Clean room purification ventilation method and system based on internet of things space monitoring
By acquiring the wind speed setting function and concentration change function, and combining them with real-time particle concentration and entry time, the corrected wind speed is calculated, which solves the problem of untimely wind speed adjustment in cleanroom purification ventilation and realizes the intelligence and energy saving of cleanrooms.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SUZHOU XINGYA PURIFICATION ENG
- Filing Date
- 2025-08-20
- Publication Date
- 2026-07-10
AI Technical Summary
Existing cleanroom ventilation technology fails to adjust airflow in real time to cope with changes in particle concentration as staff enter and exit, resulting in wasted power resources and substandard cleanroom cleanliness.
By acquiring the wind speed setting function, dividing the threshold and concentration change function, and combining the real-time particle concentration and entry time, the corrected wind speed is calculated to adjust the ventilation wind speed of the clean room.
It enables intelligent adjustment of airflow based on real-time changes in particle concentration, ensuring cleanroom cleanliness and saving power resources, thus improving the intelligence and energy efficiency of cleanroom purification and ventilation.
Smart Images
Figure CN120740180B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of cleanroom ventilation technology, specifically to a cleanroom purification and ventilation method and system based on Internet of Things (IoT) spatial monitoring. Background Technology
[0002] As the core production environment for high-end industries such as semiconductor manufacturing, biopharmaceuticals, and precision medical devices, the stability of the air cleanliness of cleanrooms is directly related to product quality and process safety. In order to maintain the air cleanliness of cleanrooms, it is usually necessary to continuously ventilate the cleanroom with the maximum airflow to ensure that the cleanroom is clean at all times. However, ventilating the cleanroom with the maximum power for a long time will waste electricity resources.
[0003] To save energy while maintaining cleanroom cleanliness, existing technologies use different fan speed settings for different cleanroom scenarios. While this is more energy-efficient than traditional methods that ventilate at maximum power for extended periods, it doesn't adjust the fan speed based on real-time particle concentration, leading to wasted energy. For example, patent application CN113932332A discloses an air purification method and system for cleanrooms. This solution uses different fan speed settings but fails to adjust the speed based on real-time particle concentration, resulting in wasted energy. Ideally, adjusting the fan speed based on real-time particle concentration would perfectly ensure both energy savings and cleanliness. However, in reality, personnel movement increases particle concentration, and existing fan speeds cannot handle sudden increases, leading to substandard cleanroom cleanliness. Existing cleanroom ventilation technologies fail to consider the particle concentration fluctuations caused by personnel movement, making it impossible to design more energy-efficient ventilation methods while maintaining cleanroom cleanliness. Summary of the Invention
[0004] This invention aims to at least partially solve one of the technical problems in the prior art by obtaining a wind speed setting function; obtaining a threshold and concentration change function; obtaining the real-time particle concentration in the cleanroom and marking it as the real-time monitoring concentration; obtaining the entry time of each person in the cleanroom and marking it as the real-time entry time; obtaining a correction value based on the real-time entry time and the threshold; obtaining a corrected wind speed based on the correction value, the real-time monitoring concentration, and the wind speed setting function; and using the corrected wind speed for cleanroom purification and ventilation. This addresses the problem that existing cleanroom purification and ventilation technologies fail to consider the changes in particle concentration caused by personnel entering and exiting, resulting in the inability to set more energy-efficient ventilation methods while ensuring cleanroom cleanliness.
[0005] To achieve the above objectives, this application provides a cleanroom purification and ventilation method based on Internet of Things (IoT) space monitoring, comprising the following steps:
[0006] Establish a laboratory cleanroom and obtain an executable air velocity based on the laboratory cleanroom;
[0007] The first and second wind speed values are obtained based on the executable wind speed.
[0008] The final wind speed is obtained based on the executable wind speed, the first wind speed value, and the second wind speed value.
[0009] The wind speed setting function is obtained based on the final wind speed.
[0010] The third experimental concentration was obtained based on the experimental cleanroom;
[0011] The division threshold and concentration change function were obtained based on the concentration from the third experiment.
[0012] Acquire the real-time particle concentration in the cleanroom and mark it as the real-time monitoring concentration; acquire the entry time of each person in the cleanroom and mark it as the real-time entry time;
[0013] The correction value is obtained based on the real-time entry time and the division threshold;
[0014] The corrected wind speed is obtained based on the correction value, real-time monitoring concentration, and wind speed setting function, and the wind speed of the corrected wind speed is used for purification and ventilation of the clean room.
[0015] Furthermore, establishing a laboratory cleanroom and obtaining an executable air velocity based on the laboratory cleanroom includes the following sub-steps:
[0016] Construct a cleanroom with controllable particle concentration, labeled as the experimental cleanroom; obtain the controllable airflow range of the cleanroom, labeled as the adjustable airflow range; set different particle concentrations, labeled as the experimental initial concentration; under different experimental initial particle concentrations, ventilate the cleanroom a first number of times at different airflow rates within the airflow range, obtain the real-time particle concentration in the cleanroom, labeled as the first experimental concentration; obtain the airflow rate at which the first experimental concentration does not exceed the upper limit threshold within the first time period, labeled as the executable airflow rate.
