A Smart Monitoring Method for Cleanroom Environment that Integrates Active and Passive Monitoring

Abstract

This invention belongs to the field of intelligent monitoring and discloses an active-passive integrated intelligent monitoring method for cleanroom environments. This method constructs an active-passive integrated monitoring system based on covariate statistics. By introducing real-time air field differences as a key covariate, a two-parameter variogram function is established to overcome the shortcomings of traditional models that ignore flow field dynamics. Utilizing the deviation between the basic information field generated by fixed sensors and the corrected information field formed by robot fusion data, combined with inherent coverage risks and joint spatial alienation, a comprehensive blind zone guidance index is constructed to accurately identify absolute monitoring blind zones. Based on this, a shift from fixed-route inspection to a "blind zone-driven active detection" control mode is achieved. This method significantly improves the overall monitoring coverage, accuracy, and response speed to sudden contamination in cleanrooms without increasing hardware costs.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring, specifically a method for intelligent monitoring of cleanroom environments that integrates active and passive monitoring. Background Technology

[0002] Cleanrooms are widely used in high-end manufacturing fields such as semiconductors and biomedicine. Their cleanliness is affected by the nonlinear coupling of multiple factors, including airflow organization, dust source distribution, personnel activities, and equipment operation, which directly affects product yield and production safety. Achieving high-precision, spatiotemporally continuous, and comprehensive environmental monitoring is crucial for revealing the pollution coupling mechanism and constructing dynamic optimization models.

[0003] Current mainstream methods rely on fixed sensor networks, such as the patent application number CN202310254146.8. However, due to high costs and sparse deployment, these methods struggle to cover the entire space and cannot effectively detect areas without sensors, resulting in significant blind spots. To expand coverage, some solutions employ fixed-path mobile robots for inspection (such as the patent application number 202410519256.7), but these suffer from rigid paths, slow response, and a tendency to miss dynamic pollution sources. While autonomous mobile robots (such as the patent application number CN202511217004.X) can freely plan their paths, they remain merely "mobile single-point sensors," lacking global real-time perception capabilities and struggling to overcome the "spatiotemporal coupling" problem in large-scale, highly time-varying flow fields.

[0004] In summary, existing technologies are either limited by static deployment or constrained by localized motion sensing, and neither can simultaneously achieve broad coverage, fast response, and global accuracy. There is an urgent need for an intelligent monitoring method that integrates active and passive monitoring to achieve efficient, continuous, and comprehensive sensing of the cleanroom environment. Summary of the Invention

[0005] Therefore, to address the aforementioned shortcomings, this invention provides an intelligent monitoring method for cleanroom environments that integrates active and passive monitoring. This method constructs an active-passive integrated monitoring system based on covariate geostatistics: by introducing real-time wind field differences as a key covariate to construct a two-parameter variogram function, it solves the problem of traditional models neglecting flow field dynamics characteristics; furthermore, by utilizing the monitoring deviation between the "basic information field" generated by fixed sensors and the "corrected information field" of fused robot data, combined with inherent coverage risks and joint spatial alienation, a comprehensive blind zone guidance index is constructed to accurately locate absolute blind zones far from all monitoring nodes; finally, based on this index, a shift from fixed-route inspection to "blind zone-driven active detection" control mode is achieved, significantly improving the overall coverage, accuracy, and response speed to sudden pollution in cleanroom environmental monitoring without increasing hardware costs.

