A laboratory air conditioning purification system for pollutant monitoring

Abstract

The application relates to the technical field of air conditioning air purification, and particularly discloses a laboratory air conditioning purification system for pollutant monitoring, which comprises a pollution source positioning module, a diffusion trend prediction module, a purification pre-deployment module and a by-product tracing and adjustment module. Relying on laboratory multi-point air monitoring data, the highest concentration measuring point is selected as a pollution source center, the surrounding adjacent measuring points are set as diffusion correlation nodes, the diffusion direction and arrival time of pollutants are calculated, and a diffusion trend prediction result is generated. A pre-purification area is selected from a laboratory adjustable purification area, and purification resources are preferentially deployed to complete pre-deployment according to air volume distribution rules before the pollutants arrive. After purification, the residual pollutants in the area are compared with the original components of the pollution source. If new components that are not contained in the original pollutants are detected, it is determined that by-products are generated during purification, a warning is simultaneously sent out, and a purification scheme adjustment instruction is output. The application can improve the pollutant monitoring efficiency of a laboratory.

Description

Technical Field

[0001] This invention relates to the field of air conditioning and air purification technology, and in particular to a laboratory air conditioning and purification system for pollutant monitoring. Background Technology

[0002] Existing laboratory air conditioning purification methods typically operate at fixed time intervals or triggered by a single concentration threshold. These methods can only reflect whether the pollutant concentration at the current sampling point exceeds the standard, but cannot predict the diffusion trend and migration path of pollutants. When the monitoring node detects that the concentration exceeds the standard, the pollutants have often already spread to a large area, causing the purification action to lag behind the pollution diffusion process. In addition, existing methods apply uniform purification parameters to all spatial areas within the laboratory, and cannot dynamically adjust according to the spatiotemporal differences in pollutant distribution. This easily leads to insufficient purification in key areas, waste of resources in non-contaminated areas, low overall purification efficiency, and difficulty in effectively intercepting pollutants in the early stages of diffusion.

[0003] Existing methods lack the ability to trace and verify the composition of air after purification. During equipment operation, certain purification methods (such as photocatalytic oxidation and chemisorption) may transform original pollutants into other gaseous byproducts. However, existing methods only focus on whether the concentration of the target pollutant decreases, without identifying whether new pollutants are introduced into the purified air. This "only reduce concentration, ignore byproducts" purification logic means that while laboratory air quality may meet the original targets, it may harbor new risks of secondary pollution, affecting the accuracy of experimental results and the health and safety of personnel. Therefore, there is an urgent need for a laboratory air conditioning purification method that can predict pollution diffusion trends, dynamically allocate purification resources, and identify purification byproducts. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a laboratory air conditioning purification system for pollutant monitoring, so as to solve the problems of existing laboratory air conditioning purification methods, such as the inability to predict the diffusion trend of pollutants, resulting in delayed purification action; insufficient purification and energy waste in key areas due to the uniform distribution of purification resources; and the risk of secondary pollution due to the lack of ability to identify purification byproducts.

[0005] This invention provides a laboratory air conditioning and purification system for pollutant monitoring. The system includes a pollutant source location module, a diffusion trend prediction module, a purification pre-deployment module, and a byproduct tracing and adjustment module, wherein: The pollution source location module is used to identify the node with the highest pollutant concentration as the pollution source center node and the adjacent nodes as diffusion association nodes based on the pollutant concentration data collected by multiple air monitoring nodes deployed in the laboratory. The diffusion trend prediction module is used to integrate the predicted diffusion direction and diffusion arrival time of pollutants into diffusion trend prediction information based on the concentration gradient between the pollutant concentration data of the pollution source center node and the current concentration data of the diffusion-related nodes, combined with the airflow velocity direction data of each air monitoring node. The purification pre-deployment module is used to dynamically mark a pre-purification area from multiple adjustable purification areas in the laboratory based on diffusion trend prediction information, and to prioritize the allocation of pre-set purification air volume to the pre-purification area before the pollutants actually diffuse to the pre-purification area, thus forming a pre-deployment of purification resources in the pre-purification area. The byproduct tracing and adjustment module is used to determine that byproduct pollution has occurred during the purification process if the composition comparison between the pollutant residue data of the pre-purified area and the original pollutant data of the pollution source center node shows that the residue contains new components that are not original pollutants, and then generates a byproduct warning signal and a purification strategy adjustment instruction.

[0006] Preferably, when the pollution source localization module selects the node with the highest pollutant concentration as the pollution source center node and the adjacent nodes as diffusion association nodes, it is specifically used for: Read the pollutant concentrations output by multiple air monitoring nodes deployed in the laboratory at the same time, and record the node with the highest pollutant concentration as the pollution source center node; Based on the spatial coordinates of the pollution source center node, calculate the Euclidean distance between the other air monitoring nodes in the laboratory and the pollution source center node; Nodes whose Euclidean distance is less than a preset neighborhood threshold are marked as diffusion-related nodes.

[0007] Preferably, when the diffusion trend prediction module performs concentration gradient calculations based on the pollutant concentration data of the pollution source center node and the current concentration data of the diffusion-related nodes, it is specifically used for: Extract the pollutant concentration at the central node of the pollution source, as well as the pollutant concentration values ​​and airflow velocity vectors at the corresponding diffusion-related nodes; For each diffusion-related node, calculate the concentration difference between the pollution source center node and each diffusion-related node, and calculate the unit vector from each diffusion-related node to the pollution source center node. Projecting the airflow velocity vector onto a unit vector yields the projected components of the airflow velocity vector. If the projection component is greater than zero, it indicates that the airflow is toward the center node of the pollution source. In this case, the direction in which the diffusion-related node corresponding to the projection component points to the center node of the pollution source will be eliminated. For the remaining diffusion-related nodes, sort them from largest to smallest concentration difference, and take the direction with the largest concentration difference as the expected main diffusion direction.

