Vehicle-mounted PM2.5 sensor fan rotating speed control method and device

Abstract

The invention relates to a vehicle-mounted PM2.5 sensor fan rotating speed control method and device, and more accurate and efficient fan rotating speed control is achieved through methods such as a new strategy for adjusting the fan rotating speed based on PI D, elimination of steady-state errors through introduction of an integral term, improvement of the response speed through a differential term, an intelligent control algorithm and the like. According to the new strategy, the heat dissipation requirement of the system can be met, energy consumption and noise can be reduced, and the overall performance and stability of the system are improved. The product quality is improved by improving a fan rotating speed climbing control strategy; the change of the fan rotating speed along with the target rotating speed is flexibly adapted by automatically adjusting the fan rotating speed strategy. The PID control algorithm can accurately control the rotating speed of the fan, so that the actual rotating speed quickly and stably reaches and is kept near the target rotating speed.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vehicle-mounted equipment, and in particular to a method, device and electronic device for controlling fan speed of a vehicle-mounted PM2.5 sensor. Background Art

[0002] The fan in the vehicle PM2.5 sensor plays a vital role:

[0003] 1. Function and effect

[0004] The on-board PM2.5 sensor fan is mainly used to inhale external air so that particles in the air can enter the sensor for detection. It is a key component in the sensor sampling process, ensuring that the sensor can accurately and real-time monitor the PM2.5 concentration inside and outside the car.

[0005] 2. Working Principle

[0006] The fan drives the blades to rotate through the motor, generating wind force, which creates negative pressure inside the sensor and draws in outside air. When the air sample passes through the sensor, the built-in light source illuminates the particles in the air, causing the light to scatter. The sensor measures the intensity of the scattered light to infer the concentration of the particles, and outputs the data to the device display or connects to other devices wirelessly for users to view.

[0007] When selecting a car PM2.5 sensor fan, you should pay attention to its performance parameters such as air volume, air pressure, noise, etc. to ensure that it meets the sensor's usage requirements. At the same time, it is also necessary to clean and maintain the fan regularly to extend its service life and maintain its performance stability. The car PM2.5 sensor fan is an important part of the sensor, and its performance directly affects the accuracy and stability of the sensor. Therefore, when selecting and maintaining the fan, you should pay attention to its performance parameters and usage environment to ensure that the sensor can accurately and real-time monitor the PM2.5 concentration inside and outside the car.

[0008] The impact of fan speed on PM2.5 in the car is mainly reflected in the performance and efficiency of the car air purifier. By forcing air circulation, the air in the car is accelerated to flow through the filter medium inside the purifier, which can ensure that the air in various places in the car is effectively purified and improve the removal efficiency of PM2.5. The PM2.5 sensor uses the wind pressure of the fan to suck dust particles from the air inlet of the sensor, and calculates the air quality by counting the number of dust particles in the channel through the channel equipped with a laser module. If the wind pressure of the fan is unstable, it will directly affect the number of dust particles in the channel, thereby affecting the accuracy of the sensor's measurement. Therefore, it is crucial to ensure that the PM2.5 sensor has a stable airflow, and the stability of the fan speed directly affects this.

[0009] However, the on-board PM2.5 sensors installed on existing vehicles have relatively simple fan speed adjustment and a single control strategy for fan speed adjustment. In PID control, there is also an unreasonable fan speed climbing rate, and it is impossible to achieve intelligent adjustment according to the fan speed request. This is mainly because when the fan speed starts, the unreasonable setting of the fan speed climbing rate causes a sudden increase in speed, resulting in abnormal fan noise. In PID control, due to its own inertia, steady-state errors (the difference between the target value and the actual value) may occur, and the fan speed cannot fully reach the target speed; in some cases, the system may respond slowly to the input signal, resulting in the fan speed being unable to follow the changes in the target speed in time. Summary of the invention

[0010] To solve the above problem:

[0011] On the one hand, the present application proposes a method for controlling the fan speed of a vehicle-mounted PM2.5 sensor, comprising the following steps:

[0012] Get the speed error value of the fan speed of the vehicle-mounted PM2.5 sensor;

