Clean room particle monitoring system and method based on temperature field information

Abstract

The application discloses a clean room particle monitoring system and method based on temperature field information, and relates to the field of intelligent monitoring; the system comprises an environment detection module, which is used for detecting real-time air parameters of a measured environment; an environment purification module, which is used for purifying air of the measured environment; a movement module, which is used for moving the environment detection module and the environment purification module to a specified position according to an inspection instruction; the inspection instruction comprises an inspection path or / and a temperature gradient change instruction; a control module, which is used for controlling the environment detection module, the environment purification module and the movement module; the application can intelligently plan a newly discovered abnormal area and automatically incorporate the abnormal area into an existing inspection route, so that the robot can significantly improve the ability of autonomous intelligent identification and dynamic path optimization while maintaining the efficiency of fixed path inspection, greatly reduce the overall workload and detection time, and significantly improve the efficiency, accuracy and intelligent level of long-term maintenance of clean room particle monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent monitoring, in particular to a clean room particle monitoring system and method based on temperature field information. BACKGROUND

[0002] Clean rooms are the core production environment for high-tech industries such as semiconductors, new energy batteries, biopharmaceuticals, etc., and have extremely stringent control requirements for dust particle concentration in the air (such as hundred-level / thousand-level clean rooms). In these environments with extremely high cleanliness requirements, even tiny particles can have irreversible effects on product quality, performance, and even production yield, and even cause serious safety hazards. For example, in semiconductor production, particles with a size of only 5 microns or more can cause short circuits in chip circuits, directly affecting product functionality; in the automotive manufacturing industry, especially in the field of new energy vehicles, there are strict limits on surface particles (such as metal shavings, fibers, etc.) of parts, otherwise it will cause part wear, circuit short circuit, and even the risk of self-ignition. The traditional manual sampling mode cannot cover the entire process, and once a product batch is scrapped due to local contamination, the economic loss can be as high as tens of millions of yuan, for example, dust exceeding the standard in lithium battery production can directly cause battery explosion. Therefore, achieving full-automatic online and accurate particle monitoring has become an inevitable choice for clean room management.

[0003] Currently, the monitoring of particles in clean rooms mainly relies on three methods. One is manual operation of sensor monitoring of particles in clean rooms. This method relies on manual holding of equipment for on-site sampling, which has problems such as increasing environmental load, unstable sampling points and time, and difficulty in establishing long-term trend analysis, and cannot achieve continuous and comprehensive monitoring. The second is fixed-point sensor, which provides continuous monitoring data in a specific area, but the coverage is limited, there are a lot of monitoring blind spots, and it cannot capture global or dynamic pollution events. For example, some existing technologies have patents on fixed multi-point air quality sensors (such as the invention patent with application number 202211539256.0 and the invention patent with application number 202310254146.8), the core of which is to achieve regional coverage by densely deploying sensor arrays at key points, but its inherent defect is that it cannot effectively detect spaces without sensor points, and it is insufficiently responsive to dynamically changing particles. The third is mobile robots for fixed path inspection. Although this method achieves automation, the "blind" detection of robots along the preset path cannot actively identify and track sudden local abnormalities at non-pre-set locations, resulting in suboptimal resource allocation, low overall inspection efficiency, and difficulty in achieving accurate tracing and rapid response of particles. For example, the invention patent with application number 202410519256.7 discloses a scheme for mobile robots to conduct periodic inspection along a preset route, aiming to improve the level of inspection automation. However, the efficiency and coverage density of such a scheme are limited by the preset of the fixed path, and it lacks the ability to actively detect real-time abnormalities and dynamically adapt to the scene. SUMMARY

[0004] Therefore, in order to solve the above problems, the present application provides a clean room particle monitoring system and method based on temperature field information, which uses temperature extreme values and their gradient changes as proactive and active indicators to guide mobile robots to autonomously and intelligently find and focus on detecting potential particle accumulation areas that are most likely to have problems based on the established fixed path inspection. The present application can intelligently plan and automatically incorporate these newly discovered abnormal areas into the existing inspection route. This allows the robot to maintain high efficiency in fixed path inspection while significantly improving the ability of autonomous and intelligent identification and dynamic path optimization, thereby greatly reducing the overall workload and detection time and significantly improving the efficiency, accuracy and intelligent level of long-term maintenance of clean room particle monitoring.

