Constant pressure management system for a compounding center based on the internet of things

Abstract

The application discloses a management system for constant pressure of a static dispensing center in an Internet of Things, and relates to the technical field of static dispensing center pressure difference management. The management system comprises the following steps: collecting air pressure data of all air pressure sensors in a to-be-managed area of the static dispensing center, calculating air pressure difference values, and obtaining air pressure difference data; performing weight configuration processing on the air pressure difference data at historical moments to obtain sensor weight data, and adjusting the sampling frequency of each pressure sensor; performing air pressure analysis processing on the air pressure difference data at the current moment of the to-be-managed area to obtain representative air pressure data; and controlling and managing the ventilation system of the to-be-managed area according to the representative air pressure data and the sensor weight data. The application is used to solve the problem that the existing static dispensing center pressure difference management technology cannot dynamically adjust the sampling frequency of the sensor according to historical data when sampling pressure difference data, optimizes the consumption of relevant resources, and accurately manages the pressure difference of the static dispensing center according to the sampled data.

Description

Technical Field

[0001] This invention relates to the field of differential pressure management technology in static dispensing centers, specifically to a constant pressure management system for static dispensing centers based on the Internet of Things. Background Technology

[0002] Differential pressure management technology in intravenous drug preparation centers refers to a comprehensive technical system that maintains a stable pressure difference between different functional areas in the clean environment control of intravenous drug preparation centers through systematic design, operation control, monitoring and maintenance, so as to achieve directional airflow, prevent cross-diffusion of pollutants and ensure the cleanliness of the preparation environment.

[0003] Existing differential pressure management technologies for static distribution centers often employ a fixed sampling frequency when collecting differential pressure data. In certain areas of the static distribution center, the differential pressure is typically stable with minimal fluctuations. In such cases, fixed-frequency sampling generates a large amount of redundant data. This redundant data not only consumes storage resources but also increases the burden on data processing, resulting in a waste of computing power and storage costs. Therefore, a fixed sampling frequency cannot be dynamically adjusted according to operating conditions, failing to meet the monitoring needs of different areas and hindering dynamic optimization of resource consumption. Furthermore, due to cost and space constraints, the number of sensors deployed in the static distribution center is limited. When using sensors to collect differential pressure data, these sensors are often mounted on walls or ceilings, making it impossible to monitor differential pressure at every point within the area. Relying solely on the differential pressure readings from edge sensors to regulate the ventilation system of the static distribution center is insufficient for precise differential pressure management. Consequently, existing differential pressure management technologies for static distribution centers cannot dynamically adjust the sensor sampling frequency based on historical data to optimize resource consumption and simultaneously achieve precise differential pressure management based on the sampled data. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It collects air pressure data from all pressure sensors in the managed area of ​​a static distribution center, calculates the air pressure difference, and obtains air pressure difference data. Weighting is then performed to obtain sensor weight data, and the sampling frequency of each pressure sensor is adjusted. Air pressure analysis is conducted based on the current air pressure difference data of the managed area to obtain representative air pressure data. The ventilation system of the managed area is then controlled and managed based on the representative air pressure data and the sensor weight data. This addresses the problem that existing static distribution center pressure difference management technologies cannot dynamically adjust the sensor sampling frequency based on historical data to optimize resource consumption, and simultaneously achieve accurate management of the static distribution center pressure difference based on the sampled data.

[0005] To achieve the above objectives, this application provides an IoT-based constant pressure management system for a static dispensing center, including a data collection module, a data acquisition and configuration module, a pressure analysis module, and a pressure control module.

[0006] The data collection module is used to collect air pressure data from all air pressure sensors in the area to be managed in the static mixing center, and calculate the air pressure difference to obtain air pressure difference data.

[0007] The acquisition and configuration module includes a weighting unit and a frequency unit. The weighting unit performs weighting configuration processing based on the air pressure difference data at historical times to obtain sensor weight data. The frequency unit adjusts the sampling frequency of each pressure sensor based on the sensor weight data.

[0008] The air pressure analysis module performs air pressure analysis based on the air pressure difference data of the area to be managed at the current moment to obtain representative air pressure data;

[0009] The pressure control module controls and manages the ventilation system of the area to be managed based on representative air pressure data and sensor weight data.

[0010] Furthermore, the data collection module is configured with a data collection strategy, which includes:

[0011] Any area in the static compounding center that needs to be managed is recorded as the area to be managed. Areas adjacent to the area to be managed but with a lower cleanliness level than the area to be managed are recorded as adjacent areas to be managed.

[0012] Obtain the allowable pressure difference range between the area to be managed and the adjacent managed area, denoted as [AP1, AP2].

[0013] Furthermore, data collection strategies also include:

[0014] Multiple pressure sensors are evenly distributed within the area to be managed, and any one of the pressure sensors is designated as the first sensor.

[0015] The air pressure of the area to be managed is continuously collected using the first sensor and recorded as the first sensor air pressure. At the same time, the air pressure of the adjacent area is acquired and recorded as the adjacent area air pressure. The pressure difference between the first sensor air pressure and the adjacent area air pressure is calculated and recorded as the sensor pressure difference. After completion, the air pressure data of the first sensor is obtained.

[0016] Repeatedly and continuously collect air pressure data from all barometers and label them as air pressure difference data.

