Clean room purification and ventilation method and system based on Internet of Things space monitoring

Abstract

The invention discloses a clean room purification and ventilation method and system based on Internet of Things space monitoring, and relates to the technical field of clean room ventilation, and the method comprises the following steps: obtaining a wind speed setting function based on a final wind speed; obtaining a division threshold and a concentration change function based on the third experiment concentration; the real-time particle concentration in the clean room is obtained and marked as the real-time monitoring concentration; obtaining the entering time of each person in the clean room, and marking the entering time as real-time entering time; obtaining a correction value based on the real-time entry time and the division threshold; obtaining a corrected wind speed based on the corrected value, the real-time monitoring concentration and a wind speed setting function, and purifying and ventilating the clean room at the corrected wind speed; the method is used for solving the problem that a more energy-saving ventilation method cannot be set under the condition that the cleanness of a clean room cannot be ensured due to the fact that the change of the particle concentration caused by entering and exiting of workers is not considered in an existing clean room purification ventilation technology.

Description

Technical Field

[0001] The present invention relates to the technical field of clean room ventilation, and in particular to a clean room purification and ventilation method and system based on Internet of Things space monitoring. Background Art

[0002] Cleanrooms are the core production environments of high-end industries such as semiconductor manufacturing, biopharmaceuticals, and precision medical devices. The stability of their air cleanliness is directly related to product quality and process safety. To maintain cleanroom air cleanliness, they are typically ventilated continuously at maximum power to ensure constant cleanliness. However, ventilating cleanrooms at maximum power for extended periods of time wastes electrical resources. To save power while ensuring cleanroom cleanliness, existing technologies use different wind speed settings based on different cleanroom scenarios. While this is more energy-efficient than traditional ventilation of the cleanroom at maximum power for extended periods, it fails to adjust the wind speed based on real-time particle concentrations, leading to a waste of some power. For example, patent application publication number CN113932332A discloses an air purification method and system for a cleanroom. This solution utilizes different wind speed settings for ventilation, fails to adjust the wind speed based on real-time particle concentrations, and also leads to a waste of some power. Ideally, adjusting the wind speed based on real-time particle concentrations would perfectly save resources while maintaining cleanroom cleanliness. However, in reality, the entry and exit of workers can cause particle concentrations to rise, and the existing wind speed cannot cope with the sudden increase in particle concentration, resulting in substandard cleanroom cleanliness. Existing cleanroom purification and ventilation technologies fail to take into account the fluctuations in particle concentration caused by the entry and exit of workers, resulting in an inability to set a more energy-efficient ventilation method while ensuring cleanroom cleanliness. Summary of the Invention

[0003] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by obtaining a wind speed setting function; obtaining a division threshold and a concentration change function; obtaining the real-time particle concentration in the clean room, marked as the real-time monitoring concentration; obtaining the entry time of each person in the clean room, marked as the real-time entry time; obtaining a correction value based on the real-time entry time and the division threshold; obtaining a corrected wind speed based on the correction value, the real-time monitoring concentration and the wind speed setting function, and purifying and ventilating the clean room with the corrected wind speed, so as to solve the problem that the existing clean room purification and ventilation technology fails to take into account that the entry and exit of staff will cause changes in particle concentration, resulting in the inability to set a more energy-saving ventilation method while ensuring the cleanliness of the clean room.

[0004] To achieve the above objectives, the present application provides a clean room purification and ventilation method based on IoT space monitoring, comprising the following steps: Establish an experimental clean room and obtain executable wind speed based on the experimental clean room; Obtaining a first wind speed value and a second wind speed value based on the executable wind speed; Obtaining a final wind speed based on the executable wind speed, the first wind speed value, and the second wind speed value; Obtaining a wind speed setting function based on the final wind speed; Obtaining a third experimental concentration based on the experimental clean room; Obtaining a division threshold and a concentration change function based on a third experimental concentration; Obtain the real-time particle concentration in the clean room and mark it as real-time monitoring concentration; obtain the entry time of each person in the clean room and mark it as real-time entry time; Obtaining a correction value based on the real-time entry time and the partition threshold; A corrected wind speed is obtained based on the correction value, the real-time monitoring concentration, and the wind speed setting function, and the clean room is purified and ventilated at the corrected wind speed.

