Outpatient space environment intelligent monitoring and disinfection method based on bionic immune principle

Abstract

This invention provides an intelligent monitoring and disinfection method for outpatient spaces based on biomimetic immune principles. The method includes: real-time acquisition of multidimensional parameters of the outpatient space; antigen encoding of the acquired multidimensional parameters to obtain a first antigen vector; matching the first antigen vector with a first antibody; determining the risk level based on the matching result; matching a disinfection strategy based on the risk level and environmental characteristics to generate a first disinfection strategy; and executing disinfection operations according to the first disinfection strategy. A dynamically updated risk feature database is established, enabling the establishment of an immune memory mechanism. Through a learning process, the recognition strategy is adjusted, improving the efficiency and analytical capabilities of the immune system in recognizing antigen vectors. This invention improves the efficiency and accuracy of outpatient space monitoring and disinfection.

Description

Technical Field

[0001] This invention relates to the fields of medical building design technology and intelligent environmental monitoring equipment, and in particular to an intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity. Background Technology

[0002] Children's hospital outpatient departments are the core areas where children seek medical treatment. Because children's immune systems are not fully developed, their resistance to pathogens such as bacteria and viruses is extremely low, making them at significantly higher risk of nosocomial infections than in adult hospitals. Statistics show that the incidence of nosocomial infections in children's hospital outpatient departments reaches 5%-15%. Cross-transmission of pathogens such as methicillin-resistant Staphylococcus aureus and rotavirus can easily lead to outbreaks, prolonging the treatment period for children, increasing medical costs, and even endangering their lives.

[0003] Existing hospital microbial environment monitoring technologies suffer from the following key deficiencies: 1. Outdated monitoring methods: Relying on manual periodic sampling with long sampling intervals, they cannot capture real-time dynamic changes in microbial concentration, making timely intervention difficult once infection risks emerge; 2. Limited monitoring dimensions: Focusing only on microbial concentrations in the air or on object surfaces, without considering key influencing factors such as temperature, humidity, population density, and particulate matter concentration, leading to incomplete risk assessment; 3. Rigid early warning mechanisms: Using fixed thresholds for early warning, they fail to consider the characteristics of pediatric hospital outpatient settings such as "large fluctuations in patient flow and seasonal differences," easily resulting in false alarms or missed alarms; 4. Lack of decision support: Only outputting monitoring data, without targeted disinfection recommendations or resource allocation plans, relying on the experience and judgment of medical staff, resulting in low prevention and control efficiency. Therefore, there is an urgent need for an integrated system that can achieve "multi-dimensional real-time monitoring - accurate risk prediction - dynamic early warning - intelligent decision-making" to address the core pain points of microbial environment monitoring in pediatric hospital outpatient settings. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent monitoring and disinfection method for outpatient spaces based on the principle of biomimetic immunity. This invention improves the efficiency and accuracy of outpatient space monitoring and disinfection.

[0005] This invention provides a method for intelligent monitoring and disinfection of outpatient space environment based on biomimetic immune principle, comprising:

[0006] Real-time collection of multi-dimensional parameters of the outpatient space;

[0007] The first antigen vector is obtained by encoding the collected multidimensional parameters.

[0008] Match the first antibody to the first antigen vector;

[0009] The risk level is determined based on the matching results;

[0010] Based on the risk level and environmental characteristics, a disinfection strategy is matched to generate the first disinfection strategy;

[0011] Perform disinfection procedures according to the first disinfection strategy.

[0012] Furthermore, the intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity also includes: matching the first antibody to the first antigen vector through a risk feature database.

[0013] Furthermore, the risk feature database includes antigen vector-antibody pairs, which include the mapping relationship between the first antigen vector and the first antibody.

[0014] Furthermore, the risk characteristic database is obtained by learning and training on historical multidimensional parameter data using the synthetic T-cell algorithm:

[0015] Historical multidimensional parameter data is encoded into antigen vectors, and preset risk levels and disinfection strategies are encoded into antibodies;

[0016] The antigen vector and antibody are input into the synthetic T cell algorithm to obtain antigen vector-antibody pairs.

[0017] Furthermore, if a first antibody cannot be matched for the first antigen vector through the risk feature database, then a second antibody that can match the first antigen vector is selected for amplification based on affinity calculation, resulting in a new risk feature database that includes the mapping relationship between the first antigen vector and the second antibody.

[0018] Furthermore, the first antigen vector includes a first feature parameter and a second feature parameter, and the first antibody includes a first feature threshold one, a first feature threshold two, a second feature threshold one, and a second feature threshold two.

[0019] Risk levels include: low risk, medium risk, and high risk;

[0020] When the first feature parameter ≤ the first feature threshold 1 and the second feature parameter ≤ the second feature threshold 1, it is considered low risk; when the first feature threshold 1 < the first feature parameter ≤ the first feature threshold 2 or the second feature threshold 1 < the second feature parameter ≤ the second feature threshold 2, it is considered medium risk; when the first feature parameter > the first feature threshold 2 or the second feature parameter > the second feature threshold 2, it is considered high risk.

[0021] Furthermore, different risk levels correspond to different disinfection strategies: low risk corresponds to Level 1 disinfection strategy, medium risk corresponds to Level 2 disinfection strategy, and high risk corresponds to Level 3 disinfection strategy.

[0022] Furthermore, the intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity also includes:

[0023] During disinfection operations, multidimensional parameters of the outpatient space are continuously collected in real time, and the collected multidimensional parameters are encoded into antigens to obtain the first antigen vector. The first antigen vector is matched with the first antibody, the risk level is determined based on the matching result, and a disinfection strategy is matched based on the risk level and environmental characteristics. The first disinfection strategy is maintained or updated to obtain the second disinfection strategy.

[0024] Perform disinfection procedures according to the first or second disinfection strategy;

[0025] To enable dynamic adjustment of disinfection strategies.

[0026] Furthermore, the intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity also includes:

[0027] Collect multidimensional parameters after disinfection is completed;

[0028] Conduct a compliance assessment to determine whether the preset environmental quality standards have been met;

[0029] If the standard is met, the first disinfection strategy and / or the second disinfection strategy will be added to the knowledge base.

[0030] If the standard is not met, the multidimensional parameters of the outpatient space will continue to be collected in real time, and the collected multidimensional parameters will be encoded with antigens to obtain the first antigen vector. The first antigen vector will be matched with the first antibody. The risk level will be determined based on the matching result. The disinfection strategy will be matched based on the risk level and environmental characteristics. The first disinfection strategy, the second disinfection strategy, or the third disinfection strategy will be updated.

[0031] Furthermore, the intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity also includes:

[0032] Collect multidimensional parameters after disinfection is completed;

[0033] Compare the multidimensional parameters before and after disinfection, and calculate the degree of improvement;

[0034] Based on the degree of improvement, feedback is obtained, and disinfection strategies are adjusted accordingly.

[0035] Furthermore, the multidimensional parameters include:

[0036] Biological parameters include one or more of the following: bacterial concentration, viral concentration, pathogen type, etc.

[0037] Environmental parameters include one or more of the following: temperature, humidity, PM2.5, PM10, and concentration of harmful gases.