[0008] Preferably, when the diffusion trend prediction module selects the direction with the largest concentration difference as the expected main diffusion direction, it is specifically used for: When the concentration difference values ​​of multiple diffusion-related nodes are the same, calculate the angle between the airflow velocity vector of each diffusion-related node and the direction vector from the pollution source center node to the diffusion-related node. Choose the direction with the smallest included angle as the expected main diffusion direction; The diffusion-related nodes along the expected main diffusion direction are designated as priority diffusion nodes, and the spatial distance and airflow velocity of the priority diffusion nodes are recorded.

[0009] Preferably, when performing the simulation of pollutant diffusion arrival time, the diffusion trend prediction module is specifically used for: Construct a diffusion driving intensity factor based on all diffusion-related nodes surrounding the central node of the pollution source; Based on the spatial distance between the priority diffusion node and the central node of the pollution source, and combined with the diffusion driving intensity factor, the diffusion arrival time of the pollutants is calculated. The formula for calculating the diffusion arrival time is as follows: ; In the formula, Indicates the time of diffusion arrival. This represents the spatial distance between the priority diffusion node and the central node of the pollution source. Indicates the diffusion-driven intensity factor. This represents the average airflow velocity of all diffusion-related nodes surrounding the central node of the pollution source.

[0010] Preferably, the diffusion trend prediction module calculates the diffusion driving strength factor using the following formula: ; In the formula, Indicates the diffusion-driven intensity factor. This represents the total number of all diffusion-related nodes surrounding the central node of the pollution source. This indicates the pollutant concentration at the central node of the pollution source. Indicates the first Pollutant concentrations at each diffusion-related node. Represents the cosine function. Indicates the first The angle between the airflow velocity direction of each diffusion-related node and the direction of expected diffusion from the center node of the pollution source.

[0011] Preferably, when the pre-deployment module dynamically marks a pre-purification area from multiple adjustable purification areas in the laboratory based on diffusion trend prediction information, it is specifically used for: Starting from the central node of the pollution source, traverse the adjacent nodes sequentially along the expected main direction of diffusion, and take the first node that meets the expected conditions as the starting node. The desired conditions are that the airflow velocity direction at the node is consistent with the expected main diffusion direction, and the pollutant concentration at the node is less than half of the concentration value at the center node of the pollution source. The space covered by two consecutive nodes starting from the starting node is defined as the pre-cleaning area of ​​the laboratory.

[0012] Preferably, when the purification pre-deployment module prioritizes allocating pre-purification airflow to the pre-purification area according to preset purification airflow allocation rules before pollutants actually diffuse into the pre-purification area, thus forming a pre-deployment of purification resources in the pre-purification area, it is specifically used for: Before the pollutants actually diffuse to the pre-purification area, read the current return air valve opening value and supply air valve opening value of each area of ​​the laboratory; According to the preset purification air volume distribution rules, the opening degree of the air supply valve corresponding to the pre-purification area is increased by one level, and the opening degree of the return air valve in the reverse diffusion direction area in the laboratory is decreased by one level, so that the air exchange rate in the pre-purification area is increased to twice the current value.

[0013] Preferably, when the byproduct tracing and adjustment module performs a composition comparison between the pollutant residue data of the pre-purified area and the original pollutant data of the pollution source center node after purification is completed, and the result shows that the residue contains new components that are not original pollutants, it determines that byproduct pollution has occurred during the purification process and generates a byproduct warning signal. Specifically, this module is used to: Collect air samples from the pre-purified area after purification and detect the absorption spectrum curves of the air samples; Read the original pollutant reference spectral curve of the central node of the pollution source, and compare the absorption spectral curve with the original pollutant reference spectral curve to generate a difference spectral curve; Check if there are peak positions in the differential spectral curve where the absorption peak intensity is more than three times the background noise. If so, identify the substance corresponding to the peak position as a new component that is not the original pollutant and generate a byproduct warning signal.

[0014] Preferably, when the byproduct tracing and adjustment module executes the purification strategy adjustment instruction, the specific adjustment process of the instruction is as follows: In response to byproduct warning signals; Disconnect the power supply to the ultraviolet lamps of the photocatalytic oxidation module in the pre-purification area; Adjust the fresh air valve of the pre-purification area to the maximum opening, and at the same time completely close the return air valve to put the pre-purification area into a direct current ventilation mode and maintain the preset purging time. Record the types of byproducts from the pre-purified area in the historical purification records into the byproduct blacklist.

[0015] As can be seen from the above technical solution, the laboratory air conditioning purification system for pollutant monitoring provided by this invention, by introducing airflow direction data and concentration gradient joint analysis, can predict the diffusion direction and arrival time of pollutants in advance, and complete the pre-deployment of purification resources before the pollutants diffuse to sensitive areas, effectively overcoming the defect of purification action lagging behind pollution diffusion in the prior art; by dynamically matching purification strategies according to pollutant type and prioritizing the scheduling of air volume to pre-purification areas, the system avoids the waste of resources caused by uniform purification and the problem of insufficient purification in key areas, significantly improving purification efficiency and spatial distribution rationality; at the same time, by comparing the composition of purified air with the original pollutants, the system can identify non-original byproducts generated during the purification process and adjust the purification strategy in a timely manner to prevent secondary pollution, ensuring the true safety of laboratory air quality and the integrity of the purification process. Attached Figure Description

[0016] Other objects and results of the invention will become more apparent and readily understood by referring to the following description taken in conjunction with the accompanying drawings, and with a more complete understanding of the invention. In the drawings: Figure 1 This is a system architecture diagram of a laboratory air conditioning and purification system for pollutant monitoring, provided as an embodiment of the present invention. Detailed Implementation

[0017] Existing laboratory air conditioning purification methods suffer from several drawbacks: they cannot predict the spread of pollutants, leading to delayed purification actions; they distribute purification resources evenly, resulting in insufficient purification in key areas and wasted energy; and they lack the ability to identify purification byproducts, leading to the risk of secondary pollution.