[0005] Furthermore, an experimental clean room is established, and obtaining executable wind speed based on the experimental clean room includes the following sub-steps: Construct a clean room that can control particle concentration, marked as an experimental clean room; obtain the controllable wind speed range of the clean room, marked as the adjusted wind speed range, set different particle concentrations, marked as the experimental starting concentration, so that the starting particle concentration in the experimental clean room is different experimental starting concentration conditions, adjust the wind speed range and perform ventilation at different wind speeds for the first number of times, obtain the real-time particle concentration in the experimental clean room, marked as the first experimental concentration; obtain the wind speed at which the first experimental concentration does not exceed the upper concentration threshold within the first time, marked as the executable wind speed.

[0006] Furthermore, obtaining the first wind speed value and the second wind speed value based on the executable wind speed includes the following sub-steps: Sort the executable wind speeds from small to large and mark them as Fs1 to Fs i ; Get the number of executable wind speeds, marked 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); Determine whether the first position value is an integer. If so, set Fs （W1） As the first wind speed value; if not, get 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 whether the second position value is an integer. If so, set Fs （W2） As the second position value; if not, get the integer part of W2, mark it as Z2, and set Fs （Z2） as the second wind speed value.

[0007] 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: The first wind speed value is marked as S1, and the second wind speed value is marked 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 starting concentration of each experiment.

[0008] Furthermore, obtaining a wind speed setting function based on the final wind speed includes the following sub-steps: With the experimental concentration as the horizontal axis data and the final wind speed as the vertical axis data, a plane rectangular coordinate system is established and marked as the wind speed setting coordinate system; The experimental starting concentration and the final wind speed corresponding to the experimental starting concentration are used as the horizontal and vertical coordinates of the coordinate point, and marked as the wind speed setting coordinate point; Plot all 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 wind speed setting scatter plot.

[0009] Furthermore, obtaining a third experimental concentration based on the experimental clean room includes the following sub-steps: Under the condition that the initial particle concentration in the experimental clean room is 0, the timing is set to start when a second person enters the experimental clean room, and the timing time is marked as the experimental time. The particle concentration of the experimental clean room is obtained once every second time interval, and marked as the second experimental concentration; the average value of the second experimental concentration in each same experimental time is obtained, and marked as the third experimental concentration.

[0010] Furthermore, obtaining the division threshold and the concentration change function based on the third experimental concentration includes the following sub-steps: With the experimental time as the horizontal axis data and the third experimental concentration as the vertical axis, a plane rectangular coordinate system is established, marked as the concentration change coordinate system; The experimental time and the third experimental concentration corresponding to the experimental time are used as the horizontal and vertical coordinates of the coordinate point, and marked as the concentration change coordinate point; Connect adjacent concentration change coordinate points to obtain line segments, marked as connecting line segments; obtain the slope of each connecting line segment, marked as connecting slope; sort the connecting slopes from small to large according to the horizontal coordinates of the right endpoints of the connecting line segments, marked as K j ; Where j is a positive integer, and j from small to large represents the horizontal coordinates of the right endpoints of the connecting line segment from small to large; In the concentration change coordinate system, determine from left to right whether K appears. j is positive, K j-1 When it is negative for the first time, get K j The endpoint of the connecting line segment with the smaller corresponding horizontal coordinate is marked as the division endpoint, and the horizontal coordinate corresponding to the division endpoint is marked as the division threshold; The concentration change coordinate points whose horizontal coordinates are less than the division threshold are fitted with a polynomial to obtain the concentration change function.

[0011] Furthermore, obtaining the correction value based on the real-time entry time and the division threshold includes the following sub-steps: 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, substitute the real-time entry time plus the third time 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.

[0012] Furthermore, obtaining a corrected wind speed based on the correction value, the real-time monitored concentration, and the wind speed setting function, and purifying and ventilating the clean room at the corrected wind speed includes the following steps: Add the correction value to the real-time monitoring concentration to obtain the corrected monitoring concentration; Substitute the corrected monitoring concentration into the wind speed setting function to obtain the corrected wind speed.

