Clean room particulate matter monitoring system and method based on temperature field information

Abstract

The invention discloses a clean room particulate matter monitoring system and method based on temperature field information, and relates to the field of intelligent monitoring. The system comprises an environment detection module used for detecting real-time air parameters of a detected environment; the environment purification module is used for purifying the air of the detected environment; the motion module is used for moving the environment detection module and the environment purification module to a designated position according to an inspection instruction, and the inspection instruction comprises an inspection path or / and a temperature gradient change instruction; the control module is used for controlling the environment detection module, the environment purification module and the movement module; according to the invention, the newly found abnormal area can be intelligently planned and automatically incorporated into the existing inspection route, so that the robot can maintain the high efficiency of fixed path inspection, the autonomous and intelligent identification and dynamic path optimization capabilities are remarkably improved, the overall workload and detection time are greatly reduced, and the inspection efficiency is improved. And the monitoring efficiency and accuracy of the particulate matters in the clean room and the intelligent level of long-term maintenance are obviously improved.

Description

Technical Field

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

[0002] Cleanrooms, as core production environments for high-tech industries like semiconductors, new energy batteries, and biopharmaceuticals, have extremely stringent control requirements for airborne dust particle concentrations (e.g., Class 100 / Class 1000 cleanrooms). In these environments, even the finest particles can irreversibly impact product quality, performance, and even production yield, potentially posing serious safety risks. For example, in semiconductor production, particles as small as 5 microns or larger can cause chip circuit shorts, directly impacting product functionality. In the automotive industry, particularly in the new energy vehicle sector, strict limits are imposed on particulate matter (such as metal shavings and fibers) on component surfaces. Failure to do so can lead to component wear, short circuits, and even the risk of spontaneous combustion. Traditional manual spot checks fail to fully cover the entire process, and if a product batch is scrapped due to localized contamination, the economic losses can reach tens of millions of yuan. For example, excessive dust levels in lithium battery production can directly cause battery explosions. Therefore, fully automated, online, and accurate particle monitoring has become an essential requirement for cleanroom management.

[0003] Currently, particulate matter monitoring in cleanrooms relies primarily on three approaches. The first is manually operated sensors for monitoring cleanroom particulate matter. This approach relies on on-site sampling with handheld equipment, which increases environmental load, instability in sampling locations and timing, and difficulty establishing long-term trend analysis. Furthermore, it lacks continuous and comprehensive monitoring. The second approach is fixed-point sensors. While these provide continuous monitoring data in specific areas, their coverage is limited, with numerous blind spots, making them unable to capture global or dynamic pollution events. For example, several patents exist for fixed multi-point air quality sensors (such as Invention Patent No. 202211539256.0 and Invention Patent No. 202310254146.8). These sensors focus on achieving regional coverage by densely deploying sensor arrays at key locations. However, these inherent drawbacks include an inability to effectively detect areas without sensor points and insufficient response to dynamically changing particulate matter. The third is that mobile robots conduct fixed-path inspections. Although this method has achieved automation, the robot's "blind" detection along the preset path cannot actively identify and track sudden local anomalies in non-preset locations, resulting in non-optimal resource allocation, low overall inspection efficiency, and difficulty in achieving accurate traceability and rapid response to particulate matter; for example, the invention patent with application number 202410519256.7 discloses a scheme for mobile robots to conduct periodic inspections along a preset route, 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. Summary of the Invention

[0004] Therefore, to address the above-mentioned shortcomings, the present invention provides a cleanroom particulate matter monitoring system and method based on temperature field information. This system uses temperature extremes and their gradient changes as proactive, proactive indicators to guide mobile robots, based on established fixed-path inspections, to autonomously and intelligently discover and focus on areas of potential particulate matter accumulation that are most likely to harbor problems. The present invention can intelligently plan these newly discovered abnormal areas and automatically incorporate them into existing inspection routes. This allows the robot to significantly enhance its autonomous, intelligent identification and dynamic path optimization capabilities while maintaining the efficiency of fixed-path inspections, significantly reducing overall workload and inspection time, and significantly improving the efficiency, accuracy, and intelligence level of cleanroom particulate matter monitoring and long-term maintenance.

[0005] On the one hand, the present invention provides a clean room particulate matter monitoring system based on temperature field information, which includes: 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. The mobile robot is used to stably carry loads within the clean room space and drive the carried environmental detection module and environmental purification module to move precisely according to a preset inspection path and in response to temperature gradient change instructions, so as to support high-spatial-resolution environmental data collection and detailed detection of local abnormal areas; the laser ranging radar is used to provide high-precision, real-time environmental perception data in the clean room environment to achieve precise positioning of the robot, local environment mapping, and detailed obstacle detection, ensuring the safety and efficiency of monitoring and purification operations.