[0013] Performing an integration operation on the rotation speed error value, and performing an integration adjustment based on an adaptive algorithm to obtain an integration value corresponding to the rotation speed;

[0014] Performing a differential operation on the speed error value, and performing a differential adjustment based on an adaptive algorithm to obtain a differential value corresponding to the speed;

[0015] Accumulating and calculating the rotation speed error value, the integral value and the differential value to generate a total control signal corresponding to the rotation speed;

[0016] The total control signal is sent to the fan control system, which adjusts the fan speed and determines whether the target speed is reached:

[0017] If achieved, maintain the current stable state and continue monitoring;

[0018] If not reached, repeat the above steps.

[0019] As an optional implementation scheme of the present application, optionally, the step of obtaining the speed error value of the fan speed of the vehicle-mounted PM2.5 sensor includes the following steps:

[0020] Read target speed: Get the target speed value from the system setting, recorded as TargetSpeed;

[0021] Read actual speed: Get the actual speed of the fan through the vehicle-mounted PM2.5 sensor, which is recorded as ActualSpeed;

[0022] Calculate the speed error: Calculate the difference between the target speed and the actual speed to obtain the speed error value, which is recorded as Error:

[0023] Error=TargetSpeed-ActualSpeed.

[0024] As an optional implementation scheme of the present application, optionally, performing an integral operation on the rotation speed error value and performing an integral adjustment based on an adaptive algorithm to obtain an integral value corresponding to the rotation speed includes the following steps:

[0025] Initialize the integral term: If it is the first calculation, define the initialization integral term Integral = 0;

[0026] Calculate the integral value: multiply the current error by the integral coefficient ki, and add it to the integral term to obtain the integral value Integral:

[0027] Integral=Integral+ki*Error*dt,

[0028] Where dt is the sampling time interval;

[0029] Adjusting the integral coefficient ki: According to the system performance requirements, the integral coefficient ki is adjusted by an adaptive algorithm and the adjusted integral value is output.

[0030] As an optional implementation scheme of the present application, optionally, performing a differential operation on the rotation speed error value and performing a differential adjustment based on an adaptive algorithm to obtain a differential value corresponding to the rotation speed includes the following steps:

[0031] Initialize the derivative term: If it is the first calculation, or the error change rate is not defined, define the initialization derivative term Derivative = 0;

[0032] Calculate the error change rate: Calculate the difference between the current error and the previous error, and divide the difference by the sampling time interval to get the error change rate ErrorRate:

[0033] ErrorRate=(Error-PreviousError) / dt,

[0034] Among them, PreviousError is the error value calculated last time;

[0035] Calculate the differential value: multiply the error change rate ErrorRate by the differential coefficient kd to obtain the differential term Derivative:

[0036] Derivative = kd*ErrorRate;

[0037] Adjusting the differential coefficient kd: According to the system performance requirements, the differential coefficient kd is adjusted by an adaptive algorithm, and the adjusted differential term Derivative is output.

[0038] As an optional implementation scheme of the present application, optionally, the accumulating calculation of the speed error value, the integral value and the differential value to generate a total control signal corresponding to the speed includes the following steps:

[0039] Calculate the proportional term of the speed error value Error according to a preset ratio;

[0040] The proportional term, the integral value and the differential value are accumulated to obtain a total speed control value of the speed, and a total control signal of the total speed control value is generated.

[0041] As an optional implementation scheme of the present application, optionally, the calculation formula of the proportional term is:

[0042] P = kp*Error,

[0043] Where kp is the proportionality factor.

[0044] On the other hand, the present application proposes a device for implementing the above-mentioned vehicle-mounted PM2.5 sensor fan speed control method, comprising:

[0045] The speed sensor is used to obtain the speed error value of the fan speed of the vehicle-mounted PM2.5 sensor and send it to the PID controller;

[0046] A PID controller is used to perform an integral operation on the speed error value, and perform an integral adjustment based on an adaptive algorithm to obtain an integral value corresponding to the speed; and, perform a differential operation on the speed error value, and perform a differential adjustment based on an adaptive algorithm to obtain a differential value corresponding to the speed; and, perform cumulative calculation on the speed error value, the integral value and the differential value to generate a total control signal corresponding to the speed; and, send the total control signal to a fan control system, and the fan control system adjusts the fan speed and determines whether the target speed is reached:

[0047] If achieved, maintain the current stable state and continue monitoring;

[0048] If not reached, repeat the above steps;

[0049] A fan control system, used for receiving and executing the general control signal;

[0050] The rotation speed sensor and the fan control system are respectively connected to the PID controller for communication.

