Intelligent driving domain controller heat dissipation control method and related equipment

Abstract

The invention discloses an intelligent driving domain controller heat dissipation control method and related equipment thereof, and relates to the technical field of control, the method comprises the following steps: collecting operation data of an intelligent driving domain controller, the operation data comprising historical operation data and real-time operation data; a target task queue about to be executed by the intelligent driving domain controller in a future preset time period is obtained, and the future preset time period is smaller than the first preset time period; through a preset temperature change trend prediction model, performing prediction processing on a future temperature change trend based on the operation data and the target task queue, and outputting a target temperature change curve in a prediction time domain; wherein the preset temperature change trend prediction model is a model which is obtained by carrying out iterative training on a preset thermal dynamic model to be trained based on training data with a preset label and is used for accurately predicting a future temperature change trend; and carrying out advanced heat dissipation control on the intelligent driving domain controller based on the target temperature change curve. The heat dissipation requirement in practical application is met.

Description

Technical Field

[0001] This application relates to the field of control technology, and in particular to a heat dissipation control method and related equipment for an intelligent driving domain controller. Background Technology

[0002] As the level of autonomous driving (L2~L4) increases, the computing power of the domain controller increases exponentially, and its power consumption and heat generation (reflected in temperature) also increase sharply. High temperature will cause the processor to run at a reduced frequency (performance degradation) and may even cause safety hazards. Therefore, it is necessary to control the heat dissipation of the domain controller.

[0003] Traditional heat dissipation control methods mainly rely on reactive control based on current temperature sensor feedback. However, there is a delay between the detection of excessive temperature and the response and effect of the heat dissipation system (such as fan, liquid pump), which is a "passive" control method and is difficult to meet the heat dissipation requirements in practical applications. Summary of the Invention

[0004] The main objective of this application is to provide a method, device, equipment, storage medium, and product for heat dissipation control of an intelligent driving domain controller, aiming to solve the technical problem that reactive heat dissipation control methods in the current technology are difficult to meet the heat dissipation requirements in practical applications.

[0005] To achieve the above objectives, this application proposes a heat dissipation control method for an intelligent driving domain controller, the heat dissipation control method for the intelligent driving domain controller comprising: Collect operational data from the intelligent driving domain controller, wherein the operational data includes historical operational data and real-time operational data; Obtain the target task queue that the intelligent driving domain controller will execute within a future preset time period, wherein the future preset time period is shorter than a first preset time period; By using a preset temperature change trend prediction model, the system performs prediction processing on the future temperature change trend based on the running data and the target task queue, and outputs the target temperature change curve in the prediction time domain. The preset temperature change trend prediction model is an accurate model for predicting future temperature change trends, obtained by iteratively training a preset thermal dynamic model to be trained using training data with preset labels. Based on the target temperature change curve, the intelligent driving domain controller is subjected to advance heat dissipation control.

[0006] In one embodiment, the step of performing advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve includes: Based on the target temperature change curve, determine the optimal heat dissipation setting time and the target heat dissipation power consumption; The optimal heat dissipation setting time and target heat dissipation power consumption are converted into CAN signals, and the CAN signals are controlled to be input to the PID heat dissipation controller, so that the PID heat dissipation controller can control the heat dissipation device based on the CAN signals to perform advance heat dissipation control on the intelligent driving domain controller.

[0007] In one embodiment, after the step of performing advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve, the method includes: The temperature during the heat dissipation control process is measured in real time to obtain the actual measured temperature. Based on the optimal heat dissipation setting time and target heat dissipation power consumption, the predicted temperature is determined during the advance heat dissipation control process of the intelligent driving domain controller; Based on the temperature difference between the actual measured temperature and the predicted temperature at the same moment, the model parameters of the preset temperature change trend prediction model are updated in real time, so as to perform the next prediction process based on the preset temperature change trend prediction model after updating the model parameters. The preset temperature change trend prediction model is different for different vehicles after updating the model parameters.

[0008] In one embodiment, the step of updating the model parameters of the preset temperature change trend prediction model in real time based on the temperature difference between the actual measured temperature and the predicted temperature at the same moment includes: Determine the target time point when the actual measured temperature drops to the preset ideal temperature; Based on the temperature difference between the actual measured temperature and the corresponding predicted temperature at the target time point, the model parameters of the preset temperature change trend prediction model are updated in real time.

[0009] In one embodiment, the step of obtaining the queue of target tasks that the intelligent driving domain controller will execute within a preset time period includes: The system obtains the driving habits of the vehicle owner corresponding to the intelligent driving domain controller and the driving conditions of the corresponding vehicle. Based on the driving habits of the corresponding vehicle owner and the driving conditions of the corresponding vehicle, the target task queue that the intelligent driving domain controller will execute in a future preset time period is determined.