[0017] Furthermore, obtaining the first wind speed value and the second wind speed value based on the executable wind speed includes the following sub-steps:
[0018] The executable wind speeds are sorted from smallest to largest and labeled as Fs1 to Fs. i ;
[0019] Get the number of executable wind speeds and mark it as Z;
[0020] The first position value is calculated as: W1 = c1 * Z; where W1 is the first position value, c1 is the first position coefficient, and the range of c1 is (0, 0.5).
[0021] Check if the value at the first position is an integer. If it is, set Fs. （W1） Use it as the first wind speed value; if not, obtain the integer part of W1, mark it as Z1, and set Fs... （Z1） As the first wind speed value;
[0022] The second position value is calculated as: W2 = c2 * Z; where W2 is the second position value, c2 is the second position coefficient, and the range of c2 is (0.5, 1).
[0023] Determine if the value at the second position is an integer; if so, set Fs. （W2） As the second position value; if not, obtain the integer part of W2, mark it as Z2, and set Fs （Z2） As the second wind speed value.
[0024] Furthermore, obtaining the final wind speed based on the executable wind speed, the first wind speed value, and the second wind speed value includes the following sub-steps:
[0025] The first wind speed value is labeled as S1, and the second wind speed value is labeled as S2;
[0026] The test wind speed threshold is obtained as follows: Sy = S1 - [(S2 - S1) / (c2 - c1)] × c1; where Sy is the test wind speed threshold.
[0027] Get the minimum executable wind speed that is greater than or equal to the test wind speed threshold, and mark it as the final wind speed;
[0028] Obtain the final wind speed corresponding to the initial concentration for each experiment.
[0029] Furthermore, obtaining the wind speed setting function based on the final wind speed includes the following sub-steps:
[0030] A Cartesian coordinate system was established with experimental concentration as the horizontal axis data and final wind speed as the vertical axis data, and it was marked as the wind speed setting coordinate system.
[0031] The initial concentration and the corresponding final wind speed are used as the x and y coordinates of the coordinate points and marked as wind speed set coordinate points.
[0032] Plot all the wind speed setting coordinate points into the wind speed setting coordinate system to obtain a wind speed setting scatter plot.
[0033] The wind speed setting function is obtained by polynomial fitting of the scatter plot of wind speed setting.
[0034] Furthermore, obtaining the third experimental concentration based on the experimental cleanroom includes the following sub-steps:
[0035] With the initial particle concentration in the experimental cleanroom set to 0, a second number of times a person enters the experimental cleanroom is used to start timing. The timing time is marked as the experimental time. The particle concentration in the experimental cleanroom is obtained every second time interval and marked as the second experimental concentration. The average value of the second experimental concentration within each identical experimental time period is obtained and marked as the third experimental concentration.
[0036] Furthermore, obtaining the division threshold and concentration change function based on the third experimental concentration includes the following sub-steps:
[0037] With experimental time as the horizontal axis and the concentration of the third experiment as the vertical axis, a Cartesian coordinate system is established and labeled as the concentration change coordinate system.
[0038] The experimental time and the third experimental concentration corresponding to the experimental time are used as the x and y coordinates of the coordinate points, and are marked as the concentration change coordinate points.
[0039] Connect adjacent concentration change coordinate points to obtain line segments, and mark them as connecting line segments; obtain the slope of each connecting line segment, and mark it as the connection slope; sort the connection slopes in ascending order according to the x-coordinate of the right endpoint of the connecting line segment, and mark them as K. j Where j is a positive integer, and j in ascending order represents the x-coordinate of the right endpoint of the connecting line segment in ascending order;
[0040] In the concentration change coordinate system, determine from left to right whether K appears. j If positive, K j-1 If the value is negative, when it first appears, obtain the value of K at that time. j The endpoints of the connecting line segments with smaller corresponding x-coordinates are marked as dividing endpoints, and the x-coordinates corresponding to the dividing endpoints are marked as dividing thresholds;
[0041] The concentration change function is obtained by performing polynomial fitting on the concentration change coordinates of points whose x-coordinates are less than the threshold.
[0042] Furthermore, obtaining the correction value based on the real-time entry time and the division threshold includes the following sub-steps:
[0043] Determine whether the real-time entry time is less than the threshold. If so, substitute the real-time entry time into the first concentration change function to obtain the first predicted concentration value. Add the real-time entry time to the third time and substitute it into the first concentration change function to obtain the second predicted concentration value. Calculate the difference between the second predicted concentration value and the first predicted concentration value and mark it as the correction value. If not, set the correction value to 0.
[0044] Furthermore, based on the correction value, real-time monitoring concentration, and wind speed setting function, a corrected wind speed is obtained. The process of using this corrected wind speed for cleanroom ventilation includes the following steps:
[0045] The corrected monitoring concentration is obtained by adding a correction value to the real-time monitoring concentration.
