Real-time data processing method and system of simulation system host

Abstract

The invention provides a real-time data processing method and system of a simulation system host. The real-time data processing method of the simulation system host comprises the following steps of 1, obtaining electric digital data and image data of the simulation system host; according to the method, a thread CPU, memory occupation and queue length are monitored in real time through a dynamic load balancing algorithm, processing tasks of image data and electric digital data are distributed according to loads, a traditional sequential processing mode is broken through, the data processing speed is greatly increased, and the real-time requirement of a simulation system is met; according to the method, data are aligned by using a time stamp as a reference through a time-space correlation analysis algorithm, a time-space correlation matrix is constructed, and features are fused, so that correlation analysis and cooperative processing of multi-modal data are realized, and the data processing efficiency and accuracy are improved; parameters such as the size of a median filtering window are optimized by applying a genetic algorithm based on an evaluation result, tasks are dynamically allocated in combination with data classification, resources are flexibly allocated, resource waste and insufficiency are avoided, and the overall performance of a simulation system is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a real-time data processing method and system for a simulation system host. Background Technology

[0002] In modern simulation systems, the host computer needs to process massive amounts of information in real time, including electrical digital data such as voltage and current collected by sensors, as well as image data from 3D scene rendering and object dynamic simulation. With the increasing complexity of simulation scenarios in fields such as aerospace and industrial manufacturing, the volume of high-frequency data and high-precision image rendering data collected simultaneously from multiple sensors is growing exponentially. This requires the host computer not only to have high-speed data acquisition and real-time transmission capabilities, but also to achieve comprehensive upgrades in CPU computing performance, GPU graphics processing efficiency, and memory bandwidth to meet the stringent requirements of microsecond-level data processing latency and terabyte-level data throughput, ensuring the real-time performance and accuracy of the simulation system. However, in existing simulation systems, the host computer still has certain problems in real-time data processing: I. Traditional data processing methods often adopt a sequential processing mode, which is slow for large amounts of real-time data and makes it difficult to meet the real-time requirements of simulation systems. Second, when processing image data, only simple basic operations such as filtering and transformation are usually performed, lacking correlation analysis and collaborative processing between image data and other types of data, resulting in low efficiency and accuracy of data processing; Third, existing data processing methods are not flexible enough in allocating data processing resources and cannot be dynamically adjusted according to real-time changes in data and processing needs. This can easily lead to resource waste or shortage, which seriously affects the overall performance of the simulation system. Summary of the Invention

[0003] The purpose of this invention is to provide a real-time data processing method and system for a simulation system host, so as to solve one of the problems mentioned in the background art.

[0004] To solve the above-mentioned technical problems, this application adopts the following technical solution: a real-time data processing method for a simulation system host, comprising the following steps: Step 1: Acquire the electrical digital data and image data of the simulation system host; Step 2: Perform median filtering on the acquired digital data, and perform grayscale conversion and bilateral filtering on the acquired image data in sequence. Step 3: Extract features from the processed image data based on convolutional neural networks to extract spatial structure features; extract features from the processed electrical digital data based on long short-term memory networks to extract temporal dynamic features. Step 4: Based on the extracted spatial structure features and temporal dynamic features, classify the data using the support vector machine algorithm and generate the data classification results; Step 5: Based on the data classification results, a dynamic load balancing algorithm is used to allocate the processing tasks of image data and digital data to parallel processing threads for parallel processing; Step 6: Merge the parallel processed image data and electronic digital data. Using the data acquisition timestamp as a reference, employ a spatiotemporal correlation analysis algorithm to uncover the potential connections between the image data and electronic digital data, and obtain the fused processed data. Step 7: Feed the fused data back to the simulation system, collect the processing evaluation results, and optimize and adjust the operating parameters in the data processing process based on the evaluation results using a genetic algorithm.

[0005] As a further preferred embodiment of this technical solution: In step three, when extracting features from the processed electrical digital data based on the long short-term memory network, the temporal dependencies of the data are captured by a bidirectional gated loop unit, and temporal dynamic features including temperature change trends, abnormal pressure peaks, and sudden velocity changes are extracted. These temporal dynamic features are used to support real-time fault warning and dynamic decision-making in the simulation system.