[0010] In one embodiment, the step of predicting future temperature change trends based on the operational data and the target task queue using a preset temperature change trend prediction model includes: The target task queue is analyzed to obtain the future load and corresponding time curve within a preset time period. Historical temperature data, historical load data, historical environmental data, and historical heat dissipation data are extracted from the operational data. By using a preset temperature change trend prediction model, based on the future load and corresponding time curve, historical temperature data, historical load data, historical environmental data, and historical heat dissipation data, the future temperature change trend is predicted.

[0011] Furthermore, to achieve the above objectives, this application also proposes a heat dissipation control device for an intelligent driving domain controller, the device comprising: The data acquisition module is used to collect the operating data of the intelligent driving domain controller, wherein the operating data includes historical operating data and real-time operating data; The acquisition module is used to acquire the target task queue that the intelligent driving domain controller will execute within a future preset time period, wherein the future preset time period is shorter than a first preset time period; The prediction module is used to predict the future temperature change trend based on the running data and the target task queue by using a preset temperature change trend prediction model, and output the target temperature change curve in the prediction time domain. The preset temperature change trend prediction model is an accurate model for predicting future temperature change trends, obtained by iteratively training a preset thermal dynamic model to be trained using training data with preset labels. A heat dissipation module is used to perform advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve.

[0012] In addition, to achieve the above objectives, this application also proposes a thermal control system for an intelligent driving domain controller, which includes the actuator as described above, the tool layer as described above, and the task planner as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a heat dissipation control device for an intelligent driving domain controller, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the heat dissipation control steps for the intelligent driving domain controller as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of heat dissipation control of the intelligent driving domain controller as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of heat dissipation control for the intelligent driving domain controller as described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: Compared to related technologies, traditional heat dissipation control methods mainly rely on reactive control based on current temperature sensor feedback, resulting in delays and other issues that make it difficult to meet the heat dissipation requirements of practical applications. This application addresses this by collecting operational data from the intelligent driving domain controller, including historical and real-time operational data; obtaining a queue of target tasks that the intelligent driving domain controller will execute within a preset future time period, where the preset future time period is shorter than a first preset time period; using a preset temperature change trend prediction model, predicting future temperature change trends based on the operational data and the target task queue, and outputting a target temperature change curve within the prediction time domain; wherein the preset temperature change trend prediction model is an accurate model for predicting future temperature change trends obtained by iteratively training a preset thermal dynamics model based on training data with preset labels; and performing advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve. It is understood that in this application, the intelligent driving domain controller is not reactive control, but rather predictive of heat generation. That is, this application collects the operating data of the intelligent driving domain controller and obtains the target task queue that the intelligent driving domain controller will execute in a future preset time period (the future preset time period is shorter than the first preset time period, therefore, the target task queue is a short-term task queue, which can be accurately predicted and will not consume too many resources). It then performs predictive processing on the future temperature change trend of the controller to obtain the target temperature change curve in the predicted time domain. In this way, the intelligent driving domain controller can perform heat dissipation control in advance, rather than reacting passively after the temperature exceeds the standard (which results in a reaction delay). In other words, this application can predict the heat trend in advance and react in advance to meet the heat dissipation requirements in practical applications. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of the intelligent driving domain controller heat dissipation control method of this application. Figure 2 This is a schematic diagram of the framework process involved in the intelligent driving domain controller heat dissipation control method of this application; Figure 3This is a schematic diagram of the control strategy involved in the intelligent driving domain controller heat dissipation control method of this application; Figure 4 This is a flowchart illustrating Embodiment 2 of the intelligent driving domain controller heat dissipation control method of this application; Figure 5 This is a schematic diagram of the module structure of the intelligent driving domain controller heat dissipation control device in an embodiment of this application; Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the heat dissipation control of the intelligent driving domain controller in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: Collecting operational data of the intelligent driving domain controller, wherein the operational data includes historical operational data and real-time operational data; obtaining a queue of target tasks that the intelligent driving domain controller will execute within a future preset time period, wherein the future preset time period is shorter than a first preset time period; using a preset temperature change trend prediction model, performing future temperature change trend prediction processing based on the operational data and the target task queue, and outputting a target temperature change curve in the prediction time domain; wherein the preset temperature change trend prediction model is an accurate model for predicting future temperature change trends obtained by iteratively training a preset thermal dynamic model to be trained based on training data with preset labels; and performing advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve.

[0024] In related technologies, heat dissipation control is generally achieved through reactive methods, which can result in either delayed or excessive heat dissipation.