[0005] In one aspect, the present application provides a clean room particle monitoring system based on temperature field information, which comprises a motion module for performing inspection tasks, an environment detection module, an environment purification module, and a control module.

[0006] The motion module includes a mobile robot and a laser ranging radar, wherein the mobile robot is used to stably carry the environment detection module and the environment purification module in the clean room space and drive the environment detection module and the environment purification module to move accurately according to a preset inspection path and a temperature gradient change instruction, so as to support high spatial resolution environment data acquisition and fine detection of local abnormal areas; and the laser ranging radar is used to provide high-precision and real-time environment sensing data in the clean room environment, so as to realize accurate positioning of the robot, local environment mapping, and careful obstacle detection, and ensure safety and efficiency of the monitoring and purification operation.

[0007] Optionally, the environment detection module includes a temperature sensor and a particulate matter sensor, the temperature sensor is used to detect the temperature in the clean room in real time, and the particulate matter sensor is used to detect the concentration of particulate matter in the clean room in real time; and the air parameters are acquired through the environment detection module.

[0008] Optionally, the environment purification module includes an air pump and a particulate matter purification device, the air pump is used to accurately guide the air in the clean room to the particulate matter purification device, so as to effectively purify the particulate matter in the air.

[0009] Optionally, the control module includes a remote host and a programmable logic controller. The data collected by the temperature sensor and the particulate matter sensor and the data of the laser radar are transmitted to the remote host in real time through a Modbus communication protocol, and the remote host judges whether the particulate matter in the inspection area is over standard according to the sensor data and calculates the moving direction of the robot.

[0010] In another aspect, the application provides a clean room particulate matter monitoring method based on temperature field information, which is realized through the clean room particulate matter monitoring system and includes the following steps.

[0011] S100, setting an inspection route of the mobile robot according to site requirements and setting initialization parameters, wherein the initialization parameters include a gradient starting threshold value , a gradient convergence threshold value , a stagnation collection time length , a particulate matter setting threshold value C f , a single movement step length α , a single movement maximum distance α max , a minimum movement distance α min , and a maximum expected temperature gradient size value ;

[0012] S200, the mobile robot inspects and collects real-time air parameters according to the set route, and the mobile robot moves in a “Z” shape during movement, each “Z” path includes three step lengths, and a single movementα max The distance is stopped, and the environmental detection module continues to collect real-time air parameters in the indoor air, i.e. real-time particulate matter concentration C t and real-time temperature T t ;

[0013] S300, calculate the particulate matter time-averaged concentration and time-averaged temperature of the mobile robot in the first i step by the following method, wherein i is an integer;

[0014] The calculation method of the time-averaged concentration is:

[0015] ,

[0016] Wherein: C i is the particulate matter time-averaged concentration of the first i step, C t is the real-time particulate matter concentration, t is the length of the stationary collection, t The variable represents any time point within the length of the stationary collection t , used to represent the time of real-time data collection 。

[0017] The calculation method of the time-averaged temperature is:

[0018] ,

[0019] Wherein: T i is the time-averaged temperature of the first i step, T t is the real-time temperature.

[0020] S400, start the monitoring strategy according to the size relationship between the particulate matter time-averaged concentration and the temperature gradient size and the size of the initialization parameter;

[0021] S500, start the temperature local extremum area finding algorithm according to the monitoring strategy situation, and the mobile robot continuously moves according to the temperature gradient direction of the current position, and continuously detects the particulate matter concentration and the temperature in the finding process;

[0022] S600, judge whether the temperature gradient size value i of the first step is less than the gradient convergence threshold value ;

[0023] If so, the mobile robot stops moving, detects the concentration of airborne particulate matter, determines that the stopping point is a local extreme point, starts the local extreme region planning algorithm, incorporates the local extreme region into the fixed inspection route, and uploads the updated route to the terminal server; if not, it continues to execute step S500.

[0024] S700, Determining the average concentration of particulate matter within a local extreme value region C i Is it greater than the set threshold for particulate matter? C f ;

[0025] If yes, start the purification device to purify the particulate matter, and then proceed to step S800; if no, proceed directly to step S800.

[0026] S800: The mobile robot returns to its position closest to the fixed route and continues to execute step S200.

