A ward air purification system

Abstract

The present invention relates to the technical field of ward air purification systems, and discloses a ward air purification system, which includes an environment perception module, a purification path planning module, a filter unit configuration module, a purification execution module, a dynamic adjustment module, a state monitoring module, and a feedback control module. The environment perception module obtains ward space information to construct an air flow topology, the purification path planning module retrieves air quality data to mark the topology, the filter unit configuration module sets purification nodes and adjustment intervals, the purification execution module implements basic purification, the dynamic adjustment module performs active purification adjustment, the state monitoring module analyzes data to determine operating status characteristics, and the feedback control module generates monitoring records and provides visual feedback. The system can accurately perceive ward air quality, scientifically plan purification paths, dynamically adjust purification strategies, effectively improve ward air quality, and protect the health of patients and medical staff, thereby having high practical value.

Description

Technical Field

[0001] The present invention relates to the technical field of ward air purification systems, and in particular to a ward air purification system. Background Art

[0002] In modern healthcare environments, air quality in hospital wards is crucial to patient recovery and the health of medical staff. Airborne pollutants vary widely, including bacteria, viruses, and harmful gases. The presence of these pollutants can increase the risk of postoperative infection for patients, prolong recovery time, and pose a threat to the health of medical staff.

[0003] Most traditional ward air purification systems use fixed purification modes and lack the ability to perceive and dynamically adjust the air quality in the ward in real time. For example, some systems only purify the air through simple filters and are unable to carry out targeted treatment based on the pollution conditions in different areas of the ward. This fixed-mode purification method has many shortcomings: on the one hand, it is impossible to accurately obtain ward spatial information and build a comprehensive air flow topology, resulting in an inaccurate grasp of the distribution and diffusion patterns of pollutants; on the other hand, during the purification process, the purification strategy cannot be adjusted in time according to changes in air quality data, resulting in low purification efficiency and difficulty in meeting the complex ward environment requirements.

[0004] Existing purification systems, when dealing with multiple pollution sources, often fail to adequately consider the upstream and downstream relationships and mutual influences between them. This leads to irrational purification node configuration and unscientific adjustment intervals. Furthermore, state monitoring and feedback control during the purification process are also inadequate, making it impossible to promptly detect and effectively address anomalies during system operation, impacting the stability and reliability of the purification system.

[0005] With the continuous development of medical technology and the increasing demand for medical environments, traditional ward air purification systems can no longer meet actual needs. Therefore, there is an urgent need for a new ward air purification system that can sense the environment in real time, scientifically plan purification paths, dynamically adjust purification strategies, and effectively monitor and feedback control the operating status to improve ward air quality and protect the health of patients and medical staff. Summary of the Invention

[0006] The object of the present invention is to provide a ward air purification system to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides a ward air purification system, the system comprising:

[0008] An environmental perception module, which is used to obtain ward spatial information and construct an air flow topology, wherein the air flow topology is a spatial topology including multiple pollution source types in a preset area;

[0009] A purification path planning module, which is used to retrieve air quality data for a preset period of time, extract key indicators, and annotate the air flow topology, wherein the key indicators include indicator location, indicator type, indicator concentration, and indicator change rate, and the key indicators have an upstream and downstream relationship;

[0010] A filter unit configuration module, the filter unit configuration module is used to traverse the air flow topology, combine the key indicators, set purification nodes and determine the adjustment interval, the purification nodes are air purification nodes;

[0011] a purification execution module, configured to perform a basic purification operation based on the purification node and determine a basic purification amount;

[0012] a dynamic adjustment module, configured to implement active purification adjustment based on the adjustment interval and determine a compensation purification amount;

[0013] A status monitoring module, which is used to combine the basic purification amount and the compensation purification amount with an analysis unit to perform data comparison and abnormality identification to determine the operating status characteristics;

[0014] A feedback control module is used to generate purification monitoring records and perform terminal visual feedback based on the operating status characteristics.

[0015] Preferably, the environment perception module is used to:

[0016] Traversing the key indicators, and dividing them into multiple indicator groups based on correlation, wherein the correlation includes numerical correlation and time correlation;

[0017] Based on the location of the pollution source, the multiple indicator groups are associated with each other upstream and downstream, and an association criterion is determined to determine the upstream and downstream relationship.

[0018] Preferably, the purification path planning module is used to:

[0019] Traversing the key indicators to determine characteristic laws of pollutant diffusion, wherein the characteristic laws include a propagation trajectory of the diffusion along the space;

[0020] Based on the characteristic law, determining an effective purification position;

[0021] The valid purification positions are traversed and the purification nodes are set.

[0022] Preferably, the dynamic adjustment module is used to:

[0023] There are differences in the adjustment intervals of the key indicators marked in the air flow topology;

[0024] Identifying the adjustment interval and determining the position of the pre-adjustment area;

[0025] Based on the purification regulator, the active purification amount is output in a directional manner to purify the air in the pre-conditioning area.

