A CPU prediction temperature-based acquisition terminal load regulation method and module

Abstract

The application belongs to the field of power equipment, and particularly relates to a CPU prediction temperature-based acquisition terminal load regulation method, module and system.S1: measuring the environmental temperature and humidity of the acquisition terminal; determining the static temperature.S2: querying the pre-calibrated load temperature rise function.S3: obtaining the operation load rate of the CPU of the acquisition terminal and calculating the dynamic temperature;S4: calculating the prediction temperature by using the static temperature and the dynamic temperature.S5: judging whether the prediction temperature value exceeds the temperature threshold, if yes, passively reducing the operation load rate, otherwise, keeping the current operation state.S6: after the operation load rate of the acquisition terminal is passively reduced, calculating the maximum allowed temperature rise amplitude of the acquisition terminal.S7: querying the inverse function of the load temperature rise function and calculating the maximum operation load rate;S8: according to the maximum operation load rate, peak-shaving distributing the subsequent tasks of the acquisition terminal.The application solves the problem that high temperature environment and multiple tasks easily cause the acquisition terminal to be down or damaged.

Description

Technical Field

[0001] The present invention belongs to the field of power equipment, and in particular relates to a method, module and system for controlling load of an acquisition terminal based on CPU predicted temperature. Background Art

[0002] The data collection terminal is an intelligent, integrated terminal device that integrates functions such as power supply and consumption information collection within distribution stations, data collection from energy meters or data collection terminals, equipment status monitoring and communication networking, localized analysis and decision-making, and collaborative computing. The device's hardware utilizes a platform-based design, supports an edge computing framework, and enables flexible functional expansion through a software-defined approach, resulting in powerful data collection and processing capabilities. With the integration of new energy distributed power generation equipment and the diversification of new user electricity demand, the operating status of distribution stations is becoming increasingly complex, making the data collection tasks of the data collection terminal more onerous and diverse.

[0003] The CPUs in existing data collection terminals typically utilize microprocessor chips similar to those used in general-purpose computers, placing high demands on the operating environment's temperature. The CPUs in data collection terminals tend to overheat when operating at high frequencies. Under high temperatures, this can cause the chip's internal temperature to exceed the operating temperature, damaging the chip or causing the data collection equipment to shut down, impacting the normal execution of various data collection tasks and, in turn, the security of station management. Existing terminals measure the CPU's ambient temperature by installing a temperature monitoring chip and, based on the monitoring results, reduce the frequency or shut down the data collection terminal. While this reduces the risk of damage to the data collection terminal, it can also impact the normal execution of data collection tasks. Furthermore, installing a CPU temperature detection chip incurs high costs for retrofitting older equipment, making it impractical. Summary of the Invention

[0004] In order to solve the problem that high temperature environment and a large number of complex acquisition tasks easily cause the acquisition terminal to crash or be damaged, the present invention provides an acquisition terminal load control method, module and system based on CPU predicted temperature.

[0005] The present invention is achieved by adopting the following technical solutions:

[0006] A method for controlling the load of an acquisition terminal based on a CPU predicted temperature comprises the following steps:

[0007] S1: Use sensors to measure the ambient temperature et and humidity hu of the acquisition terminal in real time; and use the ambient temperature et as the static temperature T of the acquisition terminal S .

[0008] S2: According to the current ambient temperature et and ambient humidity hu, a pre-calibrated load heating function f related to the current temperature and humidity is obtained. et～hu (ut ).

[0009] S3: Get the CPU load rate u of the acquisition terminal t , and according to the load heating function f et～hu (u t ) Calculate the dynamic temperature T of the current acquisition terminal D :

[0010] T D＝ f et～hu (u t ).

[0011] S4: Calculate the real-time predicted temperature value T of the device according to the following acquisition terminal operation temperature model t :

[0012] T t =T S +T D .

[0013] S5: Get the preset temperature threshold T max , determine whether the real-time predicted temperature value Tt exceeds the temperature threshold T max :

[0014] (1) The operating load rate of the acquisition terminal is passively reduced by reducing the number of terminal tasks or lowering the terminal operating frequency.

