Operation site environment intelligent control method

Abstract

The invention provides an operation site environment intelligent control method which comprises the following steps: identifying planktonic bacteria concentration distribution of a local planktonic bacteria concentration increasing area, and performing clustering grouping processing on a planktonic bacteria concentration distribution diagram according to the planktonic bacteria concentration to obtain a boundary coordinate and a center point position of a high-concentration area; according to the concentration change of planktonic bacteria in the high-concentration area and the proportion of the air flow covering the high-concentration area, the filtering strength level is enhanced so as to meet the purification requirement of the high-concentration area; integrating the updated filtering strength level and airflow path configuration, simulating the overall purification efficiency change of the operation site environment, and judging whether the simulated purification efficiency reaches a preset standard value or not; if the preset standard value is not reached, the airflow path vector and the filtering intensity level are iteratively adjusted according to deviation analysis of the simulated purification efficiency, and finally optimized purification equipment control parameters are obtained.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an intelligent control method for surgical site environment. Background Technology

[0002] In modern medicine, the cleanliness of the operating room environment directly affects patient safety and surgical success rates, making air quality control particularly crucial. Airborne bacteria can cause postoperative infections, threatening patient lives. Dynamic management of airflow distribution and airborne bacteria concentration is a core element in ensuring a sterile operating room environment. Current operating room air purification primarily relies on fixed ventilation systems and filtration equipment. These systems typically operate according to preset modes, creating airflow circulation through supply and return air vents to reduce airborne bacteria. However, equipment placement, personnel movement, and surgical procedures within the operating room can disrupt airflow, leading to uneven airflow distribution in localized areas. This uneven airflow can cause air stagnation in certain areas, allowing airborne bacteria to accumulate and increasing the risk of contamination. Existing solutions often fail to detect these dynamic changes in real time and struggle to flexibly adjust airflow paths or filtration intensity based on actual conditions, thus reducing purification effectiveness. Against this backdrop, uneven airflow distribution becomes a core technical challenge affecting airborne bacteria concentration control. Airflow distribution within the operating room is influenced by various factors, such as the location of air vents, the layout of the operating table, and disturbances caused by personnel movement. These factors lead to inconsistent airflow speed and direction in the space, potentially creating airflow "dead zones." In these dead zones, slow airflow allows airborne bacteria to accumulate, resulting in high-concentration contamination points. For example, near the operating table, if airflow fails to effectively cover the area, instrument surfaces may become contaminated due to airborne bacteria settling, increasing the risk of postoperative infection. Furthermore, because airflow conditions within the operating room are constantly changing, traditional monitoring methods, which typically rely on periodic sampling, struggle to capture real-time airborne bacteria distribution. Without dynamic monitoring, purification equipment cannot adjust airflow paths or enhance local filtration intensity based on the specific location of contamination points. For instance, during a complex surgery, frequent movement of medical staff may weaken airflow on one side of the operating table, increasing airborne bacteria concentration, but existing systems cannot detect this change in time and make targeted adjustments. Therefore, how to monitor the correlation between airflow distribution and airborne bacteria concentration in the dynamically changing operating room environment in real time, and accurately identify high-concentration contamination areas accordingly, has become a key issue in operating room air quality control. Summary of the Invention

[0003] This invention provides an intelligent control method for the surgical site environment, mainly comprising:

[0004] Data on airborne bacteria concentration in the surgical environment is collected, and airflow velocity and direction at multiple points are obtained. The airflow velocity and airflow velocity at these points are integrated to generate a real-time airflow distribution map and an airborne bacteria concentration distribution map, outputting a correlation curve between airflow velocity and airborne bacteria concentration. Based on the correlation curve, areas with increased airborne bacteria concentration are identified. The airborne bacteria concentration distribution in these areas is clustered to obtain the boundary coordinates and center point of high-concentration areas. Based on the airflow coverage area, the boundary coordinates, and the center point of the high-concentration areas, it is determined whether the high-concentration areas are covered. The airflow direction, velocity, and coverage area are adjusted based on the center point and boundary coordinates of the high-concentration areas, and the changes in airborne bacteria concentration and airflow coverage ratio in the adjusted high-concentration areas are obtained. The filtration intensity level is adjusted based on the changes in airborne bacteria concentration and the airflow coverage ratio. Combining the filtration intensity level and airflow path configuration, the purification efficiency of the surgical environment is simulated, and it is determined whether a preset standard value is reached. Based on the deviation of the simulated purification efficiency, the airflow path and filtration intensity level are iteratively adjusted, and optimized purification equipment control parameters are output.

[0005] Furthermore, airborne bacterial concentration data in the surgical environment is collected, and airflow velocity and direction at multiple points are obtained. The airborne bacterial concentration data and the airflow velocity at these multiple points are integrated to generate a real-time airflow distribution map and an airborne bacterial concentration distribution map. The correlation curve between airflow velocity and airborne bacterial concentration is output, including:

[0006] Samplers are arranged in a spatial grid within the operating room. These samplers record the airborne bacteria concentration and timestamps. Wind speed sensors are deployed to acquire the airflow velocity and direction vectors at multiple points. The airborne bacteria concentration and airflow parameters are aligned using timestamp matching to establish a correspondence between spatial coordinates and airborne bacteria concentration. A continuous airborne bacteria concentration distribution field is generated using interpolation, and the airflow velocity and direction vectors are reconstructed to form an airflow distribution field. The airflow velocity and airborne bacteria concentration values ​​of each grid in the airflow distribution field are extracted, correlation coefficients are calculated, strongly correlated regions are marked, and airflow dead zones are identified. Based on the airflow dead zone locations and correlation coefficients, real-time airflow distribution maps and airborne bacteria concentration distribution maps are drawn, with velocity vectors and concentration contour lines marked. The correlation curve between airflow velocity and airborne bacteria concentration is output.

[0007] Furthermore, based on the correlation curve, the region of increased localized airborne bacteria concentration is determined, including:

[0008] The slope data of the correlation curve is extracted, and the airflow velocity intervals are divided according to the positive and negative values ​​of the slope. The concentration values ​​of planktonic bacteria within the airflow velocity intervals are statistically analyzed, the concentration change rate is calculated, inflection points are marked, and a correspondence table between airflow velocity and planktonic bacteria concentration is generated. Based on the correspondence table and the airflow distribution map, the airflow velocity values ​​at each location are obtained, the expected range of planktonic bacteria concentration is found, and the measured concentration values ​​in the planktonic bacteria concentration distribution map are compared to calculate the deviation, generating concentration deviation distribution data. Based on the concentration deviation distribution data, a deviation threshold is set, and locations where the measured concentration exceeds the expected range are marked as abnormal rise points. Adjacent abnormal rise points are merged to form a continuous region, and the boundary coordinates of the continuous region are extracted to determine the local planktonic bacteria concentration rise area.

[0009] Furthermore, based on the planktonic bacteria concentration distribution in the areas of increased local planktonic bacteria concentration, clustering is performed to obtain the boundary coordinates and center point locations of the high-concentration areas, including:

[0010] The three-dimensional coordinates and concentration values ​​of sampling points within the region of increased local planktonic bacterial concentration are obtained. A cubic grid is then formed, and the spatial density value and concentration gradient of each grid are calculated to create gradient field distribution data. Based on this gradient field distribution data, a clustering algorithm is used to group planktonic bacterial concentrations, marking core points and connecting density-reachable points to form high-concentration clusters. The weighted average of the coordinates of sampling points within the high-concentration clusters is calculated to obtain the centroid coordinates, and the outermost sampling points are extracted to form an initial boundary contour. The initial boundary contour is smoothed, outliers are removed, and the boundary coordinate sequence and center point position of the high-concentration region are output.

[0011] Furthermore, based on the airflow coverage area, the boundary coordinates of the high-concentration area, and the center point location, it is determined whether the high-concentration area is covered, including:

[0012] Read the operating parameters of the purification equipment, construct the airflow path spatial vector, calculate the three-dimensional spatial range of the airflow coverage, and generate a set of boundary coordinates of the coverage area; compare the set of boundary coordinates of the coverage area with the boundary coordinates of the high concentration area, calculate the intersection volume, and determine the coverage ratio; check whether the center point of the high concentration area is within the coverage area, calculate the distance from the center point to the boundary of the coverage area, and output the coverage judgment result.

[0013] Furthermore, based on the center point location and boundary coordinates of the high-concentration area, the airflow direction, speed, and coverage area are adjusted to obtain the changes in the airborne bacteria concentration and airflow coverage ratio of the high-concentration area after adjustment, including:

[0014] Calculate the coordinate difference between the center point of the high-concentration area and the projection point of the airflow center axis, determine the angular offset and adjustment times, drive the air outlet to deflect, and adjust the fan speed; sample the airborne bacteria concentration value of the high-concentration area after adjustment, calculate the concentration change rate, and determine the stable state; calculate the intersection volume of the airflow coverage area and the high-concentration area, count the percentage decrease in concentration, and output the change in airborne bacteria concentration and the airflow coverage ratio.