[0018] To address the aforementioned problems, this invention provides a laboratory air conditioning and purification system for pollutant monitoring. Specific embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0019] To illustrate the laboratory air conditioning and purification system for pollutant monitoring provided by this invention, Figure 1 An exemplary illustration is provided for a laboratory air conditioning and purification system for pollutant monitoring according to an embodiment of the present invention.

[0020] The following description of exemplary embodiments is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques and equipment should be considered part of the specification.

[0021] like Figure 1 The diagram shown is a system architecture diagram of a laboratory air conditioning and purification system for pollutant monitoring provided in an embodiment of the present invention.

[0022] The laboratory air conditioning and purification system 100 for pollutant monitoring according to the present invention may include a pollution source location module 101, a diffusion trend prediction module 102, a purification pre-deployment module 103, and a by-product tracing and adjustment module 104, wherein: The pollution source location module is used to identify the node with the highest pollutant concentration as the pollution source center node and the adjacent nodes as diffusion association nodes based on the pollutant concentration data collected by multiple air monitoring nodes deployed in the laboratory. In this embodiment of the invention, when the pollution source localization module selects the node with the highest pollutant concentration as the pollution source center node and the adjacent nodes as diffusion association nodes, it is specifically used for: Read the pollutant concentrations output by multiple air monitoring nodes deployed in the laboratory at the same time, and record the node with the highest pollutant concentration as the pollution source center node; Based on the spatial coordinates of the pollution source center node, calculate the Euclidean distance between the other air monitoring nodes in the laboratory and the pollution source center node; Nodes whose Euclidean distance is less than a preset neighborhood threshold are marked as diffusion-related nodes.

[0023] The system activates the synchronous data acquisition function of all air monitoring nodes in the laboratory in real time. Through a unified time trigger signal, all air monitoring nodes stop data acquisition and output the currently monitored pollutant concentration data at the exact same time point. The pollutant concentration data output by all air monitoring nodes are completely collected into the data processing unit. The data processing unit reads and compares all the collected pollutant concentration data item by item, compares the concentration values ​​of each air monitoring node in turn, and gradually filters out the pollutant concentration data with the largest value. The unique air monitoring node corresponding to the largest concentration data is identified and officially marked as the pollution source center node.

[0024] The laboratory spatial positioning system retrieves the three-dimensional spatial coordinates of the pollution source center node within the laboratory. This coordinate information includes the specific position values ​​of the node in the length, width, and height directions of the laboratory. The three-dimensional spatial coordinates of each air monitoring node in the laboratory, excluding the pollution source center node, are retrieved sequentially. Using the three-dimensional spatial coordinates of the pollution source center node as the calculation benchmark, for each air monitoring node to be calculated, the position difference between the two nodes in the length direction, width direction, and height direction are calculated separately. The position differences in the three directions are then combined to obtain the straight-line distance between the two nodes in space, which is the Euclidean distance between the two nodes.

[0025] Using a pre-set neighborhood threshold as the screening criterion, the Euclidean distance between each of the remaining air monitoring nodes and the pollution source center node is calculated and compared with the neighborhood threshold. Each Euclidean distance is checked to see if it is less than the neighborhood threshold. All air monitoring nodes that meet the condition of having an Euclidean distance less than the preset neighborhood threshold are screened out, while those that do not meet the condition are excluded from the screening range. All screened air monitoring nodes that meet the distance requirements are uniformly marked as diffusion-related nodes.

[0026] The diffusion trend prediction module is used to integrate the predicted diffusion direction and diffusion arrival time of pollutants into diffusion trend prediction information based on the concentration gradient between the pollutant concentration data of the pollution source center node and the current concentration data of the diffusion-related nodes, combined with the airflow velocity direction data of each air monitoring node. In this embodiment of the invention, when the diffusion trend prediction module executes the concentration gradient based on the pollutant concentration data of the pollution source center node and the current concentration data of the diffusion-related node, it is specifically used for: Extract the pollutant concentration at the central node of the pollution source, as well as the pollutant concentration values ​​and airflow velocity vectors at the corresponding diffusion-related nodes; For each diffusion-related node, calculate the concentration difference between the pollution source center node and each diffusion-related node, and calculate the unit vector from each diffusion-related node to the pollution source center node. Projecting the airflow velocity vector onto a unit vector yields the projected components of the airflow velocity vector. If the projection component is greater than zero, it indicates that the airflow is toward the center node of the pollution source. In this case, the direction in which the diffusion-related node corresponding to the projection component points to the center node of the pollution source will be eliminated. For the remaining diffusion-related nodes, sort them from largest to smallest concentration difference, and take the direction with the largest concentration difference as the expected main diffusion direction.

