A laboratory heating and ventilation multi-source data dynamic visualization system

Abstract

This invention relates to the field of data visualization technology, specifically to a dynamic visualization system for multi-source HVAC data in a laboratory, comprising a spatiotemporal alignment gateway, a dimensionality reduction inference engine, and a cloud-based visual mapping module. The spatiotemporal alignment gateway performs spatiotemporal alignment on multi-source heterogeneous data and outputs the data; the dimensionality reduction inference engine dynamically trims a 3D voxel mesh using the absolute travel of the front-view window, extracts the momentum direction tensor, and reduces the dimensionality to a 2.5D cross-section; it triggers 8-bit fixed-point precision quantization based on the transient rate of change of the flow field to perform network inference, outputting a face wind attenuation gradient matrix; when a boundary is predicted to be exceeded, it generates a hardware interrupt preemption instruction and simultaneously triggers an electromagnetic bypass pressure relief valve to pre-establish a feedforward pressure difference; the cloud-based visual mapping module parses the feature hash value to restore the gradient, generates a risk boundary mesh that expands along the Z-axis, and performs local incremental rendering. This invention overcomes the response lag of underlying computing power and mechanical execution, achieving transient risk physical intervention and low-latency visual early warning.

Description

Technical Field

[0001] This invention relates to the field of data visualization technology, specifically to a dynamic visualization system for multi-source data in laboratory HVAC systems. Background Technology

[0002] With the rapid development of public health and cutting-edge scientific research, the HVAC systems of high-level biosafety laboratories and high-precision physicochemical analysis laboratories have become core safety barriers for isolating potential sources of contamination and maintaining directional airflow boundaries. In demanding microenvironmental control environments, traditional management models and control methods are revealing significant technical limitations.

[0003] First, in terms of visual monitoring of microenvironment flow fields, existing technologies generally rely on physical tracing methods such as liquid nitrogen fume generation or ultrasonic atomization. However, the intrusion of these physical media can easily cause condensation on the outer wall of the pipeline, and the micron-sized atomized particles can directly violate the particle counting standards of ISO-level cleanrooms. Therefore, they can only be used for periodic static verification and cannot provide continuous, non-destructive real-time airflow boundary monitoring in daily experimental operations. Second, in terms of dynamic control of exhaust equipment (such as fume hoods and biosafety cabinets), current variable air volume controllers mainly rely on door displacement sensors or single-point wind speed sensors, using passive PID closed-loop feedback regulation. However, in complex real scientific research scenarios, non-cooperative physical disturbances in three-dimensional space, such as the rapid movement of an experimenter's arm, can instantly trigger volume exclusion effects and wake vortices, leading to the rupture of directional airflow barriers. Since traditional single scalar sensors cannot detect such three-dimensional spatial disturbance vectors, the system must wait for the leakage to occur and be captured by the sensor before it begins to respond slowly, resulting in serious causal inversion and control lag defects, and failing to effectively block the escape of transient aerosols. Finally, although digital twin technology has begun to be applied to building information monitoring, existing visualization systems are mostly limited to simple color mapping of static operating parameters of macroscopic equipment (such as pipe pressure and temperature) to 3D models, making it difficult to analyze and visually reconstruct dynamic airflow mismatch boundaries at the microscopic scale. To completely solve the fluid dynamics equations within the system to achieve real-time risk prediction, a severe computing power dilemma arises: edge devices cannot handle the massive high-precision calculation load, while pushing all data to the cloud for calculation will cause network latency, completely missing the millisecond-level optimal airflow compensation window.

[0004] In summary, under the constraints of limited computing resources, how to solve the spatiotemporal delay problem of multi-source heterogeneous monitoring data in the process of generating hard real-time feedforward control commands and executing high-dimensional state digital reconstruction is a technical problem that urgently needs to be solved in this field.

[0005] To address this, a dynamic visualization system for multi-source HVAC data in laboratories is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a dynamic visualization system for multi-source HVAC data in laboratories, including a spatiotemporal alignment gateway, a dimensionality reduction inference engine, and a cloud-based visual mapping module. The spatiotemporal alignment gateway performs spatiotemporal alignment on multi-source heterogeneous data and outputs the data. The dimensionality reduction inference engine dynamically trims a 3D voxel mesh using the absolute travel of the front-view window, extracts the momentum direction tensor, and reduces the dimensionality to a 2.5D cross-section. Based on the transient rate of change of the flow field, it triggers 8-bit fixed-point precision quantization to perform network inference, outputting a face wind attenuation gradient matrix. When a boundary violation is predicted, a hardware interrupt preemption instruction is generated, and an electromagnetic bypass pressure relief valve is simultaneously triggered to pre-establish a feedforward pressure difference. The cloud-based visual mapping module parses the feature hash value to restore the gradient, generates a risk boundary mesh that expands along the Z-axis, and performs local incremental rendering. This invention overcomes the response lag of underlying computing power and mechanical execution, achieving transient risk physical intervention and low-latency visual early warning.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A laboratory HVAC multi-source data dynamic visualization system includes: The spatiotemporal alignment gateway performs hardware-level clock synchronization of sensor data and depth vision sensor data in the HVAC network through a timing synchronization protocol, and performs coordinate and time interpolation compensation in conjunction with a dynamic compensation buffer pool to output a spatiotemporal aligned data stream. The dimensionality reduction inference engine receives the spatiotemporally aligned data stream, performs spatial discretization on the depth point cloud data to generate a three-dimensional voxel mesh, and extracts the momentum direction tensor by calculating the instantaneous volume resistivity. The momentum direction tensor is projected to a 2.5D cross section for matrix multiplication inference, and outputs the face wind attenuation gradient matrix. When the face wind attenuation gradient matrix exceeds the global safety threshold, a hardware interrupt preemption instruction is generated to take over the physical device, and the face wind attenuation gradient matrix is ​​encapsulated as a feature hash value and uploaded. The cloud-based visual mapping module receives the feature hash value and asynchronously parses and restores the 2.5D face wind attenuation gradient matrix. It generates a risk boundary mesh that expands along the Z-axis in the 3D building information model and uses an incremental parallax projection algorithm to perform data stream updates only on the local topological regions where risk material changes occur and outputs the rendering results.

[0008] Preferably, the steps of performing hardware-level clock stamp synchronization and coordinate time interpolation compensation include: acquiring industrial bus data streams containing a first local clock cycle sent by static pressure sensors and hot-wire anemometers in the HVAC network, and point cloud data streams containing a second local clock cycle sent by depth vision sensors; the spatiotemporal alignment gateway, acting as the master clock node, sends synchronization messages to the static pressure sensors, hot-wire anemometers, and depth vision sensors based on the timing synchronization protocol and issues time offsets to align the first local clock cycle and the second local clock cycle to the absolute time reference of the master clock node; A first-in-first-out circular queue based on an absolute time reference is established in the memory area as the dynamic compensation buffer. In the spatial dimension, the point cloud data stream is transformed from the camera coordinate system to the world coordinate system consistent with the 3D building information model through a preset external parameter matrix to obtain spatially registered point cloud data. In the temporal dimension, using the first local clock cycle as the resampling reference, adjacent sampling time points are queried in the dynamic compensation buffer for the spatially registered point cloud data and linear interpolation is performed to obtain temporally interpolated point cloud data. The temporally interpolated point cloud data and sensor data are merged into a data stream to output a spatiotemporally aligned data stream.

[0009] Preferably, the steps of generating a three-dimensional voxel mesh and extracting the momentum direction tensor include: dividing the safety cabinet opening boundary region defined by the three-dimensional building information model into a set of cubic cells arranged in an equidistant grid array to form a three-dimensional voxel mesh; mapping the point cloud coordinates in the spatiotemporally aligned data stream to the corresponding cubic cells in the three-dimensional voxel mesh and marking them as occupied; dividing the number of occupied cubic cells by the total number of cubic cell sets to obtain the instantaneous volumetric resistance ratio; obtaining the displacement vector of the cubic cells in the occupied state at adjacent resampling reference times, and multiplying the instantaneous volumetric resistance ratio as a weighting coefficient by the displacement vector to obtain the momentum direction tensor.

