Gate-water flow coupled vibration space-time energy transfer visual analysis system and method

Abstract

The invention relates to the technical field of energy transfer data analysis, and discloses a gate-water flow coupled vibration space-time energy transfer visual analysis system and method, and the system comprises an energy fingerprint extraction system, an energy potential gradient network construction system and a dynamic energy potential topology mapping system. The method comprises the steps that a space-time continuous energy density distribution field is generated through a space-frequency field reconstruction technology, non-uniform sampling data is converted into a scalar field with energy fingerprint characteristics, energy fingerprint codes are aggregated into energy infinitesimal elements with directivity, and a space-time energy infinitesimal element array is formed; generating an energy potential gradient network based on the dynamic coupling relationship, and identifying a main energy path and a branch energy path to form a hierarchical energy potential gradient network structure; and mapping the energy potential gradient network to an affine space-time coordinate system, and generating a dynamic energy potential topological curved surface according to path energy flux intensity and directivity. According to the invention, multi-parameter synchronous observation in a complex energy transfer process is realized.

Description

Technical Field

[0001] This invention relates to the field of energy transfer data analysis technology, and in particular to a spatiotemporal energy transfer visualization analysis system and method for gate-water flow coupled vibration. Background Technology

[0002] The coupling vibration of gates with water flow in hydraulic engineering projects has long posed a significant safety hazard. Statistics show that in the past decade, 37% of gate structural damage accidents in my country were caused by fluid-induced vibration. The energy transfer process exhibits significant spatiotemporal multidimensional characteristics: in the temporal dimension, it manifests as the coupling and superposition of low-frequency vibration (0.1-10Hz) and high-frequency turbulent pulsations (10-100Hz); in the spatial dimension, it involves the cascade transformation of fluid kinetic energy, structural strain energy, and acoustic radiation energy. Current engineering monitoring mainly relies on discrete sensor deployment, which faces three major technical bottlenecks: ① Insufficient accuracy in energy transfer path identification (spatial resolution <0.5m); ② Lagging dynamic response (sampling frequency ≤50Hz); ③ Difficulty in multi-physics data fusion.

[0003] Prior art 1, Chinese Patent Application No. 202411629539.3, discloses a method and device for real-time vibration analysis of a diversion tunnel gate. It introduces a bio-fluid-structure interaction model combining real-time data acquisition and finite element analysis. By setting the data acquisition frequency within a monitoring cycle, it monitors and integrates biofilm thickness, growth rate, and hydrodynamic data in real time to construct a comprehensive vibration state analysis strategy. While this effectively improves the system's ability to identify and control vibration states, enhances the accuracy of analysis, and provides targeted biofilm thickness control and adjustment measures, significantly improving the stability and safety of the diversion tunnel gate operation, it only focuses on the macroscopic vibration state analysis of the bio-fluid-structure interaction and lacks the ability to continuously analyze the spatiotemporal energy transfer path, failing to establish dynamic coupling relationships at the micro-element level.

[0004] Prior art two, Chinese patent application number: 202411988246.4, discloses a method for analyzing the vibration characteristics of a turbine guide vane shaft-top cover rubbing coupling system, including the following steps: establishing a reduced dynamic model of the guide vane shaft-top cover rubbing coupling system using the finite element method combined with the fixed interface modal synthesis method; analyzing the influence of modal cutoff number, friction coefficient, and water flow surface load on the first three prestressed modal characteristics of the guide vane shaft-top cover rubbing coupling system based on the reduced dynamic model; analyzing the influence of the near-load end contact state, near-far-load end contact state, and friction coefficient on the vibration response and spectral characteristics of the guide vane shaft-top cover rubbing coupling system when the reduced dynamic model is subjected to harmonic excitation; and evaluating whether the mixed-flow turbine is operating stably and safely based on the above steps. Although the vibration characteristics of the guide vane shaft-top cover rubbing coupling system of the mixed-flow turbine can be analyzed to monitor and diagnose the structural health status and faults of the mixed-flow turbine, the modal analysis based on the reduced dynamics model is limited to the local structure and does not solve the problem of visualizing the energy interaction of multi-physics fields. The spectral characteristic analysis has not established a mapping relationship with the spatial energy distribution.

[0005] Prior art 3, Chinese patent application number: 202411705760.2, discloses a method and system for verifying the operating load of the hydraulic cylinder of a double-suspension arc gate hoist. The method includes: constructing a dynamic model of the double-suspension arc gate hoist based on the gate's dynamic safety opening, different water flow heights and speeds, and an initial model of the double-suspension arc gate hoist; determining the fluid domain and solid domain and performing meshing on each; performing coupled analysis and calculation on the fluid domain and solid domain based on the preset boundary conditions of the double-suspension arc gate hoist and the meshing of the fluid and solid domains to construct an environmental parameter and hydraulic cylinder load distribution model; and finally, based on the environmental parameter and hydraulic cylinder load distribution model... Although stress and strain simulation data of each component of the hydraulic cylinder of the double-suspension arc gate hoist under different flow velocities were obtained to verify the stress and strain range corresponding to the selection of different components and structural design of the hydraulic cylinder of the double-suspension arc gate hoist, the load verification relies on static coupling analysis under preset boundary conditions. The mesh generation method is difficult to capture transient energy transfer characteristics, and the stress and strain data are not converted into an interactive energy topology map.

[0006] Current technologies 1, 2, and 3 suffer from limitations in discrete point measurement, single-path analysis, and the problem of transforming abstract data into intuitive representation. Therefore, this invention provides a visualization analysis system and method for spatiotemporal energy transfer of gate-flow coupled vibration. Summary of the Invention

[0007] The main objective of this invention is to provide a visualization analysis system and method for spatiotemporal energy transfer of gate-flow coupled vibration, in order to solve the problems of discrete point measurement limitations, single path analysis defects, and the transformation of abstract data into intuitive expression in the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A visualization analysis system for spatiotemporal energy transfer of gate-flow coupled vibration, comprising: The dynamic potential topology mapping system 3 is used to map the potential gradient network to an affine spatiotemporal coordinate system and generate a dynamic potential topology surface based on the path energy flux intensity and directionality. It adopts a frequency domain-color domain coupled mapping strategy to map the energy flux intensity to hue change and the energy transfer direction to texture flow direction, realizing a multi-dimensional dynamic expression of the energy transfer process. Finally, it generates an interactive potential topology map that supports multi-scale exploration, drilling from the global potential structure to the local energy micro-element dynamics.

[0009] As a further improvement of the present invention, the dynamic potential topology mapping system includes: The frequency domain-hue feature mapping subsystem is used to divide the continuous energy intensity values ​​into different frequency domain intervals based on the spectral distribution characteristics of the path energy flux intensity. Each frequency domain interval corresponds to a specific hue coding reference value, converting the energy flux intensity value into a hue value, thus forming a hue distribution map based on energy intensity. The direction-texture flow direction synthesis subsystem is used to decompose the direction angle information in the potential gradient network into two components: flow direction vector and flow velocity intensity. The flow direction vector is transformed into directional texture primitives through texture field generation technology. Each texture primitive contains directional and flow characteristics. The flow velocity intensity component controls the density and sharpness of the texture primitives, synthesizing a dynamic texture field with directional characteristics. The multidimensional dynamic expression fusion subsystem is used to establish the correspondence between hue values ​​and texture parameters between the hue distribution map and the dynamic texture field. Based on the position information in the spatiotemporal coordinate system, the hue distribution and texture flow direction are spatially registered to form a unified color-texture composite field. Finally, the spatiotemporal continuous energy transfer process is visualized through dynamic rendering technology.