[0015] (2) Otherwise, the current CPU load rate and execution strategy of the acquisition task are maintained.

[0016] S6: After the load rate of the acquisition terminal is passively reduced, the maximum allowable temperature rise of the acquisition terminal is calculated by the following formula △T max :

[0017] △T max =T max -T S .

[0018] S7: Query the load temperature rise function f et～hu (u t )'s inverse function g et～hu (T), and according to the maximum allowable temperature rise △T max Calculate the maximum operating load rate u tmax ;

[0019] u tmax =g et～hu (△T max ).

[0020] S8: The subsequent tasks of the collection terminal are distributed in a staggered manner according to the maximum operation load rate to ensure that the total operation load rate of multiple collection tasks processed in parallel is not higher than the maximum operation load rate.

[0021] As a further improvement of the present invention, in step S1, the ambient temperature et and the ambient humidity hu are measured by the temperature and humidity sensor of the acquisition terminal itself. For the acquisition terminal that is not equipped with a temperature and humidity sensor, a temperature and humidity sensor is added outside the acquisition terminal, and the detection results of the temperature and humidity sensor are uploaded.

[0022] As a further improvement of the present invention, in step S2, the load temperature rise function f in the acquisition terminal is collected. et～hu (u t ) is calibrated as follows:

[0023] S01: The upper and lower limits of the ambient temperature et and ambient humidity hu during the test process are determined based on the theoretical operating temperature and humidity in the acquisition terminal equipment parameters.

[0024] S02: Divide the ambient temperature et and the ambient humidity hu into n and m segments according to a preset interval width.

[0025] S03: Select the median value of each segment, combine them into n×m test conditions including temperature and humidity values, and generate a corresponding task list.

[0026] S04: Select the first test task in the task list and obtain the corresponding load temperature rise function f through the following process et～hu (u t ):

[0027] S041: Adjust the temperature and humidity of the acquisition terminal test environment to preset test conditions.

[0028] S042: Record the CPU surface temperature of the acquisition terminal in an idle state and use it as the base temperature T0 under the current test conditions.

[0029] S043: Add parallel collection tasks to the collection terminal at a preset operation load rate increase, and record the number of each operation load.

[0030] The temperature increment △T of the CPU surface temperature after running for 3 minutes at the load rate state:

[0031] Temperature increment ΔT = current temperature - base temperature T0.

[0032] S044: Based on the data pairs of several interrelated operating load rates and temperature increments obtained from the test (u t ,△

[0033] T), and fit the function that represents the mapping relationship between the operating load rate and temperature increment under the current test conditions, that is:

[0034] f et～hu (u t ).

[0035] S05: Select the remaining test tasks in the task list in turn, and repeat steps S041 to S044 until a function representing the mapping relationship between the operating load rate and the temperature increment corresponding to all test conditions is obtained.

[0036] As a further improvement of the present invention, in the process of generating the task list in step S02, the ambient temperature et is segmented at intervals of 5°C; and the ambient humidity hu is segmented at intervals of 10°C RH.

[0037] As a further improvement of the present invention, in step S043, the preset operating load rate increase is 5% or 10%, and the detection result of the CPU surface temperature is accurate to 1 decimal place.

[0038] As a further improvement of the present invention, in step S5, when the state of passively reducing the operating load rate is triggered, the operating load rate of the CPU of the acquisition terminal is reduced to below 50%.

[0039] As a further improvement of the present invention, in step S8, the number N of collection tasks that the collection terminal allows to be processed in parallel satisfies the following expression:

[0040]

[0041] In the above formula, u t 0 indicates the load rate of the acquisition terminal in idle state; u t (i) represents the running load rate generated when the i-th collection task runs in the CPU of the collection terminal.

[0042] The present invention also includes a collection terminal load control module based on CPU predicted temperature, which adopts the collection terminal load control method based on CPU predicted temperature as described above to control the operating load rate of the CPU in the collection terminal to avoid the collection terminal from shutting down due to overheating. This type of collection terminal load control module based on CPU predicted temperature includes: a communication module and a processing module.