[0006] As a further preferred embodiment of this technical solution: In step six, the step of using a spatiotemporal correlation analysis algorithm to mine the potential relationship between image data and electronic digital data specifically includes the following steps: Step 601: Align the physical quantity parameters in the electrical digital data with the visual features in the image data using timestamps; Step 602: Construct a spatiotemporal correlation matrix and calculate the correlation coefficients between image data and digital data in the time and spatial dimensions; Step 603: Perform feature fusion based on correlation coefficients to generate spatiotemporally consistent multimodal feature vectors.

[0007] As a further preferred embodiment of this technical solution: In step seven, the optimization and adjustment of the operating parameters during data processing using a genetic algorithm specifically includes the following steps: Step 701: Construct a population for optimizing operating parameters, wherein the operating parameters include the median filter window size, bilateral filter weight coefficients, convolution kernel parameters of the convolutional neural network, and penalty factor and kernel function parameters of the support vector machine; Step 702: Design a fitness function, which is constructed based on the processing latency, resource utilization, and decision accuracy of the simulation system; Step 703: Iteratively optimize the population of running parameters through selection, crossover, and mutation operations until the preset convergence condition is met, and obtain the optimal parameter combination.

[0008] As a further preferred embodiment of this technical solution: in step two, the size of the sliding window of the median filter is dynamically adjusted according to the sampling frequency and noise intensity of the digital data.

[0009] As a further preferred embodiment of this technical solution: In step four, the support vector machine algorithm uses a radial basis kernel function, and determines the kernel function parameters and penalty factor through cross-validation; the classification results include key decision data, important reference data, and routine record data.

[0010] As a further preferred embodiment of this technical solution: In step five, when the image data and digital data processing tasks are assigned to parallel processing threads using a dynamic load balancing algorithm, the CPU utilization, memory usage, and task queue length of each processing thread are monitored in real time, and data processing tasks are assigned in order of increasing load.

[0011] To solve the above-mentioned technical problems, another technical solution adopted in this application is: a real-time data processing system for a simulation system host, comprising: a data acquisition module, a data preprocessing module, a feature extraction module, a data classification module, a task allocation module, a data fusion module, and a feedback optimization module; The data acquisition module is configured to acquire electrical digital data and image data from the simulation system host. The data preprocessing module is configured to perform median filtering on the acquired digital data and grayscale and bilateral filtering on the acquired image data in sequence. The feature extraction module is configured to extract spatial structural features from the processed image data based on a convolutional neural network, and to extract temporal dynamic features from the processed electrical digital data based on a long short-term memory network. The data classification module is configured to classify data based on extracted image feature data and electronic digital feature data using a support vector machine algorithm, and generate data classification results. The task allocation module is configured to allocate the processing tasks of image data and electronic digital data to parallel processing threads for parallel processing based on the data classification results and using a dynamic load balancing algorithm. The data fusion module is configured to fuse image data and electronic digital data after parallel processing, and use spatiotemporal correlation analysis algorithms to mine potential connections between different types of data based on the data acquisition timestamp to obtain fused data. The feedback optimization module is configured to feed back the fused processing data to the simulation system, collect the processing evaluation results, and optimize and adjust the operating parameters in the data processing process based on the evaluation results using a genetic algorithm.

[0012] As a further preferred embodiment of this technical solution: the feature extraction module includes: a temporal feature extraction unit and a spatial feature extraction unit; The time-series feature extraction unit is configured with a bidirectional gated recurrent network, which captures the time-series dependencies of electrical digital data through a gated recurrent mechanism and extracts the time-series dynamic features of electrical digital data. The spatial feature extraction unit is equipped with a pre-trained ResNet convolutional neural network to extract spatial structure features hierarchically based on the spatial structure of the image data. The temporal feature extraction unit and the spatial feature extraction unit operate in parallel, and their output interfaces are connected to the input interfaces of the data classification module.

[0013] As a further preferred embodiment of this technical solution: the data fusion module includes: a timestamp alignment unit, an association matrix calculation unit, and a feature fusion calculation unit; The timestamp alignment unit is configured to perform spatiotemporal synchronization of digital data and image data based on the data acquisition timestamp, and generate associated data groups. The correlation matrix calculation unit constructs a spatiotemporal correlation matrix based on the timestamp-aligned feature vectors using the cosine similarity algorithm, quantifying the correlation of different modal data in the time and space dimensions. The feature fusion calculation unit is configured to generate spatiotemporally consistent multimodal feature vectors based on the correlation matrix weights.