[0006] Specifically, a method for intelligent monitoring of cleanroom environments that integrates active and passive monitoring includes the following steps: S1. Construct a monitoring system, which includes a fixed sensor array and a robot. The fixed sensor array has multiple fixed sensors that transmit data to the central processing unit in real time in one direction. The robot carries the sensors and the central processing unit, transmits data to the central processing unit in real time via a wireless network, and receives the optimal inspection path instructions issued by the central processing unit. S2, Input fixed sensor array position information S =[ S 1, S 2,…, S N Information on obstacles in cleanrooms; Initialize the robot's contaminant dataset C robot Flow field dataset V robot Pollutant datasets from fixed sensor arrays C fixed Flow field dataset V fixed Blind spot dataset I Inspection location dataset I D Basic Information Field C base With modified information field C cor ; Initialize the robot's step size L and the robot's inspection starting point. R S Inspection endpoint R E Blind zone threshold I min ; S3, the robot starts from the inspection point. R S The robot starts moving along the currently set route; simultaneously, a fixed sensor array collects and records data in real time. t Position coordinates at time R ( t Pollutant information C robot ( t ) and flow field information V robot ( t ), and update the robot's contaminant dataset accordingly. C robot Flow field dataset V robot Pollutant information collected by a fixed sensor array in the middle. C fixed ( S i, t ) and flow field information V fixed ( S i , t Update the pollutant dataset to the fixed sensors respectively. Flow field dataset ; S4. Calculate the basic information field of the time based on the data collected in step S3. C base ( t ) and modified information field C cor ( t ), and update to the basic information field. C base With modified information field C cor In this process, a dual information field is constructed; S5. By comparing the dual information fields calculated in step S4, calculate the cleanroom. t Comprehensive blind spot guidance indicators at various locations at any given time I ( t And update to the blind spot dataset. middle; S6. The comprehensive blind spot guidance index I ( t ) and the preset blind zone threshold I min Compare the results to determine whether the robot needs to execute step S3 or step S7.

[0007] S7, Traversing the Blind Spot Dataset I The sequence of numbers filters out spatial location coordinates to form a candidate target set; S8. Calculate the Euclidean distance between each coordinate point in the candidate target set and the robot's current position, select the closest coordinate point as the robot's next moving target position; drive the robot to move to that position, and store the coordinates of that position in the inspection position dataset. I D middle; S9. Determine whether the robot's current position requires continued inspection. The step that needs to be performed in step S6 is either step S3 or step S7.

[0008] S10. Based on the inspection location dataset I DThe historical coordinates are dynamically updated to the robot's preset fixed inspection route, and the location coordinates in the dataset are embedded into the fixed route as new routine inspection nodes; then the state is reset, and the process jumps back to step S2 to start the monitoring of the next cycle.

[0009] The present invention has the following advantages: This invention is an intelligent cleanroom environmental monitoring method that integrates active and passive monitoring. It overcomes the limitations of existing cleanroom environmental monitoring technologies, such as the limited coverage of fixed sensor networks due to sparse locations, high costs of high-density deployment, and the difficulty in accurately capturing sudden local contamination in complex flow fields using traditional interpolation. Existing mobile inspection technologies, lacking guidance from overall environmental situational information, suffer from blind path planning, inability to respond to environmental changes in real time, and difficulty in effectively filling monitoring blind spots. This invention introduces wind field differences as a covariate to construct a two-parameter variogram model. It utilizes multi-source data fusion from fixed sensors and mobile robots to establish a basic information field and a corrected information field. Based on monitoring deviation, inherent uncertainty, and spatial alienation, it calculates a comprehensive blind spot guidance index, thereby driving the mobile robot to actively detect high-risk blind spots and dynamically optimize paths. This achieves low-cost, high-precision, and adaptive full-area cleanroom environmental monitoring. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the active-passive integrated intelligent monitoring method for cleanroom environments described in this invention. Detailed Implementation

[0011] 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.

[0012] 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.

[0013] As described in the background section, in existing cleanroom environmental monitoring technologies, fixed sensor networks have limited coverage due to sparse locations, high costs for high-density deployment, and difficulty in accurately capturing localized sudden pollution in complex flow fields by relying on traditional interpolation. Meanwhile, existing mobile inspection technologies lack guidance from overall environmental situation information, resulting in blind monitoring path planning, inability to respond to environmental changes in real time, and difficulty in effectively filling monitoring blind spots.

[0014] To address this problem, this embodiment provides a method for intelligent monitoring of cleanroom environments that integrates active and passive monitoring, such as... Figure 1 As shown, the method includes the following steps: S1. Construct a monitoring system, which includes a fixed sensor array and a robot. The fixed sensor array has multiple fixed sensors that transmit data to the central processing unit in real time in one direction. The robot carries the sensors and the central processing unit, transmits data to the central processing unit in real time via a wireless network, and receives the optimal inspection path instructions issued by the central processing unit. The central processing unit is responsible for receiving all data and executing the core algorithm calculations described in steps S3 to S10.