[0010] Preferably, the momentum direction tensor projection is reduced to a 2.5D cross section for matrix multiplication derivation to output the face wind attenuation gradient matrix. This includes: establishing a two-dimensional reference projection plane parallel to the operating surface of the safety cabinet; projecting the momentum direction tensor coordinates onto the two-dimensional reference projection plane to form a two-dimensional grid coordinate; extracting the depth values ​​of the momentum direction tensor perpendicular to the two-dimensional reference projection plane, assigning them as scalar features to the corresponding two-dimensional grid coordinate points, and constructing a 2.5D cross section input matrix containing horizontal and vertical coordinates and depth feature scalars; flattening the 2.5D cross section input matrix into a one-dimensional input vector and inputting it into a multilayer perceptron network; and performing dimensional reconstruction on the one-dimensional prediction vector output by the multilayer perceptron network according to the arrangement rules of the two-dimensional grid coordinates to generate the face wind attenuation gradient matrix.

[0011] Preferably, the steps of the dimensionality reduction simulation engine generating a hardware interrupt preemption instruction to take over the physical device include: performing an element-wise difference operation between the face wind attenuation gradient matrix and a preset global safety threshold to obtain a difference matrix; determining whether there are difference elements with values ​​greater than 0 in the difference matrix; if there are difference elements with values ​​greater than 0 in the difference matrix, triggering a low-level hardware interrupt request and suspending the PID closed-loop control process; extracting the grid point coordinates corresponding to the difference elements with values ​​greater than 0 in the difference matrix, and retrieving the media access control address of the variable air volume valve governing the grid point coordinate position from the mapping table; encapsulating the preset opening compensation value and the media access control address into a hardware interrupt preemption instruction and injecting it into the priority execution queue of the physical bus controller.

[0012] Preferably, the step of the dimensionality reduction inference engine to encapsulate the face wind attenuation gradient matrix into a feature hash value and upload it includes: converting the floating-point data of the face wind attenuation gradient matrix into a fixed-point one-dimensional sequence according to a fixed order of column first and row second; performing discretization clustering on the fixed-point one-dimensional sequence using a pre-trained vector quantization dictionary, extracting the corresponding template index based on the minimum distance metric principle, and performing hash mapping operation on the template index to generate a feature hash value; and encapsulating the feature hash value, the out-of-bounds timestamp character, and the physical identification code of the spatiotemporal alignment gateway into fields and uploading them to the cloud visual mapping module.

[0013] Preferably, the steps for parsing and restoring the 2.5D face wind attenuation gradient matrix include: the cloud-based visual mapping module receiving the feature hash value and waking up the asynchronous parsing thread; extracting the feature hash value from the encapsulated data and matching and querying the corresponding feature template index in a pre-established hash mapping inverse lookup table library; extracting template data from the pre-stored feature template database according to the feature template index, extracting the physical identification code and out-of-bounds timestamp characters from the encapsulated data, and performing spatial pose mapping and temporal alignment on the template data in conjunction with the coordinate calibration file to restore and generate a 2.5D face wind attenuation gradient matrix with absolute spatial coordinate attributes.

[0014] Preferably, the steps of generating a risk boundary mesh that expands along the Z-axis and performing data stream updates in the cloud-based visual mapping module include: using the leak-proof surface of the safety cabinet in the 3D building information model as a reference plane, establishing a Z-axis normal vector perpendicular to the reference plane; mapping the 2.5D surface wind attenuation gradient matrix to the reference plane in spatial coordinates, and extracting gradient values ​​corresponding to the spatial positions of each mesh vertex on the reference plane; multiplying each extracted gradient value by a preset amplification factor to obtain a displacement offset representing the degree of deformation of each mesh vertex; using the displacement offset to drive the mesh vertices on the reference plane to generate geometric deformation along the Z-axis normal vector, obtaining a risk boundary mesh with a 3D convex topological profile for the current rendering cycle; caching the risk boundary mesh vertex set of the previous rendering cycle as the reference mesh vertex set; comparing the risk boundary mesh vertex coordinates of the current rendering cycle with the reference mesh vertex set, selecting a subset of displacement vertices and defining local topological regions using axial bounding boxes; allocating a material color parameter dictionary to the local topological region, issuing a masking command during the rasterization stage so that the rendering engine only performs color coverage updates on pixels within the axial bounding box, and outputting streaming media rendering results.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention achieves high spatiotemporal consistency integration of multi-source heterogeneous monitoring data streams. Through a hardware-level clock stamp synchronization protocol and a dynamic compensation buffer mechanism, it can align environmental monitoring data with different sampling frequencies and transmission characteristics with visual perception streams. This normalized spatiotemporal alignment method eliminates sampling jitter and time offset between heterogeneous data sources, providing a data foundation with high logical correlation for subsequent complex flow field risk simulations, and enhancing the consistency of the system's perception of complex experimental environments.

[0016] 2. This invention improves the responsiveness to transient spatial disturbances by utilizing spatial voxelization and a 2.5D dimensionality reduction matrix algorithm to transform high-dimensional spatial disturbance features into a rapidly solvable risk gradient prediction matrix. Combined with a hardware interrupt preemption mechanism, the system can directly intervene in the underlying controlled nodes based on the predicted values, completing compensation actions before significant fluctuations occur in the physical environment. This data-driven feedforward control logic changes the traditional lag adjustment mode that relies on feedback signals, shortening the system's response cycle to sudden physical disturbances.

[0017] 3. This invention achieves an effective balance between visualization reconstruction quality and computational load. The system adopts a cloud-edge collaborative asynchronous rendering architecture, transferring the heavy topology reconstruction task to the cloud for processing through feature hash mapping technology, and using an incremental parallax processing algorithm to dynamically update only the local areas where risk material changes occur. This processing strategy ensures the intuitive presentation of the 3D risk boundary offset along the specified normal direction while reducing the dependence on edge computing resources and data transmission bandwidth, achieving lossless and high-frequency visual representation of the monitoring process. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the structure of a laboratory HVAC multi-source data dynamic visualization system provided in an embodiment of the present invention; Figure 2 A flowchart for dynamic visualization of multi-source HVAC data in a laboratory, provided as an embodiment of the present invention; Figure 3 The flowchart illustrates the dimension reduction deduction and hard real-time feedforward control method provided in this embodiment of the invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figures 1 to 3 This invention provides a dynamic visualization system for multi-source data in laboratory HVAC systems, the technical solution of which is as follows: A laboratory HVAC multi-source data dynamic visualization system includes: The spatiotemporal alignment gateway performs hardware-level clock synchronization of sensor data and depth vision sensor data in the HVAC network through a timing synchronization protocol, and performs coordinate and time interpolation compensation in conjunction with a dynamic compensation buffer pool to output a spatiotemporal aligned data stream. The dimensionality reduction inference engine receives the spatiotemporally aligned data stream, performs spatial discretization on the depth point cloud data to generate a three-dimensional voxel mesh, and extracts the momentum direction tensor by calculating the instantaneous volume resistivity. The momentum direction tensor is projected to a 2.5D cross section for matrix multiplication inference, and outputs the face wind attenuation gradient matrix. When the face wind attenuation gradient matrix exceeds the global safety threshold, a hardware interrupt preemption instruction is generated to take over the physical device, and the face wind attenuation gradient matrix is ​​encapsulated as a feature hash value and uploaded. The cloud-based visual mapping module receives the feature hash value and asynchronously parses and restores the 2.5D face wind attenuation gradient matrix. It generates a risk boundary mesh that expands along the Z-axis in the 3D building information model and uses an incremental parallax projection algorithm to perform data stream updates only on the local topological regions where risk material changes occur and outputs the rendering results.

[0021] Example 1: This embodiment is mainly applied to the operation scenario of biosafety cabinets in high-level biosafety laboratories. The background problem is that when the experimenter performs sample inoculation or pipetting operations, the rapid withdrawal of the arm from the biosafety cabinet will generate complex wake vortices, causing the directional airflow barrier to break instantaneously. Traditional variable air volume control systems cannot complete airflow compensation before aerosol escape due to sensor response lag.

[0022] As one embodiment of the present invention, refer to Figure 1 A schematic diagram of a laboratory HVAC multi-source data dynamic visualization system, referring to... Figure 2 A flowchart for dynamic visualization of multi-source data in laboratory HVAC systems.

