperature values ​​caused by sensor failure or data transmission errors). Data alignment: ensure that the temperature data of all electronic components are aligned in time, or at least can be matched to similar time points. Data interpolation: If data is missing at certain time points, a suitable interpolation method (such as linear interpolation, nearest neighbor interpolation, etc.) can be used to estimate the missing temperature values. In order to increase the robustness and generalization ability of the model, a target random function can be used to extract target historical temperature data corresponding to multiple different time points from the preprocessed historical temperature data. This target random function can be designed as follows: Random time selection: randomly select multiple time points within the preset time period, and these time points should be distributed as evenly as possible throughout the time period. Data subset selection: For each selected time point, extract the temperature values ​​of all target electronic components at that time point to form a data subset. Based on the extracted target historical temperature data, the normal distribution model corresponding to the temperature of each electronic component can be calculated. The specific steps are as follows: Calculate the temperature mean (μ): For each electronic component, calculate the average value of all its target historical temperature data; calculate the temperature standard deviation (σ). Similarly, the standard deviation of the temperature data of each electronic component can be calculated to reflect the degree of discreteness of its temperature distribution, and then use the calculated mean and standard deviation to build a normal distribution model N (μ, σ2) for each electronic component.

[0111] In this embodiment, a more accurate and reliable normal distribution model of electronic components can be constructed through a large amount of historical temperature data, and a more reasonable thermal management strategy can be formulated, such as adjusting the fan speed, adding heat sinks, etc., to ensure that the electronic components operate stably within the allowable temperature range, which helps to reduce energy consumption, extend equipment life and improve the efficiency of electronic components.

[0112] Fig.10 The following is a brief flow chart of temperature detection of electronic components in an exemplary application scenario. Fig.10 In the application scenario shown, the historical temperature data and historical environmental data of the electronic components within the preset time length are obtained; the normal distribution model corresponding to the temperature of the electronic components is calculated based on the historical temperature data, and the normal distribution model includes the temperature mean and temperature standard deviation corresponding to the electronic components; the normal distribution model is corrected based on the historical environmental data to obtain the temperature distribution model corresponding to the electronic components. The average temperature of the electronic components in the temperature distribution model is obtained to determine the thermal conductivity corresponding to the material characteristics of the electronic components; the initial temperature detection frequency corresponding to the electronic components at the average temperature is determined based on the thermal conductivity; based on the relationship between the average temperature and the real-time temperature of the electronic components, the temperature detection frequency of the electronic components under different temperature conditions is determined. If the real-time temperature of the electronic components is greater than the average temperature, the initial temperature detection frequency is adjusted based on the first adjustment rate coefficient to obtain the first target temperature detection frequency corresponding to the electronic components; if the real-time temperature of the electronic components is less than the average temperature, the initial temperature detection frequency is adjusted based on the second adjustment rate coefficient to obtain the second target temperature detection frequency corresponding to the electronic components, wherein the second adjustment rate coefficient is less than the first adjustment rate coefficient. The temperature of the electronic components is detected based on the target detection frequency to obtain the corresponding temperature detection result, and the temperature distribution model is updated based on the result of the temperature detection. For the detailed implementation process, please refer to the records in the aforementioned embodiments, which will not be described in detail here.

[0113] Fig.11 is a block diagram of a temperature detection device for electronic components shown in an exemplary embodiment of the present application. The device can be applied to Figure 1 The implementation environment shown in the figure is specifically configured in the server 120. The device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applied.

[0114] like Fig.11As shown, the exemplary temperature detection device 1100 for electronic components includes: a first acquisition module 1110, used to acquire historical temperature data of electronic components, and construct a temperature distribution model of the electronic components based on the historical temperature data; a second acquisition module 1120, used to acquire the average temperature of the electronic components in the temperature distribution model, and determine the temperature detection frequency of the electronic components under different temperature conditions based on the average temperature; a third acquisition module 1130, used to acquire the material properties corresponding to the electronic components and the environmental characteristics in which they are located, and correct the temperature detection frequency based on the material properties and the environmental characteristics to obtain a target temperature detection frequency; a detection module 1140, used to perform temperature detection on the electronic components based on the target detection frequency to obtain corresponding temperature detection results, and update the temperature distribution model based on the temperature detection results.

