Clean room pollutant purification method and system based on flow field characteristics and storage medium thereof

Abstract

The invention relates to the field of intelligent monitoring, and discloses a clean room pollutant purification method and system based on flow field characteristics and a storage medium thereof.The method comprises the steps that a mobile robot inspection route and operation parameters are set according to the requirements of a clean room; the robot inspects according to a route, stops every time the robot moves a basic distance and monitors pollutant concentration and flow velocity; calculating the time-average concentration, the time-average flow velocity, the flow velocity gradient and the change frequency of the stagnation point pollutants; executing a monitoring strategy according to a threshold value, and starting an autonomous inspection algorithm to move along a flow velocity gradient direction; judging whether the flow velocity gradient is smaller than a convergence threshold, if so, determining a local extreme point and updating the inspection route; and if the pollutant concentration at the extreme point exceeds the standard, starting the purification device, otherwise, enabling the robot to return to the fixed route for continuous inspection. According to the method, the flow field characteristics are taken as preposition indexes, the autonomous identification and path optimization capability is improved, the workload and the detection time are reduced, and the monitoring efficiency, the accuracy and the maintenance intelligence level are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring, specifically to a method, system, and storage medium for purifying pollutants in cleanrooms based on flow field characteristics. Background Technology

[0002] Cleanrooms play a crucial role in high-tech industries such as semiconductors, new energy batteries, and biopharmaceuticals, where stringent cleanliness requirements are critical to product performance and production yield. Maintaining cleanliness hinges on efficient airflow organization, ensuring the continuous and effective removal of airborne contaminants through precise airflow pattern control. However, in actual operation, factors such as equipment layout, personnel activity, and air conditioning system configuration can easily lead to the formation of localized low-velocity areas, dead zones with low air exchange efficiency, or complex vortices within cleanrooms. These abnormal flow areas cannot effectively transport contaminants, causing them to stagnate and accumulate, resulting in localized areas exceeding safety standards. Therefore, efficiently and accurately identifying and locating these abnormal flow areas, and conducting targeted monitoring and treatment of contaminants there, is essential for ensuring product quality and production safety.

[0003] Currently, particulate matter monitoring in cleanrooms mainly employs three methods: manual inspection, fixed sensor array monitoring, and robotic inspection. However, existing cleanroom contaminant monitoring and management methods, whether manual inspection or automated solutions based on fixed points / paths, suffer from inefficiencies, insufficient coverage, and untimely responses. Especially in mobile robot applications, their strategies rely on fixed paths or contaminant concentration characteristics unsuitable for the cleanroom environment, making it difficult to proactively identify and accurately locate localized anomalies. Furthermore, current robot designs generally lack systematic cleanliness considerations, and they are prone to generating particulate matter and static electricity, potentially becoming sources of contamination. Therefore, there is an urgent need for an intelligent cleanroom contaminant monitoring and control solution that comprehensively addresses both theoretical and hardware design challenges. Summary of the Invention

[0004] Therefore, to address the aforementioned shortcomings, this invention provides a cleanroom contaminant purification method, system, and storage medium based on flow field characteristics. Using flow field characteristics (including velocity gradient and wind speed change frequency) as a proactive indicator, the mobile robot can autonomously and intelligently initiate a search algorithm to accurately locate abnormal areas in the flow field based on a predetermined inspection path. This significantly enhances the autonomous and intelligent identification and dynamic path optimization capabilities, thereby greatly reducing the overall workload and detection time, and significantly improving the efficiency, accuracy, and intelligent level of long-term maintenance of cleanroom contaminant monitoring.