[0046] The corrected wind speed is obtained by substituting the corrected monitoring concentration into the wind speed setting function.
[0047] This application also provides a cleanroom purification and ventilation system based on Internet of Things space monitoring, including: an execution wind speed acquisition module, first and second wind speed acquisition modules, final wind speed acquisition module, wind speed setting function acquisition module, third experimental concentration acquisition module, concentration change function acquisition module, data acquisition module, correction value acquisition module, and correction wind speed execution module;
[0048] The execution wind speed acquisition module is used to establish an experimental cleanroom and acquire an executable wind speed based on the experimental cleanroom.
[0049] The first and second wind speed acquisition modules are used to acquire a first wind speed value and a second wind speed value based on the executable wind speed;
[0050] The final wind speed acquisition module is used to acquire the final wind speed based on the executable wind speed, the first wind speed value, and the second wind speed value.
[0051] The wind speed setting function acquisition module is used to acquire the wind speed setting function based on the final wind speed.
[0052] The third experimental concentration acquisition module is used to acquire the third experimental concentration based on the experimental cleanroom.
[0053] The concentration change function acquisition module is used to acquire the division threshold and concentration change function based on the third experimental concentration.
[0054] The data acquisition module is used to acquire the particle concentration in the clean room and mark it as the real-time monitoring concentration; and to acquire the entry time of each person in the clean room and mark it as the real-time entry time.
[0055] The correction value acquisition module is used to obtain the correction value based on the real-time entry time and the division threshold.
[0056] The corrected wind speed execution module is used to obtain a corrected wind speed based on the corrected value, real-time monitoring concentration, and wind speed setting function, and to purify and ventilate the clean room using the corrected wind speed.
[0057] The beneficial effects of this invention are as follows: This invention obtains a wind speed setting function; obtains a division threshold and a concentration change function; obtains the real-time particle concentration in the cleanroom and marks it as the real-time monitoring concentration; obtains the entry time of each person in the cleanroom and marks it as the real-time entry time; obtains a correction value based on the real-time entry time and the division threshold; and obtains a corrected wind speed based on the correction value, the real-time monitoring concentration, and the wind speed setting function. The corrected wind speed is then used for cleanroom purification and ventilation. The advantage is that it can adjust the wind speed based on the real-time particle concentration, while also addressing the increase in particle concentration caused by personnel entering and exiting, making cleanroom purification and ventilation more intelligent and more energy-efficient while ensuring cleanroom cleanliness.
[0058] This invention obtains the final wind speed based on the executable wind speed, the first wind speed value, and the second wind speed value. Its advantage is that it can screen historical experimental wind speeds to obtain the minimum wind speed that maintains the cleanliness of the cleanroom under stable conditions, making subsequent cleanroom purification and ventilation more energy-efficient. Attached Figure Description
[0059] Figure 1 This is a schematic diagram of the system of the present invention;
[0060] Figure 2 This is a schematic diagram of the wind speed setting function of the present invention;
[0061] Figure 3 This is a schematic diagram of the dividing endpoints of the present invention;
[0062] Figure 4 This is a schematic diagram of the concentration change function of the present invention;
[0063] Figure 5 This is a flowchart of the steps of the method of the present invention. Detailed Implementation
[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0065] Example 1, please refer to Figure 1 As shown, this application provides a cleanroom purification and ventilation system based on Internet of Things (IoT) space monitoring, including:
[0066] The system includes a wind speed acquisition module, a first and second wind speed acquisition module, a final wind speed acquisition module, a wind speed setting function acquisition module, a third experimental concentration acquisition module, a concentration change function acquisition module, a data acquisition module, a correction value acquisition module, and a correction wind speed execution module.
[0067] The execution wind speed acquisition module is used to establish a laboratory cleanroom and acquire an executable wind speed based on the laboratory cleanroom.
[0068] The wind speed acquisition module is configured with an execution wind speed acquisition strategy, which includes:
[0069] Construct a cleanroom with controllable particle concentration, labeled as the experimental cleanroom; obtain the controllable airflow range of the cleanroom, labeled as the adjustable airflow range; set different particle concentrations, labeled as the experimental initial concentration; under different experimental initial particle concentrations, ventilate the cleanroom a first number of times at different airflow speeds within the controllable airflow range, obtain the real-time particle concentration in the cleanroom, labeled as the first experimental concentration; obtain the airflow speed at which the first experimental concentration does not exceed the upper limit threshold within the first time period, labeled as the executable airflow speed.