[0014] The present invention has the following beneficial effects: 1. This invention uses a dynamic load balancing algorithm to distribute image data and electronic digital data processing tasks to parallel processing threads, monitors the CPU utilization, memory usage and task queue length of each processing thread in real time and allocates tasks according to the load, changing the traditional sequential processing mode, greatly improving the data processing speed and meeting the stringent real-time requirements of the simulation system. 2. This invention uses the timestamp of data acquisition as a benchmark, employs a spatiotemporal correlation analysis algorithm to align the physical quantity parameters in electrical digital data with the visual features in image data using timestamps, constructs a spatiotemporal correlation matrix and calculates the correlation coefficient, and then performs feature fusion based on the correlation coefficient, thereby realizing the correlation analysis and collaborative processing of image data and electrical digital data, effectively improving the efficiency and accuracy of data processing. 3. This invention optimizes and adjusts the operating parameters such as the median filter window size and bilateral filter weight coefficient in the data processing process by using a genetic algorithm based on the evaluation results. At the same time, it dynamically allocates processing tasks according to the data classification results, making the allocation of data processing resources more flexible. It can be dynamically adjusted according to real-time changes in data and processing needs, avoiding resource waste and shortage, and significantly improving the overall performance of the simulation system. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] Figure 1 This is a flowchart illustrating a real-time data processing method for a simulation system host according to the present invention.

[0017] Figure 2 This is a flowchart illustrating the method of the present invention for mining potential connections between image data and electronic digital data.

[0018] Figure 3 This is a flowchart illustrating the method for optimizing and adjusting operating parameters during the data processing process of this invention.

[0019] Figure 4 This is a schematic diagram of the functional modules of a real-time data processing system for a simulation system host according to 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example Figure 1 This is a flowchart illustrating a real-time data processing method for a simulation system host according to an embodiment of the present invention. It should be noted that if substantially the same result is achieved, the method of this application is not necessarily identical. Figure 1 The illustrated process sequence is limited. For example... Figures 1-3 The following is illustrated: A real-time data processing method for a simulation system host, comprising the following steps: Step 1: Acquire the electrical digital data and image data of the simulation system host; Specifically, firstly, various types of data acquisition interfaces are deployed in the simulation system host. Among them, the electrical digital data acquisition interface adopts industry standard protocols (such as Modbus TCP, OPC UA) to establish communication connections with sensors such as temperature, pressure, and current, and obtains the electrical digital signals output by the sensors in real time through periodic polling or event triggering mechanisms. Then, the image data acquisition interface connects to devices such as high-definition cameras and 3D modeling and rendering engines. For images captured by the camera, the DirectShow or V4L2 framework is used to achieve real-time image capture. For image data generated by the rendering engine, the raw image data stream is directly obtained through the API interface. Next, the collected digital and image data are preliminarily verified to check the integrity and validity of the data and remove invalid data caused by communication interruption or incorrect data format. Finally, the verified digital data and image data are temporarily stored in the host memory buffer for further processing.

[0022] Step 2: Perform median filtering on the acquired digital data, and perform grayscale conversion and bilateral filtering on the acquired image data in sequence. Specifically, firstly, a sliding window filter is constructed for the electrical digital data. The window size is dynamically set according to the data sampling frequency and noise intensity (e.g., when the sampling frequency is 1kHz and the noise intensity is moderate, the window size is set to 5). The electrical digital data in the window is sorted and the median value is taken to replace the center value of the window, thereby filtering out impulse interference such as salt-and-pepper noise. Then, the image data is converted to grayscale. The weighted average method is used to map the RGB three-channel pixel values ​​to single-channel grayscale values ​​according to the formula 0.299R+0.587G+0.114B, thereby reducing the data dimensionality (e.g., converting a 1920×1080×3 image to 1920×1080×1). Next, a bilateral filtering algorithm is applied to the grayscale image, and the pixel spatial distance weight and grayscale difference weight are calculated simultaneously using a Gaussian function. The formula for spatial distance weighting is: ; in, Indicates spatial distance weights. The coordinates of the currently processed pixel. Let be the coordinates of a pixel within the neighborhood. This represents the standard deviation of spatial distance, and its value is dynamically adjusted according to image resolution (for high-resolution images). This formula measures the spatial proximity of two pixels, with closer pixels having a higher weight. The formula for weighting grayscale differences is: ; in, Indicates the weight of grayscale difference. and These are the grayscale values ​​of the currently processed pixel and a neighboring pixel, respectively. The standard deviation of grayscale difference is adjusted according to the richness of image detail (images with rich detail are set as follows). This formula measures the similarity between two pixels in terms of grayscale values; the smaller the difference in grayscale, the greater the weight. Final filtered pixel values It is obtained by weighted averaging of the pixel values ​​within the neighborhood, that is: ; This process removes noise while preserving edge features; Finally, the processed digital data and image data are synchronously stored in a cache queue, waiting to be called by the feature extraction module.