[0025] In this application, the intelligent driving domain controller is not reactive control, but rather predicts heat generation. Specifically, this application collects the operating data of the intelligent driving domain controller and obtains the target task queue that the intelligent driving domain controller will execute in a future preset time period (the future preset time period is shorter than the first preset time period, therefore, the target task queue is a short-term task queue that can be accurately predicted and will not consume too many resources). It then performs predictive processing on the future temperature change trend of the controller to obtain the target temperature change curve in the predicted time domain. In this way, the intelligent driving domain controller can perform heat dissipation control in advance, rather than reacting passively after the temperature exceeds the limit (which results in a reaction delay). That is, this application can predict the heat trend in advance and react in advance to meet the heat dissipation requirements in practical applications.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a cooling control device for an intelligent driving domain controller. The following description uses a cooling control device for an intelligent driving domain controller as an example to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, this application provides a method for controlling the heat dissipation of an intelligent driving domain controller, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the intelligent driving domain controller heat dissipation control method of this application.

[0028] In this embodiment, the intelligent driving domain controller heat dissipation control method includes steps S10~S40: Step S10: Collect the operating data of the intelligent driving domain controller, wherein the operating data includes historical operating data and real-time operating data; It should be noted that the execution entity in this embodiment is the intelligent driving domain controller (hereinafter referred to as the controller). That is, as the level of intelligent driving (L2~L4) increases, the computing power of the (intelligent driving) domain controller increases exponentially, and its power consumption and heat generation also increase sharply. High temperature will cause the processor to operate at a reduced frequency (performance degradation), which will seriously affect the real-time performance and reliability of key tasks such as perception and decision-making, and may even cause safety hazards. Traditional heat dissipation control methods are mainly based on reactive control using current temperature sensor feedback. This method has obvious drawbacks: First, lag: that is, there is a delay from the detection of excessive temperature to the response and effect of the heat dissipation system (such as fan, liquid pump), which cannot avoid instantaneous temperature spikes; Second, overreaction: To avoid overheating, many vehicles typically adopt a conservative strategy, which is to keep the cooling system running at high power for a long time. However, running the cooling system at high power for a long time leads to high energy consumption, loud noise, and shortened component life.

[0029] In other words, overall, current heat dissipation control is a "passive" control method.

[0030] In this embodiment, the heat dissipation control adopts an "active" control method.

[0031] Specifically, in this embodiment, the intelligent driving domain controller (continuously) acquires operational data, such as... Figure 2 As shown, the operational data includes historical operational data and real-time operational data. The historical operational data is stored in a corresponding database and specifically includes: Temperature data: Temperature of multiple key points inside the intelligent driving domain controller (such as CPU, GPU, SoC (System on Chip)). The temperature data in the database can be categorized and stored according to different seasons and / or different regions.

[0032] Load data includes the CPU / GPU processor utilization and memory usage of the intelligent driving domain controller, as well as the load data of currently running intelligent driving functions (such as highway cruise load, automatic lane changing load, and traffic jam following load). The load varies significantly depending on the type of vehicle.

[0033] Environmental data: Ambient temperature, vehicle speed, etc., from the vehicle's CAN (Controller Area Network) data; Thermal data: CAN data of the PID (Proportion-Integration-Derivative) thermal controller, including fan speed, cooling water pump speed, etc.

[0034] In this embodiment, to avoid excessive consumption of computing resources during the prediction process, the acquired historical data is key data that has undergone structured processing (occupying less resources and consuming less). That is, in this embodiment, the data in the database can be pre-structured. This pre-structured processing can be based on node-attribute or node-relationship structures, and then the key information is extracted and summarized from the pre-structured data to reduce the amount of processing and save resources in the future.

[0035] In addition, in this embodiment, since the data needs to be converted into vectors during subsequent data processing, after extracting and summarizing key information from the pre-structured data, the relevant vector data can be stored in advance.

[0036] In this embodiment, the intelligent driving domain controller also acquires real-time operating data, which includes temperature data, load data, environmental data, and heat dissipation data. In this embodiment, the real-time operating data also includes scene data, which includes the current specific scene data, such as an intersection scene, a lane narrowing scene, a lane merging scene, or a turning scene, etc.

[0037] In this embodiment, specific scene data is also acquired to facilitate the early determination of the scene, thereby providing preparation for early perception or decision-making.

[0038] Step S20: Obtain the target task queue that the intelligent driving domain controller will execute within a future preset time period, wherein the future preset time period is shorter than the first preset time period; In this embodiment, as Figure 2 As shown, the system also obtains a queue of target tasks that the intelligent driving domain controller will execute in a future preset time period (short term). Here, a target task is not a single task, but a queue. For example, it also obtains the target task queue for the intelligent driving domain controller to process 10 frames of image data and perform 5 path planning operations (2 target tasks) during the use of the high-speed cruise function in the next 5 seconds, or it obtains the target task queue for the intelligent driving domain controller to process 20 frames of image data, perform 7 path planning operations, and determine whether the lane to which the controller needs to change lanes during path planning is likely to collide with any vehicles (3 target tasks) during the use of the automatic lane changing function in the next 10 seconds. In this embodiment, it should be noted that the future preset time period is shorter than the first preset time period. The first preset time period can be the next 5 seconds or the next 10 seconds, etc., and is not specifically limited.