[0070] In practical applications, a model of a corresponding cleanroom can be used to construct an experimental cleanroom. Constructing an experimental cleanroom facilitates the analysis of particle concentration. Performing an initial number of ventilation cycles (e.g., 20 cycles) helps determine the optimal ventilation velocity, ensuring the velocity maintains the cleanliness of the cleanroom. Different particle concentrations are then set; here, the appropriate particle concentration for the desired cleanliness level is obtained based on the laboratory's intended use, for example, an upper limit of 9000 particles / m³. 3 At 0 grains / m 3 Up to 9000 grains / m 3 Different particle concentrations can be set, for example, 2000 particles / m³. 3 With 2500 grains / m 3 Etc.; To demonstrate that the wind speed can maintain the cleanliness of the cleanroom, the initial observation time is usually set to be relatively long, such as 1 hour, and the wind speed remains constant for each ventilation; for example, at an initial concentration of 2000 particles / m³. 3 Under the given conditions, the executable wind speeds were obtained as 0.31 m / s, 0.32 m / s, ..., 0.40 m / s.
[0071] The first and second wind speed acquisition modules are used to acquire the first wind speed value and the second wind speed value based on the executable wind speed;
[0072] The first and second wind speed acquisition modules are configured with first and second wind speed acquisition strategies, which include:
[0073] The executable wind speeds are sorted from smallest to largest and labeled as Fs1 to Fs. i ;
[0074] Get the number of executable wind speeds and mark it as Z;
[0075] The first position value is calculated as: W1 = c1 * Z; where W1 is the first position value, c1 is the first position coefficient, and the range of c1 is (0, 0.5); in order to obtain a smaller executable wind speed i, the range of c1 is (0, 0.5), and the specific value of c1 can be selected as the middle value of (0, 0.5), which is 0.25.
[0076] Check if the value at the first position is an integer. If it is, set Fs. （W1） Use it as the first wind speed value; if not, obtain the integer part of W1, mark it as Z1, and set Fs... （Z1） As the first wind speed value;
[0077] The second position value is calculated as: W2 = c2 * Z; where W2 is the second position value, c2 is the second position coefficient, and the range of c2 is (0.5, 1); in order to obtain a smaller executable wind speed i, the range of c2 is (0.5, 1), and the specific value of c2 is selected as the middle value of (0.5, 1), which is 0.75.
[0078] Determine if the value at the second position is an integer; if so, set Fs. （W2） As the second position value; if not, obtain the integer part of W2, mark it as Z2, and set Fs （Z2） As the second wind speed value;
[0079] In practical applications, for example, when the initial concentration in an experiment is 2000 particles / m³ 3 Under the given conditions, the number of executable wind speeds is 60. The first position value is calculated as: W1 = 0.25 * 60 = 15. Then, Fs... （15） The specific value is 3.26 m / s, which is taken as the first wind speed value; the second position value is calculated as: W2 = 0.75 * 60 = 45, and Fs （45） The specific value is 3.76 m / s, which is used as the second wind speed value.
[0080] The final wind speed acquisition module is used to obtain the final wind speed based on the executable wind speed, the first wind speed value, and the second wind speed value;
[0081] The final wind speed acquisition module is configured with a final wind speed acquisition strategy, which includes:
[0082] The first wind speed value is labeled as S1, and the second wind speed value is labeled as S2;
[0083] The test wind speed threshold is obtained as: Sy = S1 - [(S2 - S1) / (c2 - c1)] × c1; where Sy is the test wind speed threshold; the test wind speed threshold is used to select the smallest executable wind speed that can stably keep the particle concentration within the standard; where all executable wind speeds can stably keep the particle concentration within the standard, the executable wind speeds should be uniformly distributed within the executable wind speed range, so the minimum threshold for uniform distribution can be obtained by calculating the test wind speed threshold;
[0084] Get the minimum executable wind speed that is greater than or equal to the test wind speed threshold, and mark it as the final wind speed;
[0085] Obtain the final wind speed corresponding to the initial concentration for each experiment;
[0086] In practical applications, for example, when the initial concentration in an experiment is 2000 particles / m³ 3 Under the given conditions, when the first peak wind speed is 3.26 m / s and the second peak wind speed is 3.76 m / s, Sy = 3.26 - [(3.76 - 3.26) / (0.75 - 0.25)] × 0.25 = 3.01 m / s. The minimum executable wind speed greater than or equal to 3.01 m / s is 3.01 m / s. Therefore, with an initial concentration of 2000 particles / m³, the optimal wind speed for this experiment is... 3 The final wind speed under these conditions is 3.01 m / s;
[0087] The wind speed setting function acquisition module is used to acquire the wind speed setting function based on the final wind speed.
[0088] The wind speed setting function acquisition module is configured with a wind speed setting function acquisition strategy, which includes:
[0089] A Cartesian coordinate system was established with experimental concentration as the horizontal axis data and final wind speed as the vertical axis data, and it was marked as the wind speed set coordinate system.
[0090] The initial concentration and the corresponding final wind speed are used as the x and y coordinates of the coordinate points and marked as wind speed set coordinate points.
[0091] Plot all the wind speed setting coordinate points into the wind speed setting coordinate system to obtain a wind speed setting scatter plot.
[0092] The wind speed setting function is obtained by polynomial fitting of the scatter plot of wind speed setting.
[0093] In practical applications, please participate. Figure 2 As shown, the wind speed setting function is obtained. The wind speed setting function is the relationship between all wind speeds in the cleanroom for different particle concentrations under undisturbed conditions.