[0010] As a further improvement of the present invention, the multidimensional dynamic expression fusion subsystem includes: The visual channel coupling relationship establishment component is used to first establish a visual correspondence between hue values ​​in the hue distribution map and texture parameters in the dynamic texture field, and associate and match the wavelength characteristics of hue values ​​with the directional features of texture primitives; by establishing a hue-texture mapping table, the hue values ​​correspond to the texture patterns. The spatiotemporal coordinate alignment processing component is used to spatially register hue distribution and texture flow based on the position information of the spatiotemporal coordinate system; it uses spatiotemporal grid alignment to accurately match the pixel coordinates of the hue distribution map with the vector coordinates of the dynamic texture field; and it eliminates the spatial deviation between the two through coordinate transformation, so that the color attribute and texture attribute of each spatial point correspond completely, forming a visual element combination with unified position. The composite field dynamic fusion generation component is used to generate a color-texture composite field from registered visual elements through field fusion technology. The hue distribution is used as the base layer and the texture flow is used as the feature layer. Through transparency adjustment and edge blending processing, the two visual elements are naturally combined. The final generated composite field retains both the color expression of energy intensity and the texture expression of energy direction, forming a complete energy transfer visualization carrier.

[0011] As a further improvement of the present invention, the composite field dynamic fusion generation component includes: The layer preprocessing and feature enhancement sub-component is used to first optimize the color saturation of the registered hue distribution layer to enhance the visual distinction of energy intensity expression; the texture flow layer is enhanced for directional consistency; and both layers are subjected to resolution normalization. The dynamic transparency adjustment sub-component is used to generate transparency control parameters based on energy flux intensity data. High-intensity energy areas correspond to lower texture layer transparency, while low-intensity energy areas correspond to higher texture layer transparency. The transparency adjustment uses a non-linear response curve to make the visual transition natural and smooth. The Edge Blending and Visual Coordination sub-component is used to process the edge areas of two layers using gradient blending technology; adaptive edge detection is used to identify the feature boundaries of color and texture, creating a blending transition zone in the boundary area; color compatibility is used to adjust the color tone and texture density of the transition area, so that the two visual elements blend naturally at the junction; ultimately forming a composite visualization field.

[0012] As a further improvement of the present invention, the edge blending and visual coordination sub-component includes: The intelligent feature boundary recognition module is used to identify potential boundary regions based on the visual feature differences between the preprocessed hue distribution layer and texture flow layer using gradient field analysis technology; it locates transition regions with significant feature differences by using the spatial gradient changes of the two layers in color saturation and texture density; and it integrates the boundary information detected at different scales into a complete feature boundary map to identify the fusion boundary regions that require special processing. The blend transition zone construction module is used to create blend transition zones in the identified feature boundary areas. It dynamically determines the width and shape of the transition zone based on the degree of visual feature difference on both sides of the boundary; it smoothly transitions color values ​​and texture parameters within the transition zone; and it generates intermediate transition features inside the transition zone, which serve as a natural bridge between visual elements of two layers. The visual compatibility optimization module is used to analyze the difference in color tone on both sides of the boundary of visual elements within the blending transition zone, and adjust the hue value of the transition area through color harmonization calculation; at the same time, based on the texture density distribution characteristics, texture gradient technology is used to smooth the rate of change of texture density; ultimately, the visual elements of the two layers are merged in the boundary area to form a visually unified and harmonious composite field.

[0013] As a further improvement of the present invention, the hybrid transition band building module includes: The submodule for quantitative evaluation of the degree of difference is used to quantitatively evaluate the differences in visual features on both sides of the boundary based on the boundary region information provided by the feature boundary map. It obtains three core parameters through multi-dimensional differences: color saturation difference, texture density difference, and orientation consistency difference. Each parameter is normalized and converted into a standard difference coefficient, and a comprehensive difference index is obtained through weighted fusion. The transition zone parameter dynamic generation submodule is used to input the comprehensive difference index into the transition zone parameter generator to dynamically determine the width and shape of the transition zone; The Smooth Transition and Intermediate Feature Synthesis submodule is used to achieve a smooth transition of color values ​​and texture parameters within a defined transition zone using a bivariate gradient technique. It also considers the variation patterns on both sides of the boundary and within the transition zone to generate a natural and continuous parameter distribution. Based on the parameter distribution, intermediate transition features are further synthesized to ultimately form a natural visual bridge connecting the two layers.

[0014] As a further improvement of the present invention, the smooth transition and intermediate feature synthesis submodule includes: The boundary feature data extraction unit is used to extract the color value and texture parameter distribution features on both sides of the boundary based on the transition zone region; the extracted data constitutes the initial boundary conditions for gradient processing. The bivariate coupled field construction unit is used to determine the spatial coordination relationship between color gradient and texture gradient by establishing a coupling weight matrix; based on the distance between each position in the transition zone and the boundary line, the color target value and texture target value of each point are obtained to form a bivariate coupled field; The continuous parameter distribution generation unit is used to generate the final parameter distribution of the bivariate coupled field through smoothness optimization. The boundary condition verification procedure checks whether the generated parameter distribution is perfectly connected with the original boundary features. Finally, a natural and continuous parameter distribution is output, realizing a seamless transition between the two visual elements.

[0015] As a further improvement of the present invention, the bivariate coupled field construction unit includes: The spatial coordination relationship modeling subunit is used to analyze the correlation between the hue change law of color gradient and the density change law of texture gradient based on the color and texture distribution features provided by the boundary feature data extraction unit, and establish a spatial coordination relationship model. The similarity measure is used to obtain the degree of consistency between the two gradient modes in spatial distribution, generate a coordination coefficient matrix, and quantitatively describe the coordination relationship between color change and texture change at each spatial location. The dynamic weight allocation processing subunit is used to dynamically allocate the weight ratio of color and texture according to the feature salience of different regions within the transition zone; The target value coupling calculation subunit is used to couple the weight matrix with the boundary distance parameter. The basic gradient value is obtained based on the distance between each point and the boundary line, and then weighted and fused together with the weight matrix; the color target value and texture target value of each spatial point are generated, and the target values ​​constitute a complete bivariate coupled field.

[0016] As a further improvement of the present invention, it also includes an energy potential gradient network construction system for receiving a spatiotemporal energy micro-element array, calculating the potential energy difference and kinetic energy flux between adjacent energy micro-elements through an energy potential gradient operator, and establishing a dynamic coupling relationship between energy micro-elements; generating an energy potential gradient network based on the dynamic coupling relationship, wherein nodes are energy accumulation centers and edges are effective transmission paths for energy potential gradients exceeding a threshold; and identifying the main energy path and branch energy paths through adaptive clustering to form a hierarchical energy potential gradient network structure.

[0017] To achieve the above objectives, the present invention also provides the following technical solution: A method for visualizing and analyzing the spatiotemporal energy transfer of gate-flow coupled vibration, applied to the aforementioned system for visualizing and analyzing the spatiotemporal energy transfer of gate-flow coupled vibration, comprising: Based on the original vibration and water flow disturbance signals acquired by the multi-source sensor array, a spatiotemporally continuous energy density distribution field is generated through the spatial frequency field reconstruction technology. The non-uniform sampling data is transformed into a scalar field with energy fingerprint characteristics, where each spatial point is assigned a time-varying energy fingerprint code, which includes energy amplitude, phase and frequency domain characteristics. The energy fingerprint codes are aggregated into directional energy micro-elements to form a spatiotemporal energy micro-element array. The system receives a spatiotemporal energy micro-element array, calculates the potential energy difference and kinetic energy flux between adjacent energy micro-elements using the potential gradient operator, and establishes a dynamic coupling relationship between the energy micro-elements. Based on the dynamic coupling relationship, an energy potential gradient network is generated, where nodes are energy accumulation centers and edges are effective transmission paths where the potential gradient exceeds a threshold. Adaptive clustering is used to identify the main energy path and branch energy paths, forming a hierarchical energy potential gradient network structure. The potential gradient network is mapped to an affine spatiotemporal coordinate system, and a dynamic potential topology surface is generated based on the path energy flux intensity and directionality. A frequency domain-color domain coupled mapping strategy is adopted to map the energy flux intensity to hue change and the energy transfer direction to texture flow, realizing a multi-dimensional dynamic expression of the energy transfer process. Finally, an interactive potential topology map supporting multi-scale exploration is generated, drilling from the global potential structure to the local energy micro-element dynamics.