[0039] In this embodiment, it should be noted that the target task queue to be executed by the intelligent driving domain controller in the future preset time period can be obtained only when the current temperature data is detected to be greater than the minimum temperature value required for heat dissipation control, thus avoiding waste of resources. In addition, in this embodiment, the task of obtaining the target task queue to be executed by the intelligent driving domain controller in the future preset time period can be omitted if the vehicle starts less than the preset start time period. That is, if the vehicle starts less than the preset start time period, the vehicle is unlikely to have an overheating situation, thus saving resources. (However, in this process, it is still necessary to collect the operating data of the intelligent driving domain controller to make the data in the database more comprehensive, or some vehicles are driving on common commuting routes, so collecting this part of the data can facilitate the improvement of subsequent temperature prediction.)

[0040] In addition, in this embodiment, whether to obtain the target task queue that the intelligent driving domain controller will execute in a future preset time period can also be optional to suit different scenario requirements.

[0041] Step S30: Using a preset temperature change trend prediction model, the future temperature change trend is predicted based on the running data and the target task queue, and the target temperature change curve in the prediction time domain is output. The preset temperature change trend prediction model is an accurate model for predicting future temperature change trends, obtained by iteratively training a preset thermal dynamic model to be trained using training data with preset labels. In this embodiment, the temperature change curve of the controller over a future period of time (prediction time domain, such as 10 seconds) can be predicted.

[0042] In this embodiment, it should be noted that the preset temperature change trend prediction model is a pre-trained model, and the training process of the preset temperature change trend prediction model can be as follows: Acquire training data with preset labels, wherein the preset labels may be actual temperature change curves or data consisting of actual collected temperature and time, wherein the training data includes training run data; After acquiring the training data, in this embodiment, the preset thermal dynamic model to be trained is iteratively trained. Specifically, the predicted temperature curve output after the training data is input into the preset thermal dynamic model to be trained is acquired, the predicted temperature curve is compared with the actual temperature change curve, and the comparison result is obtained. Based on the comparison result, the model parameters of the preset thermal dynamic model to be trained are adjusted until the preset number of training times is reached, or the preset loss function converges, until the preset temperature change trend prediction model is obtained.

[0043] The step of predicting future temperature change trends based on the operational data and the target task queue using a preset temperature change trend prediction model includes: Step S31: Analyze the target task queue to obtain the future load and corresponding time curve within a preset time period; In this embodiment, the target task queue is parsed using a preset parsing model to obtain the future load (e.g., ...) within a preset time period. Figure 2 The future load shown in the figure and the corresponding time curve.

[0044] Step S32: Extract historical temperature data, historical load data, historical environmental data, and historical heat dissipation data from the operating data; Step S33: Using a preset temperature change trend prediction model, based on the future load and corresponding time curve, historical temperature data, historical load data, historical environmental data, and historical heat dissipation data, perform prediction processing of future temperature change trends.

[0045] In this embodiment, the future load and corresponding time curve, historical temperature data, historical load data, historical environmental data, and historical heat dissipation data are input into the preset temperature change trend prediction model. That is, in this embodiment, the future load and corresponding time curve are first parsed and then input into the model, so as to facilitate subsequent comparison or comparative analysis with the corresponding target temperature change curve.

[0046] Step S40: Based on the target temperature change curve, perform advance heat dissipation control on the intelligent driving domain controller.

[0047] In this embodiment, after obtaining the target temperature change curve, based on the target temperature change curve (e.g., Figure 2 As shown), generate corresponding optimal heat dissipation instructions to perform pre-heat dissipation control (predictive heat dissipation control strategy) on the intelligent driving domain controller.

[0048] The method for pre-heating control of the intelligent driving domain controller based on the target temperature change curve can be as follows: Method 1: If the highest temperature in the future target temperature change curve is greater than the preset temperature threshold, the corresponding cooling device will be activated at the first time point before the preset temperature threshold is reached. Cooling will be carried out in advance before the vehicle temperature reaches the preset temperature threshold to avoid the vehicle temperature reaching the preset temperature threshold and affecting the vehicle's driving performance or even safety performance.

[0049] Method 2: If the future target temperature change curve has an instantaneous temperature peak, the corresponding cooling device will be activated at the corresponding second time point before the instantaneous peak. Cooling will be carried out in advance before the vehicle temperature reaches the instantaneous peak to avoid the vehicle temperature reaching the preset temperature threshold, which would affect the vehicle's driving performance or even safety performance.

[0050] In this embodiment, the specific first and second time points are calculated, and the calculation method may be as follows: Determine the heat dissipation power of the heat dissipation device, and determine the time period information from the current time to the first time point or the second time point, and determine the available heat dissipation based on the heat dissipation power and time period information; Based on the future target temperature change curve, the heat generated by the vehicle during the corresponding time period (time period information) is determined; then, based on both, the vehicle's heat dissipation control is determined in advance.