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

[0008] Optionally, the environmental purification module includes an air pump and a particulate matter purification device, and the air pump is used to accurately guide the air in the clean room to the particulate matter purification device to achieve effective purification of particulate matter in the air.

[0009] Optionally, 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 to the remote host in real time via the Modbus communication protocol. The remote host then determines whether particulate matter levels in the inspection area exceed a certain limit based on the sensor data and calculates the robot's movement direction.

[0010] On the other hand, the present invention provides a clean room particulate matter monitoring method based on temperature field information. The method is implemented by the above-mentioned clean room particulate matter monitoring system and includes the following steps: S100, set the inspection route of the mobile robot according to the site requirements and set the initialization parameters, which include the gradient start threshold , gradient convergence threshold , stagnation collection time , particulate matter threshold setting C f , single moving step length α , the maximum distance of a single move α max , minimum moving distance α min , the 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 process. Each "Z" path includes three steps. A single movement α maxThe distance is then stagnant. During the stagnation period, the environmental detection module continuously collects the real-time air parameters in the indoor air, that is, the real-time particle concentration C t and real-time temperature T t ; S300, calculate the mobile robot in the following way i The time-averaged concentration and temperature of particulate matter with a step length are i is an integer; The time-averaged concentration is calculated as follows: , in: C i For the i The average concentration of particles at each step, C t is the real-time particle concentration, τ is the duration of stagnant collection, t This variable indicates the duration of stagnant collection. τ Any time point within the range is used to represent the moment of real-time data collection. 。

[0011] The time-averaged temperature is calculated as follows: , in: T i For the i The time-averaged temperature of the step, T t It is the real-time temperature.

[0012] S400, starting a monitoring strategy based on the relationship between the time-averaged concentration of particulate matter, the temperature gradient, and the initialization parameters; S500: Initiate a temperature local extreme value area search algorithm based on the monitoring strategy. The mobile robot continuously moves in the direction of the temperature gradient at the current location and continuously detects the particle concentration and temperature during the search process. S600, determine the i The temperature gradient value of the step Is it less than the gradient convergence threshold? ; If yes, the mobile robot stops moving, detects the concentration of air particles, determines that the stopping point is a local extreme point, starts the local extreme area planning algorithm, incorporates the local extreme area into the fixed inspection route, and uploads the updated route to the terminal server; if no, continues to step S500; S700: Determine the time-averaged concentration of particulate matter in the local extreme value area C iIs it greater than the set threshold of particulate matter? C f ; If yes, the purification device is started to purify the particulate matter, and then step S800 is executed; if no, step S800 is directly executed; S800: The mobile robot returns to the position closest to the fixed route and continues to execute step S200.

[0013] Optionally, the monitoring strategy in step S400 includes: Strategy 1: Time-averaged concentration of particulate matter at the current stagnation point C i Less than the set threshold for particulate matter C f , and the temperature gradient value Less than the gradient start threshold , then execute step S200; Strategy 2: Time-averaged concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matter C f , and the temperature gradient value Less than the gradient start threshold , the purification device is started to purify the particulate matter, and after the purification is completed, step S200 is executed; Strategy 3: Time-averaged concentration of particulate matter at the current stagnation point C i Less than the set threshold for particulate matter C f , and the temperature gradient value Greater than the gradient start threshold , then execute step S500; Strategy 4: Time-averaged concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matter C f , and the temperature gradient value Greater than the gradient start threshold , the purification device is started to purify the particulate matter, and after the purification is completed, step S500 is executed; Optionally, a specific method of the algorithm for finding the local extreme temperature region in S500 is: S510: Determine the search direction and move the robot to obtain its current position Real-time temperature data , determine the initial forward direction of this local search U f , and calculate the temperature gradient at the current position using the following formula ; , in: , , Δ x and Δ y is a small spatial step used to calculate the gradient, representing a single moving step α exist x The direction component and y Directional component.