[0017] Furthermore, the weight unit is configured with a weight configuration strategy, which includes:

[0018] Intercept the data with the latest first time length from the air pressure difference data, denoted as historical pressure difference data, and the first time length is T1;

[0019] For the historical pressure difference data corresponding to the first sensor, arrange the corresponding pressure differences in chronological order from near to far, denoted as the first pressure difference sequence;

[0020] Set the second time length as T2, divide the first pressure difference sequence into multiple subsequences according to T2. For any one subsequence, denoted as the first subsequence, obtain the position serial number of the first subsequence in the first pressure difference sequence, denoted as DN; let W0 = T1 / T2;

[0021] Mark the pressure difference data exceeding the allowable pressure difference range [AP1, AP2] in the first subsequence as abnormal pressure differences. For any one abnormal pressure difference, denoted as BPi, calculate the deviation magnitude PLi of BPi. Among them, if BPi < AP1, then PLi = AP1 - BPi; if BPi > AP2, then PLi = BPi - AP2;

[0022] Repeatedly obtain the deviation magnitudes of all abnormal pressure differences, and mark the maximum deviation magnitude as the absolute abnormal value of the first subsequence, denoted as DL; if there is no abnormal pressure difference in the first subsequence, mark the absolute abnormal value DL of the first subsequence as 0;

[0023] If there is no abnormal pressure difference in the first subsequence, mark the first subsequence as a normal subsequence, otherwise mark the first subsequence as an abnormal subsequence;

[0024] Calculate the relative abnormality XL of the first subsequence, where XL = NQ*DL / (AP2 - AP1), NQ = DN / (W0 / 2), and repeatedly obtain the relative abnormalities of all subsequences and sum them, denoted as the overall abnormality ZL, and calculate the recent abnormality density RM corresponding to the first sensor, where RM = ZL / W0.

[0025] Furthermore, the weight configuration strategy further includes:

[0026] Obtain the total number of abnormal subsequences in the first pressure difference sequence, denoted as YG; for any one abnormal subsequence, if adjacent subsequences are all normal subsequences, then mark it as an abnormal independent subsequence, and obtain the total number of abnormal independent subsequences, denoted as AG;

[0027] Calculate the abnormal persistence factor RC corresponding to the first sensor according to YG and AG, where RC = (YG - AG) / YG; and calculate the weight configuration factor VR of the first sensor, where VR = RM*RC;

[0028] Repeatedly obtain the weight configuration factors of all pressure sensors and sum them, denoted as the total configuration factor WR;

[0029] The configuration weight RQ of the first sensor is calculated based on VR and WR, where RQ = VR / WR; the configuration weights of all barometric pressure sensors are obtained by repeating this process to obtain sensor weight data.

[0030] Furthermore, the frequency unit is configured with a frequency configuration strategy, which includes:

[0031] Obtain the theoretical sampling frequency range of the barometric pressure sensor, denoted as [AF, BF]. Set k1 sampling frequency levels from [AF, BF], and denot them as sampling levels 1-k1 from smallest to largest according to the corresponding sampling frequency, where k1 is the number of levels set.

[0032] Obtain the minimum and maximum values ​​of the configuration weights of all barometric pressure sensors, and denote them as AQ and BQ respectively. Divide [AQ, BQ] into k1 sub-intervals evenly; denote them as sub-intervals 1-k1 in ascending order.

[0033] Obtain the index of the sub-interval where the configuration weight RQ of the first sensor is located, denoted as k2; and denot the sampling frequency corresponding to the sampling level k2 as the setting frequency, and set the sampling frequency of the first sensor to the setting frequency; repeat the setting of the sampling frequency for all barometric pressure sensors.

[0034] Furthermore, the pressure analysis module is configured with a pressure analysis strategy, which includes:

[0035] Establish a spatial rectangular coordinate system in the area to be managed, obtain the position of each barometric pressure sensor, calculate the straight-line distance between any two barometric pressure sensors, and arrange them in ascending order, which is recorded as the sensing distance sequence.

[0036] For any two adjacent straight-line distances in the sensing distance sequence, let DR be the distance between them. j and DR j+1 , where j represents the position number in the sensing distance sequence; calculate DR j and DR j+1 Distance change ratio DV j Among them, DV j =(DR j+1 -DR j ) / DR j Repeatedly acquire the distance change ratio of all adjacent distances in the sensing distance sequence;

[0037] The median of the distance change ratio is obtained and denoted as DVZ. Starting from the beginning of the sensing distance sequence, the first distance greater than or equal to k3*DVZ is denoted as the first distance threshold YL, where k3 is the set ratio coefficient.

[0038] Furthermore, barometric analysis strategies also include:

[0039] k4 points are evenly distributed in the area to be managed, and are denoted as representative points, where k4 is the number of points set; any one of the representative points is denoted as the first representative point.

[0040] The spherical region with the first representative point as the center and YL as the radius is denoted as the first spherical region; the number of pressure sensors in the first spherical region is obtained and denoted as G0;

[0041] If G0 is greater than k5, then the k5 barometric pressure sensor closest to the first representative point within the first spherical region is selected and recorded as the reference sensor; if G0 is less than k6, then the k6 barometric pressure sensor closest to the first representative point is selected and recorded as the reference sensor; otherwise, the three-dimensional barometric pressure sensor within the first spherical region is recorded as the reference sensor; where k5 is the upper limit number set and k6 is the lower limit number set.

[0042] Furthermore, barometric analysis strategies also include:

[0043] Calculate the straight-line distance from each reference sensor to the first representative point, and obtain the sum of the reciprocals of all straight-line distances, denoted as the distance reciprocal sum FL;

[0044] For any reference sensor, denoted as the second sensor, the straight-line distance from the second sensor to the first representative point is denoted as DS. The distance weight SQ of the second sensor is calculated, where SQ = FL / DS. SQ is then multiplied by the configuration weight of the second sensor, and denoted as the product weight XQ of the second sensor.

[0045] Repeatedly obtain the product weights of all reference sensors and sum them, denoted as XW. Calculate the comprehensive weight HQ of the second sensor, where HQ = XQ / XW.

[0046] The pressure difference between the most recently collected air pressure from the second sensor and the air pressure in the adjacent area is denoted as CP. The weighted pressure difference QP of the second sensor is calculated, where QP = HQ * CP. The weighted pressure differences of all reference sensors are repeatedly obtained and summed to obtain the representative pressure difference of the first representative point.

[0047] The representative pressure difference of all representative points is repeatedly acquired at the first time interval to obtain representative air pressure data, where the first time interval is e1.

[0048] Furthermore, the pressure control module is configured with a pressure control strategy, which includes:

[0049] Based on the representative air pressure data and air pressure difference data, if the most recently acquired sensor pressure difference exceeding the allowable pressure difference range and the total number of representative pressure differences exceed k7, or if the duration of a certain air pressure sensor's sensor pressure difference exceeding the allowable pressure difference range exceeds e2, then the ventilation system's air supply volume will be reduced and the exhaust volume will be increased, where k7 is the set number and e2 is the set time length.