[0051] In other words, in this embodiment, based on the target temperature change curve, a prediction can be made in advance, and the intelligent driving domain controller can be controlled to dissipate heat in advance.

[0052] It is understood that in this embodiment, predictive control enhances heat dissipation in advance before the temperature rises sharply, effectively avoiding performance degradation of the controller or processor due to overheating, and ensuring the stability and safety of the intelligent driving system.

[0053] Furthermore, in this embodiment, by controlling heat dissipation in advance, the sudden start and stop of the cooling system is avoided, allowing it to operate more smoothly and energy-efficiently while meeting heat dissipation requirements, thus reducing overall vehicle energy consumption and operating noise. In other words, in this embodiment, the smooth temperature and control curves reduce thermal shock and mechanical wear, helping to extend the lifespan of the controller and the cooling system itself.

[0054] Compared to related technologies, traditional heat dissipation control methods mainly rely on reactive control based on current temperature sensor feedback, resulting in delays and other issues that make it difficult to meet the heat dissipation requirements of practical applications. This application addresses this by collecting operational data from the intelligent driving domain controller, including historical and real-time operational data; obtaining a queue of target tasks that the intelligent driving domain controller will execute within a preset future time period, where the preset future time period is shorter than a first preset time period; using a preset temperature change trend prediction model, predicting future temperature change trends based on the operational data and the target task queue, and outputting a target temperature change curve within the prediction time domain; wherein the preset temperature change trend prediction model is an accurate model for predicting future temperature change trends obtained by iteratively training a preset thermal dynamics model based on training data with preset labels; and performing advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve. It is understood that in this application, the intelligent driving domain controller is not reactive control, but rather predictive of heat generation. That is, this application collects the operating data of the intelligent driving domain controller and obtains the target task queue that the intelligent driving domain controller will execute in a future preset time period (the future preset time period is shorter than the first preset time period, therefore, the target task queue is a short-term task queue, which can be accurately predicted and will not consume too many resources). It then performs predictive processing on the future temperature change trend of the controller to obtain the target temperature change curve in the predicted time domain. In this way, the intelligent driving domain controller can perform heat dissipation control in advance, rather than reacting passively after the temperature exceeds the standard (which results in a reaction delay). In other words, this application can predict the heat trend in advance and react in advance to meet the heat dissipation requirements in practical applications.

[0055] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, the step of performing advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve includes: Step S41: Based on the target temperature change curve, determine the optimal heat dissipation setting time and the target heat dissipation power consumption; In this embodiment, based on the target temperature change curve, the target temperature to be lowered and the heat dissipation power required to reach the target temperature can be determined. Alternatively, based on the target temperature change curve, the heat dissipation power required to reach the target temperature can be determined directly based on historical experience or experiments.

[0056] In this embodiment, it should also be noted that as long as the target temperature change curve is determined, or as long as the heat dissipation power required to drop to the target temperature is determined, the optimal heat dissipation setting time and target heat dissipation power can be determined based on the experimental data previously obtained by the corresponding vehicle.

[0057] In this embodiment, after determining the optimal heat dissipation setting time and the target heat dissipation power consumption, the type and number of heat dissipation devices that need to be activated can be determined, as well as the rotation speed of the heat dissipation devices.

[0058] That is, in this embodiment, the target heat dissipation power consumption can be calculated based on the rotation speed of the heat dissipation device, etc. It should be noted that the heat dissipation device can be the fan speed and the cooling water pump speed. Therefore, the rotation speed of the heat dissipation device can specifically be the fan speed and the cooling water pump speed, etc.

[0059] In this embodiment, based on the target temperature change curve, determining the optimal heat dissipation setting time and target heat dissipation power consumption also requires obtaining experimental data (the heat dissipation device determined through experiments). Step S42: Convert the optimal heat dissipation setting time and target heat dissipation power consumption into a CAN signal, and control the input of the CAN signal to the PID heat dissipation controller, so that the PID heat dissipation controller can control the heat dissipation device based on the CAN signal to perform advance heat dissipation control on the intelligent driving domain controller.

[0060] In this embodiment, as Figure 3 As shown, the optimal heat dissipation setting time and target heat dissipation power consumption are converted into CAN signals, and the CAN signals are controlled to be input to the PID heat dissipation controller, so that the PID heat dissipation controller can control the heat dissipation device (such as fan speed and cooling water pump speed) based on the CAN signals, so as to perform pre-heat dissipation control on the intelligent driving domain controller.

[0061] After the step of performing advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve, the following steps are included: Step A1: Measure the temperature in real time during the heat dissipation control process to obtain the actual measured temperature; In this embodiment, after the preheating process begins, the temperature is measured at regular intervals during the heat dissipation control process, forming a sequence of actual measured temperatures. It should be noted that the actual measured temperatures may vary due to vehicle aging, different vehicles, or different usage environments.