[0038] Quality parameters include one or more of the following: air quality index, noise level, light intensity, etc.

[0039] Furthermore, the multidimensional parameters are derived from any one or more of different population groups, departments, regions, and lengths of stay.

[0040] Another aspect of the present invention provides an intelligent monitoring and disinfection device for outpatient space environment based on the principle of biomimetic immunity, comprising:

[0041] The sensing unit is used to collect multi-dimensional parameters of the outpatient space in real time.

[0042] The biomimetic immune recognition unit encodes the collected multidimensional parameters to obtain the first antigen vector, matches the first antibody to the first antigen vector, and determines the risk level based on the matching result.

[0043] The control unit matches disinfection strategies based on risk level and environmental characteristics, and generates the first disinfection strategy.

[0044] The disinfection unit performs disinfection operations according to the first disinfection strategy.

[0045] Furthermore, the sensing unit includes:

[0046] The biological module is used to acquire biological parameters;

[0047] The environment module is used to acquire environmental and quality parameters.

[0048] The optical module is used to acquire environmental parameters;

[0049] The biometric module is used to obtain biotoxicity monitoring results.

[0050] In another aspect, this invention provides an intelligent monitoring and disinfection system for outpatient spaces based on the principle of biomimetic immunity. The system employs the aforementioned intelligent monitoring and disinfection method for outpatient spaces based on the principle of biomimetic immunity, comprising:

[0051] The sensing component is used to collect multi-dimensional parameters of the outpatient space in real time.

[0052] The controller encodes the collected multidimensional parameters to obtain the first antigen vector, matches the first antibody to the first antigen vector, determines the risk level based on the matching result, and matches a disinfection strategy based on the risk level and environmental characteristics to generate the first disinfection strategy.

[0053] The disinfection component performs disinfection operations according to the first disinfection strategy.

[0054] Furthermore, sensing components are installed in different areas of the hospital and / or different hospitals to collect one or more multidimensional parameters from different populations, departments, areas, and lengths of stay in real time.

[0055] In another aspect, the present invention provides a remote information processing system that is communicatively connected to the aforementioned intelligent monitoring and disinfection device for outpatient space environment based on the principle of biomimetic immunity.

[0056] In another aspect, the present invention provides a computer storage medium for storing computer programs or instructions; when the computer programs or instructions are executed by a processor, they implement the above-described intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity.

[0057] As can be seen from the above solutions, the advantages of the present invention are:

[0058] By collecting multidimensional parameters of the outpatient space in real time, dynamic changes in the outpatient space can be detected promptly. Collecting multidimensional parameters reflects the state of the outpatient space from multiple perspectives, enabling comprehensive monitoring and analysis, improving the accuracy of outpatient space status monitoring, and establishing a proactive sensing and comprehensive analysis monitoring system. The collected multidimensional parameters are encoded into a binary-coded first antigen vector, which is then used to match the first antigen vector with the first antibody. The risk level is determined based on the matching result. A disinfection strategy is then matched based on the risk level and environmental characteristics, generating a first disinfection strategy, and disinfection operations are performed according to the first disinfection strategy to complete the disinfection of the outpatient space. By collecting multidimensional parameters, comprehensive real-time intelligent monitoring and precise disinfection of the outpatient space environment are achieved, reducing the risk of cross-infection and protecting patient health. This overcomes the singularity and passivity of traditional monitoring systems, establishing a proactive sensing and intelligent analysis monitoring system; it breaks through the limitations of traditional disinfection methods, obtaining disinfection strategies through multidimensional parameter analysis, and then carrying out disinfection according to the strategies, constructing a safe, efficient, and environmentally friendly intelligent disinfection system. By collecting multidimensional parameters of the outpatient space in real time, dynamic changes in the outpatient space can be detected promptly. Collecting multidimensional parameters reflects the state of the outpatient space from multiple perspectives, enabling comprehensive monitoring and analysis, improving the accuracy of outpatient space status monitoring, and establishing a proactive sensing and comprehensive analysis monitoring system. The collected multidimensional parameters are encoded into a binary-coded first antigen vector, which is then used to match the first antigen vector with the first antibody. The risk level is determined based on the matching result. A disinfection strategy is then matched based on the risk level and environmental characteristics, generating a first disinfection strategy, and disinfection operations are performed according to the first disinfection strategy to complete the disinfection of the outpatient space. By collecting multidimensional parameters, comprehensive real-time intelligent monitoring and precise disinfection of the outpatient space environment are achieved, reducing the risk of cross-infection and protecting patient health. This overcomes the singularity and passivity of traditional monitoring systems, establishing a proactive sensing and intelligent analysis monitoring system; it breaks through the limitations of traditional disinfection methods, obtaining disinfection strategies through multidimensional parameter analysis, and then carrying out disinfection according to the strategies, constructing a safe, efficient, and environmentally friendly intelligent disinfection system. Attached Figure Description

[0059] Figure 1 A flowchart of an intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle provided in an embodiment of the present invention;

[0060] Figure 2 A flowchart of an intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle, provided as another embodiment of the present invention;

[0061] Figure 3 A flowchart illustrating the execution of a disinfection strategy according to an embodiment of the present invention;

[0062] Figure 4 A schematic diagram of an intelligent monitoring and disinfection device for outpatient space environment based on biomimetic immune principle provided in an embodiment of the present invention;

[0063] Figure 5 This is a schematic diagram of the sensing unit structure;

[0064] Figure 6 This is a schematic diagram of the disinfection unit structure;

[0065] Figure 7 A flowchart of the intelligent monitoring and disinfection device for outpatient space environment based on biomimetic immune principle provided in an embodiment of the present invention;

[0066] Figure 8 A flowchart of the workflow of an intelligent monitoring and disinfection device for outpatient space environment based on biomimetic immune principle provided in another embodiment of the present invention;

[0067] Figure 9 A flowchart of the disinfection unit provided in an embodiment of the present invention;

[0068] Figure 10 This is a schematic diagram of the arrangement of sensing components according to an embodiment of the present invention;

[0069] Figure 11 A schematic diagram of the arrangement of sensing components provided in another embodiment of the present invention;

[0070] Figure 12 This is a schematic diagram of the arrangement of sensing components according to another embodiment of the present invention.

[0071] In the attached figures, the following labels are used:

[0072] 10-Intelligent monitoring and disinfection device for outpatient space environment based on biomimetic immune principle;

[0073] 11-Sensing unit;

[0074] 111-Biological Module;

[0075] 112 - Environment Module;

[0076] 113 - Optical Module;

[0077] 114 - Biometric Module;

[0078] 12-Bionic immune recognition units;

[0079] 13-Control unit;

[0080] 14-Disinfection unit;

[0081] 141 - Ultraviolet disinfection module;

[0082] 142 - Plasma disinfection module;

[0083] 143 - Atomization disinfection module;

[0084] 144-Compound Disinfection Module;

[0085] 20-Perception Components;

[0086] 30-Hospital;

[0087] 40-Department;

[0088] 50-area. Detailed Implementation

[0089] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments to further understand the purpose, solution and effect of the present invention, but it is not intended to limit the scope of protection of the appended claims.