[0027] Optionally, the monitoring strategy in step S400 includes:

[0028] Strategy 1: Hourly average concentration of particulate matter at the current stagnation point C i Less than the set threshold for particulate matter C f And the magnitude of the temperature gradient Less than the gradient initiation threshold Then proceed to step S200;

[0029] Strategy 2: Average hourly concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matter C f And the magnitude of the temperature gradient Less than the gradient initiation threshold If the purification device is activated to purify particulate matter, step S200 will be executed after purification is completed.

[0030] Strategy 3: Average hourly concentration of particulate matter at the current stagnation point C i Less than the set threshold for particulate matter C f And the magnitude of the temperature gradient Greater than the gradient initiation threshold Then proceed to step S500;

[0031] Strategy 4: Hourly average concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matter C f And the magnitude of the temperature gradient Greater than the gradient initiation threshold If the purification device is activated to purify particulate matter, step S500 will be executed after purification is completed.

[0032] Optionally, the specific method for finding local temperature extrema regions in S500 is as follows:

[0033] S510: Determine the search direction; the mobile robot obtains its current position. Real-time temperature data Determine the initial direction of travel for this local search. U f The temperature gradient at the current location is calculated using the following formula. ;

[0034] ,

[0035] in: , ,

[0036] Δ x With Δ y These are the tiny spatial step sizes used to calculate the gradient, representing the step size of a single movement. α exist x directional components and y The directional component.

[0037] yes( x i , y i )along x Move a small step Δ in the positive direction of the axis x The temperature value afterwards;

[0038] yes( x i , y i )along x Move a small step Δ in the negative axis direction x The temperature value afterwards;

[0039] yes( x i , y i )along y Move a small step Δ in the positive direction of the axis y The temperature value afterwards;

[0040] yes( x i , yi )along y Move a small step Δ in the negative axis direction y The temperature value afterwards;

[0041] S520: Motion control; the mobile robot updates its position according to the following formula. P i+1 :

[0042] , P i+1 This is the updated location;

[0043] in α i The step size factor for the current iteration is used to control the distance the mobile robot travels along the actual direction of movement. Its formula is:

[0044] ,

[0045] ,

[0046] in It is the current actual motion direction vector of the mobile robot, and the method to determine it is:

[0047] Calculate the temperature gradient at the current location. In the initial direction of travel U f Projection values ​​on:

[0048] ,

[0049] if If the temperature rises in the direction behind the robot, the robot will tend to retreat as a whole. To avoid retreating while simultaneously searching for the temperature change point, the robot will use the opposite direction of the gradient as its actual direction of movement.

[0050] ,

[0051] if If the temperature rises in the direction in front of the robot, and the robot does not tend to retreat as a whole, the robot will move along the gradient direction as its actual direction of motion.

[0052] .

[0053] in It is a random perturbation vector, sampled from a Gaussian distribution with a mean of zero, i.e. in The identity matrix ensures that the perturbations are independent and of equal strength in all dimensions. The formula for calculating the intensity of random disturbances is:

[0054] ;

[0055] in This represents the maximum value of the random disturbance intensity. This represents the minimum value of the random disturbance intensity.

[0056] Optionally, the specific method of the local extremum region planning algorithm in step S600 is as follows:

[0057] The mobile robot sets the starting point as the position where it first detects that the temperature gradient exceeds the activation threshold, and sets the ending point as the position where it moves along the gradient direction until the gradient is less than the convergence threshold. The total distance actually moved by the mobile robot between these two points is calculated as the diameter, and this area is marked as a local extremum region.

[0058] The present invention has the following advantages:

[0059] This invention relates to a cleanroom particulate matter monitoring system and method based on temperature field information, effectively overcoming the blind and inefficient problems of fixed-point and fixed-path inspections in existing cleanroom particulate matter monitoring. This invention innovatively utilizes temperature anomalies as a leading indicator to guide a robot to autonomously and intelligently discover and accurately locate potential particulate matter accumulation areas. Through a dynamic search strategy and automatic optimization of the inspection route, it significantly reduces the robot's overall workload and detection time, greatly improving monitoring efficiency, accuracy, and the level of intelligence in cleanroom management, providing a more proactive and reliable pollution control solution for cleanrooms. Attached Figure Description

[0060] Figure 1 This is a modular block diagram of a cleanroom particulate matter monitoring system based on temperature field information.