[0062] Step A2: Based on the optimal heat dissipation setting time and target heat dissipation power consumption, determine the predicted temperature during the advance heat dissipation control process of the intelligent driving domain controller; In this embodiment, based on the optimal heat dissipation setting time and target heat dissipation power consumption, the predicted temperature during the advance heat dissipation control process of the intelligent driving domain controller is determined. It should be noted that the predicted temperature is the theoretical temperature.

[0063] Step A3: Based on the temperature difference between the actual measured temperature and the predicted temperature at the same time, update the model parameters of the preset temperature change trend prediction model in real time, so as to perform the next prediction process based on the preset temperature change trend prediction model after updating the model parameters. The preset temperature change trend prediction model is different for different vehicles after updating the model parameters.

[0064] Based on the temperature difference between the actual measured temperature and the predicted temperature at the same moment, the model parameters of the preset temperature change trend prediction model are updated in real time. The next prediction process is then performed based on the preset temperature change trend prediction model with the updated model parameters. Figure 3 As shown, it should be noted that the preset temperature change trend prediction model after updating the model parameters can only be applied to this vehicle and not extended to other vehicles. That is, the preset temperature change trend prediction model after updating the model parameters is different for different vehicles.

[0065] In this embodiment, the built-in online learning mechanism enables the model to adapt to different vehicles, different usage environments, and the effects of vehicle aging, thereby improving the personalized heat dissipation control experience of the vehicle.

[0066] The step of updating the model parameters of the preset temperature change trend prediction model in real time based on the temperature difference between the actual measured temperature and the predicted temperature at the same moment includes: Determine the target time point when the actual measured temperature drops to the preset ideal temperature; Based on the temperature difference between the actual measured temperature and the corresponding predicted temperature at the target time point, the model parameters of the preset temperature change trend prediction model are updated in real time.

[0067] In this embodiment, the target time point when the actual measured temperature drops to the preset ideal temperature is determined to be 2:30. Based on the temperature difference between the actual measured temperature and the corresponding predicted temperature at the target time point (2:30), the model parameters of the preset temperature change trend prediction model can be updated, and the updated model can be obtained.

[0068] In this embodiment, the actual measured temperature is obtained by measuring the temperature during the heat dissipation control process in real time. Based on the optimal heat dissipation setting time and target heat dissipation power consumption, the predicted temperature during the advance heat dissipation control process of the intelligent driving domain controller is determined. Based on the temperature difference between the actual measured temperature and the predicted temperature at the same moment, the model parameters of the preset temperature change trend prediction model are updated in real time. The next prediction process is then performed based on the preset temperature change trend prediction model with updated model parameters. The preset temperature change trend prediction model differs for different vehicles after updating the model parameters. This allows the model to better meet the personalized needs of the vehicle.

[0069] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first or second embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, the step of obtaining the target task queue that the intelligent driving domain controller will execute within a future preset time period includes: Step B1: Obtain the driving habits of the vehicle owner corresponding to the intelligent driving domain controller and the driving conditions of the corresponding vehicle; In this embodiment, the driver's driving habits are also obtained, including information such as the driver's usual driving routes, driving style, and driving time. The driving conditions include whether the area where the vehicle is driven is mountainous, and whether the area is bumpy.

[0070] Step B2: Based on the driving habits of the corresponding vehicle owner and the driving conditions of the corresponding vehicle, determine the target task queue that the intelligent driving domain controller will execute in a future preset time period.

[0071] In this embodiment, the driving habits of the corresponding vehicle owner and the driving conditions of the corresponding vehicle are input into a preset target task generation model. Based on the preset target task generation model, the target task queue that the intelligent driving domain controller will execute in a preset time period in the future is determined. The model is a trained model.

[0072] In this embodiment, the driving habits of the vehicle owner corresponding to the intelligent driving domain controller and the driving conditions of the corresponding vehicle are obtained. Based on the driving habits of the vehicle owner and the driving conditions of the corresponding vehicle, a target task queue to be executed by the intelligent driving domain controller within a preset time period is determined. That is, in this embodiment, the target task queue to be executed is determined based on the actual driving situation, thus the target task queue can be accurately determined.

[0073] This application also provides a heat dissipation control device for an intelligent driving domain controller, such as... Figure 5 As shown, the device includes: The acquisition module 10 is used to acquire the operating data of the intelligent driving domain controller, wherein the operating data includes historical operating data and real-time operating data; The acquisition module 20 is used to acquire the target task queue that the intelligent driving domain controller will execute in a future preset time period, wherein the future preset time period is shorter than a first preset time period; The prediction module 30 is used to predict the future temperature change trend based on the running data and the target task queue by using a preset temperature change trend prediction model, and output the target temperature change curve in the prediction time domain. The preset temperature change trend prediction model is an accurate model for predicting future temperature change trends, obtained by iteratively training a preset thermal dynamic model to be trained using training data with preset labels. The heat dissipation module 40 is used to perform advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve.