[0005] In a first aspect, the present invention provides a cleanroom contaminant purification method based on flow field characteristics, comprising: S100. Based on the cleanroom site requirements, cleanliness level and expected monitoring accuracy, set the mobile robot inspection route and initialize various operating parameters, including gradient start threshold, gradient convergence threshold, stagnation time, contaminant setting threshold, maximum single movement distance, minimum movement distance, maximum expected flow rate gradient value and maximum expected flow rate gradient frequency. S200: The mobile robot performs inspections according to the inspection route. During the movement, the mobile robot dynamically adjusts its movement based on the main inspection algorithm after each basic distance is covered. After completing the basic distance, the mobile robot stops and monitors the pollutant concentration and flow rate during the stop period. S300, Calculation in the... i The hourly average concentration of pollutants at each stagnation point, the hourly average flow velocity, the magnitude of the flow velocity gradient, and the frequency of flow velocity changes; S400: Implement a monitoring strategy based on the relationship between the pollutant's time-averaged concentration, the magnitude of the flow velocity gradient, and the set threshold. The S500 mobile robot initiates an autonomous inspection algorithm and continues to move according to the direction of the flow velocity gradient at its current location. S600, Determine the first i If the velocity gradient at each stagnation point is less than the gradient convergence threshold, the mobile robot stops moving, detects the pollutant concentration, determines that the stagnation point is a local extremum, starts the polluted area marking algorithm, and updates the polluted area to the fixed inspection route; otherwise, it continues to execute step 500. S700: Determine whether the average concentration of pollutants at the local extreme point is greater than the set threshold for pollutants. If yes, start the purification device on the mobile robot to purify the pollutants, and then proceed to step S800; otherwise, proceed directly to step S800.

[0006] S800: The mobile robot returns to its position closest to the fixed route and continues to execute step S200.

[0007] Optionally, the monitoring strategy described in step S400 includes: Strategy 1: The i The hourly average concentration of pollutants at each stagnation point C i Less than the pollutant set threshold C f And the magnitude of the velocity gradient |▽ V i | Less than the gradient initiation threshold Then proceed to step S200; Strategy 2: Hourly average concentration of pollutants at the current stagnation point C i greater than the pollutant set threshold Cf And the magnitude of the velocity gradient |▽ V i If the pollutant exceeds the set threshold, the pollutant purification device will be activated, and step S500 will be executed after purification is completed. Strategy 3: Average hourly concentration of pollutants at the current stagnation point C i Less than the pollutant set threshold C f And the magnitude of the velocity gradient |▽ V i If the value is greater than the set threshold, then proceed to step S500; Strategy 4: Average hourly concentration of pollutants at the current stagnation point C i greater than the pollutant set threshold C f And the magnitude of the velocity gradient |▽ V i If the pollutant level is less than the set threshold, the purification device will be activated to purify the pollutants. After purification is completed, step S200 will be executed.

[0008] Optionally, the autonomous inspection algorithm in step S500 includes: S510: Information collection; the mobile robot obtains its current location. X i =( x i , y i Real-time flow rate data, average flow rate V i Determine the initial direction of travel for this local search. X f The velocity gradient at the current location is calculated using the following formula: ▽ V i With the magnitude of the velocity gradient |▽ V i |; in, , ; , ; V ( x i + Δx,y i ) is at point ( x i , y i Move a small step Δ along the positive x-axis x The subsequent flow rate; V ( xi - Δx, y i ) is at point ( x i , y i Move a small step Δ along the negative x-axis x The subsequent flow rate; V ( x i ,y i + Δy ): At point ( x i , y i Move a small step Δ along the positive y-axis y The subsequent flow rate; V ( x i ,y i - D y ): At point ( x i , y i Move a small step Δ along the negative y-axis y The subsequent flow rate.

[0009] S520. Determine the search direction; the mobile robot determines its next search direction Δ X i ; S530, adaptive parameter adjustment, the mobile robot adjusts its parameters according to the real-time detected flow velocity gradient. |▽ V i |and frequency of flow velocity change| dV i / dt | Dynamically adjust its movement step size L i and the intensity of random disturbances ; in, , Δ t The time step is equal to one-quarter of the stagnation time.

[0010] S540 Continuous detection and iterative execution: The mobile robot continuously collects information on pollutant concentration, wind speed and wind direction during its movement, and repeats S510 to S530 until the stopping condition of step S600 is met.

[0011] Secondly, the present invention provides a cleanroom contaminant purification system based on flow field characteristics, for performing the cleanroom contaminant purification method based on flow field characteristics. The system includes: a mobile robot, which includes a mobile robot chassis, a sensor module, a purification module and an industrial control computer installed on the mobile robot chassis; The sensor module includes a lidar, a flow meter, and a pollutant sensor; The purification module includes an air inlet, an air pump, a pollutant purification device, an air outlet, and an airflow control valve. The air pump draws in polluted air from the air inlet, passes it through the pollutant purification device, and finally discharges clean air through the air outlet. The industrial control computer is a central processing and control unit used to receive and process environmental data from the sensor module in real time. Based on the real-time environmental data, it performs flow field characteristic analysis, calculates the movement direction of the mobile robot, and controls the movement direction of the purification module and the mobile robot based on the calculation results.

