Temperature detection method and device for electronic components, electronic equipment and storage medium

Abstract

The embodiments of the present application disclose a method, apparatus, device, and medium for detecting the temperature of electronic components. The method includes: 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 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 a corresponding temperature detection result, and updating the temperature distribution model based on the temperature detection result. The embodiments of the present application improve the rationality and accuracy of temperature detection of electronic components.

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 within the ECU is becoming increasingly higher, and power consumption is also increasing accordingly. High temperature environments 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 device thermal management systems, to prevent damage from overheating to chips such as SOCs and MCUs, and certain hardware such as camera screens, it is usually necessary to monitor the temperature of each chip and hardware. Application-layer software then periodically reads all temperature data through an API at a certain frequency. When a monitored chip or hardware overheats, appropriate measures are taken. However, due to significant differences in thermal conductivity between different materials, the temperature variation range and overheating interval of each monitored object vary. 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 method and device for detecting the temperature of an electronic component, 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 a corresponding temperature detection result, and updating the temperature distribution model based on the temperature detection result.

[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 component under different temperature conditions is determined based on the relationship between the average temperature and the real-time temperature of the electronic component, including: 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 less 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 component, wherein the second adjustment rate coefficient is less than the first adjustment rate coefficient.

[0008] According to one aspect of an embodiment of the present application, the method further includes: determining the 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 the environmental characteristics corresponding to the electronic components at the 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 acquiring of historical temperature data of electronic components and the construction of a temperature distribution model of the electronic components based on the historical temperature data include: acquiring the 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 component 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 component based on the target historical temperature data.

[0012] According to one aspect of an embodiment of the present application, a temperature detection device for an electronic component is provided, the device comprising: a first acquisition module, for acquiring historical temperature data of the electronic component, and constructing a temperature distribution model of the electronic component based on the historical temperature data; a second acquisition module, for acquiring the average temperature of the electronic component in the temperature distribution model, and determining the temperature detection frequency of the electronic component under different temperature conditions based on the average temperature; a third acquisition module, for acquiring the material properties corresponding to the electronic component and the environmental characteristics in which it is located, and correcting the temperature detection frequency based on the material properties and the environmental characteristics to obtain a target temperature detection frequency; a detection module, for 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.

[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 the electronic component as described above.

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

[0016] In the technical solution provided in the embodiments of the present application, by obtaining historical temperature data of electronic components and constructing temperature distribution models 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. By correcting the temperature detection frequency by considering the material properties and real-time environmental characteristics of the electronic components, the temperature detection strategy is made more consistent with actual working conditions. This dynamic adjustment capability enables efficient temperature monitoring to be maintained in the face of different working environments and conditions. A closed-loop feedback loop is formed through real-time temperature detection and the updating of the temperature distribution model. By continuously acquiring new temperature data and updating the model, the temperature detection of electronic components is made 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 accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, serving to explain the principles of the present application. It is obvious that the drawings described below are merely some embodiments of the present application, and a person of ordinary skill in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

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

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

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

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

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

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

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

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

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

[0028] Figure 10 This is a schematic diagram of a simplified process for temperature detection of electronic components in an exemplary application scenario;

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

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

[0031] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, 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 for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0034] In this application, "plurality" refers to two or more. "And / or" describes the relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, 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 ensuring the stable operation and efficient performance of the ECU.

[0036] Different chips and hardware have different thermal management requirements. For example, the central processing unit (CPU) is the core of the ECU. Its computing speed and processing power directly impact the ECU's overall performance. The CPU generates a significant amount of heat during operation, which needs to be dissipated promptly by the thermal management module to ensure stable operation. The thermal management module precisely controls the CPU's temperature to prevent performance degradation or damage caused by overheating. The power management chip provides a stable power supply to other chips and hardware within the ECU. This chip also generates a certain amount of heat during operation. Poor heat dissipation can cause power supply fluctuations, impacting ECU stability. Therefore, the thermal management module must ensure that the temperature of the power management chip remains within a reasonable range. Communication interface chips, such as the CAN bus interface chip, are responsible for communication between the ECU and other onboard systems. These chips also require thermal management during operation to ensure stable and reliable communication. Sensors and actuators: Although sensors and actuators generate relatively little heat themselves, their performance is significantly affected by temperature. The thermal management module must ensure that these sensors and actuators operate within an appropriate temperature range to improve 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, as 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 based on 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 real-time environmental characteristics to obtain the target temperature detection frequency. Then, the electronic component is temperature detected according to the target detection frequency to obtain the corresponding detection result, and the temperature distribution model can be further optimized based on the obtained detection result to make the calculated target temperature detection frequency more accurate.