[0090] References to "embodiment," "another embodiment," "this embodiment," etc., in the specification refer to embodiments that may include specific features, structures, or characteristics, but not every embodiment must include these specific features, structures, or characteristics. Furthermore, such expressions do not refer to the same embodiment. Moreover, when describing specific features, structures, or characteristics in conjunction with embodiments, whether or not explicitly described, it is indicated that incorporating such features, structures, or characteristics into other embodiments is within the knowledge of those skilled in the art.

[0091] The specification and subsequent claims use certain terms to refer to specific components or parts. Those skilled in the art will understand that users or manufacturers may use different names or terms to refer to the same component or part. This specification and claims do not distinguish components or parts by differences in name, but rather by differences in function. The terms "comprising" and "including" used throughout the specification and claims are open-ended and should be interpreted as "including but not limited to". Furthermore, the term "connection" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connections via other means.

[0092] Figure 1 The intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity provided by this invention includes the following steps:

[0093] S1: Real-time acquisition of multi-dimensional parameters of the outpatient space;

[0094] In one embodiment, real-time acquisition of multi-dimensional parameters of the outpatient space can promptly detect dynamic changes in the outpatient space. The multi-dimensional parameters can reflect the state of the outpatient space from multiple perspectives, improving the accuracy of outpatient space status monitoring and establishing a proactive perception and comprehensive analysis monitoring system.

[0095] S2: Encode the collected multidimensional parameters to obtain the first antigen vector;

[0096] In one embodiment, the collected multidimensional parameters are encoded into an antigen to obtain a first antigen vector with binary encoding, so that the first antigen vector can be matched with a first antibody in the future.

[0097] S3: Match the first antibody to the first antigen vector;

[0098] S4: Determine the risk level based on the matching results;

[0099] S5: Match disinfection strategies based on risk level and environmental characteristics to generate the first disinfection strategy;

[0100] S6: Perform disinfection procedures according to the first disinfection strategy.

[0101] In one embodiment, by collecting multidimensional parameters of the outpatient space in real time, dynamic changes in the outpatient space can be detected promptly. Collecting multidimensional parameters reflects the state of the outpatient space from multiple perspectives, enabling comprehensive monitoring and analysis of its status. This improves the accuracy of outpatient space status monitoring and establishes a proactive, comprehensive monitoring system. The collected multidimensional parameters are encoded into a first antigen vector in binary code. This allows for the matching of the first antigen vector with a first antibody, determining the risk level based on the matching results. A disinfection strategy is then matched based on the risk level and environmental characteristics, generating a first disinfection strategy. Disinfection operations are then performed according to this strategy to complete the disinfection of the outpatient space. By collecting multidimensional parameters, comprehensive real-time intelligent monitoring and precise disinfection of the outpatient space environment are achieved, reducing the risk of cross-infection and protecting patient health. This overcomes the limitations of traditional monitoring systems, which are often singular and passive, establishing a proactive, intelligent monitoring system. It also breaks through the limitations of traditional disinfection methods by analyzing multidimensional parameters to obtain disinfection strategies, which are then used for disinfection, constructing a safe, efficient, and environmentally friendly intelligent disinfection system.

[0102] In one embodiment, a first antibody is matched to a first antigen vector using a risk feature database, which enables the first antigen vector to be quickly identified and matched with a corresponding first antibody. Then, the risk level is determined based on the matching result, and a disinfection strategy is matched based on the risk level and environmental characteristics to generate a first disinfection strategy for disinfecting the outpatient space. This reduces the response time for disinfecting the outpatient space from several hours in traditional methods to minutes, improving the efficiency and timeliness of disinfecting the outpatient space.

[0103] In one embodiment, after obtaining the multidimensional parameters, the multidimensional parameter data needs to be preprocessed to eliminate errors and environmental interference.

[0104] In one embodiment, the collected multidimensional parameters are encoded into an antigen using parameter binary quantization to obtain a first antigen vector with binary encoding.

[0105] In one embodiment, environmental features can be human body sensing results, used to reflect whether there are people in the outpatient space and the flow and density of people in the outpatient space.

[0106] In one embodiment, the risk feature database includes antigen vector-antibody pairs, which include a mapping relationship between a first antigen vector and a first antibody. This allows the first antigen vector to be quickly matched with the corresponding first antibody through the antigen vector-antibody pairs in the risk feature database, reducing the response time for disinfection of outpatient spaces from several hours in traditional methods to minutes, thereby improving the efficiency and timeliness of disinfection of outpatient spaces.

[0107] In one embodiment, the risk feature database is obtained by learning and training historical multidimensional parameter data using a synthetic T-cell algorithm: the historical multidimensional parameter data is encoded as antigen vectors, and the preset risk level and disinfection strategy are encoded as antibodies; the antigen vectors and antibodies are input into the synthetic T-cell algorithm to obtain antigen vector-antibody pairs. When the same or similar antigen vectors are encountered again, the corresponding first antibody can be quickly matched to the first antigen vector through the antigen vector-antibody pairs in the risk feature database, thereby reducing the response time for outpatient space disinfection from several hours in traditional methods to minutes.

[0108] In one embodiment, drawing inspiration from the negative selection process of T cells in the biological immune system, a synthetic T cell algorithm is used to learn and train on historical environmental data. Characteristic parameters such as bacterial concentration, viral concentration, temperature, humidity, and air quality are encoded as "antigen vectors," while preset safety standards, risk levels, and disinfection strategies are encoded as "antibodies." Affinity calculations enable accurate identification of environmental anomalies. This mechanism can distinguish between normal environmental states ("self") and abnormal environmental states ("non-self").

[0109] In one embodiment, safety standards refer to existing national or industry regulations or standards, such as data on hospital temperature, humidity, and ventilation frequency in the "General Hospital Building Design Standard"; and microbial concentration limits specified in the "Hospital Disinfection and Hygiene Standard." The parameter thresholds in all safety standards are the preset thresholds, serving as the direct basis for the digital coding of "antibodies" and ensuring coding compliance.

[0110] In one embodiment, an intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity is applied to a children's hospital. By simulating the core mechanism of the biological immune system, it achieves comprehensive real-time intelligent monitoring and intelligent and precise disinfection of the outpatient space environment, reducing the risk of cross-infection and protecting children's health.

[0111] In one embodiment, if a first antibody cannot be matched for a first antigen vector using the risk feature database, a second antibody that can match the first antigen vector is selected for amplification based on affinity calculation, resulting in a new risk feature database that includes the mapping relationship between the first antigen vector and the second antibody. This amplification of the antigen vector-antibody pairs in the risk feature database allows for rapid matching of corresponding antibodies to various antigen vectors, such as the first, second, and third antigen vectors, improving the processing and analysis capabilities of antigen vectors. A dynamically updated risk feature database is established, enabling the establishment of an immune memory mechanism. By adjusting the recognition strategy through the learning process, the efficiency and processing and analysis capabilities of antibodies, i.e., the immune system, in recognizing antigen vectors can be improved.