[0013] The present application also provides a clean room purification and ventilation system based on Internet of Things space monitoring, comprising: an execution 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 clean room and acquire the executable wind speed based on the experimental clean room; 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 obtain the third experimental concentration based on the experimental clean room; The concentration change function acquisition module is used to acquire a division threshold and a concentration change function based on the third experimental concentration; The data acquisition module is used to obtain the particle concentration in the clean room, marked as real-time monitoring concentration; obtain the entry time of each person in the clean room, marked as 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 modified wind speed execution module is used to obtain the modified wind speed based on the correction value, the real-time monitoring concentration and the wind speed setting function, and purify and ventilate the clean room at the modified wind speed.

[0014] Beneficial effects of the present invention: The present invention obtains a wind speed setting function; obtains a division threshold and a concentration change function; obtains the real-time particle concentration in the clean room and marks it as the real-time monitoring concentration; obtains the entry time of each person in the clean room and marks it as the real-time entry time; obtains a correction value based on the real-time entry time and the division threshold; obtains a corrected wind speed based on the correction value, the real-time monitoring concentration and the wind speed setting function, and purifies and ventilates the clean room with the corrected wind speed. The advantage of the present invention is that the wind speed can be adjusted based on the real-time particle concentration, and at the same time, the increase in particle concentration caused by the entry and exit of staff can be addressed, making the purification and ventilation of the clean room more intelligent and more energy-saving while ensuring the cleanliness of the clean room. The present invention obtains the final wind speed based on the executable wind speed, the first wind speed value and the second wind speed value. The advantage is that the historical experimental wind speeds can be screened to obtain the minimum wind speed for maintaining the cleanliness of the clean room under stable conditions, making subsequent clean room purification and ventilation more energy-efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a functional block diagram of the system of the present invention; Figure 2 A schematic diagram of a wind speed setting function of the present invention; Figure 3 A schematic diagram of dividing endpoints of the present invention; Figure 4 is a schematic diagram of the concentration change function of the present invention; Figure 5 Flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] Example 1, please refer to Figure 1 As shown, this application provides a clean room purification and ventilation system based on IoT space monitoring, including: Execution wind speed acquisition module, first and second wind speed acquisition modules, final wind speed acquisition module, wind speed setting function acquisition module, third experiment concentration acquisition module, concentration change function acquisition module, data acquisition module, correction value acquisition module and correction wind speed execution module; The execution wind speed acquisition module is used to establish an experimental clean room and obtain the executable wind speed based on the experimental clean room; The wind speed acquisition module is configured with a wind speed acquisition strategy, which includes: Construct a clean room that can control particle concentration, marked as an experimental clean room; obtain the controllable wind speed range of the clean room, marked as the adjustment wind speed range, set different particle concentrations, marked as the experimental starting concentration, so that the starting particle concentration in the experimental clean room is different experimental starting concentration conditions, adjust the wind speed range and perform ventilation at different wind speeds for the first number of times, obtain the real-time particle concentration in the experimental clean room, marked as the first experimental concentration; obtain the wind speed at which the first experimental concentration does not exceed the upper concentration threshold within the first time, marked as the executable wind speed; In practical applications, the construction of an experimental clean room can be replaced by a model corresponding to the clean room. The construction of an experimental clean room is convenient for analyzing the particle concentration; the first number of ventilations is convenient for selecting the optimal ventilation speed, for example, the first number is 20 times, so that the wind speed can maintain the cleanliness of the clean room; different particle concentrations are set respectively. Here, the particle concentration of the corresponding cleanliness is obtained according to the purpose of the laboratory, for example, the upper limit of the particle concentration is 9000 particles / m 3 , at 0 particles / m 3 Up to 9000 grains / m 3 Set different particle concentrations, such as 2000 particles / m 3 With 2500 particles / m 3 In order to show that the wind speed can maintain the cleanliness of the clean room, the first observation time is usually set to be longer, for example, the first time is 1 hour, and the wind speed of each ventilation is unchanged; for example, when the initial concentration of the experiment is 2000 particles / m 3 Under the condition of , the executable wind speeds are 0.31m / s, 0.32m / s, ..., 0.40m / s.