[0014] yes( x i , y i )along x Axis positive direction moves in small steps Δ x The temperature value after yes( x i , y i )along x Axis negative direction moves in small steps Δ x The temperature value after yes( x i , y i )along y Axis positive direction moves in small steps Δ y The temperature value after yes( x i , y i )along y Axis negative direction moves in small steps Δ y The temperature value after S520: Motion control, the mobile robot updates its position according to the following formula P i+1 : , P i+1 is the updated position; in α i is the moving step factor of the current iteration, which is used to control the distance of the mobile robot along the actual movement direction. Its determination formula is: , , in is the actual motion direction vector of the mobile robot, which is determined by: Calculate the temperature gradient at the current location In the initial forward direction U f Projection value on : , if , the temperature rise direction is behind the robot, and the robot has a tendency to retreat as a whole. In order to avoid retreating as a whole while searching for the temperature change point, the robot will move in the opposite direction of the gradient as the actual direction: , if , the temperature rise direction is in front of the robot, the robot has no overall tendency to retreat, and the robot will follow the gradient direction as the actual movement direction: .

[0015] in is a random perturbation vector sampled from a Gaussian distribution with zero mean, i.e. in is the identity matrix, ensuring that the perturbations are independent and of equal strength in all dimensions. The formula for calculating the random disturbance intensity is: ; in represents the maximum value of random perturbation intensity, Indicates the minimum value of random perturbation intensity.

[0016] Optionally, the specific method of the local extreme value region planning algorithm in step S600 is: The mobile robot sets the position where it first detects that the temperature gradient exceeds the starting threshold as the starting point, and the position where it moves along the gradient direction until the gradient is less than the convergence threshold and stops as the ending point. The total distance actually moved by the mobile robot between these two points is calculated as the diameter, and a circle is drawn to mark the area as the local extreme value area.

[0017] The present invention has the following advantages: This invention is a cleanroom particulate matter monitoring system and method based on temperature field information, effectively overcoming the blind and inefficient inspections of fixed points and fixed paths in existing cleanroom particulate matter monitoring. The invention innovatively utilizes temperature anomalies as a leading indicator, guiding the robot to autonomously and intelligently discover and accurately locate areas of potential particulate matter accumulation. Through dynamic search strategies and automatic optimization of inspection routes, the robot's overall workload and inspection time are significantly reduced, significantly improving the efficiency and accuracy of monitoring and the intelligent level of cleanroom management, providing a more proactive and reliable pollution control solution for cleanrooms. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a modular block diagram of a clean room particulate matter monitoring system based on temperature field information; Figure 2 This is a flow chart of the clean room particulate matter monitoring method based on temperature field information; Figure 3 A schematic diagram of the algorithm for finding the local extreme temperature region according to the present invention; In the figure: 100, environmental detection module; 200, environmental purification module; 300, motion module; 400, control module. DETAILED DESCRIPTION

[0019] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0020] In this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0021] As described in the background, particulate matter monitoring in cleanrooms currently relies primarily on three approaches. The first is manually operated sensors for monitoring cleanroom particulate matter. This approach relies on on-site sampling with handheld equipment, which increases environmental load, creates instability in sampling points and timing, and makes it difficult to establish long-term trend analysis. Furthermore, it fails to achieve continuous, comprehensive monitoring. The second approach is fixed-point sensors, which provide continuous monitoring data in specific areas. However, these sensors have limited coverage, numerous blind spots, and are unable to capture global or dynamic pollution events. For example, several patents exist in the prior art for fixed multi-point air quality sensors (such as Patent Application No. 202211539256.0 and Patent Application No. 202310254146.8). These sensors focus on achieving regional coverage by densely deploying sensor arrays at key locations. However, these sensors inherently lack effective detection in spaces without sensor points and are insufficiently responsive to dynamically changing particulate matter. The third is that mobile robots conduct fixed-path inspections. Although this method has achieved automation, the robot's "blind" detection along the preset path cannot actively identify and track sudden local anomalies in non-preset locations, resulting in non-optimal resource allocation, low overall inspection efficiency, and difficulty in achieving accurate traceability and rapid response to particulate matter; for example, the invention patent with application number 202410519256.7 discloses a scheme for mobile robots to conduct periodic inspections along a preset route, 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.

[0022] Based on the above reasons, Figure 1 As shown, this embodiment provides a clean room particulate matter monitoring system based on temperature field information, which includes: a motion module 300 for performing inspection tasks, an environment detection module 100, an environment purification module 200, and a control module 400.

[0023] The motion module includes a mobile robot and a laser ranging radar. The mobile robot is used to stably carry loads within the clean room space and drive the carried environmental detection module and environmental purification module to move precisely according to a preset inspection path and in response to temperature gradient change instructions, so as to support high-spatial-resolution environmental data collection and detailed detection of local abnormal areas; the laser ranging radar is used to provide high-precision, real-time environmental perception data in the clean room environment to achieve precise positioning of the robot, local environment mapping, and detailed obstacle detection, ensuring the safety and efficiency of monitoring and purification operations.