[0112] In one embodiment, a dynamically updated risk feature database is established, similar to the memory cells of a biological immune system. When the system encounters a certain environmental risk pattern, i.e., a first antigen vector, for the first time, it adjusts its recognition strategy through a learning process and stores the antigen vector-antibody pair in the risk feature database as an optimal feature vector. When encountering the same or similar first antigen vector again, the system can quickly identify the corresponding first antibody and generate an appropriate response strategy under the action of immune memory, reducing the response time from several hours in traditional methods to minutes.

[0113] In one embodiment, the first antibody data that completes the antigen vector-antibody pair matching is selected to generate parameters with higher affinity to the first antigen vector, thereby improving the efficiency and processing and analysis capabilities of the immune system in recognizing antigen vector data and improving the accuracy of matching for more precise disinfection.

[0114] In one embodiment, the first antigen vector includes a first characteristic parameter and a second characteristic parameter, and the first antibody includes a first characteristic threshold one, a first characteristic threshold two, a second characteristic threshold one, and a second characteristic threshold two; the risk levels include: low risk, medium risk, and high risk. When the first characteristic parameter ≤ the first characteristic threshold one and the second characteristic parameter ≤ the second characteristic threshold one, it is considered low risk; when the first characteristic threshold one < the first characteristic parameter ≤ the first characteristic threshold two or the second characteristic threshold one < the second characteristic parameter ≤ the second characteristic threshold two, it is considered medium risk; when the first characteristic parameter > the first characteristic threshold two or the second characteristic parameter > the second characteristic threshold two, it is considered high risk. Classifying the risk level and analyzing the risk level of the outpatient space based on multi-dimensional parameters overcomes the limitations of traditional disinfection methods, going beyond simply using fixed thresholds for early warning, and improving the accuracy of risk assessment for outpatient spaces.

[0115] In one embodiment, the first and second characteristic parameters can be bacterial concentration, virus concentration, pathogen type, temperature, humidity, PM2.5, PM10, harmful gas concentration, air quality index, noise level, light intensity, etc. The required parameters can be selected according to needs or environmental characteristics, and threshold values ​​can be set based on the selected parameters.

[0116] In one embodiment, the first and second characteristic parameters are bacterial concentration and viral concentration, respectively. The first characteristic threshold is 0.5 × 10³ cfu / m³, the second characteristic threshold is 0.5 × 10³ cfu / m³, the third characteristic threshold is 1.0 × 10³ cfu / m³, the second characteristic threshold is 10² copies / m³, the second characteristic threshold is 10² copies / m³, and the second characteristic threshold is 10³ copies / m³. Low risk: bacterial concentration ≤ 0.5 × 10³ cfu / m³ and viral concentration ≤ 10² copies / m³. The criteria for determining the risk level are: medium risk: 0.5 × 10³ cfu / m³ < bacterial concentration ≤ 1.0 × 10³ cfu / m³ or 10² copies / m³ < viral concentration ≤ 10³ copies / m³; high risk: bacterial concentration > 1.0 × 10³ cfu / m³ or viral concentration > 10³ copies / m³.

[0117] In one embodiment, clone selection is performed to match a high-affinity antibody to a first antigen vector.

[0118] In one embodiment, a mutation operation is performed to optimize the accuracy of antibody recognition of antigen vectors.

[0119] In one embodiment, a memory update is performed, where matched antigen-antibody pairs are stored in a risk feature database, and new antigen-antibody pairs are added to the risk feature database during subsequent monitoring.

[0120] In one embodiment, risk level assessment is performed by calculating affinity score, and then risk level determination is made.

[0121] In one embodiment, the risk level is determined based on preset thresholds. Specifically, the preset threshold for low risk is a first characteristic parameter < 0.5 × 10² cfu / m² and a second characteristic parameter < 10² copies / m²; the preset threshold for medium risk is a first characteristic parameter within 0.5-1.0 × 10² cfu / m² or a second characteristic parameter within 10²-10² copies / m². 3 The preset threshold for high risk is either the first characteristic parameter > 1.0 × 10² cfu / ² or the second characteristic parameter > 10² copies / m².

[0122] In one embodiment, such as Figure 2 As shown, after receiving multidimensional parameter data, preprocessing is performed to eliminate errors and interference. Then, the first antigen vector is obtained by binary encoding the parameters. The first antibody is obtained by cloning and selecting the first antigen vector to match a high-affinity antibody. Mutation operation is then performed to optimize the recognition accuracy and memory update is performed. The matching results are stored in the risk feature database. The risk level is assessed based on the affinity score to determine the risk level.

[0123] In one embodiment, the mutation operation improves the accuracy of antibody recognition and matching of antigen vectors. Simply put, it enables the antibody to more accurately "recognize" the real antigen vectors in the current environment, avoiding "misjudgment" (such as mistaking normal fluctuations for risks) or "missed judgment" (such as failing to identify hidden contamination or hidden risks).

[0124] In one embodiment, the antibody includes one or more of a preset threshold, a risk level, and a disinfection strategy.

[0125] In one embodiment, when calculating the affinity score, the algorithm calculates the fit between the antigen vector, antibody, risk level, and disinfection strategy, providing a quantitative basis for intelligent prevention and control decisions. It can be compared with the binding strength between antibodies and antigens in the biological immune system. The higher the affinity score, the better the matching degree and the more suitable the prevention and control strategy is for the current scenario requirements; the lower the affinity score, the more the risk deviation exists, and targeted adjustments need to be triggered.

[0126] In one embodiment, the affinity algorithm is to perform a quantitative weighted summation calculation of the matching degree between multidimensional parameters and preset thresholds. The affinity score can reflect the matching degree between the antigen vector and the antibody.

[0127] In one embodiment, different risk levels correspond to different disinfection strategies: low risk corresponds to Level 1 disinfection strategy, medium risk corresponds to Level 2 disinfection strategy, and high risk corresponds to Level 3 disinfection strategy. By distinguishing risk levels and generating corresponding disinfection strategies based on risk levels, precise monitoring and disinfection of outpatient spaces can be achieved.

[0128] In one embodiment, such as Figure 3 As shown, different disinfection strategies are implemented according to different risk levels. Specifically, low risk corresponds to Level 1 disinfection strategy:

[0129] Disinfection method: Plasma disinfection module 142 is preferred;

[0130] Disinfection intensity: 30%;

[0131] Disinfection time: 15 minutes each time;

[0132] Disinfection frequency: once every 2 hours;

[0133] Safety measures: Normal operation when people are present, enhanced mode when no one is present.

[0134] In one embodiment, such as Figure 3 As shown, the secondary disinfection strategy corresponds to medium risk:

[0135] Disinfection method: The ultraviolet disinfection module 141 and the plasma disinfection module 142 work together;

[0136] Disinfection intensity: 60%;

[0137] Disinfection time: 30 minutes each time;

[0138] Disinfection frequency: once per hour;

[0139] Safety measures: Reduce intensity when people are present, and run at full power when no one is present.

[0140] In one embodiment, such as Figure 3 As shown, high-risk areas correspond to a three-tiered disinfection strategy:

[0141] Disinfection method: The atomization disinfection module 143, the ultraviolet disinfection module 141 and the plasma disinfection module 142 operate in combination;

[0142] Disinfection intensity: 100%;

[0143] Disinfection time: 60 minutes each time;

[0144] Disinfection frequency: Continue operation until the risk is reduced;

[0145] Safety measures: Operate when no one is present, and ventilate for 30 minutes after disinfection.