[0015] Furthermore, adjusting the filtration intensity level based on the changes in the airborne bacteria concentration and the airflow coverage ratio includes:

[0016] Obtain the changes in airborne bacteria concentration and airflow coverage ratio, determine the intensity adjustment range, query the filtration intensity mapping table, and determine the basic filtration level; adjust the basic filtration level according to the cleanliness requirements, check the filter pressure difference, and adjust the filtration intensity level; calculate the running time corresponding to the adjusted filtration intensity level, monitor concentration changes, and output the filtration intensity level setting.

[0017] Furthermore, based on the filtration intensity level and airflow path configuration, the purification efficiency of the simulated surgical site environment is assessed to determine whether a preset standard value has been achieved, including:

[0018] The filtration intensity level and airflow path configuration parameters are obtained, a three-dimensional flow field simulation grid is established, and a numerical simulation environment is constructed. The trajectory of airborne bacteria particles is tracked, the purification efficiency of each grid unit is statistically analyzed, and a spatial purification efficiency distribution is formed. The removal rate of the monitoring points in the spatial purification efficiency distribution is calculated, the overall purification rate and purification ratio are obtained, the purification ratio is compared with the preset standard value, and the compliance judgment result is output.

[0019] Furthermore, based on the simulated purification efficiency deviation, the airflow path and filtration intensity level are iteratively adjusted to output optimized purification equipment control parameters, including:

[0020] Calculate the deviation between the simulated purification efficiency and the preset standard value, mark the priority adjustment area, and determine the angle offset and adjustment vector; construct candidate parameter combinations, evaluate the expected purification efficiency, and mark feasible solutions; select the parameter combination with the smallest evaluation index, verify physical feasibility, and output the optimized purification equipment control parameters, including airflow direction, speed, coverage area, and filtration intensity level.

[0021] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0022] This invention discloses an intelligent control method for surgical site environments. Addressing the problem of locally elevated airborne bacteria concentration and insufficient coverage by purification equipment in surgical environments, the method collects airborne bacteria concentration, multi-point airflow velocity, and direction data, integrates them to generate real-time airflow distribution maps and airborne bacteria concentration distribution maps, and analyzes the correlation curve between airflow velocity and airborne bacteria concentration to determine the boundary coordinates and center point location of high-concentration areas. By comparing the airflow coverage range of the purification equipment with the location of high-concentration areas, this invention dynamically adjusts the airflow direction, velocity, and coverage range to enhance filtration intensity. It also monitors changes in airborne bacteria concentration and coverage ratio in real time, iteratively optimizing equipment parameters until the simulated purification efficiency reaches a preset standard. Through dynamic coupling analysis and feedback adjustment of airflow and airborne bacteria concentration, this invention achieves precise control of air cleanliness in surgical sites, significantly improving purification efficiency, reducing infection risks, and providing efficient and reliable air quality assurance for the surgical environment. Attached Figure Description

[0023] Figure 1 This is a flowchart of an intelligent control method for the surgical site environment according to the present invention.

[0024] Figure 2 This is a schematic diagram of an intelligent control method for the surgical site environment according to the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] like Figure 1-2 This embodiment of an intelligent control method for surgical site environment may specifically include:

[0027] Step S101: Collect airborne bacteria concentration data in the surgical environment using a portable airborne bacteria sampler. Simultaneously, obtain airflow velocity and airflow direction at multiple points. By integrating the airborne bacteria concentration data and the airflow velocity at multiple points, obtain a real-time airflow distribution map and a corresponding airborne bacteria concentration distribution map, and output the correlation curve between airflow velocity and airborne bacteria concentration.

[0028] Portable airborne bacteria samplers are deployed in the operating room according to a pre-defined spatial grid. Each sampling point forms a three-dimensional sampling matrix at predetermined intervals. The samplers simultaneously record the airborne bacteria concentration value and the corresponding timestamp. Simultaneously, wind speed sensors are deployed at the same points to acquire airflow velocity data and direction vector information. The airborne bacteria concentration data and airflow parameters are aligned using a timestamp matching method to establish a correspondence between spatial coordinates and airborne bacteria concentration. Based on this correspondence, a three-dimensional spatial interpolation function is constructed. Kriging interpolation is used to estimate the airborne bacteria concentration in the region between sampling points, generating a continuous airborne bacteria concentration distribution field. Simultaneously, the airflow velocity data and direction vector are reconstructed into a vector field, and the airflow velocity value within each spatial grid is calculated to obtain the complete airflow distribution field in the operating room. For each spatial grid in the airflow distribution field, the corresponding airflow velocity value and airborne bacteria concentration value are extracted to form a data pair. The linear correlation between airflow velocity and airborne bacteria concentration is calculated using the Pearson correlation coefficient. If the absolute value of the correlation coefficient is greater than a preset threshold, it is marked as a strongly correlated region. The location of airflow dead zones is identified based on the spatial distribution characteristics of the strongly correlated regions. Based on the location of the airflow dead zone and the results of the correlation coefficient calculation, a real-time airflow distribution map and a planar bacteria concentration distribution map are plotted. The velocity vector arrows and concentration contour lines are marked on the distribution map. The positive or negative correlation coefficient is used to determine whether the airflow velocity and the planar bacteria concentration are inversely or in the same direction. The correlation curve between airflow velocity and planar bacteria concentration, which includes the goodness-of-fit value, is output.

[0029] For example, in one implementation, the sampling points in the operating room are arranged using a three-dimensional gridding strategy, and the grid density is determined according to the spatial dimensions of the operating room.

[0030] Specifically, in a standard operating room measuring 8 meters long, 6 meters wide, and 3 meters high, sampling points are set at predetermined intervals along the length, and similarly at equal intervals along the width and height, forming a three-dimensional sampling network. The portable airborne bacteria sampler employs an impact sampling principle, using a built-in sampling pump to draw air at a constant flow rate, causing airborne bacteria to collide with and be captured on the culture medium surface. Each sampler is equipped with a high-precision clock module, ensuring that the time synchronization error of all devices is controlled within milliseconds, achieving precise time matching between airborne bacteria concentration data and airflow parameters. The wind speed sensor uses a hot-wire or ultrasonic measurement principle, capable of simultaneously acquiring the speed and direction information of the airflow. The direction vector output by the sensor is represented in three-dimensional coordinates, containing three components: x, y, and z, corresponding to the length, width, and height of the operating room, respectively. Through a timestamp matching method, the airborne bacteria concentration values ​​and airflow velocity vectors at the same time and spatial location are fused to establish a database of correspondence between spatial coordinates and airborne bacteria concentration. Each record contains five elements: spatial coordinates, timestamp, airborne bacteria concentration value, airflow speed, and direction vector.

[0031] Preferably, the application of Kriging interpolation involves two key steps: calculating the variogram and determining the weighting coefficients. The variogram describes the spatial correlation of airborne bacterial concentration with distance. By analyzing the relationship between the differences in airborne bacterial concentration and spatial distance between known sampling points, theoretical variograms such as spherical or Gaussian models are fitted. Based on the variogram, Kriging interpolation determines the weighting coefficients of each sampling point for the estimated point by solving a system of linear equations. These weighting coefficients satisfy the conditions of unbiasedness and minimum variance. In the three-dimensional space of the operating room, for any unsampled spatial location, Kriging interpolation estimates the airborne bacterial concentration at that location based on the airborne bacterial concentration values ​​of surrounding sampling points and the corresponding weighting coefficients, generating a continuous and smooth airborne bacterial concentration distribution field. During the vector field reconstruction process, airflow velocity data and direction vectors are spatially interpolated using bilinear interpolation or cubic spline interpolation methods to ensure the continuity and differentiability of the airflow field. The calculation of airflow volume is based on the principles of fluid mechanics. Each spatial grid is regarded as a control volume. The volumetric flow rate of the airflow through the grid is obtained by the dot product of the airflow velocity vector at the grid boundary and the boundary area, which reflects the air exchange intensity in the local area.

