A smart park multi-scene fusion visual management and control system and method

Abstract

The present application relates to the field of visual control technology, especially to a kind of intelligent park multi-scene fusion visual control system and method, comprising: obtaining infrared data and operating load data by data acquisition module;Feature injection module configures inflation tensor, deformation matrix and emissivity function at model vertex;Electromagnetic decoupling module constructs magnetic field tensor based on load data and spatial topology, and generates decoupled infrared data by using phase-locked filtering to inverse transform infrared data;Refraction index compensation module solves medium refractive index gradient and executes inverse ray tracing integration, corrects texture mapping intersection coordinates;Grid reconstruction module adjusts the spatial geometric boundary coordinates of model vertex using dynamically updated deformation matrix;Obstruction mapping module constructs obstruction area mask to intercept invalid projection, and solves heat conduction differential equation to repair and render heat field of obstruction area grid.The present application eliminates electromagnetic, refractive index and thermal deformation interference, and realizes full-scene accurate blind area control.

Description

Technical Field

[0001] This invention relates to the field of visual management and control technology, and in particular to a smart park multi-scenario integrated visual management and control system and method. Background Technology

[0002] With the deepening development of smart park construction, the use of infrared thermal imaging technology combined with 3D virtual reality technology for visual management and control of core power distribution equipment, power supply lines, and key energy-consuming terminals within the park has become an important means of monitoring equipment operation status and identifying safety hazards. In existing visual management and control systems, the surface radiation energy of each device within the target area is typically acquired through an infrared thermal imaging sensor array and converted into infrared data. This infrared data is then directly used as a two-dimensional texture and projected onto the surface of a pre-constructed digital twin 3D model, thereby intuitively displaying the spatial thermal field distribution of key monitoring equipment in the park in a 3D virtual space.

[0003] Existing visualization and control systems generally ignore the physical interference of time-varying electromagnetic fields generated during the operation of high-load power distribution equipment on infrared imaging pipelines or spatial coordinate calibration. This results in severe pixel misalignment and geometric distortion when infrared thermal image data is spatially superimposed on the surface of a three-dimensional model, making it impossible to achieve high-precision spatiotemporal alignment and twin perception. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a smart park multi-scenario fusion visualization management and control system and method, which aims to improve the problem that infrared thermal image data is prone to serious pixel misalignment and geometric distortion when spatially superimposed on the surface of a three-dimensional model, and that it is impossible to achieve high-precision spatiotemporal alignment and twin perception.

[0005] In a first aspect, the present invention provides the following technical solution: a smart park multi-scenario integrated visual management and control system, comprising: The data acquisition module is used to acquire infrared data and operational load data of the target area; The feature injection module is used to configure the dilation tensor, deformation matrix, and emissivity function with temperature and viewing angle as independent variables for the corresponding objects in the vertices of the 3D model. The electromagnetic decoupling module is used to construct a time-varying magnetic field tensor based on the operating load data and spatial topological location, and to perform inverse transformation processing on the infrared data using a phase-locked filtering algorithm synchronized with the fluctuation period of the operating load data to generate decoupled infrared data. The refractive index compensation module is used to solve the refractive index gradient of the medium surrounding the corresponding object based on the operating load data, and to perform inverse ray tracing integral calculation based on the refractive index gradient to correct the texture mapping intersection coordinates of the decoupled infrared data on the surface of the three-dimensional model. The mesh reconstruction module is used to perform differential geometric matrix transformation using the deformation matrix that is dynamically updated with the operating load data, and to adjust the spatial geometric boundary coordinates of the vertices of the three-dimensional model. The occlusion mapping module is used to construct an occlusion area mask through spatial geometric projection to intercept the decoupled infrared data projection of the physical occlusion area, and solve the thermal conduction differential equation based on the emissivity function and the material thermal conductivity of the corresponding object to repair the thermal field data and perform 3D rendering on the mesh in the occlusion area.

[0006] Secondly, the present invention provides the following technical solution: a smart park multi-scenario integrated visual management and control method, the method comprising: S1. Acquire infrared data and operational load data for the target area; S2. Configure the dilation tensor, deformation matrix, and emissivity function with temperature and observation viewpoint as independent variables for the corresponding objects in the vertices of the 3D model; S3. Construct a time-varying magnetic field tensor based on the operating load data and spatial topology location, and use a phase-locked filtering algorithm synchronized with the fluctuation period of the operating load data to perform inverse transformation processing on the infrared data to generate decoupled infrared data. S4. Solve the refractive index gradient of the medium surrounding the corresponding object based on the operating load data, and perform inverse ray tracing integral calculation based on the refractive index gradient to correct the texture mapping intersection coordinates of the decoupled infrared data on the surface of the three-dimensional model. S5. Using the deformation matrix that is dynamically updated with the operating load data, perform differential geometric matrix transformation to adjust the spatial geometric boundary coordinates of the vertices of the three-dimensional model; S6. Construct a mask for the occlusion area through spatial geometric projection to intercept the decoupled infrared data projection of the physical occlusion area, and solve the thermal conduction differential equation according to the emissivity function and the thermal conductivity of the corresponding object's material to repair the thermal field data and perform 3D rendering on the mesh in the occlusion area.

[0007] The present invention has the following beneficial effects: 1. In this invention, the time-varying magnetic field tensor is inverted using the operating load data through the electromagnetic decoupling module, and the infrared data is inversely geometrically affinely transformed by the phase-locked loop filter. This achieves adaptive real-time decoupling of electromagnetic interference and accurate correction of infrared pixel misalignment, solving the problem of geometric distortion and parallax misalignment when infrared thermal images are spatially superimposed due to the neglect of electromagnetic field interference from high-load equipment in traditional systems.

[0008] 2. In this invention, the inverse ray tracing integral is performed by the refractive index compensation module, and the differential geometric matrix transformation is performed by the mesh reconstruction module using the thermo-mechanical coupling reduced-order model and the Laplacian deformation algorithm. This realizes the dynamic adjustment of the model's geometric boundary with load and temperature fluctuations, as well as the real-time correction of the ray deflection trajectory. This solves the problem of texture mapping misalignment and rendering disconnect caused by ray deflection due to air thermal convection and thermal expansion and contraction of the equipment when the equipment is under high load and the ambient temperature changes drastically.