[0012] Optionally, the drive motor of the mobile robot chassis is a brushless DC motor, and a flexible dustproof skirt made of antistatic and low-friction material is installed on the edge of the mobile robot chassis.

[0013] Thirdly, the present invention provides a storage medium having a computer program stored thereon, which is executed by a processor to implement the cleanroom contaminant purification method based on flow field characteristics.

[0014] The present invention has the following advantages: This invention relates to a cleanroom contaminant purification method, system, and storage medium based on flow field characteristics. It addresses challenges in existing cleanroom contaminant monitoring methods, such as inefficiency of manual operation, blindness and limited detection range of fixed-point and fixed-path inspections, and the inapplicability of contaminant concentration-based inspections. Furthermore, it addresses the potential for the robot itself to become a source of contamination. The invention proposes using flow field characteristics (including velocity gradient and wind speed change frequency) as a proactive and preemptive indicator. This system and method enable a mobile robot to autonomously and intelligently initiate a search algorithm to accurately locate abnormal areas in the flow field, based on a predetermined inspection path. Finally, it intelligently plans and automatically incorporates the located local contaminant accumulation areas into the predetermined inspection route. In addition, the robot chassis incorporates low-dust and anti-static designs. This invention significantly improves the efficiency, accuracy, and intelligence of cleanroom contaminant monitoring, effectively reducing the overall workload and detection time of the robot. Attached Figure Description

[0015] Figure 1 This is a schematic flowchart of the cleanroom pollutant purification method based on flow field characteristics described in this invention. Figure 2 This is a schematic diagram of the base plate; In the diagram: 100, chassis; 200, flexible dustproof skirt. Detailed Implementation

[0016] 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.

[0017] 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.

[0018] As described in the background section, existing methods for monitoring and managing contaminants in cleanrooms, whether manual inspections or automated solutions based on fixed points / paths, suffer from inefficiencies, insufficient coverage, and slow response times. This is particularly true in mobile robot applications, where strategies rely on fixed paths or contaminant concentration characteristics unsuitable for cleanroom environments, making it difficult to proactively identify and accurately locate localized anomalies. Furthermore, current robot designs generally lack systematic cleanliness considerations, easily generating particulate matter and static electricity, potentially becoming sources of contamination. Therefore, there is an urgent need for an intelligent cleanroom contaminant monitoring and control solution that comprehensively addresses both theoretical and hardware design challenges.

[0019] Based on the above, this embodiment provides a cleanroom contaminant purification method based on flow field characteristics, including: S100. Based on the cleanroom site requirements, cleanliness level, and expected monitoring accuracy, set the mobile robot inspection route and initialize various operating parameters, including: gradient start threshold. =0.1 / s Gradient convergence threshold =0.01 / s The duration of stagnation τ = 600 s Pollutant threshold settings C f (Refer to cleanroom standards), maximum distance L for a single movement max =1 m Minimum travel distance L min =0.5m Maximum expected velocity gradient value |▽ V | max =1 / s and the maximum expected flow rate frequency | dV i / dt | max =0.2 m 2 / s ; S200: The mobile robot performs inspections according to the inspection route. During the movement, the mobile robot dynamically adjusts its movement based on the main inspection algorithm after each basic distance L is covered. After completing the basic distance, the mobile robot stops, and the pollutant concentration is monitored during the stop period. C t and flow rate V t ; S300, Calculation in the... i The hourly average concentration of pollutants at each stagnation point C i Average flow rate V i Flow velocity gradient |▽ V i |Size and frequency of flow rate changes ; S400, based on the time-averaged concentration of pollutants C i Magnitude of velocity gradient |▽ V i The monitoring strategy is executed based on the relationship between the value of the set threshold; The S500 mobile robot initiates an autonomous inspection algorithm and continues to move according to the direction of the flow velocity gradient at its current location. S600, Determine the first i Velocity gradient at stagnation points |▽ V i Is it less than the gradient convergence threshold? If yes, the mobile robot stops moving, detects the pollutant concentration, determines that the stopping point is a local extreme point, starts the polluted area marking algorithm, and updates the polluted area to the fixed inspection route; if not, it continues to execute step 500. S700, Determining the average concentration of pollutants at local extreme points C i Is it greater than the pollutant threshold? C f If yes, then activate the purification device on the mobile robot to purify the pollutants, and then proceed to step S800; otherwise, proceed directly to step S800.