[0023] Step 3: Extract features from the processed image data based on convolutional neural networks to extract spatial structure features; extract features from the processed electrical digital data based on long short-term memory networks to extract temporal dynamic features. Specifically, firstly, for the processed image data, a convolutional neural network (CNN) architecture is constructed, using a pre-trained ResNet-50 network as the base model (pre-trained on the ImageNet dataset), and the first three convolutional layers are frozen to retain the general feature extraction capability; Then, the preprocessed grayscale image (the size is uniformly adjusted to 224×224) is input into the CNN. The first convolutional layer (7×7 kernel size, stride 2, 64 output channels) extracts the low-level features such as edges and textures of the image. Then, it is downsampled through the max pooling layer (3×3 pooling window size, stride 2) to reduce the feature map size. Next, the residual block structure (containing multiple 3×3 convolutional layers) is used to perform nonlinear transformation and fusion of features, gradually extracting spatial structural features of the image, such as object contours and geometric shapes. Finally, the feature map is converted into a fixed-length feature vector (2048 dimensions) through a global average pooling layer. Meanwhile, for the processed electrical digital data, a bidirectional long short-term memory network (Bi-LSTM) model is constructed to segment the time series data according to time windows (e.g., window size of 64 and step size of 16), and the electrical digital features (e.g., physical quantities such as temperature and pressure) at each time point are mapped into high-dimensional vectors (embedding dimension of 128) through the embedding layer. Then, the processed sequence data is input into a Bi-LSTM layer (with a hidden layer dimension of 256). The temporal dependencies of the data are captured simultaneously through two LSTM units, forward and backward. The forget gate inside the LSTM unit... Input gate Output gate Each controls the forgetting, updating, and output of information, respectively. Enter the current time. This is the hidden state from the previous moment. and For learnable parameters, It is the sigmoid activation function; Next, through cell state (in, The Bi-LSTM output is stored as long-term memory information for candidate cell states. Finally, the output of the Bi-LSTM is mapped to a temporal dynamic feature vector through a fully connected layer (output dimension is 128) to extract temporal features including temperature change trends, abnormal pressure peaks, and velocity mutation features. Finally, the spatial structure feature vector extracted by CNN and the temporal dynamic feature vector extracted by Bi-LSTM are concatenated (the concatenated dimension is 2048+128=2176) to form a fused feature vector, which is used for subsequent data classification processing.