[0061] Figure 2 This is a schematic diagram of a cleanroom particulate matter monitoring method based on temperature field information;

[0062] Figure 3 This is a schematic diagram of the algorithm for finding local temperature extrema regions described in this invention;

[0063] In the diagram: 100, Environmental Detection Module; 200, Environmental Purification Module; 300, Motion Module; 400, Control Module. Detailed Implementation

[0064] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0065] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0066] As described in the background section, current particulate matter monitoring in cleanrooms mainly relies on three methods. First, manual operation of sensors for cleanroom particulate matter monitoring. This method relies on manual on-site sampling, which increases environmental load, results in unstable sampling points and times, and makes it difficult to establish long-term trend analysis. Furthermore, it cannot achieve continuous and comprehensive monitoring. Second, fixed-point sensors provide continuous monitoring data in specific areas, but their coverage is limited, with numerous monitoring blind spots, making it impossible to capture global or dynamic pollution events. For example, some existing patents concern fixed multi-point air quality sensors (such as invention patents with application numbers 202211539256.0 and 202310254146.8), whose core principle is to achieve area coverage by densely deploying sensor arrays at key points. However, their inherent drawback is the inability to effectively detect spaces without sensor locations and insufficient response to dynamically changing particulate matter. Third, mobile robots perform fixed-path inspections. While this method achieves automation, the "blind" detection by the robot along the preset path cannot actively identify and track sudden local anomalies in non-preset locations, resulting in suboptimal resource allocation, low overall inspection efficiency, and difficulty in achieving accurate source tracing and rapid response to particulate matter. For example, the invention patent with application number 202410519256.7 discloses a scheme for mobile robots to perform periodic inspections along preset routes, aiming to improve the level of inspection automation. However, the efficiency and coverage density of such schemes are limited by the preset fixed path and lack the ability to actively detect and dynamically adapt to real-time anomalies on site.

[0067] For the reasons mentioned above, such as Figure 1 As shown, this embodiment provides a cleanroom particulate matter monitoring system based on temperature field information. The system includes: a motion module 300 for performing inspection tasks, an environmental detection module 100, an environmental purification module 200, and a control module 400.

[0068] The motion module includes a mobile robot and a laser ranging radar. The mobile robot is used to stably carry and precisely move the onboard environmental detection and environmental purification modules within the cleanroom space according to a preset inspection path and in response to temperature gradient changes. This supports high spatial resolution environmental data acquisition and fine detection of local anomalies. The laser ranging radar provides high-precision, real-time environmental perception data in the cleanroom environment to enable precise robot positioning, local environmental mapping, and detailed obstacle detection, ensuring the safety and efficiency of monitoring and purification operations.

[0069] Furthermore, the environmental monitoring module includes a temperature sensor and a particulate matter sensor. The temperature sensor is used to detect the temperature inside the cleanroom in real time; the particulate matter sensor is used to detect the particulate matter concentration inside the cleanroom in real time; and air parameters are obtained through the environmental monitoring module.

[0070] Furthermore, the environmental purification module includes an air pump and a particulate matter purification device. The air pump is used to precisely guide the air in the clean room to the particulate matter purification device to achieve effective purification of particulate matter in the air.

[0071] Furthermore, the control module includes a remote host and a programmable logic controller. Data collected by the temperature sensor and particulate matter sensor, along with data from the lidar, are transmitted in real time to the remote host via the Modbus communication protocol. The remote host uses the sensor data to determine whether the particulate matter level in the inspection area exceeds the standard and calculates the robot's movement direction.

[0072] In another embodiment, such as Figure 2 and Figure 3 As shown, this invention provides a cleanroom particulate matter monitoring method based on temperature field information. This method is implemented using the aforementioned cleanroom particulate matter monitoring system and includes the following steps:

[0073] S100. Set the inspection route of the mobile robot according to the site requirements, and set the initialization parameters, including the gradient start threshold. Gradient convergence threshold Duration of data collection pause t Particulate matter setting threshold C f single movement step length α Maximum distance moved in a single move α max Minimum movement distance α min Maximum expected temperature gradient magnitude ;

[0074] For example, setting a gradient initiation threshold = 0.2℃ / mGradient convergence threshold = 0.02℃ / m Duration of data collection pause t =60 s Particulate matter concentration setting threshold C f Based on cleanroom standards, the maximum distance for a single movement α max =1 m Minimum movement distance α min =0.5 m Maximum expected temperature gradient value = 0.2℃ / m .