[0015] S2, Input fixed sensor array position information S =[ S 1, S 2,…, S N Information on obstacles in cleanrooms; Initialize the robot's contaminant dataset C robot Flow field dataset V robot Pollutant datasets from fixed sensor arrays C fixed Flow field dataset V fixed Blind spot dataset I Inspection location dataset I D Basic Information Field C base With modified information field C cor ; Initialize the robot's step size L and the robot's inspection starting point. R S Inspection endpoint R E Blind zone threshold I min .

[0016] Parameter configuration can be achieved through step S2.

[0017] S3, the robot starts from the inspection point. R S The robot starts moving along the currently set route; simultaneously, a fixed sensor array collects and records data in real time. t Position coordinates at time R ( t Pollutant information C robot ( t ) and flow field information V robot ( t ), and update the robot's contaminant dataset accordingly. C robot Flow field dataset V robot Pollutant information collected by a fixed sensor array in the middle. C fixed ( S i , t ) and flow field information V fixed ( S i , t Update the pollutant dataset to the fixed sensors respectively. Flow field dataset ; Data collection and updating can be achieved through step S3.

[0018] S4. Calculate based on the data collected in step S3. t The basic information field of time C base ( t ) and modified information field C cor ( t ), and update to the basic information field. C base With modified information field C cor In this process, a dual information field is constructed. This dual information field construction can be achieved through step S4.

[0019] Calculate in step S4 t The basic information field of time C base ( t ) and modified information field C cor ( t The methods include: Step S4.1: Calculate using the following formula t Effective distance between any two fixed sensors at any given timeh eff ( S i , S j ,t ), ; In the formula S i ,S j ∈ S , S This indicates the position information of a fixed sensor array. S i It is the first i Fixed sensor position vector, S j yes j A fixed sensor position vector, and i Not equal to j , V fixed ( S i ,t ), V fixed ( S j ,t )∈ V fixed , V fixed This represents a flow field dataset from a fixed sensor. V fixed ( S i ,t ) is the first i A fixed sensor in t The velocity vector of the flow field at time t. V fixed ( S j , t ) is the first j A fixed sensor in t The velocity vector of the flow field at time t; C fixed ( S i ,t ), C fixed ( S j ,t )∈ C fixed , C fixedThis represents a dataset of pollutants from a fixed sensor. C fixed ( S i ,t ) is the first i A fixed sensor in t Pollutant data at any given time, C fixed ( S j ,t ) is the first j A fixed sensor in t Pollutant data at any given time; S i S j Point S i With point S j The vector, Indicates the distance between two points. α This is a correction factor; Step S4.2: Set the effective distance h eff ( S i ,S j ,t The range of values ​​for ) is divided into K The intervals are respectively h 1 , h 2 , ..., h k , h k It is the first K The effective distance of each interval is determined by the following formula: ; ; in , N for S Number of elements in the dataset; Step S4.3: Calculate the empirical variation value within different distance ranges using the following formula: ; in: N ( h k ) is the first K Logarithm of data points within a range ξ ( h k , t) is the empirical variation value. Step S4.4: Fit the empirical variance value calculated in step S4.3 to... t Empirical variation function of cleanroom at all times ξ ( Si , Sj , t ); Step S4.5: Construct the mutation value matrix and solve for the optimal weight vector. W With Lagrange multiplier Its defining formula is: ; in ξ i,j The matrix of variability values ​​among all sensors, ξ i,p for P Points and all sensors S i Variations between them; Step S4.6: Calculation t time, P Basic Information C base ( P,t ) and uncertainty indicators U base ( P,t ): C base ( P,t )= W · C fixed ( t ); ; in, ; Step S4.7: Repeat step S4.6 to calculate. t Time-based basic information field C base ( t ) and uncertainty field U base ( t ).