[0024] Furthermore, the environmental detection module includes a temperature sensor and a particle sensor. The temperature sensor is used to detect the temperature in the clean room in real time; the particle sensor is used to detect the particle concentration in the clean room in real time; and the air parameters are obtained through the environmental detection module.

[0025] Furthermore, the environmental purification module includes an air pump and a particulate matter purification device, and the air pump is used to accurately guide the air in the clean room to the particulate matter purification device to achieve effective purification of particulate matter in the air.

[0026] 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 to the remote host in real time via the Modbus communication protocol. The remote host then determines whether particulate matter levels in the inspection area exceed the permitted level based on the sensor data and calculates the robot's movement direction.

[0027] In another embodiment, if Figure 2 and Figure 3 As shown, the present invention provides a clean room particulate matter monitoring method based on temperature field information. The method is implemented by the above-mentioned clean room particulate matter monitoring system and includes the following steps: S100, set the inspection route of the mobile robot according to the site requirements and set the initialization parameters, which include the gradient start threshold , gradient convergence threshold , stagnation collection time τ , particulate matter threshold setting C f , single moving step length α , the maximum distance of a single move α max , minimum moving distance α min , the maximum expected temperature gradient value ; For example, set the gradient start threshold = 0.2℃ / m , gradient convergence threshold = 0.02℃ / m , stagnation collection time τ =60 s , setting threshold for particulate matter concentration C f Refer to clean room standards, maximum distance of single movement α max =1 m , minimum moving distance α min =0.5 m , the maximum expected temperature gradient value = 0.2℃ / m.

[0028] 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 process. Each "Z" path includes three steps. A single movement α max The distance is then stagnant. During the stagnation period, the environmental detection module continuously collects the real-time air parameters in the indoor air, that is, the real-time particle concentration C t and real-time temperature T t ; S300, calculate the mobile robot in the following way i The time-averaged concentration and temperature of particulate matter with a step length are i is an integer; The time-averaged concentration is calculated as follows: , in: C i For the i The average concentration of particles at each step, C t is the real-time particle concentration, τ is the duration of stagnant collection, t This variable indicates the duration of stagnant collection. τ Any time point within the range is used to represent the moment of real-time data collection. 。

[0029] The time-averaged temperature is calculated as follows: , in: T i For the i The time-averaged temperature of the step, T t It is the real-time temperature.

[0030] S400: Initiate the following monitoring strategy based on the relationship between the time-averaged concentration of particulate matter, the temperature gradient, and the initialization parameters; Strategy 1: Time-averaged concentration of particulate matter at the current stagnation point C i Less than the set threshold for particulate matter C f , and the temperature gradient value Less than the gradient start threshold , then execute step S200; Strategy 2: Time-averaged concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matterC f , and the temperature gradient value Less than the gradient start threshold , the purification device is started to purify the particulate matter, and after the purification is completed, step S200 is executed; Strategy 3: Time-averaged concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matter C f , and the temperature gradient value Greater than the gradient start threshold , then execute step S500; Strategy 4: Time-averaged concentration of particulate matter at the current stagnation point C i Greater than the set threshold for particulate matter C f , and the temperature gradient value Greater than the gradient start threshold , the purification device is started to purify the particulate matter, and after the purification is completed, step S500 is executed; S500: Start the temperature local extreme value area search algorithm according to the monitoring strategy. The mobile robot continues to move according to the temperature gradient direction of the current position and continuously detects the particle concentration and temperature during the search process.

[0031] The specific method of the algorithm for finding the local extreme temperature area is (such as Figure 3 shown): S510: Determine the search direction and move the robot to obtain its current position Real-time temperature data , determine the initial forward direction of this local search U f , and calculate the temperature gradient at the current position using the following formula ; , in: , , Δ x and Δ y is a small spatial step used to calculate the gradient, representing a single moving step α exist x The direction component and y Directional component.

[0032] yes( x i , y i )along xAxis positive direction moves in small steps Δ x The temperature value after yes( x i , y i )along x Axis negative direction moves in small steps Δ x The temperature value after yes( x i , y i )along y Axis positive direction moves in small steps Δ y The temperature value after yes( x i , y i )along y Axis negative direction moves in small steps Δ y The subsequent temperature value.

