A hospital infection monitoring system based on Internet of Things technology

Abstract

The invention relates to the technical field of intelligent monitoring, and specifically discloses a hospital infection monitoring system based on the Internet of Things technology. By acquiring the concentration data of toxic gases and particulate matter in each hospital room, the air quality abnormal value of each hospital room is obtained, and the air quality abnormal value of each hospital room is compared with the air quality abnormal value of a fresh air unit and a fresh air channel, so as to complete the evaluation of the air quality in each hospital room. Based on the evaluation result of the air quality in each hospital room, the valve of the fresh air channel corresponding to the hospital room is controlled. When a negative closing signal of the fresh air channel is obtained, it indicates that the current air quality of the hospital room is poor, and there may be a potential risk of spillover, such as viruses or other factors that may affect the health of patients in the hospital room. A decision analysis module closes the valve of the fresh air channel based on the negative closing signal of the fresh air channel, so as to prevent the air in the hospital room from being spread to other hospital rooms through a fresh air duct, causing the risk of cross-infection.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to a hospital infection monitoring system based on Internet of Things technology. Background Art

[0002] Hospital infection refers to the infection acquired by hospitalized patients in the hospital, including infections that occur during hospitalization and infections that occur after discharge. Many hospitals have implemented information-based hospital infection monitoring and management. With the development of machine learning, WeChat Enterprise Accounts and hospital information platforms, hospital infection information management has ushered in opportunities for further improvement.

[0003] In existing hospitals, most wards share the same set of fresh air units. When the fresh air units fail to meet the required purification standards for the ward environment, the patient's symptoms will be aggravated in severe cases. Secondly, since multiple wards share one set of fresh air units, the environments of each ward are connected. When the infection risk in one ward is significantly increased, if the ventilation connection between the ward and other wards cannot be blocked in time, it will lead to the risk of infection spillover. Summary of the invention

[0004] The purpose of the present invention is to provide a hospital infection monitoring system based on Internet of Things technology. The present invention arranges a plurality of air quality sensors and particle monitors on a fresh air unit and a fresh air duct to obtain toxic gas concentration data and particle concentration data in the environment of the fresh air unit and the fresh air duct. By processing the toxic gas concentration data and the particle concentration data, the air quality anomalies of the fresh air unit and the fresh air duct are obtained. Thus, by identifying the air quality anomalies, the purification effects of the fresh air unit and the fresh air duct are identified, so that the fresh air unit and the fresh air duct can meet the use requirements of the hospital room.

[0005] The technical problems solved by the present invention are:

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A hospital infection monitoring system based on Internet of Things technology, comprising:

[0008] An air quality detection module is used to monitor the air quality at the entrance of the fresh air channel of each courtyard room and the output end of the fresh air unit, obtain air quality abnormal values, and transmit the air quality abnormal values ​​to the cloud management and control platform;

[0009] A quality assessment module, wherein the quality assessment module receives the air quality abnormal value transmitted by the cloud management and control platform, compares the air quality abnormal value with the air quality abnormal value threshold, obtains the fresh air unit quality signal, and sends the fresh air unit quality signal to the cloud management and control platform;

[0010] The quality signal of the fresh air unit includes a normal quality signal and an abnormal quality signal;

[0011] A first decision analysis module, wherein the first decision analysis module receives a normal quality signal transmitted by the cloud management and control platform, identifies the air quality in each courtyard room, and controls the opening and closing of the fresh air duct corresponding to the courtyard room based on the air quality in the courtyard room;

[0012] The second decision analysis module receives the quality abnormality signal transmitted by the cloud management and control platform, and the second decision analysis module identifies the operating status of the fresh air unit to realize intelligent management and control of the operation of the fresh air unit.

[0013] As a further solution of the present invention: the air quality detection module includes a plurality of gas sampling units and a plurality of particle sampling units;

[0014] The gas sampling unit includes an air quality sensor, which is used to monitor the concentration of toxic gases in the air;

[0015] The particle sampling unit includes a particle monitor, which is used to monitor the concentration of the particle;

[0016] Among them, multiple air quality sensors and particulate matter monitors are provided.

[0017] As a further solution of the present invention: the process of obtaining the abnormal value of air quality is:

[0018] Acquiring toxic gas concentration data monitored by the air quality sensor;

[0019] Acquiring concentration data of particulate matter monitored by the particulate matter monitor;

[0020] The toxic gas concentration data and the particulate matter concentration data are processed to obtain the air quality anomalies of the fresh air unit and the fresh air duct.