[0026] Preferably, the dynamic adjustment module is further used to:

[0027] Determine the initial adjustment amount;

[0028] Correcting the initial adjustment amount as an active purification amount;

[0029] Among them, the adjustment and correction methods include:

[0030] Determine the multi-cycle intensity, and modify and mark the initial adjustment amount as a type of active purification amount;

[0031] The tracking medium is introduced and combined with the initial adjustment amount as the second type of active purification amount.

[0032] Preferably, the status monitoring module is used to:

[0033] The analysis unit includes an active analysis area and a passive analysis area;

[0034] receiving the basic purification amount and activating the active analysis area to perform data comparison and anomaly identification;

[0035] The compensation purification amount is received, and the passive analysis area is activated to perform data comparison and abnormality identification, wherein the active analysis area and the passive analysis area are relatively independent.

[0036] Preferably, the feedback control module is used to:

[0037] Prioritize the purification monitoring records based on the indicator concentration as the first priority feature and the impact range as the second priority feature to determine the risk sequence;

[0038] Classifying the operating status characteristics by homology and determining a classification result;

[0039] Based on the classification results, the risk sequence is marked, and terminal visualization feedback and purification equipment operation and maintenance management are carried out.

[0040] Preferably, the environment perception module is used to:

[0041] Acquiring pollution source type information, distribution location information, and air flow relationship information as the ward space information;

[0042] Based on the ward space information, the air flow topology is constructed by using a region connection method, wherein the region connection method includes contaminated area definition and flow relationship mapping.

[0043] Preferably, the purification path planning module is further used to:

[0044] Performing time verification on the multiple indicator groups and determining verification criteria;

[0045] Based on the verification criterion, the association criterion is modified to form a final association criterion.

[0046] Preferably, the purification monitoring record includes information on indicator location, indicator type, indicator concentration, abnormality type and abnormality location;

[0047] The terminal visual feedback is displayed through interface partitions, wherein high index concentration abnormalities are displayed in red areas, medium index concentration abnormalities are displayed in yellow areas, and low index concentration abnormalities are displayed in green areas.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The system's environmental perception module captures ward spatial information and constructs an air flow topology encompassing multiple pollution source types. This enables the system to comprehensively and accurately understand the pollution status within the ward, laying a solid foundation for subsequent purification efforts. By analyzing the correlation of key indicators and linking upstream and downstream data, the system can gain a deeper understanding of the distribution and dynamics of pollutants, providing a more scientific basis for purification path planning.

[0050] The purification path planning module retrieves air quality data from a preset time period, extracts key indicators, and annotates the air flow topology. This module can determine the characteristics and patterns of pollutant diffusion and effective purification locations, thereby rationally setting purification nodes. This data- and pattern-based planning approach greatly improves the scientific and effective nature of purification node placement, enabling more targeted purification operations and effectively enhancing purification efficiency.

[0051] The filter unit configuration module traverses the air flow topology and sets purification nodes and determines adjustment intervals based on key indicators, ensuring that the purification system can be rationally configured based on actual pollution conditions, making purification more accurate and efficient. The dynamic adjustment module implements active purification adjustments based on adjustment intervals, enabling differentiated adjustments based on pollution levels in different areas. By correcting the initial adjustment amount, such as determining multi-cycle intensity correction annotations and introducing tracking media, the active purification amount is more accurate, enabling more effective response to pollution changes and further improving purification effectiveness.

[0052] The status monitoring module compares data and identifies anomalies for both the basic and compensatory purification volumes through active and passive analysis areas, achieving comprehensive and detailed monitoring of the system's operating status. This allows for timely detection of anomalies in system operation, ensuring stable system operation. The feedback control module prioritizes purification monitoring records based on indicator concentration as the first priority feature and impact range as the second priority feature, determines risk sequences, and performs homologous classification and terminal visual feedback. This allows medical staff to intuitively understand the air quality status and system operating status within the ward, facilitating timely operation and maintenance management of purification equipment, and improving system reliability and maintainability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a working principle diagram of the ward air purification system of the present invention;

[0054] Figure 2 Flowchart of the dynamic adjustment module;

[0055] Figure 3 Revised flowchart for the dynamic adjustment module;

[0056] Figure 4 Flowchart of the feedback control module;

[0057] Figure 5 Flowchart for visual feedback to the terminal. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] See also Figure 1-Figure 5The present invention provides a ward air purification system, which includes: an environment perception module, a purification path planning module, a filter unit configuration module, a purification execution module, a dynamic adjustment module, a status monitoring module and a feedback control module. The specific implementation steps are as follows:

[0060] The environmental perception module acquires ward spatial information and constructs an air flow topology. This information includes pollution source type, distribution location, and air flow relationship information. This topology is constructed using a regional connectivity method that includes pollution area definition and flow relationship mapping.

[0061] The purification path planning module retrieves the air quality data of the preset time period, extracts key indicators and marks the air flow topology. The key indicators include indicator position-indicator type-indicator concentration-indicator change rate, and the key indicators have upstream and downstream relationships.