[0051] In another aspect, the present application further provides an electronic device, comprising:

[0052] processor;

[0053] a memory for storing processor-executable instructions;

[0054] Wherein, the processor is configured to implement the method for controlling the fan speed of a vehicle-mounted PM2.5 sensor when executing the executable instructions.

[0055] Technical effects of the present invention:

[0056] The present invention uses a new strategy based on PID to adjust the fan speed, by introducing an integral term to eliminate steady-state errors, using a differential term to improve the response speed, and an intelligent control algorithm to achieve more accurate and efficient fan speed control. This new strategy can not only meet the heat dissipation requirements of the system, but also reduce energy consumption and noise problems, and improve the overall performance and stability of the system. By improving the problems of the fan speed climbing control strategy, the product quality is improved; by automatically adjusting the fan speed strategy, the fan speed can be flexibly adapted to the changes in the target speed. The PID control algorithm can achieve precise control of the fan speed, so that the actual speed can quickly and smoothly reach and remain near the target speed.

[0057] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0059] Figure 1 It is shown as a schematic diagram of the implementation process of the present invention;

[0060] Figure 2 It is a schematic diagram showing the structure of the application system of the present invention;

[0061] Figure 3 It is a schematic diagram showing the application of the electronic device of the present invention. DETAILED DESCRIPTION

[0062] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0063] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0064] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present disclosure.

[0065] Example 1

[0066] Please understand the structure and application control system of the vehicle-mounted PM2.5 sensor in conjunction with the existing vehicle-mounted system. The structure and principle of the vehicle-mounted PM2.5 sensor will not be described in detail in this embodiment.

[0067] The on-board PM2.5 sensor is controlled by the vehicle technology PID controller.

[0068] like Figure 1 As shown, on one hand, the present application proposes a method for controlling the fan speed of a vehicle-mounted PM2.5 sensor, comprising the following steps:

[0069] Get the speed error value of the fan speed of the vehicle-mounted PM2.5 sensor;

[0070] Performing an integration operation on the rotation speed error value, and performing an integration adjustment based on an adaptive algorithm to obtain an integration value corresponding to the rotation speed;

[0071] Performing a differential operation on the speed error value, and performing a differential adjustment based on an adaptive algorithm to obtain a differential value corresponding to the speed;

[0072] Accumulating and calculating the rotation speed error value, the integral value and the differential value to generate a total control signal corresponding to the rotation speed;

[0073] The total control signal is sent to the fan control system, which adjusts the fan speed and determines whether the target speed is reached:

[0074] If achieved, maintain the current stable state and continue monitoring;

[0075] If not reached, repeat the above steps.

[0076] The new strategy of adjusting the fan speed based on PID achieves more accurate and efficient fan speed control by introducing integral terms to eliminate steady-state errors, using differential terms to improve response speed, and intelligent control algorithms. This new strategy can not only meet the heat dissipation requirements of the system, but also reduce energy consumption and noise problems, and improve the overall performance and stability of the system.

[0077] The main technical solutions are as follows:

[0078] Step 1: Collection of speed error

[0079] Read target speed: Get the target speed value from the system settings, recorded as TargetSpeed.

[0080] Read actual speed: Get the actual speed of the fan through the sensor, which is recorded as ActualSpeed.

[0081] Calculate the speed error: Subtract the target speed from the actual speed to obtain the speed error value, which is recorded as Error = TargetSpeed ​​- ActualSpeed.

[0082] Step 2: Integrate the error and adjust the integral coefficient (ki value)

[0083] Initialize the integral term: If this is the first calculation, initialize the integral term Integral = 0.

[0084] Calculate the integral value: multiply the current error by the integral coefficient ki and add it to the integral term, that is, Integral = Integral + ki*Error*dt, where dt is the sampling time interval.