[0018] This invention achieves holographic analysis and dynamic visualization of the energy transfer process of gate-flow coupled vibration. By using spatial frequency field reconstruction technology, discrete sensor data is transformed into a continuous energy density field with complete spatiotemporal characteristics, solving the energy path breakage problem caused by traditional point measurements. An energy fingerprint encoding mechanism provides a standardized mathematical representation of the dynamic energy characteristics of each spatial point, offering structured input for subsequent network analysis. The potential gradient network construction system breaks through the traditional single-path analysis mode, identifying the dynamic coupling relationship between potential energy difference and kinetic energy flux to establish a three-dimensional transfer network including main and branch paths. An adaptive clustering algorithm automatically classifies energy accumulation centers and transfer paths, revealing preferred paths and dissipation nodes for energy conduction. The dynamic potential topology mapping system transforms the abstract potential gradient network into an interactive map with affine spatiotemporal characteristics. Through dual-channel visual encoding of hue and texture, it simultaneously expresses the intensity and direction dimensions of energy flux, supporting seamless scale switching from macroscopic potential structures to microscopic energy elements, enabling multi-parameter synchronous observation of complex energy transfer processes. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the functional modules of the spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration of the present invention. Figure 2 This is a schematic diagram of the functional modules of the energy fingerprint extraction system, which is an embodiment of the spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration of the present invention. Figure 3 This is a schematic diagram of the functional modules of the potential gradient network construction system, which is an embodiment of the spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration of the present invention. Figure 4 This is a schematic diagram of the functional modules of the dynamic potential topology mapping system, which is an embodiment of the spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration of the present invention. Figure 5 This is a schematic diagram of the steps of the spatiotemporal energy transfer visualization analysis method of the gate-water flow coupled vibration of the present invention; Figure 6 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention; Figure 7This is a schematic diagram of the structure of one embodiment of the storage medium of the present invention. Detailed Implementation

[0020] 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 a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] The terms "first," "second," and "third" used in this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this invention are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the accompanying drawings). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] like Figure 1 As shown, this embodiment provides an example of a visualization analysis system for spatiotemporal energy transfer of gate-flow coupled vibration. In this embodiment, the visualization analysis system for spatiotemporal energy transfer of gate-flow coupled vibration includes an energy fingerprint extraction system 1, an energy potential gradient network construction system 2, and a dynamic energy potential topology mapping system 3, which are connected in sequence. The energy fingerprint extraction system 1 is used to obtain raw vibration and water flow disturbance signals based on a multi-source sensor array. It generates a spatiotemporally continuous energy density distribution field through spatial-frequency field reconstruction technology, transforming non-uniform sampling data into a scalar field with energy fingerprint characteristics. Each spatial point is assigned a time-varying energy fingerprint code, including energy amplitude, phase, and frequency domain characteristics. The energy fingerprint codes are aggregated into directional energy micro-elements, forming a spatiotemporal energy micro-element array. The potential gradient network construction system 2 receives the spatiotemporal energy micro-element array, calculates the potential energy difference and kinetic energy flux between adjacent energy micro-elements using the potential gradient operator, and establishes a dynamic coupling relationship between the energy micro-elements. Based on this dynamic coupling relationship, it generates the energy potential. A gradient network is constructed, where nodes are energy accumulation centers and edges are effective transmission paths where the energy potential gradient exceeds a threshold. Adaptive clustering identifies the main energy path and branch energy paths, forming a hierarchical energy potential gradient network structure. A dynamic energy potential topology mapping system 3 maps the energy potential gradient network to an affine spatiotemporal coordinate system, generating a dynamic energy potential topology surface based on the path energy flux intensity and directionality. A frequency domain-color domain coupled mapping strategy is employed to map energy flux intensity to hue changes and energy transmission direction to texture flow, achieving a multi-dimensional dynamic expression of the energy transmission process. Finally, an interactive energy potential topology map supporting multi-scale exploration is generated, drilling down from the global energy potential structure to the local energy micro-element dynamics.

[0024] Preferably, this embodiment realizes the holographic analysis and dynamic visualization of the gate-flow coupled vibration energy transfer process; by using spatial frequency field reconstruction technology, discrete sensor data is transformed into a continuous energy density field with complete spatiotemporal characteristics, solving the problem of energy path breakage caused by traditional point measurement; the energy fingerprint encoding mechanism enables the dynamic energy characteristics of each spatial point to obtain standardized mathematical representation, providing structured input for subsequent network analysis. The potential gradient network construction system breaks through the traditional single-path analysis mode, and establishes a three-dimensional transfer network including main paths and branch paths by identifying the dynamic coupling relationship between potential energy difference and kinetic energy flux; the adaptive clustering algorithm realizes the automatic classification of energy accumulation centers and transfer paths, revealing the priority paths and dissipation nodes of energy conduction. The dynamic potential topology mapping system transforms the abstract potential gradient network into an interactive map with affine spatiotemporal characteristics; through hue-texture dual-channel visual encoding, it simultaneously expresses the intensity dimension and direction dimension of energy flux, supports seamless scale switching from macroscopic potential structure to microscopic energy micro-elements, and realizes multi-parameter synchronous observation of complex energy transfer processes.

[0025] Furthermore, such as Figure 2 As shown, the energy fingerprint extraction system 1 specifically includes: The energy fingerprint primitive generation subsystem processes the original vibration and water flow disturbance signals acquired by the multi-source sensor array using spatial frequency field reconstruction technology. This process reconstructs the non-uniformly sampled discrete signals into a continuous spatiotemporal field. Each spatial point is assigned a set of energy fingerprint primitives based on the frequency domain characteristics and energy distribution of its neighboring sampling points. The energy fingerprint primitives contain the energy amplitude spectrum characteristics, phase coherence, and frequency domain energy distribution pattern of the spatial points, forming an initial energy fingerprint scalar field. The feature fusion subsystem is used to generate a complete energy fingerprint with directional identification by encoding and fusion processing of energy fingerprint primitives; extract the phase gradient and amplitude change rate between adjacent spatial point energy fingerprint primitives to obtain the potential direction vector of energy transfer; then fuse the direction vector with the original energy fingerprint primitives to generate an enhanced energy fingerprint code containing energy intensity and direction information; the energy fingerprint code of each spatial point not only records the energy state of that point, but also includes the energy interaction features with the surrounding area; The energy micro-element aggregation and array construction subsystem is used to enhance energy fingerprint encoding by clustering and aggregating energy micro-elements. Based on spatial proximity and energy feature similarity, spatial points with coherent energy transfer directions are aggregated into energy micro-elements. Each energy micro-element is assigned overall energy attribute parameters, including the dominant energy intensity, main transfer direction, and frequency domain feature range. Finally, the energy micro-elements are organized into a structured array in spatiotemporal order.