[0038] in, Figure 1 The server 120 shown can be, for example, an independent physical server, 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, without limitation. It can also be an intelligent terminal, such as a smartphone, in-vehicle computer, tablet computer, laptop computer, 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 within the ECU is becoming increasingly higher, and power consumption is also increasing accordingly. High temperature environments 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, existing thermal management systems collect 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 to general thermal management processes. To address these problems, the present application provides a method for detecting the temperature of an electronic component, a device for detecting the temperature of an electronic component, an electronic device, a computer-readable storage medium, and a computer program product, which are described in detail below.

[0042] See also Figure 2 , Figure 2This is a flow chart of a temperature detection method for electronic components shown in an exemplary embodiment of the present application. This 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 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 , obtaining historical temperature data of electronic components, and constructing a temperature distribution model of the electronic components based on the historical temperature data.

[0045] Specifically, to build a normal temperature distribution model for the various chips and hardware components in an intelligent driving ECU, sufficient historical temperature data must be collected. The key to data collection is to ensure that the model accurately reflects the actual temperature distribution of each chip and hardware component. Data sources include: Equipment monitoring data: The most direct data source is monitoring data from electronic components in real-world applications. This data is typically collected in real time by the device's temperature sensors and stored in a database or data warehouse; Experimental data: In a laboratory setting, by simulating the operation of electronic components under different temperature conditions, a large amount of experimental data can be obtained. This data is crucial for understanding the performance of electronic components under different temperatures; Public datasets: For example, some research institutions or companies may publicly release their experimental or monitoring data, which can serve as a supplement to model building. Select an appropriate model based on the data characteristics and requirements. Common models include linear regression, nonlinear regression, neural networks, and support vector machines. Temperature distribution models for electronic components may need to account for temperature variations over time and space, necessitating the use of spatiotemporal or dynamic models.

[0046] Step S220 , obtaining the average temperature of the electronic components in the temperature distribution model, and determining the temperature detection frequencies 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 should be 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 real-time environmental characteristics to obtain a target temperature detection frequency.

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

[0050] Highly conductive materials (such as copper and aluminum) are often used to make metal conductors and electrodes in electronic components. The material's conductivity can affect its heat generation when current flows through it, which in turn affects the temperature detection frequency setting. Insulation: Insulating materials (such as plastic, silicone, and rubber) are used to prevent short circuits between electronic components. The thermal conductivity of the insulating material can also affect the temperature distribution and detection frequency of the electronic components. Thermal stability: Materials with strong thermal stability (such as organic polymers, ceramics, and high-temperature alloys) can maintain stable performance in high-temperature environments. For electronic components operating in high-temperature environments, the thermal stability of the material will directly affect the temperature detection frequency setting. Corrosion resistance: Materials with strong corrosion resistance can resist corrosion from corrosive agents, extending the service life of electronic components. The corrosion resistance of the material can indirectly affect the heat dissipation performance and temperature detection requirements of electronic components. Real-time environmental characteristics include ambient temperature, humidity, airflow velocity, electromagnetic radiation, and other factors that may affect the temperature of electronic components. Ambient temperature directly affects the operating temperature and heat dissipation of electronic components, requiring real-time monitoring using devices 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. Airflow velocity affects the heat dissipation effect of electronic components and can be monitored through an anemometer or airflow sensor.