[0018] 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 first and second wind speed acquisition modules are configured with first and second wind speed acquisition strategies, and the first and second wind speed acquisition strategies include: Sort the executable wind speeds from small to large and mark them as Fs1 to Fs i ; Get the number of executable wind speeds, marked as Z; 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). In order to obtain the executable wind speed with a smaller i, the range of c1 is: (0, 0.5). The specific value of c1 is 0.25, which is the middle value of (0, 0.5). Determine whether the first position value is an integer. If so, set Fs （W1） As the first wind speed value; if not, get the integer part of W1, mark it as Z1, and set Fs （Z1） As the first wind speed value; 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). In order to obtain the executable wind speed with a smaller i, the range of c2 is: (0.5, 1). The specific value of c2 is 0.75, which is the middle value of (0.5, 1). Determine whether the second position value is an integer. If so, set Fs （W2） As the second position value; if not, get the integer part of W2, mark it as Z2, and set Fs （Z2） As the second wind speed value; In practical applications, for example, when the initial concentration of the experiment is 2000 particles / m 3 Under the condition of , the number of executable wind speeds is 60, and the first position value is calculated as: W1=0.25*60=15, and Fs （15） The specific value is 3.26m / s as the first wind speed value; calculate the second position value: W2=0.75*60=45, and Fs （45） The specific value is 3.76m / s as the second wind speed value.

[0019] 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 final wind speed acquisition module is configured with a final wind speed acquisition strategy, which includes: The first wind speed value is marked as S1, and the second wind speed value is marked 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. The test wind speed threshold is used to screen out the minimum executable wind speed that can stably keep the particle concentration below the standard. If all executable wind speeds can stably keep the particle concentration below the standard, the executable wind speeds should be evenly distributed within the executable wind speed range. Therefore, the minimum threshold for uniform distribution can be obtained by calculating 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 starting concentration of each experiment; In practical applications, for example, when the initial concentration of the experiment is 2000 particles / m 3 Under the conditions of , when the first wind speed value is 3.26m / s and the second peak wind speed value is 3.76m / s, Sy=3.26-[(3.76-3.26) / (0.75-0.25)]×0.25=3.01m / s, the minimum executable wind speed greater than or equal to 3.01m / s is 3.01m / s, then when the initial concentration of the experiment is 2000 particles / m 3 The final wind speed under the conditions is 3.01m / s; The wind speed setting function acquisition module is used to obtain the wind speed setting function based on the final wind speed; The wind speed setting function acquisition module is configured with a wind speed setting function acquisition strategy, which includes: With the experimental concentration as the horizontal axis data and the final wind speed as the vertical axis data, a plane rectangular coordinate system is established and marked as the wind speed setting coordinate system; The experimental starting concentration and the final wind speed corresponding to the experimental starting concentration are used as the horizontal and vertical coordinates of the coordinate point, and marked as the wind speed setting coordinate point; Plot all wind speed setting coordinate points into the wind speed setting coordinate system to obtain a wind speed setting scatter plot; Perform polynomial fitting on the wind speed setting scatter plot to obtain the wind speed setting function; In actual application, please participate Figure 2 As shown, the wind speed setting function is obtained, which is the relationship between all wind speeds in the clean room under different particle concentrations without interference; The third experimental concentration acquisition module is used to obtain the third experimental concentration based on the experimental clean room; The third experimental concentration acquisition module is configured with a third experimental concentration acquisition strategy, and the third experimental concentration acquisition strategy includes: Under the condition that the initial particle concentration in the experimental clean room is 0, the timing is set to start when a second person enters the experimental clean room, and the timing time is marked as the experimental time. The particle concentration in the experimental clean room is obtained once every second time interval, and marked as the second experimental concentration; the average of the second experimental concentration within each same experimental time is obtained, and marked as the third experimental concentration. Because when someone enters the experimental clean room, the particle concentration will increase for a short time, so analysis is required; In practical applications, the second number of times can reduce the error of the acquisition. The larger the second number is, the more accurate the third experimental concentration is. For example, if the second number is 20 times, the smaller the second time is, the more accurate the change of particle concentration can be observed. For example, if the second time is 10s, an example is that when the experimental time is 10s, the third experimental concentration obtained is 502 particles / m 3 ; The concentration change function acquisition module is used to acquire a partition threshold and a concentration change function based on the third experimental concentration; The concentration change function acquisition module is configured with a concentration change function acquisition strategy, which includes: With the experimental time as the horizontal axis data and the third experimental concentration as the vertical axis, a plane rectangular coordinate system is established, marked as the concentration change coordinate system; The experimental time and the third experimental concentration corresponding to the experimental time are used as the horizontal and vertical coordinates of the coordinate point, and marked as the concentration change coordinate point; Connect adjacent concentration change coordinate points to obtain line segments, marked as connecting line segments; obtain the slope of each connecting line segment, marked as connecting slope; sort the connecting slopes from small to large according to the horizontal coordinates of the right endpoints of the connecting line segments, marked as K j ; Where j is a positive integer, and j from small to large represents the horizontal coordinates of the right endpoints of the connecting line segment from small to large; In the concentration change coordinate system, determine from left to right whether K appears. j is positive, K j-1 When it is negative for the first time, get K j The endpoint of the connecting line segment with the smaller horizontal coordinate is marked as the partition endpoint, and the horizontal coordinate corresponding to the partition endpoint is marked as the partition threshold. Because when a person enters the clean room, the particle concentration will first increase and then decrease. Here we need to obtain the change in particle concentration during the increase. Because when the concentration increases, we need to increase the air volume to maintain the clean room cleanliness. Perform polynomial fitting on the concentration change coordinate points whose abscissas are less than the division threshold to obtain the concentration change function; In practical applications, please refer to Figure 3 and Figure 4As shown, the concentration change coordinate points, division endpoints and concentration change function are obtained, the division threshold is 60s, and the concentration change coordinate points with abscissas less than 60s are fitted with polynomials to obtain the concentration change function.