[0021] As a further solution of the present invention: the air quality abnormal value of the fresh air unit and the fresh air duct is marked as WF, the air quality abnormal value threshold of the fresh air unit is preset as wf, and the air quality abnormal value WF of the fresh air unit is compared with the air quality abnormal value threshold wf of the fresh air machine;

[0022] If the air quality abnormal value WF of the fresh air unit is less than the air quality abnormal value threshold value wf of the fresh air unit, it means that the output air quality of the fresh air unit is normal, a normal quality signal is obtained, and the fresh air unit is turned on normally;

[0023] When the air quality abnormal value WF of the fresh air unit ≥ the air quality abnormal value threshold of the fresh air unit is wf, it means that the output air quality of the fresh air unit is abnormal, a quality abnormality signal is obtained, and the fresh air unit is shut down.

[0024] As a further solution of the present invention: the abnormal value of air quality in the courtyard is obtained and marked as WFY, and the ratio of the abnormal value of air quality WFY in the courtyard is calculated with the abnormal value of air quality WF of the fresh air unit to obtain the air quality conversion ratio.

[0025] As a further solution of the present invention: if 0.6≤air quality conversion ratio≤1.5, it means that the air quality in the courtyard room is average, and a fresh air channel opening signal is obtained. The first decision analysis module keeps the valve of the fresh air channel in an open state based on the fresh air channel opening signal;

[0026] If the air quality conversion ratio is less than 0.6, it means that the air quality in the courtyard is good, and a positive closing signal of the fresh air channel is obtained. The first decision analysis module keeps the valve of the fresh air channel closed based on the positive closing signal of the fresh air channel;

[0027] If the air quality conversion ratio is greater than 1.5, it indicates that the air quality in the courtyard is poor, and a negative closing signal of the fresh air channel is obtained. The first decision analysis module keeps the valve of the fresh air channel closed based on the positive and negative signals of the fresh air channel.

[0028] As a further solution of the present invention: the second decision analysis module identifies the operating status of the fresh air unit as follows:

[0029] Obtain the number of leakage points corresponding to all fresh air ducts, record it as the total number of leakage points, calculate the ratio of the total number of leakage points to the length value of all fresh air ducts, and obtain the leakage number ratio;

[0030] Obtain the area value corresponding to each leakage point on each fresh air duct, sum up the area values ​​of all leakage points to obtain the total leakage area value, calculate the ratio of the total leakage area value to the length value of all fresh air ducts, and obtain the leakage area ratio;

[0031] The leakage number ratio is marked as Lg;

[0032] The leakage area ratio is marked as Ls;

[0033] That is, the fresh air duct operation parameter value Li is calculated by the formula Li=γ*(Lg+Ls), wherein γ is the pre-trial proportional coefficient, and γ>0.

[0034] As a further solution of the present invention: obtaining operation behavior data of the fresh air unit, the operation behavior data including the operation sound value, the total operation time and the operation failure rate of the fresh air unit;

[0035] Among them, the operating sound value of the fresh air unit is the maximum decibel value during the operation of the fresh air unit;

[0036] Mark the operating sound value of the fresh air unit as Fs;

[0037] Mark the total operating duration of the fresh air unit as Ft;

[0038] Mark the operating failure rate of the fresh air unit as Fg;

[0039] That is, through the formula Calculate the operating failure value Fi of the fresh air unit, where a1, a2, and a3 are preset proportionality coefficients.

[0040] As a further solution of the present invention: perform weighted processing on the operating parameter value Li of the fresh air duct and the operating failure value Fi of the fresh air unit; obtain the operating state evaluation value LF of the fresh air unit and the fresh air duct.

[0041] As a further solution of the present invention: preset the limit values of the operating state evaluation value of the fresh air unit and the fresh air duct as LF1 and LF2, where LF1 < LF2:

[0042] When LF < LF1, it indicates that the operating state of the fresh air unit and the fresh air duct is relatively stable, and a first-level abnormal signal is obtained;

[0043] When LF1 ≤ LF ≤ LF2, it indicates that the operating state of the fresh air unit and the fresh air duct is relatively stable, and a second-level abnormal signal is obtained;

[0044] When LF > LF2, it indicates that the operating state of the fresh air unit and the fresh air duct is relatively stable, and a third-level abnormal signal is obtained.