[0043] The communication module communicates with the data collection terminal and the master server. The processing module includes a data collection unit, a storage unit, a query unit, a calculation unit, a judgment unit, and an interaction unit. The data collection unit is used to obtain the current ambient temperature and humidity of the data collection terminal, as well as the real-time CPU load rate. The storage unit stores pre-calibrated load-temperature-rise functions and their inverses under different temperature and humidity levels. The storage unit also stores the load rate corresponding to each data collection task. The query unit is used to retrieve the load-temperature-rise functions and their inverses under the current temperature and humidity conditions, as well as the load rate corresponding to the pending data collection task, from the storage unit. The calculation unit first calculates the real-time predicted temperature based on the ambient temperature and load rate; then, based on the real-time predicted temperature and a temperature threshold, calculates the maximum load rate of the CPU being collected. The judgment unit determines whether the calculated real-time predicted temperature is greater than the temperature threshold and, based on the judgment result, issues a command to the data collection terminal's CPU to passively reduce its load. The interaction unit uploads the maximum load rate and the load rate corresponding to the pending data collection task to the master server and receives task assignment instructions from the master server.

[0044] The master server reallocates the collection tasks to be processed according to the received data to ensure that the total operating load rate of multiple collection tasks processed in parallel does not exceed the maximum operating load rate.

[0045] As a further improvement of the present invention, the acquisition terminal load control module based on CPU predicted temperature provided by the present invention also includes a temperature sensor and a humidity sensor, both of which are installed on the acquisition device and electrically connected to the processing module.

[0046] The present invention also includes a collection terminal load control system based on predicted CPU temperature, comprising the aforementioned collection terminal load control module based on predicted CPU temperature and a master station server. The collection terminal load control module based on predicted CPU temperature employs the aforementioned collection terminal load control method based on predicted CPU temperature to generate the maximum operating load rate of the collection terminal CPU and the operating load rate corresponding to the collection task to be processed, and uploads these values ​​to the master station server. Based on the received data, the master station server reallocates the collection tasks to be processed in a staggered manner to ensure that the total operating load rate of multiple collection tasks being processed in parallel does not exceed the maximum operating load rate.

[0047] The technical solution provided by the present invention has the following beneficial effects:

[0048] The solution provided by the present invention pre-calibrates the changes in the CPU temperature of the acquisition terminal under different temperature and humidity conditions as the operating load rate changes, and obtains a load temperature rise function. Then, during the operation of the acquisition terminal, the ambient temperature is used as the static temperature, the temperature rise amplitude of the CPU that changes dynamically with the operating load rate is used as the dynamic temperature, and the fusion temperature of the two is used as the predicted temperature of the acquisition terminal operation process. Finally, by adjusting the acquisition task execution strategy in the CPU, it is ensured that the predicted temperature is not higher than the preset temperature threshold. Among them, during the task control process of the acquisition terminal, the present invention also calculates the operating load rate that allows the acquisition terminal to work normally at different ambient temperatures based on the inverse function of the calibrated load temperature rise function. This ensures that the acquisition terminal is always in the best working state and prevents the acquisition terminal from shutting down or being damaged in a high-temperature multi-tasking state.

[0049] The solution provided by the present invention can be externally connected to existing old equipment or embedded in new equipment. It has high practicality and wide adaptability and is suitable for large-scale promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flowchart of the steps of a method for controlling the load of an acquisition terminal based on the predicted CPU temperature provided in Example 1 of the present invention.

[0051] Figure 2 This is a flowchart of the steps of the calibration method of the load temperature rise function of the acquisition terminal in Example 1 of the present invention.

[0052] Figure 3 This is an architectural diagram of the acquisition terminal load control module based on CPU predicted temperature provided in Example 2 of the present invention.

[0053] Figure 4 This is an architectural diagram of the load control module of the acquisition terminal including the temperature and humidity sensor provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] Example 1

[0056] This embodiment provides a method for controlling the load of an acquisition terminal based on the CPU predicted temperature. Figure 1 As shown, it includes the following steps:

[0057] S1: Use sensors to measure the ambient temperature et and humidity hu of the acquisition terminal in real time; and use the ambient temperature et as the static temperature T of the acquisition terminalS .