[0074] In one embodiment, the intelligent driving domain controller heat dissipation control device is used to achieve: Based on the target temperature change curve, determine the optimal heat dissipation setting time and the target heat dissipation power consumption; The optimal heat dissipation setting time and target heat dissipation power consumption are converted into CAN signals, and the CAN signals are controlled to be input to the PID heat dissipation controller, so that the PID heat dissipation controller can control the heat dissipation device based on the CAN signals to perform advance heat dissipation control on the intelligent driving domain controller.

[0075] In one embodiment, the intelligent driving domain controller heat dissipation control device is used to achieve: The temperature during the heat dissipation control process is measured in real time to obtain the actual measured temperature. Based on the optimal heat dissipation setting time and target heat dissipation power consumption, the predicted temperature is determined during the advance heat dissipation control process of the intelligent driving domain controller; Based on the temperature difference between the actual measured temperature and the predicted temperature at the same moment, the model parameters of the preset temperature change trend prediction model are updated in real time, so as to perform the next prediction process based on the preset temperature change trend prediction model after updating the model parameters. The preset temperature change trend prediction model is different for different vehicles after updating the model parameters.

[0076] In one embodiment, the intelligent driving domain controller heat dissipation control device is used to achieve: Determine the target time point when the actual measured temperature drops to the preset ideal temperature; Based on the temperature difference between the actual measured temperature and the corresponding predicted temperature at the target time point, the model parameters of the preset temperature change trend prediction model are updated in real time.

[0077] In one embodiment, the intelligent driving domain controller heat dissipation control device is used to achieve: The system obtains the driving habits of the vehicle owner corresponding to the intelligent driving domain controller and the driving conditions of the corresponding vehicle. Based on the driving habits of the corresponding vehicle owner and the driving conditions of the corresponding vehicle, the target task queue that the intelligent driving domain controller will execute in a future preset time period is determined.

[0078] In one embodiment, the intelligent driving domain controller heat dissipation control device is used to achieve: The target task queue is analyzed to obtain the future load and corresponding time curve within a preset time period. Historical temperature data, historical load data, historical environmental data, and historical heat dissipation data are extracted from the operational data. By using a preset temperature change trend prediction model, based on the future load and corresponding time curve, historical temperature data, historical load data, historical environmental data, and historical heat dissipation data, the future temperature change trend is predicted.

[0079] Compared with the prior art, the heat dissipation control device for intelligent driving domain controller provided in this application has the same beneficial effects as the heat dissipation control device for intelligent driving domain controller provided in the above embodiments, and other technical features in the heat dissipation control device for intelligent driving domain controller are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0080] This application provides a heat dissipation control device for an intelligent driving domain controller. The heat dissipation control device for an intelligent driving domain controller includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the heat dissipation control of the intelligent driving domain controller in the above embodiment 1.

[0081] The following is for reference. Figure 6 The diagram illustrates a structural schematic of a heat dissipation control device suitable for implementing embodiments of this application. The heat dissipation control device for the intelligent driving domain controller in this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, tablets, digital radio receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The intelligent driving domain controller heat dissipation control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0082] like Figure 6 As shown, the intelligent driving domain controller thermal management device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the intelligent driving domain controller thermal management device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the intelligent driving domain controller thermal control device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows an intelligent driving domain controller thermal control device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0083] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0084] The intelligent driving domain controller heat dissipation control device provided in this application adopts the intelligent driving domain controller heat dissipation control in the above embodiments, which can solve the technical problem of intelligent driving domain controller heat dissipation control. Compared with the prior art, the beneficial effects of the intelligent driving domain controller heat dissipation control device provided in this application are the same as the beneficial effects of the intelligent driving domain controller heat dissipation control provided in the above embodiments, and other technical features in the intelligent driving domain controller heat dissipation control device are the same as the features disclosed in the method of the previous embodiment, and will not be repeated here.

[0085] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0086] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0087] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to perform the intelligent driving domain controller heat dissipation control in the above embodiments.

[0088] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0089] The aforementioned computer-readable storage medium may be included in the thermal management control device of the intelligent driving domain controller; or it may exist independently and not be assembled into the thermal management control device of the intelligent driving domain controller.

[0090] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the intelligent driving domain controller heat dissipation control device, the intelligent driving domain controller heat dissipation control device: collects access information from different application sources through an access acquisition program and stores the access information in a message queue, wherein the access acquisition program is deployed in the runtime environment of the application; performs service call dependency deduction on the access information in the message queue to obtain the call relationship between the different applications, and generates an application topology architecture based on the call relationship.

[0091] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0093] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0094] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described intelligent driving domain controller heat dissipation control, thereby solving the technical problem of intelligent driving domain controller heat dissipation control. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the intelligent driving domain controller heat dissipation control provided in the above embodiments, and will not be repeated here.

[0095] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of heat dissipation control for the intelligent driving domain controller as described above.