[0026] Preferably, the spatial frequency field reconstruction technology in this embodiment ensures the transformation from non-uniform sampled data to a continuous energy field, providing a computational basis for energy fingerprint primitives; the phase gradient and amplitude change rate are derived from wave energy propagation theory and applied to the extraction of energy directional features; clustering and aggregation are specifically optimized for energy feature similarity and spatial continuity; the entire processing forms a complete link from the original signal to energy fingerprint primitives, then to enhanced energy fingerprint encoding, and finally to a spatiotemporal energy micro-element array. This achieves the transformation from raw sensing data to a structured energy micro-element array, providing basic data units with complete energy attributes and directional features for subsequent construction of energy potential gradient networks.

[0027] Furthermore, the feature fusion subsystem specifically includes: The orientation feature quantization component is used to quantize the orientation features of the phase gradient and amplitude change rate extracted from the energy fingerprint primitives of adjacent spatial points; it discretizes continuous orientation changes into standard orientation vectors while retaining the relative magnitude of the original orientation intensity; it removes abnormal orientation data through orientation consistency checks to ensure the reliability of the orientation vectors; the processed orientation vectors are transformed into standardized orientation descriptors with intensity weights and orientation confidence. An energy-direction coupled coding generation component is used to combine the standardized direction descriptor with the original energy fingerprint primitive; the energy amplitude spectrum features in the energy fingerprint primitive are coupled with the intensity weights of the direction descriptor to generate an energy-direction joint distribution matrix; the phase coherence features and direction confidence are cross-validated to enhance the credibility of the features; and a composite feature code that simultaneously contains energy characteristics and direction characteristics is formed, namely the primary energy-direction fingerprint. An enhanced energy fingerprint coding synthesis component is used to generate an enhanced energy fingerprint code by performing feature enhancement processing on the primary energy-direction fingerprint. Through neighborhood energy field consistency analysis, the weight allocation of each feature in the energy-direction fingerprint is adjusted. Adaptive feature weighting is adopted to keep the energy intensity feature and direction feature spatially consistent. The final generated enhanced energy fingerprint code not only contains the energy state description of the point, but also embeds the energy interaction mode with the surrounding area, forming a complete energy feature expression.

[0028] Preferably, the orientation consistency check in this embodiment ensures the reliability of the orientation vector, providing high-quality input for subsequent fusion; the multi-dimensional feature fusion innovatively couples energy features and orientation features in a matrix manner to form a composite code; the adaptive feature weighting strategy dynamically adjusts the feature weights according to the characteristics of the neighborhood energy field to ensure spatial consistency of the code; it realizes the transformation from basic energy features to enhanced energy fingerprint coding, providing a complete feature description that simultaneously contains energy intensity and orientation information for subsequent energy micro-element aggregation.

[0029] Furthermore, such as Figure 3 As shown, the potential gradient network construction system 2 specifically includes: A dynamic coupling relationship establishment subsystem is used to process the spatiotemporal energy micro-element array through the energy potential gradient operator. Based on the dominant energy intensity and main transmission direction recorded in the energy micro-element, the potential energy difference between adjacent energy micro-elements is calculated. At the same time, the kinetic energy flux intensity is derived based on the frequency domain characteristic range of the energy micro-elements. Two sets of key data are generated: the potential energy difference matrix and the kinetic energy flux tensor. The two sets of data are fused through the coupling coefficient to generate a dynamic coupling relationship map between energy micro-elements, where each connection relationship includes intensity weight and direction attribute. The calculation of the potential energy difference between adjacent energy micro-elements is determined based on the difference in dominant energy intensity and the angle between the main transmission directions of the two micro-elements. The potential gradient network generation subsystem is used to form an initial network structure by thresholding the dynamic coupling relationship graph. The critical value of the effective transmission path is automatically determined according to the strength distribution characteristics of the coupling relationship. Coupling relationships exceeding the threshold are retained as network edges, and the energy micro-elements connected to them are transformed into network nodes. Network nodes are assigned type identifiers according to their energy accumulation characteristics to distinguish core energy nodes and ordinary energy nodes. The attributes of the edges include flux intensity and direction information, forming a potential gradient network with complete topological characteristics. A hierarchical network structure construction subsystem is used to hierarchically organize the energy potential gradient network, identify high-density connection regions in the network, and determine candidate paths for the backbone energy pathway based on flux intensity distribution. Through path importance assessment, the energy transfer contribution of each path in the network is calculated, and the backbone energy pathway is selected. The remaining paths are divided into branch energy pathways of different levels based on their connection relationship with the backbone pathway and flux intensity. Finally, an energy potential gradient network with a clear hierarchical structure is formed, including three levels: backbone layer, branch layer, and terminal layer. The energy transfer contribution of each path in the network is calculated by combining the flux intensity of the path and the number of key nodes it connects in the network.

[0030] Preferably, this embodiment realizes the transformation from an energy micro-element array to a hierarchical energy potential gradient network, providing a structured network foundation for subsequent dynamic energy potential topology mapping.

[0031] Furthermore, the potential gradient network generation subsystem specifically includes: The coupling strength distribution feature extraction component is used to first analyze the distribution characteristics of the strength weights of all connections in the dynamic coupling relationship spectrum, identify the clustering state of the strength values, and determine the distribution characteristics of high-strength clustering areas, medium-strength dispersion areas, and low-strength sparse areas; and obtain the statistical characteristic parameters of the strength values ​​through distribution curve fitting, including the main distribution intervals, peak positions, and distribution dispersion. The high-intensity clustered region is defined as follows: the intensity weight value is located in the top 15-20% percentile of the overall distribution, showing a clear peak shape in the kernel density estimation map, and the connection density is more than 3 times the average network density; the medium-intensity dispersed region is defined as follows: the intensity value is distributed in the 25-75% percentile range, showing a gentle "plateau" shape in the distribution curve, and the connection density is comparable to the average network density; the low-intensity sparse region is identified as follows: the intensity value is distributed in the bottom 10-15% percentile, showing as discrete points with a trailing phenomenon in the distribution map, and the connection density is less than 1 / 5 of the average density; the division of the three regions is not static, but dynamically updated through a sliding window algorithm (usually a 500ms time window); The adaptive critical value calculation component is used to determine the upper critical value based on the intensity distribution characteristic parameters and the lower limit of the high-intensity clustered area to screen significant energy transfer paths; and to determine the lower critical value based on the distribution characteristics of the medium-intensity dispersed area to retain potential energy interaction channels; a dynamic threshold interval is formed between the upper and lower critical values ​​to ensure that effective connections of different intensities can be reasonably screened. The network topology constraint optimization component is used to further optimize and adjust the calculated critical values ​​based on network topology characteristics; it analyzes the overall density and node degree distribution of network connections and fine-tunes the critical values ​​according to network connectivity requirements.

[0032] Preferably, in this embodiment, the intensity distribution characteristics of the coupling relationship graph are extracted to generate statistical parameters. The statistical parameters are then calculated using an adaptive algorithm to obtain an initial critical value. The initial critical value is then optimized through network topology to form a final critical value.

[0033] Furthermore, the adaptive critical value calculation component specifically includes: The high-intensity clustering area boundary identification sub-component is used to calculate the boundary of the high-intensity area by coupling the statistical feature parameters obtained by the intensity distribution feature extraction component. Based on the main distribution interval and peak position data, the lower boundary of the high-intensity clustering area is determined by the distribution canyon identification. By analyzing the change of the second derivative of the distribution curve, the transition point from the high-intensity area to the medium-intensity area is located, and this transition point is used as the initial candidate value of the upper critical value. The sub-component for analyzing the characteristics of medium-intensity dispersion regions processes the distribution characteristics of these regions through dispersion quantification. It calculates the stability index of the medium-intensity region based on the distribution dispersion parameter and determines the representative intensity range of the region by combining the peak position. Through dynamic dispersion interval partitioning technology, the medium-intensity region is divided into stable and fluctuating sub-regions, with the lower limit of the stable sub-region selected as a benchmark reference for the lower critical value. The stability index of the medium-intensity region, calculated based on the distribution dispersion parameter, quantifies the fluctuation characteristics and concentration of the region's intensity values ​​by analyzing the degree of deviation of data points from the central trend. A dynamic threshold interval generation sub-component is used to integrate the upper critical value candidate and the lower critical value reference through interval coordination; calculate the relative distance between the two critical values ​​and adjust the critical value spacing according to the overall characteristics of the intensity distribution; ensure that the upper critical value can cover significant energy transfer paths, while the lower critical value can retain potentially meaningful energy interaction channels; and finally generate an adaptive dynamic threshold interval to achieve effective screening of connections with different intensities.