[0075] The S200 mobile robot inspects and collects real-time air parameters according to a set route. During its movement, the robot moves in a "Z" pattern, with each "Z" path consisting of three steps. A single movement... α max The distance was measured, and then the reading stopped. During the pause, the environmental monitoring module continuously collected real-time air parameters in the indoor air, namely the real-time particulate matter concentration. C t and real-time temperature T t ;

[0076] S300, the mobile robot in the following manner is calculated at the... i The time-averaged concentration and time-averaged temperature of particulate matter at each step, where i It is an integer;

[0077] The average concentration over time is calculated as follows:

[0078] ,

[0079] in: C i For the first i The time-average concentration of particulate matter at the step size C t It is the real-time particulate matter concentration. t It is the duration of data collection pause. t This variable represents the duration of data collection pause. t Any point in time within the time frame is used to represent the moment of real-time data acquisition. 。

[0080] The average temperature is calculated as follows:

[0081] ,

[0082] in: T i For the firsti The average temperature over time is the step size. T t It's the real-time temperature.

[0083] S400. Based on the relationship between the time-averaged concentration of particulate matter, the magnitude of the temperature gradient, and the magnitude of the initialization parameters, the following monitoring strategy is initiated;

[0084] Strategy 1: Hourly average concentration of particulate matter at the current stagnation point C i Less than the set threshold for particulate matter C f And the magnitude of the temperature gradient Less than the gradient initiation threshold Then proceed to step S200;

[0085] Strategy 2: Average hourly concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matter C f And the magnitude of the temperature gradient Less than the gradient initiation threshold If the purification device is activated to purify particulate matter, step S200 will be executed after purification is completed.

[0086] Strategy 3: Average hourly concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matter C f And the magnitude of the temperature gradient Greater than the gradient initiation threshold Then proceed to step S500;

[0087] Strategy 4: Hourly average concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matter C f And the magnitude of the temperature gradient Greater than the gradient initiation threshold If the purification device is activated to purify particulate matter, step S500 will be executed after purification is completed.

[0088] S500: Based on the monitoring strategy, the algorithm for finding local extreme temperature regions is activated. The mobile robot continues to move according to the temperature gradient direction at its current location, and continuously detects particulate matter concentration and temperature during the search process.

[0089] The specific method for finding local extreme temperature regions is as follows (e.g.) Figure 3 (as shown)

[0090] S510: Determine the search direction; the mobile robot obtains its current position. Real-time temperature data Determine the initial direction of travel for this local search. U f The temperature gradient at the current location is calculated using the following formula. ;

[0091] ,

[0092] in: , ,

[0093] Δ x With Δ y These are the tiny spatial step sizes used to calculate the gradient, representing the step size of a single movement. α exist x directional components and y The directional component.

[0094] yes( x i , y i )along x Move a small step Δ in the positive direction of the axis x The temperature value afterwards;

[0095] yes( x i , y i )along x Move a small step Δ in the negative axis direction x The temperature value afterwards;

[0096] yes( x i , y i )along y Move a small step Δ in the positive direction of the axis y The temperature value afterwards;

[0097] yes( x i , y i )along y Move a small step Δ in the negative axis direction y The temperature value afterwards.

[0098] S520: Motion control; the mobile robot updates its position according to the following formula. P i+1 :

[0099] , P i+1 This is the updated location;

[0100] in α i The step size factor for the current iteration is used to control the distance the mobile robot travels along the actual direction of movement. Its formula is:

[0101] ,

[0102] ,

[0103] in It is the current actual motion direction vector of the mobile robot, and the method to determine it is:

[0104] Calculate the temperature gradient at the current location. In the initial direction of travel U f Projection values ​​on:

[0105] ,

[0106] if If the temperature rises in the direction behind the robot, the robot will tend to retreat as a whole. To avoid retreating while simultaneously searching for the temperature change point, the robot will use the opposite direction of the gradient as its actual direction of movement.

[0107] ,

[0108] if If the temperature rises in the direction in front of the robot, and the robot does not tend to retreat as a whole, the robot will move along the gradient direction as its actual direction of motion.

[0109] .