[0009] 3. In this invention, the occlusion mapping module uses projection depth comparison to construct an occlusion mask to intercept invalid projections, and transforms the known temperature and emissivity function into boundary conditions to solve the heat conduction differential equation by spatiotemporal finite difference extrapolation. This achieves accurate numerical repair and reconstruction rendering of the unknown thermal field distribution in the physical line of sight blind zone, and solves the problem that traditional infrared visualization is easily affected by physical perspective, pipeline overlap or shell occlusion, resulting in data discontinuity in the occlusion blind zone and inability to perceive the continuous internal thermal field state. Attached Figure Description

[0010] Figure 1 This is an architecture diagram of a smart park multi-scenario integrated visual management and control system proposed in this invention; Figure 2 This is a data processing flowchart of the refractive index compensation module of a smart park multi-scenario integrated visual control system proposed in this invention; Figure 3 This is a flowchart of the mask generation and thermal field repair process for the occlusion mapping module of a smart park multi-scenario fusion visualization control system proposed in this invention. Figure 4 This is a flowchart of a smart park multi-scenario integrated visual management and control method proposed in this invention. Detailed Implementation

[0011] The technical solutions in 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.

[0012] Example 1: In the first embodiment of the present invention, the present invention provides a smart park multi-scenario integrated visual management and control system, such as... Figures 1-3 As shown, it includes the following steps: The data acquisition module is used to acquire infrared data and operational load data of the target area; Specifically, when the smart park multi-scenario integrated visualization control system is in operation, its data processing pipeline is driven by the data acquisition module as the starting point. This module continuously captures physical parameters of a preset target area to obtain basic infrared data and operational load data. An infrared thermal imaging sensor array with a fixed sampling frequency and a power parameter acquisition gateway are deployed within the target area; these two types of hardware terminals are the original data sources for all subsequent digital twin algorithm models.

[0013] The acquisition of infrared data involves an infrared thermal imaging sensor array spatially discretely sampling the surface radiation energy of each monitoring device within the target area. The sensor converts the received infrared radiation signal into an electrical signal, which is then converted from analog to digital to generate a two-dimensional pixel matrix. Each pixel in this matrix carries spatial coordinates and a corresponding quantized radiation intensity value, which directly reflects the apparent radiance distribution on the surface of the monitoring device. In the specific spatiotemporal alignment process, the system converts the two-dimensional pixel matrix into an infrared radiation intensity dataset with absolute three-dimensional spatial coordinates based on the installation three-dimensional coordinates and reference orientation angle of the infrared thermal imaging sensor. This dataset is stored in a buffer indexed by a timestamp, forming the direct input stream for the subsequent electromagnetic decoupling module.

[0014] The process of acquiring operational load data involves the power parameter acquisition gateway monitoring the electrical physical quantities of each distribution circuit and power terminal within the target area in real time. The acquisition gateway periodically extracts current and voltage waveform signals from the power supply lines via current transformers. After receiving these waveform signals, the data acquisition module performs a Discrete Fourier Transform on the on-chip system to extract the fundamental current amplitude, RMS value, active power, reactive power, and total harmonic distortion. These electrical physical quantities collectively constitute the operational load dataset. In actual data flow, the operational load data does not exist independently. The system uses a synchronous clock source to assign a high-precision timestamp to each set of power parameters, ensuring complete microsecond-level alignment with the infrared radiation intensity dataset in the time dimension, generating a composite monitoring matrix under a unified spatiotemporal reference.

[0015] To transform discrete operating load data into parameters that can be directly accessed in subsequent physics calculations, the system incorporates real-time heat loss conversion logic within the data acquisition module. This logic couples active power with the preset impedance of the distribution circuit using physical equations; the calculation formula is as follows: ; In the above formula, This indicates the real-time heating power of the heating element; This represents the effective value of the power supply line current measured by the power parameter acquisition gateway; This represents the preset equivalent impedance of the power distribution circuit, in ohms. In this embodiment, the impedance is set to a fixed 0.05 ohms. This represents the total additional harmonic loss power generated by higher harmonics (excluding the fundamental frequency) in the core and conductor, measured in watts. In the specific operating logic, the system dynamically calculates this total additional harmonic loss power based on the product of the total harmonic distortion rate and the active power. This real-time heating power, as a core intermediate operating state parameter, is injected into the dynamic data bus in real time. Simultaneously, the system combines the total harmonic distortion rate and the effective current value to convert it into a current variability factor, which characterizes the potential fluctuation intensity of the interference source.

[0016] After local verification and normalization, the acquired infrared radiation intensity dataset and runtime load dataset are synchronously distributed to the subsequent feature injection module and electromagnetic decoupling module via a high-speed data channel. The infrared data serves as the original spatial texture benchmark to be corrected, while the runtime load data serves as the core independent variable that excites changes in the spatial electromagnetic field and drives the thermal deformation of the 3D mesh. Together, these two types of data constitute the underlying real-time data flow closed loop for multi-scene fusion visualization and control, providing continuous and deterministic discrete boundary conditions for the entire digital twin rendering pipeline.

[0017] The feature injection module is used to configure the dilation tensor, deformation matrix, and emissivity function with temperature and viewing angle as independent variables for the corresponding objects in the vertices of the 3D model. Specifically, in the data processing flow of the smart park multi-scenario integrated visualization control system, after receiving the static geometric data of the smart park transmitted from the previous stage, the feature injection module begins to statically configure and initialize the embedding of multi-dimensional physical features in the vertex data structure of the 3D model. This process transforms the original 3D mesh, which only has spatial geometric coordinates, into a digital twin entity that carries thermodynamic and optical radiation characteristics. The system first reads the 3D model file of the corresponding object within the target area, which contains the parsed vertex array. In addition to storing traditional infrared geometric position information, each vertex also reserves dedicated data storage slots for the dilation tensor, deformation matrix, and emissivity function by expanding the vertex attribute buffer.

[0018] In the processing flow of configuring the expansion tensor at the vertex, the system retrieves the coefficient of thermal expansion from a pre-defined material feature database based on the material's physical properties. The expansion tensor, a second-order symmetric tensor describing the strain correlation along various axes of an object under heat, has its initialization matrix form determined by the isotropic or anisotropic characteristics of the material. Its specific calculation formula is as follows: ; In the above formula, This represents the thermal expansion tensor matrix located at the vertices of the model; These represent the positive axial expansion components of the corresponding object's material in a Cartesian coordinate system. These represent the shear expansion components. The system iterates through each vertex of the 3D model, writing each component of this second-order symmetric tensor into the attribute buffer of the corresponding vertex. This expansion tensor, as a fundamental material feature, will be used as a multiplier factor in the subsequent mesh reconstruction module to directly map temperature changes into the thermal expansion displacement increment of the vertex.