[0094] The third experimental concentration acquisition module is used to acquire the third experimental concentration based on the experimental cleanroom.
[0095] The third experimental concentration acquisition module is configured with a third experimental concentration acquisition strategy, which includes:
[0096] With the initial particle concentration in the experimental cleanroom set to 0, a second set of timers was set to begin when one person entered the cleanroom. The timers were marked as the experimental time. The particle concentration in the cleanroom was measured at each second time interval and marked as the second experimental concentration. The average value of the second experimental concentration within each identical experimental time interval was measured and marked as the third experimental concentration. Because the particle concentration increases briefly when someone enters the cleanroom, this needs to be analyzed.
[0097] In practical applications, the second number of repetitions reduces errors. A larger second number yields a more accurate third experimental concentration. For example, a second number of 20 repetitions. A smaller second time setting allows for better observation of particle concentration changes. For instance, a second time of 10 seconds yields a third experimental concentration of 502 particles / m³. 3 ;
[0098] The concentration change function acquisition module is used to obtain the division threshold and concentration change function based on the concentration of the third experiment;
[0099] The concentration change function acquisition module is configured with a concentration change function acquisition strategy, which includes:
[0100] With experimental time as the horizontal axis and the concentration of the third experiment as the vertical axis, a Cartesian coordinate system is established and labeled as the concentration change coordinate system.
[0101] The experimental time and the third experimental concentration corresponding to the experimental time are used as the x and y coordinates of the coordinate points, and are marked as the concentration change coordinate points.
[0102] Connect adjacent concentration change coordinate points to obtain line segments, and mark them as connecting line segments; obtain the slope of each connecting line segment, and mark it as the connection slope; sort the connection slopes in ascending order according to the x-coordinate of the right endpoint of the connecting line segment, and mark them as K. j Where j is a positive integer, and j in ascending order represents the x-coordinate of the right endpoint of the connecting line segment in ascending order;
[0103] In the concentration change coordinate system, determine from left to right whether K appears. j If positive, K j-1 If the value is negative, when it first appears, obtain the value of K at that time. jThe endpoints of the connecting line segments with smaller x-coordinates are marked as dividing endpoints, and the x-coordinates corresponding to the dividing endpoints are marked as dividing thresholds. Because when a person enters the cleanroom, the particle concentration will first rise and then fall, it is necessary to obtain the change in particle concentration during the rise. Because when the concentration rises, the air volume needs to be increased to maintain the cleanliness of the cleanroom.
[0104] The concentration change function is obtained by performing polynomial fitting on the concentration change coordinates of the x-coordinates that are less than the threshold.
[0105] For practical applications, please refer to Figure 3 and Figure 4 As shown, the concentration change coordinate points, dividing endpoints, and concentration change function are obtained. The dividing threshold is 60s. Polynomial fitting is performed on the concentration change coordinate points with abscissa less than 60s to obtain the concentration change function.
[0106] The data acquisition module is used to acquire the particle concentration in the cleanroom and mark it as the real-time monitoring concentration; it also acquires the entry time of each person in the cleanroom and marks it as the real-time entry time.
[0107] In practical applications, for example, real-time monitoring of a concentration of 2000 particles / m³ 3 A person's real-time entry time is 20 seconds.
[0108] The correction value acquisition module is used to obtain correction values based on real-time entry time and division threshold;
[0109] The correction value acquisition module is configured with a correction value acquisition strategy, which includes:
[0110] Determine if the real-time entry time is less than the threshold. If so, substitute the real-time entry time into the first concentration change function to obtain the first predicted concentration value. Add the real-time entry time to the third time and substitute it into the first concentration change function to obtain the second predicted concentration value. Calculate the difference between the second predicted concentration value and the first predicted concentration value and mark it as the correction value. If not, set the correction value to 0. This is because when the concentration rises, the airflow needs to be increased to maintain the cleanliness of the cleanroom. Predict the concentration change after the third time and suggest increasing the airflow to maintain the cleanliness of the cleanroom, for example, if the third time is 1 second.
[0111] In practical applications, when a person's real-time entry time is 20 seconds, which is less than the threshold of 60 seconds, substituting 20 seconds into the first concentration change function yields a first predicted concentration value of 1400 particles / m³. 3 Substituting the real-time entry time plus the third time into the first concentration change function, the second predicted concentration value is 1470 particles / m³. 3 The correction value is 70 grains / m 3 .
[0112] The corrected wind speed execution module is used to obtain the corrected wind speed based on the correction value, real-time monitoring concentration and wind speed setting function, and to purify and ventilate the clean room with the corrected wind speed.
[0113] The wind speed correction execution module is configured with a wind speed correction execution strategy, which includes:
[0114] The corrected monitoring concentration is obtained by adding a correction value to the real-time monitoring concentration; because when the concentration rises, the air volume needs to be increased to maintain the cleanliness of the cleanroom; the concentration change after the third time period is predicted, and the air velocity is increased to maintain the cleanliness of the cleanroom.