[0027] When the diffusion trend prediction module selects the direction with the largest concentration difference as the expected main diffusion direction, it is specifically used for: When the concentration difference values ​​of multiple diffusion-related nodes are the same, calculate the angle between the airflow velocity vector of each diffusion-related node and the direction vector from the pollution source center node to the diffusion-related node. Choose the direction with the smallest included angle as the expected main diffusion direction; The diffusion-related nodes along the expected main diffusion direction are designated as priority diffusion nodes, and the spatial distance and airflow velocity of the priority diffusion nodes are recorded.

[0028] The diffusion trend prediction module, when performing the simulation of pollutant diffusion arrival time, is specifically used for: Construct a diffusion driving intensity factor based on all diffusion-related nodes surrounding the central node of the pollution source; Based on the spatial distance between the priority diffusion node and the central node of the pollution source, and combined with the diffusion driving intensity factor, the diffusion arrival time of the pollutants is calculated. The formula for calculating the diffusion arrival time is as follows: ; In the formula, Indicates the time of diffusion arrival. This represents the spatial distance between the priority diffusion node and the central node of the pollution source. Indicates the diffusion-driven intensity factor. This represents the average airflow velocity of all diffusion-related nodes surrounding the central node of the pollution source.

[0029] The diffusion trend prediction module calculates the diffusion driving strength factor using the following formula: ; In the formula, Indicates the diffusion-driven intensity factor. This represents the total number of all diffusion-related nodes surrounding the central node of the pollution source. This indicates the pollutant concentration at the central node of the pollution source. Indicates the first Pollutant concentrations at each diffusion-related node. Represents the cosine function. Indicates the first The angle between the airflow velocity direction of each diffusion-related node and the direction of expected diffusion from the center node of the pollution source.

[0030] From the air monitoring equipment connected to the pollution source center node that has been located and marked, directly extract the pollutant concentration value that the node is stably outputting at the current moment. At the same time, access the air monitoring equipment connected to each of the identified diffusion-related nodes in turn, extract the pollutant concentration value corresponding to each diffusion-related node at the current moment, and then extract the airflow velocity vector composed of the airflow velocity magnitude and airflow direction currently monitored by each diffusion-related node from the airflow sensor installed with each diffusion-related node.

[0031] For each independent diffusion-related node, the pollutant concentration value extracted from the pollution source center node is subtracted from the pollutant concentration value extracted from the current diffusion-related node to obtain a clear concentration difference between the two. Taking the spatial location of the pollution source center node as the endpoint and the spatial location of the current diffusion-related node as the starting point, the straight-line spatial direction between the two points is determined. The direction is standardized according to the standard unit length to obtain the unit vector from the current diffusion-related node to the pollution source center node.

[0032] The airflow velocity vector corresponding to each diffusion-related node is projected parallel to the direction indicated by the unit vector corresponding to that node. The actual extension length of the airflow velocity vector in the direction of the unit vector is calculated. This extension length is the projection component of the airflow velocity vector on the unit vector. The projection component is calculated for each diffusion-related node one by one.

[0033] Each diffusion-related node's calculated projection component value is checked one by one. When the projection component value is greater than zero, it is determined that the overall airflow of the diffusion-related node is flowing towards the pollution source center node. The direction of the diffusion-related node corresponding to the projection component pointing to the pollution source center node is completely removed from the set of possible diffusion directions and will no longer participate in the subsequent diffusion direction judgment.

[0034] For all diffusion-related nodes retained after directional elimination, the concentration difference value corresponding to each node is used as the sorting criterion. Starting from the node with the largest concentration difference value, the nodes are arranged sequentially towards the node with the smallest concentration difference value, forming a diffusion-related node sequence strictly arranged in descending order of concentration difference value.

[0035] When multiple diffusion-related nodes have the same concentration difference after being sorted by concentration difference, for these nodes with the same concentration difference, the direction vector between the two points is determined with the pollution source center node as the starting point and the corresponding diffusion-related node as the ending point. Then, the spatial angle between the airflow velocity vector and the corresponding direction vector of each diffusion-related node is measured.

[0036] Among all the spatial angles corresponding to diffusion-related nodes with the same concentration difference, find the angle with the smallest value, and formally determine the direction from the pollution source center node to the diffusion-related node corresponding to the smallest angle as the expected main diffusion direction of the pollutant.

[0037] All diffusion-related nodes along the expected main diffusion direction extension path are uniformly marked as priority diffusion nodes. The straight-line spatial distance between each priority diffusion node and the pollution source center node is measured one by one using spatial ranging and recorded completely. At the same time, the airflow velocity corresponding to each priority diffusion node is read from the airflow sensor and recorded properly.

[0038] Based on all diffusion-related nodes surrounding the central node of the pollution source, and considering the magnitude of the concentration difference and the degree of alignment between the airflow direction and the expected main diffusion direction for each diffusion-related node, the ratio of the concentration difference to the baseline concentration is first calculated for each individual diffusion-related node. This conversion relies on the measured concentrations at the central node of the pollution source and the corresponding measured concentrations at the diffusion-related nodes to obtain a proportional value that reflects the magnitude of the local concentration gradient. The included angle corresponding to each diffusion-related node is then cosine-converted to obtain an azimuth correction coefficient. This coefficient characterizes the degree of alignment between the current node's airflow direction and the predetermined expected main diffusion direction; a higher degree of alignment corresponds to a larger correction coefficient value. Finally, the concentration proportions and azimuth correction coefficients obtained for each node are combined to generate the local diffusion contribution for that single node. This contribution quantifies the driving effect of a single diffusion-related node on the overall pollutant diffusion. The local diffusion contribution of each of the diffusion-related nodes is accumulated one by one to obtain the total contribution of all nodes. The total contribution is then divided by the total number of diffusion-related nodes to complete the averaging process, and finally a diffusion driving intensity factor is generated. This factor is used to comprehensively characterize the overall driving force of the pollution source's surrounding environment on the outward diffusion of pollutants along the expected main diffusion direction.