[0062] The filter unit configuration module traverses the air flow topology, combines key indicators, sets purification nodes and determines the adjustment interval. The purification nodes are air purification nodes.

[0063] The purification execution module implements basic purification operations based on the purification nodes and determines the basic purification amount.

[0064] The dynamic adjustment module implements active purification adjustment based on the adjustment interval and determines the compensation purification amount.

[0065] The status monitoring module combines the basic purification amount and the compensation purification amount with the analysis unit to perform data comparison and abnormality identification to determine the operating status characteristics. The analysis unit includes an active analysis area and a passive analysis area.

[0066] The feedback control module generates purification monitoring records and provides terminal visual feedback based on the operating status characteristics.

[0067] Example 1

[0068] When the environmental perception module of the system in this embodiment is implemented, it is necessary to obtain pollution source type information, distribution location information and air flow relationship information, and use this information as ward space information. The pollution source type information here can be obtained by detecting possible pollution sources in the ward through various sensors, such as detecting whether there are pollutants generated by the operation of medical equipment, gases discharged by patients' breathing, etc., to clarify the specific type of pollution source. The distribution location information requires determining the specific coordinate position of each pollution source in the ward space, which can be achieved with the help of positioning technology or manual measurement. Air flow relationship information mainly refers to parameters such as the flow direction and speed of the air in the ward, which can be obtained through real-time monitoring through equipment such as wind speed sensors and wind direction sensors.

[0069] After obtaining the above-mentioned ward space information, the regional connection method is used to construct the air flow topology based on this information. The regional connection method includes pollution area definition and flow relationship mapping. The pollution area definition is to divide the ward space into different pollution areas based on the distribution location and pollution level of the pollution source. Each pollution area has specific pollution characteristics. For example, the area close to the pollution source may be defined as a high pollution area, while the area far from the pollution source may be defined as a low pollution area. The flow relationship mapping is to map and represent the air flow relationship between each pollution area, clarifying the air flow path and method between different pollution areas.

[0070] After constructing the air flow topology, the environmental perception module traverses key indicators and divides them into multiple groups based on correlation. Correlation includes both numerical and temporal correlation. Key indicators include location, type, concentration, and rate of change. While traversing these key indicators, the module analyzes their numerical correlations. This involves determining whether there is a degree of correlation between the numerical changes of different indicators, such as whether changes in the concentration of one indicator are positively or negatively correlated with changes in another. Furthermore, the module analyzes temporal correlations to determine whether there is a sequential or simultaneous relationship between the changes in different indicators over time. Based on these correlation analyses, key indicators with similar correlations are divided into multiple groups. Within each group, the indicators exhibit strong correlations in both numerical and temporal terms.

[0071] Based on the location of the pollution source, multiple indicator groups are associated with each other upstream and downstream, and the association criteria are determined to further determine the upstream and downstream relationships. The location of the pollution source is an important basis for determining the upstream and downstream relationships of the indicator groups. Starting from the pollution source, the position of each indicator group on the air flow path is determined according to the direction of air flow. The indicator group located upstream of the pollution source may have an impact on the downstream indicator group, while the downstream indicator group is affected by the upstream indicator group. When determining the association criteria, factors such as the speed of air flow and the diffusion characteristics of pollutants need to be considered to clarify the degree and method of influence between different indicator groups. For example, in areas with faster air flow, the impact of the upstream indicator group on the downstream indicator group may be more rapid and obvious.

[0072] The purification path planning module will perform time verification on multiple indicator groups and determine the verification criteria. Time verification mainly tests and verifies the data of the indicator group in the time series to ensure the accuracy and reliability of the data. The determination of the verification criteria needs to consider factors such as the time span and sampling frequency of the data. For example, whether the time span of the set data meets the requirements of the preset time period, whether the sampling frequency meets the needs of the analysis, etc. Based on the verification criteria, the association criteria are revised to form the final association criteria. When revising the association criteria, it is necessary to combine the results of the time verification and adjust the errors or unreasonableness that may exist in the previously determined association criteria to ensure that the final association criteria can accurately reflect the upstream and downstream relationships and the degree of influence between the indicator groups.

[0073] The environmental perception module provides a basis for building an accurate air flow topology by obtaining detailed ward space information. The constructed air flow topology provides a spatial structure reference for the subsequent division of indicator groups and determination of upstream and downstream relationships. The correlation analysis of key indicators and the division of indicator groups help to more clearly understand the distribution and change patterns of pollutants in the ward. Determining the upstream and downstream relationships of indicator groups based on the location of pollution sources and the direction of air flow provides an important basis for subsequent purification path planning and filter unit configuration. The purification path planning module's correction of the time verification and association criteria of the indicator group further improves the accuracy and reliability of the system's analysis of the ward air quality status, thereby enabling the entire ward air purification system to operate more efficiently and achieve purification of the ward air.