[0051] Based on the material properties of electronic components and real-time environmental characteristics, the temperature detection frequency can be modified as follows: The impact 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 effect of the 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 do not overheat. In addition to increasing the temperature detection frequency, attention should also be paid to moisture-proofing measures for electronic components when humidity is high. When airflow is slow, the heat dissipation of electronic components may be poor, necessitating an increase in the temperature detection frequency. When electromagnetic radiation is high, in addition to considering the electromagnetic compatibility of electronic components, interference with temperature detection equipment should also be considered. Based on the monitoring results of the electronic component's material properties and real-time environmental characteristics, their impact on the temperature detection frequency should be comprehensively evaluated. The performance and stability of the electronic components at different detection frequencies should be verified through experiments or simulations. Based on the verification results, the temperature detection frequency should be adjusted to the target value to ensure safe and reliable operation of the electronic components.

[0052] Step S240 , 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.

[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 to be 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 obtaining the historical temperature data of electronic components and constructing a temperature distribution model of the 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. By considering the material properties and real-time environmental characteristics of the electronic components to correct the temperature detection frequency, 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 embodiment, please refer to Figure 3 In one of the exemplary embodiments provided in this application, the specific implementation process of determining the temperature detection frequency of the electronic component under different temperature conditions based on the average temperature may further include steps S310 to S330, which are described in detail as follows:

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

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

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

[0059] Specifically, first, it's necessary to understand the material properties of electronic components, particularly their thermal conductivity. Thermal conductivity is a physical quantity that measures a material's thermal conductivity and determines how quickly heat is transferred within the material. For electronic components, thermal conductivity directly impacts their heat dissipation performance and temperature stability.

[0060] In step S310, it is necessary to determine the thermal conductivity corresponding to the material properties of the electronic components. This 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 change 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 that the temperature changes are monitored in time.

[0062] In step S330, 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. This generally involves an in-depth analysis of the temperature characteristics of the electronic component. For example, when the real-time temperature of the electronic component is much higher than the average temperature, it may mean that the electronic component is experiencing severe heat accumulation or poor heat dissipation. In this case, the temperature detection frequency should be increased to more closely monitor temperature changes. Conversely, when the real-time temperature is close to or slightly lower than the average temperature, the temperature detection frequency can be appropriately reduced to conserve resources.

[0063] In summary, determining the temperature monitoring frequency for electronic components requires comprehensive consideration of their material properties (especially thermal conductivity), average temperature, and the relationship between the real-time temperature and the average temperature. By rationally and appropriately setting the temperature monitoring frequency, electronic components can maintain a stable temperature under various operating conditions, thereby extending their service life and improving system reliability.

[0064] In this embodiment, setting an appropriate initial temperature detection frequency based on thermal conductivity allows for timely detection and resolution of potential overheating issues, preventing component failure due to excessive temperatures. Dynamically adjusting the temperature detection frequency based on the deviation between the real-time and average temperatures increases the number of detections during large temperature fluctuations, ensuring stable component operation under extreme conditions. By monitoring and adjusting the temperature detection frequency in real time, temperature anomalies can be promptly detected and addressed, improving the reliability and service life of electronic components.

[0065] Further, based on the above embodiment, please refer to Figure 4 In one of the exemplary embodiments provided in this application, the specific implementation process of determining the temperature detection efficiency of electronic components under different temperature conditions based on the relationship between the average temperature and the real-time temperature of the electronic components may further include steps S410 and 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 corresponding second target temperature detection frequency of the electronic component, where the second adjustment rate coefficient is less than the first adjustment rate coefficient.

[0068] Specifically, define the conditions and coefficients: The real-time temperature (Treal) and average temperature (Tavg) of the electronic component are known. 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 according to 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, and necessary heat dissipation measures or workload adjustments can be taken in a timely manner to prevent the component from overheating and being damaged. 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, thus improving the overall efficiency.

[0071] Further, based on the above embodiment, please refer to Figure 5 In one of the exemplary embodiments provided in this 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 period: First, use the temperature distribution model to predict the temperature change of the electronic components within a preset time period (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 period, 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 logarithmic, 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 amplitude of reducing the detection frequency is usually smaller than the amplitude when increasing). 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 that distinguishes 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 into the previous formula to adjust the value of k(x,h) so that it depends not only on temperature but also on humidity. For example, the humidity feature of the environmental characteristics is introduced into the detection frequency.

[0082] When the real-time temperature of an electronic component is greater than the average temperature, 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 dynamically adjusted depending on the environment).