[0032] In one possible implementation, the Pearson correlation coefficient is calculated using a sliding window method, performing local correlation analysis on data from each spatial grid and its neighboring areas. The correlation coefficient is calculated as the ratio of the covariance of airflow velocity and airborne bacterial concentration to their respective standard deviations, ranging from -1 to 1. When the correlation coefficient is close to -1, it indicates a strong negative correlation between airflow velocity and airborne bacterial concentration, meaning that the higher the airflow velocity, the lower the airborne bacterial concentration; a correlation coefficient close to 1 indicates a positive correlation. The preset threshold is determined based on operating room cleanliness standards and historical data analysis, typically set between 0.6 and 0.8. Areas exceeding this threshold are marked as strongly correlated areas. The identification of airflow dead zones is based on flow field analysis and correlation distribution characteristics. In areas such as under the operating table or behind equipment, airflow velocity is significantly reduced due to physical obstruction, and the airflow velocity in these areas is usually below a certain percentage threshold of the overall average. Simultaneously, these low-velocity areas often exhibit a localized increase in airborne bacterial concentration. By analyzing the spatial connectivity and geometric characteristics of strongly correlated areas, airflow dead zone locations with closed or semi-closed features are identified.

[0033] Understandably, real-time airflow distribution maps employ vector field visualization technology, using arrows to represent the direction and speed of airflow in two-dimensional or three-dimensional space. The length of the arrows is proportional to the airflow speed, and color coding reflects the speed level. Planktonic bacteria concentration distribution maps use contour lines or cloud maps, with different colors representing different concentration levels, from blue to red, indicating changes in concentration from low to high.

[0034] For example, during surgery, the movement of medical staff can cause local airflow disturbances, resulting in a temporary weakening of airflow on one side of the operating table. After this change is captured by a real-time monitoring system, correlation analysis reveals that the airflow velocity in this area has decreased from the normal value to below the threshold, while the concentration of airborne bacteria shows an upward trend. This abnormal area is immediately marked on the distribution map, and the corresponding data points deviating from the normal distribution range are displayed on the correlation curve. Furthermore, the correlation curve is plotted using a scatter plot combined with regression fitting. The horizontal axis represents the numerical range of airflow velocity, dynamically adjusted according to the actual conditions of the operating room; the vertical axis represents the corresponding airborne bacteria concentration, expressed in colony-forming units per cubic meter. The data points are fitted using the least squares method or other regression methods to obtain a curve equation reflecting the overall trend. The goodness-of-fit value is obtained by calculating the coefficient of determination, reflecting the degree to which the regression model interprets the actual data. The closer the value is to 1, the better the fit, providing a quantitative basis for subsequent airflow optimization and adjustment.

[0035] Step S102: Analyze the correlation between airflow velocity and planktonic bacteria concentration using correlation curves, and determine the local area of ​​increased planktonic bacteria concentration by comparing and analyzing the airflow distribution map and the planktonic bacteria concentration distribution map, combined with the correlation between airflow velocity and planktonic bacteria concentration.

[0036] The slope data of the correlation curve is extracted, and the relationship between airflow velocity and airborne bacteria concentration is determined based on the sign of the slope. The airflow velocity is divided into multiple velocity intervals according to a preset interval. The airborne bacteria concentration values ​​of all sampling points in each velocity interval are statistically analyzed. The concentration change rate is calculated by the difference between the average concentration values ​​of adjacent intervals. When the change rate changes from a negative value to a positive value or exceeds a preset threshold, it is marked as a turning point, resulting in a correspondence table between airflow velocity and airborne bacteria concentration. The correspondence table is compared with the airflow distribution map, and the actual airflow velocity values ​​at each spatial location on the airflow distribution map are read. The expected airborne bacteria concentration range corresponding to each location is found according to the velocity interval in the correspondence table. The expected concentration range is compared with the measured concentration value at the same location on the airborne bacteria concentration distribution map, and the deviation of the measured value from the expected range is calculated, generating concentration deviation distribution data. For the concentration deviation distribution data, a deviation threshold judgment standard is set. If the measured concentration at a certain location exceeds the expected upper limit of concentration and reaches a preset ratio, the location is marked as an abnormal rise point. The spatial continuity of adjacent abnormal rise points is judged by the eight-neighbor connectivity. Abnormal points with a distance less than a preset interval are merged to form a continuous region. The outer boundary coordinates of the merged region are extracted to determine the local area of ​​increased airborne bacteria concentration.

[0037] For example, in one implementation, the slope of the correlation curve is extracted by the difference method, which divides the difference in airflow velocity by the difference in airflow concentration between adjacent data points on the curve to obtain the local slope value.

[0038] Specifically, a negative slope indicates that increased airflow velocity leads to a decrease in airborne bacteria concentration, which aligns with the principle of airflow dilution. A positive slope suggests abnormal airborne bacteria aggregation within that velocity range. Velocity ranges are divided using an equal-interval method, segmenting from the lowest to the highest airflow velocity with a fixed step size. Each range contains multiple sampling data points.

[0039] It should be noted that the concentration change rate is calculated based on the difference in the average concentration between adjacent velocity intervals. By statistically analyzing the airborne bacteria concentration at all sampling points within each velocity interval, the arithmetic mean is calculated as the representative concentration for that interval. The difference in the average concentration between adjacent intervals divided by the width of the velocity interval gives the concentration change rate. Inflection points are identified based on changes in the sign or abrupt changes in the value of the rate of change. When the rate of change changes from negative to positive, the corresponding velocity value is marked as an inflection point. These inflection points reflect the critical state of the airflow purification effect.

[0040] Preferably, the process of comparing the correspondence table with the distribution map uses a coordinate matching method. The three-dimensional coordinates and corresponding airflow velocity values ​​of each grid node on the airflow distribution map are read. Based on the velocity value, the corresponding velocity interval is found in the correspondence table, and the upper and lower limits of the expected concentration range for that interval are obtained. The measured value at the same coordinate position in the planktonic bacteria concentration distribution map is compared with the expected range. When the measured value falls within the expected range, the deviation is recorded as zero. When it exceeds the upper limit, the excess is calculated as a positive deviation; when it is below the lower limit, the difference is calculated as a negative deviation. In one possible embodiment, eight-neighbor connectivity judgment is used to identify spatially adjacent abnormal rise points. For each abnormal point in three-dimensional space, it is checked whether its 26 neighboring locations (up, down, left, right, front, back, and diagonal directions) are also marked as abnormal. If there are abnormal points in neighboring locations and the distance between the two points is less than a preset merging threshold, these points are grouped into the same connected region. The region merging is implemented using the disjoint-set data structure algorithm. Initially, each outlier point is treated as an independent set. Neighboring sets are continuously merged through neighborhood checks to form several unconnected connected regions. Each region represents a local area with increased airborne bacterial concentration. The coordinates of the outermost point of each region are extracted as the boundary.

[0041] Step S103: Identify the distribution of planktonic bacteria concentration in areas where the local planktonic bacteria concentration is increased, and perform clustering and grouping processing on the planktonic bacteria concentration distribution map according to the planktonic bacteria concentration to obtain the boundary coordinates and center point location of the high concentration area.

[0042] The three-dimensional coordinates and corresponding concentration values ​​of all sampling points within the region of increased local planktonic bacterial concentration are obtained. The space is divided into a cubic grid, and the total amount of planktonic bacteria in each grid is divided by the grid volume to obtain the spatial density value. The ratio of the density difference between adjacent grids to the distance between the grid centers is calculated as the concentration gradient, and gradient field distribution data is formed based on the gradient direction and magnitude. Based on the gradient field distribution data, the DBSCAN clustering algorithm is used to group the planktonic bacterial concentrations. A minimum density value and a neighborhood radius are set as clustering parameters. When the concentration value of a spatial point within its neighborhood radius exceeds the minimum density value and the neighborhood contains more than a predetermined number of high-concentration points, the point is marked as a core point. Starting from the core point, other points are connected through density accessibility and density reachability relationships to form high-concentration clusters. For each high-concentration cluster, the weighted average of the coordinates of all sampling points within the cluster is calculated, with the weight being the proportion of each point's concentration value to the total concentration within the cluster. The centroid coordinates of the cluster center are obtained, and the outermost sampling points of each cluster are extracted as boundary candidate points. The initial boundary contour is determined by calculating the minimum bounding polygon of the candidate point set. The initial boundary contour is smoothed, the curvature change between adjacent points on the boundary is calculated, and if the curvature change exceeds a preset threshold, an interpolation point is added at that position. Cubic spline interpolation is used to connect all boundary points to form a smooth curve. Abnormal points whose distance to the centroid exceeds a preset multiple of the average distance are removed, and the sequence of boundary coordinates and center point position of the high-concentration region are output.

[0043] For example, in one implementation, the spatial grid is divided using a uniform cubic grid method, with the grid side length determined based on the actual size of the operating room and the sampling accuracy requirements. For a standard laminar flow operating room, the space is divided into cubic grids with a side length of 0.5 meters, each grid serving as an independent statistical unit. The total amount of airborne bacteria within a grid is obtained by summing the concentration values ​​of all sampling points within the grid's coverage area. The summation value divided by the grid volume of 0.125 cubic meters yields the spatial density value of that grid. The concentration gradient is calculated based on the density difference between the center points of adjacent grids, using the central difference method to calculate the three-dimensional gradient vector. The x-component of the gradient is equal to the density difference between adjacent east-west grids divided by the grid center distance. The y-component and z-component are calculated using the same method. The three components are combined to form complete gradient field distribution data.