[0039] The total number of diffusion-related nodes is derived from a thorough count of all diffusion-related nodes retained after the initial screening process. The count was conducted by systematically counting each node on the marked list, and the final total number of nodes was determined upon completion. The pollutant concentration at the pollution source center node is obtained from the real-time pollutant concentration readings taken from the air monitoring devices mounted on the pollution source center node during the synchronous acquisition phase. This reading is the actual monitoring data directly output by the equipment. The pollutant concentration at a single diffusion-related node is derived from the actual pollutant concentration values ​​output by the air monitoring sensors on the corresponding diffusion-related node at a unified acquisition time. Each diffusion-related node has its own set of dedicated monitoring concentration data. The angle between the airflow direction and the expected main diffusion direction is determined through spatial orientation measurement comparison. The operation uses the expected main diffusion direction extending from the pollution source center node as a reference, aligns with the actual airflow direction of the corresponding diffusion-related node, and measures the spatial angle formed by the two spatial directions.

[0040] When the pollutant concentration difference between the central node of the pollution source and the diffusion-related nodes continues to increase, the concentration proportion of each individual node increases synchronously. With the included angle remaining constant, the diffusion contribution of each individual node increases synchronously, and the final diffusion-driving intensity factor, after aggregation and mean conversion, also increases synchronously. When the airflow direction of the diffusion-related nodes continuously approaches the expected main diffusion direction, the measured included angle continuously narrows, and the azimuth correction coefficient obtained through cosine conversion continuously increases. With the concentration proportion remaining constant, the diffusion contribution of each individual node increases synchronously, and the final diffusion-driving intensity factor, after aggregation and mean conversion, also increases synchronously. When all diffusion-related nodes show a narrowing of the pollutant concentration difference while the airflow direction gradually deviates from the expected main diffusion direction, the diffusion contribution of all individual nodes decreases synchronously, and the final diffusion-driving intensity factor, after aggregation and mean conversion, decreases synchronously.

[0041] Based on the recorded spatial distance between the priority diffusion node and the pollution source center node, and combined with the diffusion driving intensity factor that has been constructed, the time required for pollutants to move from the pollution source center node to the priority diffusion node is calculated step by step by taking into account the spatial distance and diffusion driving capacity, and finally the accurate diffusion arrival time is obtained.

[0042] The overall pollutant diffusion rate is obtained by multiplying the diffusion driving intensity factor by the average airflow velocity. This rate reflects the speed at which pollutants diffuse due to the combined effect of the concentration gradient and ambient airflow. The diffusion arrival time is then calculated by dividing the spatial distance between the priority diffusion node and the pollution source center node by the calculated overall diffusion rate. This arrival time represents the time required for pollutants to travel from the pollution source center node along the expected main diffusion direction to the priority diffusion node.

[0043] The spatial distance between the priority diffusion node and the pollution source center node is derived from the on-site spatial distance measurement operation carried out in the previous step of marking the priority diffusion node. The distance measurement operation is carried out along the spatial straight line between the two points to complete the length measurement, and the measured length data retained after the measurement is directly used as the basis for the value of this distance. The diffusion driving intensity factor is derived from the final result obtained by summing and averaging the concentration data and azimuth angle data of all diffusion-related nodes in the previous step. This value is the calculation product that has been constructed and fixed. The average airflow velocity of all diffusion-related nodes around the pollution source center node is calculated based on the real-time airflow velocity values ​​synchronously collected by the airflow sensors of each diffusion-related node. First, the measured airflow velocity values ​​corresponding to all diffusion-related nodes are accumulated one by one to obtain the velocity sum. Then, the velocity sum is divided by the total number of diffusion-related nodes to complete the average conversion. The final result after conversion is the average airflow velocity.

[0044] As the spatial distance between the priority diffusion node and the pollution source center node increases, the calculated diffusion arrival time increases synchronously while the overall diffusion rate remains constant. When the diffusion driving intensity factor continuously increases, the overall diffusion rate increases while the spatial distance and average airflow velocity remain constant, resulting in a synchronous decrease in the calculated diffusion arrival time. When the average airflow velocity of all diffusion-related nodes around the pollution source center node continuously increases, the overall diffusion rate increases while the spatial distance and diffusion driving intensity factor remain constant, resulting in a synchronous decrease in the calculated diffusion arrival time. When the spatial distance decreases synchronously and the diffusion driving intensity and average airflow velocity increase synchronously, the overall diffusion rate continues to rise, resulting in a significant decrease in the calculated diffusion arrival time.

[0045] The expected main direction of pollutant diffusion, derived from node analysis and angle judgment in the early stage, is integrated with the pollutant diffusion arrival time calculated from spatial distance and driving intensity in the later stage. This integrates directional and temporal information into a complete and directly usable diffusion trend prediction information.

[0046] The purification pre-deployment module is used to dynamically mark a pre-purification area from multiple adjustable purification areas in the laboratory based on diffusion trend prediction information, and to prioritize the allocation of pre-set purification air volume to the pre-purification area before the pollutants actually diffuse to the pre-purification area, thus forming a pre-deployment of purification resources in the pre-purification area. In this embodiment of the invention, when the purification pre-deployment module dynamically marks a pre-purification area from multiple adjustable purification areas in the laboratory based on diffusion trend prediction information, it is specifically used for: Starting from the central node of the pollution source, traverse the adjacent nodes sequentially along the expected main direction of diffusion, and take the first node that meets the expected conditions as the starting node. The desired conditions are that the airflow velocity direction at the node is consistent with the expected main diffusion direction, and the pollutant concentration at the node is less than half of the concentration value at the center node of the pollution source. The space covered by two consecutive nodes starting from the starting node is defined as the pre-cleaning area of ​​the laboratory.