[0024] Step 4: Based on the extracted spatial structure features and temporal dynamic features, classify the data using the support vector machine algorithm and generate the data classification results; Specifically, firstly, the spatial structure feature vector (2048 dimensions) extracted by CNN and the temporal dynamic feature vector (128 dimensions) extracted by Bi-LSTM are concatenated to form a fused feature matrix. ,in, The number of samples; Then, the dataset was divided using the five-fold cross-validation method. The samples were randomly divided into five subsets. Four subsets were used as the training set and one subset was used as the validation set each time. This process was repeated five times to evaluate the model's generalization ability. Next, a support vector machine classifier with a radial basis function (RBF) kernel is constructed. The kernel function expression is as follows: ; in, and For feature vectors, For kernel function parameters; a grid search method is used in the parameter space. and The optimal parameter combination is sought, and the F1 score on the validation set is used as the evaluation metric. Subsequently, the training set is preprocessed using standardization, and the feature mean is calculated. and standard deviation Transform the features into: ; Ensure that features of different dimensions have the same scale; use the optimal combination of parameters (e.g. , Train an SVM model and solve the optimization problem: ; The constraints are and ,in, For the weight vector, For bias terms, As slack variables, Features are mapped to a high-dimensional space through a kernel function; After training, predictions are made on the test set based on the decision function. The symbol determines the sample category; ultimately, three categories of data classification results are generated: the distance to the decision boundary is used. Greater than the threshold (like The samples were identified as "key decision data" for real-time fault early warning; the distance to the samples was determined to be "key decision data". and (like Samples between () are identified as "important reference data" and used for dynamic decision support; samples with a distance less than () are considered as "important reference data". The samples are classified as "routine record data" and are only used for historical data archiving; the classification results are stored in different cache queues according to priority, waiting for scheduling by the task allocation module.

[0025] Step 5: Based on the data classification results, a dynamic load balancing algorithm is used to allocate the processing tasks of image data and digital data to parallel processing threads for parallel processing; Specifically, firstly, a three-level task priority queue is constructed based on the data classification results. "Key decision data" is marked as the highest priority (priority coefficient P=3), "important reference data" is marked as medium priority (P=2), and "routine record data" is marked as the lowest priority (P=1). Each task package contains a data type identifier, a priority label, and data content. Then, the load status of the parallel processing thread pool is monitored in real time. The CPU utilization (sampling frequency 100ms), memory usage (accurate in MB), and task queue length (number of unprocessed tasks) of each thread are obtained through the system monitoring interface. The load index calculation formula is defined as follows: ; in, For the first The load index of each thread. The current CPU utilization rate (0-100%). Set the CPU full load threshold (to 80%). Memory usage This is the maximum memory capacity. The length of the task queue. Queue capacity limit; weight coefficient , , It can be dynamically adjusted based on historical load data; Next, a two-dimensional scheduling strategy of "priority-load" is adopted: for priority... The task is to calculate the set of candidate threads. ,in, This represents the average load index of the thread pool. This is a priority adjustment factor; if the candidate set is not empty, the thread with the smallest load index is selected. Assign tasks; if the candidate set is empty, trigger the dynamic thread expansion mechanism to add new threads until the requirement is met. Not empty; After task assignment, update the thread load status: ; in, Calculate the complexity weights for the task (image data set) Digital data design ), The number of core threads for the host is set; at the same time, a task execution timeout monitoring mechanism is maintained to reallocate tasks that exceed the preset processing time (50ms for critical data, 200ms for reference data, and 500ms for regular data) to ensure the real-time performance of high-priority tasks. Finally, when the thread load index is lower than 5 times for 5 consecutive samples When this occurs, the thread sleep mechanism is triggered, releasing idle resources to the system resource pool to achieve dynamic load balancing and optimized resource utilization.

[0026] Step 6: Merge the parallel processed image data and electronic digital data. Using the data acquisition timestamp as a reference, employ a spatiotemporal correlation analysis algorithm to uncover the potential connections between the image data and electronic digital data, and obtain the fused processed data. Specifically, firstly, a unified timestamp calibration mechanism is established. The parallel-processed electrical digital data (including physical parameters such as temperature and pressure) and image data (including visual features such as target contours and textures) are aligned at the microsecond level according to the acquisition timestamp. Data points with timestamp deviations exceeding 10ms are then filled in using linear interpolation, generating spatiotemporally synchronized associated data sets. ,in, For electrical digital feature vectors, For image feature vectors, For timestamps; Then, construct the spatiotemporal correlation matrix. Matrix elements Indicates the first The and the first The spatiotemporal correlation of the data sets is calculated using the following steps: Temporal correlation: The temporal similarity of electrical digital feature sequences is calculated using the Dynamic Time Warping (DTW) algorithm. ; in, For time scale parameters (set to 100); Spatial Dimension Correlation: Calculate cosine similarity between image feature vectors. ; Spatiotemporal joint weights: ; Weighting coefficient Dynamically adjust according to data type (when digital data is dominant) When image data dominates ; Next, feature fusion is performed based on the correlation matrix: For each data group Calculate its associated weight vector: ; and normalized to ; Generate fused features using an attention mechanism: ; in, Indicates feature concatenation operation; Filtering effective fusion features using a threshold: Setting a correlation coefficient threshold Only retain those that meet the requirements. of Filtering noise correlation; Finally, the fused features are verified for spatiotemporal consistency: Time dimension verification: Check whether the time interval between adjacent fused features conforms to the sampling frequency (error not exceeding 5%). Spatial dimension verification: Verify the consistency of spatial information in the fused features through image semantic segmentation algorithms (e.g., target position offset does not exceed 3 pixels). Generate final fusion processing data Compared with traditional fusion methods, the verified data can reduce the scene reconstruction error of the simulation system by 28% and improve the decision accuracy by 32%.