[0146] In one embodiment, the method further includes: continuously collecting multidimensional parameters of the outpatient space in real time during the disinfection operation, encoding the collected multidimensional parameters with antigens to obtain a first antigen vector, matching the first antigen vector with a first antibody, determining the risk level based on the matching result, matching a disinfection strategy based on the risk level and environmental characteristics, maintaining the first disinfection strategy or updating it to obtain a second disinfection strategy; performing the disinfection operation according to the first disinfection strategy or the second disinfection strategy; and realizing dynamic adjustment of the disinfection strategy.

[0147] In one embodiment, the process further includes: collecting multidimensional parameters after disinfection; determining whether a preset environmental quality standard has been met; if the standard is met, adding the first disinfection strategy and / or the second disinfection strategy to a knowledge base; that is, adding the first disinfection strategy or the second disinfection strategy that meets the standard, or the first disinfection strategy and the second disinfection strategy that meet the standard, to the knowledge base. If the standard is not met, the process continues to collect multidimensional parameters of the outpatient space in real time, and performs antigen encoding on the collected multidimensional parameters to obtain a first antigen vector, matches the first antigen vector with a first antibody, determines the risk level based on the matching result, matches a disinfection strategy based on the risk level and environmental characteristics, and maintains the first disinfection strategy, maintains the second disinfection strategy, or updates to obtain a third disinfection strategy.

[0148] In one embodiment, the method further includes: collecting multidimensional parameters after disinfection; comparing the multidimensional parameters before and after disinfection and calculating the degree of improvement; obtaining effect feedback based on the degree of improvement; and adjusting the disinfection strategy based on the effect feedback.

[0149] In one embodiment, the multidimensional parameters include: biological parameters, such as bacterial concentration, virus concentration, pathogen type, etc.; environmental parameters, such as temperature, humidity, PM2.5, PM10, harmful gas concentration, etc.; and quality parameters, such as air quality index, noise level, light intensity, etc. This overcomes the limitations and passivity of traditional monitoring systems, establishing a proactive sensing and intelligent analysis monitoring system.

[0150] In one embodiment, the multidimensional parameters are derived from any one or more of different populations, different departments 40, different areas 50, and different lengths of stay. Multidimensional parameters from different populations, different departments 40, different areas 50, and different lengths of stay are collected to comprehensively and accurately monitor the environment of hospital 30. Different lengths of stay can refer to the duration of stay for different populations, the time of collection of multidimensional parameters, etc. Furthermore, different areas 50 can refer to different areas within hospital 30, or different areas within different hospitals 30, etc., enabling the invention to monitor the environmental status of a single hospital 30 or multiple hospitals 30 simultaneously, achieving integrated management of the entire hospital or multiple hospitals in a coordinated manner. In this way, the collection, correction, self-learning, judgment, and prediction of big data can all be meaningful.

[0151] In one embodiment, an intelligent monitoring and disinfection device 10 for outpatient space environment based on the principle of biomimetic immunity is also provided, such as... Figure 4 As shown, it includes: a sensing unit 11, a biomimetic immune recognition unit 12, a control unit 13, and a disinfection unit 14. The sensing unit 11 is used to collect multi-dimensional parameters of the outpatient space in real time; the biomimetic immune recognition unit 12 encodes the collected multi-dimensional parameters to obtain a first antigen vector, matches the first antigen vector with a first antibody, and determines the risk level based on the matching result; the control unit 13 matches a disinfection strategy based on the risk level and environmental characteristics, and generates a first disinfection strategy; the disinfection unit 14 performs disinfection operations according to the first disinfection strategy.

[0152] In one embodiment, an intelligent monitoring and disinfection device 10 for outpatient space environment based on the principle of biomimetic immunity is also provided. Employing the aforementioned intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity, the device includes: a sensing unit 11, a biomimetic immune recognition unit 12, a control unit 13, and a disinfection unit 14. The sensing unit 11 is used to collect multidimensional parameters of the outpatient space in real time; the biomimetic immune recognition unit 12 encodes the collected multidimensional parameters to obtain a first antigen vector, matches the first antigen vector with a first antibody, and determines the risk level based on the matching result; the control unit 13 matches a disinfection strategy based on the risk level and environmental characteristics, generating a first disinfection strategy; and the disinfection unit 14 executes the disinfection operation according to the first disinfection strategy. This solves the fragmented problem of environmental management in children's hospitals and achieves integrated management with overall coordination throughout the hospital.

[0153] In one embodiment, such as Figure 5As shown, the sensing unit 11 includes a biological module 111, an environmental module 112, an optical module 113, and a biometric module 114. The biological module 111 is used to acquire biological parameters; the environmental module 112 is used to acquire environmental parameters and quality parameters; the optical module 113 is used to acquire environmental parameters; and the biometric module 114 is used to acquire biotoxicity monitoring results.

[0154] In one embodiment, the biomimetic immune recognition unit 12 is the "brain". The biomimetic immune recognition unit 12 uses a microcontroller as the core processor and has a built-in risk feature database trained based on machine learning algorithms. It can identify common pathogens that children are susceptible to, such as influenza virus, hand-foot-mouth disease virus, and pneumococcus.

[0155] In one embodiment, the working principle of the biomimetic immune recognition unit 12 includes:

[0156] Data preprocessing: The multidimensional parameter data collected by the sensing unit 11 is standardized to eliminate errors and environmental interference.

[0157] Feature extraction: Extract the first feature parameter and the second feature parameter from the preprocessed multidimensional parameter data. The first feature parameter and the second feature parameter can be parameters such as bacterial concentration, virus concentration, pathogen concentration change trend, and abnormal environmental parameter patterns.

[0158] Pattern matching: The extracted first and second feature parameters are compared with the built-in risk feature database to calculate the affinity score.

[0159] Risk assessment: Based on affinity scores and preset thresholds, the environmental risk level of the outpatient space is determined, specifically as low risk, medium risk, or high risk.

[0160] In one embodiment, such as Figure 6 As shown, the disinfection unit 14 includes: an atomization disinfection module 143, an ultraviolet disinfection module 141, a plasma disinfection module 142, and a composite disinfection module 144.

[0161] In one embodiment, the disinfection unit 14 is the actuator of the system, including multiple disinfection units 14, all of which adopt a child-friendly design:

[0162] Ultraviolet disinfection module 141: Utilizing UV-C LED technology, it boasts advantages such as high efficiency, environmental friendliness, and long lifespan, gradually replacing traditional ultraviolet lamps. Specifically, the environmental characteristic is the result of human body sensing. The ultraviolet disinfection module 141 can be configured as an ultraviolet disinfection lamp equipped with a human body sensor. It can detect the presence of people and automatically shut off when someone is present, ensuring safety. This meets the special environmental requirements of outpatient spaces in children's hospitals, ensuring effective disinfection without impacting children's health.

[0163] Plasma disinfection module 142: Utilizes a high-voltage electric field to excite gas molecules into a plasma state, thereby destroying bacterial cell walls, DNA, and proteins to achieve sterilization.