[0044] It's important to note that the core of the DBSCAN clustering algorithm lies in its density-based clustering approach, eliminating the need to pre-specify the number of clusters. The algorithm defines two key parameters: the neighborhood radius ε and the minimum number of points MinPts. In the operating room airborne bacteria concentration clustering application, the neighborhood radius is set to 1 meter, and the minimum number of points is set to 5. These parameters are determined based on the spatial characteristics of the operating room and the distribution characteristics of airborne bacteria. For each sampling point in the space, all points within its ε-neighborhood are calculated. If the number of high-concentration points within the neighborhood is greater than or equal to MinPts, then the point is marked as a core point. The density reachability relation is defined as follows: if point q is within the ε-neighborhood of core point p, then q is density-reachable from p. The density reachability relation is extended through transitivity: if there exists a chain of points p1, p2, ..., pn, where p1 = p, pn = q, and for all i, pi+1 is density-reachable from pi, then q is density-reachable from p. The algorithm starts from any unvisited core point and expands continuously through density reachability relationships, grouping all density-reachable points into the same cluster to form a high-concentration region based on density connectivity. The centroid calculation after cluster formation uses a concentration-weighted average method to avoid the bias that may arise from simple arithmetic averaging. Specifically, the calculation first sums the concentration values ​​of all sampling points within a cluster, then calculates the concentration weight of each point, i.e., the ratio of that point's concentration value to the total concentration. The x-coordinate of the centroid is equal to the sum of the products of the x-coordinates of all points and their corresponding weights; the y-coordinates and z-coordinates are calculated using the same weighting method. This weighted centroid more accurately reflects the actual center location of the high-concentration region, especially when the concentration distribution is uneven. Boundary candidate point extraction is achieved by judging the neighborhood characteristics of points within each cluster. If a point's neighborhood contains points that do not belong to that cluster or blank areas, that point is marked as a boundary candidate point. The determination of the minimum bounding polygon uses an incremental construction method. The point with the smallest y-coordinate is selected from the set of boundary candidate points as the starting point, and then all other candidate points are sorted according to their polar angle. Each point is examined sequentially, and its turning relationship with the existing boundary is determined. If a right turn is formed, the previous point is deleted, and this process continues until a left turn or collinearity is achieved. This process gradually constructs a convex polygon contour containing all candidate points. In the operating room environment, due to the influence of equipment and personnel activities, areas with high concentrations of airborne bacteria often exhibit irregular shapes. The convex hull algorithm can quickly determine their outer boundaries.

[0045] In one possible implementation, the smoothing of the boundary profile begins with calculating the local curvature between adjacent boundary points. Curvature is calculated using the three-point circular arc method; for three consecutive points on the boundary, the reciprocal of the radius of the circle defined by these three points is used as the curvature estimate at the midpoint. When the rate of change of curvature exceeds a preset threshold, it indicates a sharp transition at that location, requiring additional interpolation points to achieve a smooth transition. The application of cubic spline interpolation in boundary smoothing ensures the second-order continuity of the curve. The spline function is a cubic polynomial within each interval, satisfying the continuity conditions of function value, first derivative, and second derivative at the connection points. The spline coefficients are determined by solving a tridiagonal linear equation system, resulting in a smooth curve passing through all boundary points. During interpolation, the interpolation density is dynamically adjusted based on the distance between adjacent points; more interpolation points are added to segments with larger distances to ensure the uniformity of the curve. Furthermore, outlier removal is based on statistical analysis methods. The distance from all boundary points to the centroid is calculated, yielding the mean distance and standard deviation. Points whose distance exceeds the mean plus twice the standard deviation are identified as potential outliers, which may be caused by measurement errors or local disturbances. After removing outliers, spline interpolation is performed again to obtain a more accurate boundary profile.

[0046] For example, during complex surgeries, multiple independent high-concentration areas may form around the operating table. After the above processing, each area obtains its own boundary coordinate sequence and center point position.

[0047] Step S104: Obtain the current airflow direction, speed, coverage area, and filtration intensity setting of the purification device. By comparing the airflow coverage area with the boundary coordinates and center point position of the high concentration area, determine whether the airflow path parameters and filtration intensity settings cover the high concentration area.

[0048] The system acquires current purification equipment operating parameters, including airflow direction angle, wind speed, and filter level settings. Based on the airflow outlet location and airflow direction angle, it constructs a spatial vector of the airflow path. Multiplying the wind speed by a preset diffusion coefficient yields the airflow propagation range in each direction. Combining this with the vertical distance from the air outlet to the floor within the operating room, it determines the three-dimensional spatial range of the airflow coverage, resulting in a set of boundary coordinates for the coverage area. This set of boundary coordinates is then spatially compared with the boundary coordinates of the high-concentration area. The intersection volume of the two areas in three-dimensional space is calculated. If the ratio of the intersection volume to the total volume of the high-concentration area is less than a preset coverage threshold, the current airflow path is determined not to completely cover the high-concentration area, and the spatial coordinate range of the uncovered portion of the high-concentration area is recorded. For the uncovered portion, it checks whether the center point of the high-concentration area is located inside the coverage area boundary. If the center point is outside the coverage area, the filtration intensity setting is deemed insufficient to handle the high-concentration area. The degree of coverage deviation is determined by calculating the Euclidean distance from the center point to the nearest coverage area boundary, and the coverage judgment result for the high-concentration area based on the airflow path parameters and filtration intensity settings is output.

[0049] For example, in one implementation, operating parameters are transmitted in real time via a digital interface, including the horizontal deflection angle and vertical pitch angle of the air outlet, the wind speed value corresponding to the fan speed, and the filtration level of the high-efficiency filter. The airflow direction angle consists of two components: the horizontal deflection angle ranges from -45 degrees to 45 degrees, and the vertical pitch angle ranges from 0 degrees to 30 degrees. These two angle values, combined with the installation position coordinates of the air outlet, are used to determine the initial direction vector of the airflow through trigonometric function calculations.

[0050] It should be noted that the diffusion coefficient reflects the diffusion characteristics of airflow during propagation, and its value is determined according to the laminar flow class of the operating room. The diffusion coefficient is set to 0.8 to 1.2 for a Class 100 laminar flow operating room, and 1.5 to 2.0 for a Class 10,000 laminar flow operating room. The calculation of the airflow coverage area is based on the jet diffusion principle in fluid mechanics. The maximum horizontal coverage radius is obtained by multiplying the air outlet velocity by the diffusion coefficient. The vertical coverage depth is determined by the height of the air outlet from the ground and the downward velocity of the airflow. The coverage area has an inverted cone shape in space, and its boundary coordinates are described by parametric equations.

[0051] Preferably, the intersection volume is approximated using the Monte Carlo method. A large number of sampling points are randomly generated within a minimum cube containing both regions. The number of points simultaneously located within both the covered area and the high-concentration area is counted. The ratio of this number to the total number of sampling points, multiplied by the cube volume, gives an approximate value for the intersection volume. The coverage threshold is set according to the cleanliness requirements of the operating room; extra-clean operating rooms require a coverage rate of 95% or higher, while standard clean operating rooms require 85% or higher. The calculation of Euclidean distance involves the straight-line distance between two points in three-dimensional space. The distance between the center point of the high-concentration area and all points on the boundary of the covered area is obtained by taking the square root of the sum of the squares of their coordinate differences. The minimum value is selected as the quantitative indicator of the coverage deviation. When the deviation exceeds a preset safety threshold, it indicates that the existing airflow parameters cannot effectively handle the high-concentration area, requiring adjustment of the air supply angle or enhancement of the filtration intensity.

[0052] In one embodiment, the coverage determination result includes three levels: complete coverage, partial coverage, and no coverage. Complete coverage indicates that the ratio of the intersection volume to the high-concentration region volume is greater than 95%, partial coverage indicates that the ratio is between 50% and 95%, and no coverage indicates that the ratio is less than 50%.

[0053] Step S105: If the airflow coverage does not completely cover the high-concentration area, the airflow direction, speed, and coverage are adjusted according to the center point and boundary coordinates of the high-concentration area. The change in airborne bacteria concentration in the high-concentration area and the proportion of the high-concentration area covered by the airflow are obtained through the feedback adjustment system.