[0019] In the configuration flow of the deformation matrix, the system configures an initial fourth-order homogeneous deformation matrix for each vertex. This deformation matrix contains translational components that control linear spatial displacement and rotational shear components that control spatial attitude. The system establishes the computational logic for this deformation matrix, making it an identity matrix in the initial state, and then dynamically refreshes it by receiving thermodynamic inversion parameters from the running load data. The spatial arrangement of the deformation matrix at the vertex is related to the bone weights or local curvature of the 3D model, thus directly participating as intermediate state parameters in the differential geometric matrix transformation of the subsequent mesh reconstruction module.

[0020] In a processing flow configured with an emissivity function as independent variables (temperature and observation angle), the system establishes a multidimensional nonlinear polynomial fitting model. Emissivity is not a fixed constant, but a physical quantity that dynamically shifts with changes in real-time surface temperature and sensor observation angle. The system inputs experimentally measured emissivity sampling points of the corresponding object's surface material at different temperatures and polarization angles into the fitting algorithm to construct an explicit continuous mathematical function relationship. The specific calculation formula is as follows: ; In the above formula, The calculated real-time surface emissivity is represented by a dimensionless coefficient value used in solving the subsequent thermal conduction differential equation; T represents the real-time surface temperature of the corresponding object, obtained through subsequent thermal field inversion or injection from an external sensor. This represents the angle between the virtual camera's line of sight and the surface normal vector of the current mesh vertex; These all represent material-specific polynomial fitting coefficients. The feature injection module, through compilation logic, directly encapsulates the algebraic calculation formula of this multivariate function and its corresponding fitting coefficient array into the fragment shader configuration script of the 3D model vertex.

[0021] After injecting the dilation tensor, deformation matrix, and emissivity function, the system packages this dataset of 3D model vertices carrying multi-dimensional physical properties into the vertex buffer object of the graphics processing hardware. This dataset serves as the underlying geometric and physical coupling reference data, and is globally shared and flows to subsequent electromagnetic decoupling modules, refractive index compensation modules, mesh reconstruction modules, and occlusion mapping modules to support the subsequent correction of infrared texture intersection coordinates and iterative solution of blind zone thermal fields by the entire control system.

[0022] The electromagnetic decoupling module is used to construct a time-varying magnetic field tensor based on the operating load data and spatial topology, and to perform inverse transformation processing on the infrared data using a phase-locked filtering algorithm synchronized with the fluctuation period of the operating load data to generate decoupled infrared data. Furthermore, in the electromagnetic decoupling module, the construction of a time-varying magnetic field tensor based on operating load data and spatial topological location includes: Topological discrete elements are used to divide spatial topological locations into three-dimensional mesh nodes and multiple discrete line segment elements. The signal conversion unit is used to convert operating load data into current intensity timing signals; The field strength calculation unit is used to calculate the magnetic induction intensity vector at each three-dimensional grid node by using the current intensity time sequence signal and the position of discrete line segment elements, according to the Biot-Savart law. Tensor building units are used to spatially superimpose magnetic induction intensity vectors at each 3D mesh node to generate a magnetic field tensor that updates dynamically over time.

[0023] Furthermore, in the electromagnetic decoupling module, the infrared data is inversely transformed using a phase-locked loop filter algorithm synchronized with the fluctuation cycle of the operating load data, including: The cycle locking unit is used to extract time-domain features from the operating load data, identify the adaptive main frequency of the electromagnetic interference source, and lock the adaptive main frequency to the fluctuation period. An adaptive filtering unit is used to perform real-time tracking and filtering of amplitude interference and phase drift in infrared data belonging to the period using an adaptive notch filter algorithm synchronized with the fluctuation period. The inverse transformation unit is used to perform an inverse spatial geometric affine transformation on the filtered infrared data based on the magnetic field tensor to generate decoupled infrared data.

[0024] Specifically, when the smart park multi-scenario integrated visualization control system is in the data processing stage, the infrared data and operating load data collected and distributed by the front-end are synchronously input into the electromagnetic decoupling module to eliminate the physical distortion caused by spatial electromagnetic interference to the infrared imaging quality.

[0025] The electromagnetic decoupling module first initiates the construction process of the time-varying magnetic field tensor. The topology discretization unit retrieves the spatial topology location information of the park's equipment and power supply lines, and uses a spatial discretization operator to divide the spatial topology region and construct a virtual coordinate space composed of interwoven three-dimensional mesh nodes. Simultaneously, the topology discretization unit divides the continuous power supply lines into multiple discrete line segments based on the three-dimensional spatial orientation of the line routes. Each discrete line segment is assigned a starting point three-dimensional coordinate vector and an ending point three-dimensional coordinate vector. The signal conversion unit receives time-aligned operating load data, extracts the current waveform or load power, and maps and converts it into a continuous current intensity time-series signal by dividing by a standard voltage term. The field strength calculation unit combines this current intensity time-series signal with the spatial geometric boundary data of each discrete line segment element, and calculates the magnetic field distribution at each three-dimensional mesh node based on the Biot-Savart law. The specific calculation formula is as follows: ; In the above formula, This represents the differential magnetic induction vector generated by any discrete line segment element at a node of the target 3D mesh. Indicates the permeability of free space; This represents the real-time current intensity timing signal value output by the signal conversion unit; Represents the spatial geometric direction vector of a discrete line segment element; This represents the spatial relative distance displacement vector from the current discrete line segment element to the target 3D mesh node; This represents the magnitude of the spatial relative distance displacement vector. The tensor building blocks traverse all virtual space meshes, performing spatial algebraic superposition on the differential magnetic induction intensity vectors generated by all discrete line segment elements within the target region at each 3D mesh node. By calculating the field strength components along each orthogonal axis, a dynamically updated magnetic field tensor array is finally generated, whose expression is: ; In the above formula, This represents the spatial magnetic field tensor matrix of a specific three-dimensional grid node at time t; These represent the magnetic induction intensity components along the three orthogonal axes of the spatial rectangular coordinate system in the virtual coordinate space, obtained by algebraically superimposing the differential magnetic induction intensity vectors of all discrete line segment elements.

[0026] While constructing the magnetic field tensor, the electromagnetic decoupling module uses a phase-locked loop (PLL) filtering algorithm synchronized with the fluctuation period of the operating load data to perform noise reduction and affine correction on the raw infrared data. The period-locking unit receives the current or power signal from the operating load data, performs time-domain autocorrelation function analysis or zero-crossing detection calculations, thereby identifying and separating the dominant frequency variation characteristics of the electromagnetic interference source, and locking the period corresponding to this dominant frequency as the fluctuation period. The adaptive filtering unit injects this fluctuation period as the core tracking step size parameter into the digital filter, constructing a highly selective adaptive notch matrix. This notch matrix directly acts on the infrared radiation intensity dataset of the infrared data, dynamically filtering out high-frequency amplitude interference signals and periodic phase drift components within this specific period frequency band.