[0074] Example 2

[0075] In actual operation, the purification path planning module in this embodiment needs to retrieve air quality data for a preset period of time. The preset period can be set according to the actual use and needs of the ward, such as one day, one week, or one month. This air quality data contains information on multiple dimensions, such as pollutant concentrations, pollutant types, temperature, humidity, etc. at different locations, and can be collected in real time by various air quality monitoring sensors distributed throughout the ward.

[0076] After obtaining the air quality data for the preset time period, the purification path planning module extracts key indicators and marks the air flow topology. Key indicators include indicator location, indicator type, indicator concentration, indicator change rate, and key indicators have upstream and downstream relationships. When extracting key indicators, it is necessary to screen and analyze the large amount of air quality data collected to find indicators that have a greater impact on the air quality in the ward and can reflect the trend of air quality changes. For example, for common pollutants in the ward such as bacteria, viruses, volatile organic compounds, etc., their concentration and change rate are important key indicators. When marking the air flow topology, the key indicators are mapped to specific locations and areas in the air flow topology, so that the air flow topology can more intuitively reflect the air quality conditions at different locations.

[0077] After extracting key indicators and annotating the air flow topology, the purification path planning module traverses the key indicators to determine the characteristic patterns of pollutant diffusion, including the diffusion trajectory along the space. While traversing the key indicators, the module analyzes the temporal and spatial variations of different indicators. By analyzing historical data, the module identifies general patterns of pollutant diffusion. For example, by observing changes in pollutant concentration over different time periods and the direction and speed of pollutant diffusion within the ward space, the module can determine the diffusion trajectory along the space.

[0078] Based on the determined pollutant diffusion characteristics, the purification path planning module identifies effective purification locations. This determination requires comprehensive consideration of factors such as the pollutant diffusion trajectory, air flow direction, and ward layout. For example, effective purification locations are set along key pollutant diffusion paths and in areas where poor air flow can easily lead to pollutant accumulation, to maximize purification efficiency.

[0079] The purification path planning module traverses valid purification locations and sets purification nodes. Purification nodes are air purification nodes. When traversing valid purification locations, appropriate purification equipment and technology are selected based on the specific conditions of each valid purification location to set purification nodes. For example, in some highly polluted areas, high-efficiency air filters or ultraviolet disinfection equipment may be required as purification nodes.

[0080] The filter unit configuration module traverses the air flow topology and, based on key indicators, sets purification nodes and determines adjustment intervals. While traversing the air flow topology, it analyzes the pollution and air flow conditions in each area of ​​the topology in detail. Based on the air quality information reflected by key indicators, it determines where to set purification nodes to achieve the best air purification results. The determination of adjustment intervals requires consideration of factors such as the rate of change of pollutants and the processing capacity of the purification equipment. For example, in areas where pollutant concentrations change rapidly, shorter adjustment intervals are set to enable timely adjustments to the operating status of the purification equipment.

[0081] Throughout the implementation process, each step cooperates with each other to jointly realize the planning of ward air purification paths and the setting of purification nodes. Retrieving air quality data for a preset time period provides a data basis for subsequent extraction and analysis of key indicators. The extraction of key indicators and the annotation of air flow topology enable the system to more accurately understand the air quality conditions in the ward. The determination of the diffusion characteristics of pollutants provides a basis for the selection of effective purification locations, and the determination of effective purification locations and the setting of purification nodes directly affect the effect of air purification. The filter unit configuration module combines the air flow topology and key indicators to set purification nodes and determine the adjustment interval, further optimizing the operating efficiency and effect of the purification system, enabling the entire ward air purification system to adjust the purification strategy in real time according to changes in the air quality in the ward, thereby achieving efficient purification of the ward air.

[0082] When determining the diffusion characteristics of pollutants and the effective purification locations, it is necessary to fully consider the actual conditions within the ward, such as the size and shape of the ward, the location of doors and windows, and the activities of personnel. These factors will affect the diffusion of pollutants and the flow of air. For example, the movement of personnel may change the direction of air flow, thereby affecting the diffusion trajectory of pollutants. Therefore, these factors need to be taken into consideration when planning the purification path. In addition, different types of pollutants may have different diffusion characteristics. It is necessary to adopt corresponding purification measures and set up appropriate purification nodes for different pollutant types to ensure that they can effectively purify various pollutants.

[0083] After setting up the purification nodes and determining the adjustment intervals, the operating status of the purification nodes needs to be monitored and evaluated in real time to ensure that they are operating properly and achieving the expected purification effect. If the purification node is found to be operating poorly or the adjustment interval is set improperly, timely adjustments and optimizations are required to ensure the stable operation and efficient work of the entire ward air purification system.