[0054] Based on the coordinate difference between the center point coordinates of the high-concentration area and the coordinates of the projection point on the current airflow center axis, the angular offsets in the horizontal and vertical directions are calculated. The total offset angle is divided by the preset single adjustment angle to obtain the number of adjustments. The servo motor drives the air outlet to deflect gradually according to the single adjustment angle, so that the airflow center axis gradually points towards the center point of the high-concentration area. At the same time, the fan speed is adjusted to the corresponding level according to the farthest distance between the boundary coordinates and the air outlet, resulting in the adjusted airflow direction and speed configuration. The feedback adjustment mechanism is activated. After the airflow direction and speed configuration takes effect, sampling begins after a preset delay. The airborne bacteria concentration values ​​are collected in real time by multiple monitoring points arranged in the high-concentration area. The difference between the current concentration value and the concentration value before adjustment at each monitoring point is calculated. If the difference is negative, it indicates that the concentration is decreasing, so the current adjustment direction is maintained. If the difference is positive, it indicates that the concentration is increasing, so the adjustment direction is reversed. The concentration value at each sampling moment is recorded to form time-series data. Based on the time-series data, the concentration change at adjacent sampling times is calculated. The change is divided by the sampling time interval to obtain the concentration change rate. When the absolute value of the change rate within a preset number of sampling periods is less than a convergence threshold, a stable state is determined to have been reached. The intersection volume of the airflow coverage area and the high-concentration area is calculated, and the ratio of the intersection volume to the high-concentration area volume is used as the coverage ratio. The average percentage decrease in concentration at all monitoring points within the area is calculated. If the coverage ratio does not reach the target value or the average percentage decrease does not meet the purification requirements, the air outlet diffusion angle is adjusted according to the difference between the coverage ratio and the target value. By increasing the diffusion angle, the airflow coverage range is expanded. The feedback adjustment process is repeated until the preset iteration limit is reached, and the airflow concentration change data and the proportion of the airflow covering the high-concentration area are output.

[0055] For example, in one implementation, the angular offset is calculated based on a three-dimensional spatial coordinate system, with the air outlet position as the origin and the coordinates of the center point of the high-concentration area as the target position.

[0056] Specifically, the spatial deflection angle is obtained by calculating the angle between the target position vector and the current airflow direction vector. This angle is decomposed into the azimuth offset in the horizontal plane and the pitch offset in the vertical plane. The horizontal azimuth angle is calculated using the arctangent function to determine the angle of the target point on the horizontal projection plane, and the vertical pitch angle is calculated using the ratio of the height difference of the target point to the horizontal distance. The single adjustment angle is set between 2 and 5 degrees to avoid airflow turbulence caused by excessive adjustments. The number of adjustments equals the total offset angle divided by the single adjustment angle and rounded up, ensuring that the airflow accurately points to the target area after a limited number of adjustments. The servo motor's stepping control uses a pulse drive method, with each pulse corresponding to a fixed rotation angle. The deflection mechanism of the air outlet includes two degrees of freedom: a horizontal rotation axis and a vertical swing axis, each controlled by an independent servo motor. The horizontal rotation axis adjusts the left-right direction of the airflow, while the vertical swing axis adjusts the up-down angle. The frequency of the motor drive signal determines the deflection speed, typically set to a rotation rate of 10 to 20 degrees per second, ensuring timely adjustment while avoiding mechanical shock. The fan speed adjustment is based on the correlation between boundary distance and wind speed. When the boundary of the high-concentration area is more than 5 meters from the air outlet, the fan speed is increased to the high setting; when the distance is between 3 and 5 meters, the medium setting is used; and when the distance is less than 3 meters, the low setting is used. Stepless speed adjustment is achieved through a frequency converter. The core of the feedback adjustment mechanism lies in establishing the relationship between concentration changes and adjustment actions. The delay time setting considers airflow propagation time and airborne bacteria diffusion time, typically 30 to 60 seconds after the adjustment action is completed. Monitoring points are arranged according to a uniform distribution principle, set up in a grid pattern within the high-concentration area, with one airborne bacteria concentration sensor placed at each grid node. The sensor uses the laser scattering principle to measure the concentration of particulate matter in the air in real time, identifying airborne bacteria particles through particle size analysis. Each monitoring point collects data every 10 seconds, and the data from all monitoring points is wirelessly transmitted and aggregated to the central processing unit. The concentration difference is calculated using a moving average method, taking the average of the most recent 5 samples as the current concentration, and comparing it with the baseline concentration before adjustment to obtain the difference. When more than 60% of the monitoring points show a decrease in concentration, the adjustment direction is determined to be correct; when more than 40% of the monitoring points show an increase in concentration, the direction is reversed, and the horizontal and vertical adjustment directions are reversed simultaneously.

[0057] In one possible implementation, the concentration change rate is calculated using a first-order difference method. The instantaneous change rate is obtained by dividing the concentration difference between two adjacent sampling times by the time interval. To eliminate the influence of measurement noise, median filtering is applied to five consecutive instantaneous rate values. Convergence is determined using a dual criterion: the absolute value of the change rate is less than a preset threshold, and the variance of the rate is less than a stability threshold. The system is considered to have reached a stable state when both criteria are met for 10 consecutive sampling periods. The airflow coverage area is then determined through flow field simulation. A computational fluid dynamics model is established based on the air outlet parameters and environmental conditions to obtain the three-dimensional flow field distribution. The calculation of the coverage ratio requires determining the intersection volume of the two three-dimensional regions. The high-concentration region is defined by the boundary point set obtained by a clustering algorithm, and the airflow coverage area is defined by the velocity isosurface obtained from the flow field calculation. The numerical integration of the intersection volume is achieved by discretizing the space into small cubic units. It is determined whether each unit is simultaneously located within both regions, and the intersection volume is obtained by multiplying the number of units that meet the conditions by the unit volume. The average percentage decrease is calculated by combining data from all monitoring points. The percentage decrease at each monitoring point is equal to the concentration reduction divided by the initial concentration. The arithmetic mean of all monitoring points serves as the overall purification effect index for the region. Furthermore, the diffusion angle is adjusted by changing the angle of the air outlet guide vanes. These vanes are radially distributed around the air outlet, and the tilt angle of each vane can be adjusted independently. When a larger diffusion angle is needed, the outer vanes deflect outwards, creating a larger cone angle for the airflow; when a smaller diffusion angle is needed, the vanes retract inwards, concentrating the airflow. The adjustment amount of the diffusion angle is directly proportional to the proportion of insufficient coverage; for every 10% decrease in coverage, the diffusion angle increases by 5 degrees.

[0058] Understandably, the termination conditions for the iterative process include three scenarios: achieving the target coverage ratio and purification effect, reaching the maximum number of iterations, or the system parameters reaching their physical limits. The maximum number of iterations is typically set to 20 to prevent the system from entering an infinite loop. Physical limits include the maximum deflection angle of the air outlet, the maximum fan speed, and the maximum diffusion angle. The results of each iteration are recorded in the database, including information such as adjustment parameters, concentration distribution, and coverage ratio, forming a complete record of the optimization process.

[0059] For example, during a surgical procedure, frequent instrument transfer created a high-concentration area on the right side of the operating table. After detecting this area, it was calculated that the airflow needed to be deflected 15 degrees to the right, and this angle adjustment was completed in five steps. Feedback monitoring showed that the concentration began to decrease, but the coverage rate only reached 70%. The diffusion angle was increased from 30 degrees to 45 degrees, and after three iterative adjustments, the coverage rate increased to 92%, and the concentration decreased by 65%, meeting the preset purification requirements. The entire adjustment process took 8 minutes, achieving precise purification control of the localized high-concentration area.

[0060] Step S106: Increase the filtration intensity level according to the changes in airborne bacteria concentration in the high-concentration area and the proportion of airflow covering the high-concentration area to match the purification needs of the high-concentration area.

[0061] Data on changes in airborne bacterial concentration and airflow coverage ratio in high-concentration areas are acquired. The intensity adjustment range is determined based on the difference between the concentration reduction percentage and the preset purification standard. The coverage ratio is multiplied by a preset coefficient to obtain a weight value. A pre-established filtration intensity mapping table is consulted to determine the basic filtration level, which records the correspondence between airborne bacterial concentration ranges and corresponding filtration levels. Based on the basic filtration level and the cleanliness requirements of the operating room, the actual filtration intensity is adjusted. If the current operating room is a Class 100 cleanroom, a preset high-level increment is added to the basic level; if it is a Class 10,000 cleanroom, a preset standard increment is added. Simultaneously, the degree of filter clogging is judged based on the ratio of the pressure difference across the filter to the initial pressure difference. When the ratio exceeds a preset threshold, the intensity level is reduced by one level. For the adjusted filtration intensity level, the runtime required to reach the target cleanliness is calculated based on the corresponding filtration efficiency and the current airborne bacterial concentration. Real-time changes in airborne bacterial concentration are monitored during operation. If the concentration recovery rate exceeds a preset value, the filtration level is increased by one level, and the filtration intensity level setting is output to match the purification needs of reducing airborne bacterial concentration and maintaining air cleanliness in high-concentration areas.