[0047] The purification pre-deployment module, before pollutants actually diffuse into the pre-purification area, prioritizes the allocation of pre-set purification airflow to the pre-purification area, thus forming a pre-deployment of purification resources in the pre-purification area. Specifically, it is used for: Before the pollutants actually diffuse to the pre-purification area, read the current return air valve opening value and supply air valve opening value of each area of ​​the laboratory; According to the preset purification air volume distribution rules, the opening degree of the air supply valve corresponding to the pre-purification area is increased by one level, and the opening degree of the return air valve in the reverse diffusion direction area in the laboratory is decreased by one level, so that the air exchange rate in the pre-purification area is increased to twice the current value.

[0048] Using the established expected main direction of diffusion as a reference benchmark, starting from the location of the marked pollution source center node, the adjacent monitoring nodes along the path are visited one by one along the extension of the expected main direction of diffusion. Each node is checked to see if it matches the expected conditions to be defined. The first node that meets all the criteria is selected in the entire traversal path and marked as the starting node.

[0049] The airflow velocity and direction data measured by the airflow sensor equipment of the traversed nodes are retrieved, and it is compared with the spatial direction of the expected diffusion direction to see if they completely coincide. Then, the real-time pollutant concentration readings output by the air monitoring equipment of the corresponding nodes are retrieved, and the readings are compared with half of the measured pollutant concentration value left at the pollution source center node. Only nodes whose airflow direction completely matches the expected diffusion direction and whose pollutant concentration does not reach half of the concentration at the pollution source center node are considered to have met the complete expected conditions.

[0050] Using the confirmed starting node as the starting point for defining the scope, continue to lock the next node immediately adjacent to the starting node along the expected main direction of diffusion. Define the entire space enclosed by the starting node and the adjacent node within the laboratory space as the pre-purification area, and fully record the laboratory space boundary and area affiliation information corresponding to this area.

[0051] Based on the previously calculated diffusion arrival time, the time nodes are controlled. Before the time node when the pollutants arrive at the pre-purification area according to the predicted pattern arrives, the mechanical opening scale of the air supply valves and return air valves installed in each zone of the laboratory is checked one by one. The actual opening value of each valve body at that time is recorded in a true manner, and the opening data of the two types of valves in all areas of the entire laboratory are completely collected.

[0052] The pre-compiled purification air volume distribution rules are retrieved as the valve body adjustment execution standard. The air supply valve specifically configured for the pre-purification area is located. According to the valve body's graded adjustment level, the actual opening of the air supply valve is increased by one fixed level in the direction of opening larger. At the same time, the area in the laboratory that is located in the opposite direction of the expected diffusion direction is located, and the actual opening of all return air valves in this area is reduced by one fixed level in the direction of closing smaller.

[0053] By adjusting the valve body to change the air volume, the changes in the air exchange frequency in the pre-purification area are checked. The air supply valve is opened to increase the total amount of fresh air supplied, and the return air valve in the reverse diffusion zone is narrowed to reduce the loss of polluted gas. The synchronous adjustment of the two types of valves together accurately increases the original air exchange rate in the pre-purification area to twice the original air exchange rate.

[0054] The byproduct tracing and adjustment module is used to determine that byproduct pollution has occurred during the purification process if the composition comparison between the pollutant residue data of the pre-purified area and the original pollutant data of the pollution source center node shows that the residue contains new components that are not original pollutants, and then generates a byproduct warning signal and a purification strategy adjustment instruction.

[0055] In this embodiment of the invention, when the byproduct tracing and adjustment module performs a component comparison between the pollutant residue data of the pre-purified area after purification and the original pollutant data of the pollution source center node, and the result shows that the residue contains new components that are not original pollutants, it determines that byproduct pollution has occurred during the purification process and generates a byproduct warning signal. Specifically, this module is used to: Collect air samples from the pre-purified area after purification and detect the absorption spectrum curves of the air samples; Read the original pollutant reference spectral curve of the central node of the pollution source, and compare the absorption spectral curve with the original pollutant reference spectral curve to generate a difference spectral curve; Check if there are peak positions in the differential spectral curve where the absorption peak intensity is more than three times the background noise. If so, identify the substance corresponding to the peak position as a new component that is not the original pollutant and generate a byproduct warning signal.

[0056] When the byproduct tracing and adjustment module executes the purification strategy adjustment command, the specific adjustment process of the command is as follows: In response to byproduct warning signals; Disconnect the power supply to the ultraviolet lamps of the photocatalytic oxidation module in the pre-purification area; Adjust the fresh air valve of the pre-purification area to the maximum opening, and at the same time completely close the return air valve to put the pre-purification area into a direct current ventilation mode and maintain the preset purging time. Record the types of byproducts from the pre-purified area in the historical purification records into the byproduct blacklist.

[0057] A sealed air sampling probe is placed at multiple different heights and horizontal positions in the pre-purified area after complete purification treatment. The purified air inside the area is drawn out at a constant speed by a sampling pump. All the drawn air is collected and packaged in a sealed sampling container. The packaged air sample is then placed stably in the sample detection chamber of the spectrometer. The spectrometer continuously emits detection beams in a fixed wavelength change sequence to penetrate the air sample. The photosensitive components inside the instrument record the light energy loss data formed by the absorption of the light beam by the air after passing through the sample. The loss data corresponding to all bands are arranged in a continuous sequence according to the wavelength, and finally a complete absorption spectrum curve is formed.