[0050] If the most recently acquired differential pressure is less than the allowable differential pressure range and the total number of differential pressures exceeds k7, or if the duration of a differential pressure of a certain pressure sensor being less than the allowable differential pressure range is greater than e2, then increase the supply air volume of the ventilation system and decrease the exhaust air volume.

[0051] The beneficial effects of this invention are as follows: This invention collects air pressure data from all air pressure sensors in the managed area of ​​the static distribution center and calculates the air pressure difference to obtain air pressure difference data; it performs weighting processing based on historical air pressure difference data to obtain sensor weight data, and adjusts the sampling frequency of each pressure sensor according to the sensor weight data; it performs air pressure analysis processing based on the current air pressure difference data of the managed area to obtain representative air pressure data; and it controls and manages the ventilation system of the managed area based on the representative air pressure data and the sensor weight data. When sampling pressure difference data, the sampling frequency of the sensors can be dynamically adjusted according to historical data to optimize the consumption of related resources, and the pressure difference of the static distribution center can be accurately managed based on the sampled data.

[0052] This invention calculates the anomaly density and anomaly persistence factor of each barometric pressure sensor using historical data, thereby obtaining the configuration weight of each sensor. The barometric pressure sensor more prone to pressure differential anomalies receives a higher weight, and its sampling frequency is correspondingly increased. Compared to fixed-frequency sampling, dynamic weighting allows the system to focus on locations prone to anomalies, reducing the probability of missed detections and improving the accuracy of pressure management. By uniformly selecting multiple representative points within a region, and choosing the optimal reference sensor within each representative point according to a first distance threshold, the representative pressure difference at each point is calculated. The advantage lies in the fact that the representative point layout and weighted calculation ensure high coverage and accuracy of pressure difference changes across the entire region, making pressure management less reliant on data from a single sensor and more robust. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the system of the present invention;

[0054] Figure 2 This is a flowchart of the steps of the method of the present invention;

[0055] Figure 3 This is a flowchart of the weight configuration strategy of the present invention;

[0056] Figure 4This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0057] 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.

[0058] Example 1, please refer to Figure 1 As shown, this application provides an IoT-based constant pressure management system for a static dispensing center, including a data collection module, a data acquisition and configuration module, a pressure analysis module, and a pressure control module.

[0059] The data collection module is used to collect air pressure data from all air pressure sensors in the area to be managed in the static mixing center, and calculate the air pressure difference to obtain air pressure difference data;

[0060] The data collection module is configured with a data collection strategy, which includes: any area in the static compounding center that needs to be managed is recorded as the area to be managed; areas adjacent to the area to be managed but with a lower cleanliness level than the area to be managed are recorded as adjacent areas to be managed; the static compounding center must use strict air pressure control to ensure that the airflow flows from high to low cleanliness, thereby preventing contaminants from low cleanliness areas from entering high cleanliness areas and ensuring the sterility and safety of intravenous drug preparation;

[0061] Obtain the allowable pressure difference range between the area to be managed and the adjacent managed areas, denoted as [AP1, AP2]; the allowable pressure difference range can be set according to the actual application scenario;

[0062] Multiple pressure sensors are evenly distributed within the area to be managed, and any one of the pressure sensors is designated as the first sensor.

[0063] The air pressure of the area to be managed is continuously collected using the first sensor and recorded as the first sensor air pressure. At the same time, the air pressure of the adjacent area is acquired and recorded as the adjacent area air pressure. The pressure difference between the first sensor air pressure and the adjacent area air pressure is calculated and recorded as the sensor pressure difference. After completion, the air pressure data of the first sensor is obtained, which provides basic data for subsequent historical trend analysis.

[0064] Repeatedly and continuously collect air pressure data from all barometers and label them as air pressure difference data;

[0065] In the specific implementation process, the differential pressure in the intravenous admixture center refers to the air pressure difference between different functional areas inside it, which is a key control index for maintaining a clean environment and preventing cross-contamination. The adjustment of the differential pressure in the intravenous admixture center is mainly achieved through the ventilation equipment system. The core is to precisely control the air supply volume, exhaust volume, and air flow direction in different areas to form a stable air pressure gradient.

[0066] The acquisition and configuration module includes a weight unit and a frequency unit. The weight unit performs weight configuration processing based on the air pressure difference data at historical moments to obtain sensor weight data. The frequency unit adjusts the sampling frequency of each pressure sensor according to the sensor weight data.

[0067] The weight unit is configured with a weight configuration strategy, which includes: intercepting the data of the latest first time length from the air pressure difference data, denoted as historical pressure difference data, and the first time length is T1; T1 can be flexibly set. In this embodiment, T1 = 1 month. Only focus on the pressure difference fluctuations within the recent T1 time period, avoiding the influence of outdated anomalies on weight calculation, making the weight configuration more timely.

[0068] Please refer to Figure 3 As shown, for the historical pressure difference data corresponding to the first sensor, arrange the corresponding pressure differences in chronological order from near to far, denoted as the first pressure difference sequence.

[0069] Set the second time length as T2. Divide the first pressure difference sequence into multiple subsequences according to T2. For any one subsequence, denoted as the first subsequence, obtain the position serial number of the first subsequence in the first pressure difference sequence, denoted as DN. Let W0 = T1 / T2. In this embodiment, T2 = 1 hour, and W0 is the number of subsequences. Because the sampling frequencies of each air pressure sensor may be different, divide the first pressure difference sequence into multiple subsequences with a certain time length and compare them under this unified time unit to eliminate the influence of the amount of data.

[0070] Mark the pressure difference data exceeding the allowable pressure difference range [AP1, AP2] in the first subsequence as abnormal pressure differences. For any one abnormal pressure difference, denoted as BPi, calculate the deviation magnitude PLi of BPi. Among them, if BPi < AP1, then PLi = AP1 - BPi; if BPi > AP2, then PLi = BPi - AP2. That is, take the maximum value of all deviation amounts in the first subsequence as the absolute abnormal value.