[0027] The inverse transformation unit receives the adaptively filtered infrared radiation intensity dataset and retrieves the spatial magnetic field tensor array, which precisely corresponds to its high-precision timestamp, from memory. Since electromagnetic fields can cause geometric deflection disturbances to the imaging link or calibration space, the inverse transformation unit uses the magnitude of the magnetic field tensor and its spatial gradient as independent variables to calculate the spatial coordinate distortion operator. It then performs an inverse spatial geometric affine transformation on the filtered infrared data in the spatial pixel mapping. This affine transformation corrects pixel misalignment and distortion caused by electromagnetic interference through matrix inverse operations. The affine transformation formula is: ; In the above formula, This represents the corrected clean pixel coordinate vector; This represents the inverse geometric distortion transformation matrix constructed using the spatial magnetic field tensor array as the independent variable; This represents the distorted pixel spatial coordinate vector in the original infrared data. After spatial coordinate geometric correction, the system outputs clean, decoupled infrared data. This decoupled infrared data is written to globally shared video memory and used in subsequent ray integration compensation calculations by the refractive index compensation module.

[0028] The refractive index compensation module is used to solve the refractive index gradient of the medium surrounding the corresponding object based on the operating load data, and to perform inverse ray tracing integral calculation based on the refractive index gradient to correct the texture mapping intersection coordinates of the decoupled infrared data on the surface of the three-dimensional model. Furthermore, in the refractive index compensation module, the calculation of the refractive index gradient of the medium surrounding the corresponding object based on the operating load data includes: The power consumption mapping unit is used to calculate the real-time surface dissipation power of the corresponding object based on the operating load data and the preset impedance characteristics of the corresponding object. The temperature field solving unit is used to take the real-time surface dissipation power as a heat source input and solve the non-uniform air temperature field distribution of the medium surrounding the corresponding object. The gradient calculation unit is used to convert the air temperature field distribution into a spatial refractive index field based on the Gladstone-Dale formula, and to obtain the refractive index gradient by calculating the first-order partial derivative of the spatial refractive index field in three dimensions.

[0029] Furthermore, in the refractive index compensation module, inverse ray tracing integral calculation is performed based on the refractive index gradient to correct the coordinates of the texture mapping intersection points of the decoupled infrared data on the surface of the 3D model, including: An adaptive integration step unit is used to dynamically reduce or increase the Runge-Kutta integration step size along the direction of backlight projection, based on the rate of change of the modulus of the refractive index gradient. The trajectory tracing unit is used to iteratively solve the differential equation of light propagation using the Runge-Kutta integral step size, and to calculate the deflection and bending trajectory of light as it passes through the surrounding medium. The texture intersection correction unit is used to retrieve the actual geometric intersection points between the inverted light rays and the surface of the 3D model based on the deflected and bent trajectories, and to correct the coordinates of the texture mapping intersection points.

[0030] Specifically, during the operation of the smart park multi-scenario integrated visualization control system, the refractive index compensation module receives the operating load data and decoupled infrared data distributed by the front-end to eliminate the light deflection disturbance caused by the non-uniform temperature field, thereby accurately correcting the texture mapping coordinates.

[0031] The refractive index compensation module first drives the power consumption mapping unit, temperature field solving unit, and gradient calculation unit to solve for the refractive index gradient of the medium surrounding the corresponding object. The power consumption mapping unit retrieves real-time input operating load data, extracts current and voltage waveform characteristics, and retrieves the impedance characteristic parameters preset during system initialization for the corresponding object. By performing a complex multiplication oscillatory transformation on the real-time current value and impedance characteristic parameters in the operating load data, the real-time surface dissipation power of the corresponding object is calculated. The temperature field solving unit receives this real-time surface dissipation power, uses it as input as the volumetric heat source term in the thermal boundary conditions, and combines it with preset fluid dynamics control equations to perform spatial discrete mesh iteration, solving for the non-uniform air temperature field distribution of the air medium surrounding the corresponding object. The gradient calculation unit introduces the Gladstone-Dale formula to map and convert the solved air temperature field distribution into a discrete three-dimensional spatial refractive index field. The conversion formula is as follows: ; In the above formula, This represents the calculated real-time air refractive index at each grid point in space. This represents the Gladstone-Dale constant; Indicates the reference atmospheric pressure constant; The gas constant representing air; This represents the real-time air temperature value at the corresponding spatial grid point output by the temperature field solving unit. After establishing the spatial refractive index field, the gradient solving unit uses the three-dimensional spatial central difference operator to calculate the first-order partial derivatives of the spatial refractive index field along the three orthogonal directions of the virtual spatial coordinate axes, thereby generating a three-dimensional spatial refractive index gradient vector field with directional attributes.

[0032] The refractive index compensation module activates the adaptive integration step size unit, the trajectory tracing unit, and the texture intersection correction unit to perform inverse ray tracing integral calculations. The adaptive integration step size unit uses the optical center of the virtual camera as the starting point for inverse ray tracing and establishes a ray propagation path along the direction of inverse ray projection. At each integration node, the refractive index gradient corresponding to that position is read, and the rate of change of the magnitude of the refractive index gradient between adjacent nodes is calculated. When the rate of change of the magnitude exceeds a preset fluctuation threshold, the adaptive integration step size unit executes a dynamic step size reduction command to capture the severe ray deflection in high-gradient regions; conversely, it executes a step size expansion command. The trajectory tracing unit uses this adaptively adjusted Runge-Kutta integration step size to numerically iterate and solve the ray propagation differential equation. The solved ray propagation differential equation satisfies: ; In the above formula, s represents the arc length parameter along the path of the reverse ray propagation; n represents the real-time refractive index of the air at the current spatial location; This represents the three-dimensional spatial position vector of the backward tracing ray; This represents the three-dimensional spatial first-order partial derivative refractive index gradient of the current spatial position, output by the gradient calculation unit. The trajectory tracing unit updates the propagation direction and spatial position of the light ray through multi-level Runge-Kutta formula integration, thereby fully calculating the deflection and bending trajectory of the light ray as it passes through the surrounding non-uniform air medium.