[0115] According to one aspect of an embodiment of the present application, the above-mentioned third acquisition module 1130 is also used to determine the thermal conductivity corresponding to the material characteristics of the electronic components; determine the initial temperature detection frequency corresponding to the electronic components at the average temperature based on the thermal conductivity; and determine the temperature detection frequency of the electronic components under different temperature conditions based on the relationship between the average temperature and the real-time temperature of the electronic components.

[0116] According to one aspect of an embodiment of the present application, the above-mentioned third acquisition module 1130 is also used to, if the real-time temperature of the electronic component is greater than the average temperature, adjust the initial temperature detection frequency based on the first adjustment rate coefficient to obtain a first target temperature detection frequency corresponding to the electronic component; if the real-time temperature of the electronic component is lower than the average temperature, adjust the initial temperature detection frequency based on the second adjustment rate coefficient to obtain a second target temperature detection frequency corresponding to the electronic component, wherein the second adjustment rate coefficient is smaller than the first adjustment rate coefficient.

[0117] According to one aspect of an embodiment of the present application, the third acquisition module 1130 is further used to determine the temperature change difference of the electronic component within a preset time period based on the temperature distribution model; and determine the first adjustment rate coefficient and the second adjustment rate coefficient based on the temperature change difference.

[0118] According to one aspect of an embodiment of the present application, the third acquisition module 1130 is also used to obtain environmental characteristics corresponding to the electronic components at the current moment, the environmental characteristics including ambient temperature and ambient humidity; and determine the first adjustment rate coefficient and the second adjustment rate coefficient based on the ambient temperature, ambient humidity and thermal conductivity.

[0119] According to one aspect of an embodiment of the present application, the above-mentioned first acquisition module 1110 is also used to obtain historical temperature data and historical environmental data of electronic components within a preset time period; calculate the normal distribution model corresponding to the temperature of the electronic components based on the historical temperature data, the normal distribution model includes the temperature mean and temperature standard deviation corresponding to the electronic components; correct the normal distribution model based on the historical environmental data to obtain the temperature distribution model corresponding to the electronic components.

[0120] According to one aspect of an embodiment of the present application, the above-mentioned first acquisition module 1110 is also used to obtain historical temperature data of electronic components within a preset time period; extract target historical temperature data corresponding to multiple different time points from the historical temperature data through a target random function; and calculate the normal distribution model corresponding to the temperature of the electronic components based on the target historical temperature data.

[0121] It should be noted that the temperature detection device for electronic components provided in the above embodiment and the temperature detection method for electronic components provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In practical applications, the temperature detection device for electronic components provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0122] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by one or more processors, the electronic device implements the temperature detection method of electronic components provided in the above-mentioned embodiments.

[0123] Fig.12 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Fig.12 The computer system 1200 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0124] like Fig.12As shown, the computer system 1200 includes a central processing unit (CPU) 1201, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1202 or the program loaded from the storage part 1208 to the random access memory (RAM) 1203, such as executing the method in the above embodiment. In RAM 1203, various programs and data required for system operation are also stored. CPU 1201, ROM 1202 and RAM 1203 are connected to each other through a bus 1204. An input / output (I / O) interface 1205 is also connected to the bus 1204.

[0125] The following components are connected to the I / O interface 1205: an input section 1206 including a keyboard, a mouse, etc.; an output section 1207 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to the I / O interface 1205 as needed. A removable medium 1212, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1210 as needed so that a computer program read therefrom is installed into the storage section 1208 as needed.

[0126] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 1209, and / or installed from a removable medium 1211. When the computer program is executed by a central processing unit (CPU) 1201, various functions defined in the system of the present application are executed.

[0127] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0128] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0129] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0130] Another aspect of the present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the temperature detection method of the electronic component as described above is implemented. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0131] Another aspect of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the temperature detection method of the electronic component provided in each of the above embodiments.

[0132] The above content is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. A person skilled in the art can easily make corresponding changes or modifications based on the main concept and spirit of the present application. Therefore, the scope of protection of the present application shall be based on the scope of protection required by the claims.