[0087] When the real-time temperature of an electronic component is lower than the average temperature, 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 dynamically adjusted depending on the environment).

[0092] Optionally, in some feasible embodiments, the temperature of the chip and hardware is monitored in real time, its 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 shuts down the algorithm's peripheral expansion functions; 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 using a temperature distribution model to predict the temperature variation of electronic components over a preset period of time, a more accurate understanding of the component's temperature variation trends can be achieved. Based on this prediction, the first and second adjustment rate coefficients can be set to more accurately reflect the actual temperature variation, thereby improving the accuracy of temperature management.

[0094] Further, based on the above embodiment, please refer to Figure 6 In one of the exemplary embodiments provided in this 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 ambient temperature and ambient 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 humidity (RH). Consider the impact of thermal conductivity: The thermal conductivity (λ) of electronic components is known, which determines the efficiency of heat transfer in the material. Higher thermal conductivity means faster heat transfer, which may require more frequent temperature checks to capture rapid temperature changes. Define the calculation logic for the adjustment rate coefficients: First, the adjustment rate coefficient k1: When the ambient temperature is high or the humidity is low (both of which may exacerbate heat dissipation issues for electronic components), the temperature check frequency should be increased. Therefore, k1 can be a function of the ambient temperature, humidity, and thermal conductivity, and increases as the degree of these factors becomes more unfavorable. Second, the adjustment rate coefficient k2: When the ambient temperature is low or the humidity is high (both of which are conducive to heat dissipation for electronic components), the temperature check frequency can be appropriately reduced. Therefore, k2 should be less than k1 and decrease as these "favorable" conditions become stronger, but generally does 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 this application, the specific implementation process of obtaining historical temperature data of electronic components and constructing a temperature distribution model of the electronic components based on the historical temperature data may further 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 component.

[0103] Specifically, obtain the temperature data of electronic components within a preset time period (such as one week, one month, etc.) from the temperature monitoring system. These data should contain enough sample points to accurately reflect the temperature distribution of electronic components. Historical environmental data: Acquire environmental data within the same time period at the same time, including ambient temperature, ambient humidity and other factors 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 historical temperature data of electronic components over a preset period of time, a normal distribution model of the component temperature can be constructed. The normal distribution model can describe the natural fluctuations and distribution of component temperature, providing a solid foundation for subsequent temperature prediction. Further correction of the normal distribution model by combining historical environmental data (such as ambient temperature and humidity) can more accurately reflect the temperature behavior of components under different environmental conditions. This correction makes the temperature distribution model more realistic and improves the accuracy of predictions.

[0105] Further, based on the above embodiment, please refer to Figure 9 In one of the exemplary embodiments provided in this application, the specific implementation process of the temperature detection method of the electronic component may further 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 : calculating 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 strategy is given: Temperature data from the previous day: When optimizing temperature management, the temperature data from the previous day is collected and used first. This data best reflects the current system status and environmental impact, providing the model with the latest temperature characteristics. Randomly sample historical data from the previous week: 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] For example, first, a reasonable preset time period (e.g., one week, one month, etc.) is set and temperature data for all target electronic components (e.g., chips, sensors, etc.) within that time period is obtained from the intelligent driving ECU's monitoring system. This data should include timestamps and corresponding temperature values ​​to facilitate subsequent time series analysis and processing. Before extracting the target historical temperature data, necessary data preprocessing steps are performed to ensure data accuracy and reliability: Data cleaning: Remove outliers (e.g., extreme temperature values ​​due to sensor failure or data transmission errors). Data alignment: Ensure that the temperature data for all electronic components are temporally aligned, or at least matched to similar time points. Data interpolation: If data is missing at certain time points, appropriate interpolation methods (e.g., linear interpolation, nearest neighbor interpolation, etc.) can be used to estimate the missing temperature values. 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 as evenly distributed 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 each electronic component's temperature data can be calculated to reflect the degree of dispersion of its temperature distribution. Then, using the calculated mean and standard deviation, a normal distribution model N(μ,σ2) is constructed 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] Figure 10 This is a brief flow chart of temperature detection of electronic components in an exemplary application scenario. Figure 10 In the illustrated application scenario, historical temperature data and historical environmental data of electronic components over a preset time period are obtained. A normal distribution model corresponding to the electronic component temperature is calculated based on the historical temperature data. The normal distribution model includes the temperature mean and temperature standard deviation corresponding to the electronic component. The normal distribution model is then modified based on the historical environmental data to obtain a temperature distribution model corresponding to the electronic component. The average temperature of the electronic component in the temperature distribution model is obtained, and the thermal conductivity corresponding to the material properties of the electronic component is determined. Based on the thermal conductivity, an initial temperature detection frequency corresponding to the electronic component at the average temperature is determined. 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. If the real-time temperature of the electronic component is greater than the average temperature, the initial temperature detection frequency is adjusted 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 less 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, where the second adjustment rate coefficient is less than the first adjustment rate coefficient. Temperature detection of the electronic component is performed 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. For the detailed implementation process, please refer to the description in the aforementioned embodiments, which will not be described in detail here.