[0020] Step S4.8: Integrate the robot's position information, flow field information, and pollutant concentration information into the original fixed sensor dataset to construct an expanded sensor set. N represents the number of fixed sensors. R ( t )express t The location of the time-lapse robot is... tThe position information of the robot and the fixed sensors is stored in a single data sequence for easy data processing. ; ; in, C robot ( t Robots are t Pollutant information at any given time V robot ( t Robots are t Flow field information at any given time C fixed,robot It is the merged pollutant dataset. V fixed,robot This is the fused flow field dataset; and steps S4.1 to S4.7 are repeated to calculate the corrected information field. C cor ( t ) and uncertainty field U cor ( t ).

[0021] S5. By comparing the dual information fields calculated in step S4, calculate the cleanroom. t Comprehensive blind spot guidance indicators at various locations at any given time I ( t And update to the blind spot dataset. In the middle. Step S5 can complete the comprehensive blind spot determination.

[0022] The calculation method for the comprehensive blind spot guidance index is as follows: ; In the formula Δ C ( t The deviation between the base field and the correction field is represented by . The larger the deviation, the less accurate the fixed sensor's monitoring at that point is, and the greater the possibility of a blind zone. The formula for calculating this deviation is: ; ω 1 represents the difference weight, indicating the system's sensitivity to correcting real-time monitoring deviations. ω 2 represents the inherent weight, indicating the system's focus on exploring inherent coverage blind spots.

[0023] S6. The comprehensive blind spot guidance index I ( t ) and the preset blind zone threshold I min Comparison: like I ( t )< Imin If the current area monitoring is deemed valid, the robot continues to move along the currently set route and executes step S3. like I ( t )≥ I min If a monitoring blind spot is detected, the active detection mechanism is triggered, and step S7 is executed.

[0024] S7. Candidate blind zone filtering: Traverse the blind zone dataset and filter the spatial location coordinates that simultaneously meet the following two conditions to form a candidate target set: A. The blind spot guidance indicator value at this location is greater than the preset threshold. I min ; B. This position is in the area in front of the robot's current direction of movement.

[0025] S8. Dynamic Target Execution: Calculate the Euclidean distance between each coordinate point in the candidate target set and the robot's current position, select the closest coordinate point as the robot's next moving target position, drive the robot to move to that position, and store the coordinates of that position in the inspection position dataset. I D middle; S9. Determine whether the robot still needs to continue the inspection at its current position.

[0026] For example, the specific method of step S9 is: determine the robot's current position relative to the inspection endpoint. R E Or the starting point of the inspection R S Is the distance D less than or equal to 5L, where L is the robot's step size? If D≤5L, the robot proceeds to the inspection endpoint. R E Or the starting point of the inspection R S And execute step S10; If D > 5L, then proceed to step S3.

[0027] S10. Based on the inspection location dataset I D The system dynamically updates the historical coordinates and the robot's preset fixed inspection route, embedding the location coordinates in the dataset as new routine inspection nodes into the fixed route; then, it resets the state, jumps back to step S2, and starts the monitoring for the next cycle. This step achieves path correction and iteration.

[0028] The above steps S1-S10 overcome the limitations of existing cleanroom environmental monitoring technologies. Fixed sensor networks suffer from limited coverage due to sparse locations, high costs for high-density deployment, and difficulty in accurately capturing sudden localized contamination in complex flow fields using traditional interpolation. Existing mobile inspection technologies, lacking guidance from overall environmental situational information, suffer from blind path planning, inability to respond to environmental changes in real time, and difficulty in effectively filling monitoring blind spots. This invention introduces wind field differences as a covariate to construct a two-parameter variogram model. It utilizes multi-source data fusion from fixed sensors and mobile robots to establish a basic information field and a corrected information field. Based on monitoring deviation, inherent uncertainty, and spatial alienation, it calculates a comprehensive blind spot guidance index, thereby driving the mobile robot to actively detect high-risk blind spots and dynamically optimize paths. This achieves low-cost, high-precision, and adaptive cleanroom environmental monitoring across the entire area.