[0033] The texture intersection correction unit acquires the deflected and curved trajectory and, upon termination of the integral iteration, calculates the geometric intersection relationship between this trajectory and the 3D model surface constructed in the feature injection module. By obtaining the analytical solution for the intersection of the trajectory vector and the triangular facets in model space, the actual geometric intersection point between the ray and the 3D model surface is calculated. The texture intersection correction unit replaces and updates the original texture mapping intersection point coordinates with parallax offset to these actual geometric intersection points, thus correcting the texture mapping intersection point coordinates. The corrected coordinate data is output to the subsequent mesh reconstruction module and occlusion mapping module as a precise positioning reference for determining spatial texture shading relationships.

[0034] The mesh reconstruction module is used to perform differential geometric matrix transformations using the deformation matrix that is dynamically updated with the operating load data, and to adjust the spatial geometric boundary coordinates of the vertices of the 3D model. Furthermore, in the mesh reconstruction module, the differential geometric matrix transformation using the deformation matrix dynamically updated with the operating load data includes: The thermal stress mapping unit is used to input the operating load data into the preset thermal-mechanical coupled finite element reduced-order model of the corresponding object in real time, and extrapolate to calculate the three-dimensional strain field of each mesh vertex under the current thermal load. The deformation matrix update unit is used to calculate the spatial scaling operator and rotational shear operator of each mesh vertex using the three-dimensional strain field, and dynamically update the deformation matrix.

[0035] Furthermore, in the mesh reconstruction module, adjusting the spatial geometric boundary coordinates of the vertices of the 3D model includes: Topology-preserving elements are used to extract the local differential geometric features and spatial topological connectivity of vertices in a 3D model based on the Laplacian mesh deformation algorithm. The coordinate transformation unit is used to apply the updated deformation matrix to the local differential geometric features. While keeping the local topology of the surface unchanged, the matrix iteratively solves for the new spatial geometric boundary coordinates of the vertices of the 3D model.

[0036] Specifically, during the operation of the smart park multi-scenario integrated visualization control system, the grid reconstruction module receives real-time operating load data distributed from the front-end, and through the collaborative processing logic of each internal unit, drives the 3D model to undergo thermodynamic deformation that conforms to the actual physical state, thereby accurately adjusting the spatial geometric boundary coordinates of the grid vertices.

[0037] The mesh reconstruction module first calls the thermal stress mapping unit and the deformation matrix update unit to execute the preparation flow for differential geometric matrix transformation. The thermal stress mapping unit captures the running load data in real time and injects it as a time-varying excitation source into the pre-set thermo-mechanical coupled finite element reduced-order model of the corresponding object. This thermo-mechanical coupled finite element reduced-order model is constructed based on a pre-trained eigenorthogonal decomposition operator, which can project high-dimensional physical field partial differential equations to low-dimensional space online. The internal calculation logic of the model first converts the load current or power into heat flux density boundary conditions, solves the transient temperature increment on the model surface, and then, under the constraint of the solid mechanics constitutive relation, quickly extrapolates and solves the three-dimensional strain field of each mesh vertex under the current thermal load. The deformation matrix update unit obtains this three-dimensional strain field and extracts the normal strain component and shear strain component of each mesh vertex in the spatial rectangular coordinate system, the expression of which is: ; In the above formula, This represents the calculated symmetric three-dimensional strain tensor at the mesh vertices; These represent the spatial normal strain components; These represent the spatial shear strain components. The deformation matrix update unit uses these strain components to construct a spatial scaling operator and a rotational shear operator, and then performs homogeneous matrix multiplication between them to dynamically update the deformation matrix initialized in the feature injection module. The updated deformation matrix carries in real time the local volume expansion and shear distortion features of the mesh vertices caused by thermal expansion and contraction.

[0038] The mesh reconstruction module activates topology-preserving elements and coordinate transformation elements to implement an adjustment flow for the spatial geometric boundary coordinates of the 3D model vertices. The topology-preserving elements retrieve the original geometric data of the 3D model and construct a Laplacian operator matrix based on the Laplacian mesh deformation algorithm. This algorithm extracts the local differential geometric features of the 3D model vertices, i.e., the Laplacian coordinates, by calculating the relative displacement vector between each mesh vertex and the geometric center of its first-order adjacent vertices. Its discrete extraction formula is as follows: ; In the above formula, Indicates the first Local differential geometric eigenvectors of each grid vertex; Represents the original three-dimensional geometric coordinate vector of the vertex; Indicates the relationship with the first A set of adjacent vertices for each grid vertex that has a first-order spatial topological connection; This represents the total number of adjacent vertices within the set. Simultaneously, the topology-preserving unit maintains and locks the original vertex adjacency matrix, thereby representing the static spatial topological connectivity of the mesh.

[0039] The coordinate transformation unit acquires the local differential geometric features and the deformation matrix updated by the previous stage. To prevent distortion and fragmentation of the local surface topology during mesh deformation, the coordinate transformation unit applies the updated deformation matrix as a linear transformation operator to the Laplacian coordinates, constructing a global least-squares energy functional equation whose matrix iterative solution satisfies: ; In the above formula, This represents the Laplacian operator matrix constructed from topology-preserving units; This represents the new spatial geometric boundary coordinate matrix of the vertices of the 3D model to be solved; This represents the set of deformation matrix transformations output by the deformation matrix update unit, which includes spatial scaling and rotational shearing features. Represents the local differential geometric eigenvectors of each vertex of the entire mesh. The overall feature matrix is ​​composed of these elements. The coordinate transformation unit solves the sparse linear equation system through a series of iterations using the conjugate gradient method. While maintaining the local topological connectivity of the mesh surface, it calculates the new spatial geometric boundary coordinates of the 3D model vertices. The newly calculated boundary coordinate matrix is ​​written back to the vertex shader of the graphics rendering pipeline, directly changing the spatial shape contour of the 3D model and providing a precise post-deformation geometric reference for the subsequent occlusion mapping module.

[0040] The occlusion mapping module is used to construct an occlusion area mask through spatial geometric projection to intercept the decoupled infrared data projection of the physical occlusion area, and solve the thermal conduction differential equation based on the emissivity function and the material thermal conductivity of the corresponding object to repair the thermal field data and perform 3D rendering on the mesh in the occlusion area.

[0041] Furthermore, in the occlusion mapping module, constructing an occlusion area mask through spatial geometric projection to intercept the decoupled infrared data projection of the physically occluded area includes: The view frustum clipping unit is used to obtain the current viewing angle and projection matrix of the virtual camera, and to perform spatial geometric projection on the 3D model to construct the projection view frustum; The depth comparison unit is used to read the geometric depth values ​​of each spatial grid point within the projected frustum and mark the physical occlusion areas whose depth values ​​are greater than the current visible grid depth on the surface. The mask generation unit is used to generate a binarized mask of the occlusion area based on the physical occlusion area, so as to intercept the decoupled infrared data of the occluded part.