[0033] S520: Motion control, the mobile robot updates its position according to the following formula P i+1 : , P i+1 is the updated position; in α i is the moving step factor of the current iteration, which is used to control the distance of the mobile robot along the actual movement direction. Its determination formula is: , , in is the actual motion direction vector of the mobile robot, which is determined by: Calculate the temperature gradient at the current location In the initial forward direction U f Projection value on : , if , the temperature rise direction is behind the robot, and the robot has a tendency to retreat as a whole. In order to avoid retreating as a whole while searching for the temperature change point, the robot will move in the opposite direction of the gradient as the actual direction: , if , the temperature rise direction is in front of the robot, the robot has no overall tendency to retreat, and the robot will follow the gradient direction as the actual movement direction: .

[0034] in is a random perturbation vector sampled from a Gaussian distribution with zero mean, i.e. where is the identity matrix, ensuring that the perturbations are independent and have the same strength in all dimensions, The formula for calculating the random disturbance intensity is: ; in Indicates the maximum value of random perturbation intensity (for example, the value is 0.1), Indicates the minimum value of the random perturbation intensity (for example, the value is 0.01).

[0035] S600, determine the i The temperature gradient value of the step Is it less than the gradient convergence threshold? ; If yes, the mobile robot stops moving, detects the concentration of air particles, determines that the stopping point is a local extreme point, starts the local extreme area planning algorithm, incorporates the local extreme area into the fixed inspection route, and uploads the updated route to the terminal server; if no, continues to step S500; The specific method of the local extreme value region planning algorithm is: The mobile robot sets the position where it first detects that the temperature gradient exceeds the starting threshold as the starting point, and the position where it moves along the gradient direction until the gradient is less than the convergence threshold and stops as the ending point. The total distance actually moved by the mobile robot between these two points is calculated as the diameter, and a circle is drawn to mark the area as the local extreme value area.

[0036] S700: Determine the time-averaged concentration of particulate matter in the local extreme value area C i Is it greater than the set threshold of particulate matter? ; If yes, the purification device is started to purify the particulate matter, and then step S800 is executed; if no, step S800 is directly executed; S800: The mobile robot returns to the position closest to the fixed route and continues to execute step S200.

[0037] The above-mentioned clean room particulate matter monitoring method addresses the problems of inefficient manual operation, blind and inefficient inspections at fixed points and fixed paths in clean room particulate matter monitoring, and the inability to accurately and dynamically identify local particulate matter accumulation areas. It innovatively uses temperature anomalies as a pre-indicator to guide the robot 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 robot's overall workload and detection time, greatly improves the efficiency and accuracy of monitoring and the intelligence level of clean room management, and provides a more proactive and reliable pollution control solution for clean rooms.

[0038] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The clean room particulate matter monitoring system based on temperature field information is characterized by: include: An environmental detection module is used to detect real-time air parameters of the measured environment, which include at least temperature and particulate matter concentration; Environmental purification module, used to purify the air in the tested environment; A motion module, used to move the environment detection module and the environment purification module to a designated position according to an inspection instruction, wherein the inspection instruction includes an inspection path and / or a temperature gradient change instruction; The control module is used to control the environment detection module, the environment purification module and the motion module.

2. The clean room particulate matter monitoring system based on temperature field information according to claim 1 is characterized in that: The environment detection module includes: Temperature sensor, used to detect the temperature in the clean room in real time; Particle sensor, used to detect the concentration of particulate matter in the clean room in real time.

3. The clean room particulate matter monitoring system based on temperature field information according to claim 1, characterized in that: The environmental purification module includes: A particle purification device is used to purify particles in the air; An air pump is used to accurately guide the air in the clean room to the particle purification device.

4. The clean room particulate matter monitoring system based on temperature field information according to claim 1, characterized in that: The motion module includes: The mobile robot is used to carry and move the environmental detection module, environmental purification module and control module carried by it within the clean room space according to the preset inspection path and in response to temperature gradient change instructions; LiDAR is used to provide high-precision, real-time environmental perception data in cleanroom environments.