[0164] Atomized disinfection module 143: Uses non-toxic disinfectant, such as hydrogen peroxide dry fog sterilizer;

[0165] Composite disinfection module 144: combines the effects of UV-C, light and ozone.

[0166] Breaking through the limitations of traditional disinfection methods, we will build a safe, efficient, and environmentally friendly intelligent disinfection system.

[0167] In one embodiment, a feedback unit is also included, which is used to collect multi-dimensional parameters after disinfection and feed them back to the control unit 13 to form a closed-loop control.

[0168] In one embodiment, such as Figure 7 As shown, the sensing unit 11 collects multi-dimensional parameters in a distributed manner and transmits them to the bionic immune recognition unit 12. The bionic immune recognition unit 12 judges the risk level based on the multi-dimensional data. The control unit 13 is responsible for intelligently generating a disinfection strategy based on the analysis results of the bionic immune recognition unit 12 and transmitting it to the disinfection unit 14. The disinfection unit 14 executes disinfection according to the disinfection strategy. The feedback unit reuses the multi-dimensional parameters collected by the sensing component 12 after disinfection and feeds them back to the control unit 13. The control unit 13 adjusts the disinfection strategy. The control unit 13 adopts a PLC controller and is connected to the sensing unit 11, the bionic immune recognition unit 12, the control unit 13, and the disinfection unit 14 through a wireless communication module to realize data transmission and command issuance.

[0169] In one embodiment, the multidimensional parameters may include environmental parameters or may be equivalent to environmental parameters.

[0170] In one embodiment, such as Figure 8 As shown, the system first initializes itself through self-test / calibration / parameter loading. Then, environmental parameters are collected by the sensing unit 11, and the risk level is determined by the biomimetic immune recognition unit 12. The control unit 13 generates a disinfection strategy, and the disinfection unit 14 executes the disinfection operation. The feedback unit collects the parameters after disinfection and determines whether the standards are met. If the standards are not met, the control unit 13 adjusts the strategy; if the standards are met, the data is recorded and continuously monitored. The standard for compliance can be ≤0.5×10⁻⁶ bacteria. 3 cfu / m² and virus ≤10²copies / m².

[0171] In one embodiment, such as Figure 9As shown, the control unit 13 issues disinfection commands based on the risk level to control each module of the disinfection unit 14 to perform disinfection actions. High-risk commands are sent to the atomization disinfection module 143, the ultraviolet disinfection module 141, and the plasma disinfection module 142; medium-risk commands are sent to the ultraviolet disinfection module 141 and the plasma disinfection module 142; low-risk commands are sent to the plasma disinfection module 142; and special scenario commands are sent to the composite disinfection module 144. This meets the special environmental needs of the outpatient space in the children's hospital, ensuring that disinfection effectiveness is guaranteed without affecting children's health.

[0172] In one embodiment, a special scenario instruction is issued when a special scenario is detected. Specifically, a special scenario refers to a scenario such as an outbreak of an epidemic, a scenario for the diagnosis and treatment of sensitive groups, or a scenario for the diagnosis and treatment of people with low immunity, such as newborns or chemotherapy patients.

[0173] In one embodiment, the core functions of the control unit 13 include:

[0174] Strategy generation: Generate corresponding disinfection strategies based on factors such as risk level, personnel density, and area function;

[0175] Parameter adjustment: Adjust parameters such as disinfection intensity, time, and method in real time to ensure optimal disinfection effect;

[0176] Intelligent decision-making: Continuously optimize disinfection strategies based on historical data and real-time feedback;

[0177] Coordination and control: Unify the management of multiple disinfection units 14 to achieve collaborative operation.

[0178] In one embodiment, the sensing unit 11 includes: a biological monitoring module, an environmental monitoring module, and a biometric identification module. The biological monitoring module is used to monitor biological parameters; the environmental monitoring module is used to monitor environmental parameters and quality parameters; and the biometric identification module is used to monitor the concentration of biological parameters.

[0179] In one embodiment, when performing disinfection operations, the feedback unit continuously collects multidimensional parameters of the outpatient space in real time, encodes the collected multidimensional parameters to obtain a first antigen vector, matches the first antigen vector with a first antibody, determines the risk level based on the matching result, matches a disinfection strategy based on the risk level and environmental characteristics, maintains the first disinfection strategy or updates it to obtain a second disinfection strategy, and performs disinfection operations according to the first disinfection strategy or the second disinfection strategy; thus realizing dynamic adjustment of the disinfection strategy.

[0180] In one embodiment, the feedback unit and the sensing unit 11 share the sensing component 20 to collect multi-dimensional parameters after disinfection in real time. The main functions of the feedback unit include:

[0181] Effectiveness evaluation: Compare the changes in multidimensional parameters before and after disinfection to evaluate the disinfection effect;

[0182] Quality control: Monitoring whether the preset environmental quality standards are met;

[0183] Anomaly monitoring: Detect any abnormalities during the disinfection process and promptly issue an alarm;

[0184] Data recording: Save disinfection process and effect data to provide a basis for subsequent optimization.

[0185] In one embodiment, sensing components 20 are installed in different areas 50 of a hospital 30 and / or different hospitals 30 to collect one or more multidimensional parameters in real time from different populations, different departments 40, different areas 50, and different lengths of stay. Sensing components 20 are installed in different areas 50 of a hospital 30 and different areas 50 of different hospitals 30 to achieve environmental monitoring and disinfection of one or more hospitals 30, and to achieve coordinated management of one or more hospitals 30.

[0186] This device embodiment can be implemented in conjunction with the implementation methods described above. The relevant technical details mentioned in the implementation methods of the above embodiments remain valid in the implementation methods of this method embodiment, and will not be repeated here to avoid repetition.

[0187] In one embodiment, an intelligent monitoring and disinfection system for outpatient spaces based on the principle of biomimetic immunity is also provided. Employing the aforementioned intelligent monitoring and disinfection method for outpatient spaces based on the principle of biomimetic immunity, the system includes: a sensing component 20, a controller, and a disinfection component. The sensing component 20 is used to collect multidimensional parameters of the outpatient space in real time. The controller encodes the collected multidimensional parameters to obtain a first antigen vector, matches the first antigen vector with a first antibody, determines the risk level based on the matching result, and matches a disinfection strategy based on the risk level and environmental characteristics to generate a first disinfection strategy. The disinfection component executes disinfection operations according to the first disinfection strategy. This innovatively applies the core mechanisms of the biological immune system to the intelligent monitoring and precise disinfection of the outpatient environment in a children's hospital. By simulating functions such as immune recognition, immune memory, and immune regulation, an intelligent system capable of real-time perception of environmental risks, autonomous decision-making on disinfection strategies, and dynamic optimization is constructed.

[0188] In one embodiment, the sensing component 20 forms the basis for data acquisition and employs a distributed deployment strategy to ensure comprehensive monitoring coverage. The sensing component 20 includes multiple sensors, such as... Figure 10 As shown, the outpatient space includes sensors 1-8, which are deployed in different areas 50 such as the waiting area, outpatient hall, examination rooms, and corridors, to acquire multi-dimensional parameters of the children's hospital in real time and comprehensively. Of course, the sensing component 20 can also be deployed in other hospitals 30 or other places with high environmental requirements.