[0045] The beneficial effects of the present invention:

[0046] (1) By setting a number of air quality sensors and particulate monitors on the fresh air unit and the fresh air duct, the present invention obtains the concentration data of toxic gases and the concentration data of particulate matter in the environment of the fresh air unit and the fresh air duct. By processing the concentration data of toxic gases and the concentration data of particulate matter, the air quality abnormal value of the fresh air unit and the fresh air duct is obtained, so as to complete the identification of the purification effect of the fresh air unit and the fresh air duct by identifying the air quality abnormal value, enabling the fresh air unit and the fresh air duct to meet the usage requirements of the hospital ward;

[0047] (2) The present invention obtains the concentration data of toxic gases and particulate matter in each hospital room to obtain the air quality abnormal value of each hospital room, compares the air quality abnormal value of each hospital room with the air quality abnormal value of the fresh air unit and the fresh air channel, completes the evaluation of the air quality in each hospital room, and controls the valve of the fresh air channel corresponding to the hospital room based on the air quality evaluation result in each hospital room. When a negative closing signal of the fresh air channel is obtained, it means that the current air quality in the hospital room is poor, and there may be potential risk spillover, such as viruses or other factors that may affect the health of patients in the hospital room. The decision analysis module closes the valve of the fresh air channel based on the negative closing signal of the fresh air channel to prevent the air in the hospital room from spreading to other hospitals through the fresh air duct, causing the risk of cross-infection;

[0048] (3) The present invention is based on the operating status data of the fresh air unit and the fresh air duct, namely, the number of leakage points, the leakage area ratio, the operating sound value of the fresh air unit, the total operating time and the operating failure rate during the operation of the fresh air duct. By processing the operating status data of the fresh air unit and the fresh air duct, an operating status evaluation value is obtained. Based on the operating status evaluation value of the fresh air unit and the fresh air duct, the degree of abnormality of the fresh air unit and the fresh air duct is identified, thereby realizing the visual management of the fresh air unit and the fresh air duct during operation by the management personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] Figure 1 It is a flowchart of a hospital infection monitoring system based on Internet of Things technology of the present invention;

[0051] Figure 2 This is a schematic diagram of the air quality flow of a fresh air unit and a fresh air duct of a hospital infection monitoring system based on the Internet of Things technology of the present invention;

[0052] Figure 3 The present invention is a schematic diagram of the opening and closing process of the valve status of the fresh air channel of the hospital infection monitoring system based on the Internet of Things technology. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0054] See also Figure 1As shown, the present invention is a hospital infection monitoring system based on Internet of Things technology, including an air quality detection module, a quality assessment module, a first decision analysis module, a second decision analysis module and a cloud management and control platform;

[0055] The air quality detection module is used to monitor the air quality at the entrance of the fresh air channel of each courtyard room and the output end of the fresh air unit, obtain the abnormal air quality value, and transmit the abnormal air quality value to the cloud management and control platform;

[0056] Among them, the same group of fresh air units corresponds to multiple fresh air channels, and each fresh air channel corresponds to a courtyard room;

[0057] The quality assessment module receives the air quality anomaly values ​​transmitted by the cloud management and control platform, compares the air quality anomaly values ​​with the air quality anomaly threshold, obtains the fresh air unit quality signal, and sends the fresh air unit quality signal to the cloud management and control platform;

[0058] The quality signal of the fresh air unit includes a normal quality signal and an abnormal quality signal;

[0059] The first decision analysis module receives the normal quality signal transmitted by the cloud management and control platform, identifies the air quality in each hospital room, and controls the opening and closing of the fresh air duct corresponding to the hospital room based on the air quality in the hospital room;

[0060] The second decision analysis module receives the quality abnormality signal transmitted by the cloud management and control platform. The second decision analysis module identifies the operating status of the fresh air unit to realize intelligent management and control of the operation of the fresh air unit.

[0061] The air quality detection module includes a plurality of gas sampling units and a plurality of particle sampling units;

[0062] The gas sampling unit includes an air quality sensor, which is used to monitor the concentration of toxic gases in the air;

[0063] Among them, air toxic gases include carbon monoxide, carbon dioxide and formaldehyde in the measured environment;

[0064] The particle sampling unit includes a particle monitor, which is used to monitor the concentration of the particle;

[0065] Specific:

[0066] Several air quality sensors are installed on the fresh air unit and the fresh air channel, and the toxic gas concentration data monitored by the air quality sensors are marked as Wtu;