[0084] Example 3

[0085] During the specific implementation of the dynamic adjustment module in this embodiment, since the adjustment intervals of the key indicators marked in the air flow topology are differentiated, it is necessary to identify the adjustment intervals and determine the location of the pre-adjustment area. The key indicators at different positions in the air flow topology, such as indicator concentration, indicator change rate, etc., have different changing speeds and rules, which leads to the differentiation of the adjustment intervals. For example, in areas close to pollution sources, the pollutant concentration may change more frequently, and its adjustment interval is relatively short; while in areas far from pollution sources, the pollutant concentration changes relatively slowly, and the adjustment interval is relatively long. When identifying the adjustment interval, the adjustment interval duration corresponding to each key indicator is determined by analyzing the key indicator data marked in the air flow topology. The determination of the location of the pre-adjustment area is based on the location of the key indicators with shorter adjustment intervals. These areas need to be given priority for active purification adjustment because of the rapid changes in pollutant concentration.

[0086] After identifying the adjustment interval and determining the location of the pre-conditioning area, the active purification amount is output in a directional manner based on the purification regulator to purify the air in the pre-conditioning area. The purification regulator is a device or apparatus used to implement active purification adjustment, which can adjust the output purification amount according to the system's instructions. When the active purification amount is output in a directional manner, it is necessary to ensure that the purification amount acts accurately on the pre-conditioning area to avoid unnecessary impact on other areas. For example, by controlling the output direction and intensity of the purification regulator, the active purification amount can accurately cover the pre-conditioning area, thereby purifying the air in the area.

[0087] The dynamic adjustment module also determines an initial adjustment amount and corrects it as the active purification amount. The determination of the initial adjustment amount requires comprehensive consideration of factors such as the pollution level in the pre-conditioning area, air flow conditions, and the purification equipment's processing capacity. For example, the initial adjustment amount is calculated based on the pollutant concentration and area of ​​the pre-conditioning area, combined with the purification capacity per unit time of the purification equipment. When correcting the initial adjustment amount, various adjustment correction methods are used to improve the accuracy and effectiveness of the active purification amount.

[0088] The adjustment and correction methods include: determining the multi-cycle intensity, correcting and marking the initial adjustment amount as a type of active purification amount; introducing a tracking medium, combined with the initial adjustment amount, as a type of active purification amount. When determining the multi-cycle intensity, analyze the changes in pollution intensity in the pre-adjustment area over multiple cycles, such as hours or days, and count the peak and valley values ​​of pollutant concentrations in each cycle, as well as the trend of changes. Based on the analysis results of the multi-cycle intensity, correct the initial adjustment amount. If the pollution intensity in a certain cycle is high, increase the initial adjustment amount accordingly; if the pollution intensity is low, reduce the initial adjustment amount appropriately. After correction, mark the type of active purification amount for subsequent management and tracing.

[0089] When introducing a tracking medium as a correction method for the Class II active purification rate, select an appropriate tracking medium with good traceability and diffusion characteristics similar to those of the pollutants. Introduce the tracking medium into the pre-conditioning area and monitor its diffusion and distribution to gain real-time insights into the pollutant's diffusion dynamics. Based on the monitoring data from the tracking medium and the initial adjustment rate, the initial adjustment rate is corrected. For example, if the tracking medium diffuses faster than expected, it indicates that the pollutants may also be spreading faster, and the initial adjustment rate needs to be increased to ensure timely purification. Otherwise, the initial adjustment rate should be reduced.

[0090] The purification execution module performs basic purification operations based on purification nodes and determines the basic purification capacity. Purification nodes are air purification nodes. During basic purification operations, the corresponding purification equipment, such as air filters and disinfection devices, is activated based on the type and configuration requirements of the purification node and operates according to preset operating parameters. The basic purification capacity is calculated based on parameters such as the operating time and processing efficiency of the purification equipment. For example, an air filter can purify a certain volume of air per hour, and the basic purification capacity can be determined based on its operating time.

[0091] Throughout the implementation process, the dynamic adjustment module and the purification execution module collaborate to purify the ward air. By identifying adjustment intervals and determining the location of pre-adjustment areas, the dynamic adjustment module accurately locates and proactively adjusts purification in areas with rapidly changing pollution levels. The purification execution module, on the other hand, implements basic purification operations based on purification nodes, providing continuous purification capabilities. The application of adjustment corrections allows the active purification volume to more accurately adapt to changes in pollutants, improving the flexibility and adaptability of the purification system.

[0092] When determining the initial adjustment and making corrections, it is necessary to fully consider the actual conditions within the ward, such as personnel activities, the opening and closing of doors and windows, and other factors. These factors may affect the diffusion of pollutants and air flow, thereby affecting the accuracy of the initial adjustment. For example, when people frequently enter and exit the ward, air flow may intensify, causing the diffusion of pollutants to accelerate. In this case, the initial adjustment and correction method need to be adjusted promptly based on the actual situation. In addition, the selection and use of tracking media must strictly comply with relevant standards and specifications to ensure that they do not adversely affect the environment and personnel health within the ward.

[0093] After implementing active purification and adjustment, it is necessary to monitor the air quality in the pre-conditioned area in real time to evaluate the effectiveness of active purification and adjustment. If the air quality after active purification and adjustment does not meet the expected target, it is necessary to analyze the reasons, such as whether the adjustment interval setting is reasonable and whether the initial adjustment amount correction is accurate. Timely adjustments and optimizations should be made to ensure the normal operation of the dynamic adjustment module and the effectiveness of the purification effect.