[0085] Adjust the integral coefficient (ki value): According to the system performance requirements, the integral coefficient ki is adjusted through an adaptive algorithm. A smaller ki value will make the integral effect weaker, and a larger ki value will make the integral effect stronger.

[0086] Use the integral term: The integral term is used as part of the PID control output and added to other terms to form the control signal.

[0087] The steps of adjusting the integral coefficient ki by an adaptive algorithm and dynamically finding the optimal integral coefficient ki can be described as follows:

[0088] 1. Define system goals and performance indicators

[0089] First, it is necessary to clarify the system objectives and the indicators used to evaluate system performance. These indicators may include system stability, response speed, overshoot, steady-state error, etc. These indicators will be used to evaluate system performance under different integral coefficients Ki and serve as the basis for the adaptive algorithm to adjust Ki.

[0090] 2. Select or design an adaptive algorithm

[0091] According to the characteristics and requirements of the system, select or design an adaptive algorithm to dynamically adjust the integral coefficient Ki. Common adaptive algorithms include gradient descent, genetic algorithm, particle swarm algorithm, etc. These algorithms can dynamically adjust the value of Ki based on real-time feedback of system performance.

[0092] 3. Initialize parameters and set boundary conditions

[0093] Before the algorithm starts, it is necessary to initialize relevant parameters, such as the initial value of Ki, the learning rate of the algorithm, the number of iterations, etc. At the same time, it is also necessary to set the adjustment range or boundary conditions of Ki to prevent the value of Ki from being too large or too small, causing system instability.

[0094] 4. Real-time monitoring of system performance

[0095] During the operation of the algorithm, it is necessary to monitor the system performance in real time and collect relevant performance indicator data. These data will be used to evaluate the system performance under the current Ki value and serve as the basis for the algorithm to adjust Ki.

[0096] 5. Adjust the integral coefficient Ki according to performance indicators

[0097] Based on the real-time monitored system performance indicators, the adaptive algorithm will dynamically adjust the value of the integral coefficient Ki. If the system performance is not optimal, the algorithm will adjust the value of Ki according to the preset optimization strategy and continue to monitor the system performance. This process will be iterated until the system performance reaches the optimal level or meets the preset stop condition.

[0098] 6. Verification and testing

[0099] After the algorithm adjusts the Ki value to achieve the best state for the system, verification and testing are required to ensure the stability and performance of the system. This can include running the system in a real environment and observing its response and performance. If the system performance is still not ideal, it may be necessary to readjust the adaptive algorithm or system parameters.

[0100] When adjusting the integral coefficient Ki, it is necessary to balance the stability and performance of the system. A Ki that is too large may cause the system to be over-adjusted or even unstable, while a Ki that is too small may cause the system to respond slowly and fail to meet performance requirements.

[0101] The selection and design of the adaptive algorithm needs to be based on the characteristics and requirements of the system. Different algorithms may have different optimization effects and computational complexities. The system performance requirements, such as the default value of the optimal state (speed value), are set by the administrator. The integral coefficient Ki calculated when the optimal value is reached is the optimal value.

[0102] In practical applications, the real-time and robustness of the algorithm also need to be considered to ensure that the system can operate stably in various environments.

[0103] Through the above steps, the adaptive algorithm can be used to dynamically adjust the integral coefficient Ki so that the system reaches the optimal state. However, in the specific implementation, it may be necessary to adjust and optimize according to the actual situation and needs of the system.

[0104] The same applies to the following differential coefficients.

[0105] Step 3: Perform differential operation on the error and adjust the differential coefficient (kd value)

[0106] Initialize the derivative term: If this is the first calculation, or the error rate of change is undefined, you can initialize the derivative term Derivative = 0).

[0107] Calculate the error change rate: Calculate the error change rate by dividing the difference between the current error and the previous error by the sampling time interval, that is, ErrorRate = (Error-PreviousError) / dt, where PreviousError is the error value calculated last time.

[0108] Calculate the differential value: multiply the error change rate by the differential coefficient kd to obtain the differential term, that is, Derivative = kd*ErrorRate.