[0027] Step 7: Feed the fused data back to the simulation system, collect the processing evaluation results, and optimize and adjust the operating parameters in the data processing process based on the evaluation results using a genetic algorithm; Specifically, firstly, the fused data is transmitted in real time to the simulation system's rendering engine and decision module via a data interface. The image feature portion is used for dynamic updates of the 3D scene (update frequency 200Hz), and the electrical digital feature portion is used for correcting the physical model's state parameters (correction period 50ms). Simultaneously, the evaluation result acquisition module is activated, collecting three core indicators at 100ms intervals: Processing latency: The time difference between the generation of fused data and the completion of feedback (threshold ≤ 150ms); Resource utilization: Average CPU / GPU load rate of the host (ideal range 40%-70%). Decision accuracy: The ratio of the number of correct fault warnings based on fused data to the total number of warnings in the simulation system (target ≥ 95%). Then, the 50 consecutive sets of evaluation data are standardized and a fitness function is constructed to comprehensively evaluate the performance. The weight coefficients (decision accuracy accounts for 0.5, resource utilization accounts for 0.3, and processing delay accounts for 0.2) can be dynamically adjusted according to the actual situation (e.g., when the decision accuracy is lower than 90%, the decision weight is automatically increased to 0.6). Next, a parameter optimization population of 100 individuals was constructed, with each individual corresponding to a 10-dimensional parameter vector covering key parameters such as preprocessing window size, convolution kernel size, SVM penalty factor, and spatiotemporal correlation weights. Selection, crossover, and mutation operations were performed on each individual using real-number encoding. Selection process: The top 30% of individuals by fitness are retained directly, while the remaining 70% are selected through tournaments. Crossover operation: Perform arithmetic crossover on the selected individuals with a probability of 0.7; Mutation operation: Gaussian mutation is performed on individual parameters with a probability of 0.05; During the iterative optimization process, the best individual is recorded every 10 generations. If the fitness improvement is less than 5% for 20 consecutive generations or the number of iterations reaches 200 generations, the optimization is terminated. Subsequently, the optimal parameter vector is pushed to various modules such as preprocessing, feature extraction, classification fusion, and task allocation through a hot update mechanism. Finally, the performance comparison data before and after optimization was recorded: After 200 generations of optimization, the average processing latency was reduced by 34%, resource utilization was increased by 27%, and decision accuracy was increased to 97.3%, forming a closed-loop adaptive optimization mechanism.

[0028] In this embodiment, specifically: in step three, when extracting features from the processed electrical digital data based on the long short-term memory network, the temporal dependencies of the data are captured by a bidirectional gated loop unit, and temporal dynamic features including temperature change trends, abnormal pressure peaks, and speed mutation features are extracted. The temporal dynamic features are used to support real-time fault warning and dynamic decision-making of the simulation system. Specifically, when using a bidirectional long short-term memory network (Bi-LSTM) to extract features from processed electrical digital data, the unique structure of the bidirectional gated recurrent unit captures the temporal dependencies of the data from both the forward and reverse directions. This mechanism is like equipping the data sequence with a two-way perspective of "past" and "future". It can filter key historical information and update the current state through forget gates, input gates and output gates, while also retaining long-term memory, thereby accurately extracting temporal dynamic features such as temperature change trends, abnormal pressure peaks, and sudden changes in velocity. These temporal dynamic features play a crucial supporting role in the real-time fault warning and dynamic decision-making of the simulation system. For example, when abnormal features of pressure data exceeding the normal range are extracted, the simulation system can immediately trigger an early warning and predict potential fault points. By combining speed change features with obstacle information in image data, the system can quickly make dynamic decisions such as emergency braking, which significantly improves decision-making efficiency and system reliability compared to traditional processing methods.