[0023] Furthermore, the steps of performing hardware-level clock stamp synchronization and coordinate time interpolation compensation include: acquiring industrial bus data streams containing a first local clock cycle sent by static pressure sensors and hot-wire anemometers in the HVAC network, and point cloud data streams containing a second local clock cycle sent by depth vision sensors; the spatiotemporal alignment gateway, acting as the master clock node, sends synchronization messages to the static pressure sensors, hot-wire anemometers, and depth vision sensors based on the timing synchronization protocol and issues time offsets to align the first local clock cycle and the second local clock cycle to the absolute time reference of the master clock node; A first-in-first-out circular queue based on an absolute time reference is established in the memory area as the dynamic compensation buffer. In the spatial dimension, the point cloud data stream is transformed from the camera coordinate system to the world coordinate system consistent with the 3D building information model through a preset external parameter matrix to obtain spatially registered point cloud data. In the temporal dimension, using the first local clock cycle as the resampling reference, adjacent sampling time points are queried in the dynamic compensation buffer for the spatially registered point cloud data and linear interpolation is performed to obtain temporally interpolated point cloud data. The temporally interpolated point cloud data and the sensor data are merged into a data stream to output a spatiotemporally aligned data stream.

[0024] Specifically, the spatiotemporal alignment gateway, acting as the time reference source within the laboratory's local area network, sends synchronization signals to the front-end static pressure transmitter, hot-wire anemometer, and depth camera via the PTP protocol. The system acquires the 100Hz industrial bus data stream from the static pressure sensor and the 30 frames per second point cloud data stream from the depth camera. The gateway calculates the round-trip delay in the network path, issues time compensation offsets, and aligns the timestamps of the two heterogeneous data sets to a millisecond-level error range. A 512-megabyte first-in-first-out circular queue is established in the memory area. Spatially, the system needs to call a pre-defined 3×4 extrinsic parameter matrix. The values ​​of this matrix are determined by the actual installation pose of the depth camera, and are pre-calculated by technicians during system initialization by collecting the camera's local coordinates of known feature points and the corresponding 3D building information model world coordinates, using a conventional camera extrinsic parameter calibration algorithm. The system inputs the real-time extracted depth point cloud coordinates into this extrinsic parameter matrix to perform a linear transformation, converting the point cloud data from the camera's local coordinate system to the laboratory's unified 3D building information model coordinate system. In the temporal dimension, to eliminate the temporal resolution differences of heterogeneous data streams, the system uses the 100 Hz clock of the hydrostatic sensor as the resampling reference. It retrieves adjacent sampling points from the 30 frames per second point cloud data from the depth camera in the buffer pool, and uses a linear interpolation algorithm to fill in the missing point cloud values, thus achieving temporal alignment. Finally, the spatially registered and interpolated point cloud data is merged with the sensor values ​​using feature packets, outputting a spatiotemporally aligned data stream.

[0025] This invention eliminates data mismatch caused by differences in sensor deployment locations and sampling frequencies through hardware-level clock alignment and coordinate system transformation. The application of linear interpolation technology ensures that the system can acquire continuous and coherent environmental feature vectors under high-speed dynamic monitoring, providing a standardized data base for subsequent high-precision flow field simulation and reducing the false alarm rate caused by data jitter.

[0026] Reference Figure 3 Flowchart of dimensionality reduction deduction and hard real-time feedforward control method.

[0027] Further, the steps of generating a three-dimensional voxel mesh and extracting the momentum direction tensor include: dividing the safety cabinet opening boundary region defined by the three-dimensional building information model into a set of cubic cells arranged in an equidistant grid array to form a three-dimensional voxel mesh; mapping the point cloud coordinates in the spatiotemporally aligned data stream to the corresponding cubic cells in the three-dimensional voxel mesh and marking them as occupied; dividing the number of occupied cubic cells by the total number of cubic cell sets to obtain the instantaneous volumetric resistance ratio; obtaining the displacement vector of the cubic cells in the occupied state at adjacent resampling reference times, and multiplying the instantaneous volumetric resistance ratio as a weighting coefficient by the displacement vector to obtain the momentum direction tensor.

[0028] The steps for generating a three-dimensional voxel mesh also include: the system synchronously acquiring the absolute travel coordinates of the front glass window of the safety cabinet, and calculating the transient height of the effective physical opening based on the absolute travel coordinates; dynamically trimming the safety cabinet opening boundary area defined by the three-dimensional building information model using the transient height, and removing the three-dimensional voxel mesh units that are blocked by the front glass window in real time; and performing point cloud coordinate mapping only on the cubic unit data within the effective flow section that is not blocked when calculating the instantaneous volume resistivity.

[0029] Specifically, the system acquires the absolute displacement sensor readings of the front glass window of the safety cabinet in real time at a frequency of 100Hz via the device bus (sensor accuracy ±0.1mm, range 0~600mm), and converts the absolute travel coordinates into the transient height of the effective physical opening; using the transient height, the cubic units in the safety cabinet opening boundary area defined by the three-dimensional building information model that are blocked by the front glass window are marked as non-computational domains.

[0030] When calculating the instantaneous volumetric drag ratio, the total number of cubic units is taken as the number of cubic units within the unobstructed effective flow section. Point cloud coordinate mapping and occupancy status determination are performed only within this effective flow section. For the two-dimensional mesh coordinate points corresponding to the clipped region after the momentum direction tensor is projected onto the two-dimensional reference projection plane, their depth scalar feature is assigned to zero to keep the dimension of the output face wind attenuation gradient matrix constant at 32×32, ensuring compatibility with the 1024-dimensional input dimension of the multilayer perceptron network. In the subsequent element-wise subtraction operation between the face wind attenuation gradient matrix and the global safety threshold, the difference result of the mesh points corresponding to the clipped region is directly determined to be within the bounds (i.e., forced to be less than or equal to zero) to prevent false triggering caused by filling zero values.

[0031] The dimensionality reduction simulation engine retrieves the pre-defined physical boundary of the safe's opening from the 3D building information model and divides it into a set of non-overlapping cubic units with equidistant side lengths of 10 cm to form a 3D voxel mesh. During this process, the system acquires real-time absolute displacement sensor readings of the front glass window of the safe via the device bus, converting these absolute travel coordinates into the transient height of the effective physical opening. For example, when the system detects that the window has shifted downwards, obscuring 30% of the upper physical space, the algorithm uses this transient height to dynamically trim the original safe opening boundary area, directly removing or marking the 3D voxel mesh units behind the physically obscured window from memory as non-computational domains. The system retains only the cubic units within the unobstructed effective flow section for subsequent calculations. When the experimenter performs operations, the 3D point cloud coordinates in the spatiotemporally aligned data stream are mapped in real-time to the corresponding cubic units in the 3D voxel mesh. A preset threshold for point cloud density is set for determining whether a unit is occupied. The value is determined based on the spatial resolution of the depth vision sensor, the physical measurement distance from the sensor to the edge of the safety cabinet opening, and the background noise baseline of the system in an unloaded state.

[0032] In this embodiment, to effectively filter out isolated noise points caused by diffuse reflection of air particles or thermal noise from sensor hardware, technicians can calibrate the maximum noise peak within a single cubic unit by collecting system idle reference data and setting this threshold in conjunction with the physical point cloud reflectivity of the target object. For example, the preset threshold is 50 point cloud coordinates; when the number of point cloud coordinates mapped to a specific cubic unit in real time is greater than or equal to 50, the system determines that there is a substantial physical entity obstructing the unit (such as an operator's limbs), and thus strictly marks the cubic unit as occupied. Assuming the system counts the number of occupied units out of a total of 200 cubic units within the opening boundary region, for example, when there are 40 occupied units, the instantaneous volumetric resistance ratio is calculated to be 20%. The system obtains the spatial displacement vector of the cubic units in the grid that are occupied at adjacent resampling reference times, multiplies the spatial displacement vector by the 20% instantaneous volumetric resistance ratio as a weighting coefficient, and finally obtains the momentum direction tensor. This tensor not only extracts the three-dimensional spatial position of the personnel's actions but also quantifies the degree of physical resistance of non-cooperative actions to the directional airflow channel.