[0094] Example 4

[0095] In this embodiment, when the status monitoring module is actually working, the analysis unit includes an active analysis area and a passive analysis area. These two areas are functionally independent and can process and analyze data from different sources respectively. When the system is running, the purification execution module will perform basic purification operations based on the purification nodes and determine the basic purification amount. These basic purification amount data will be transmitted to the status monitoring module. At this time, the status monitoring module receives the basic purification amount and activates the active analysis area for data comparison and anomaly identification. The active analysis area will process the basic purification amount data in real time, compare it with preset standard data or historical data, and analyze the data change trends and patterns to identify whether there are any abnormalities.

[0096] For example, if the purification efficiency of a particular purification node in the basic purification data remains below the preset normal range for a period of time, the active analysis area will capture this change, determine through data comparison that the purification node may have a fault or abnormal operation, and record the abnormal information. The active analysis area operates in real time and proactively, enabling timely identification of potential problems in basic purification operations.

[0097] After the dynamic adjustment module implements active purge adjustments based on the adjustment interval and determines the compensation purge amount, this compensation purge amount data is also transmitted to the condition monitoring module. The condition monitoring module receives the compensation purge amount and activates the passive analysis area for data comparison and anomaly identification. The passive analysis area also processes the compensation purge amount data and compares and analyzes it with related data, but its operation is relatively passive, primarily performing corresponding processing after receiving the compensation purge amount data.

[0098] For example, if the active purification volume in a pre-conditioned area of ​​the compensation purification volume data suddenly fluctuates significantly, the passive analysis area will analyze this fluctuation to determine whether it is due to normal pollution changes or other abnormal factors. Because the active and passive analysis areas are relatively independent, they can process the basic purification volume and compensation purification volume data separately, avoiding mutual interference during data processing and improving the accuracy and efficiency of anomaly identification.

[0099] At the same time, the status monitoring module combines the basic and compensating purification outputs with the analysis unit to perform data comparisons and identify anomalies, thereby determining the operational status characteristics. This requires a comprehensive analysis of the data processed by the active and passive analysis areas, integrating the overall effectiveness of basic purification and active purification adjustments. For example, by comprehensively considering the stability of the basic purification output and the adjustment of the compensating purification output, it is possible to determine whether the entire purification system is operating normally and whether any further adjustments are needed.

[0100] When determining the operating status characteristics, the data after comprehensive analysis is summarized and summarized to form characteristic parameters that can reflect the system's operating status. For example, the system's overall purification efficiency, the operating stability of each purification node, the frequency and amplitude of compensation purification adjustments, etc. These characteristic parameters can help staff intuitively understand the system's operating status.

[0101] Furthermore, the feedback control module generates purification monitoring records based on operational status characteristics and provides visual feedback to the terminal. These characteristics are crucial to the feedback control module's operation. Based on these characteristic parameters, the module generates detailed purification monitoring records containing information such as indicator location, indicator type, indicator concentration, anomaly type, and anomaly location.

[0102] For example, if the status monitoring module identifies a purification node with abnormal operating characteristics, the feedback control module will record the location, abnormality type, and possible cause of the purification node in the purification monitoring record. This information is then presented in an intuitive manner through terminal visual feedback, making it easier for staff to review and manage.

[0103] Throughout the implementation process, the analysis unit of the condition monitoring module processes basic and compensating purification data in the active and passive analysis areas, respectively, achieving comprehensive monitoring of system operating data and identifying anomalies. The real-time, proactive processing in the active analysis area enables timely identification of problems in basic purification operations, while the processing of compensating purification data in the passive analysis area ensures effective monitoring of the effectiveness of active purification adjustments. The relative independence of the two ensures accurate and efficient data processing, while the comprehensive analysis of basic and compensating purification data provides a comprehensive understanding of purification effectiveness, providing a sufficient basis for determining operational status characteristics.

[0104] The feedback control module generates purification monitoring records and terminal visual feedback based on operational status characteristics, enabling staff to promptly understand the system's operating status and facilitate appropriate operation and maintenance management. For example, if the terminal visual feedback indicates abnormal concentrations in a certain area, staff can quickly locate the abnormality and cause based on the information in the purification monitoring records and take appropriate measures to address it.

[0105] Furthermore, in practical applications, the parameter settings of the active and passive analysis areas of the analysis unit can be adjusted based on the actual conditions of the ward. For example, in wards with more severe pollution, the active analysis area's comparison frequency and sensitivity to basic purification data can be increased to detect problems more promptly. In areas with rapidly changing pollution, the passive analysis area's processing of compensatory purification data can be optimized to better adapt to changes in pollution.