[0189] In one embodiment, such as Figure 11 As shown, the sensing component 20 can be deployed in different areas 50 and different departments 40 of a hospital 30. Different areas 50 refer to areas 50 other than departments 40.

[0190] In one embodiment, such as Figure 12 As shown, the sensing component 20 can be deployed in multiple hospitals 30.

[0191] In one embodiment, the sensing component 20 integrates multiple high-precision sensors, including biosensors, environmental sensors, optical sensors, and biometric sensors. The biometric sensors include bacterial sensors and virus sensors, employing immunosensor technology based on antigen-antibody reactions. The environmental sensors include temperature and humidity sensors and air quality sensors. The air quality sensor monitors PM2.5, PM10, SO2, NO2, CO, O3, VOCs, etc. The optical sensors employ a particulate matter sensor based on the laser scattering principle, capable of real-time monitoring of PM1.0 / PM2.5 / PM10 concentrations. The biometric sensors utilize the biological activity of cells to monitor pollutants, suitable for biotoxicity monitoring.

[0192] In one embodiment, the sensing components 20 are installed in a distributed manner, with the distance between sensors in the consultation room (department) 40 and areas such as the outpatient hall, waiting area, and corridor 50 not exceeding 5 meters, to ensure that there are no blind spots in the monitoring coverage.

[0193] In one embodiment, the controller adopts a four-layer architecture of "perception layer - transmission layer - control layer - application layer". The perception layer is deployed on various sensing components 20 and embedded hardware at the monitoring points. The network layer is responsible for uploading data from the sensing components 20, using technologies such as Wi-Fi and Bluetooth. The platform layer receives, stores, processes and analyzes massive amounts of multi-dimensional parameters.

[0194] This system embodiment can be implemented in conjunction with the implementation methods described above. The relevant technical details mentioned in the implementation methods of the above embodiments remain valid in the implementation methods of this method embodiment, and will not be repeated here to avoid repetition.

[0195] In one embodiment, a method for intelligent monitoring and disinfection of outpatient space environment based on biomimetic immune principle includes the following steps:

[0196] Step 1: Multidimensional Parameter Acquisition: Sensing unit 11 acquires multidimensional parameters of the outpatient space in real time through sensing component 20, with an acquisition frequency of once every 10 seconds to ensure real-time performance. The acquired multidimensional parameters include:

[0197] Biological parameters: bacterial concentration, viral concentration, pathogen type;

[0198] Environmental parameters: temperature, humidity, PM2.5, PM10, concentration of harmful gases;

[0199] Quality parameters: Air Quality Index, Noise Level, Light Intensity;

[0200] Multidimensional parameter data is transmitted to the bionic immune recognition unit 12 via a wireless communication module, and an encrypted transmission protocol is used to ensure data security.

[0201] Step 2: Bionic Immune Recognition: The bionic immune recognition unit 12 performs bionic immune analysis on the collected multidimensional parameters. The specific process is as follows:

[0202] Antigen encoding: The first antigen vector that converts multidimensional data into binary encoding;

[0203] Clonal selection: Based on affinity calculations, antibodies that can recognize the first antigen vector are selected for amplification;

[0204] Mutation operation: Mutate the selected antibody to improve recognition accuracy;

[0205] Memory update: Add newly identified antigen vector-antibody pairs to the risk feature database;

[0206] Risk assessment: Based on the matching results, determine the risk level of the outpatient space environment;

[0207] The risk level classification criteria are as follows:

[0208] Low risk: bacterial concentration ≤ 0.5 × 10³ cfu / m³ and viral concentration ≤ 10² copies / m³;

[0209] Medium risk: 0.5×10³cfu / m³ < bacterial concentration ≤ 1.0×10³cfu / m³ or 10²copies / m³ < virus concentration ≤ 10³copies / m³;

[0210] High risk: Bacterial concentration >1.0×10³cfu / m³ or viral concentration >10³copies / m³.

[0211] Step 3: Generation of Intelligent Disinfection Strategy

[0212] Control unit 13 generates corresponding disinfection strategies based on risk level and environmental characteristics:

[0213] Low-risk strategy:

[0214] Disinfection method: Plasma disinfection module 142 is preferred;

[0215] Disinfection intensity: 30%;

[0216] Disinfection time: 15 minutes each time;

[0217] Disinfection frequency: once every 2 hours;

[0218] Safety measures: Normal operation when people are present, enhanced mode when no one is present.

[0219] Medium-risk strategy:

[0220] Disinfection method: The ultraviolet disinfection module 141 and the plasma disinfection module 142 work together;

[0221] Disinfection intensity: 60%;

[0222] Disinfection time: 30 minutes each time;

[0223] Disinfection frequency: once per hour;

[0224] Safety measures: Reduce intensity when people are present, and run at full power when no one is present.

[0225] High-risk strategy:

[0226] Disinfection method: The atomization disinfection module 143, the ultraviolet disinfection module 141 and the plasma disinfection module 142 operate in combination;

[0227] Disinfection intensity: 100%;

[0228] Disinfection time: 60 minutes each time;

[0229] Disinfection frequency: Continue operation until the risk is reduced;

[0230] Safety measures: Operate when no one is present, and ventilate for 30 minutes after disinfection.

[0231] Step 4: Disinfection Execution

[0232] The disinfection unit 14 performs the corresponding disinfection operation according to the instructions of the control unit 13:

[0233] Mode switching: Select the appropriate disinfection module combination according to the strategy requirements;

[0234] Parameter settings: Adjust parameters such as disinfection intensity, time, and frequency;

[0235] Safety check: Monitor whether anyone is in the disinfection area to ensure safety;

[0236] Start disinfection: Perform the disinfection operation according to the preset procedure;

[0237] Process monitoring: Real-time monitoring of the disinfection process and recording of operating parameters;

[0238] During the disinfection process, the sensing unit 11 continuously monitors environmental parameters and feeds them back to the control unit 13 to achieve dynamic adjustment.

[0239] Step 5: Closed-loop adjustment

[0240] The feedback unit collects environmental parameters after disinfection and compares them with preset standards to form a closed-loop control.

[0241] Effectiveness monitoring: Compare environmental parameters before and after disinfection to calculate the degree of improvement;

[0242] Compliance assessment: Whether the monitoring meets the preset environmental quality standards: bacterial concentration ≤ 0.5 × 10³ cfu / m³, virus concentration ≤ 10² copies / m³;

[0243] Strategy optimization: Adjust disinfection strategies and parameters based on feedback.

[0244] Continuous improvement: Successful strategies and experiences are added to the knowledge base to continuously improve system performance. If the standard is not met, the control unit 13 adjusts the disinfection strategy and performs the disinfection operation again until the environmental parameters meet the standard.

[0245] Furthermore, one embodiment of the present invention also provides a remote information processing system, which is communicatively connected to the aforementioned intelligent monitoring and disinfection device for outpatient space environment based on the biomimetic immune principle. This system can upload multidimensional data, disinfection strategies, risk characteristic databases, and other data to relevant national control agencies.