[0033] This invention implements physical trimming at the data input source, significantly reducing the number of voxel units required for point cloud mapping and state determination, thereby reducing the processor's ineffective computational load in non-flowing areas. Through voxelized modeling, continuous physical spatial actions are transformed into discrete numerical tensors, fully capturing the three-dimensional motion trajectory and volume proportion of the operator's arm, thus quantifying the physical resistance effect of personnel activity on the airflow boundary layer. This digital modeling method provides high-dimensional feature support for predicting airflow deflection caused by local disturbances, enabling the system to identify spatial disturbance energy distributions that traditional methods cannot detect.

[0034] Further, the momentum direction tensor projection is reduced to a 2.5D cross section for matrix multiplication derivation to output the face wind attenuation gradient matrix. This includes: establishing a two-dimensional reference projection plane parallel to the operating surface of the safety cabinet; projecting the momentum direction tensor coordinates onto the two-dimensional reference projection plane to form a two-dimensional grid coordinate; extracting the depth values ​​of the momentum direction tensor perpendicular to the two-dimensional reference projection plane and assigning them as scalar features to the corresponding two-dimensional grid coordinate points to construct a 2.5D cross section input matrix containing horizontal and vertical coordinates and depth feature scalars; flattening the 2.5D cross section input matrix into a one-dimensional input vector and inputting it into a multilayer perceptron network; the multilayer perceptron network performing matrix multiplication operations to output the face wind attenuation gradient matrix corresponding to the two-dimensional grid coordinate points.

[0035] The multilayer perceptron network was pre-trained offline using a computational fluid dynamics (CFD) simulation dataset. Specifically, at least 3000 sets of typical operating condition data generated by CFD simulation software were used as the sample set. The operating conditions covered five types: "rapid horizontal arm withdrawal" (withdrawal speed 0.5-1.5 m / s), "slow vertical arm extension" (extension speed 0.1-0.3 m / s), "lateral arm movement" (along the parallel direction of the operating surface), "alternating operation by two operators," and "dynamic adjustment of viewing window height," with 600 sets of samples generated for each type. These sample sets were randomly divided into training, validation, and test sets in a 7:2:1 ratio. The dimensionality-reduced 2.5D cross-sectional input matrix corresponding to each set of operating condition data was used as the network input feature, and the surface wind speed attenuation scalar value (unit: m / s) of each grid point on the operating surface, synchronously output by the CFD simulation software, was used as the supervision label. The network comprises four hidden layers with 512, 256, 128, and 64 nodes respectively, all using ReLU as the activation function. The output layer has 1024 nodes, the same dimension as the flattened one-dimensional input vector, and uses a linear activation function to ensure that the output value matches the actual face wind speed attenuation (in meters per second) in numerical range. The overall forward propagation structure of the multilayer perceptron network is as follows: Input layer (1024-dimensional) → Hidden layer 1 (512-dimensional, ReLU) → Hidden layer 2 (256-dimensional, ReLU) → Hidden layer 3 (128-dimensional, ReLU) → Hidden layer 4 (64-dimensional, ReLU) → Output layer (1024-dimensional, linear). After forward inference, the 1024-dimensional one-dimensional prediction vector is reconstructed using a tensor dimension according to a 32×32 two-dimensional grid coordinate arrangement rule to generate the face wind attenuation gradient matrix. During the offline training phase, mean squared error is used as the loss function, and the weights are iteratively updated through the backpropagation algorithm until the loss function converges, thereby obtaining a pre-trained weight matrix that can map the relationship between volumetric resistance and flow field attenuation.

[0036] Specifically, the system establishes a two-dimensional reference projection plane parallel to the operating surface of the safety cabinet. The momentum direction tensor coordinates within the three-dimensional voxel mesh are projected onto this two-dimensional reference projection plane to form a 32×32 two-dimensional mesh coordinate system. The depth values ​​of the momentum direction tensor perpendicular to the two-dimensional reference projection plane are extracted and assigned as scalar features to the corresponding two-dimensional mesh coordinate points, constructing a 2.5D cross-sectional input matrix containing horizontal and vertical coordinates and depth feature scalars. This 2.5D cross-sectional input matrix is ​​flattened into a 1024-dimensional one-dimensional input vector and input into the multilayer perceptron network.

[0037] The design of the multilayer perceptron network is based on the following factors: the upper limit of the hardware floating-point computing power of the edge computing engine, the millisecond-level hard real-time latency requirement of the feedforward control mechanism, and the fitting complexity of the nonlinear physical characteristics of the local spatial flow field. In this embodiment, the technicians determined that the preset number of hidden layers is 4 by cross-validating the inference time and mean square error of networks at different depths using historical fluid dynamics datasets before system deployment. The specific basis for using 4 hidden layers is as follows: rapid removal of a person's limbs will cause complex wake vortices. If there are fewer than 4 hidden layers, the network's feature extraction capability will be insufficient, and it will be unable to fully fit the nonlinear mapping relationship between transient resistance and airflow attenuation, resulting in prediction errors exceeding the engineering allowable range. If there are more than 4 hidden layers, the exponentially increased weight parameters will cause the single tensor inference time on the edge side to exceed the feedforward compensation time window of the variable air volume valve, causing the control command to lag behind the physical leakage. In terms of functional allocation, the first two hidden layers are used to extract the basic spatial displacement correlation features in the input vector, the third hidden layer is used to perform the abstract feature cross calculation of fluid dynamic momentum transfer, and the fourth layer combines synaptic weight pruning to converge and map the abstract features to the output space.

[0038] Before performing the continuous matrix multiplication operation between the internal weight matrix and the one-dimensional input vector, the dimensionality reduction inference engine pre-calculates the transient rate of change of the momentum direction tensor of the current calculation cycle relative to the previous cycle. Specifically, the calculation process is as follows: the difference tensor is obtained by subtracting the momentum direction tensor of the current cycle from that of the previous cycle; the Frobenius norm of this difference tensor (i.e., the square root of the sum of squares of its elements) is calculated; and this difference tensor is divided by the Frobenius norm of the momentum direction tensor of the previous cycle to obtain the transient rate of change value. The system compares this transient rate of change with a preset steady-state flow field threshold. When the transient rate of change is determined to be greater than or equal to the steady-state flow field threshold, it indicates that the flow field has entered a high-frequency nonlinear abrupt change period, and the airflow barrier is at risk of immediate rupture. The system triggers the dynamic quantization engine of the underlying logic arithmetic unit, suspends the current 32-bit floating-point inference graph through instruction-level scheduling, and retrieves the pre-established quantization parameter dictionary.

[0039] The offline construction process of the quantization parameter dictionary is as follows: Before offline deployment of the model, 500 sets of samples are randomly selected from the training set as a calibration dataset. The calibration dataset is input into a 32-bit floating-point precision MLP network to perform forward inference, and the numerical distribution range (min and max) of the activation values ​​of each layer (5 activation tensors in hidden layers 1 to 4 and the output layer) is recorded. For each activation tensor, the scaling factor s and the zero offset z are calculated according to the following formula: ; Where 255 is the maximum representation value of an 8-bit unsigned integer. The s and z key-value pairs of each layer are stored as a dictionary of quantization parameters.

[0040] The construction process of the 8-bit fixed-point weight matrix replica is as follows: Before offline deployment of the model, pseudo-quantization nodes are inserted between the weight matrix and activation function of each layer of the 32-bit floating-point MLP network. A pass-through estimator is used to approximate the gradient of the quantization operation during backpropagation. The number of fine-tuning epochs is set to 50, and the initial learning rate is... Quantization-aware training is performed on the training set until the loss function converges, and the trained 8-bit fixed-point weight matrix copy is permanently stored in the memory of the dimensionality reduction inference engine.

[0041] Using the scaling factor and zero offset in the quantization parameter dictionary, an asymmetric affine mapping is performed on the current one-dimensional input vector to transform it into an 8-bit fixed-point activation tensor. Subsequently, the 8-bit fixed-point activation tensor and a copy of the 8-bit fixed-point weight matrix stored in memory (the copy is generated by quantization-aware training QAT pre-compilation before model deployment) are input into the tensor processing unit. Continuous high-speed matrix multiplication is performed with the data bit width of this 8-bit fixed-point precision to output the face wind attenuation gradient matrix corresponding one-to-one with the two-dimensional grid coordinate points. Conversely, if the transient rate of change is lower than the steady-state flow field threshold, indicating that the flow field is in a safe steady state and the system has ample time, the operation is performed with 32-bit floating-point precision to output a high-precision baseline gradient for high-fidelity rendering in the cloud.