[0062] For example, in one embodiment, the filtration intensity mapping table adopts a two-dimensional lookup table structure, with the horizontal axis representing the airborne bacteria concentration range and the vertical axis representing the filtration level.

[0063] Specifically, the concentration range is divided into five intervals: 0-100 CFU / m³ corresponds to pre-filtering, 100-500 CFU / m³ to medium-efficiency filtration, 500-1000 CFU / m³ to high-medium efficiency filtration, 1000-5000 CFU / m³ to sub-high-efficiency filtration, and above 5000 CFU / m³ to high-efficiency filtration. The weighting coefficient conversion is based on a linear mapping relationship, with coverage ratios from 0 to 100% mapping to weight values ​​ranging from 0.5 to 1.5. When the coverage ratio is 50%, the weight value is 1.0, representing standard filtration intensity. For every 10% increase in coverage ratio, the weight value increases by 0.1.

[0064] It should be noted that the impact of the operating room's cleanliness level on filtration intensity is reflected in the differentiated settings of the level increments. A Class 100 clean operating room requires no more than 3,520 particles of 0.5 micrometers or larger per cubic meter, with the corresponding higher-level increment being two levels above the base level. A Class 10,000 clean operating room requires no more than 352,000 particles, with a standard increment of one level. Each level corresponds to a different operating power of the filter, and the filtration efficiency difference between levels is approximately 15% to 20%.

[0065] Preferably, the degree of filter clogging is monitored in real time using a differential pressure sensor. The initial differential pressure is the pressure difference when the filter is first installed. As usage time increases, more particles accumulate on the filter screen, causing the differential pressure to gradually rise. When the ratio of the measured differential pressure to the initial differential pressure reaches 1.5, it indicates that the filtration resistance has increased by 50%. At this point, the filtration efficiency begins to decline, and the system automatically reduces the intensity level by one level to maintain stable airflow. A filter replacement warning is triggered when the differential pressure ratio reaches 2.0.

[0066] For example, the runtime is calculated based on a purification kinetic model. The runtime t is estimated using an exponential decay formula, based on the current airborne bacteria concentration C0, the target concentration Ct, and the purification efficiency η of the selected filtration level. The purification efficiency for different filtration levels ranges from 60% for pre-filters to 99.97% for high-efficiency filters. Real-time monitoring uses a sampling frequency of once per minute to calculate the average rate of concentration increase over the past 5 minutes. When the rate exceeds 10 cfu / m³ / min, it indicates that the current filtration intensity is insufficient, and the filtration level is automatically upgraded to the next level.

[0067] In one embodiment, during a surgical procedure, a localized area was found to have an airborne bacterial concentration of 3000 CFU / m³, covering 70% of the area. Referring to a pre-defined table, the basic filtration level was determined to be sub-high efficiency (HEPA) with a weighting factor of 1.2. Considering the requirements of Class 100 cleanroom, two additional levels were added, setting it to HEPA filtration. After 15 minutes of operation, the concentration dropped to 500 CFU / m³, but monitoring revealed a recovery rate of 12 CFU / m³ / min. The system maintained HEPA filtration and activated the auxiliary purification unit, ultimately achieving a stable clean environment.

[0068] Step S107: Based on the updated filtration intensity level and airflow path configuration, simulate the overall purification efficiency change of the surgical site environment, and determine whether the simulated purification efficiency reaches the preset standard value.

[0069] The updated filtration intensity level and airflow path configuration parameters, including the air outlet angle, wind speed, diffusion range, and filtration grade, are obtained. A three-dimensional flow field simulation mesh is established based on the geometry of the operating room. Each mesh node contains position coordinates, airflow velocity vectors, and airborne bacteria concentration values. The fluid control equations are integrated over each mesh cell and transformed into a system of algebraic equations. The air outlet is set as the velocity inlet boundary, the return air outlet as the pressure outlet boundary, and the wall as the non-slip wall boundary to construct a numerical simulation environment for the operating room. Airborne bacteria particle trajectory tracking is introduced into the simulation environment. The terminal settling velocity is calculated based on the particle diameter and density using Stokes' law. A random walk method is used to simulate particle diffusion motion, tracking the complete path of each particle in the airflow field from the release point to being captured by the filter or discharged from the return air outlet. The cumulative residence time of particles in each mesh cell is calculated, and the reciprocal of the residence time is used as the purification efficiency of that cell to form a spatial purification efficiency distribution. Based on the spatial purification efficiency distribution, the airborne bacteria removal rate at each monitoring point is calculated. The removal rate is equal to the product of the purification efficiency at that point and the current concentration. The removal rates of all monitoring points are summed to obtain the overall purification rate. The overall purification rate is divided by the airborne bacteria production rate to obtain the purification ratio. This process is iteratively solved until the change in the purification ratio is less than a convergence threshold for a preset number of iterations, yielding a steady-state purification efficiency value. The steady-state purification efficiency value is compared with a preset standard value corresponding to the operating room cleanliness standard. If the purification efficiency reaches the standard value, the current configuration is deemed to meet the purification requirements. If the standard value is not reached, the efficiency difference and the spatial location of the low-efficiency area are recorded, and the result of whether the simulated purification efficiency meets the standard is output.

[0070] For example, in one implementation, the three-dimensional flow field simulation mesh is established using a hybrid mesh strategy that combines structured and unstructured meshes.

[0071] Specifically, unstructured tetrahedral meshes are used in areas with drastic flow field changes, such as near the air supply and return inlets, to flexibly adapt to complex geometries; structured hexahedral meshes are used in areas with relatively uniform flow fields, such as the center of the operating room, to improve computational efficiency and accuracy. The mesh size is adaptively adjusted according to the local flow field gradient, with denser meshes in areas of large gradients and sparser meshes in areas of small gradients. Each mesh node stores physical quantities including three-dimensional coordinates, three components of the velocity vector, pressure, temperature, and airborne bacteria concentration. The fluid control equations are the Navier-Stokes equations. Gauss's theorem is applied to each mesh cell to transform the partial differential equations into algebraic equations for flux balance. Upwind schemes are used for flux calculations to ensure numerical stability. The partial differential equations can be...

[0072]

[0073] ρ represents fluid density, u represents velocity vector, t represents time, p represents pressure, μ represents dynamic viscosity, and f represents volume force. This is the Navier-Stokes momentum equation, which describes the conservation of momentum in fluid motion. The left side represents inertial force, and the right side represents pressure gradient force, viscous force, and volume force, respectively.

[0074] It should be noted that the boundary conditions directly affect the accuracy of the simulation results. The velocity inlet boundary of the air supply outlet is set with a velocity vector based on the actual wind speed and air supply angle. The velocity distribution adopts a fully developed turbulent velocity profile, with the highest velocity at the center and decreasing towards the edges. The pressure outlet boundary of the return air outlet is set to atmospheric pressure, allowing free fluid flow. The no-slip boundary condition on the wall requires the fluid velocity at the wall surface to be zero, which conforms to actual physical conditions. The surfaces of obstacles such as the operating table and shadowless lamp are also set as no-slip walls. The temperature boundary condition considers human body heat generation and equipment heat dissipation; the human body surface temperature is set to 36.5 degrees Celsius, and the heat generation on the equipment surface is determined based on the power. These boundary conditions are discretized into a large sparse linear equation system, which is solved using the preprocessed conjugate gradient method.

[0075] Preferably, Stokes' law is used to calculate the settling velocity of small-diameter planktonic bacteria, applicable to laminar settling conditions with Reynolds numbers less than 1. The terminal settling velocity is equal to the difference between particle gravity and buoyancy divided by Stokes drag, specifically expressed as the square of the particle diameter multiplied by the density difference, then multiplied by the gravitational acceleration, and finally divided by 18 times the aerodynamic viscosity. For a typical planktonic bacterium with a diameter of 1 micrometer, the density is approximately 1050 kg / m³, and the terminal settling velocity under standard atmospheric conditions is approximately 0.03 mm / s. The random walk method simulates particle diffusion caused by Brownian motion. Within each time step, the random displacement of particles in three directions follows a Gaussian distribution, with the standard deviation proportional to the diffusion coefficient and the square root of the time step. The diffusion coefficient is calculated using Einstein's relation, equal to the Boltzmann constant multiplied by the temperature and divided by the Stokes drag coefficient. The numerical integration of particle trajectories uses the fourth-order Runge-Kutta method to ensure the accuracy of trajectory calculation. The formation of the spatial purification efficiency distribution is based on statistical principles. At the start of the simulation, a large number of tracer particles, each representing a certain number of airborne bacteria, are released from the source of contamination within the operating room. The particles move under the influence of the airflow field, simultaneously experiencing gravitational settling and diffusion. When particles come into contact with the filter surface, they are captured and removed; when they reach the return air vent, they are discharged with the airflow. The cumulative residence time of particles within each grid cell is calculated; a longer residence time indicates slower air renewal and lower purification efficiency at that location. Purification efficiency is defined as the ratio of the number of particles removed per unit time to the total number of particles, reflecting the purification capacity of that location.