[0034] Preferably, in this embodiment, the distribution characteristics of the high-intensity clustering area are analyzed to generate candidate upper critical values, and the feature analysis of the medium-intensity dispersion area generates a lower critical value reference. The two critical values ​​are integrated through an interval coordination algorithm to form the final dynamic threshold interval. The entire process is based on the statistical feature parameters obtained from the previous processing, and gradually derives a scientific and reasonable critical value setting to ensure that the selection of effective transmission paths is both strict and inclusive.

[0035] Furthermore, the dynamic threshold range generation sub-component specifically includes: The relative distance quantization calculation module uses a normalized distance metric to convert the absolute difference between the upper critical value candidate and the lower critical value reference into a percentage distance relative to the overall intensity distribution range; at the same time, it calculates the position ratio of the two critical values ​​on the distribution curve to obtain the distance ratio coefficient. The distribution morphology analysis module is used to perform morphological analysis based on the overall characteristics of the intensity distribution, analyze the width ratio between high-intensity and medium-intensity regions to obtain the distribution symmetry index; and derive the central tendency index and dispersion index of the intensity distribution by combining the peak position and dispersion parameters. The adaptive spacing adjustment module is used to adjust the weight allocation of relative distance parameters and morphological feature parameters according to the distribution symmetry index, and then determine the basic scale of the spacing by combining the central tendency index; fine-tuning is performed according to the dispersion index, and the adjusted critical value spacing finally forms an adaptive dynamic threshold range.

[0036] Preferably, in this embodiment, relative distance calculation generates quantitative parameters, and distribution morphology analysis provides characteristic parameters. The two types of parameters are processed together by a spacing coordination algorithm to generate the final adjustment result. The entire process is based on the candidate critical values ​​and benchmark references obtained from the previous processing, combined with the overall characteristics of the intensity distribution, to gradually derive a scientific and reasonable critical value spacing setting.

[0037] Furthermore, such as Figure 4 As shown, the dynamic potential topology mapping system 3 specifically includes: The frequency domain-hue feature mapping subsystem is used to analyze the spectral distribution characteristics of path energy flux intensity. It employs frequency banding to divide continuous energy intensity values ​​into different frequency domain intervals. Each frequency domain interval corresponds to a specific hue coding reference value, converting the energy flux intensity value into a hue value. High-intensity energy flux is mapped to a high-frequency hue region, and low-intensity energy flux is mapped to a low-frequency hue region, forming a hue distribution map based on energy intensity. The process of converting energy flux intensity values ​​into hue values ​​is based on frequency banding technology, dividing the continuous energy intensity spectrum into several intervals, each interval corresponding to a specific reference hue value. Then, a nonlinear mapping function is used to convert the specific intensity values ​​into precise hue values, achieving a quantitative conversion from energy intensity to color space. The direction-texture flow direction synthesis subsystem decomposes the directional angle information in the potential gradient network into two components: flow direction vector and flow velocity intensity. The flow direction vector is transformed into directional texture primitives through texture field generation technology. Each texture primitive contains directional and flow characteristics. The flow velocity intensity component controls the density and sharpness of the texture primitives. High-intensity flow velocity regions generate dense and sharp texture patterns, while low-intensity regions generate sparse and blurry texture patterns, ultimately synthesizing a dynamic texture field with directional characteristics. The process of transforming the flow direction vector into directional texture primitives through texture field generation technology first encodes the directional information of the vector field into the principal direction angle of the texture primitive, and then adjusts the morphological characteristics of the primitive according to the vector magnitude, ultimately generating basic texture units with directional and flow visual characteristics. The multidimensional dynamic expression fusion subsystem is used to establish the correspondence between hue values ​​and texture parameters in the hue distribution map and dynamic texture field. Based on the position information in the spatiotemporal coordinate system, the hue distribution and texture flow direction are spatially registered to form a unified color-texture composite field. Finally, the spatiotemporal continuous energy transfer process is visualized through dynamic rendering technology, supporting multi-scale exploration from macroscopic color distribution observation to microscopic texture flow direction analysis.

[0038] Preferably, in this embodiment, the energy flux intensity data of the potential gradient network is processed to generate a hue distribution map, and the directional attribute data is processed to generate a dynamic texture field. The two visualization elements are integrated through a multi-dimensional fusion algorithm to form the final multi-dimensional dynamic expression. The entire process is based on the potential gradient network structure obtained by the preceding processing, and is gradually converted into visual elements to realize a multi-dimensional visualization expression of the energy transfer process.

[0039] Furthermore, the multidimensional dynamic expression fusion subsystem specifically includes: The visual channel coupling relationship establishment component is used to first establish a visual correspondence between hue values ​​in the hue distribution map and texture parameters in the dynamic texture field, and associate and match the wavelength characteristics of hue values ​​with the directional features of texture primitives; by establishing a hue-texture mapping table, hue values ​​within a specific range correspond to specific types of texture patterns; The spatiotemporal coordinate alignment processing component is used to spatially register hue distribution and texture flow based on the position information of the spatiotemporal coordinate system; it uses spatiotemporal grid alignment to accurately match the pixel coordinates of the hue distribution map with the vector coordinates of the dynamic texture field; and it eliminates the spatial deviation between the two through coordinate transformation to ensure that the color attributes and texture attributes of each spatial point are completely consistent, forming a visual element combination with unified position. The composite field dynamic fusion generation component is used to generate a color-texture composite field from registered visual elements through field fusion technology. The hue distribution is used as the base layer and the texture flow is used as the feature layer. Through transparency adjustment and edge blending processing, the two visual elements are naturally combined. The final generated composite field retains both the color expression of energy intensity and the texture expression of energy direction, forming a complete energy transfer visualization carrier.

[0040] Preferably, in this embodiment, the hue distribution map and dynamic texture field establish a corresponding relationship through visual channel coupling, achieve spatial registration through spatiotemporal coordinate alignment, and finally generate a unified color-texture composite field through field fusion technology. The entire process is based on the hue distribution and texture field data obtained from the previous processing, and gradually realizes the organic integration of the two visualization elements to form the final multi-dimensional dynamic expression effect.

[0041] Furthermore, the composite field dynamic fusion generation component specifically includes: The layer preprocessing and feature enhancement sub-components are used to first optimize the color saturation of the registered hue distribution layer to enhance the visual distinction of energy intensity expression; the texture flow layer is enhanced to strengthen directional consistency and eliminate local inconsistencies through flow smoothing; both layers are subjected to resolution normalization to ensure complete spatial scale matching, laying the foundation for subsequent fusion operations; The dynamic transparency adjustment sub-component is used to generate transparency control parameters based on energy flux intensity data. High-intensity energy areas correspond to lower texture layer transparency, making texture features clearly visible; low-intensity energy areas correspond to higher texture layer transparency, ensuring that the background color information is not completely covered; the transparency adjustment uses a non-linear response curve to make the visual transition natural and smooth. The Edge Blending and Visual Coordination sub-component processes the edge areas of two layers using gradient blending techniques; it employs adaptive edge detection to identify the characteristic boundaries of color and texture, creating a blending transition zone in the boundary area; and it adjusts the color tone and texture density of the transition area through color compatibility, allowing the two visual elements to blend naturally at the intersection; ultimately forming a composite visualization field that maintains its own characteristics while achieving harmony and unity.