[0115] Substitute the corrected monitoring concentration into the wind speed setting function to obtain the corrected wind speed;
[0116] In practical applications, please participate. Figure 2 As shown, the corrected monitoring concentration is: 2000 + 70 = 2070 particles / m³ 3 2070 grains / m 3 Substituting the value into the wind speed setting function, we obtain 0.286 m / s, and then correct the wind speed to 0.286 m / s; the cleanroom is then ventilated with a wind speed of 0.286 m / s.
[0117] Example 2, please refer to Figure 5 As shown, this application provides a cleanroom purification and ventilation method based on IoT space monitoring, including the following steps:
[0118] Step S1: Establish an experimental cleanroom and obtain an executable air velocity based on the experimental cleanroom; Step S1 includes the following sub-steps:
[0119] Step S101: Construct a cleanroom with controllable particle concentration, labeled as experimental cleanroom; obtain the controllable wind speed range of the cleanroom, labeled as adjustable wind speed range; set different particle concentrations, labeled as experimental initial concentrations; under different experimental initial concentration conditions, adjust the wind speed range and ventilate at different wind speeds for a first number of times to obtain the real-time particle concentration in the experimental cleanroom, labeled as the first experimental concentration; obtain the wind speed at which the first experimental concentration does not exceed the upper limit threshold within the first time period, labeled as the executable wind speed.
[0120] Step S2: Obtain the first wind speed value and the second wind speed value based on the executable wind speed; Step S2 includes the following sub-steps:
[0121] Step S201: Sort the executable wind speeds in ascending order and label them as Fs1 to Fs. i ;
[0122] Step S202: Obtain the number of executable wind speeds and mark them as Z;
[0123] Step S203, calculate the first position value as: W1=c1*Z; where W1 is the first position value, c1 is the first position coefficient, and the range of c1 is (0, 0.5).
[0124] Step S204: Determine if the value at the first position is an integer. If so, set Fs... （W1） Use it as the first wind speed value; if not, obtain the integer part of W1, mark it as Z1, and set Fs... （Z1） As the first wind speed value;
[0125] Step S205, calculate the second position value as: W2 = c2 * Z; where W2 is the second position value, c2 is the second position coefficient, and the range of c2 is (0.5, 1);
[0126] Step S206: Determine if the value at the second position is an integer. If so, set Fs... （W2） As the second position value; if not, obtain the integer part of W2, mark it as Z2, and set Fs （Z2） As the second wind speed value.
[0127] Step S3: Obtain the final wind speed based on the executable wind speed, the first wind speed value, and the second wind speed value; Step S3 includes the following sub-steps:
[0128] Step S301: Mark the first wind speed value as S1 and the second wind speed value as S2;
[0129] Step S302, obtain the test wind speed threshold as: Sy=S1-[(S2-S1) / (c2-c1)]×c1; where Sy is the test wind speed threshold;
[0130] Step S303: Obtain the minimum executable wind speed that is greater than or equal to the test wind speed threshold, and mark it as the final wind speed;
[0131] Step S304: Obtain the final wind speed corresponding to the initial concentration of each experiment.
[0132] Step S4: Obtain the wind speed setting function based on the final wind speed; Step S4 includes the following sub-steps:
[0133] Step S401: Establish a Cartesian coordinate system with experimental concentration as the horizontal axis data and final wind speed as the vertical axis data, and mark it as the wind speed setting coordinate system;
[0134] Step S402: Use the initial concentration of the experiment and the final wind speed corresponding to the initial concentration of the experiment as the x-coordinate and y-coordinate of the coordinate point, and mark them as the wind speed setting coordinate point.
[0135] Step S403: Plot all the wind speed setting coordinate points into the wind speed setting coordinate system to obtain a wind speed setting scatter plot.
[0136] Step S404: Perform polynomial fitting on the scatter plot of wind speed setting to obtain the wind speed setting function.
[0137] Step S5: Obtain the third experimental concentration based on the experimental cleanroom; Step S5 includes the following sub-steps:
[0138] Step S501: Under the condition that the initial particle concentration in the experimental cleanroom is 0, start timing when a person enters the experimental cleanroom a second number of times, mark the timing time as the experimental time, obtain the particle concentration in the experimental cleanroom once every second time interval, and mark it as the second experimental concentration; obtain the average value of the second experimental concentration in each same experimental time period, and mark it as the third experimental concentration.