[0113] Figure 11 This 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 FIG1 is specifically configured in the server 120. The apparatus may also be applicable to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the apparatus is applicable.

[0114] like Figure 11As shown, the exemplary temperature detection device 1100 for electronic components includes: a first acquisition module 1110, 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 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 the 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 the second target temperature detection frequency corresponding to the electronic component, wherein the second adjustment rate coefficient is lower 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 above-mentioned third acquisition module 1130 is also used to obtain the 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, and 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-mentioned embodiment and the temperature detection method for electronic components provided in the above-mentioned embodiment are based on the same concept, wherein the specific manner in which each module and unit performs operations has been described in detail in the method embodiment and will not be repeated here. In actual applications, the temperature detection device for electronic components provided in the above-mentioned embodiment can allocate the above-mentioned 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, wherein when the one or more programs are executed by the one or more processors, the electronic device implements the temperature detection method of electronic components provided in the above-mentioned embodiments.

[0123] Figure 12 The following is a schematic diagram showing the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application. Figure 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 Figure 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 into the random access memory (RAM) 1203, such as executing the method in the above embodiment. Various programs and data required for system operation are also stored in the RAM 1203. The CPU 1201, ROM 1202 and RAM 1203 are connected to each other via 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, and the like; an output section 1207 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 1208 including a hard disk; and a communication section 1209 including a network interface card such as a LAN (Local Area Network) card or a modem. 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. Removable media 1212, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 1210 as needed, so that computer programs read from the removable media can be installed in 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 via the communication section 1209, and / or installed from a removable medium 1211. When the computer program is executed by the central processing unit (CPU) 1201, the 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 embodiments 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 can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. 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 that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0128] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that 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 the 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. In some cases, the names of these units do not constitute limitations on the units themselves.

[0130] Another aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the temperature detection method for electronic components as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device.

[0131] Another aspect of the present application provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the temperature detection method for electronic components 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. Ordinary technicians in this field can easily make corresponding changes or modifications based on the main ideas 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: Acquiring historical temperature data of electronic components, and constructing a temperature distribution model of the electronic components based on the historical temperature data; Obtaining an average temperature of the electronic component in the temperature distribution model, and determining a temperature detection frequency corresponding to the electronic component under different temperature conditions based on the average temperature; Obtaining material properties and real-time environmental characteristics corresponding to the electronic component, and correcting 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 temperature 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, wherein The determining, based on the average temperature, the temperature detection frequency of the electronic component under different temperature conditions includes: 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, wherein 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 includes: 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, wherein The method further comprises: Determining a 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, wherein The method further comprises: Acquire environmental characteristics corresponding to the electronic component at a current moment, the environmental characteristics including 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, wherein 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: Obtain 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, wherein The method further comprises: Obtaining historical temperature data of the electronic component 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 is 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 configured to acquire an average temperature of the electronic component in the temperature distribution model, and determine a temperature detection frequency of the electronic component under different temperature conditions based on the average temperature; a third acquisition module, configured to acquire material properties corresponding to the electronic component and environmental characteristics thereof, 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 configured to perform temperature detection on the electronic component based on the target temperature 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 according to 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 for an electronic component according to any one of claims 1 to 7.

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