[0042] Preferably, in this embodiment, the two preprocessed layers are initially merged through dynamic transparency adjustment, and then the final visual coordination is achieved through edge blending. The entire process is based on the registration data obtained from the previous processing, and gradually realizes the transformation from separate layers to merged visualization, ensuring the complete expression of energy intensity and direction information.

[0043] Furthermore, the edge blending and visual coordination sub-component specifically includes: The intelligent feature boundary recognition module is used to identify potential boundary regions based on the visual feature differences between the preprocessed hue distribution layer and texture flow layer using gradient field analysis technology; it locates transition regions with significant feature differences by using the spatial gradient changes of the two layers in color saturation and texture density; and it integrates the boundary information detected at different scales into a complete feature boundary map to accurately identify the fusion boundary regions that require special processing. The blend transition zone construction module is used to create blend transition zones in the identified feature boundary areas. It dynamically determines the width and shape of the transition zone based on the degree of visual feature difference on both sides of the boundary. It smoothly transitions color values ​​and texture parameters within the transition zone to ensure the continuity of visual changes. It generates intermediate transition features inside the transition zone, which serve as a natural bridge between visual elements of two layers. The visual compatibility optimization module is used to analyze the difference in color tone on both sides of the boundary of visual elements within the blending transition zone. It adjusts the hue value of the transition area through color harmonization calculation to ensure a natural and harmonious color transition. At the same time, based on the texture density distribution characteristics, it uses texture gradient technology to smooth the rate of change of texture density. Ultimately, it enables the visual elements of the two layers to merge in the boundary area, forming a visually unified and coordinated composite field. The visual compatibility optimization process is achieved through three stages: First, within the transition zone, the color features on both sides of the boundary are quantitatively analyzed. Then, using a hue ring mapping technique, the hue values ​​on both sides of the boundary are converted into angular coordinates to obtain the minimum hue angle difference between them. Based on this difference, a harmonic coefficient algorithm is used to determine the color harmonic intensity parameter, which is positively correlated with the hue difference. Simultaneously, the harmonic gradient step size is calculated based on the transition zone width to establish a baseline parameter system for color harmonics. Based on the harmonic baseline parameters, a transition hue sequence is generated using a hue interpolation algorithm. This algorithm employs a nonlinear interpolation method to find the shortest harmonic path on the hue ring, generating a series of intermediate hue values ​​along this path. Each intermediate hue value is assigned a corresponding weight coefficient based on its position within the transition zone, ensuring that the hue change conforms to the uniformity of visual perception. The generated hue sequence constitutes the basis for color harmonics in the transition region. Texture density distribution is optimized using density gradient technology. First, the texture density difference rate on both sides of the boundary is analyzed to obtain a reasonable gradient for density change. A density distribution curve is generated based on the texture density difference rate and the transition zone width. This curve ensures that the transition of texture density from one side to the other maintains both continuity and visual comfort, ultimately achieving a natural gradient effect in texture density.

[0044] Preferably, in this embodiment, feature boundary recognition generates a boundary map, which drives the construction of a hybrid transition zone. The transition zone achieves the final fusion effect through visual compatibility optimization. The entire process is based on two standardized layers obtained through processing, gradually realizing a complete processing flow from boundary recognition to visual coordination, ensuring that the fusion effect maintains the visual characteristics of each layer while achieving a natural transition.

[0045] Furthermore, the hybrid transition band building block specifically includes: The submodule for quantitative evaluation of the degree of difference is used to quantitatively evaluate the differences in visual features on both sides of the boundary based on the boundary region information provided by the feature boundary map. It obtains three core parameters through multi-dimensional differences: color saturation difference, texture density difference, and orientation consistency difference. Each parameter is normalized and converted into a standard difference coefficient. The comprehensive difference index is obtained through weighted fusion, which reflects the overall degree of difference in visual features on both sides of the boundary. The transition zone parameter dynamic generation submodule is used to input the comprehensive difference index into the transition zone parameter generator to dynamically determine the width and shape of the transition zone. The width is determined by a non-linear response program. The greater the difference, the wider the transition zone, providing sufficient space for a smooth transition. The shape is determined by a curvature optimization program, which automatically adjusts the curvature of the transition zone according to the complexity of the boundary line to ensure that it can perfectly fit the original boundary features. The Smooth Transition and Intermediate Feature Synthesis submodule is used to achieve a smooth transition of color values ​​and texture parameters within a defined transition zone using a bivariate gradient technique. It also considers the variation patterns on both sides of the boundary and within the transition zone to generate a natural and continuous parameter distribution. Based on the parameter distribution, intermediate transition features are further synthesized to ultimately form a natural visual bridge connecting the two layers.

[0046] Preferably, in this embodiment, the feature boundary map provides boundary information to drive the assessment of the degree of difference. The assessment result generates transition band parameters, which guide the smooth transition processing and finally synthesize intermediate transition features. The entire process is based on the boundary information obtained through processing, gradually realizing a complete workflow from difference quantification to transition band construction, ensuring the natural connection and continuity between visual elements.

[0047] Furthermore, the smooth transition and intermediate feature synthesis submodule specifically includes: The boundary feature data extraction unit is used to extract the color value and texture parameter distribution features on both sides of the boundary based on the determined transition zone area. Color value extraction uses hue sampling to obtain the hue distribution pattern within a specific range on both sides of the boundary line. Texture parameter extraction obtains the distribution pattern of texture density and flow direction features through directional consistency analysis. The extracted data constitutes the initial boundary conditions for gradient processing. The bivariate coupled field construction unit is used to determine the spatial coordination relationship between color gradient and texture gradient by establishing a coupling weight matrix; based on the distance between each position in the transition zone and the boundary line, the color target value and texture target value of each point are obtained to form a bivariate coupled field; The continuous parameter distribution generation unit is used to generate the final parameter distribution of the bivariate coupled field through smoothness optimization. The boundary condition verification procedure checks whether the generated parameter distribution is perfectly connected with the original boundary features. Finally, a natural and continuous parameter distribution is output, realizing a seamless transition between the two visual elements.

[0048] Preferably, in this embodiment, boundary feature data extraction provides input conditions for gradient processing. This data is processed by a bivariate coupling algorithm to form an initial coupling field, which is then optimized for smoothness to generate the final parameter distribution. The entire process is based on the determined transition zone region and boundary features, gradually realizing the complete process from feature extraction to parameter distribution generation, ensuring the naturalness and continuity of the visual transition.

[0049] Furthermore, the bivariate coupled field building unit specifically includes: The spatial coordination relationship modeling subunit is used to establish a spatial coordination relationship model based on the color and texture distribution features provided by the boundary feature data extraction unit. It employs multi-dimensional feature mapping technology to correlate the hue change law of color gradient with the density change law of texture gradient. The similarity measure is used to obtain the degree of consistency between the two gradient patterns in spatial distribution, and a coordination coefficient matrix is ​​generated to quantitatively describe the coordination relationship between color change and texture change at each spatial location. The dynamic weight allocation processing subunit is used to dynamically allocate the weight ratio of color and texture according to the feature salience of different regions within the transition zone; higher texture weight is assigned to regions with drastic feature changes, and higher color weight is assigned to regions with obvious color contrast, ensuring that the two gradient processes remain optimized and coordinated in space. The target value coupling calculation subunit is used to couple the weight matrix with the boundary distance parameter. Based on the distance between each point and the boundary line, the basic gradient value is obtained, and then weighted and fused with the weight matrix. This generates the color target value and texture target value for each spatial point, and the target values ​​constitute a complete bivariate coupled field. First, the basic gradient value is calculated based on the relative distance between each spatial point and the boundary line. This value reflects the color and texture parameters of that point under ideal conditions. Then, the basic gradient value is weighted and fused with the corresponding color weight and texture weight in the coupling weight matrix. The color weight adjusts the transition intensity of the hue value, and the texture weight controls the significance of density changes. Finally, the weighted output yields the precise color target value and texture target value for each spatial point. These values ​​together constitute a bivariate coupled field reflecting the coordinated change law of the two variables.