[0139] Step S6: Obtain the division threshold and concentration change function based on the concentration from the third experiment; Step S6 includes the following sub-steps:
[0140] Step S601: Establish a Cartesian coordinate system with experimental time as the horizontal axis and the third experimental concentration as the vertical axis, and mark it as the concentration change coordinate system;
[0141] Step S602: Use the experimental time and the third experimental concentration corresponding to the experimental time as the x-coordinate and y-coordinate of the coordinate point, and mark them as the concentration change coordinate point;
[0142] Step S603: Connect adjacent concentration change coordinate points to obtain line segments, and mark them as connecting line segments; obtain the slope of each connecting line segment, and mark it as the connection slope; sort the connection slopes according to the x-coordinate of the right endpoint of the connecting line segment from smallest to largest, and mark them as K. j Where j is a positive integer, and j in ascending order represents the x-coordinate of the right endpoint of the connecting line segment in ascending order;
[0143] Step S604: Determine whether K appears sequentially from left to right in the concentration change coordinate system. j If positive, K j-1 If the value is negative, when it first appears, obtain the value of K at that time. j The endpoints of the connecting line segments with smaller corresponding x-coordinates are marked as dividing endpoints, and the x-coordinates corresponding to the dividing endpoints are marked as dividing thresholds;
[0144] Step S605: Perform polynomial fitting on the concentration change coordinates of the points whose x-coordinates are less than the division threshold to obtain the concentration change function.
[0145] Step S7: Obtain the real-time particle concentration in the cleanroom and mark it as the real-time monitoring concentration; obtain the entry time of each person in the cleanroom and mark it as the real-time entry time.
[0146] Step S8: Obtain the correction value based on the real-time entry time and the division threshold; Step S8 includes the following sub-steps:
[0147] Step S801: Determine whether the real-time entry time is less than the division threshold. If so, substitute the real-time entry time into the first concentration change function to obtain the first predicted concentration value, add the third time to the real-time entry time and substitute it into the first concentration change function to obtain the second predicted concentration value, and calculate the difference between the second predicted concentration value and the first predicted concentration value as the correction value; otherwise, set the correction value to 0.
[0148] Step S9 involves obtaining a corrected wind speed based on the correction value, real-time monitored concentration, and wind speed setting function, and then using the corrected wind speed for cleanroom purification and ventilation. Step S9 includes the following sub-steps:
[0149] Step S901: Add the correction value to the real-time monitoring concentration to obtain the corrected monitoring concentration;
[0150] Step S902: Substitute the corrected monitoring concentration into the wind speed setting function to obtain the corrected wind speed.
[0151] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. 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 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0152] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.
Claims
1. A cleanroom purification and ventilation method based on IoT spatial monitoring, characterized in that, Includes the following steps: Establish a laboratory cleanroom and obtain an executable air velocity based on the laboratory cleanroom; The first and second wind speed values are obtained based on the executable wind speed. The final wind speed is obtained based on the executable wind speed, the first wind speed value, and the second wind speed value. The wind speed setting function is obtained based on the final wind speed. The third experimental concentration was obtained based on the experimental cleanroom; The division threshold and concentration change function were obtained based on the concentration from the third experiment. Acquire the real-time particle concentration in the cleanroom and mark it as the real-time monitoring concentration; acquire the entry time of each person in the cleanroom and mark it as the real-time entry time; The correction value is obtained based on the real-time entry time and the division threshold; The corrected wind speed is obtained based on the correction value, real-time monitoring concentration, and wind speed setting function, and the corrected wind speed is used for cleanroom purification and ventilation. Establishing a cleanroom for testing and obtaining an executable air velocity based on the cleanroom includes the following sub-steps: Construct a cleanroom with controllable particle concentration, labeled as the experimental cleanroom; obtain the controllable airflow range of the cleanroom, labeled as the adjustable airflow range; set different particle concentrations, labeled as the experimental initial concentration; under different experimental initial particle concentrations, ventilate the cleanroom a first number of times at different airflow speeds within the controllable airflow range, obtain the real-time particle concentration in the cleanroom, labeled as the first experimental concentration; obtain the airflow speed at which the first experimental concentration does not exceed the upper limit threshold within the first time period, labeled as the executable airflow speed. Obtaining the first and second wind speed values based on the executable wind speed includes the following sub-steps: The executable wind speeds are sorted from smallest to largest and labeled as Fs1 to Fs. i ; Get the number of executable wind speeds and mark it as Z; The first position value is calculated as: W1 = c1 * Z; where W1 is the first position value, c1 is the first position coefficient, and the range of c1 is (0, 0.5). Check if the value at the first position is an integer. If it is, set Fs. （W1） Use it as the first wind speed value; if not, obtain the integer part of W1, mark it as Z1, and set Fs... （Z1） As the first wind speed value; The second position value is calculated as: W2 = c2 * Z; where W2 is the second position value, c2 is the second position coefficient, and the range of c2 is (0.5, 1). Determine if the value at the second position is an integer; if so, set Fs. （W2） As the second position value; if not, obtain the integer part of W2, mark it as Z2, and set Fs （Z2） As the second wind speed value; Obtaining the final wind speed based on the executable wind speed, the first wind speed value, and the second wind speed value includes the following sub-steps: The first wind speed value is labeled as S1, and the second wind speed value is labeled as S2; The test wind speed threshold is obtained as follows: Sy = S1 - [(S2 - S1) / (c2 - c1)] × c1; where