[0071] Repeat to obtain the deviation magnitudes of all abnormal pressure differences, and mark the maximum deviation magnitude as the absolute abnormal value of the first subsequence, denoted as DL. If there is no abnormal pressure difference in the first subsequence, mark the absolute abnormal value DL of the first subsequence as 0.

[0072] If there is no abnormal pressure difference in the first subsequence, then the first subsequence is marked as a normal subsequence; otherwise, the first subsequence is marked as an abnormal subsequence.

[0073] Calculate the relative anomaly XL of the first subsequence, where XL = NQ * DL / (AP2 - AP1), NQ = DN / (W0 / 2), and repeat the acquisition of relative anomalies for all subsequences, summing them and denoting the total anomaly ZL. Calculate the recent anomaly density RM corresponding to the first sensor, where RM = ZL / W0. The absolute anomaly DL quantifies the maximum deviation, reflecting the most severe offset. The relative anomaly XL is normalized to the allowable range width and considers time and location weights, making large recent offsets more prominent. Through relative anomalies, we can see both the magnitude and timing of the deviation. The anomaly density RM reflects the average anomaly intensity of the sensor per unit time. Compared with the RM of other sensors, a high RM indicates that this sensor has deviated from the limit more frequently or more severely recently, and should be given more attention.

[0074] Obtain the total number of abnormal subsequences in the first differential pressure sequence, denoted as YG; for any abnormal subsequence, if the adjacent subsequences are all normal subsequences, mark it as an abnormal independent subsequence, and obtain the total number of abnormal independent subsequences, denoted as AG;

[0075] The anomaly persistence factor RC corresponding to the first sensor is calculated based on YG and AG, where RC = (YG - AG) / YG; and the weight configuration factor VR of the first sensor is calculated, where VR = RM * RC; if the anomaly segments often appear continuously, it indicates that the sensor state or environment anomaly is continuous, and RC is close to 1. For occasional and isolated anomalies, RC is smaller to avoid excessive weighting due to a single sudden occurrence; VR reflects both anomaly density and anomaly persistence characteristics, and is a quantitative indicator of the degree to which barometric pressure sensors need to be monitored.

[0076] Repeatedly obtain the weight configuration factors of all barometric pressure sensors and sum them, denoted as the total configuration factor sum WR;

[0077] The configuration weight RQ of the first sensor is calculated based on VR and WR, where RQ = VR / WR; the configuration weights of all barometric pressure sensors are obtained by repeating this process to obtain sensor weight data; after normalization of RQ, the relative importance of each sensor in the system can be compared horizontally.

[0078] The frequency unit is configured with a frequency configuration strategy, which includes: obtaining the theoretical sampling frequency range of the barometric pressure sensor, denoted as [AF, BF], setting k1 sampling frequency levels from [AF, BF], and denoting them as sampling levels 1-k1 from small to large according to the corresponding sampling frequency, where k1 is the number of levels set; k1 can be set flexibly, and in this embodiment, k1=10;

[0079] Obtain the minimum and maximum values ​​of the configuration weights of all barometric pressure sensors, and denote them as AQ and BQ respectively. Divide [AQ, BQ] into k1 sub-intervals evenly; denote them as sub-intervals 1-k1 in ascending order.

[0080] Obtain the index of the sub-interval where the configuration weight RQ of the first sensor is located, denoted as k2; and denote the sampling frequency corresponding to the sampling level k2 as the setting frequency, and set the sampling frequency of the first sensor as the setting frequency; repeat the setting of the sampling frequency for all barometric pressure sensors; for example, if the configuration weight of a barometric pressure sensor falls in the 3rd sub-interval, then configure the sampling frequency of that barometric pressure sensor as sampling level 3; the mapping relationship between sampling frequency and configuration weight can be flexibly set;

[0081] In practice, sensors with high configuration weights are assigned to higher sampling frequencies to capture further fluctuations in a timely manner, while the sampling frequency of sensors with low weights can be appropriately reduced to save network bandwidth and processing resources and ensure the overall efficiency of the system.

[0082] The air pressure analysis module performs air pressure analysis based on the air pressure difference data of the area to be managed at the current moment, and obtains representative air pressure data;

[0083] The barometric pressure analysis module is configured with a barometric pressure analysis strategy, which includes: establishing a spatial rectangular coordinate system in the area to be managed, obtaining the position of each barometric pressure sensor, calculating the straight-line distance between any two barometric pressure sensors, and arranging them in ascending order as a sensing distance sequence.

[0084] For any two adjacent straight-line distances in the sensing distance sequence, let DR be the distance between them. j and DR j+1 , where j represents the position number in the sensing distance sequence; calculate DR j and DR j+1 Distance change ratio DV j Among them, DV j =(DR j+1 -DR j ) / DR j Repeatedly acquire the distance change ratio of all adjacent distances in the sensing distance sequence; convert the original distance between barometric pressure sensors into a distance change ratio index to more sensitively capture the transition characteristics from dense sensor areas to sparse sensor areas.

[0085] The median of the distance change ratio is obtained and denoted as DVZ. Starting from the beginning of the sensing distance sequence, the first distance greater than or equal to k3*DVZ is denoted as the first distance threshold YL, where k3 is the set ratio coefficient; in this embodiment, k3=1.5.

[0086] By anchoring the general pattern of the data through the median of the distance change ratio, and then using k3*DVZ as the threshold, the location point that first exceeds k3*DVZ is located. This location point is the dividing point between the end of the dense region and the beginning of the sparse region. This avoids the subjectivity of manually setting a fixed threshold in traditional methods. The first distance threshold YL is used as the radius of the spherical region for subsequent representative points, ensuring that the spherical region can cover a sufficient number of sensors while preventing noise from being introduced by data from too far away.