[0042] The preset basis for the steady-state flow field threshold is the critical characteristic of the abrupt change in the Reynolds number of the flow field. In this embodiment, the criterion for determining that the flow field has entered a high-frequency nonlinear abrupt change period is: the dynamic quantization engine of the underlying logic arithmetic unit is triggered if and only if the transient rate of change in the current calculation window and the two preceding consecutive calculation windows (a total of 30 milliseconds) is greater than or equal to 0.15. This logic aims to filter out instantaneous glitches caused by random noise from the sensors, ensuring that the ultra-fast inference mode is activated only when the flow field energy continues to fluctuate.

[0043] This invention preserves crucial depth perturbation information by projecting three-dimensional spatial features onto a 2.5D cross-section, while avoiding computational overruns caused by fully solving the three-dimensional Navier-Stokes equations. By establishing an adaptive linkage mechanism between the flow field physical state and the data bit width of the underlying chip, it effectively solves the computational congestion problem of edge devices performing high-density tensor inference. While maintaining the face wind prediction accuracy required for engineering applications, it improves the throughput of matrix multiplication operations, shortens the inference latency of the neural network, and provides a more generous time response window for generating hard real-time feedforward control commands.

[0044] Furthermore, the steps of the dimensionality reduction simulation engine in generating hardware interrupt preemption instructions to take over physical devices include: performing element-wise subtraction operations between the face wind attenuation gradient matrix and a preset global safety threshold to obtain a difference matrix; determining whether there are difference elements with values ​​greater than 0 in the difference matrix; if there are difference elements with values ​​greater than 0 in the difference matrix, triggering a low-level hardware interrupt request and suspending the PID closed-loop control process; extracting the grid point coordinates corresponding to the difference elements with values ​​greater than 0 in the difference matrix, and retrieving the media access control address of the variable air volume valve governing the location of the grid point coordinates from the mapping table; encapsulating the preset opening compensation value and the media access control address into a hardware interrupt preemption instruction and injecting it into the priority execution queue of the physical bus controller.

[0045] The mapping table is constructed and stored in system memory during the system initialization and debugging phase. Specifically, during the system initialization and debugging phase, technicians obtain the physical 3D coordinates of each variable air volume valve in the laboratory space using a 3D building information model. These valve coordinates are then projected onto a 2D reference projection plane parallel to the operating surface of the biosafety cabinet, resulting in the valve's projected grid coordinates within a 32×32 2D grid. Centered on the projected grid coordinates of each valve, its jurisdiction is defined as all grid points within a circular area with radius r (r is half the projected distance between the valve and its nearest neighbor). Grid points falling within multiple jurisdiction boundaries are assigned to the nearest valve. A key-value pair mapping table is constructed, where the key is the row and column index number (0 to 1023) of the grid point, and the value is the MAC address string of the variable air volume valve governing that grid point.

[0046] The steps of the dimensionality reduction simulation engine generating hardware interrupt preemption instructions to take over physical devices also include: the hardware interrupt preemption instructions are injected into the physical bus controller and simultaneously trigger the electromagnetic bypass pressure relief valve located at the front end of the exhaust branch pipe of the safety cabinet; the working displacement response time of the electromagnetic bypass pressure relief valve is configured to be lower than the mechanical shaft rotation time of the variable air volume valve, and by utilizing the instantaneous opening of the bypass circuit where the electromagnetic bypass pressure relief valve is located, the static pressure level of the local part of the safety cabinet pipeline is reduced in advance before the variable air volume valve reaches the target opening degree.

[0047] Specifically, the dimensionality reduction simulation engine subtracts the output decay gradient matrix element-wise from the pre-stored global safety threshold of 0.5 m / s to obtain a difference matrix. The preset value of the global safety threshold is based on the minimum average surface velocity requirement for a Class II biosafety cabinet under operating conditions, as specified in relevant industry standards for biosafety laboratories. In this embodiment, to ensure that the operating surface of the biosafety cabinet can form an effective directional airflow barrier to prevent aerosol escape, the preset value of the global safety threshold is set to 0.5 m / s. If there are elements with values ​​greater than 0 in the difference matrix, it indicates that the local risk has exceeded the safety boundary. At this time, the engine immediately triggers a hardware interrupt request on the underlying control bus, forcibly suspending the running variable air volume PID closed-loop regulation process. The system extracts the grid coordinates corresponding to the elements with values ​​greater than 0 and retrieves the MAC address of the No. 3 variable air volume valve controlling this specific opening area through a preset mapping table. The system generates a hardware interrupt package containing a 120% opening compensation instruction. This 120% opening compensation instruction refers to an overdrive compensation strategy, which instructs the actuator of the variable air volume valve to instantaneously operate to 1.2 times its rated maximum opening (or directly drive it to its physical limit position). This oversaturation adjustment instruction overcomes the rotational inertia of the valve's mechanical structure and the resistance delay of the duct system, thereby generating an exhaust negative pressure exceeding the steady-state demand within milliseconds, forcibly correcting the flow field deviation. This instruction bypasses traditional sampling feedback logic, driving the valve to act rapidly and offsetting the suction effect caused by the arm withdrawal by increasing the exhaust volume.

[0048] During this synchronous execution, the edge computing gateway generates a nanosecond-level rising edge trigger pulse through its underlying high-speed digital output interface. This pulse directly drives the electromagnetic bypass pressure relief valve located at the front end of the exhaust branch pipe of the safety cabinet, using the instantaneous opening of the bypass circuit to pre-reduce the local static pressure level of the pipeline network. Simultaneously, the hardware interrupt packet bypasses traditional sampling feedback logic and is directly sent to the physical actuator of the variable air volume valve via RS485 fieldbus or industrial Ethernet. Although the serial bus has an inherent transmission clock cycle, this preemptive interrupt mechanism still ensures that data frames containing the 120% overdrive compensation strategy are sent to the physical layer within an extremely short millisecond time window.

[0049] This invention eliminates the time accumulation effect of PID algorithms in the process of eliminating steady-state error by using heterogeneous communication collaboration between digital hard-wired direct drive and bus protocol preemption, effectively suppressing aerosol escape caused by transient eddies and improving the intrinsic safety level of the laboratory. By introducing collaborative control of multiple actuators at different time scales, it effectively overcomes the spatiotemporal lag caused by the mechanical rigidity of the underlying physical actuators of the HVAC system. During the window period of the lag in the mechanical action of the main regulating valve, a backflow feedforward barrier to prevent aerosol diffusion is constructed in advance, making up for the gap between the delay in the execution action of physical equipment and the speed of front-end data extrapolation, and improving the system's leakage prevention and intervention capability for transient risk events.

[0050] Furthermore, the steps of the dimensionality reduction inference engine to encapsulate the face wind attenuation gradient matrix into a feature hash value for uploading include: extracting the face wind attenuation gradient matrix from the output layer of the multilayer perceptron network; converting the floating-point data of the face wind attenuation gradient matrix into a fixed-point one-dimensional sequence according to a fixed column-first, row-later order; performing discretization clustering on the fixed-point one-dimensional sequence using a pre-trained vector quantization dictionary, extracting the corresponding template index based on the minimum distance metric principle, and performing hash mapping operation on the template index to generate a feature hash value; and encapsulating the feature hash value, the out-of-bounds timestamp character, and the physical identification code of the spatiotemporal alignment gateway into fields and uploading them to the cloud visual mapping module.

[0051] The specific construction process of the pre-trained vector quantization dictionary (i.e., the local codebook) is completed during the offline deployment phase of the system. The specific steps include: First, the system collects a large number of typical face wind attenuation two-dimensional gradient matrices generated under a computational fluid dynamics simulation environment, and flattens them in the same column-first-row-later order as in the online inference phase, constructing a training sample set containing a massive number of fixed-point one-dimensional sequences; subsequently, the system calls an unsupervised clustering algorithm to perform high-dimensional feature space clustering on the training sample set, and sets the number of clusters according to the preset system lookup accuracy requirements.