[0029] 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 intelligent monitoring of cleanroom environment that integrates active and passive monitoring, characterized in that, Includes the following steps: S1. Construct a monitoring system, which includes a fixed sensor array and a robot. The fixed sensor array has multiple fixed sensors that transmit data to the central processing unit in real time in one direction. The robot carries the sensors and the central processing unit, transmits data to the central processing unit in real time via a wireless network, and receives the optimal inspection path instructions issued by the central processing unit. S2, Input fixed sensor array position information S =[ S 1, S 2,…, S N Information on obstacles in cleanrooms; Initialize the robot's contaminant dataset C robot Flow field dataset V robot Pollutant datasets from fixed sensor arrays C fixed Flow field dataset V fixed Blind spot dataset I Inspection location dataset I D Basic Information Field C base With modified information field C cor ; Initialize the robot's step size L and the robot's inspection starting point. R S Inspection endpoint R E Blind zone threshold I min ; S3, the robot starts from the inspection point. R S The robot starts moving along the currently set route; simultaneously, a fixed sensor array collects and records data in real time. t The coordinates of the engraving position R ( t Pollutant information C robot ( t ) and flow field information V robot ( t ), and update the robot's contaminant dataset accordingly. C robot Flow field dataset V robot Pollutant information collected by a fixed sensor array in the middle. C fixed ( S i , t ) and flow field information V fixed ( S i , t Update the pollutant dataset to the fixed sensors respectively. C fixed Flow field dataset V fixed ; S4. Calculate based on the data collected in step S3. t The basic information field of time C base ( t ) and modified information field C cor ( t ), and update to the basic information field. C base With modified information field C cor In this process, a dual information field is constructed; S5. Calculate the comprehensive blind area guiding index at each position in the clean room by comparing the dual information fields obtained in step S4 t at the moment I ( t ) and update it to the blind area data set I =|[[]] I(0) ,…, I(t) ,…|; S6. The comprehensive blind spot guidance index I ( t ) and the preset blind zone threshold I min Compare the steps to determine the robot needs to perform. The basic information field for calculating time in step S4 C base ( t ) and modified information field C cor ( t The methods include: Step S4.1: Calculate using the following formula t Effective distance between any two fixed sensors at any given time h eff ( S i ,S j , t ), ； In the formula S i ,S j ∈ S , S This indicates the position information of a fixed sensor array. S i It is the first i Fixed sensor position vector, S j yes j A fixed sensor position vector, and i Not equal to j , V fixed ( S i ,t ), V fixed ( S j ,t )∈ V fixed , V fixed This represents a flow field dataset from a fixed sensor. V fixed ( S i ,t ) is the first i A fixed sensor in t The velocity vector of the flow field at time t. V fixed ( S j ,t ) is the first j A fixed sensor in t The velocity vector of the flow field at time t; C fixed ( S i ,t ), C fixed ( S j ,t )∈ C fixed , C fixed This represents a dataset of pollutants from a fixed sensor. C fixed ( S i ,t ) is the first i A fixed sensor in t Pollutant data at any given time, C fixed ( S j ,t ) is the first j A fixed sensor in t Pollutant data at any given time; S i S j Point S i With point S j The vector, This represents the distance between two points, where α is a correction factor. Step S4.2: Set the effective distance h eff ( S i ,S j ,t The range of values ​​for ) is divided into K The intervals are respectively h 1 , h 2 , ..., h k , h k It is the first K The effective distance of each interval K The formula is: ； ； in: , N for S Number of elements in the dataset; Step S4.3: Calculate the empirical variation value within different distance ranges using the following formula: ； in: N ( h k ) is the first K Logarithm of data points within a range ξ ( h k , t ) is the empirical variation value. Step S4.4: Fit the empirical variance value calculated in step S4.3 to... t The empirical variation function of a cleanroom at any given time ξ ( Si , Sj,t ); Step S4.5: Construct the mutation value matrix and solve for the optimal weight vector. W With Lagrange multiplier Its defining formula is: ； in ξ i,j The matrix of variability values ​​among all sensors, ξ i,p for P Points and all sensors S i Variations between them; Step S4.6: Calculation t time, P Basic Information C base ( P,t ) and fundamental uncertainty indicators U base ( P,t ): C base ( P,t )= W · C fixed （ t ）； U base ( P,t )= W · ξ i,p ＋ ； in: C fixed ( t )=| C fixed (S1, t ),…, C fixed (S i , t ),... C fixed (S N , t )|, Step S4.7: Repeat step S4.6 to calculate. t Time-based basic information field C base ( t ) and fundamental uncertainty field U base ( t It contains basic information and uncertainty indicators for all indoor locations; Step S4.8: Integrate the robot's position information, flow field information, and pollutant concentration information into the original fixed sensor dataset to construct an expanded sensor set SR(t) = |S1,…,S N , R ( t ) |; ； ； in, C robot ( t Robots are t Pollutant information at any given time V robot ( t Robots are t The flow field information at time t, where N represents the number of fixed sensors. R ( t () represents the position of the robot at time t. C fixed,robot It is the merged pollutant dataset. V fixed,robot This is the fused flow field dataset; and steps S4.1 to S4.7 are repeated to calculate the corrected information field. C cor ( t ) and modified uncertainty field U cor ( t ).