[0110] in It is a random perturbation vector, sampled from a Gaussian distribution with a mean of zero, i.e. Here, the identity matrix ensures that the perturbation is independent and of the same strength in all dimensions. The formula for calculating the intensity of random disturbances is:

[0111] ;

[0112] in This represents the maximum value of the random disturbance strength (for example, a value of 0.1). This represents the minimum value of the random disturbance strength (for example, a value of 0.01).

[0113] S600, Determine the firsti The magnitude of the temperature gradient over each step Is it less than the gradient convergence threshold? ;

[0114] If so, the mobile robot stops moving, detects the concentration of airborne particulate matter, determines that the stopping point is a local extreme point, starts the local extreme region planning algorithm, incorporates the local extreme region into the fixed inspection route, and uploads the updated route to the terminal server; if not, it continues to execute step S500.

[0115] The specific method of the local extremum region planning algorithm is as follows:

[0116] The mobile robot sets the starting point as the position where it first detects that the temperature gradient exceeds the activation threshold, and sets the ending point as the position where it moves along the gradient direction until the gradient is less than the convergence threshold. The total distance actually moved by the mobile robot between these two points is calculated as the diameter, and this area is marked as a local extremum region.

[0117] S700, Determining the average concentration of particulate matter within a local extreme value region C i Is it greater than the set threshold for particulate matter? ;

[0118] If yes, start the purification device to purify the particulate matter, and then proceed to step S800; if no, proceed directly to step S800.

[0119] S800: The mobile robot returns to its position closest to the fixed route and continues to execute step S200.

[0120] The aforementioned cleanroom particulate matter monitoring method addresses the problems of inefficiency in manual operation, blind and inefficient fixed-point and fixed-path inspections, and the inability to accurately and dynamically identify local particulate matter accumulation areas in cleanroom particulate matter monitoring. It innovatively uses temperature anomalies as a leading indicator to guide robots to autonomously and intelligently discover and accurately locate potential particulate matter accumulation areas. Through dynamic search strategies and automatic optimization of inspection routes, it significantly reduces the overall workload and detection time of robots, greatly improves the efficiency and accuracy of monitoring, and enhances the level of intelligence in cleanroom management, providing a more proactive and reliable pollution control solution for cleanrooms.

[0121] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring particulate matter in cleanrooms based on temperature field information, characterized in that: This is achieved through a cleanroom particulate matter monitoring system based on temperature field information. The system includes: The environmental monitoring module is used to detect real-time air parameters of the environment under test, which include at least temperature and particulate matter concentration. The environmental purification module is used to purify the air in the tested environment. The motion module is used to move the environmental detection module and the environmental purification module to a designated position according to the inspection instructions, which include the inspection path and / or temperature gradient change instructions. The control module is used to control the environmental detection module, the environmental purification module, and the motion module; The method includes: S100. Set the inspection route of the mobile robot in the motion module according to the site requirements, and set the initialization parameters. The initialization parameters include gradient start threshold, gradient convergence threshold, stagnation collection time, particulate matter setting threshold, single movement step size, single movement maximum distance, minimum movement distance, and maximum expected temperature gradient value. S200: The mobile robot inspects and collects real-time air parameters according to the set route. The mobile robot moves in a Z-shape during the movement. Each Z-shaped path includes three steps. The maximum distance of a single movement is the distance of a single movement. Then it stops. During the stop, the environmental detection module continuously collects real-time air parameters in the indoor air. S300, Computational Mobile Robot in the... i The time-averaged concentration and time-averaged temperature of particulate matter at each step, where i It is an integer; S400. A monitoring strategy is initiated based on the relationship between the time-averaged particulate matter concentration, the temperature gradient, and the initialization parameters. This monitoring strategy includes: Strategy 1: If the hourly average concentration of particulate matter at the current stagnation point is less than the set threshold for particulate matter and the magnitude of the temperature gradient is less than the gradient activation threshold, then proceed to step S200. Strategy 2: If the hourly average concentration of particulate matter at the current stagnation point is greater than the set threshold for particulate matter and the magnitude of the temperature gradient is less than the gradient activation threshold, then the purification device is activated to purify the particulate matter. After purification is completed, step S200 is executed. Strategy 3: If the hourly average concentration of particulate matter at the current stagnation point is less than the set threshold for particulate matter and the magnitude of the temperature gradient is greater than the gradient activation threshold, then proceed to step S500. Strategy 4: If the hourly average concentration of particulate matter at the current stagnation point is greater than the set threshold for particulate matter, and the magnitude of the temperature gradient is greater than the gradient activation threshold, then the purification device is activated to purify the particulate matter. After purification is completed, step S500 is executed. S500: Based on the monitoring strategy, the algorithm for finding local extreme temperature regions is activated. The mobile robot moves continuously according to the temperature gradient direction at its current location and continuously detects particulate matter concentration and temperature during the search process. S600, Determine the first i Whether the magnitude of the temperature gradient at each step is less than the gradient convergence threshold; If so, the mobile robot stops moving, detects the concentration of airborne particulate matter, determines that the stopping point is a local extreme point, starts the local extreme region planning algorithm, incorporates the local extreme region into the fixed inspection route, and uploads the updated route to the terminal server; if not, it continues to execute step S500. S700: Determine whether the time-averaged concentration of particulate matter in the local extreme value region is greater than the set threshold for particulate matter. If yes, start the purification device to purify the particulate matter, and then proceed to step S800; if no, proceed directly to step S800. S800: The mobile robot returns to its position closest to the fixed route and continues to execute step S200.