[0076] In one possible implementation, the iterative solution for the purification ratio employs a fixed-point iteration method. An initial purification ratio is assumed, and the concentration distribution at each point is calculated based on this value. Then, the removal rate and generation rate are recalculated to obtain a new purification ratio. The iterative formula is that the new ratio equals the removal rate divided by the generation rate, where the removal rate is the sum of the products of purification efficiency and concentration at all grid points, and the generation rate is estimated based on the number of personnel and activity intensity in the operating room. The convergence criterion uses a relative error criterion; convergence is considered achieved when the relative change in the ratio between two adjacent iterations is less than 0.01. Furthermore, the steady-state purification efficiency value reflects the purification capacity of the system when it reaches dynamic equilibrium. Under steady-state conditions, the generation rate of airborne bacteria is equal to the removal rate, and the concentration distribution no longer changes with time. A higher steady-state efficiency value indicates a stronger purification capacity of the system and a higher level of cleanliness that can be maintained.

[0077] Understandably, the preset standard values ​​are determined based on the requirements of different cleanliness levels. Class 100 operating rooms require a purification efficiency of 99% or higher, Class 1000 operating rooms require 95% or higher, and Class 10,000 operating rooms require 90% or higher. The efficiency difference is calculated by subtracting the actual value from the standard value; a positive value indicates the extent to which the standard is not met. Low-efficiency areas are identified using an efficiency distribution map, and areas with efficiency values ​​below 60% of the average are marked as locations requiring focused improvement.

[0078] For example, simulation results from a cardiac operating room showed that, under the current airflow configuration, the purification efficiency in the area directly above the operating table reached 98.5%, meeting the Class 100 cleanroom requirements. However, the efficiency near the instrument table on the side of the operating table was only 85%, lower than the standard requirement. The output judgment was that the local area did not meet the standard, and the specific location of the low-efficiency area was marked.

[0079] In step S108, if the preset standard value is not reached, the airflow path vector and filtration intensity level are iteratively adjusted based on the deviation analysis of the simulated purification efficiency to obtain the final optimized control parameters of the purification equipment.

[0080] Based on the deviation between the simulated purification efficiency and the preset standard value, the efficiency gap value of each spatial region is calculated. The region with the largest deviation is marked as the priority adjustment region. The center coordinates of this region are extracted, and the position difference between this coordinate and the current airflow centerline is calculated. The position difference is decomposed into angular offsets in the horizontal and vertical directions. The offset is divided by the preset number of iterations to obtain the angular increment for each adjustment, forming an initial adjustment vector. After determining the adjustment direction based on the initial adjustment vector, candidate parameter combinations are constructed within a preset parameter range. The parameter range includes the upper and lower limits of the airflow direction angle, the minimum and maximum wind speed levels, the adjustment range of the diffusion angle, and the level range of the filtration intensity. Multiple value points are selected at uniform intervals within each parameter range, and the value points of different parameters are arranged and combined to form a parameter candidate set. For each set of parameters in the parameter candidate set, the expected purification efficiency is evaluated through simplified flow field calculation. The absolute value of the difference between the expected efficiency and the target efficiency is calculated as the evaluation index. If the evaluation index is less than a preset threshold, the set of parameters is marked as a feasible solution; otherwise, the parameter values ​​are adjusted according to the proportion of the influence of each parameter change on the efficiency. The influence proportion is calculated by the perturbation method. The iteration continues until a feasible solution is found. Select the parameter combination with the smallest evaluation index from the feasible solutions, and verify the physical feasibility of the combination, including that the airflow direction does not exceed the mechanical limit range, the wind speed does not exceed the rated value of the equipment, and the filtration intensity does not exceed the highest level of the filter. If all constraints are met, output the combination as the final optimized control parameters of the purification equipment. The parameters include airflow direction, speed, coverage area and filtration intensity level.

[0081] For example, in one implementation, the efficiency gap value is calculated based on a spatial discretization method, which divides the operating room into multiple cubic evaluation units, and the efficiency gap of each unit is equal to the target purification efficiency of that unit minus the actual simulated efficiency.

[0082] Specifically, for a Class 100 clean operating room, the target efficiency is set at 99%. If the actual efficiency of a unit is 85%, the gap is 14%. The priority adjustment areas are determined using a weighted scoring method, with weighting factors including gap size, area importance, and contamination risk level. The area directly above the operating table has an importance weight of 1.5, the instrument table area has a weight of 1.2, and other areas have a weight of 1.0. The calculation of positional differences involves a three-dimensional coordinate transformation, converting the positional difference in the Cartesian coordinate system to an angular offset in the spherical coordinate system. The horizontal angular offset is calculated using the arctangent function, and the vertical angular offset is calculated using the arcsine function.

[0083] It should be noted that the determination of the angle increment takes into account the response characteristics and stability requirements of the mechanical system. The stepper motor of the air outlet deflection mechanism has a minimum resolution limit, typically 0.5 degrees; therefore, the angle increment is set to an integer multiple of this resolution. The selection of the number of iterations is based on a balance between convergence speed and settling time. Too many iterations will prolong the settling time, while too few may lead to oscillations. Empirical data shows that the increment obtained by dividing the total offset by 5 to 10 iterations can achieve smooth adjustment. The initial adjustment vector contains four components: horizontal angle increment, vertical angle increment, fan speed change, and filtration intensity level change. These components together constitute the basic unit of parameter adjustment.

[0084] Preferably, the parameter range setting needs to comprehensively consider equipment performance limitations and actual application requirements. The airflow direction angle range is limited by the mechanical structure of the air outlet, typically -60 degrees to 60 degrees horizontally and 0 degrees to 45 degrees vertically. The fan speed settings are divided according to the control accuracy of the fan inverter, ranging from a low speed of 30Hz to a high speed of 50Hz, with each 5Hz setting representing a different speed. The diffusion angle is achieved by adjusting the air outlet guide vanes, with an adjustment range of 15 degrees to 60 degrees; a larger angle results in a wider airflow coverage but a lower center velocity. The filtration intensity level corresponds to different filter combinations, ranging from G4 pre-filter to H14 high-efficiency filter, totaling six levels. The arrangement of parameter combinations needs to consider the interrelationships between parameters; high airflow combined with a small diffusion angle enables long-distance air delivery, while low airflow combined with a large diffusion angle is suitable for short-distance, wide-area coverage.

[0085] For example, the process of calculating the parameter influence ratio using the perturbation method includes two stages: baseline assessment and perturbation assessment. The baseline assessment uses the current parameter combination to perform simplified flow field calculations to obtain a baseline purification efficiency value. The perturbation assessment applies a small perturbation to each parameter individually, keeping other parameters constant, and recalculates the purification efficiency. The influence ratio equals the efficiency change divided by the parameter perturbation amount, reflecting the sensitivity of that parameter. The choice of perturbation amplitude needs to balance computational accuracy and numerical stability; too small a perturbation may be overwhelmed by numerical errors, while too large a perturbation violates the linear approximation assumption. In practice, 1% to 5% of the parameter variation range is used as the perturbation amplitude. The influence ratio matrix obtained through the perturbation method guides the direction of parameter adjustment; parameters with a large influence ratio are adjusted first, while parameters with a small influence ratio remain relatively stable.

[0086] In one possible implementation, the simplified flow field calculation employs a potential flow theory approximation method, simplifying the complex Navier-Stokes equations into Laplace's equations. This simplification neglects fluid viscosity and turbulence effects but retains the main flow characteristics, increasing the computational speed by two orders of magnitude. The velocity potential function is solved using the Green's function method, and the source-sink distribution is determined based on the flow rates at the supply and return air inlets. The simplified flow field is used to quickly evaluate the effects of different parameter combinations; although the accuracy is lower than that of a full simulation, it is sufficient to guide the optimization direction. Furthermore, the iterative adjustment process employs an adaptive step-size strategy. When the evaluation index continuously decreases, the adjustment step size is maintained or increased to accelerate convergence; when the evaluation index oscillates, the step size is decreased to improve stability. The step-size adjustment factor is determined based on the trend of the index changes in the last three iterations: 1.2 for monotonically decreasing indices and 0.5 for oscillations.