[0058] In this embodiment, the ambient temperature et and ambient humidity hu are measured by the temperature and humidity sensor of the acquisition terminal itself. For an acquisition terminal that is not equipped with a temperature and humidity sensor, a temperature and humidity sensor is added outside the acquisition terminal, and the detection results of the temperature and humidity sensor are uploaded.

[0059] S2: According to the current ambient temperature et and ambient humidity hu, a pre-calibrated load heating function f related to the current temperature and humidity is obtained. et～hu (u t ).

[0060] like Figure 2 As shown, the load temperature rise function f of the acquisition terminal in this embodiment is et～hu (u t ) is calibrated as follows:

[0061] S01: The upper and lower limits of the ambient temperature et and ambient humidity hu during the test process are determined based on the theoretical operating temperature and humidity in the acquisition terminal equipment parameters.

[0062] S02: Divide the ambient temperature et and ambient humidity hu into n and m segments according to preset intervals. In this embodiment, the ambient temperature et is segmented at intervals of 5°C; the ambient humidity hu is segmented at intervals of 10°CRH.

[0063] S03: Select the median value of each segment, combine them into n×m test conditions including temperature and humidity values, and generate a corresponding task list.

[0064] S04: Select the first test task in the task list and obtain the corresponding load temperature rise function f through the following process: et～hu (u t ):

[0065] S041: Adjust the temperature and humidity of the acquisition terminal test environment to preset test conditions.

[0066] S042: Record the CPU surface temperature of the acquisition terminal in an idle state and use it as the base temperature T0 under the current test conditions.

[0067] S043: Add parallel collection tasks to the collection terminal at a preset operation load rate increment, and record the temperature increment △T of the CPU surface temperature after running for 3 minutes under each operation load rate state:

[0068] Temperature increment ΔT = current temperature - base temperature T0.

[0069] In this embodiment, the preset operating load rate increase is 5% or 10%, and the detection result of the CPU surface temperature is accurate to 1 decimal place.

[0070] S044: Based on the data pairs of several interrelated operating load rates and temperature increments obtained from the test (u t , △T), fit the function that characterizes the mapping relationship between the operating load rate and temperature increment under the current test conditions, that is: f et～hu (u t ).

[0071] S05: Select the remaining test tasks in the task list in turn, and repeat steps S041 to S044 until a function representing the mapping relationship between the operating load rate and the temperature increment corresponding to all test conditions is obtained.

[0072] S3: Get the CPU load rate u of the acquisition terminal t , and according to the load heating function f et～hu (u t ) Calculate the dynamic temperature T of the current acquisition terminal D :

[0073] T D =f et～hu (u t ).

[0074] S4: Calculate the real-time predicted temperature value T of the device according to the following acquisition terminal operation temperature model t :

[0075] T t =T S +T D .

[0076] S5: Get the preset temperature threshold T max , determine whether the real-time predicted temperature value Tt exceeds the temperature threshold T max :

[0077] (1) The operating load rate of the acquisition terminal is passively reduced by reducing the number of terminal tasks or lowering the terminal operating frequency.

[0078] In this embodiment, when the state of passively reducing the operating load rate is triggered, the operating load rate of the CPU of the acquisition terminal is reduced to below 50%.

[0079] (2) Otherwise, the current CPU load rate and execution strategy of the acquisition task are maintained.

[0080] S6: After the load rate of the acquisition terminal is passively reduced, the maximum allowable temperature rise of the acquisition terminal is calculated by the following formula △T max :

[0081] △T max = T max -T S .

[0082] S7: query the inverse function g et～hu (u t ) of the load temperature rise function f et～hu (T) and calculate the maximum operating load rate u max according to the maximum allowed temperature rise amplitude △T tmax ;

[0083] u tmax = g et～hu (△T max )。

[0084] S8: according to the maximum operating load rate, the subsequent tasks of the collection terminal are allocated in off-peak to ensure that the total operating load rate of the multiple collection tasks processed in parallel is not higher than the maximum operating load rate.