Claims

1. A temperature detection method for electronic components, characterized in that: include: Acquire historical temperature data of electronic components, and construct a temperature distribution model of the electronic components based on the historical temperature data; Acquire the average temperature of the electronic component in the temperature distribution model, and determine the temperature detection frequency corresponding to the electronic component under different temperature conditions based on the average temperature; Acquire material properties and real-time environmental characteristics corresponding to the electronic components, and correct the temperature detection frequency based on the material properties and the real-time environmental characteristics to obtain a target temperature detection frequency; The electronic component is subjected to temperature detection based on the target detection frequency to obtain a corresponding temperature detection result, and the temperature distribution model is updated based on the temperature detection result.

2. The method according to claim 1, characterized in that: The determining, based on the average temperature, the temperature detection frequency of the electronic component under different temperature conditions comprises: Determining the thermal conductivity corresponding to the material properties of the electronic component; Determining an initial temperature detection frequency of the electronic component corresponding to the average temperature based on the thermal conductivity; Based on the relationship between the average temperature and the real-time temperature of the electronic component, the temperature detection frequency of the electronic component under different temperature conditions is determined.

3. The method according to claim 2, characterized in that The determining, based on the relationship between the average temperature and the real-time temperature of the electronic component, the temperature detection efficiency of the electronic component under different temperature conditions comprises: If the real-time temperature of the electronic component is greater than the average temperature, adjusting the initial temperature detection frequency based on a first adjustment rate coefficient to obtain a first target temperature detection frequency corresponding to the electronic component; If the real-time temperature of the electronic component is lower than the average temperature, the initial temperature detection frequency is adjusted based on a second adjustment rate coefficient to obtain a second target temperature detection frequency corresponding to the electronic component, wherein the second adjustment rate coefficient is lower than the first adjustment rate coefficient.

4. The method according to claim 3, characterized in that The method further comprises: Determine the temperature change difference of the electronic component within a preset time period based on the temperature distribution model; The first adjustment rate coefficient and the second adjustment rate coefficient are determined based on the temperature change difference.

5. The method according to claim 3, characterized in that The method further comprises: Acquire environmental characteristics corresponding to the electronic component at the current moment, wherein the environmental characteristics include ambient temperature and ambient humidity; The first adjustment rate coefficient and the second adjustment rate coefficient are determined based on the ambient temperature, the ambient humidity, and the thermal conductivity.

6. The method according to claim 1, characterized in that The acquiring of historical temperature data of electronic components and constructing a temperature distribution model of the electronic components based on the historical temperature data includes: Obtaining historical temperature data and historical environmental data of the electronic components within a preset time period; Calculating a normal distribution model corresponding to the temperature of the electronic component based on the historical temperature data, wherein the normal distribution model includes a temperature mean and a temperature standard deviation corresponding to the electronic component; The normal distribution model is modified based on the historical environmental data to obtain a temperature distribution model corresponding to the electronic component.

7. The method according to claim 6, characterized in that The method further comprises: Obtaining historical temperature data of the electronic components within a preset time period; Extracting target historical temperature data corresponding to a plurality of different time points from the historical temperature data by using a target random function; A normal distribution model corresponding to the temperature of the electronic component is calculated based on the target historical temperature data.

8. A temperature detection device for electronic components, characterized in that: The device comprises: A first acquisition module, used to acquire historical temperature data of electronic components and construct a temperature distribution model of the electronic components based on the historical temperature data; A second acquisition module is used to acquire the average temperature of the electronic component in the temperature distribution model, and determine the temperature detection frequency of the electronic component under different temperature conditions based on the average temperature; A third acquisition module is used to obtain material properties corresponding to the electronic components and environmental characteristics, and to correct the temperature detection frequency based on the material properties and the environmental characteristics to obtain a target temperature detection frequency; The detection module is used to perform temperature detection on the electronic component based on the target detection frequency to obtain a corresponding temperature detection result, and to update the temperature distribution model based on the temperature detection result.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the temperature detection method for electronic components as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the temperature detection method of an electronic component according to any one of claims 1 to 7.

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