[0020] The data acquisition module is used to obtain the particle concentration in the clean room, marked as real-time monitoring concentration; obtain the entry time of each person in the clean room, marked as real-time entry time; In practical applications, for example, the real-time monitoring concentration is 2000 particles / m 3 , the real-time entry time for a person is 20s.

[0021] The correction value acquisition module is used to obtain the correction value based on the real-time entry time and the division threshold; The correction value acquisition module is configured with a correction value acquisition strategy, which includes: 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 a first predicted concentration value. Substitute the real-time entry time plus the third time into the first concentration change function to obtain a second predicted concentration value. Calculate the difference between the second predicted concentration value and the first predicted concentration value and mark it as a correction value. If not, set the correction value to 0. Because when the concentration rises, the air volume needs to be increased to maintain the cleanliness of the clean room. Predict the concentration change after the third time and suggest increasing the wind speed to maintain the cleanliness of the clean room. For example, the third time is 1 second. In actual application, when a person's real-time entry time is 20s, which is less than the classification threshold of 60s, the first predicted concentration value is 1400 particles / m by substituting 20s into the first concentration change function. 3 , add the real-time entry time and the third time to the first concentration change function to obtain the second predicted concentration value of 1470 particles / m 3 , then the correction value is 70 particles / m 3 .

[0022] The wind speed correction execution module is used to obtain the corrected wind speed based on the correction value, the real-time monitoring concentration and the wind speed setting function, and purify and ventilate the clean room at the corrected wind speed; The modified wind speed execution module is configured with a modified wind speed execution strategy, which includes: Add the real-time monitoring concentration to the correction value to obtain the corrected monitoring concentration. When the concentration rises, the air volume needs to be increased to maintain the cleanliness of the clean room. The concentration change after the third time is predicted, and the wind speed is increased to maintain the cleanliness of the clean room. Substitute the corrected monitoring concentration into the wind speed setting function to obtain the corrected wind speed; In actual application, please participate Figure 2 As shown, the corrected monitoring concentration is: 2000+70=2070 particles / m3 , 2070 particles / m 3 Substitute 0.286m / s into the wind speed setting function, and the corrected wind speed is 0.286m / s; the clean room is purified and ventilated at a wind speed of 0.286m / s; Example 2, please refer to Figure 5 As shown, the present application provides a clean room purification and ventilation method based on Internet of Things space monitoring, comprising the following steps: Step S1: Establish an experimental clean room and obtain an executable wind speed based on the experimental clean room. Step S1 includes the following sub-steps: Step S101, construct a clean room that can control the particle concentration, marked as an experimental clean room; obtain the controllable wind speed range of the clean room, marked as the adjustment wind speed range, set different particle concentrations, marked as the experimental starting concentration, so that the starting particle concentration in the experimental clean room is different experimental starting concentration conditions, adjust the wind speed range and perform ventilation at different wind speeds for the first number of times, obtain the real-time particle concentration in the experimental clean room, marked as the first experimental concentration; obtain the wind speed at which the first experimental concentration does not exceed the upper concentration threshold within the first time, marked as the executable wind speed.