[0087] k4 points are evenly set in the area to be managed, and are denoted as representative points, where k4 is the number of points set; any one of the representative points is denoted as the first representative point; k4 can be set according to the actual application scenario and time, in this embodiment, k4=200;

[0088] The spherical region with the first representative point as the center and YL as the radius is denoted as the first spherical region; the number of pressure sensors in the first spherical region is obtained and denoted as G0;

[0089] If G0 is greater than k5, then the k5 barometric pressure sensor closest to the first representative point within the first spherical region is selected and designated as the reference sensor; if G0 is less than k6, then the k6 barometric pressure sensor closest to the first representative point is selected and designated as the reference sensor; otherwise, all three-dimensional barometric pressure sensors within the first spherical region are designated as reference sensors; where k5 is the upper limit number set and k6 is the lower limit number set; the upper and lower limits k5 and k6 prevent excessive redundancy or information sparsity caused by too many or too few sensors, ensuring that each representative point can obtain sufficient and stable local data support; in this embodiment, k5=5 and k6=2.

[0090] Calculate the straight-line distance from each reference sensor to the first representative point, and obtain the sum of the reciprocals of all straight-line distances, denoted as the distance reciprocal sum FL;

[0091] For any reference sensor, denoted as the second sensor, the straight-line distance from the second sensor to the first representative point is denoted as DS. The distance weight SQ of the second sensor is calculated, where SQ = FL / DS. SQ is then multiplied by the configuration weight of the second sensor, denoted as the product weight XQ of the second sensor. The distance weight SQ emphasizes that the data from a nearby barometric pressure sensor is more representative of the barometric pressure at the representative point than the data from a distant barometric pressure sensor; that is, the closer the distance, the higher the weight. The configuration weight RQ reflects the importance of the sensor in historical anomaly analysis.

[0092] Repeatedly obtain the product weights of all reference sensors and sum them, denoted as XW. Calculate the comprehensive weight HQ of the second sensor, where HQ = XQ / XW. Multiply the distance weight and the configuration weight and normalize them to take both into account and obtain a more accurate local representative weight.

[0093] The pressure difference between the most recently collected air pressure from the second sensor and the air pressure in the adjacent area is denoted as CP. The weighted pressure difference QP of the second sensor is calculated, where QP = HQ * CP. The weighted pressure differences of all reference sensors are repeatedly acquired and summed to obtain the representative pressure difference of the first representative point. The real-time pressure differences of multiple reference sensors are fused into a single index, which not only retains the historical importance information of each sensor, but also takes into account the current spatial distance characteristics. The output representative pressure difference can be directly used for subsequent control decisions of the ventilation system.

[0094] Representative pressure differences at all representative points are repeatedly acquired at a first time interval to obtain representative air pressure data, where the first time interval is e1; e1 can be flexibly set according to the sampling frequency. In this embodiment, e1 = 0.1 seconds.

[0095] In the specific implementation process, by setting representative points and calculating the representative pressure difference of the representative points, a high coverage and high representativeness model of the air pressure distribution in the area was achieved, providing reliable and accurate data reference for subsequent ventilation control. The layout of representative points and weighted calculation ensures high coverage and accuracy of the pressure difference changes in the entire area, so that subsequent ventilation control does not rely on data from a single air pressure sensor, making the pressure control of the static distribution center more robust.

[0096] The pressure control module controls and manages the ventilation system of the area under management based on representative air pressure data and sensor weight data;

[0097] The pressure control module is configured with a pressure control strategy, which includes: based on representative air pressure data and air pressure difference data, if the most recently acquired sensor pressure difference exceeding the allowable pressure difference range and the total number of representative pressure differences exceed k7, or if the duration of a certain air pressure sensor's sensor pressure difference exceeding the allowable pressure difference range exceeds e2, then the ventilation system's supply air volume is reduced and the exhaust air volume is increased. Here, k7 is the set number and e2 is the set duration. In this embodiment, k7=3 and e2=3 seconds, which can be flexibly set according to the actual application scenario. The total number of exceedances and the duration are counted at the current moment. Only when the number exceeds k7 or the duration exceeds e2 is it considered a pressure difference abnormality, avoiding overreaction to single noise or vibration. The dual triggering of counting and duration can respond to multi-point imbalances and adjust in a timely manner for single-point continuous imbalances, taking into account both robustness and sensitivity.

[0098] If the most recently acquired pressure difference is less than the allowable pressure difference range and the total number of pressure differences exceeds k7, or if the pressure difference of a certain pressure sensor is less than the allowable pressure difference range for a duration greater than e2, then increase the supply air volume of the ventilation system and decrease the exhaust air volume.

[0099] In practice, reducing the supply air volume and increasing the exhaust air volume can reduce the total amount of airflow entering this area from the outside or the upper level area, accelerate the discharge speed of gas in this area to the return air or the outside, actively release excess gas, suppress further increase in indoor pressure, and quickly restore the target pressure difference; increasing the supply air volume and reducing the exhaust air volume can increase the airflow from the fresh air or the upper level area into this area, slow down the indoor gas discharge rate, and compensate for insufficient pressure.

[0100] Example 2, please refer to Figure 2 As shown, this application provides a method for managing constant pressure in a static dispensing center based on the Internet of Things, including the following steps:

[0101] Step S1: Collect air pressure data from all air pressure sensors in the managed area of ​​the static mixing center, and calculate the air pressure difference to obtain air pressure difference data; Step S1 includes the following sub-steps:

[0102] Step S101: For any area in the static compounding center that needs to be managed, it is recorded as the area to be managed. The area adjacent to the area to be managed and whose cleanliness level is lower than that of the area to be managed is recorded as the adjacent area to be managed.

[0103] Step S102: Obtain the allowable pressure difference range between the area to be managed and the adjacent managed area, denoted as [AP1, AP2];

[0104] Step S103: Distribute multiple air pressure sensors evenly within the area to be managed, and designate any one of the air pressure sensors as the first sensor.

[0105] Step S104: Continuously collect the air pressure of the area to be managed using the first sensor, and record it as the first sensing air pressure. At the same time, acquire the air pressure of the adjacent area to be managed, and record it as the adjacent area air pressure. Calculate the pressure difference between the first sensing air pressure and the adjacent area air pressure, and record it as the sensing pressure difference. After completion, the air pressure data of the first sensor is obtained.

[0106] Step S105: Repeatedly and continuously collect air pressure data from all air pressure sensors and mark them as air pressure difference data.