[0020] S800: The mobile robot returns to its position closest to the fixed route and continues to execute step S200.

[0021] For example, the monitoring strategy described in step S400 includes: Strategy 1: The i The hourly average concentration of pollutants at each stagnation point C i Less than the pollutant set threshold C f And the magnitude of the velocity gradient |▽ V i | Less than the gradient initiation threshold Then proceed to step S200; Strategy 2: Hourly average concentration of pollutants at the current stagnation point C i greater than the pollutant set threshold C f And the magnitude of the velocity gradient |▽ V i If the pollutant exceeds the set threshold, the pollutant purification device will be activated, and step S500 will be executed after purification is completed. Strategy 3: Average hourly concentration of pollutants at the current stagnation point C i Less than the pollutant set threshold C f And the magnitude of the velocity gradient |▽ V i If the value is greater than the set threshold, then proceed to step S500; Strategy 4: Average hourly concentration of pollutants at the current stagnation point C i greater than the pollutant set threshold C f And the magnitude of the velocity gradient |▽ V i If the pollutant level is less than the set threshold, the purification device will be activated to purify the pollutants. After purification is completed, step S200 will be executed.

[0022] For example, the autonomous inspection algorithm in step S500 includes: S510: Information collection; the mobile robot obtains its current location. X i =( x i , y i Real-time flow rate data, average flow rate V i Determine the initial direction of travel for this local search. X f And calculate the velocity gradient at the current location.V i With the magnitude of the velocity gradient |▽ V i |, x i It is the first mobile robot i The position of the stagnation point on the X-axis y i It is the first mobile robot i The point of stagnation is located on the Y-axis.

[0023] in, , ; , ; V ( x i + Δx,y i ) is at point ( x i , y i Move a small step Δ along the positive x-axis x The subsequent flow rate; V ( x i - Δx, y i ) is at point ( x i , y i Move a small step Δ along the negative x-axis x The subsequent flow rate; V ( x i ,y i + Δy ): At point ( x i , y i Move a small step Δ along the positive y-axis y The subsequent flow rate; V ( x i ,y i - D y ): At point ( x i , y i Move a small step Δ along the negative y-axis y The subsequent flow rate.

[0024] Step S520: Determine the search direction. The mobile robot determines its next search direction Δ according to the following formula. X i : Δ X i = L i · D i + e i ; in D i The current actual motion direction vector of the mobile robot is determined by the following formula: ; In the formula Angle Reflecting the velocity gradient ▽ V i relative to the initial direction of travel X f The included angle is calculated using the following formula: Angle =▽ V i ·X f ; in L i Let be the step size for the current iteration, used to control the distance the mobile robot travels along the actual direction of motion. Its formula is: L i = L min +( L max - L min )· G i ; in the formula G i To adjust the flow field state of the mobile robot's current environment in order to adjust the robot's step size, the coefficients include the degree of drastic change in flow velocity. and the frequency of flow field fluctuations The calculation formula is: ; ; ; oh 1 and oh 2, which are weighting coefficients used to adjust the relative importance of spatial gradient information and flow field stability information in the final decision.

[0025] in e i The random perturbation vector is a random vector that follows a normal distribution with an expected value of zero, and its covariance matrix is ​​determined by the intensity of the random perturbation. Strong determination, its calculation formula is: s i = s min +( s max - s min )· s 0 ; s 0 To adjust the parameters non-linearly, as the mobile robot gradually moves from a flat area with a small flow velocity gradient to an area with an increasingly steep slope, s i It will smoothly transition to the maximum disturbance intensity s max With minimal disturbance intensity s min This switching between different modes makes the mobile robot's search behavior more stable and natural; s 0 The calculation formula is: s 0 = e -c·Gi ; in the formula c This is the attenuation coefficient.

[0026] S530: Adaptive parameter adjustment. The mobile robot adjusts its parameters based on the real-time detected flow velocity gradient. V i |and frequency of flow velocity change| dV i / dt | Dynamically adjust its movement step size L i and the intensity of random disturbances The intensity of random perturbation expresses the overall activity and dispersion of the random perturbation vector. The greater the intensity, the larger the value of the covariance matrix is ​​usually.

[0027] in, ; Δ t The time step is equal to one-quarter of the stagnation time.

[0028] S540: Continuous detection and iterative execution: The mobile robot continuously collects information on pollutant concentration, wind speed and wind direction during its movement, and repeats S510 to S530 until the stopping condition of step S600 is met.