[0058] The original pollutant reference spectral curves measured and saved in the original pollutant monitoring stage at the central node of the pollution source are retrieved from the local dedicated spectral archive. The absorption spectral curves and the original pollutant reference spectral curves are compared one by one in a completely consistent wavelength order. The numerical difference between the two spectral curves at the same wavelength position is recorded one by one. The difference data of all positions are connected in sequence according to the wavelength arrangement. The difference data after being connected and summarized are a complete difference spectral curve.

[0059] The system sequentially inspects all peaks with upward convexity along the wavelength arrangement of the differential spectral curve. It retrieves background noise baseline data pre-calibrated using clean, impurity-free blank air and checks the relationship between the actual peak intensity of each peak and three times the standard background noise. If a peak intensity exceeds three times the background noise, the corresponding chemical substance is determined to be not an original pollutant released from the pollution source. This substance is then classified as a new component generated during the purification process. The system simultaneously connects the signal output hardware module, which transmits a fixed-format byproduct warning signal.

[0060] After receiving the byproduct warning signal from the hardware module, the system hardware automatically retrieves the full text of the purification strategy adjustment instructions pre-entered in the equipment control console account, and uses the operation specifications recorded in the instructions as the execution standard to initiate each subsequent control operation for the purification equipment.

[0061] Operate the air switch of the corresponding UV lamp tube in the photocatalytic oxidation module inside the power distribution control cabinet of the pre-purification area, turn the switch to the circuit breaker position to cut off the power supply of the entire power supply circuit, completely terminate the power supply of all UV lamp tubes, and the UV lamp tubes will immediately stop emitting UV light after losing power input.

[0062] Turn the manual adjustment handwheel of the fresh air valve in the pre-purification area to continuously open the valve until the valve body baffle reaches the maximum opening position of the mechanical limit. Then turn the locking control component of the return air valve to push the baffle to fully fit the sealing end face of the valve body to achieve full shut-off. Outside air can only be sent into the pre-purification area in one direction through the fresh air duct. The original gas inside the area can only be discharged out through the independent exhaust duct. Relying on the one-way airflow structure formed by the valve body, the pre-purification area can stably switch to the direct current ventilation mode. From the moment the valve is adjusted, the preset fixed purging time is maintained continuously.

[0063] Extract the specific category information of the new components, i.e., by-products, obtained from this spectral detection process. Open the paper ledger and electronic registration form for the by-product blacklist, which are specifically used for control and recording. Enter the types of by-products identified in this instance into the blank record fields of the form to complete the archiving and registration of this type of by-product in the by-product blacklist.

[0064] As can be seen from the above embodiments, the laboratory air conditioning purification system for pollutant monitoring provided by the present invention, by introducing airflow direction data and concentration gradient joint analysis, can predict the diffusion direction and arrival time of pollutants in advance, and complete the pre-deployment of purification resources before the pollutants diffuse to sensitive areas, effectively overcoming the defect of purification action lagging behind pollutant diffusion in the prior art; by dynamically matching purification strategies according to pollutant type and prioritizing the scheduling of air volume to pre-purification areas, the system avoids the waste of resources caused by uniform purification and the problem of insufficient purification in key areas, significantly improving purification efficiency and spatial distribution rationality; at the same time, by comparing the composition of purified air with the original pollutants, the system can identify non-original byproducts generated during the purification process and adjust the purification strategy in a timely manner to prevent secondary pollution, ensuring the true safety of laboratory air quality and the integrity of the purification process.

[0065] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A laboratory air conditioning and purification system for pollutant monitoring, characterized in that, The system includes a pollution source location module, a diffusion trend prediction module, a purification pre-deployment module, and a byproduct source tracing and adjustment module, wherein: The pollution source location module is used to identify the node with the highest pollutant concentration as the pollution source center node and the adjacent nodes as diffusion association nodes based on the pollutant concentration data collected by multiple air monitoring nodes deployed in the laboratory. The diffusion trend prediction module is used to integrate the predicted diffusion direction and diffusion arrival time of pollutants into diffusion trend prediction information based on the concentration gradient between the pollutant concentration data of the pollution source center node and the current concentration data of the diffusion-related nodes, combined with the airflow velocity direction data of each air monitoring node. Specifically, it is used for: Extract the pollutant concentration at the central node of the pollution source, as well as the pollutant concentration values ​​and airflow velocity vectors at the corresponding diffusion-related nodes; For each diffusion-related node, calculate the concentration difference between the pollution source center node and each diffusion-related node, and calculate the unit vector from each diffusion-related node to the pollution source center node. Projecting the airflow velocity vector onto a unit vector yields the projected components of the airflow velocity vector. If the projection component is greater than zero, it indicates that the airflow is toward the center node of the pollution source. In this case, the direction in which the diffusion-related node corresponding to the projection component points to the center node of the pollution source will be eliminated. For the remaining diffusion-related nodes, sort them from largest to smallest concentration difference, and take the direction with the largest concentration difference as the expected main diffusion direction, specifically used for: Construct a diffusion driving intensity factor based on all diffusion-related nodes surrounding the central node of the pollution source; Based on the spatial distance between the priority diffusion node and the central node of the pollution source, and combined with the diffusion driving intensity factor, the diffusion arrival time of the pollutants is calculated. The formula for calculating the diffusion arrival time is as follows: ; In the formula, Indicates the time of diffusion arrival. This represents the spatial distance between the priority diffusion node and the central node of the pollution source. Indicates the diffusion-driven intensity factor. This represents the average airflow velocity of all diffusion-related nodes surrounding the central node of the pollution source; The purification pre-deployment module is used to dynamically mark a pre-purification area from multiple adjustable purification areas in the laboratory based on diffusion trend prediction information, and to prioritize the allocation of pre-set purification air volume to the pre-purification area before the pollutants actually diffuse to the pre-purification area, thus forming a pre-deployment of purification resources in the pre-purification area. The byproduct tracing and adjustment module is used to determine that byproduct pollution has occurred during the purification process if the composition comparison between the pollutant residue data of the pre-purified area and the original pollutant data of the pollution source center node shows that the residue contains new components that are not original pollutants, and then generates a byproduct warning signal and a purification strategy adjustment instruction.