2. The cleanroom particulate matter monitoring method based on temperature field information according to claim 1, characterized in that, The environmental detection module includes: Temperature sensors are used to detect the temperature inside the cleanroom in real time. Particulate matter sensors are used to detect the concentration of particulate matter in cleanrooms in real time.

3. The cleanroom particulate matter monitoring method based on temperature field information according to claim 1, characterized in that, The environmental purification module includes: Particulate matter purification devices are used to purify particulate matter in the air; An air pump is used to precisely direct air from the cleanroom to the particulate matter purification device.

4. The cleanroom particulate matter monitoring method based on temperature field information according to claim 1, characterized in that, The motion module includes: Mobile robots are used to carry and move their onboard environmental detection, environmental purification, and control modules within a cleanroom space, following a preset inspection path and responding to temperature gradient changes. Laser ranging radar is used to provide high-precision, real-time environmental perception data in cleanroom environments.

5. The cleanroom particulate matter monitoring method based on temperature field information according to claim 1, characterized in that, The time-average concentration mentioned in step S300 is calculated as follows: ， in: C i For the first i The time-average concentration of particulate matter at the step size C t It is the real-time particulate matter concentration. τ It is the duration of data collection pause. t The variable represents any point in time within the stagnation period τ; The average temperature is calculated as follows: ， in: T i For the first i The average temperature over time is the step size. T t It's the real-time temperature.

6. The cleanroom particulate matter monitoring method based on temperature field information according to claim 1, characterized in that, The specific method for finding local temperature extrema regions in S500 is as follows: S510: Determine the search direction; the mobile robot obtains its current position. Real-time temperature data Determine the initial direction of travel for this local search. And calculate the temperature gradient at the current location. ; S520: Motion control, the mobile robot updates its position to the next position according to the following formula. : ， in α i The step size factor for the current iteration is used to control the distance the mobile robot travels along the actual direction of movement. Its formula is: ， ， in It is the current actual motion direction vector of the mobile robot; in It is a random perturbation vector, sampled from a Gaussian distribution with a mean of zero; in, It is the maximum expected temperature gradient value.

7. The cleanroom particulate matter monitoring method based on temperature field information according to claim 6, characterized in that, The current actual motion direction vector of the mobile robot The methods for determining this include: Calculate the temperature gradient at the current location. In the initial direction of travel Projection values ​​on: ， if If the temperature rises in the direction behind the robot, the robot will tend to retreat as a whole. To avoid retreating while simultaneously searching for the temperature change point, the robot will use the opposite direction of the gradient as its actual direction of movement. ， if If the temperature rises in the direction in front of the robot, and the robot does not tend to retreat as a whole, the robot will move along the gradient direction as its actual direction of motion. 。 8. The cleanroom particulate matter monitoring method based on temperature field information according to claim 1, characterized in that, The specific method of the local extremum region planning algorithm in step S600 is as follows: The mobile robot sets the starting point as the position where it first detects that the temperature gradient exceeds the activation threshold, and sets the ending point as the position where it moves along the gradient direction until the gradient is less than the convergence threshold. The total distance actually moved by the mobile robot between these two points is calculated as the diameter, and this area is marked as a local extremum region.

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