5. A clean room particulate matter monitoring method based on temperature field information, characterized in that: The method is implemented by the clean room particulate matter monitoring system based on temperature field information according to any one of claims 1 to 4, comprising: S100. Set the inspection route of the mobile robot according to the site requirements and set initialization parameters, including the gradient start threshold, gradient convergence threshold, stagnation collection time, particle 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 along a set route. The mobile robot moves in a Z-shaped manner. Each Z-shaped path includes three steps, with the maximum distance of a single movement being the distance of a single movement. The mobile robot then stops. During the stoppage, the environmental detection module continuously collects real-time air parameters from the indoor air. S300, calculate the mobile robot in the i The time-averaged concentration and temperature of particulate matter with a step length are i is an integer; S400, starting a monitoring strategy based on the relationship between the time-averaged concentration of particulate matter, the temperature gradient, and the initialization parameters; S500: Initiate a temperature local extreme value area search algorithm based on the monitoring strategy. The mobile robot continuously moves in the direction of the temperature gradient at the current location and continuously detects the particle concentration and temperature during the search process. S600, determine the i Whether the temperature gradient value of each step is less than the gradient convergence threshold; If yes, the mobile robot stops moving, detects the concentration of air particles, determines that the stopping point is a local extreme point, starts the local extreme area planning algorithm, incorporates the local extreme area into the fixed inspection route, and uploads the updated route to the terminal server; if no, continues to step S500; S700: Determine whether the time-averaged concentration of particulate matter in the local extreme value area is greater than a set particulate matter threshold; If yes, the purification device is started to purify the particulate matter, and then step S800 is executed; if no, step S800 is directly executed; S800: The mobile robot returns to the position closest to the fixed route and continues to execute step S200.

6. The clean room particulate matter monitoring method based on temperature field information according to claim 5 is characterized in that: In step S300, the time-averaged concentration is calculated as follows: ， in: C i For the i The average concentration of particles at each step, C t is the real-time particle concentration, τ is the duration of stagnant collection, t This variable represents any time point within the stagnation acquisition duration τ; The time-averaged temperature is calculated as follows: ， in: T i For the i The time-averaged temperature of the step, T t It is the real-time temperature.

7. The clean room particulate matter monitoring method based on temperature field information according to claim 5, characterized in that: In step S400, the monitoring strategy includes: Strategy 1: If the time-averaged concentration of particulate matter at the current stagnation point is less than the set particulate matter threshold, and the temperature gradient is less than the gradient start threshold, then execute step S200; Strategy 2: If the time-averaged concentration of particulate matter at the current stagnation point is greater than the set particulate matter threshold and the temperature gradient is less than the gradient start threshold, the purification device is activated to purify the particulate matter. After the purification is completed, step S200 is executed. Strategy 3: If the time-averaged concentration of particulate matter at the current stagnation point is less than the set particulate matter threshold, and the temperature gradient magnitude is greater than the gradient start threshold, then execute step S500; Strategy 4: If the time-averaged concentration of particulate matter at the current stagnation point is greater than the set particulate matter threshold and the temperature gradient is greater than the gradient start threshold, the purification device is started to purify the particulate matter. After the purification is completed, step S500 is executed.

8. The clean room particulate matter monitoring method based on temperature field information according to claim 5, characterized in that: The specific method of the algorithm for finding the local extreme temperature area in S500 is: S510: Determine the search direction and move the robot to obtain its current position Real-time temperature data , determine the initial forward direction of this local search , and calculate the temperature gradient at the current position ; S520: Motion control, the mobile robot updates its position to the next position according to the following formula : ， in α i is the moving step factor of the current iteration, which is used to control the distance of the mobile robot along the actual movement direction. Its determination formula is: ， ， in is the actual motion direction vector of the mobile robot; in is a random perturbation vector, sampled from a Gaussian distribution with zero mean.

9. The clean room particulate matter monitoring method based on temperature field information according to claim 8, characterized in that: The actual movement direction vector of the mobile robot Methods for determining include: Calculate the temperature gradient at the current location In the initial forward direction Projection value on : ， if , the temperature rise direction is behind the robot, and the robot has a tendency to retreat as a whole. In order to avoid retreating as a whole while searching for the temperature change point, the robot will move in the opposite direction of the gradient as the actual direction: ， if , the temperature rise direction is in front of the robot, the robot has no overall tendency to retreat, and the robot will follow the gradient direction as the actual movement direction: 。 10. The clean room particulate matter monitoring method based on temperature field information according to claim 5, characterized in that: The specific method of the local extreme value region planning algorithm in step S600 is: The mobile robot sets the position where it first detects that the temperature gradient exceeds the starting threshold as the starting point, and the position where it moves along the gradient direction until the gradient is less than the convergence threshold and stops as the ending point. The total distance actually moved by the mobile robot between these two points is calculated as the diameter, and a circle is drawn to mark the area as the local extreme value area.

Images