[0107] Step S2 involves performing weighted configuration processing based on historical air pressure difference data to obtain sensor weight data, and adjusting the sampling frequency of each pressure sensor according to the sensor weight data. Step S2 includes the following sub-steps:

[0108] Step S201: Extract the latest data of the first time length from the air pressure difference data and record it as historical pressure difference data. The first time length is T1.

[0109] Step S202: For the historical differential pressure data corresponding to the first sensor, arrange the corresponding differential pressures in chronological order from near to far, and record them as the first differential pressure sequence.

[0110] Step S203, set the second time length as T2, divide the first pressure difference sequence into multiple subsequences according to T2. For any one of the subsequences, denoted as the first subsequence, obtain the position serial number of the first subsequence in the first pressure difference sequence, denoted as DN; let W0 = T1 / T2;

[0111] Step S204, mark the pressure difference data exceeding the allowable pressure difference range [AP1, AP2] in the first subsequence as abnormal pressure difference. For any one of the abnormal pressure differences, denoted as BPi, calculate the deviation magnitude PLi of BPi. Among them, if BPi < AP1, then PLi = AP1 - BPi; if BPi > AP2, then PLi = BPi - AP2;

[0112] Step S205, repeatedly obtain the deviation magnitudes of all abnormal pressure differences, and mark the maximum deviation magnitude as the absolute abnormal value of the first subsequence, denoted as DL; if there is no abnormal pressure difference in the first subsequence, mark the absolute abnormal value DL = 0 of the first subsequence;

[0113] Step S206, if there is no abnormal pressure difference in the first subsequence, mark the first subsequence as a normal subsequence, otherwise mark the first subsequence as an abnormal subsequence.

[0114] Step S207, calculate the relative abnormality XL of the first subsequence, where XL = NQ*DL / (AP2 - AP1), NQ = DN / (W0 / 2), and repeatedly obtain the relative abnormalities of all subsequences and sum them, denoted as the overall abnormality ZL, and calculate the nearest abnormality density RM corresponding to the first sensor, where RM = ZL / W0;

[0115] Step S208, obtain the total number of abnormal subsequences in the first pressure difference sequence, denoted as YG; for any one of the abnormal subsequences, if the adjacent subsequences are all normal subsequences, then mark it as an abnormal independent subsequence, and obtain the total number of abnormal independent subsequences, denoted as AG;

[0116] Step S209, calculate the abnormal duration factor RC corresponding to the first sensor according to YG and AG, where RC = (YG - AG) / YG; and calculate the weight configuration factor VR of the first sensor, where VR = RM*RC;

[0117] Step S210, repeatedly obtain the weight configuration factors of all pressure sensors and sum them, denoted as the total configuration factor WR.

[0118] Step S211, calculate the configuration weight RQ of the first sensor according to VR and WR, where RQ = VR / WR; repeatedly obtain the configuration weights of all pressure sensors to obtain the sensor weight data;

[0119] Step S212: Obtain the theoretical sampling frequency range of the barometric pressure sensor, denoted as [AF, BF]. Set k1 sampling frequency levels from [AF, BF], and denot them as sampling levels 1-k1 from small to large according to the corresponding sampling frequencies, where k1 is the number of levels set.

[0120] Step S213: Obtain the minimum and maximum values ​​of the configuration weights of all barometric pressure sensors, and denote them as AQ and BQ respectively in order. Divide [AQ, BQ] into k1 sub-intervals evenly; denote them as sub-intervals 1-k1 in ascending order.

[0121] Step S214: Obtain the sequence number of the sub-interval where the configuration weight RQ of the first sensor is located, denoted as k2; and denot the sampling frequency corresponding to the sampling level k2 as the setting frequency, and set the sampling frequency of the first sensor as the setting frequency; repeat the setting of the sampling frequency for all barometric pressure sensors.

[0122] Step S3 involves performing air pressure analysis based on the current air pressure difference data of the area to be managed, to obtain representative air pressure data. Step S3 includes the following sub-steps:

[0123] Step S301: Establish a spatial rectangular coordinate system in the area to be managed, obtain the position of each barometric pressure sensor, calculate the straight-line distance between any two barometric pressure sensors, and arrange them in ascending order as the sensing distance sequence.

[0124] Step S302: For any two adjacent straight-line distances in the sensing distance sequence, denoted as DR j and DR j+1 , where j represents the position number in the sensing distance sequence; calculate DR j and DR j+1 Distance change ratio DV j Among them, DV j =(DR j+1 -DR j ) / DR j Repeatedly acquire the distance change ratio of all adjacent distances in the sensing distance sequence;

[0125] Step S303, and obtain the median of the distance change ratio, denoted as DVZ. Search sequentially from the starting position in the sensing distance sequence, and record the first distance greater than or equal to k3*DVZ as the first distance threshold YL, where k3 is the set ratio coefficient.

[0126] Step S304: Evenly set k4 points in the area to be managed, and record them as representative points, where k4 is the number of points set; record any one of the representative points as the first representative point;

[0127] Step S305: The spherical region with the first representative point as the center and YL as the radius is denoted as the first spherical region; the number of pressure sensors in the first spherical region is obtained and denoted as G0;

[0128] Step S306: If G0 is greater than k5, then obtain the k5 barometric pressure sensor that is closest to the first representative point within the first spherical region and record it as the reference sensor; if G0 is less than k6, then obtain the k6 barometric pressure sensor that is closest to the first representative point and record it as the reference sensor; otherwise, record the three-dimensional barometric pressure sensor within the first spherical region as the reference sensor; where k5 is the upper limit number set and k6 is the lower limit number set.

[0129] Step S307: Calculate the straight-line distance from each reference sensor to the first representative point, and obtain the sum of the reciprocals of all straight-line distances, denoted as the distance reciprocal sum FL;

[0130] Step S308: For any reference sensor, denoted as the second sensor, the straight-line distance from the second sensor to the first representative point is denoted as DS. The distance weight SQ of the second sensor is calculated, where SQ = FL / DS. SQ is then multiplied by the configuration weight of the second sensor and denoted as the product weight XQ of the second sensor.

[0131] Step S309: Repeatedly obtain the product weights of all reference sensors and sum them, denoted as XW. Calculate the comprehensive weight HQ of the second sensor, where HQ = XQ / XW.