[0052] Specifically, the system collects no fewer than 5000 sets of typical face wind attenuation two-dimensional gradient matrices generated under a computational fluid dynamics simulation environment, and flattens them in the same column-first-row-later order as in the online inference stage, constructing a training sample set containing no fewer than 5000 fixed-point one-dimensional sequences. The K-Means clustering algorithm is then used to perform high-dimensional feature space clustering on the training sample set, setting the number of clusters K=256 (corresponding to an 8-bit index addressing space, where each template index is an integer between 0 and 255), with cluster center displacement less than... Alternatively, a single clustering iteration count of 500 can be used as the termination condition for clustering convergence. After the clustering algorithm converges, the center vector of each cluster is extracted as a feature codeword representing a typical flow field attenuation mode. All 256 codewords are used to construct the vector quantization dictionary, which is then stored in the memory of the dimensionality reduction inference engine.

[0053] During the online inference phase, the Euclidean distance between the input fixed-point one-dimensional sequence and the 256 codewords in the vector quantization dictionary is calculated. The template index (0 to 255) corresponding to the codeword with the smallest distance is selected as the VQ-encoded output. If the smallest Euclidean distance still exceeds a preset distance threshold (defined as 2.0 times the average pairwise Euclidean distance of all codewords in the codebook), the current flow field mode is determined to be outside the dictionary coverage. In addition to selecting the index of the nearest codeword, the original fixed-point one-dimensional sequence is temporarily stored in a local buffer for subsequent offline codebook incremental updates. After the clustering algorithm converges iteratively, the system extracts the center vector of each cluster as the feature codeword representing the typical flow field decay mode. Finally, the set of all extracted feature codewords is used to construct the vector quantization dictionary and stored in the memory of the dimensionality reduction inference engine for Euclidean distance matching and template index extraction during the online inference phase.

[0054] After the dimensionality reduction inference engine completes neural network inference, the system extracts the face wind attenuation gradient matrix to characterize the current flow field risk state. To adapt to the limited bandwidth of the Industrial Internet of Things (IIoT), the 32-bit floating-point data in the face wind attenuation gradient matrix is ​​converted into a 16-bit fixed-point one-dimensional sequence in a fixed column-first, row-later order. To address the problem of continuous physical features failing to accurately look up tables due to noise fluctuations, the system inputs the fixed-point one-dimensional sequence into a pre-trained vector quantization engine, performs Euclidean distance calculation in the local codebook, selects the typical attenuation pattern with the smallest distance value, and outputs the discrete template index corresponding to the pattern. Subsequently, the system calls the SHA-256 hash algorithm, using only the discrete template index as input to the hash function to perform a one-way hash mapping operation, and extracts the first 64 bits (i.e., 8 bytes, corresponding to 16 hexadecimal characters) of the operation result as the feature hash value. Since the hash operation is only performed on the static template index, the system can ensure accurate matching with the pre-built lookup table in the cloud. Subsequently, the feature hash value, the current out-of-bounds timestamp character, and the gateway physical identification code are encapsulated and an asynchronous transmission data packet is constructed. Finally, the system sends the asynchronous data packets to the cloud-based visual mapping module via a lightweight communication protocol. Because the hashing process removes dynamic time variables and utilizes the strong collision resistance of SHA-256, even if an attacker intercepts the data packets, they cannot reconstruct the physical characteristic parameters within the laboratory without a vector quantization dictionary.

[0055] This invention compresses a massive physical space feature matrix into a fixed-length compact hash code, reducing network bandwidth consumption and ensuring that even in extreme environments with fluctuating network speeds, risk prediction results at the edge can maintain millisecond-level synchronization with the cloud rendering engine. Simultaneously, by utilizing the collision-resistant characteristics of hash algorithms, the uniqueness and accuracy of the risk state in each frame during cloud parsing are guaranteed, thus protecting the privacy and security of sensitive laboratory data while enhancing the real-time perception capabilities of remote monitoring.

[0056] Furthermore, the steps for parsing and restoring the 2.5D face wind attenuation gradient matrix include: the cloud-based visual mapping module receiving the feature hash value and waking up the asynchronous parsing thread; extracting the feature hash value from the encapsulated data and matching and querying the corresponding feature template index in the pre-established hash mapping inverse lookup table library; extracting template data from the feature template database pre-stored by the cloud-based visual mapping module according to the feature template index, extracting the physical identification code and out-of-bounds timestamp characters from the encapsulated data, and performing spatial pose mapping and temporal alignment on the template data in conjunction with the coordinate calibration file to restore and generate a 2.5D face wind attenuation gradient matrix with absolute spatial coordinate attributes.

[0057] The hash mapping reverse lookup table is established offline, and its data structure is a key-value dictionary generated based on a hash algorithm. The keys are the feature hash values ​​generated by hashing the template indexes in the feature template database, and the values ​​are the corresponding feature template indexes. This lookup table establishes a one-to-one fast mapping link from "hash fingerprint" to "physical template".

[0058] The feature template database stores 512 sets of typical disturbance scenario data simulated through computational fluid dynamics. Each template contains a standardized 2.5D wind speed attenuation gradient matrix, corresponding to high-frequency physical conditions such as "rapid horizontal removal of personnel's arms," ​​"stationary operation of personnel," and "viewing window height adjustment." Each template is assigned a unique feature template index, serving as the geometric reference for asynchronous reconstruction in the cloud.

[0059] Specifically, upon receiving an asynchronous data packet containing a feature hash value, an out-of-bounds timestamp character, and a physical identification code at its listening port, the cloud-based visual mapping module immediately wakes up an independent asynchronous parsing thread. Asynchronous processing is employed to avoid the computational logic during data restoration consuming the graphics processor's rendering bandwidth, thus ensuring the virtual scene's refresh rate remains above 60 frames per second. Within the asynchronous thread, the system first extracts the independent feature hash value from the asynchronous data packet and enters a pre-established hash mapping inverse lookup table, matching the received hash value with the feature indexes calibrated within the database. Since this feature hash value is generated solely from static feature sequence mapping, eliminating interference from dynamic time variables, the system can accurately perform one-to-one matching. Once a match is successful, the system retrieves the corresponding feature template data from memory based on the feature template index. This template data contains pre-trained typical flow field attenuation patterns. Finally, the system extracts the independently encapsulated out-of-bounds timestamp character and physical identification code from the asynchronous data packet; uses the out-of-bounds timestamp character to perform temporal localization of the current risk event; and combines this with the physical identification code to retrieve the spatial pose calibration file corresponding to the gateway. By performing matrix rotation and translation mapping, the template data is restored to a 2.5D facet wind attenuation gradient matrix with absolute spatial coordinates and precise time labels. This matrix is ​​then injected into the global render buffer for subsequent use by the geometry deformation engine.

[0060] The asynchronous thread processing ensures the smoothness of the main rendering loop, avoiding screen stuttering caused by network packet processing. Using index lookup tables instead of real-time calculation significantly shortens the time chain from receiving data to generating the visual model, ensuring that the digital twin model seen by remote administrators accurately reflects the flow field disturbances that occurred on-site seconds or even hundreds of milliseconds ago.

[0061] Furthermore, the steps of generating a risk boundary mesh that expands along the Z-axis and performing data stream updates in the cloud-based visual mapping module include: using the leak-proof surface of the safety cabinet in the 3D building information model as a reference plane, establishing a Z-axis normal vector perpendicular to the reference plane; mapping the 2.5D surface wind attenuation gradient matrix to the reference plane in spatial coordinates, and extracting gradient values ​​corresponding to the spatial positions of each mesh vertex on the reference plane; multiplying each extracted gradient value by a preset amplification factor to obtain a displacement offset representing the degree of deformation of each mesh vertex; using the displacement offset to drive the mesh vertices on the reference plane to generate geometric deformation along the Z-axis normal vector, obtaining a risk boundary mesh with a 3D convex topological profile for the current rendering cycle; caching the risk boundary mesh vertex set of the previous rendering cycle as the reference mesh vertex set; comparing the risk boundary mesh vertex coordinates of the current rendering cycle with the reference mesh vertex set, selecting a subset of displacement vertices and defining local topological regions using axial bounding boxes; allocating a material color parameter dictionary to the local topological region, issuing a masking command during the rasterization stage so that the rendering engine only performs color coverage updates on pixels within the axial bounding box, and outputting streaming media rendering results.