[0050] Preferably, in this embodiment, the boundary feature data is modeled through spatial coordination relationships to generate a coordination coefficient matrix. This matrix drives dynamic weight allocation to produce a coupling weight matrix. The weight matrix and distance parameters are then interpolated using a bivariate interpolation algorithm to finally generate the target value coupling field. The entire process is based on the extracted boundary feature data, gradually realizing a complete workflow from feature analysis to field construction, ensuring the coordination and unity of color and texture gradations.

[0051] like Figure 5 As shown, this embodiment also provides an embodiment of a visualization analysis method for spatiotemporal energy transfer of gate-flow coupled vibration. In this embodiment, the visualization analysis method for spatiotemporal energy transfer of gate-flow coupled vibration is applied to the visualization analysis system for spatiotemporal energy transfer of gate-flow coupled vibration as described in the above embodiment. The visualization analysis method for spatiotemporal energy transfer of gate-flow coupled vibration specifically includes the following steps: Step S1: Based on the original vibration and water flow disturbance signals acquired by the multi-source sensor array, a spatiotemporally continuous energy density distribution field is generated through the spatial frequency field reconstruction technology. The non-uniform sampling data is transformed into a scalar field with energy fingerprint characteristics, where each spatial point is assigned a time-varying energy fingerprint code, including energy amplitude, phase and frequency domain characteristics. The energy fingerprint codes are aggregated into directional energy micro-elements to form a spatiotemporal energy micro-element array. Step S2: Receive the spatiotemporal energy micro-element array, calculate the potential energy difference and kinetic energy flux between adjacent energy micro-elements using the potential gradient operator, and establish a dynamic coupling relationship between energy micro-elements; generate an energy potential gradient network based on the dynamic coupling relationship, where nodes are energy accumulation centers and edges are effective transmission paths where the potential gradient exceeds a threshold; identify the main energy path and branch energy paths through adaptive clustering to form a hierarchical energy potential gradient network structure. Step S3: Map the potential gradient network to an affine spatiotemporal coordinate system, and generate a dynamic potential topology surface based on the path energy flux intensity and directionality; adopt a frequency domain-color domain coupled mapping strategy to map the energy flux intensity to hue change and the energy transfer direction to texture flow direction, realizing a multi-dimensional dynamic expression of the energy transfer process; finally, generate an interactive potential topology map that supports multi-scale exploration, drilling from the global potential structure to the local energy micro-element dynamics.

[0052] Preferably, this embodiment achieves the mathematical transformation from discrete multi-source sensing data to a spatiotemporally continuous scalar field through spatial-frequency field reconstruction technology and energy fingerprinting mechanism. This solves the problem of insufficient spatial resolution caused by sparse sampling points in traditional analysis methods, providing a foundation for a global energy state description for subsequent network construction. The energy potential gradient operator and adaptive clustering algorithm work together to transform energy micro-elements in the scalar field into a conduction network with a topological structure, breaking through the limitations of traditional single-path tracing methods and simultaneously identifying primary / secondary energy conduction paths and their dynamic coupling relationships. Affine spatiotemporal mapping and frequency-domain-spatial coupling strategies achieve synchronous visualization encoding of energy flux intensity and directionality, establishing a transformation channel from abstract network data to interactive maps, making multi-scale observation of complex energy transfer processes technically feasible. For the first time, a computable modeling and structured representation of the entire spatiotemporal energy transfer process in gate-flow coupled vibration is realized.

[0053] like Figure 6 As shown, this embodiment provides an embodiment of an electronic device 8, which includes a processor 41 and a memory 42 coupled to the processor 41.

[0054] The memory 42 stores program instructions for implementing the spatiotemporal energy transfer visualization analysis method for gate-flow coupled vibration of any of the above embodiments.

[0055] The processor 41 is used to execute program instructions stored in the memory 42 to lay out the chemical pump body processing equipment.

[0056] The processor 41 can also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip with signal processing capabilities. The processor 41 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0057] Furthermore, Figure 7This is a schematic diagram of the structure of a storage medium according to an embodiment of this application. The storage medium 5 of this embodiment stores program instructions 51 capable of implementing all the methods described above. These program instructions 51 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0058] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0059] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

[0060] The specific embodiments of the invention have been described in detail above, but these are merely examples, and the invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this invention. Therefore, all equivalent transformations, modifications, and improvements made without departing from the spirit and principles of this invention should be included within the scope of this invention.

Claims

1. A visualization analysis system for spatiotemporal energy transfer of gate-flow coupled vibration, characterized in that, The spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration includes: The dynamic potential topology mapping system 3 is used to map the potential gradient network to an affine spatiotemporal coordinate system and generate a dynamic potential topology surface based on the path energy flux intensity and directionality. It adopts a frequency domain-color domain coupled mapping strategy to map the energy flux intensity to hue change and the energy transfer direction to texture flow direction, realizing a multi-dimensional dynamic expression of the energy transfer process. Finally, it generates an interactive potential topology map that supports multi-scale exploration, drilling from the global potential structure to the local energy micro-element dynamics.

2. The spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration according to claim 1, characterized in that, Dynamic potential topology mapping system, including: The frequency domain-hue feature mapping subsystem is used to divide the continuous energy intensity values ​​into different frequency domain intervals based on the spectral distribution characteristics of the path energy flux intensity. Each frequency domain interval corresponds to a specific hue coding reference value, converting the energy flux intensity value into a hue value, thus forming a hue distribution map based on energy intensity. The direction-texture flow direction synthesis subsystem is used to decompose the direction angle information in the potential gradient network into two components: flow direction vector and flow velocity intensity. The flow direction vector is transformed into directional texture primitives through texture field generation technology. Each texture primitive contains directional and flow characteristics. The flow velocity intensity component controls the density and sharpness of the texture primitives, synthesizing a dynamic texture field with directional characteristics. The multidimensional dynamic expression fusion subsystem is used to establish the correspondence between hue values ​​and texture parameters between the hue distribution map and the dynamic texture field. Based on the position information in the spatiotemporal coordinate system, the hue distribution and texture flow direction are spatially registered to form a unified color-texture composite field. Finally, the spatiotemporal continuous energy transfer process is visualized through dynamic rendering technology.

3. The spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration according to claim 2, characterized in that, The multidimensional dynamic expression fusion subsystem includes: The visual channel coupling relationship establishment component is used to first establish a visual correspondence between hue values ​​in the hue distribution map and texture parameters in the dynamic texture field, and associate and match the wavelength characteristics of hue values ​​with the directional features of texture primitives; by establishing a hue-texture mapping table, the hue values ​​correspond to the texture patterns. The spatiotemporal coordinate alignment processing component is used to spatially register hue distribution and texture flow based on the position information of the spatiotemporal coordinate system; it uses spatiotemporal grid alignment to accurately match the pixel coordinates of the hue distribution map with the vector coordinates of the dynamic texture field; and it eliminates the spatial deviation between the two through coordinate transformation, so that the color attribute and texture attribute of each spatial point correspond completely, forming a visual element combination with unified position. The composite field dynamic fusion generation component is used to generate a color-texture composite field from registered visual elements through field fusion technology. The hue distribution is used as the base layer and the texture flow is used as the feature layer. Through transparency adjustment and edge blending processing, the two visual elements are naturally combined. The final generated composite field retains both the color expression of energy intensity and the texture expression of energy direction, forming a complete energy transfer visualization carrier.