[0042] Furthermore, in the occlusion mapping module, the thermal conduction differential equation is solved based on the emissivity function and the corresponding object's material thermal conductivity. This process involves thermal field data repair and 3D rendering of the mesh within the occlusion area, including: The boundary condition extraction unit is used to take the actual temperature at the intersection coordinates of the texture mapping of the unmasked area as the first type of boundary condition and the emissivity function as the second type of boundary condition. The equation discretization unit is used to perform spatiotemporal finite difference discretization of the thermal conduction differential equation by combining the material thermal conductivity of the corresponding object, and to iteratively extrapolate and solve the equation by combining the first type of boundary conditions and the second type of boundary conditions, and to inversely calculate the unknown thermal field distribution of the mesh in the shading area. The repair rendering unit is used to reconstruct the texture and color data of the mesh in the occluded area using the unknown thermal field distribution, and complete the thermal field data repair and 3D reconstruction rendering.

[0043] Specifically, when the smart park multi-scenario integrated visualization control system processes graphics rendering and physical field mapping, the occlusion mapping module receives the boundary coordinates of the three-dimensional model after reconstruction by the previous stage and the corrected texture mapping intersection coordinates, in order to identify physical blind spots and complete the thermal field numerical repair within the blind spots.

[0044] The occlusion mapping module first calls the view frustum clipping unit, depth comparison unit, and mask generation unit to drive the spatial geometric projection and occlusion blind zone determination processing flow. The view frustum clipping unit obtains the current viewing angle parameters and projection matrix of the virtual camera in real time, and multiplies these matrix parameters with the homogeneous coordinates of the spatial geometric boundary of the 3D model vertices after deformation by the mesh reconstruction module to construct a normalized projection view frustum from the virtual camera's perspective. The depth comparison unit reads the converted screen space coordinates of each spatial mesh point within the projection view frustum and extracts its corresponding geometric depth value.

[0045] The depth comparison unit reads the reference depth value of the current visible mesh from the depth buffer of the graphics hardware. The depth comparison unit compares the geometric depth value of the spatial mesh point with this reference depth value pixel by pixel. If the geometric depth value of the spatial mesh point is greater than the depth of the current visible mesh, it determines that the mesh point is located behind an invisible object or is occluded by a foreground object, and marks it as a physically occluded area. The mask generation unit obtains the spatial boundary of the physically occluded area, establishes a binary two-dimensional matrix in the image space, fills the pixel values ​​belonging to the physically occluded area with zeros, and fills the pixel values ​​of the visible area with ones, thereby generating a binary occlusion mask. This mask acts as a hardware clipping and interception mechanism in the subsequent infrared texture projection pipeline, preventing decoupled infrared data from being directly projected onto the physically occluded area.

[0046] The occlusion mapping module activates the boundary condition extraction unit, the equation discretization unit, and the repair rendering unit to implement the thermal field data repair and 3D reconstruction rendering process for the mesh within the occluded area. The boundary condition extraction unit retrieves the actual temperature at the texture mapping intersection coordinates of the unoccluded area. This actual temperature is obtained by inverting the emissivity function from the decoupled infrared data combined with the feature injection module, and the boundary condition extraction unit uses it as the first type of Dirichlet boundary condition for the heat conduction differential equation. Simultaneously, the boundary condition extraction unit extracts the corresponding emissivity function, calculates its radiative heat transfer flux at the boundary, and uses it as the second type of Newman boundary condition. The equation discretization unit retrieves the material thermal conductivity parameters of the corresponding object and establishes the heat conduction differential equation on the occluded area mesh. To achieve discrete numerical solutions, the equation discretization unit uses the spatiotemporal finite difference method to discretize and expand the first and second order partial derivatives of the spatial geometric mesh, satisfying the following discrete control formula: ; In the above formula, This represents any mesh node within the occlusion region to be solved in the future... Transient temperature values ​​at each time step; Indicates the current number Known temperature values ​​at each time step; Indicates the time step increment; These represent the discrete grid spatial step size along the three orthogonal axes of the Cartesian coordinate system, respectively. This indicates the thermal conductivity of the material of the corresponding object, as read by the system. Indicates the density of the material; This represents the specific heat capacity of the material. The discrete solution unit substitutes the first type of Dirichlet boundary conditions and the second type of Newman boundary conditions into the boundary nodes of the aforementioned discrete control formula, and uses an explicit stepwise forward difference or implicit matrix alternating iterative algorithm to perform spatiotemporal series extrapolation. By advancing layer by layer from the boundary of the unshaded area to the interior of the shaded area, the unknown thermal field distribution of each grid node within the shaded area is inverted and calculated.

[0047] The patching rendering unit acquires the unknown thermal field distribution matrix and transforms it into a continuous temperature scalar field. Within the shader, the patching rendering unit establishes a mapping between temperature scalar values ​​and a color lookup table. Based on the temperature levels of each node, it dynamically reconstructs the RGB texture color data of the occluded area mesh online, completing the numerical patching of the thermal field data. The patching rendering unit seamlessly stitches this color data with the infrared texture of the unoccluded area. Finally, the graphics processor performs 3D reconstruction rendering, outputting a full-scene, blind-spot-free, multi-scene fusion visualization control screen to the management desktop.

[0048] Example 2: In scenarios such as high-voltage substations and core power distribution rooms in smart parks, large transformers, bundled busbars, and high-voltage switchgear generate strong alternating electromagnetic fields and spatial harmonic interference due to extremely high current densities during peak load operation. At this time, conventional infrared thermal imaging sensors, when capturing surface radiation energy from these critical devices, are highly susceptible to physical coupling disturbances from the alternating magnetic field within their signal conditioning circuits and analog-to-digital conversion chips. This results in a large amount of periodic amplitude noise and phase drift mixed into the acquired infrared data. If this distorted infrared data with interference is directly projected onto the three-dimensional geometric model of the substation, it will cause high-frequency jitter, pixel misalignment, and blurred boundaries in the thermal field texture of the 3D twin image, making it impossible for maintenance personnel to accurately pinpoint the specific spatial coordinates of potential heat sources. To solve these problems, this invention provides a multi-scenario fusion visualization management method for smart parks, the structure of which is as follows: Figure 4 As shown. The specific implementation process of this method is as follows: S1. Acquire infrared data and operational load data for the target area; S2. Configure the dilation tensor, deformation matrix, and emissivity function with temperature and observation viewpoint as independent variables for the corresponding objects in the vertices of the 3D model; S3. Construct a time-varying magnetic field tensor based on the operating load data and spatial topology location, and use a phase-locked filtering algorithm synchronized with the fluctuation period of the operating load data to perform inverse transformation processing on the infrared data to generate decoupled infrared data. S4. Solve the refractive index gradient of the medium surrounding the corresponding object based on the operating load data, and perform inverse ray tracing integral calculation based on the refractive index gradient to correct the texture mapping intersection coordinates of the decoupled infrared data on the surface of the three-dimensional model. S5. Use the deformation matrix that is dynamically updated with the operating load data to perform differential geometric matrix transformation and adjust the spatial geometric boundary coordinates of the vertices of the three-dimensional model. S6. Construct a mask for the occlusion area through spatial geometric projection to intercept the decoupled infrared data projection of the physical occlusion area, and solve the thermal conduction differential equation according to the emissivity function and the thermal conductivity of the corresponding object's material to repair the thermal field data and perform 3D rendering on the mesh in the occlusion area.