Sy is the test wind speed threshold. Get the minimum executable wind speed that is greater than or equal to the test wind speed threshold, and mark it as the final wind speed; Obtain the final wind speed corresponding to the initial concentration for each experiment; The wind speed setting function, which is based on the final wind speed, includes the following sub-steps: A Cartesian coordinate system was established with experimental concentration as the horizontal axis data and final wind speed as the vertical axis data, and it was marked as the wind speed setting coordinate system. The initial concentration and the corresponding final wind speed are used as the x and y coordinates of the coordinate points and marked as wind speed set coordinate points. Plot all the wind speed setting coordinate points into the wind speed setting coordinate system to obtain a wind speed setting scatter plot. The wind speed setting function is obtained by performing polynomial fitting on the scatter plot of wind speed setting. Obtaining the third experimental concentration based on the experimental cleanroom includes the following sub-steps: With the initial particle concentration in the experimental cleanroom set to 0, a second number of times a person enters the experimental cleanroom is used to start timing, and the timing time is marked as the experimental time. The particle concentration in the experimental cleanroom is obtained every second time interval and marked as the second experimental concentration. The average value of the second experimental concentration within each identical experimental time period is obtained and marked as the third experimental concentration. The steps for obtaining the threshold and concentration change function based on the third experimental concentration are as follows: With experimental time as the horizontal axis and the concentration of the third experiment as the vertical axis, a Cartesian coordinate system is established and labeled as the concentration change coordinate system. The experimental time and the third experimental concentration corresponding to the experimental time are used as the x and y coordinates of the coordinate points, and are marked as the concentration change coordinate points. Connect adjacent concentration change coordinate points to obtain line segments, and mark them as connecting line segments; obtain the slope of each connecting line segment, and mark it as the connection slope; sort the connection slopes in ascending order according to the x-coordinate of the right endpoint of the connecting line segment, and mark them as K. j Where j is a positive integer, and j in ascending order represents the x-coordinate of the right endpoint of the connecting line segment in ascending order; In the concentration change coordinate system, determine from left to right whether K appears. j If positive, K j-1 If the value is negative, when it first appears, obtain the value of K at that time. j The endpoints of the connecting line segments with smaller corresponding x-coordinates are marked as dividing endpoints, and the x-coordinates corresponding to the dividing endpoints are marked as dividing thresholds; The concentration change function is obtained by performing polynomial fitting on the concentration change coordinates of points whose x-coordinates are less than the threshold.
2. The cleanroom purification and ventilation method based on IoT spatial monitoring according to claim 1, characterized in that, Obtaining the correction value based on the real-time entry time and the segmentation threshold includes the following sub-steps: Determine whether the real-time entry time is less than the threshold. If so, substitute the real-time entry time into the first concentration change function to obtain the first predicted concentration value. Add the real-time entry time to the third time and substitute it into the first concentration change function to obtain the second predicted concentration value. Calculate the difference between the second predicted concentration value and the first predicted concentration value and mark it as the correction value. If not, set the correction value to 0.
3. The cleanroom purification and ventilation method based on IoT spatial monitoring according to claim 2, characterized in that, The corrected wind speed is obtained based on the correction value, real-time monitoring concentration, and wind speed set function. The airflow for cleanroom purification and ventilation is adjusted using the corrected wind speed, and the following steps are included: The corrected monitoring concentration is obtained by adding a correction value to the real-time monitoring concentration. The corrected wind speed is obtained by substituting the corrected monitoring concentration into the wind speed setting function.
4. A cleanroom purification and ventilation system based on Internet of Things (IoT) spatial monitoring, used to implement the cleanroom purification and ventilation method based on IoT spatial monitoring as described in any one of claims 1-3, characterized in that, include: The system includes a wind speed acquisition module, a first and second wind speed acquisition module, a final wind speed acquisition module, a wind speed setting function acquisition module, a third experimental concentration acquisition module, a concentration change function acquisition module, a data acquisition module, a correction value acquisition module, and a correction wind speed execution module. The execution wind speed acquisition module is used to establish an experimental cleanroom and acquire an executable wind speed based on the experimental cleanroom. The first and second wind speed acquisition modules are used to acquire a first wind speed value and a second wind speed value based on the executable wind speed; The final wind speed acquisition module is used to acquire the final wind speed based on the executable wind speed, the first wind speed value, and the second wind speed value. The wind speed setting function acquisition module is used to acquire the wind speed setting function based on the final wind speed. The third experimental concentration acquisition module is used to acquire the third experimental concentration based on the experimental cleanroom. The concentration change function acquisition module is used to acquire the division threshold and concentration change function based on the third experimental concentration. The data acquisition module is used to acquire the particle concentration in the clean room and mark it as the real-time monitoring concentration; and to acquire the entry time of each person in the clean room and mark it as the real-time entry time. The correction value acquisition module is used to obtain the correction value based on the real-time entry time and the division threshold. The corrected wind speed execution module is used to obtain a corrected wind speed based on the corrected value, real-time monitoring concentration, and wind speed setting function, and to purify and ventilate the clean room using the corrected wind speed.