[0023] Step S2, obtaining a first wind speed value and a second wind speed value based on the executable wind speed; Step S2 includes the following sub-steps: Step S201: sort the executable wind speeds from small to large and mark them as Fs1 to Fs2. i ; Step S202, obtaining the number of executable wind speeds, marked as Z; Step S203 , calculating the first position value as: W1=c1*Z; wherein W1 is the first position value, c1 is the first position coefficient, and the range of c1 is: (0, 0.5); Step S204: determine whether the first position value is an integer. If so, set Fs （W1） As the first wind speed value; if not, get the integer part of W1, mark it as Z1, and set Fs （Z1） As the first wind speed value; Step S205 , calculating the second position value as: W2=c2*Z; wherein W2 is the second position value, c2 is the second position coefficient, and the range of c2 is: (0.5, 1); Step S206, determine whether the second position value is an integer, if so, set Fs （W2） As the second position value; if not, get the integer part of W2, mark it as Z2, and set Fs （Z2） as the second wind speed value.

[0024] Step S3, obtaining a 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: Step S301, marking the first wind speed value as S1, and marking the second wind speed value as S2; Step S302 , obtaining a test wind speed threshold value: Sy=S1-[(S2-S1) / (c2-c1)]×c1; wherein Sy is the test wind speed threshold value; Step S303: obtaining the minimum executable wind speed that is greater than or equal to the test wind speed threshold, and marking it as the final wind speed; Step S304: Obtain the final wind speed corresponding to the initial concentration of each experiment.

[0025] Step S4, obtaining a wind speed setting function based on the final wind speed; Step S4 includes the following sub-steps: Step S401, using the experimental concentration as the horizontal axis data and the final wind speed as the vertical axis data, a plane rectangular coordinate system is established, which is marked as the wind speed setting coordinate system; Step S402: Taking the experimental starting concentration and the final wind speed corresponding to the experimental starting concentration as the horizontal coordinate and vertical coordinate of the coordinate point, and marking it as the wind speed setting coordinate point; Step S403: plotting all wind speed setting coordinate points into the wind speed setting coordinate system to obtain a wind speed setting scatter plot; Step S404: Perform polynomial fitting on the wind speed setting scatter plot to obtain a wind speed setting function.

[0026] Step S5, obtaining a third experimental concentration based on the experimental clean room; Step S5 includes the following sub-steps: Step S501: Under the condition that the initial particle concentration in the experimental clean room is 0, the timing is set to start when a second number of people enter the experimental clean room, and the timing time is marked as the experimental time. The particle concentration of the experimental clean room is obtained once every second time interval and marked as the second experimental concentration; the average value of the second experimental concentration in each same experimental time is obtained and marked as the third experimental concentration.