[0085] The number N of collection tasks allowed to be processed in parallel by the collection terminal satisfies the following expression:

[0086]

[0087] In the above expression, u t 0 represents the operating load rate of the collection terminal in the idle state; u t (i) represents the operating load rate generated when the i-th collection task runs in the CPU of the collection terminal.

[0088] The scheme provided by the present application calibrates the change of the CPU temperature of the collection terminal under different temperature and humidity conditions with the operating load rate in advance to obtain a load temperature rise function. Then, during the operation of the collection terminal, the environmental temperature is taken as a static temperature, the temperature rise amplitude of the CPU dynamically changing with the operating load rate is taken as a dynamic temperature, and the fusion temperature of the two is taken as the predicted temperature of the operation process of the collection terminal. Finally, the predicted temperature is ensured to be not higher than the preset temperature threshold by adjusting the collection task execution strategy in the CPU. In the task control process of the collection terminal, the present application also calculates the operating load rate allowed for normal work of the collection terminal under different environmental temperatures according to the inverse function of the calibrated load temperature rise function. So as to ensure that the collection terminal is always in the best working state, and prevent the collection terminal from being down or damaged in the high-temperature multi-task state.

[0089] The scheme provided by the present application can be externally connected to existing old equipment, or embedded in new equipment, has high practicability, wide adaptability, and is suitable for large-scale popularization and application.

[0090] Example 2

[0091] This embodiment provides a collection terminal load control module based on CPU predicted temperature, which uses the collection terminal load control method based on CPU predicted temperature as in Example 1 to control the CPU operating load rate in the collection terminal to avoid the collection terminal from shutting down due to overheating. Figure 3 As shown, this CPU temperature prediction-based data acquisition terminal load control module includes a communication module and a processing module. The communication module communicates with the data acquisition terminal and the master server. The processing module includes a data acquisition unit, a storage unit, a query unit, a calculation unit, a judgment unit, and an interaction unit.

[0092] The data acquisition unit is used to obtain, in real time, the current ambient temperature and humidity of the acquisition terminal, as well as the real-time operating load rate of the CPU. The storage unit stores pre-calibrated load temperature rise functions and their inverse functions for different temperature and humidity levels. The storage unit also stores the operating load rate corresponding to each acquisition task. The query unit is used to retrieve, from the storage unit, the load temperature rise functions and their inverse functions for the current temperature and humidity conditions, as well as the operating load rate corresponding to the pending acquisition task. The calculation unit is used to first calculate the real-time predicted temperature based on the ambient temperature and operating load rate; then, based on the real-time predicted temperature and a temperature threshold, calculate the maximum operating load rate of the acquired CPU. The judgment unit is used to determine whether the calculated real-time predicted temperature is greater than the temperature threshold and, based on the judgment result, issue a command to the acquisition terminal's CPU to passively reduce the operating load. The interaction unit is used to upload the maximum operating load rate and the operating load rate corresponding to the pending acquisition task to the master server and receive task allocation instructions from the master server.

[0093] The master server reallocates the collection tasks to be processed according to the received data to ensure that the total operating load rate of multiple collection tasks processed in parallel does not exceed the maximum operating load rate.

[0094] like Figure 4 As shown, the acquisition terminal load regulation module based on CPU predicted temperature provided in this embodiment also includes a temperature and humidity sensor, which is installed on the acquisition device and electrically connected to the processing module.

[0095] The acquisition terminal load control module based on CPU predicted temperature provided in this embodiment is essentially a computer device for realizing data processing and instruction generation, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0096] The embedded data processing module of the computer device provided in the embodiment may also be an intelligent terminal, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server, or cabinet server (including a standalone server or a server cluster composed of multiple servers) capable of executing programs. The computer device of this embodiment includes at least, but is not limited to, a memory and a processor that can be interconnected via a system bus.

[0097] In this embodiment, the memory (i.e., readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device.

[0098] In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc., equipped with the computer device. Of course, the memory may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store the operating system and various application software installed on the computer device. In addition, the memory may also be used to temporarily store various types of data that have been output or are about to be output.

[0099] In some embodiments, the processor may be a central processing unit (CPU), a graphics processing unit (GPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run program code stored in the memory or process data.