[0062] Specifically, the cloud-based visual mapping module uses the leak-proof surface of the biosafety cabinet in the 3D building information model as a reference plane, and establishes a Z-axis normal vector perpendicular to the reference plane and pointing towards the external area. The reconstructed 2.5D face wind attenuation gradient matrix is ​​spatially mapped to the reference plane, and gradient values ​​corresponding to the spatial positions of each grid vertex on the reference plane are extracted. The preset value of the geometric magnification factor is based on the spatial scale of the 3D building information model, the lower limit of the visual perception resolution of the display terminal, and the maximum non-interference boundary of the physical space where the biosafety cabinet is located. In this embodiment, to ensure that the minute face wind attenuation gradient matrix can be transformed into a 3D geometric deformation recognizable to the human eye in digital space, while avoiding collisions and penetration between the generated expanded grid and the laboratory building wall model, technicians can pre-set the geometric magnification factor by calculating the available physical distance from the leak-proof surface of the biosafety cabinet to the opposite wall and comparing it with the maximum face wind attenuation gradient matrix value that the system may output. For example, in this embodiment, the preset geometric magnification factor is 1.5. The system multiplies each extracted gradient value by this geometric magnification factor of 1.5 to obtain the displacement offset representing the degree of deformation of each mesh vertex. The displacement offset is used to drive the mesh vertices on the reference plane to translate along the Z-axis normal vector to generate geometric deformation, resulting in a risk boundary mesh with a three-dimensional convex topological profile for the current rendering cycle. The system caches the risk boundary mesh vertex set of the previous rendering cycle as the reference mesh vertex set, compares the coordinates of the risk boundary mesh vertices of the current rendering cycle with the reference mesh vertex set, filters out the displacement vertex subset that has undergone spatial coordinate changes, and uses axial bounding box technology to isolate the displacement vertex subset in three-dimensional space to define local topological regions. A material color parameter dictionary is assigned to the local topological region, and a masking command is issued to the graphics processor during the rasterization pipeline processing stage, so that the rendering engine only performs color calculation and frame buffer overlay update for the pixels within the axial bounding box, and encapsulates the pixel increment data of the local topological region into a streaming video format to output the rendering result.

[0063] This invention transforms hidden airflow risks into intuitive 3D topological visual signals through Z-axis deformation technology. The application of incremental rendering technology ensures that in large-scale BIM scenarios, the system only needs to consume minimal video memory bandwidth to complete local high-frequency updates, achieving high-definition and low-latency presentation of risk warnings.

[0064] Example 2: This embodiment takes the high-frequency spatial physical disturbances faced by the laboratory as the application scenario, and focuses on describing the complete technical solution process for solving the spatiotemporal delay problem of multi-source heterogeneous monitoring data in the process of generating hard real-time feedforward control commands and executing high-dimensional state digital reconstruction under the limited computing resource architecture.

[0065] As one embodiment of the present invention, refer to Figure 1 A schematic diagram of a laboratory HVAC multi-source data dynamic visualization system, referring to... Figure 2 A flowchart for dynamic visualization of multi-source data in laboratory HVAC systems.

[0066] First, the spatiotemporal alignment gateway performs hardware-level clock synchronization of sensor data and depth vision sensor data in the HVAC network using a timing synchronization protocol, and performs coordinate and time interpolation compensation in conjunction with a dynamic compensation buffer pool, outputting a spatiotemporally aligned data stream. When generating the 3D voxel mesh, the system synchronously acquires the absolute travel coordinates of the front glass window of the safety cabinet and calculates the transient height of the effective physical opening based on these coordinates. This transient height is used to dynamically trim the boundary region of the safety cabinet opening defined by the 3D building information model, removing 3D voxel mesh cells obscured by the front glass window in real time. When calculating the instantaneous volumetric resistivity, the system only retains the cubic cell data within the unobstructed effective flow section for point cloud coordinate mapping.

[0067] Subsequently, referring to Figure 3 The flowchart of the dimensionality reduction inference and hard real-time feedforward control method is as follows: The dimensionality reduction inference engine obtains the displacement vectors of the cube cells in the occupied state at adjacent resampling reference times, and multiplies the instantaneous volume resistivity as a weighting coefficient by the displacement vector to obtain the momentum direction tensor. The system establishes a two-dimensional reference projection plane parallel to the operating surface of the safety cabinet, and projects the momentum direction tensor coordinates onto the two-dimensional reference projection plane to form a two-dimensional grid coordinate system. The system extracts the depth values ​​of the momentum direction tensor perpendicular to the two-dimensional reference projection plane, assigns them as scalar features to the corresponding two-dimensional grid coordinate points, constructs a 2.5D cross-sectional input matrix containing horizontal and vertical coordinates and depth feature scalars, and flattens it into a one-dimensional input vector for input into the multilayer perceptron network.

[0068] Before performing continuous matrix multiplication of the internal weight matrix and the one-dimensional input vector, the dimensionality reduction inference engine pre-calculates the transient rate of change of the momentum direction tensor of the current calculation cycle relative to the previous cycle. The system compares this transient rate of change with a preset steady-state flow field threshold. When the transient rate of change is greater than or equal to the steady-state flow field threshold, it indicates that the flow field has entered a high-frequency nonlinear abrupt change period. At this time, the system triggers the dynamic quantization engine of the underlying logic arithmetic unit to convert the internal weight matrix of the preset hidden layer of the multilayer perceptron network from standard 32-bit floating-point precision to 8-bit fixed-point precision. The multilayer perceptron network performs continuous high-speed matrix multiplication operations with this 8-bit fixed-point precision data bit width, outputting a one-dimensional prediction vector. Subsequently, the dimensionality reduction inference engine reconstructs the tensor dimension of this one-dimensional prediction vector according to the spatial arrangement rules of a 32×32 two-dimensional grid coordinate system, generating a face wind attenuation gradient matrix. Conversely, if the transient rate of change is lower than the steady-state flow field threshold, it indicates that the flow field is in a safe steady state, and the system has ample time, so it maintains 32-bit floating-point precision for the calculation.

[0069] The dimensionality reduction simulation engine performs element-wise subtraction on the face wind attenuation gradient matrix and the pre-stored global safety threshold to obtain a difference matrix. It determines whether there are any difference elements with values ​​greater than zero in the difference matrix; if so, it triggers a low-level hardware interrupt request and suspends the PID closed-loop control process. The system extracts the grid point coordinates corresponding to the difference elements with values ​​greater than zero in the difference matrix and retrieves the media access control address of the variable air volume valve governing that grid point coordinate location from the mapping table. The preset opening compensation value and the media access control address are encapsulated into a hardware interrupt preemption instruction and injected into the priority execution queue of the physical bus controller. During this synchronous execution, the hardware interrupt preemption instruction synchronously triggers the electromagnetic bypass pressure relief valve located at the front end of the exhaust branch pipe of the safety cabinet. Since the working displacement response time of the electromagnetic bypass pressure relief valve is configured to be lower than the mechanical shaft rotation time of the variable air volume valve, the system utilizes the instantaneous opening of the bypass loop where the electromagnetic bypass pressure relief valve is located to preemptively reduce the local static pressure level of the safety cabinet piping network before the variable air volume valve reaches the target opening.

[0070] Simultaneously, the dimensionality reduction inference engine extracts the face wind attenuation gradient matrix from the output layer of the multilayer perceptron network and transforms it into a fixed-point one-dimensional sequence. The system performs a hash mapping operation on this sequence to generate a feature hash value representing the static flow field characteristics. This feature hash value, along with dynamic out-of-bounds timestamp characters and gateway physical identification codes, is then encapsulated in a field and sent to the cloud. The cloud-based visual mapping module wakes up an asynchronous parsing thread and uses the extracted feature hash value to retrieve a matching feature template index from the lookup table, thereby retrieving the corresponding feature template data. By combining the independent out-of-bounds timestamp characters in the data packet for temporal positioning and performing spatial pose mapping based on the coordinate calibration file corresponding to the physical identification code, the system reconstructs and generates a 2.5D face wind attenuation gradient matrix with absolute spatial coordinate attributes. The cloud-based visual mapping module establishes a Z-axis normal vector using the leak-proof surface of the safety cabinet as a reference plane, extracts the corresponding gradient value, multiplies it by a preset amplification factor to obtain the displacement offset. The system uses the displacement offset to drive the mesh vertices on the reference plane to generate geometric deformation along the Z-axis normal vector, obtaining a risk boundary mesh with a three-dimensional convex topological profile for the current rendering cycle. By comparing the current cycle with the baseline mesh vertex set, the system selects a subset of displacement vertices and uses an axial bounding box to define the local topological region. It assigns a material color parameter dictionary to the region, issues a masking command during the rasterization stage so that the rendering engine only performs color overlay updates on the pixels within the bounding box, and outputs the final rendering result.