[0027] Step S6, obtaining a division threshold and a concentration change function based on the third experimental concentration; Step S6 includes the following sub-steps: Step S601, establishing a plane rectangular coordinate system with the experimental time as the horizontal axis data and the third experimental concentration as the vertical axis, marked as the concentration change coordinate system; Step S602, using the experimental time and the third experimental concentration corresponding to the experimental time as the horizontal coordinate and vertical coordinate of the coordinate point, marking it as a concentration change coordinate point; Step S603: Connect adjacent concentration change coordinate points to obtain line segments, which are marked as connecting line segments; obtain the slope of each connecting line segment, which is marked as connecting slope; sort the connecting slopes from small to large according to the horizontal coordinates of the right endpoints of the connecting line segments, which are marked as K. j; Where j is a positive integer, and j from small to large represents the horizontal coordinates of the right endpoints of the connecting line segment from small to large; Step S604: determine whether K appears in the concentration change coordinate system from left to right. j is positive, K j-1 When it is negative for the first time, get K j The endpoint of the connecting line segment with the smaller corresponding horizontal coordinate is marked as the division endpoint, and the horizontal coordinate corresponding to the division endpoint is marked as the division threshold; Step S605 , performing polynomial fitting on the concentration change coordinate points whose horizontal coordinates are smaller than the dividing threshold value to obtain a concentration change function.

[0028] Step S7: obtain the real-time particle concentration in the clean room and mark it as the real-time monitoring concentration; obtain the entry time of each person in the clean room and mark it as the real-time entry time.

[0029] Step S8, obtaining a correction value based on the real-time entry time and the division threshold; Step S8 includes the following sub-steps: 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, substitute the real-time entry time plus the third time 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.

[0030] Step S9, obtaining a corrected wind speed based on the correction value, the real-time monitored concentration, and the wind speed setting function, and purifying and ventilating the clean room at the corrected wind speed; Step S9 includes the following sub-steps: Step S901, adding the correction value to the real-time monitoring concentration to obtain the corrected monitoring concentration; Step S902: Substitute the corrected monitoring concentration into the wind speed setting function to obtain the corrected wind speed.

[0031] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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 may 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 read-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 memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0032] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

Claims

1. A clean room purification and ventilation method based on Internet of Things space monitoring, characterized in that: The steps include: Establish an experimental clean room and obtain executable wind speed based on the experimental clean room; Obtaining a first wind speed value and a second wind speed value based on the executable wind speed; Obtaining a final wind speed based on the executable wind speed, the first wind speed value, and the second wind speed value; Obtaining a wind speed setting function based on the final wind speed; Obtaining a third experimental concentration based on the experimental clean room; Obtaining a division threshold and a concentration change function based on a third experimental concentration; Obtain the real-time particle concentration in the clean room and mark it as real-time monitoring concentration; obtain the entry time of each person in the clean room and mark it as real-time entry time; Obtaining a correction value based on the real-time entry time and the partition threshold; A corrected wind speed is obtained based on the correction value, the real-time monitoring concentration, and the wind speed setting function, and the clean room is purified and ventilated at the corrected wind speed.

2. The clean room purification and ventilation method based on Internet of Things space monitoring according to claim 1 is characterized in that: Establishing an experimental clean room and obtaining executable wind speed based on the experimental clean room includes the following sub-steps: Construct a clean room that can control particle concentration, marked as an experimental clean room; obtain the controllable wind speed range of the clean room, marked as the adjusted wind speed range, set different particle concentrations, marked as the experimental starting concentration, so that the starting particle concentration in the experimental clean room is different experimental starting concentration conditions, adjust the wind speed range and perform ventilation at different wind speeds for the first number of times, obtain the real-time particle concentration in the experimental clean room, marked as the first experimental concentration; obtain the wind speed at which the first experimental concentration does not exceed the upper concentration threshold within the first time, marked as the executable wind speed.

3. The clean room purification and ventilation method based on Internet of Things space monitoring according to claim 2 is characterized in that: Acquiring the first wind speed value and the second wind speed value based on the executable wind speed includes the following sub-steps: Sort the executable wind speeds from small to large and mark them as Fs1 to Fs i ; Get the number of executable wind speeds, marked 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); Determine whether the first position value is an integer. If so, set Fs （W1） As the first wind speed value; if not, get 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 whether the second position value is an integer. If so, set Fs （W2） As the second position value; if not, get the integer part of W2, mark it as Z2, and set Fs （Z2） as the second wind speed value.

4. The clean room purification and ventilation method based on Internet of Things space monitoring according to claim 3 is characterized in that: 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 marked as S1, and the second wind speed value is marked 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 starting concentration of each experiment.