[0100] Example 3

[0101] This embodiment provides a collection terminal load control system based on CPU predicted temperature, which includes the collection terminal load control module based on CPU predicted temperature as in Example 2 and a master station server.

[0102] The acquisition terminal load control module based on the CPU predicted temperature adopts the acquisition terminal load control method based on the CPU predicted temperature as in Example 1 to generate the maximum operating load rate of the acquisition terminal CPU and the operating load rate corresponding to the acquisition task to be processed, and upload them to the main station server. The main station server re-allocates the acquisition tasks to be processed based on the received data to ensure that the total operating load rate of multiple acquisition tasks processed in parallel is not higher than the maximum operating load rate.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the load of an acquisition terminal based on CPU predicted temperature, characterized in that: It includes the following steps: S1: Use sensors to measure the ambient temperature et and humidity hu of the acquisition terminal in real time; and use the ambient temperature et as the static temperature T of the acquisition terminal S ; S2: According to the current ambient temperature et and ambient humidity hu, a pre-calibrated load heating function f related to the current temperature and humidity is obtained. et～hu (u t ); S3: Get the CPU load rate u of the acquisition terminal t , and according to the load heating function f et～hu (u t ) Calculate the dynamic temperature T of the current acquisition terminal D : T D＝ f et～hu (u t ); S4: Calculate the real-time predicted temperature value T of the device according to the following acquisition terminal operation temperature model t : T t =T S +T D ; S5: Get the preset temperature threshold T max , determine whether the real-time predicted temperature value Tt exceeds the temperature threshold T max : (1) Passively reduce the operating load rate of the acquisition terminal by reducing the number of terminal tasks or reducing the terminal operating frequency; (2) Otherwise, maintain the current CPU load rate and collection task execution strategy of the acquisition terminal; S6: After the load rate of the acquisition terminal is passively reduced, the maximum allowable temperature rise of the acquisition terminal is calculated by the following formula △T max : △T max =T max -T S S7: Query the load temperature rise function f et～hu (u t )'s inverse function g et～hu (T), and according to the maximum allowable temperature rise △T max Calculate the maximum operating load rate u tmax ; u tmax =g et～hu (△T max ) S8: The subsequent tasks of the collection terminal are distributed in a staggered manner according to the maximum operation load rate to ensure that the total operation load rate of multiple collection tasks processed in parallel is not higher than the maximum operation load rate.

2. The method for controlling the load of a data acquisition terminal based on the predicted CPU temperature according to claim 1, wherein: In step S1, the ambient temperature et and ambient humidity hu are measured by the temperature and humidity sensor of the acquisition terminal itself. For acquisition terminals that are not equipped with temperature and humidity sensors, a temperature and humidity sensor is added outside the acquisition terminal and the detection results of the temperature and humidity sensor are uploaded.

3. The method for controlling the load of a data acquisition terminal based on the predicted CPU temperature according to claim 1, wherein: In step S2, the load temperature rise function f in the acquisition terminal is et～hu (u t ) is calibrated as follows: S01: The upper and lower limits of the ambient temperature et and ambient humidity hu during the test process are determined based on the theoretical operating temperature and humidity in the collected terminal equipment parameters; S02: Divide the ambient temperature et and the ambient humidity hu into n and m segments according to a preset interval; S03: Select the median value of each segment, combine them into n×m test conditions containing temperature and humidity values, and generate a corresponding task list; S04: Select the first test task in the task list and obtain the corresponding load temperature rise function f through the following process et～hu (u t ): S041: Adjust the temperature and humidity of the test environment of the acquisition terminal to the preset test conditions; S042: Record the CPU surface temperature of the acquisition terminal in the idle state and use it as the base temperature T0 under the current test conditions; S043: Add parallel collection tasks to the collection terminal at a preset operating load rate increment, and record the temperature increment △T of the CPU surface temperature after running for 3 minutes under each operating load rate state: Temperature increment △T = current temperature - base temperature T0; S044: Based on the data pairs of several interrelated operating load rates and temperature increments obtained from the test (u t , △T), fit the function that characterizes the mapping relationship between the operating load rate and temperature increment under the current test conditions, that is: f et～hu (u t ); S05: Select the remaining test tasks in the task list in turn, and repeat steps S041 to S044 until a function representing the mapping relationship between the operating load rate and the temperature increment corresponding to all test conditions is obtained.