[0132] Step S310: Record the pressure difference between the most recently collected air pressure of the second sensor and the air pressure of the adjacent area as CP, calculate the weighted pressure difference QP of the second sensor, where QP=HQ*CP, repeatedly obtain the weighted pressure differences of all reference sensors, and sum them to obtain the representative pressure difference of the first representative point.

[0133] Step S311: Repeatedly acquire the representative pressure difference of all representative points at the first time interval to obtain representative air pressure data, where the first time interval is e1.

[0134] Step S4 involves controlling and managing the ventilation system of the area to be managed based on representative air pressure data and sensor weight data. Step S4 includes the following sub-steps:

[0135] Step S401: Based on the representative air pressure data and air pressure difference data, if the most recently acquired sensor pressure difference exceeding the allowable pressure difference range and the total number of representative pressure differences exceed k7, or the duration of a certain air pressure sensor's sensor pressure difference exceeding the allowable pressure difference range exceeds e2, then reduce the ventilation system's supply air volume and increase the exhaust air volume, where k7 is the set number and e2 is the set time length.

[0136] In step S402, if the most recently acquired sensing pressure difference that is less than the allowable pressure difference range and the total number of pressure differences exceeds k7, or if the sensing pressure difference of a certain air pressure sensor is less than the allowable pressure difference range for a duration greater than e2, then increase the air supply volume of the ventilation system and decrease the exhaust volume.

[0137] Example 3, please refer to Figure 4 As shown, Figure 4 A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, which the processor can call. When the processor executes a computer-readable instruction, it performs steps such as those in the IoT-based constant pressure management method for a static distribution center to achieve the following functions: collecting air pressure data from all pressure sensors in the managed area of ​​the static distribution center and calculating the air pressure difference to obtain air pressure difference data; performing weighted configuration processing based on historical air pressure difference data to obtain sensor weight data, and adjusting the sampling frequency of each pressure sensor based on the sensor weight data; performing air pressure analysis processing based on the current air pressure difference data of the managed area to obtain representative air pressure data; and controlling and managing the ventilation system of the managed area based on the representative air pressure data and the sensor weight data.

[0138] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] Example 4: This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs steps such as those in the IoT-based constant pressure management method for a static distribution center to achieve the following functions: collecting air pressure data from all air pressure sensors in the area to be managed in the static distribution center and calculating the air pressure difference to obtain air pressure difference data; performing weight configuration processing based on historical air pressure difference data to obtain sensor weight data, and adjusting the sampling frequency of each pressure sensor based on the sensor weight data; performing air pressure analysis processing based on the current air pressure difference data of the area to be managed to obtain representative air pressure data; and controlling and managing the ventilation system of the area to be managed based on the representative air pressure data and the sensor weight data.

[0140] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0141] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A constant pressure management system for a static distribution center based on the Internet of Things, characterized in that, It includes a data collection module, a collection configuration module, a pressure analysis module, and a pressure control module; The data collection module is used to collect the air pressure data of all air pressure sensors in the area to be managed in the intravenous admixture center, calculate the air pressure difference, and obtain the air pressure difference data; The collection configuration module includes a weight unit and a frequency unit. The weight unit performs weight configuration processing according to the air pressure difference data at historical moments to obtain sensor weight data, and the frequency unit adjusts the sampling frequency of each pressure sensor according to the sensor weight data; The air pressure analysis module performs air pressure analysis processing according to the air pressure difference data at the current moment in the area to be managed to obtain representative air pressure data; The pressure control module controls and manages the ventilation system in the area to be managed according to the representative air pressure data and the sensor weight data.

2. The IoT-based constant pressure management system for a static distribution center according to claim 1, characterized in that, The data collection module is configured with a data collection strategy, and the data collection strategy includes: For any area that needs to be managed in the intravenous admixture center, denoted as the area to be managed, the area adjacent to the area to be managed and with a lower cleanliness level than the area to be managed is denoted as the managed adjacent area; Obtain the allowable pressure difference range between the area to be managed and the managed adjacent area, denoted as [AP1, AP2].

3. The IoT-based constant pressure management system for a static distribution center according to claim 2, characterized in that, The data collection strategy also includes: Uniformly set multiple air pressure sensors in the area to be managed, and denote any one air pressure sensor as the first sensor; Use the first sensor to continuously collect the air pressure magnitude in the area to be managed, denoted as the first sensed air pressure, and at the same time obtain the air pressure magnitude in the managed adjacent area, denoted as the adjacent area air pressure, and calculate the pressure difference between the first sensed air pressure and the adjacent area air pressure, denoted as the sensed pressure difference; after completion, obtain the air pressure data of the first sensor; Repeat continuously collecting the air pressure data of all air pressure sensors, marked as air pressure difference data.

4. The IoT-based constant pressure management system for a static distribution center according to claim 3, characterized in that, The weight unit is configured with a weight configuration strategy, and the weight configuration strategy includes: Intercept the data of the latest first time length from the air pressure difference data, denoted as historical pressure difference data, and the first time length is T1; For the historical pressure difference data corresponding to the first sensor, arrange the corresponding pressure differences in chronological order from near to far, denoted as the first pressure difference sequence; Set the second time length as T2, divide the first pressure difference sequence into multiple subsequences according to T2. For any one subsequence, denoted as the first subsequence, obtain the position serial number of the first subsequence in the first pressure difference sequence, denoted as DN; let W0 = T1 / T2; Mark the pressure difference data exceeding the allowable pressure difference range [AP1, AP2] in the first subsequence as abnormal pressure difference. For any one abnormal pressure difference, denoted as BPi, calculate the deviation magnitude PLi of BPi, where, if BPi < AP1, then PLi = AP1 - BPi; if BPi > AP2, then PLi = BPi - AP2; Repeat to obtain the deviation magnitudes of all abnormal pressure differences, and mark the maximum deviation magnitude as the absolute abnormal value of the first subsequence, denoted as DL; if there is no abnormal pressure difference in the first subsequence, mark the absolute abnormal value DL of the first subsequence as 0; If there is no abnormal pressure difference in the first subsequence, mark the first subsequence as a normal subsequence, otherwise mark the first subsequence as an abnormal subsequence; Calculate the relative anomaly XL of the first subsequence, where XL = NQ * DL / (AP2 - AP1) and NQ = DN / (W0 / 2). Repeat this process to obtain the relative anomalies of all subsequences and sum them up, denoted as the overall anomaly ZL. Calculate the nearest anomaly density RM corresponding to the first sensor, where RM = ZL / W0.