5. The clean room purification and ventilation method based on Internet of Things space monitoring according to claim 4 is characterized in that: Obtaining a wind speed setting function based on the final wind speed includes the following sub-steps: With the experimental concentration as the horizontal axis data and the final wind speed as the vertical axis data, a plane rectangular coordinate system is established and marked as the wind speed setting coordinate system; The experimental starting concentration and the final wind speed corresponding to the experimental starting concentration are used as the horizontal and vertical coordinates of the coordinate point, and marked as the wind speed setting coordinate point; Plot all 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 wind speed setting scatter plot.

6. The clean room purification and ventilation method based on Internet of Things space monitoring according to claim 5, characterized in that: Obtaining the third experimental concentration based on the experimental clean room includes the following sub-steps: Under the condition that the initial particle concentration in the experimental clean room is 0, the timing is set to start when a second person enters the experimental clean room, and the timing time is marked as the experimental time. The particle concentration of the experimental clean room is obtained once every second time interval, and marked as the second experimental concentration; the average value of the second experimental concentration in each same experimental time is obtained, and marked as the third experimental concentration.

7. The clean room purification and ventilation method based on Internet of Things space monitoring according to claim 6, characterized in that: Obtaining the partition threshold and the concentration change function based on the third experimental concentration includes the following sub-steps: With the experimental time as the horizontal axis data and the third experimental concentration as the vertical axis, a plane rectangular coordinate system is established, marked as the concentration change coordinate system; The experimental time and the third experimental concentration corresponding to the experimental time are used as the horizontal and vertical coordinates of the coordinate point, and marked as the concentration change coordinate point; Connect adjacent concentration change coordinate points to obtain line segments, marked as connecting line segments; obtain the slope of each connecting line segment, marked as connecting slope; sort the connecting slopes from small to large according to the horizontal coordinates of the right endpoints of the connecting line segments, marked as K j ; Where j is a positive integer, and j from small to large represents the horizontal coordinates of the right endpoints of the connecting line segment from small to large; In the concentration change coordinate system, determine from left to right whether K appears. j is positive, K j-1 When it is negative for the first time, get K j The endpoint of the connecting line segment with the smaller corresponding horizontal coordinate is marked as the division endpoint, and the horizontal coordinate corresponding to the division endpoint is marked as the division threshold; The concentration change coordinate points whose horizontal coordinates are less than the division threshold are fitted with a polynomial to obtain the concentration change function.

8. The clean room purification and ventilation method based on Internet of Things space monitoring according to claim 7, characterized in that: Obtaining a correction value based on the real-time entry time and the partition threshold includes the following sub-steps: 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, substitute the real-time entry time plus the third time 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.

9. The clean room purification and ventilation method based on Internet of Things space monitoring according to claim 8, characterized in that: The corrected wind speed is obtained based on the correction value, the real-time monitoring concentration, and the wind speed setting function, and the clean room purification and ventilation using the corrected wind speed includes the following steps: Add the correction value to the real-time monitoring concentration to obtain the corrected monitoring concentration; Substitute the corrected monitoring concentration into the wind speed setting function to obtain the corrected wind speed.

10. A clean room purification and ventilation system based on Internet of Things space monitoring, used to implement the clean room purification and ventilation method based on Internet of Things space monitoring according to any one of claims 1 to 9, characterized in that: include: Execution wind speed acquisition module, first and second wind speed acquisition modules, final wind speed acquisition module, wind speed setting function acquisition module, third experiment concentration acquisition module, concentration change function acquisition module, data acquisition module, correction value acquisition module and correction wind speed execution module; The execution wind speed acquisition module is used to establish an experimental clean room and acquire the executable wind speed based on the experimental clean room; 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 obtain the third experimental concentration based on the experimental clean room; The concentration change function acquisition module is used to acquire a division threshold and a concentration change function based on the third experimental concentration; The data acquisition module is used to obtain the particle concentration in the clean room, marked as real-time monitoring concentration; obtain the entry time of each person in the clean room, marked as 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 modified wind speed execution module is used to obtain the modified wind speed based on the correction value, the real-time monitoring concentration and the wind speed setting function, and purify and ventilate the clean room at the modified wind speed.

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