[0087] Understandably, physical feasibility verification is a crucial step in ensuring the safe operation of the system. Mechanical limit switches use both the actual angle feedback from the encoder and the limit switch signal to prevent damage to the transmission mechanism due to overtravel. Equipment ratings include parameters such as the maximum power of the fan, the rated current of the motor, and the allowable speed of the bearings; exceeding these ratings triggers a protection mechanism. In one optimization process, the initial parameter combination achieved a purification efficiency of 88%, with a target efficiency of 95%. After eight iterations, three feasible solutions were found: the first adjusted the airflow direction by 15 degrees and increased the wind speed by two levels; the second kept the direction unchanged and increased the filtration intensity by two levels; the third comprehensively adjusted all parameters. The evaluation indices were 0.02, 0.03, and 0.01, respectively, and the third solution was selected as the final solution. This solution adjusted the horizontal airflow angle by 8 degrees, the vertical angle by 5 degrees, increased the wind speed by one level, and increased the filtration intensity by one level, achieving a purification efficiency of 95.5%, meeting the cleanliness requirements of the operating room.

[0088] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for intelligent control of the surgical site environment, characterized in that, include: Data on airborne bacteria concentration in the surgical environment is collected, and airflow velocity and direction at multiple points are obtained. The airflow velocity and airflow velocity at these points are integrated to generate a real-time airflow distribution map and an airborne bacteria concentration distribution map, outputting a correlation curve between airflow velocity and airborne bacteria concentration. Based on the correlation curve, areas with increased airborne bacteria concentration are identified. The airborne bacteria concentration distribution in these areas is clustered to obtain the boundary coordinates and center point of high-concentration areas. Based on the airflow coverage area, the boundary coordinates, and the center point of the high-concentration areas, it is determined whether the high-concentration areas are covered. The airflow direction, velocity, and coverage area are adjusted based on the center point and boundary coordinates of the high-concentration areas, and the changes in airborne bacteria concentration and airflow coverage ratio in the adjusted high-concentration areas are obtained. The filtration intensity level is adjusted based on the changes in airborne bacteria concentration and the airflow coverage ratio. Combining the filtration intensity level and airflow path configuration, the purification efficiency of the surgical environment is simulated, and it is determined whether a preset standard value is reached. Based on the deviation of the simulated purification efficiency, the airflow path and filtration intensity level are iteratively adjusted, and optimized purification equipment control parameters are output.

2. The intelligent control method for the surgical site environment according to claim 1, characterized in that, The process involves collecting airborne bacterial concentration data from the surgical site environment, obtaining airflow velocity and direction at multiple points, integrating the airborne bacterial concentration data and the airflow velocity at multiple points, generating a real-time airflow distribution map and an airborne bacterial concentration distribution map, and outputting a correlation curve between airflow velocity and airborne bacterial concentration, including: Samplers are arranged in a spatial grid within the operating room. These samplers record the airborne bacteria concentration and timestamps. Wind speed sensors are deployed to acquire the airflow velocity and direction vectors at multiple points. The airborne bacteria concentration and airflow parameters are aligned using timestamp matching to establish a correspondence between spatial coordinates and airborne bacteria concentration. A continuous airborne bacteria concentration distribution field is generated using interpolation, and the airflow velocity and direction vectors are reconstructed to form an airflow distribution field. The airflow velocity and airborne bacteria concentration values ​​of each grid in the airflow distribution field are extracted, correlation coefficients are calculated, strongly correlated regions are marked, and airflow dead zones are identified. Based on the airflow dead zone locations and correlation coefficients, real-time airflow distribution maps and airborne bacteria concentration distribution maps are drawn, with velocity vectors and concentration contour lines marked. The correlation curve between airflow velocity and airborne bacteria concentration is output.

3. The intelligent control method for the surgical site environment according to claim 1, characterized in that, The step of determining the region of increased local airborne bacteria concentration based on the correlation curve includes: The slope data of the correlation curve is extracted, and the airflow velocity intervals are divided according to the positive and negative values ​​of the slope. The concentration values ​​of planktonic bacteria within the airflow velocity intervals are statistically analyzed, the concentration change rate is calculated, inflection points are marked, and a correspondence table between airflow velocity and planktonic bacteria concentration is generated. Based on the correspondence table and the airflow distribution map, the airflow velocity values ​​at each location are obtained, the expected range of planktonic bacteria concentration is found, and the measured concentration values ​​in the planktonic bacteria concentration distribution map are compared to calculate the deviation, generating concentration deviation distribution data. Based on the concentration deviation distribution data, a deviation threshold is set, and locations where the measured concentration exceeds the expected range are marked as abnormal rise points. Adjacent abnormal rise points are merged to form a continuous region, and the boundary coordinates of the continuous region are extracted to determine the local planktonic bacteria concentration rise area.

4. The intelligent control method for the surgical site environment according to claim 1, characterized in that, The step of clustering and grouping the planktonic bacteria concentration based on the localized increased planktonic bacteria concentration area to obtain the boundary coordinates and center point location of the high-concentration area includes: The three-dimensional coordinates and concentration values ​​of sampling points within the region of increased local planktonic bacterial concentration are obtained. A cubic grid is then formed, and the spatial density value and concentration gradient of each grid are calculated to create gradient field distribution data. Based on this gradient field distribution data, a clustering algorithm is used to group planktonic bacterial concentrations, marking core points and connecting density-reachable points to form high-concentration clusters. The weighted average of the coordinates of sampling points within the high-concentration clusters is calculated to obtain the centroid coordinates, and the outermost sampling points are extracted to form an initial boundary contour. The initial boundary contour is smoothed, outliers are removed, and the boundary coordinate sequence and center point position of the high-concentration region are output.

5. The intelligent control method for the surgical site environment according to claim 1, characterized in that, The step of determining whether to cover the high-concentration area based on the airflow coverage area, the boundary coordinates of the high-concentration area, and the center point location includes: Read the operating parameters of the purification equipment, construct the airflow path spatial vector, calculate the three-dimensional spatial range of the airflow coverage, and generate a set of boundary coordinates of the coverage area; compare the set of boundary coordinates of the coverage area with the boundary coordinates of the high concentration area, calculate the intersection volume, and determine the coverage ratio; check whether the center point of the high concentration area is within the coverage area, calculate the distance from the center point to the boundary of the coverage area, and output the coverage judgment result.

6. The intelligent control method for the surgical site environment according to claim 1, characterized in that, The step of adjusting the airflow direction, speed, and coverage area based on the center point location and boundary coordinates of the high-concentration area, and obtaining the changes in the airborne bacteria concentration and airflow coverage ratio of the high-concentration area after adjustment, includes: Calculate the coordinate difference between the center point of the high-concentration area and the projection point of the airflow center axis, determine the angular offset and adjustment times, drive the air outlet to deflect, and adjust the fan speed; sample the airborne bacteria concentration value of the high-concentration area after adjustment, calculate the concentration change rate, and determine the stable state; calculate the intersection volume of the airflow coverage area and the high-concentration area, count the percentage decrease in concentration, and output the change in airborne bacteria concentration and the airflow coverage ratio.

7. The intelligent control method for the surgical site environment according to claim 1, characterized in that, The step of adjusting the filtration intensity level based on the change in airborne bacteria concentration and the airflow coverage ratio includes: Obtain the changes in airborne bacteria concentration and airflow coverage ratio, determine the intensity adjustment range, query the filtration intensity mapping table, and determine the basic filtration level; adjust the basic filtration level according to the cleanliness requirements, check the filter pressure difference, and adjust the filtration intensity level; calculate the running time corresponding to the adjusted filtration intensity level, monitor concentration changes, and output the filtration intensity level setting.

8. The intelligent control method for the surgical site environment according to claim 1, characterized in that, The process of simulating the purification efficiency of the surgical site environment by combining the filtration intensity level and airflow path configuration, and determining whether a preset standard value has been reached, includes: The filtration intensity level and airflow path configuration parameters are obtained, a three-dimensional flow field simulation grid is established, and a numerical simulation environment is constructed. The trajectory of airborne bacteria particles is tracked, the purification efficiency of each grid unit is statistically analyzed, and a spatial purification efficiency distribution is formed. The removal rate of the monitoring points in the spatial purification efficiency distribution is calculated, the overall purification rate and purification ratio are obtained, the purification ratio is compared with the preset standard value, and the compliance judgment result is output.

9. The intelligent control method for the surgical site environment according to claim 1, characterized in that, The process of iteratively adjusting the airflow path and filtration intensity level based on the simulated purification efficiency deviation, and outputting optimized purification equipment control parameters, includes: Calculate the deviation between the simulated purification efficiency and the preset standard value, mark the priority adjustment area, and determine the angle offset and adjustment vector; construct candidate parameter combinations, evaluate the expected purification efficiency, and mark feasible solutions; select the parameter combination with the smallest evaluation index, verify physical feasibility, and output the optimized purification equipment control parameters, including airflow direction, speed, coverage area, and filtration intensity level.

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