5. The IoT-based constant pressure management system for a static distribution center according to claim 4, characterized in that, Weight configuration strategies also include: Obtain the total number of abnormal subsequences in the first differential pressure sequence, denoted as YG; for any abnormal subsequence, if the adjacent subsequences are all normal subsequences, mark it as an abnormal independent subsequence, and obtain the total number of abnormal independent subsequences, denoted as AG; Calculate the anomaly persistence factor RC corresponding to the first sensor based on YG and AG, where RC = (YG - AG) / YG; and calculate the weight configuration factor VR of the first sensor, where VR = RM * RC; Repeatedly obtain the weight configuration factors of all barometric pressure sensors and sum them, denoted as the total configuration factor sum WR; The configuration weight RQ of the first sensor is calculated based on VR and WR, where RQ = VR / WR; the configuration weights of all barometric pressure sensors are obtained by repeating this process to obtain sensor weight data.

6. The IoT-based constant pressure management system for a static distribution center according to claim 5, characterized in that, The frequency unit is configured with a frequency configuration strategy, which includes: Obtain the theoretical sampling frequency range of the barometric pressure sensor, denoted as [AF, BF]. Set k1 sampling frequency levels from [AF, BF], and denot them as sampling levels 1-k1 from smallest to largest according to the corresponding sampling frequency, where k1 is the number of levels set. Obtain the minimum and maximum values ​​of the configuration weights of all barometric pressure sensors, and denote them as AQ and BQ respectively. Divide [AQ, BQ] into k1 sub-intervals evenly; denote them as sub-intervals 1-k1 in ascending order. Obtain the index of the sub-interval where the configuration weight RQ of the first sensor is located, denoted as k2; and denot the sampling frequency corresponding to the sampling level k2 as the setting frequency, and set the sampling frequency of the first sensor to the setting frequency; repeat the setting of the sampling frequency for all barometric pressure sensors.

7. The IoT-based constant pressure management system for a static distribution center according to claim 6, characterized in that, The barometric pressure analysis module is configured with barometric pressure analysis strategies, which include: Establish a spatial rectangular coordinate system in the area to be managed, obtain the position of each barometric pressure sensor, calculate the straight-line distance between any two barometric pressure sensors, and arrange them in ascending order, which is recorded as the sensing distance sequence. For any two adjacent straight-line distances in the sensing distance sequence, let DR be the distance between them. j and DR j+1 , where j represents the position number in the sensing distance sequence; calculate DR j and DR j+1 Distance change ratio DV j Among them, DV j =(DR j+1 -DR j ) / DR j Repeatedly acquire the distance change ratio of all adjacent distances in the sensing distance sequence; The median of the distance change ratio is obtained and denoted as DVZ. Starting from the beginning of the sensing distance sequence, the first distance greater than or equal to k3*DVZ is denoted as the first distance threshold YL, where k3 is the set ratio coefficient.

8. The IoT-based constant pressure management system for a static distribution center according to claim 7, characterized in that, Barometric analysis strategies also include: k4 points are evenly distributed in the area to be managed, and are denoted as representative points, where k4 is the number of points set; any one of the representative points is denoted as the first representative point. The spherical region with the first representative point as the center and YL as the radius is denoted as the first spherical region; the number of pressure sensors in the first spherical region is obtained and denoted as G0; If G0 is greater than k5, then the k5 barometric pressure sensor closest to the first representative point within the first spherical region is selected and recorded as the reference sensor; if G0 is less than k6, then the k6 barometric pressure sensor closest to the first representative point is selected and recorded as the reference sensor; otherwise, the three-dimensional barometric pressure sensor within the first spherical region is recorded as the reference sensor; where k5 is the upper limit number set and k6 is the lower limit number set.

9. The IoT-based constant pressure management system for a static distribution center according to claim 8, characterized in that, Barometric analysis strategies also include: Calculate the straight-line distance from each reference sensor to the first representative point, and obtain the sum of the reciprocals of all straight-line distances, denoted as the distance reciprocal sum FL; For any reference sensor, denoted as the second sensor, the straight-line distance from the second sensor to the first representative point is denoted as DS. The distance weight SQ of the second sensor is calculated, where SQ = FL / DS. SQ is then multiplied by the configuration weight of the second sensor, and denoted as the product weight XQ of the second sensor. Repeatedly obtain the product weights of all reference sensors and sum them, denoted as XW. Calculate the comprehensive weight HQ of the second sensor, where HQ = XQ / XW. The pressure difference between the most recently collected air pressure from the second sensor and the air pressure in the adjacent area is denoted as CP. The weighted pressure difference QP of the second sensor is calculated, where QP = HQ * CP. The weighted pressure differences of all reference sensors are repeatedly obtained and summed to obtain the representative pressure difference of the first representative point. The representative pressure difference of all representative points is repeatedly acquired at the first time interval to obtain representative air pressure data, where the first time interval is e1.

10. The IoT-based constant pressure management system for a static distribution center according to claim 9, characterized in that, The pressure control module is configured with a pressure control strategy, which includes: Based on the representative air pressure data and air pressure difference data, if the most recently acquired sensor pressure difference exceeding the allowable pressure difference range and the total number of representative pressure differences exceed k7, or if the duration of a certain air pressure sensor's sensor pressure difference exceeding the allowable pressure difference range exceeds e2, then the ventilation system's air supply volume will be reduced and the exhaust volume will be increased, where k7 is the set number and e2 is the set time length. If the most recently acquired differential pressure is less than the allowable differential pressure range and the total number of differential pressures exceeds k7, or if the duration of a differential pressure of a certain pressure sensor being less than the allowable differential pressure range is greater than e2, then increase the supply air volume of the ventilation system and decrease the exhaust air volume.

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