[0109] Adjust the differential coefficient (kd value): According to the system performance requirements, adjust the differential coefficient kd manually or through an adaptive algorithm. A smaller kd value will make the differential effect weaker, and a larger kd value will make the differential effect stronger.

[0110] Use the derivative term: The derivative term is used as part of the PID control output and added to other terms to form the control signal.

[0111] Step 4: Feedback and Control

[0112] Calculate the total control signal: add the proportional term (P = kp*Error, where kp is the proportional coefficient), the integral term and the differential term to obtain the total control signal ControlSignal = kp*Error+Integral+Derivative.

[0113] Apply control signal: Send the calculated control signal to the fan control system to adjust the fan speed.

[0114] Loop feedback: Repeat steps 1 to 3, continuously collect speed error, calculate control signal and adjust fan speed until the error of the target speed setting value is met, plus or minus 200rpm.

[0115] Obviously, those skilled in the art should understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated as: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0116] Example 2

[0117] like Figure 2 As shown, based on the implementation principle of Example 1, on the other hand, the present application proposes a device for implementing the above-mentioned vehicle-mounted PM2.5 sensor fan speed control method, including:

[0118] The speed sensor is used to obtain the speed error value of the fan speed of the vehicle-mounted PM2.5 sensor and send it to the PID controller;

[0119] A PID controller is used to perform an integral operation on the speed error value, and perform an integral adjustment based on an adaptive algorithm to obtain an integral value corresponding to the speed; and, perform a differential operation on the speed error value, and perform a differential adjustment based on an adaptive algorithm to obtain a differential value corresponding to the speed; and, perform cumulative calculation on the speed error value, the integral value and the differential value to generate a total control signal corresponding to the speed; and, send the total control signal to a fan control system, and the fan control system adjusts the fan speed and determines whether the target speed is reached:

[0120] If achieved, maintain the current stable state and continue monitoring;

[0121] If not reached, repeat the above steps;

[0122] A fan control system, used for receiving and executing the general control signal;

[0123] The rotation speed sensor and the fan control system are respectively connected to the PID controller for communication.

[0124] Please understand this in conjunction with the corresponding steps in Example 1, and this example will not be repeated any more.

[0125] The modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0126] Example 3

[0127] like Figure 3 As shown, further, in another aspect, the present application also proposes an electronic device, including:

[0128] processor;

[0129] a memory for storing processor-executable instructions;

[0130] Wherein, the processor is configured to implement the method for controlling the fan speed of a vehicle-mounted PM2.5 sensor when executing the executable instructions.

[0131] The electronic device according to the embodiment of the present disclosure includes a processor and a memory for storing instructions executable by the processor, wherein the processor is configured to implement any of the above-mentioned methods for controlling the fan speed of a vehicle-mounted PM2.5 sensor when executing the executable instructions.

[0132] Here, it should be noted that the number of processors can be one or more. At the same time, the electronic device of the embodiment of the present disclosure may also include an input device and an output device. Among them, the processor, memory, input device and output device may be connected through a bus or in other ways, which are not specifically limited here.

[0133] The memory, as a computer-readable storage medium, can be used to store software programs, computer executable programs and various modules, such as the program or module corresponding to the fan speed control method of a vehicle-mounted PM2.5 sensor in the embodiment of the present disclosure. The processor executes various functional applications and data processing of the electronic device by running the software programs or modules stored in the memory.

[0134] The input device can be used to receive input numbers or signals. The signal can be a key signal related to user settings and function control of the device / terminal / server. The output device can include a display device such as a display screen.

[0135] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for controlling the fan speed of a vehicle-mounted PM2.5 sensor, characterized in that: The steps include: Get the speed error value of the fan speed of the vehicle-mounted PM2.5 sensor; Performing an integration operation on the rotation speed error value, and performing an integration adjustment based on an adaptive algorithm to obtain an integration value corresponding to the rotation speed; Performing a differential operation on the speed error value, and performing a differential adjustment based on an adaptive algorithm to obtain a differential value corresponding to the speed; Accumulating and calculating the rotation speed error value, the integral value and the differential value to generate a total control signal corresponding to the rotation speed; The total control signal is sent to the fan control system, which adjusts the fan speed and determines whether the target speed is reached: If achieved, maintain the current stable state and continue monitoring; If not reached, repeat the above steps.