[0067] Several particle monitors are installed on the fresh air unit and the fresh air channel, and the concentration data of the particles monitored by the particle monitors are marked as Ftj;

[0068] The toxic gas concentration data and particulate matter concentration data obtained by monitoring are quantified and numerically calculated. The air quality outlier WF of the fresh air unit is calculated, and t is the time of data collection;

[0069] in,

[0070] u=1, 2, ..., u; where u is the number of air quality sensors monitored on the fresh air unit and the fresh air channel;

[0071] j = 1, 2, ..., j; where j is the number of particulate matter monitors monitored on the fresh air unit and the fresh air channel;

[0072] The air quality abnormal value threshold of the fresh air unit is preset as wf, and the air quality abnormal value WF of the fresh air unit is compared with the air quality abnormal value threshold wf of the fresh air unit;

[0073] If the air quality abnormal value WF of the fresh air unit is less than the air quality abnormal value threshold value wf of the fresh air unit, it means that the output air quality of the fresh air unit is normal, a normal quality signal is obtained, and the fresh air unit is turned on normally;

[0074] When the air quality abnormal value WF of the fresh air unit ≥ the air quality abnormal value threshold of the fresh air unit is wf, it means that the output air quality of the fresh air unit is abnormal, a quality abnormality signal is obtained, and the fresh air unit is shut down.

[0075] Based on the normal quality signal, the air quality of each independent courtyard room is monitored, specifically:

[0076] An air quality sensor and a particle monitor are independently installed in each independent courtyard room, and the air quality abnormal value WFY in the courtyard room is obtained through the air quality sensor and particle monitor in the independent courtyard room;

[0077] The air quality conversion ratio is obtained by calculating the ratio of the air quality abnormal value WFY in the courtyard room to the air quality abnormal value WF of the fresh air unit;

[0078] If 0.6≤air quality conversion ratio≤1.5, it means that the air quality in the courtyard is average, and a fresh air channel opening signal is obtained. The decision analysis module keeps the valve of the fresh air channel open based on the fresh air channel opening signal;

[0079] If the air quality conversion ratio is less than 0.6, it means that the air quality in the courtyard is good, and a positive closing signal of the fresh air channel is obtained. The decision analysis module keeps the valve of the fresh air channel closed based on the positive closing signal of the fresh air channel;

[0080] Among them, when the decision analysis module receives the positive closing signal of the fresh air channel, it means that the current air quality in the courtyard is good, and the fresh air unit does not need to purify the air in the courtyard, thus achieving energy saving of the fresh air unit;

[0081] If the air quality conversion ratio is greater than 1.5, it means that the air quality in the courtyard is poor, and a negative closing signal of the fresh air channel is obtained. The decision analysis module keeps the valve of the fresh air channel closed based on the positive and negative signals of the fresh air channel;

[0082] Among them, when the decision analysis module receives a negative closing signal of the fresh air channel, it means that the current air quality in the hospital room is poor, and there may be potential risks of spillover. For example, there may be viruses or other factors that affect the health of patients in the hospital room. The decision analysis module closes the valve of the fresh air channel based on the negative closing signal of the fresh air channel to prevent the air in the hospital room from spreading to other hospital rooms through the fresh air duct, causing the risk of cross-infection.

[0083] Based on the quality abnormality signal, the operating status of the fresh air unit and the fresh air channel is evaluated, specifically:

[0084] Obtain the number of leakage points corresponding to all fresh air ducts, record it as the total number of leakage points, calculate the ratio of the total number of leakage points to the length value of all fresh air ducts, and obtain the leakage number ratio;

[0085] Obtain the area value corresponding to each leakage point on each fresh air duct, sum up the area values ​​of all leakage points to obtain the total leakage area value, calculate the ratio of the total leakage area value to the length value of all fresh air ducts, and obtain the leakage area ratio;

[0086] The leakage number ratio is marked as Lg;

[0087] The leakage area ratio is marked as Ls;

[0088] That is, the fresh air duct operation parameter value Li is calculated by the formula Li=γ*(Lg+Ls), where γ is the pre-trial proportional coefficient, and γ>0;

[0089] Obtaining the operation behavior data of the fresh air unit, the operation behavior data including the operation sound value, total operation time and operation failure rate of the fresh air unit;

[0090] Among them, the operating sound value of the fresh air unit is the maximum decibel value of the fresh air unit during operation;

[0091] The operating sound value of the fresh air unit is marked as Fs;

[0092] The total operation time of the fresh air unit is marked as Ft;

[0093] The operation failure rate of the fresh air unit is marked as Fg;

[0094] That is, through the formula calculate the operating fault value Fi of the fresh air unit, where a1, a2, and a3 are preset proportionality coefficients.