[0029] For example, the contaminated area marking algorithm in step S600 is as follows: the mobile robot sets the position where it first detects that the velocity gradient exceeds the starting threshold as the starting point, and sets 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 the area is marked as a local extreme value region by drawing a circle.

[0030] The aforementioned cleanroom contaminant purification method based on flow field characteristics uses flow field characteristics (including velocity gradient and wind speed change frequency) as a proactive indicator, providing advanced algorithmic strategies. On a fixed inspection route, the robot can integrate the spatial gradient information and temporal stability information of the flow field as an intelligent decision-making basis. Once an abnormality in the flow field is detected, the robot deviates from the fixed route, initiates autonomous search, and dynamically adjusts its search step size according to the aforementioned decision-making basis, thereby achieving a more stable and natural search behavior.

[0031] Secondly, the present invention provides a cleanroom contaminant purification system based on flow field characteristics, for performing the cleanroom contaminant purification method based on flow field characteristics. The system includes: a mobile robot, which includes a mobile robot chassis, a sensor module, a purification module and an industrial control computer installed on the mobile robot chassis; The mobile robot chassis has the core function of stably and accurately supporting the sensor modules, purification modules, industrial control computers and other payloads within the cleanroom space, and moving with high precision according to the motion signals of the industrial control computers to support high spatial resolution acquisition of flow field characteristics and pollutant data, and ensure the accurate execution of subsequent purification operations; in addition, the mobile robot chassis adopts a low-dust design and an anti-static design.

[0032] The low-dust design includes: the drive motor preferably uses a brushless DC motor, which has the advantage of no brush wear and avoids the generation of particulate matter that pollutes the environment; the main structure of the chassis uses cleanroom-compatible materials, which have characteristics such as corrosion resistance, easy cleaning, low gas release, and low particulate matter generation; the chassis edges are equipped with flexible dustproof skirts made of antistatic, low-friction materials, which can effectively block dust particles that may be generated at the bottom from spreading upwards and limit the entry of external particulate matter into the chassis interior, while protecting the internal mechanisms; in addition, all exposed surfaces are smoothed to prevent dust accumulation and dirt buildup, thereby preventing non-contaminated areas from being contaminated by the chassis itself.

[0033] The antistatic design includes: a smooth chassis surface to reduce or avoid sharp edges and easily rubbed protruding parts, thereby reducing the possibility of triboelectric charging; and establishing reliable electrical connections between all conductive parts of the chassis and with the external grounding system to ensure that static charges generated during operation can be guided and dissipated in a timely manner, avoiding static charge accumulation.

[0034] The sensor module includes a lidar, a flow meter, and a contaminant sensor. The lidar provides high-precision, real-time environmental spatial information, supporting the robot's autonomous navigation, precise positioning, environmental mapping, and obstacle detection. The flow meter is used to detect the flow rate within the cleanroom in real time. The contaminant sensor is used to detect the contaminant concentration within the cleanroom in real time. All information detected by the lidar, flow meter, and contaminant sensor is transmitted to the industrial control computer for processing.

[0035] For example, the purification module includes: an air inlet, an air pump, a pollutant purification device, an air outlet, and an airflow control valve; the working principle of the purification module is: the air pump draws in polluted air from the air inlet, passes it through the purification device, and finally discharges clean air through the air outlet.

[0036] The industrial control computer serves as the central processing and control unit of the entire system. Its core function is to receive and process environmental data from the sensor modules in real time. Based on the real-time environmental data, the industrial control computer can execute algorithms such as flow field feature analysis and robot motion direction calculation; and control the motion direction of the mobile robot according to the calculation results.

[0037] Among the aforementioned technical features, the mobile robot design incorporates "low dust" and "anti-static" features specifically designed for cleanroom environments. Low dust is achieved through the use of brushless motors, cleanroom-compatible materials, and a flexible dustproof skirt 200 mounted on the chassis 100. Static electricity protection is achieved through effective grounding and anti-static materials, giving the system inherent cleanliness characteristics for operation in cleanrooms with high cleanliness requirements. This provides a reliable hardware foundation for accurate environmental data collection. This mobile robot, combining the aforementioned purification methods with an integrated hardware and software innovation, enables the robot to shift from passive inspection to proactive, intelligent traceability, significantly improving the efficiency, accuracy, and intelligent level of long-term cleanroom contaminant monitoring.