[0106] Furthermore, the condition monitoring module needs to continuously update and refine its reference data during data comparison and anomaly identification to improve recognition accuracy. For example, as the system operates, more historical data accumulates, which can be incorporated into the comparison process to make anomaly identification more accurate. Furthermore, the analysis unit's algorithms need to be optimized to increase the speed and accuracy of data processing and ensure the efficient operation of the condition monitoring module.

[0107] Example 5

[0108] In the specific implementation of the feedback control module in this embodiment, it is necessary to generate purification monitoring records based on the operating status characteristics and perform terminal visual feedback. The operating status characteristics are obtained by the status monitoring module in combination with the basic purification amount and the compensation purification amount analysis, such as the operating efficiency fluctuations of the purification nodes in a certain ward, the changing trend of pollutant concentrations, etc. The purification monitoring record contains the indicator location, indicator type, indicator concentration, abnormality type and abnormal location information. Taking a certain ward as an example, when the status monitoring module finds that the PM2.5 concentration index near the window position is continuously higher than the preset threshold, and the purification node at this position operates abnormally, the feedback control module will record in the purification monitoring record: the indicator location is the window area of ​​the ward, the indicator type is PM2.5, and the indicator concentration is 0.08mg / m 3 (higher than 0.035mg / m 3 ), the abnormality type is insufficient purification efficiency, and the abnormality location is the filter position of the corresponding purification node.

[0109] The feedback control module prioritizes the purification monitoring records with the indicator concentration as the first priority feature and the impact range as the second priority feature to determine the risk sequence. Taking the ward as an example, if there are two anomalies at the same time: one is the PM2.5 concentration of 0.08mg / m in the window area, the other is the PM2.5 concentration of 0.08mg / m 3 The affected area is 10 square meters; secondly, the bacterial concentration in the central area of ​​the ward exceeds the standard to 1500 CFU / m 3 , with an impact area of ​​25 square meters. Although the latter has a larger impact area, the indicator concentration takes precedence, so the PM2.5 concentration anomaly is ranked first in the risk sequence. During sorting, the system automatically extracts the indicator concentration value and impact area data for each anomaly record, calculates them using a preset priority algorithm, and generates an ordered risk sequence.

[0110] After completing the risk sequence ranking, the feedback control module classifies the operating status characteristics into homologous categories and determines the classification results. Homologous classification groups operating status characteristics with the same cause or source into the same category. For example, if multiple purification nodes simultaneously exhibit status characteristics indicating decreased operating efficiency, and analysis reveals that this is due to a program failure in the central control system, these characteristics will be classified as "control system failure." During the classification process, the system analyzes the causes and influencing factors of each status characteristic, and uses data mining and association analysis techniques to determine the category to which it belongs.

[0111] Based on the classification results, the feedback control module marks the risk sequence and performs terminal visual feedback and purification equipment operation and maintenance management. The terminal visual feedback is displayed through the interface partition, where high index concentration abnormalities are displayed in red areas, medium index concentration abnormalities are displayed in yellow areas, and low index concentration abnormalities are displayed in green areas. Continuing with the above ward as an example, on the terminal interface, the window area will be marked as a red block, showing a PM2.5 concentration of 0.08mg / m 3 and abnormal types; the central area of ​​the ward is marked as a yellow block, showing a bacterial concentration of 1500 CFU / m 3 At the same time, the risk sequence will be displayed in the sidebar of the interface in the form of a list, with the first one marked as "Red - High Concentration PM2.5 Abnormal" and the second one marked as "Yellow - Medium Concentration Bacteria Abnormal".

[0112] In terms of purification equipment operation and maintenance management, classification results can directly guide maintenance work. If the classification result is "filter clogged," the system automatically generates an operation and maintenance work order and assigns maintenance personnel to replace the filter at the corresponding purification node. For example, if multiple purification nodes are classified as having reduced purification efficiency due to filter clogs, the feedback control module will generate work orders based on the priority of the risk sequence, first handling the filter replacement tasks corresponding to high-concentration anomalies.

[0113] Throughout the implementation process, the feedback control module achieved efficient management of purification system anomalies through priority sorting and homologous classification. Using indicator concentration as the primary characteristic ensured that pollution issues that directly threaten patient health were prioritized; using impact range as the secondary characteristic enabled the system to account for the potential risks of large-scale contamination. Homologous classification helped maintenance personnel quickly locate the root cause of problems, improving operational efficiency. Terminal visual feedback, using intuitive color zoning and lists, enabled medical staff and maintenance personnel to quickly understand ward air quality and equipment operating status.

[0114] For example, when a nurse on night duty checks the terminal interface, a red area immediately draws attention. Combined with the risk sequence, the abnormal PM2.5 level in the window area warrants priority attention. Furthermore, the classification results indicate that the abnormality is related to a clogged filter, allowing the nurse to promptly notify the maintenance department for resolution. This visual feedback method avoids the clutter of text messages and improves response time.