2. The method for intelligent monitoring of cleanroom environment integrating active and passive monitoring according to claim 1, characterized in that, The method also includes: S7, Traversing the Blind Spot Dataset I The sequence of numbers filters out spatial location coordinates to form a candidate target set; S8. Calculate the Euclidean distance between each coordinate point in the candidate target set and the robot's current position, select the closest coordinate point as the robot's next moving target position; drive the robot to move to that position, and store the coordinates of that position in the inspection position dataset. I D middle; S9. Determine whether the robot's current position requires continued inspection. The step that needs to be performed in step S6 is either step S3 or step S7.

3. The method for intelligent monitoring of cleanroom environment integrating active and passive monitoring according to claim 2, characterized in that, The method further includes: S10. Based on the inspection location dataset I D The historical coordinates are dynamically updated to the robot's preset fixed inspection route, and the location coordinates in the dataset are embedded into the fixed route as new routine inspection nodes; then the state is reset, and the process jumps back to step S2 to start the monitoring of the next cycle.

4. The method for intelligent monitoring of cleanroom environment integrating active and passive monitoring according to claim 1, characterized in that, The calculation method for the comprehensive blind spot guidance index in step S5 is as follows: ； In the formula Δ C ( t The deviation between the base field and the modified field is represented by the formula: ; ω 1 represents the difference weight, indicating the system's sensitivity to correcting real-time monitoring deviations. ω 2 represents the inherent weight, indicating the system's focus on exploring inherent coverage blind spots.

5. The method for intelligent monitoring of cleanroom environment integrating active and passive monitoring according to claim 2, characterized in that, In step S6, the blind zone guidance index is compared with the preset blind zone threshold to determine whether the robot should execute step S3 or step S7. The specific method is as follows: The comprehensive blind spot guidance index I ( t ) and the preset blind zone threshold I min Comparison: like I ( t )< I min If the current area monitoring is deemed valid, the robot continues to move along the currently set route and executes step S3. like I ( t )≥ I min If a monitoring blind spot is detected, the active detection mechanism is triggered, and step S7 is executed.

6. The method for intelligent monitoring of cleanroom environment integrating active and passive monitoring according to claim 2, characterized in that, The spatial coordinates selected from the sequence in step S7 must simultaneously meet the following two conditions: Condition 1: The blind spot guidance indicator value at this location is greater than a preset threshold. I min ; Condition 2: The position is in front of the robot's current direction of movement.

7. The method for intelligent monitoring of cleanroom environment integrating active and passive monitoring according to claim 2, characterized in that, The specific method for determining whether the robot's current position still needs to continue the inspection in step S9 is as follows: Determine the robot's current position and the inspection endpoint. R E Or the starting point of the inspection R S Is the distance D less than or equal to 5L, where L is the robot's step size? If D≤5L, the robot proceeds to the inspection endpoint. R E Or the starting point of the inspection R S And execute step S10; If D > 5L, then proceed to step S3.

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