[0038] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0039] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure one One or more processes and / or boxes Figure one A device that provides the functions specified in one or more boxes.

[0040] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure one One or more processes and / or boxes Figure one The function specified in one or more boxes.

[0041] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure one One or more processes and / or boxes Figure one The steps of the function specified in one or more boxes.

[0042] This application also provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the above-described method for cleanroom contaminant purification based on flow field characteristics.

[0043] If the modules / units integrated into the cleanroom contaminant purification system / terminal equipment based on flow field characteristics are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0044] The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0045] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0046] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0047] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0048] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The above embodiments are only used to illustrate the technical solution of this application and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of this application.

[0049] 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 cleanroom contaminant purification method based on flow field characteristics, characterized in that, include: S100. Based on the cleanroom site requirements, cleanliness level and expected monitoring accuracy, set the mobile robot inspection route and initialize various operating parameters, including gradient start threshold, gradient convergence threshold, stagnation time, contaminant setting threshold, maximum single movement distance, minimum movement distance, maximum expected flow rate gradient value and maximum expected flow rate frequency. S200: The mobile robot performs inspections according to the inspection route. During the movement, the mobile robot dynamically adjusts its movement based on the main inspection algorithm after each basic distance is covered. After completing the basic distance, the mobile robot stops and monitors the pollutant concentration and flow rate during the stop period. S300, Calculation in the... i The hourly average concentration of pollutants at each stagnation point, the hourly average flow velocity, the magnitude of the flow velocity gradient, and the frequency of flow velocity changes; S400: Implement a monitoring strategy based on the relationship between the pollutant's time-averaged concentration, the magnitude of the flow velocity gradient, and the set threshold. The S500 mobile robot initiates an autonomous inspection algorithm and continues to move according to the direction of the flow velocity gradient at its current location. S600, Determine the first i If the velocity gradient at each stagnation point is less than the gradient convergence threshold, the mobile robot stops moving, detects the pollutant concentration, determines that the stagnation point is a local extremum, starts the polluted area marking algorithm, and updates the polluted area to the fixed inspection route; otherwise, it continues to execute step 500. S700: Determine whether the average concentration of pollutants at the local extreme point is greater than the set threshold for pollutants. If yes, start the purification device on the mobile robot to purify the pollutants, and then proceed to step S800; otherwise, 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 contaminant purification method based on flow field characteristics according to claim 1, characterized in that, The monitoring strategy described in step S400 includes: Strategy 1: The i The hourly average concentration of pollutants at each stagnation point C i Less than the pollutant set threshold C f And the magnitude of the velocity gradient |▽ V i | Less than the gradient initiation threshold Then proceed to step S200; Strategy 2: Hourly average concentration of pollutants at the current stagnation point C i greater than the pollutant set threshold C f And the velocity gradient |▽ V i If the pollutant exceeds the set threshold, the pollutant purification device will be activated, and step S500 will be executed after purification is completed. Strategy 3: Average hourly concentration of pollutants at the current stagnation point C i Less than the pollutant set threshold C f And the velocity gradient |▽ V i If the value is greater than the set threshold, then proceed to step S500; Strategy 4: Average hourly concentration of pollutants at the current stagnation point C i greater than the pollutant set threshold C f And the velocity gradient |▽ V i If the pollutant level is less than the set threshold, the purification device will be activated to purify the pollutants. After purification is completed, step S200 will be executed.

3. The cleanroom contaminant purification method based on flow field characteristics according to claim 1, characterized in that, The autonomous inspection algorithm described in step S500 includes: S510: Information collection; the mobile robot obtains its current location. X i =( x i , y i Real-time flow rate data, average flow rate V i Determine the initial direction of travel for this local search. X f The velocity gradient at the current location is calculated using the following formula: ▽ V i With the magnitude of the velocity gradient |▽ V i |, in, , ; ， ； V ( x i + Δx,y i ) is at point ( x i , y i Move along the positive x-axis by a small step size Δ x The subsequent flow rate; V ( x i - Δx,y i ) is at point ( x i , y i Move a small step Δ along the negative x-axis x The subsequent flow rate; V ( x i ,y i + Δy ): At point ( x i , y i Move a small step Δ along the positive y-axis y The subsequent flow rate; V ( x i ,y i - Δy ): At point ( x i , y i Move a small step Δ along the negative y-axis y The subsequent flow rate; S520. Determine the search direction; the mobile robot determines its next search direction Δ X i ; S530, adaptive parameter adjustment, the mobile robot adjusts its parameters according to the real-time detected flow velocity gradient. |▽ V i |and frequency of flow velocity change| dV i / dt | Dynamically adjust its movement step size L i and the intensity of random disturbances ; ； Δ t The time step is equal to one-quarter of the stagnation time; S540 Continuous detection and iterative execution: The mobile robot continuously collects information on pollutant concentration, wind speed and wind direction during its movement, and repeats S510 to S530 until the stopping condition of step S600 is met.