4. The spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration according to claim 3, characterized in that, The composite field dynamic fusion generation component includes: The layer preprocessing and feature enhancement sub-component is used to first optimize the color saturation of the registered hue distribution layer to enhance the visual distinction of energy intensity expression; the texture flow layer is enhanced for directional consistency; and both layers are subjected to resolution normalization. The dynamic transparency adjustment subcomponent is used to generate transparency control parameters based on energy flux intensity data, with high-intensity energy areas corresponding to lower texture layer transparency; The Edge Blending and Visual Coordination sub-component is used to process the edge areas of two layers using gradient blending technology; adaptive edge detection is used to identify the feature boundaries of color and texture, creating a blending transition zone in the boundary area; color compatibility is used to adjust the color tone and texture density of the transition area, so that the two visual elements blend naturally at the junction; ultimately forming a composite visualization field.

5. The spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration according to claim 4, characterized in that, Edge blending and visual coordination sub-components include: The intelligent feature boundary recognition module is used to identify potential boundary regions based on the visual feature differences between the preprocessed hue distribution layer and texture flow layer using gradient field analysis technology; it locates transition regions with significant feature differences by using the spatial gradient changes of the two layers in color saturation and texture density; and it integrates the boundary information detected at different scales into a complete feature boundary map to identify the fusion boundary regions that require special processing. The blend transition zone construction module is used to create blend transition zones in the identified feature boundary areas. It dynamically determines the width and shape of the transition zone based on the degree of visual feature difference on both sides of the boundary; it smoothly transitions color values ​​and texture parameters within the transition zone; and it generates intermediate transition features inside the transition zone, which serve as a natural bridge between visual elements of two layers. The visual compatibility optimization module is used to analyze the difference in color tone on both sides of the boundary of visual elements within the blending transition zone, and adjust the hue value of the transition area through color harmonization calculation; at the same time, based on the texture density distribution characteristics, texture gradient technology is used to smooth the rate of change of texture density; ultimately, the visual elements of the two layers are merged in the boundary area to form a visually unified and harmonious composite field.

6. The spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration according to claim 5, characterized in that, Hybrid transition band building blocks include: The submodule for quantitative evaluation of the degree of difference is used to quantitatively evaluate the differences in visual features on both sides of the boundary based on the boundary region information provided by the feature boundary map. It obtains three core parameters through multi-dimensional differences: color saturation difference, texture density difference, and orientation consistency difference. Each parameter is normalized and converted into a standard difference coefficient, and a comprehensive difference index is obtained through weighted fusion. The transition zone parameter dynamic generation submodule is used to input the comprehensive difference index into the transition zone parameter generator to dynamically determine the width and shape of the transition zone; The Smooth Transition and Intermediate Feature Synthesis submodule is used to achieve a smooth transition of color values ​​and texture parameters within a defined transition zone using a bivariate gradient technique. It also considers the variation patterns on both sides of the boundary and within the transition zone to generate a natural and continuous parameter distribution. Based on the parameter distribution, intermediate transition features are further synthesized to ultimately form a natural visual bridge connecting the two layers.

7. The spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration according to claim 6, characterized in that, The smooth transition and intermediate feature synthesis submodule includes: The boundary feature data extraction unit is used to extract the color value and texture parameter distribution features on both sides of the boundary based on the transition zone region; the extracted data constitutes the initial boundary conditions for gradient processing. The bivariate coupled field construction unit is used to determine the spatial coordination relationship between color gradient and texture gradient by establishing a coupling weight matrix; based on the distance between each position in the transition zone and the boundary line, the color target value and texture target value of each point are obtained to form a bivariate coupled field; The continuous parameter distribution generation unit is used to generate the final parameter distribution of the bivariate coupled field through smoothness optimization. The boundary condition verification procedure checks whether the generated parameter distribution is perfectly connected with the original boundary features. Finally, a natural and continuous parameter distribution is output, realizing a seamless transition between the two visual elements.

8. The spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration according to claim 7, characterized in that, The bivariate coupled field building block includes: The spatial coordination relationship modeling subunit is used to analyze the correlation between the hue change law of color gradient and the density change law of texture gradient based on the color and texture distribution features provided by the boundary feature data extraction unit, and establish a spatial coordination relationship model. The similarity measure is used to obtain the degree of consistency between the two gradient modes in spatial distribution, generate a coordination coefficient matrix, and quantitatively describe the coordination relationship between color change and texture change at each spatial location. The dynamic weight allocation processing subunit is used to dynamically allocate the weight ratio of color and texture according to the feature salience of different regions within the transition zone; The target value coupling calculation subunit is used to couple the weight matrix with the boundary distance parameter. The basic gradient value is obtained based on the distance between each point and the boundary line, and then weighted and fused together with the weight matrix; the color target value and texture target value of each spatial point are generated, and the target values ​​constitute a complete bivariate coupled field.

9. The spatiotemporal energy transfer visualization analysis system for gate-flow coupled vibration according to claim 1, characterized in that, It also includes an energy potential gradient network construction system for receiving spatiotemporal energy micro-element arrays, calculating the potential energy difference and kinetic energy flux between adjacent energy micro-elements through the energy potential gradient operator, and establishing the dynamic coupling relationship between energy micro-elements; An energy potential gradient network is generated based on dynamic coupling relationships, where nodes are energy accumulation centers and edges are effective transmission paths for energy potential gradients exceeding thresholds. Adaptive clustering is used to identify the main energy path and branch energy paths, forming a hierarchical energy potential gradient network structure.

10. A method for visualizing and analyzing the spatiotemporal energy transfer of gate-flow coupled vibration, applied to the visualization and analysis system for the spatiotemporal energy transfer of gate-flow coupled vibration as described in any one of claims 1 to 9, characterized in that, The method for visualizing and analyzing the spatiotemporal energy transfer of gate-flow coupled vibration includes: Based on the original vibration and water flow disturbance signals acquired by the multi-source sensor array, a spatiotemporally continuous energy density distribution field is generated through the spatial frequency field reconstruction technology. The non-uniform sampling data is transformed into a scalar field with energy fingerprint characteristics, where each spatial point is assigned a time-varying energy fingerprint code, which includes energy amplitude, phase and frequency domain characteristics. The energy fingerprint codes are aggregated into directional energy micro-elements to form a spatiotemporal energy micro-element array. The system receives a spatiotemporal energy micro-element array, calculates the potential energy difference and kinetic energy flux between adjacent energy micro-elements using the potential gradient operator, and establishes a dynamic coupling relationship between the energy micro-elements. Based on the dynamic coupling relationship, an energy potential gradient network is generated, where nodes are energy accumulation centers and edges are effective transmission paths where the potential gradient exceeds a threshold. Adaptive clustering is used to identify the main energy path and branch energy paths, forming a hierarchical energy potential gradient network structure. The potential gradient network is mapped to an affine spatiotemporal coordinate system, and a dynamic potential topology surface is generated based on the path energy flux intensity and directionality. A frequency domain-color domain coupled mapping strategy is adopted to map the energy flux intensity to hue change and the energy transfer direction to texture flow, realizing a multi-dimensional dynamic expression of the energy transfer process. Finally, an interactive potential topology map supporting multi-scale exploration is generated, drilling from the global potential structure to the local energy micro-element dynamics.

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