[0095] Perform weighted processing on the operating parameter value Li of the fresh air duct and the operating fault value Fi of the fresh air unit; obtain the operating status evaluation value LF of the fresh air unit and the fresh air duct;

[0096] Specifically:

[0097] Divide the weight proportion of the operating parameter value Li of the fresh air duct into n1, and divide the weight proportion of the operating fault value Fi of the fresh air unit into n2, where both n1 and n2 are greater than 0, and n1 is greater than n2;

[0098] Obtain the operating status evaluation value LF of the fresh air unit and the fresh air duct through the formula LF = Li * n1 + Fi * n2;

[0099] Preset the limit values of the operating status evaluation value of the fresh air unit and the fresh air duct as LF1 and LF2, where LF1 < LF2:

[0100] When LF < LF1, it indicates that the operating status of the fresh air unit and the fresh air duct is relatively stable, and a first-level abnormal signal is obtained;

[0101] When LF1 ≤ LF ≤ LF2, it indicates that the operating status of the fresh air unit and the fresh air duct is relatively stable, and a second-level abnormal signal is obtained;

[0102] When LF > LF2, it indicates that the operating status of the fresh air unit and the fresh air duct is relatively stable, and a third-level abnormal signal is obtained;

[0103] Among them, the abnormal degree of the third-level abnormal signal is higher than that of the second-level abnormal signal, and the abnormal degree of the second-level abnormal signal is higher than that of the first-level abnormal signal;

[0104] The higher the abnormal degree of the fresh air unit and the fresh air duct, the higher the emergency degree of the abnormality of the fresh air unit and the fresh air duct. Specifically, it indicates that the leakage is high when the fresh air unit and the fresh air duct are in use, the failure rate is high when the fresh air unit and the fresh air duct are in use, and the service life is long when the fresh air unit and the fresh air duct are in use. By obtaining the abnormal level of the fresh air unit and the fresh air duct, the visualization management of the fresh air unit and the fresh air duct by the management personnel during the operation process is realized.

[0105] The above has described a specific embodiment of the present invention in detail, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A hospital infection monitoring system based on Internet of Things technology, characterized in that: include: An air quality detection module is used to monitor the air quality at the entrance of the fresh air channel of each courtyard room and the output end of the fresh air unit, obtain air quality abnormal values, and transmit the air quality abnormal values ​​to the cloud management and control platform; A quality assessment module, wherein the quality assessment module receives the air quality abnormal value transmitted by the cloud management and control platform, compares the air quality abnormal value with the air quality abnormal value threshold, obtains the fresh air unit quality signal, and sends the fresh air unit quality signal to the cloud management and control platform; The quality signal of the fresh air unit includes a normal quality signal and an abnormal quality signal; A first decision analysis module, wherein the first decision analysis module receives a normal quality signal transmitted by the cloud management and control platform, identifies the air quality in each courtyard room, and controls the opening and closing of the fresh air duct corresponding to the courtyard room based on the air quality in the courtyard room; A second decision analysis module, wherein the second decision analysis module receives the quality abnormality signal transmitted by the cloud management and control platform, and the second decision analysis module identifies the operation status of the fresh air unit to realize intelligent management and control of the operation of the fresh air unit; The process of obtaining air quality abnormal values ​​is as follows: Several air quality sensors are installed on the fresh air unit and the fresh air channel, and the toxic gas concentration data monitored by the air quality sensors are marked as Wtu; Several particle monitors are installed on the fresh air unit and the fresh air channel, and the concentration data of the particles monitored by the particle monitors are marked as Ftj; The toxic gas concentration data and particulate matter concentration data obtained by monitoring are quantified and numerically calculated. The air quality abnormal value WF of the fresh air unit is calculated, and t is the time of data collection; in, u=1, 2, ..., u; where u is the number of air quality sensors monitored on the fresh air unit and the fresh air channel; j=1,2,……,j; Where j is the number of particulate matter monitors monitored on the fresh air unit and fresh air channel; The air quality outliers of the fresh air unit and the fresh air duct are marked as WF, the air quality outlier threshold of the fresh air unit is preset as wf, and the air quality outlier WF of the fresh air unit is compared with the air quality outlier threshold wf of the fresh air fan; If the air quality abnormal value WF of the fresh air unit is less than the air quality abnormal value threshold value wf of the fresh air unit, it means that the output air quality of the fresh air unit is normal, a normal quality signal is obtained, and the fresh air unit is turned on normally; When the air quality abnormal value WF of the fresh air unit ≥ the air quality abnormal value threshold of the fresh air unit is wf, it means that the output air quality of the fresh air unit is abnormal, a quality abnormality signal is obtained, and the fresh air unit is shut down.