[0096] The computer program product provided in this application can solve the technical problem of heat dissipation control for intelligent driving domain controllers. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of heat dissipation control for intelligent driving domain controllers provided in the above embodiments, and will not be repeated here.

[0097] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A method for heat dissipation control of an intelligent driving domain controller, characterized in that, The heat dissipation control method for the intelligent driving domain controller includes: Collect operational data from the intelligent driving domain controller, wherein the operational data includes historical operational data and real-time operational data; Obtain the target task queue that the intelligent driving domain controller will execute within a future preset time period, wherein the future preset time period is shorter than a first preset time period; By using a preset temperature change trend prediction model, the system performs prediction processing on the future temperature change trend based on the running data and the target task queue, and outputs the target temperature change curve in the prediction time domain. The preset temperature change trend prediction model is an accurate model for predicting future temperature change trends, obtained by iteratively training a preset thermal dynamic model to be trained using training data with preset labels. Based on the target temperature change curve, the intelligent driving domain controller is subjected to advance heat dissipation control.

2. The intelligent driving domain controller heat dissipation control method as described in claim 1, characterized in that, The step of performing advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve includes: Based on the target temperature change curve, determine the optimal heat dissipation setting time and the target heat dissipation power consumption; The optimal heat dissipation setting time and target heat dissipation power consumption are converted into CAN signals, and the CAN signals are controlled to be input to the PID heat dissipation controller, so that the PID heat dissipation controller can control the heat dissipation device based on the CAN signals to perform advance heat dissipation control on the intelligent driving domain controller.

3. The intelligent driving domain controller heat dissipation control method as described in claim 2, characterized in that, After the step of performing advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve, the following steps are included: The temperature during the heat dissipation control process is measured in real time to obtain the actual measured temperature. Based on the optimal heat dissipation setting time and target heat dissipation power consumption, the predicted temperature is determined during the advance heat dissipation control process of the intelligent driving domain controller; Based on the temperature difference between the actual measured temperature and the predicted temperature at the same moment, the model parameters of the preset temperature change trend prediction model are updated in real time, so as to perform the next prediction process based on the preset temperature change trend prediction model after updating the model parameters. The preset temperature change trend prediction model is different for different vehicles after updating the model parameters.

4. The intelligent driving domain controller heat dissipation control method as described in claim 3, characterized in that, The step of updating the model parameters of the preset temperature change trend prediction model in real time based on the temperature difference between the actual measured temperature and the predicted temperature at the same moment includes: Determine the target time point when the actual measured temperature drops to the preset ideal temperature; Based on the temperature difference between the actual measured temperature and the corresponding predicted temperature at the target time point, the model parameters of the preset temperature change trend prediction model are updated in real time.

5. The intelligent driving domain controller heat dissipation control method as described in claim 1, characterized in that, The step of obtaining the queue of target tasks that the intelligent driving domain controller will execute within a preset time period includes: The system obtains the driving habits of the vehicle owner corresponding to the intelligent driving domain controller and the driving conditions of the corresponding vehicle. Based on the driving habits of the corresponding vehicle owner and the driving conditions of the corresponding vehicle, the target task queue that the intelligent driving domain controller will execute in a future preset time period is determined.

6. The intelligent driving domain controller heat dissipation control method as described in claim 1, characterized in that, The step of predicting future temperature change trends based on the operational data and the target task queue using a preset temperature change trend prediction model includes: The target task queue is analyzed to obtain the future load and corresponding time curve within a preset time period. Historical temperature data, historical load data, historical environmental data, and historical heat dissipation data are extracted from the operational data. By using a preset temperature change trend prediction model, based on the future load and corresponding time curve, historical temperature data, historical load data, historical environmental data, and historical heat dissipation data, the future temperature change trend is predicted.

7. A heat dissipation control device for an intelligent driving domain controller, characterized in that, The device includes: The data acquisition module is used to collect the operating data of the intelligent driving domain controller, wherein the operating data includes historical operating data and real-time operating data; The acquisition module is used to acquire the target task queue that the intelligent driving domain controller will execute within a future preset time period, wherein the future preset time period is shorter than a first preset time period; The prediction module is used to predict the future temperature change trend based on the running data and the target task queue by using a preset temperature change trend prediction model, and output the target temperature change curve in the prediction time domain. The preset temperature change trend prediction model is an accurate model for predicting future temperature change trends, obtained by iteratively training a preset thermal dynamic model to be trained using training data with preset labels. A heat dissipation module is used to perform advance heat dissipation control on the intelligent driving domain controller based on the target temperature change curve.

8. A heat dissipation control device for an intelligent driving domain controller, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of thermal control of the intelligent driving domain controller as claimed in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, it implements the steps of heat dissipation control of the intelligent driving domain controller as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of heat dissipation control for the intelligent driving domain controller as described in any one of claims 1 to 6.

Images