4. The cleanroom contaminant purification method based on flow field characteristics according to claim 3, characterized in that, In step S520, the search direction Δ is determined using the following formula. X i : D X i = L i · D i + ε i , in D i The current actual motion direction vector of the mobile robot is determined by the following formula: ； In the formula Angle Reflecting the velocity gradient ▽ V i relative to the initial direction of travel X f The included angle; in L i The step size for the current iteration is used to control the distance the mobile robot travels along the actual direction of motion. in ε i The random perturbation vector is a random vector that follows a normal distribution with an expected value of zero, and its covariance matrix is ​​determined by the intensity of the random perturbation. The intensity of random perturbation determines the overall activity and dispersion of the random perturbation vector; the greater the intensity, the larger the value of the covariance matrix is ​​usually.

5. The cleanroom contaminant purification method based on flow field characteristics according to claim 4, characterized in that, Reflecting the velocity gradient ▽ V i relative to the initial direction of travel X f The included angle Angle The calculation formula is as follows: Angle =▽ V i ·X f ； The moving step size of the current iteration L i The calculation formula is as follows: L i = L min +（ L max - L min ）· G i ； in the formula G i To adjust the flow field state of the mobile robot's current environment in order to adjust the robot's step size, the coefficients include the degree of drastic change in flow velocity. and the frequency of flow field fluctuations The calculation formula is: ； ； ； in: ω 1 and ω 2 These are weighting coefficients used to adjust the relative importance of spatial gradient information and flow field stability information in the final decision; |▽ V | max It is the maximum expected velocity gradient value, | dV i / dt | is the frequency of flow velocity change, | dV i / dt | max It is the frequency of maximum flow velocity change.

6. The cleanroom contaminant purification method based on flow field characteristics according to claim 5, characterized in that, random disturbance strength The calculation formula is: σ i = σ min +（ σ max - σ min ）· σ 0 ； σ 0 To adjust the parameters non-linearly, as the mobile robot gradually moves from a flat area with a small flow velocity gradient to an area with an increasingly steep slope, σ i It will smoothly transition to the maximum disturbance intensity σ max With minimal disturbance intensity σ min The switching between these modes makes the mobile robot's search behavior more stable and natural; among which... σ 0 The calculation formula is: σ 0 = e -c·Gi ； in the formula c This is the attenuation coefficient.

7. The cleanroom contaminant purification method based on flow field characteristics according to claim 5, characterized in that: The contaminated area marking algorithm in step S600 is: The mobile robot sets the starting point as the position where it first detects that the velocity gradient exceeds the starting threshold, and 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.

8. A cleanroom contaminant purification system based on flow field characteristics, characterized in that: For performing the cleanroom contaminant purification method based on flow field characteristics as described in any one of claims 1-7, the system includes: a mobile robot, the mobile robot including a mobile robot chassis, a sensor module, a purification module and an industrial control computer mounted on the mobile robot chassis; The sensor module includes a lidar, a flow meter, and a pollutant sensor; The purification module includes an air inlet, an air pump, a pollutant purification device, an air outlet, and an airflow control valve. The air pump draws in polluted air from the air inlet, passes it through the pollutant purification device, and finally discharges clean air through the air outlet. The industrial control computer is a central processing and control unit used to receive and process environmental data from the sensor module in real time. Based on the real-time environmental data, it performs flow field characteristic analysis, calculates the movement direction of the mobile robot, and controls the movement direction of the purification module and the mobile robot based on the calculation results.

9. The cleanroom contaminant purification system based on flow field characteristics according to claim 8, characterized in that: The drive motor of the mobile robot chassis is a brushless DC motor, and a flexible dustproof skirt made of antistatic and low-friction material is installed on the edge of the mobile robot chassis.

10. A storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the cleanroom contaminant purification method based on flow field characteristics as described in any one of claims 1-7.

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