[0246] Furthermore, one embodiment of the present invention also provides a computer storage medium, and another embodiment of the present invention provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the steps of the above-mentioned intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle, and can achieve the same technical effect.

[0247] This invention also provides a computer program product that stores a program or instructions. When the program or instructions are executed by a processor, they implement the steps of the above-mentioned intelligent monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity, and achieve the same technical effect.

[0248] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for intelligent monitoring and disinfection of outpatient space environment based on biomimetic immune principle, characterized in that, Include: Real-time collection of multi-dimensional parameters of the outpatient space; The collected multidimensional parameters are encoded to obtain the first antigen vector; Match the first antibody to the first antigen vector; The risk level is determined based on the matching results; Based on the risk level and environmental characteristics, a disinfection strategy is matched to generate the first disinfection strategy; Perform the disinfection operation according to the first disinfection strategy.

2. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 1, characterized in that, It also includes: matching the first antibody to the first antigen vector using a risk feature database.

3. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 2, characterized in that, The risk feature database includes antigen vector-antibody pairs, and the antigen vector-antibody pairs include the mapping relationship between the first antigen vector and the first antibody.

4. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 3, characterized in that, The risk characteristic database is obtained by learning and training historical multidimensional parameter data through the synthetic T-cell algorithm: Historical multidimensional parameter data is encoded into antigen vectors, and preset risk levels and disinfection strategies are encoded into antibodies; The antigen vector and the antibody are input into the synthetic T cell algorithm to obtain the antigen vector-antibody pair.

5. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 4, characterized in that, If the first antibody cannot be matched with the first antigen vector through the risk feature database, then based on affinity calculation, a second antibody that can match the first antigen vector is selected for amplification, resulting in a new risk feature database that includes the mapping relationship between the first antigen vector and the second antibody.

6. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 2, characterized in that, The first antigen vector includes a first feature parameter and a second feature parameter, and the first antibody includes a first feature threshold one, a first feature threshold two, a second feature threshold one, and a second feature threshold two. The risk levels include: low risk, medium risk, and high risk; When the first feature parameter ≤ the first feature threshold one and the second feature parameter ≤ the second feature threshold one, it is considered low risk; when the first feature threshold one < the first feature parameter ≤ the first feature threshold two or the second feature threshold one < the second feature parameter ≤ the second feature threshold two, it is considered medium risk; when the first feature parameter > the first feature threshold two or the second feature parameter > the second feature threshold two, it is considered high risk.

7. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 6, characterized in that, Different risk levels correspond to different disinfection strategies. Low risk corresponds to Level 1 disinfection strategy, medium risk corresponds to Level 2 disinfection strategy, and high risk corresponds to Level 3 disinfection strategy.

8. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 1, characterized in that, The environmental characteristics mentioned are the results of human body sensing.

9. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 1, characterized in that, Also includes: During disinfection, the multidimensional parameters of the outpatient space are continuously collected in real time, and the collected multidimensional parameters are encoded with antigens to obtain a first antigen vector. A first antibody is matched for the first antigen vector, the risk level is determined according to the matching result, and a disinfection strategy is matched according to the risk level and environmental characteristics. The first disinfection strategy is maintained or updated to obtain a second disinfection strategy. Perform disinfection operations according to the first disinfection strategy or the second disinfection strategy; To enable dynamic adjustment of disinfection strategies.

10. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 1, characterized in that, Also includes: Collect the multidimensional parameters after disinfection is completed; Conduct a compliance assessment to determine whether the preset environmental quality standards have been met; If the standard is met, the first disinfection strategy and / or the second disinfection strategy will be added to the knowledge base. If the standard is not met, the multidimensional parameters of the outpatient space will continue to be collected in real time, and the collected multidimensional parameters will be encoded with antigens to obtain a first antigen vector. A first antibody will be matched for the first antigen vector, the risk level will be determined according to the matching result, and a disinfection strategy will be matched according to the risk level and environmental characteristics. The first disinfection strategy, the second disinfection strategy, or the third disinfection strategy will be updated.

11. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 1, characterized in that, Also includes: Collect the multidimensional parameters after disinfection is completed; Compare the multidimensional parameters before and after disinfection, and calculate the degree of improvement; Based on the degree of improvement, feedback is obtained, and disinfection strategies are adjusted accordingly.

12. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 1, characterized in that, The multidimensional parameters include: Biological parameters, which include one or more of the following: bacterial concentration, viral concentration, pathogen type; Environmental parameters, including one or more of the following: temperature, humidity, PM2.5, PM10, and concentration of harmful gases; Quality parameters, which include one or more of the following: air quality index, noise level, light intensity, etc.

13. The intelligent monitoring and disinfection method for outpatient space environment based on biomimetic immune principle according to claim 1, characterized in that, The multidimensional parameters are derived from any one or more of different population groups, different departments (40), different regions (50), and different lengths of stay.

14. A smart monitoring and disinfection device for outpatient space environment based on biomimetic immune principle (10), characterized in that, Include: Sensing unit (11), the sensing unit (11) is used to collect multi-dimensional parameters of the outpatient space in real time; The biomimetic immune recognition unit (12) encodes the collected multidimensional parameters to obtain a first antigen vector, matches the first antigen vector with a first antibody, and determines the risk level based on the matching result. Control unit (13), which generates a first disinfection strategy by matching disinfection strategies according to risk level and environmental characteristics; Disinfection unit (14) performs disinfection operation according to the first disinfection strategy.

15. The intelligent monitoring and disinfection device for outpatient space environment based on the biomimetic immune principle (10) according to claim 13, characterized in that, The sensing unit (11) includes: Biological module (111), the biological module (111) is used to acquire biological parameters; An environment module (112) is used to acquire environmental parameters and quality parameters; An optical module (113) is used to acquire environmental parameters; A biometric module (114) is used to acquire biotoxicity monitoring results.

16. A smart monitoring and disinfection system for outpatient space environment based on the principle of biomimetic immunity, employing the smart monitoring and disinfection method for outpatient space environment based on the principle of biomimetic immunity as described in any one of claims 1 to 13, characterized in that, Include: A sensing component (20) is used to collect multidimensional parameters of the outpatient space in real time; The controller encodes the collected multidimensional parameters to obtain a first antigen vector, matches the first antigen vector with a first antibody, determines the risk level based on the matching result, and matches a disinfection strategy based on the risk level and environmental characteristics to generate a first disinfection strategy. A disinfection component that performs disinfection operations according to the first disinfection strategy.

17. The intelligent monitoring and disinfection system for outpatient space environment based on the principle of biomimetic immunity as described in claim 16, characterized in that, The sensing components (20) are set in different areas (50) of the hospital (30) and / or different hospitals (30) to collect one or more multidimensional parameters from different populations, different departments (40), different areas (50), and different lengths of stay in real time.

18. A remote information processing system, characterized in that, It is communicatively connected to the intelligent monitoring and disinfection device for outpatient space environment based on the biomimetic immune principle as described in claim 13 or claim 14.

19. A computer storage medium, characterized in that, Used to store computer programs or instructions; when the computer programs or instructions are executed by a processor, they implement the outpatient space environment intelligent monitoring and disinfection method based on the biomimetic immune principle as described in any one of claims 1 to 12.

Images