[0049] Specifically, in step S1, the system synchronously collects the original infrared radiation intensity dataset of the high-voltage switchgear in the substation and reads its time-series operating load data.

[0050] In step S2, the system parses the 3D model file of the switch cabinet, expands the vertex attribute buffer, initializes the preset thermal expansion tensor, initial deformation matrix, and emissivity function formula and corresponding coefficients of the polynomial fitting, and writes them into the fragment shader configuration script of each mesh vertex.

[0051] In step S3, the system retrieves the spatial topology location of the switchgear and discretizes it into grid nodes and line segments. After converting the operating load data into current signals, it calculates the differential magnetic induction intensity of each node based on the Biot-Savart law and spatially superimposes them to generate a time-varying magnetic field tensor array. Simultaneously, the system extracts the time-domain characteristics of the load data to lock the dominant frequency period of the interference source, uses a synchronous adaptive notch filter matrix to filter out co-frequency noise in the infrared data, and constructs an inverse transformation matrix based on the magnetic field tensor array. It then performs an inverse spatial geometric affine transformation on the filtered infrared data to generate decoupled infrared data.

[0052] In step S4, the system calculates the real-time surface dissipation power based on the operating load data and the preset impedance characteristics of the switchgear. Using this power as a heat source, it solves the fluid dynamics control equations to obtain the air temperature field. Then, based on the Gladstone-Dale formula, it converts this into a spatial refractive index field and calculates the three-dimensional first-order partial derivative to obtain the refractive index gradient vector field. Subsequently, the system establishes a path along the reverse ray projection direction, starting from the optical center of the virtual camera. It dynamically adjusts the Runge-Kutta integral step size according to the rate of change of the refractive index gradient magnitude, and numerically iterates to solve the ray propagation differential equation to calculate the deflection and bending trajectory. Based on this, it inverts the actual geometric intersection point between the ray and the model surface and corrects the texture mapping intersection point coordinates.

[0053] In step S5, the system inputs the load data into a preset thermo-mechanical coupled finite element reduced-order model, extrapolates and calculates the three-dimensional strain field of each mesh vertex under the current thermal load, and constructs spatial scaling and rotational shear operators accordingly, dynamically updating the deformation matrix of the vertices. Next, the system extracts the local differential geometric features of the vertices based on the Laplace mesh deformation algorithm while maintaining the original spatial topological connections. Finally, the updated deformation matrix is ​​applied as a linear transformation operator to the local differential geometric features, and the matrix is ​​iteratively solved to obtain the new spatial geometric boundary coordinates of the switchgear after thermal expansion deformation.

[0054] In step S6, the system acquires the current viewing angle and projection matrix of the virtual camera, performs spatial projection on the model vertices to construct projection frustums, compares the geometric depth values ​​of each grid point with the reference depth values ​​to mark the physical occlusion areas, and generates a binarized occlusion area mask to intercept the projection of decoupled infrared data. Subsequently, the system uses the actual temperature at the intersection of the corrected texture in the unoccluded area as the first type of boundary condition, and the heat transfer flux calculated by the emissivity function as the second type of boundary condition. Combining the material's thermal conductivity, the system performs spatiotemporal finite difference discretization on the thermal conduction differential equation and iteratively extrapolates to solve it, inverting and calculating the unknown thermal field distribution of the grid within the occlusion area. Finally, the system maps this unknown thermal field distribution to color texture data to repair the blind zone thermal field, and the graphics processor performs 3D reconstruction rendering, outputting a full-scene, blind-spot-free substation thermal field fusion image to the control desktop.

[0055] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart park multi-scenario integrated visual management and control system, characterized in that, include: The data acquisition module is used to acquire infrared data and operational load data of the target area; The feature injection module is used to configure the dilation tensor, deformation matrix, and emissivity function with temperature and viewing angle as independent variables for the corresponding objects in the vertices of the 3D model. The electromagnetic decoupling module is used to construct a time-varying magnetic field tensor based on the operating load data and spatial topological location, and to perform inverse transformation processing on the infrared data using a phase-locked filtering algorithm synchronized with the fluctuation period of the operating load data to generate decoupled infrared data. In the electromagnetic decoupling module, constructing a time-varying magnetic field tensor based on the operating load data and spatial topological location includes: Topological discrete unit, used to divide the spatial topological location into three-dimensional mesh nodes and multiple discrete line segment elements; A signal conversion unit is used to convert the operating load data into a current intensity timing signal; The field strength calculation unit is used to calculate the magnetic induction intensity vector at each of the three-dimensional grid nodes using the current intensity time-series signal and the position of the discrete line segment elements, according to the Biot-Savart law. Tensor construction unit is used to spatially superimpose the magnetic induction intensity vector at each of the three-dimensional mesh nodes to generate a magnetic field tensor that is dynamically updated over time. In the electromagnetic decoupling module, the infrared data is inversely transformed using a phase-locked loop (PLL) filtering algorithm synchronized with the fluctuation cycle of the operating load data, including: The cycle locking unit is used to extract time-domain features from the operating load data, identify the adaptive main frequency of the electromagnetic interference source, and lock the adaptive main frequency as the fluctuation period. An adaptive filtering unit is used to perform real-time tracking filtering of amplitude interference and phase drift in infrared data belonging to the period using an adaptive notch filter algorithm synchronized with the fluctuation period. The inverse transformation unit is used to perform an inverse spatial geometric affine transformation on the filtered infrared data according to the magnetic field tensor to generate decoupled infrared data; The refractive index compensation module is used to solve the refractive index gradient of the medium surrounding the corresponding object based on the operating load data, and to perform inverse ray tracing integral calculation based on the refractive index gradient to correct the texture mapping intersection coordinates of the decoupled infrared data on the surface of the three-dimensional model. The mesh reconstruction module is used to perform differential geometric matrix transformation using the deformation matrix that is dynamically updated with the operating load data, and to adjust the spatial geometric boundary coordinates of the vertices of the three-dimensional model. The occlusion mapping module is used to construct an occlusion area mask through spatial geometric projection to intercept the decoupled infrared data projection of the physical occlusion area, and solve the thermal conduction differential equation based on the emissivity function and the material thermal conductivity of the corresponding object to repair the thermal field data and perform 3D rendering on the mesh in the occlusion area.