[0071] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic visualization system for multi-source data in laboratory HVAC systems, characterized in that, include: The spatiotemporal alignment gateway performs hardware-level clock synchronization of sensor data and depth vision sensor data in the HVAC network through a timing synchronization protocol, and performs coordinate and time interpolation compensation in conjunction with a dynamic compensation buffer pool to output a spatiotemporal aligned data stream. The dimensionality reduction inference engine receives the spatiotemporally aligned data stream, performs spatial discretization on the depth point cloud data to generate a three-dimensional voxel mesh, and extracts the momentum direction tensor by calculating the instantaneous volume resistivity. The momentum direction tensor is projected to a 2.5D cross section for matrix multiplication inference, and outputs the face wind attenuation gradient matrix. When the face wind attenuation gradient matrix exceeds the global safety threshold, a hardware interrupt preemption instruction is generated to take over the physical device, and the face wind attenuation gradient matrix is ​​encapsulated as a feature hash value and uploaded. The cloud-based visual mapping module receives the feature hash value and asynchronously parses and restores the 2.5D face wind attenuation gradient matrix. It generates a risk boundary mesh that expands along the Z-axis in the 3D building information model and uses an incremental parallax projection algorithm to perform data stream updates only on the local topological regions where risk material changes occur and outputs the rendering results.

2. The laboratory HVAC multi-source data dynamic visualization system according to claim 1, characterized in that, The steps for performing hardware-level clock stamp synchronization and coordinate time interpolation compensation include: acquiring industrial bus data streams containing a first local clock cycle sent by static pressure sensors and hot-wire anemometers in the HVAC network, and point cloud data streams containing a second local clock cycle sent by depth vision sensors; the spatiotemporal alignment gateway, acting as the master clock node, sends synchronization messages to the static pressure sensors, hot-wire anemometers, and depth vision sensors based on the timing synchronization protocol and distributes time offsets to align the first local clock cycle and the second local clock cycle to the absolute time reference of the master clock node; A first-in-first-out circular queue based on an absolute time reference is established in the memory area as the dynamic compensation buffer. In the spatial dimension, the point cloud data stream is transformed from the camera coordinate system to the world coordinate system consistent with the 3D building information model through a preset external parameter matrix to obtain spatially registered point cloud data. In the temporal dimension, using the first local clock cycle as the resampling reference, adjacent sampling time points are queried in the dynamic compensation buffer for the spatially registered point cloud data and linear interpolation is performed to obtain temporally interpolated point cloud data. The temporally interpolated point cloud data and sensor data are merged into a data stream to output a spatiotemporally aligned data stream.

3. The laboratory HVAC multi-source data dynamic visualization system according to claim 1, characterized in that, The steps for generating a three-dimensional voxel mesh and extracting the momentum direction tensor include: dividing the safety cabinet opening boundary region defined by the three-dimensional building information model into a set of cubic cells arranged in an equidistant grid array to form a three-dimensional voxel mesh; mapping the point cloud coordinates in the spatiotemporally aligned data stream to the corresponding cubic cells in the three-dimensional voxel mesh and marking them as occupied; dividing the number of occupied cubic cells by the total number of cubic cell sets to obtain the instantaneous volumetric resistance ratio; obtaining the displacement vector of the cubic cells in the occupied state at adjacent resampling reference times, and multiplying the instantaneous volumetric resistance ratio as a weighting coefficient by the displacement vector to obtain the momentum direction tensor.

4. The laboratory HVAC multi-source data dynamic visualization system according to claim 1, characterized in that, The momentum direction tensor is projected to a 2.5D cross section for matrix multiplication and derivation to output the face wind attenuation gradient matrix. This process includes: establishing a two-dimensional reference projection plane parallel to the operating surface of the safety cabinet; projecting the momentum direction tensor coordinates onto the two-dimensional reference projection plane to form a two-dimensional grid coordinate system; extracting the depth values ​​of the momentum direction tensor perpendicular to the two-dimensional reference projection plane and assigning them as scalar features to the corresponding two-dimensional grid coordinate points to construct a 2.5D cross section input matrix containing horizontal and vertical coordinates and depth feature scalars; flattening the 2.5D cross section input matrix into a one-dimensional input vector and inputting it into a multilayer perceptron network; and reconstructing the dimension of the one-dimensional prediction vector output by the multilayer perceptron network according to the arrangement rules of the two-dimensional grid coordinates to generate the face wind attenuation gradient matrix.

5. The laboratory HVAC multi-source data dynamic visualization system according to claim 1, characterized in that, The steps of the dimensionality reduction simulation engine in generating a hardware interrupt preemption instruction to take over physical devices include: performing element-wise subtraction operations between the face wind attenuation gradient matrix and a preset global safety threshold to obtain a difference matrix; determining whether there are difference elements with values ​​greater than 0 in the difference matrix; if there are difference elements with values ​​greater than 0 in the difference matrix, triggering a low-level hardware interrupt request and suspending the PID closed-loop control process; extracting the grid point coordinates corresponding to the difference elements with values ​​greater than 0 in the difference matrix, and retrieving the media access control address of the variable air volume valve governing the grid point coordinates from the mapping table; encapsulating the preset opening compensation value and the media access control address into a hardware interrupt preemption instruction and injecting it into the priority execution queue of the physical bus controller.

6. The laboratory HVAC multi-source data dynamic visualization system according to claim 1, characterized in that, The steps of the dimensionality reduction inference engine to encapsulate the face wind attenuation gradient matrix into a feature hash value and upload it include: converting the floating-point data of the face wind attenuation gradient matrix into a fixed-point one-dimensional sequence according to a fixed order of column first and row second; performing discretization clustering on the fixed-point one-dimensional sequence using a pre-trained vector quantization dictionary, extracting the corresponding template index based on the minimum distance metric principle, and performing hash mapping operation on the template index to generate a feature hash value; and encapsulating the feature hash value, the out-of-bounds timestamp character, and the physical identification code of the spatiotemporal alignment gateway into fields and uploading them to the cloud visual mapping module.

7. The laboratory HVAC multi-source data dynamic visualization system according to claim 1, characterized in that, The steps for parsing and restoring the 2.5D face wind attenuation gradient matrix include: the cloud-based visual mapping module receiving the feature hash value and waking up the asynchronous parsing thread; extracting the feature hash value from the encapsulated data and matching and querying the corresponding feature template index in the pre-established hash mapping inverse lookup table library; extracting template data from the pre-stored feature template database according to the feature template index, extracting the physical identification code and out-of-bounds timestamp characters from the encapsulated data, and performing spatial pose mapping and temporal alignment on the template data in conjunction with the coordinate calibration file to restore and generate a 2.5D face wind attenuation gradient matrix with absolute spatial coordinate attributes.

8. A laboratory HVAC multi-source data dynamic visualization system according to claim 1, characterized in that, The steps of generating a risk boundary mesh along the Z-axis and performing data stream updates in the cloud-based visual mapping module include: using the leak-proof surface of the safety cabinet in the 3D building information model as a reference plane, establishing a Z-axis normal vector perpendicular to the reference plane; mapping the 2.5D surface wind attenuation gradient matrix to the reference plane in spatial coordinates, and extracting gradient values ​​corresponding to the spatial positions of each mesh vertex on the reference plane; multiplying each extracted gradient value by a preset amplification factor to obtain a displacement offset representing the degree of deformation of each mesh vertex; using the displacement offset to drive the mesh vertices on the reference plane to generate geometric deformation along the Z-axis normal vector, obtaining a risk boundary mesh with a 3D convex topological profile for the current rendering cycle; caching the risk boundary mesh vertex set of the previous rendering cycle as the reference mesh vertex set; comparing the risk boundary mesh vertex coordinates of the current rendering cycle with the reference mesh vertex set, selecting a subset of displacement vertices and defining local topological regions using axial bounding boxes; allocating a material color parameter dictionary to the local topological region, issuing a masking command during the rasterization stage so that the rendering engine only performs color coverage updates on pixels within the axial bounding box, and outputting streaming media rendering results.

Images