[0115] Furthermore, the feedback control module optimizes its prioritization algorithm and homology classification rules based on historical purification monitoring records and operation and maintenance data. For example, if a certain type of anomaly frequently occurs and is consistently classified as a "ventilation system design defect," the system can automatically adjust its priority and give it greater attention in subsequent monitoring. This self-optimization mechanism enables the feedback control module to better adapt to changes in the ward environment and the actual operation of the system.

[0116] In terms of data interaction, the feedback control module connects with the status monitoring module in real time to ensure access to the latest operating status characteristics. It also connects to the purification equipment's control system to automatically push maintenance work orders and update processing status in real time. For example, when maintenance personnel complete a filter replacement and confirm the results at the terminal, the feedback control module updates the purification monitoring record, marking the abnormal status as "handled," reassesses the risk sequence, and adjusts the terminal display content.

[0117] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0118] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A ward air purification system, characterized in that: The system comprises: An environmental perception module, which is used to obtain ward spatial information and construct an air flow topology, wherein the air flow topology is a spatial topology including multiple pollution source types in a preset area; A purification path planning module, which is used to retrieve air quality data for a preset period of time, extract key indicators, and annotate the air flow topology, wherein the key indicators include indicator location, indicator type, indicator concentration, and indicator change rate, and the key indicators have an upstream and downstream relationship; A filter unit configuration module, the filter unit configuration module is used to traverse the air flow topology, combine the key indicators, set purification nodes and determine the adjustment interval, the purification nodes are air purification nodes; a purification execution module, configured to perform a basic purification operation based on the purification node and determine a basic purification amount; a dynamic adjustment module, configured to implement active purification adjustment based on the adjustment interval and determine a compensation purification amount; A status monitoring module, which is used to combine the basic purification amount and the compensation purification amount with an analysis unit to perform data comparison and abnormality identification to determine the operating status characteristics; A feedback control module, configured to generate a purification monitoring record and provide terminal visual feedback based on the operating status characteristics; The environment perception module is used to: Traversing the key indicators, and dividing them into multiple indicator groups based on correlation, wherein the correlation includes numerical correlation and time correlation; Based on the location of the pollution source, the multiple indicator groups are associated with each other upstream and downstream, and an association criterion is determined to determine the upstream and downstream relationship; The purification path planning module is used to: Traversing the key indicators to determine characteristic laws of pollutant diffusion, wherein the characteristic laws include a propagation trajectory of the diffusion along the space; Based on the above characteristics, the effective purification location is determined by comprehensively considering the diffusion trajectory of pollutants, the direction of air flow and the layout of the ward; The valid purification positions are traversed and the purification nodes are set.

2. A ward air purification system according to claim 1, characterized in that: The dynamic adjustment module is used for: There are differences in the adjustment intervals of the key indicators marked in the air flow topology; Identifying the adjustment interval and determining the position of the pre-adjustment area; Based on the purification regulator, the active purification amount is output in a directional manner to purify the air in the pre-conditioning area.

3. A ward air purification system according to claim 2, characterized in that: The dynamic adjustment module is also used for: Determine the initial adjustment amount; Correcting the initial adjustment amount as an active purification amount; Among them, the adjustment and correction methods include: Determine the multi-cycle intensity, analyze the change of pollution intensity in the pre-adjustment area over multiple cycles, and correct and mark the initial adjustment amount as a type of active purification amount; A tracking medium is introduced, and the diffusion and distribution of the tracking medium are monitored, combined with the initial adjustment amount, as the second type of active purification amount.

4. A ward air purification system according to claim 1, characterized in that: The status monitoring module is used to: The analysis unit includes an active analysis area and a passive analysis area; receiving the basic purification amount and activating the active analysis area to perform data comparison and anomaly identification; The compensation purification amount is received, and the passive analysis area is activated to perform data comparison and abnormality identification, wherein the active analysis area and the passive analysis area are relatively independent.

5. The ward air purification system according to claim 1, characterized in that: The feedback control module is used to: Prioritize the purification monitoring records based on the indicator concentration as the first priority feature and the impact range as the second priority feature to determine the risk sequence; Classifying the operating status characteristics by homology and determining a classification result; Based on the classification results, the risk sequence is marked, and terminal visualization feedback and purification equipment operation and maintenance management are carried out.

6. The ward air purification system according to claim 1, characterized in that: The environment perception module is used to: Acquiring pollution source type information, distribution location information, and air flow relationship information as the ward space information; Based on the ward space information, the air flow topology is constructed by using a region connection method, wherein the region connection method includes contaminated area definition and flow relationship mapping.

7. The ward air purification system according to claim 1, characterized in that: The purification path planning module is also used for: Performing time verification on the multiple indicator groups and determining verification criteria; Based on the verification criterion, the association criterion is modified to form a final association criterion.

8. The ward air purification system according to claim 5, characterized in that: The purification monitoring record includes indicator location, indicator type, indicator concentration, abnormality type and abnormality location information; The terminal visual feedback is displayed through interface partitions, wherein high index concentration abnormalities are displayed in red areas, medium index concentration abnormalities are displayed in yellow areas, and low index concentration abnormalities are displayed in green areas.

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