2. The laboratory air conditioning and purification system for pollutant monitoring as described in claim 1, characterized in that, When the pollution source localization module selects the node with the highest pollutant concentration as the pollution source center node and the adjacent nodes as diffusion association nodes, it is specifically used for: Read the pollutant concentrations output by multiple air monitoring nodes deployed in the laboratory at the same time, and record the node with the highest pollutant concentration as the pollution source center node; Based on the spatial coordinates of the pollution source center node, calculate the Euclidean distance between the other air monitoring nodes in the laboratory and the pollution source center node; Nodes whose Euclidean distance is less than a preset neighborhood threshold are marked as diffusion-related nodes.

3. The laboratory air conditioning and purification system for pollutant monitoring as described in claim 1, characterized in that, When the diffusion trend prediction module selects the direction with the largest concentration difference as the expected main diffusion direction, it is specifically used for: When the concentration difference values ​​of multiple diffusion-related nodes are the same, calculate the angle between the airflow velocity vector of each diffusion-related node and the direction vector from the pollution source center node to the diffusion-related node. Choose the direction with the smallest included angle as the expected main diffusion direction; The diffusion-related nodes along the expected main diffusion direction are designated as priority diffusion nodes, and the spatial distance and airflow velocity of the priority diffusion nodes are recorded.

4. The laboratory air conditioning and purification system for pollutant monitoring as described in claim 1, characterized in that, The diffusion trend prediction module calculates the diffusion driving strength factor using the following formula: ; In the formula, Indicates the diffusion-driven intensity factor. This represents the total number of all diffusion-related nodes surrounding the central node of the pollution source. This indicates the pollutant concentration at the central node of the pollution source. Indicates the first Pollutant concentrations at each diffusion-related node Represents the cosine function. Indicates the first The angle between the airflow velocity direction of each diffusion-related node and the direction of expected diffusion from the center node of the pollution source.

5. The laboratory air conditioning and purification system for pollutant monitoring as described in claim 4, characterized in that, When the purification pre-deployment module dynamically marks a pre-purification area from multiple adjustable purification areas in the laboratory based on diffusion trend prediction information, it is specifically used for: Starting from the central node of the pollution source, traverse the adjacent nodes sequentially along the expected main direction of diffusion, and take the first node that meets the expected conditions as the starting node. The desired conditions are that the airflow velocity direction at the node is consistent with the expected main diffusion direction, and the pollutant concentration at the node is less than half of the concentration value at the center node of the pollution source. The space covered by two consecutive nodes starting from the starting node is defined as the pre-cleaning area of ​​the laboratory.

6. The laboratory air conditioning and purification system for pollutant monitoring as described in claim 5, characterized in that, The purification pre-deployment module, before pollutants actually diffuse into the pre-purification area, prioritizes the allocation of pre-set purification airflow to the pre-purification area, thus forming a pre-deployment of purification resources in the pre-purification area. Specifically, it is used for: Before the pollutants actually diffuse to the pre-purification area, read the current return air valve opening value and supply air valve opening value of each area of ​​the laboratory; According to the preset purification air volume distribution rules, the opening degree of the air supply valve corresponding to the pre-purification area is increased by one level, and the opening degree of the return air valve in the reverse diffusion direction area in the laboratory is decreased by one level, so that the air exchange rate in the pre-purification area is increased to twice the current value.

7. A laboratory air conditioning and purification system for pollutant monitoring as described in claim 6, characterized in that, When the byproduct tracing and adjustment module performs a composition comparison between the pollutant residue data of the pre-purified area and the original pollutant data of the pollution source center node after purification is completed, and the result shows that the residue contains new components that are not original pollutants, it determines that byproduct pollution has occurred during the purification process and generates a byproduct warning signal. Specifically, this is used for: Collect air samples from the pre-purified area after purification and detect the absorption spectrum curves of the air samples; Read the original pollutant reference spectral curve of the central node of the pollution source, and compare the absorption spectral curve with the original pollutant reference spectral curve to generate a difference spectral curve; Check if there are peak positions in the differential spectral curve where the absorption peak intensity is more than three times the background noise. If so, identify the substance corresponding to the peak position as a new component that is not the original pollutant and generate a byproduct warning signal.

8. The laboratory air conditioning and purification system for pollutant monitoring as described in claim 7, characterized in that, When the byproduct tracing and adjustment module executes the purification strategy adjustment command, the specific adjustment process of the command is as follows: In response to byproduct warning signals; Disconnect the power supply to the ultraviolet lamps of the photocatalytic oxidation module in the pre-purification area; Adjust the fresh air valve of the pre-purification area to the maximum opening, and at the same time completely close the return air valve to put the pre-purification area into a direct current ventilation mode and maintain the preset purging time. Record the types of byproducts from the pre-purified area in the historical purification records into the byproduct blacklist.

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