4. The method for controlling the load of a data acquisition terminal based on the predicted CPU temperature according to claim 3, wherein: In the process of generating the task list in step S02, the ambient temperature et is segmented at intervals of 5°C; the ambient humidity hu is segmented at intervals of 10°CRH.

5. The method for controlling the load of a data acquisition terminal based on the predicted CPU temperature according to claim 1, wherein: In step S043 , the preset operating load rate increase is 5% or 10%, and the detection result of the CPU surface temperature is accurate to 1 decimal place.

6. The method for controlling the load of a data acquisition terminal based on the predicted CPU temperature according to claim 5, wherein: In step S5, when the state of passively reducing the operating load rate is triggered, the operating load rate of the CPU of the acquisition terminal is reduced to below 50%.

7. The method for controlling the load of a data acquisition terminal based on the predicted CPU temperature according to claim 5, wherein: In step S8, the number N of collection tasks that the collection terminal allows to be processed in parallel satisfies the following expression: In the above formula, u t0 Indicates the operating load rate of the acquisition terminal in idle state; u t (i) represents the running load rate generated when the i-th collection task runs in the CPU of the collection terminal.

8. A collection terminal load control module based on CPU predicted temperature, characterized in that: The method for controlling the load of a data acquisition terminal based on the predicted CPU temperature as described in any one of claims 1 to 7 is used to control the operating load rate of the CPU in the data acquisition terminal to prevent the data acquisition terminal from shutting down due to overheating. The data acquisition terminal load control module based on the predicted CPU temperature includes: a communication module, which is in communication with the acquisition terminal and the master station server; and A processing module, comprising a data acquisition unit, a storage unit, a query unit, a calculation unit, a judgment unit and an interaction unit; the data acquisition unit is used to obtain in real time the ambient temperature and ambient humidity of the current working environment of the acquisition terminal, as well as the real-time operating load rate of the CPU; the storage unit stores a pre-calibrated load temperature rise function and its inverse function under different temperature and humidity levels; the storage unit also stores the operating load rate corresponding to each acquisition task; the query unit is used to obtain from the storage unit the load temperature rise function and its inverse function under the current temperature and humidity conditions, as well as the operating load rate corresponding to the acquisition task to be processed; the calculation unit is used to first calculate the real-time predicted temperature based on the ambient temperature and the operating load rate; and then calculate the maximum operating load rate of the collected CPU based on the real-time predicted temperature and the temperature threshold; the judgment unit is used to determine whether the calculated real-time predicted temperature is greater than the temperature threshold, and send an instruction to the CPU of the acquisition terminal to passively reduce the operating load based on the judgment result; the interaction unit is used to upload the maximum operating load rate and the operating load rate corresponding to the acquisition task to be processed to the main station server, and accept the task allocation instruction from the main station server; The master station server reallocates the collection tasks to be processed according to the received data to ensure that the total operation load rate of multiple collection tasks processed in parallel is not higher than the maximum operation load rate.

9. The acquisition terminal load control module based on CPU predicted temperature according to claim 8, characterized in that: It also includes a temperature sensor and a humidity sensor, which are installed on the collection device and are electrically connected to the processing module.

10. A collection terminal load control system based on CPU predicted temperature, characterized in that: It includes a collection terminal load control module based on CPU predicted temperature as described in claim 8 or 9 and a main station server; the collection terminal load control module based on CPU predicted temperature adopts the collection terminal load control method based on CPU predicted temperature as described in any one of claims 1-7 to generate the maximum operating load rate of the collection terminal CPU and the operating load rate corresponding to the collection task to be processed, and upload them to the main station server. The main station server re-allocates the collection tasks to be processed according to the received data to ensure that the total operating load rate of multiple collection tasks processed in parallel is not higher than the maximum operating load rate.

Images