2. The smart park multi-scenario integrated visual management and control system according to claim 1, characterized in that, In the refractive index compensation module, calculating the refractive index gradient of the medium surrounding the corresponding object based on the operating load data includes: The power consumption mapping unit is used to calculate the real-time surface dissipation power of the corresponding object based on the operating load data and the preset impedance characteristics of the corresponding object. The temperature field solving unit is used to take the real-time surface dissipation power as a heat source input and solve the non-uniform air temperature field distribution of the medium surrounding the corresponding object. The gradient calculation unit is used to convert the air temperature field distribution into a spatial refractive index field based on the Gladstone-Dale formula, and to obtain the refractive index gradient by calculating the first-order partial derivative of the spatial refractive index field in three dimensions.

3. The smart park multi-scenario integrated visual management and control system according to claim 1, characterized in that, In the refractive index compensation module, the inverse ray tracing integral calculation is performed based on the refractive index gradient to correct the coordinates of the texture mapping intersection points of the decoupled infrared data on the surface of the 3D model, including: An adaptive integration step unit is used to dynamically reduce or increase the Runge-Kutta integration step size along the direction of reverse ray projection, based on the rate of change of the modulus of the refractive index gradient. The trajectory tracking unit is used to iteratively solve the light propagation differential equation using the Runge-Kutta integral step size, and calculate the deflection and bending trajectory of light as it passes through the surrounding medium. The texture intersection correction unit is used to correct the texture mapping intersection coordinates by inverting the actual geometric intersection of the light rays with the three-dimensional model surface based on the deflected and bent trajectory.

4. The smart park multi-scenario integrated visual control system according to claim 1, characterized in that, In the mesh reconstruction module, the differential geometric matrix transformation using the deformation matrix that is dynamically updated with the operating load data includes: The thermal stress mapping unit is used to input the operating load data into the preset thermal-mechanical coupled finite element reduced-order model of the corresponding object in real time, and extrapolate to calculate the three-dimensional strain field of each mesh vertex under the current thermal load. The deformation matrix update unit is used to calculate the spatial scaling operator and rotation shear operator of each mesh vertex using the three-dimensional strain field, and dynamically update the deformation matrix.

5. The smart park multi-scenario integrated visual management and control system according to claim 1, characterized in that, In the mesh reconstruction module, adjusting the spatial geometric boundary coordinates of the vertices of the 3D model includes: Topology-preserving elements are used to extract the local differential geometric features and spatial topological connectivity of the vertices of the 3D model based on the Laplacian mesh deformation algorithm. The coordinate transformation unit is used to apply the updated deformation matrix to the local differential geometric features, and to solve the new spatial geometric boundary coordinates of the vertices of the three-dimensional model by matrix iteration while keeping the local topology of the surface unchanged.

6. The smart park multi-scenario integrated visual management and control system according to claim 1, characterized in that, In the occlusion mapping module, constructing an occlusion area mask through spatial geometric projection to intercept decoupled infrared data projection from physically occluded areas includes: The view frustum clipping unit is used to obtain the current viewing angle and projection matrix of the virtual camera, and to perform spatial geometric projection on the three-dimensional model to construct a projection view frustum; The depth comparison unit is used to read the geometric depth values ​​of each spatial grid point within the projection frustum and mark the physical occlusion areas whose depth values ​​are greater than the current visible grid depth on the surface. The mask generation unit is used to generate a binarized masking area based on the physical masking area to intercept the decoupled infrared data of the masked portion.

7. The smart park multi-scenario integrated visual management and control system according to claim 1, characterized in that, In the occlusion mapping module, the thermal conduction differential equation is solved based on the emissivity function and the corresponding object's material thermal conductivity. Thermal field data repair and 3D rendering of the mesh within the occlusion area are then performed, including: The boundary condition extraction unit is used to take the actual temperature at the intersection coordinates of the texture mapping in the unmasked area as the first type of boundary condition and the emissivity function as the second type of boundary condition. The equation discretization unit is used to perform spatiotemporal finite difference discretization of the thermal conduction differential equation by combining the material thermal conductivity of the corresponding object, and to iteratively extrapolate and solve the equation by combining the first type of boundary conditions and the second type of boundary conditions, and to inversely calculate the unknown thermal field distribution of the mesh in the shading area. The repair rendering unit is used to reconstruct the texture and color data of the mesh in the occluded area using the unknown thermal field distribution, and complete the thermal field data repair and three-dimensional reconstruction rendering.

8. A method for integrated visual management and control of multiple scenarios in a smart park, characterized in that, The method for a smart park multi-scenario fusion visualization control system according to any one of claims 1-7 includes: S1. Acquire infrared data and operational load data for the target area; S2. Configure the dilation tensor, deformation matrix, and emissivity function with temperature and observation viewpoint as independent variables for the corresponding objects in the vertices of the 3D model; S3. Construct a time-varying magnetic field tensor based on the operating load data and spatial topology location, and use a phase-locked filtering algorithm synchronized with the fluctuation period of the operating load data to perform inverse transformation processing on the infrared data to generate decoupled infrared data. S4. Solve the refractive index gradient of the medium surrounding the corresponding object based on the operating load data, and perform inverse ray tracing integral calculation based on the refractive index gradient to correct the texture mapping intersection coordinates of the decoupled infrared data on the surface of the three-dimensional model. S5. Using the deformation matrix that is dynamically updated with the operating load data, perform differential geometric matrix transformation to adjust the spatial geometric boundary coordinates of the vertices of the three-dimensional model; S6. Construct a mask for the occlusion area through spatial geometric projection to intercept the decoupled infrared data projection of the physical occlusion area, and solve the thermal conduction differential equation according to the emissivity function and the thermal conductivity of the corresponding object's material to repair the thermal field data and perform 3D rendering on the mesh in the occlusion area.

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