2. A vehicle-mounted PM2.5 sensor fan speed control method according to claim 1, characterized in that: The method of obtaining the speed error value of the fan speed of the vehicle-mounted PM2.5 sensor comprises the following steps: Read target speed: Get the target speed value from the system setting, recorded as TargetSpeed; Read actual speed: Get the actual speed of the fan through the vehicle-mounted PM2.5 sensor, which is recorded as ActualSpeed; Calculate the speed error: Calculate the difference between the target speed and the actual speed to obtain the speed error value, which is recorded as Error: Error=TargetSpeed-ActualSpeed.

3. A vehicle-mounted PM2.5 sensor fan speed control method according to claim 2, characterized in that: The step of performing an integral operation on the rotation speed error value and performing an integral adjustment based on an adaptive algorithm to obtain an integral value corresponding to the rotation speed includes the following steps: Initialize the integral term: If it is the first calculation, define the initialization integral term Integral = 0; Calculate the integral value: multiply the current error by the integral coefficient ki, and add it to the integral term to obtain the integral value Integral: Integral=Integral+ki*Error*dt, Where dt is the sampling time interval; Adjusting the integral coefficient ki: According to the system performance requirements, the integral coefficient ki is adjusted by an adaptive algorithm and the adjusted integral value is output.

4. A vehicle-mounted PM2.5 sensor fan speed control method according to claim 1, characterized in that: The differential operation is performed on the speed error value, and the differential adjustment is performed based on the adaptive algorithm to obtain the differential value of the corresponding speed, including the following steps: Initialize the derivative term: If it is the first calculation, or the error change rate is not defined, define the initialization derivative term Derivative = 0; Calculate the error change rate: Calculate the difference between the current error and the previous error, and divide the difference by the sampling time interval to get the error change rate ErrorRate: ErrorRate=(Error-PreviousError) / dt, Among them, PreviousError is the error value calculated last time; Calculate the differential value: multiply the error change rate ErrorRate by the differential coefficient kd to obtain the differential term Derivative: Derivative = kd*ErrorRate; Adjusting the differential coefficient kd: According to the system performance requirements, the differential coefficient kd is adjusted by an adaptive algorithm, and the adjusted differential term Derivative is output.

5. The method for controlling the fan speed of a vehicle-mounted PM2.5 sensor according to claim 1, characterized in that: The step of accumulating and calculating the speed error value, the integral value and the differential value to generate a total control signal corresponding to the speed includes the following steps: Calculate the proportional term of the speed error value Error according to a preset ratio; The proportional term, the integral value and the differential value are accumulated to obtain a total speed control value of the speed, and a total control signal of the total speed control value is generated.

6. A vehicle-mounted PM2.5 sensor fan speed control method according to claim 5, characterized in that: The calculation formula of the proportional term is: P = kp*Error, Where kp is the proportionality factor.

7. A device for implementing the vehicle-mounted PM2.5 sensor fan speed control method according to any one of claims 1 to 6, characterized in that: include: The speed sensor is used to obtain the speed error value of the fan speed of the vehicle-mounted PM2.5 sensor and send it to the PID controller; A PID controller is used to perform an integral operation on the speed error value and perform an integral adjustment based on an adaptive algorithm to obtain an integral value corresponding to the speed; and to perform a differential operation on the speed error value and perform a differential adjustment based on an adaptive algorithm to obtain a differential value corresponding to the speed; and, accumulating and calculating the rotation speed error value, the integral value and the differential value to generate a total control signal corresponding to the rotation speed; And, the total control signal is sent to a fan control system, and the fan control system adjusts the fan speed and determines whether the target speed is reached: If achieved, maintain the current stable state and continue monitoring; If not reached, repeat the above steps; A fan control system, used for receiving and executing the general control signal; The rotation speed sensor and the fan control system are respectively connected to the PID controller for communication.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the vehicle-mounted PM2.5 sensor fan speed control method described in any one of claims 1-6 when executing the executable instructions.

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