2. According to claim 1, a hospital infection monitoring system based on Internet of Things technology is characterized in that: The air quality detection module includes a plurality of gas sampling units and a plurality of particle sampling units; The gas sampling unit includes an air quality sensor, which is used to monitor the concentration of toxic gases in the air; The particle sampling unit includes a particle monitor, which is used to monitor the concentration of the particle; Among them, multiple air quality sensors and particulate matter monitors are provided.

3. According to claim 2, a hospital infection monitoring system based on Internet of Things technology is characterized in that: The abnormal value of air quality in the courtyard is obtained and marked as WFY. The ratio of the abnormal value of air quality WFY in the courtyard and the abnormal value of air quality WF of the fresh air unit is calculated to obtain the air quality conversion ratio.

4. According to claim 3, a hospital infection monitoring system based on Internet of Things technology is characterized in that: If 0.6 ≤ air quality conversion ratio ≤ 1.5, it indicates that the air quality in the courtyard house is average, and a fresh air duct opening signal is obtained. The first decision analysis module keeps the valve of the fresh air duct open based on the fresh air duct opening signal; If the air quality conversion ratio < 0.6, it indicates that the air quality in the courtyard house is good, and a fresh air duct forward closing signal is obtained. The first decision analysis module keeps the valve of the fresh air duct closed based on the fresh air duct forward closing signal; If the air quality conversion ratio > 1.5, it indicates that the air quality in the courtyard house is poor, and a fresh air duct reverse closing signal is obtained. The first decision analysis module keeps the valve of the fresh air duct closed based on the fresh air duct reverse closing signal.

5. According to claim 4, a hospital infection monitoring system based on Internet of Things technology is characterized in that: The process of the second decision analysis module identifying the operating state of the fresh air unit is as follows: Obtain the total number of leakage points corresponding to all fresh air ducts, denoted as the total number of leakage points, and calculate the ratio of the total number of leakage points to the length values of all fresh air ducts to obtain the leakage number ratio; Obtain the area value corresponding to each leakage point on each fresh air duct, sum up the area values of all leakage points to obtain the total leakage area value, and calculate the ratio of the total leakage area value to the length values of all fresh air ducts to obtain the leakage area ratio; Mark the leakage number ratio as Lg; Mark the leakage area ratio as Ls; That is, the operating parameter value Li of the fresh air duct is calculated by the formula Li = γ * (Lg + Ls), where γ is a pre-trial proportionality coefficient and γ > 0.

6. The hospital infection monitoring system based on Internet of Things technology according to claim 5 is characterized in that: Obtain the operating behavior data of the fresh air unit, and the operating behavior data includes the operating sound value, total operating duration, and operating failure rate of the fresh air unit; Among them, the operating sound value of the fresh air unit is the maximum decibel value during the operation of the fresh air unit; Mark the operating sound value of the fresh air unit as Fs; Mark the total operating duration of the fresh air unit as Ft; Mark the operating failure rate of the fresh air unit as Fg; That is, through the formula The fresh air unit operation fault value Fi is calculated, where a1, a2, and a3 are preset proportional coefficients.

7. The hospital infection monitoring system based on Internet of Things technology according to claim 6 is characterized in that: Perform weighted processing on the operating parameter value Li of the fresh air duct and the operating failure value Fi of the fresh air unit to obtain the operating state evaluation value LF of the fresh air unit and the fresh air duct.

8. The hospital infection monitoring system based on Internet of Things technology according to claim 7 is characterized in that: Preset the limit values of the operating state evaluation value of the fresh air unit and the fresh air duct as LF1 and LF2, where LF1 < LF2: When LF < LF1, a first-level abnormal signal is obtained; When LF1 ≤ LF ≤ LF2, a second-level abnormal signal is obtained; When LF > LF2, a third-level abnormal signal is obtained; Among them, the abnormal degree of the third-level abnormal signal is higher than that of the second-level abnormal signal, and the abnormal degree of the second-level abnormal signal is higher than that of the first-level abnormal signal.

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