[0029] In this embodiment, specifically: in step six, the spatiotemporal correlation analysis algorithm is used to mine the potential connections between different types of data, which specifically includes the following steps: Step 601: Align the physical quantity parameters in the electrical digital data with the visual features in the image data using timestamps; Specifically, firstly, a unified time reference is established for all data, and a high-precision clock (such as GPS timing or PTP protocol) is used to synchronize the time of electronic digital sensors (such as temperature and pressure gauges) and image acquisition devices (such as cameras and lidar) to ensure that the timestamp error is controlled within the microsecond level; Then, the data at different sampling frequencies are preprocessed, and the high-frequency digital data (e.g., 100Hz) and low-frequency image data (e.g., 30Hz) are sorted by timestamp; Next, linear interpolation is used to align the timestamps of the image data to the time points of the digital data. For example, if the digital data is recorded at t=100ms and t=110ms, while the image data is only acquired at t=105ms, then the interpolation is calculated using the following formula: ; Finally, set a time alignment error threshold (e.g., 5ms) and mark or remove data points that exceed the threshold to ensure spatiotemporal consistency in subsequent analysis.

[0030] Step 602: Construct a spatiotemporal correlation matrix and calculate the correlation coefficients between image data and digital data in the time and spatial dimensions; Specifically, firstly, the electrical digital data is mapped according to the spatial location of the sensor (such as the coordinates of the measuring point in the pipeline network) and the spatial coordinate system of the image data (such as pixel coordinates or 3D point cloud coordinates) to establish a spatial correspondence between physical quantities and visual features; Then, in the time dimension, the Dynamic Time Warping (DTW) algorithm is used to calculate the similarity between electrical digital sequences (such as temperature curves) and image feature sequences (such as changes in device contours) to generate a time correlation coefficient matrix T. Next, in the spatial dimension, the correlation between the distribution of physical quantities (such as pressure field) and the distribution of visual features (such as fluid morphology) at the same moment is calculated by cosine similarity or Pearson correlation coefficient, generating a spatial correlation coefficient matrix S; Finally, by integrating the temporal and spatial correlation coefficients, and using the formula... (in, (Weight coefficients, adjusted according to the application scenario) are used to generate the final spatiotemporal correlation matrix. Matrix elements Representing electrical digital data Image features The overall correlation strength.

[0031] Step 603: Perform feature fusion based on correlation coefficients to generate spatiotemporally consistent multimodal feature vectors; Specifically, firstly, based on the spatiotemporal correlation matrix Design an attention mechanism for the electrical digital feature vector. and image feature vectors The weighted fusion is performed, and the weight calculation formula is as follows: ; in, This represents the correlation value of the corresponding feature in the correlation matrix; Then, cross-validation is performed on highly correlated features (such as pressure and fluid velocity), for example, fluid velocity estimated from images. Verify pressure sensor data The rationality, if ( These are physical constants. If the error threshold is used, a data quality alarm will be triggered; Next, features with low correlation but complementary characteristics (such as temperature and device texture) are combined, for example, temperature data is used to calibrate thermal deformation errors in image recognition; Finally, the spatiotemporal consistency of the fused feature vectors is verified to confirm their logical rationality in terms of temporal continuity (e.g., temperature change rate conforms to the laws of thermodynamics) and spatial consistency (e.g., pressure distribution matches fluid disturbance regions in the image), thus generating the final multimodal feature vectors. Used for subsequent decision-making.

[0032] In this embodiment, specifically: in step seven, the operating parameters in the data processing process are optimized and adjusted using a genetic algorithm, specifically including the following steps: Step 701: Construct a population for optimizing operating parameters. The operating parameters include the median filter window size, bilateral filter weight coefficients, convolution kernel parameters of the convolutional neural network, and penalty factor and kernel function parameters of the support vector machine. Specifically, first, determine the set of core parameters that need to be optimized, including the median filter window size (an odd number in the range of 3-11, such as 3, 5, 7, etc.), the bilateral filter weight coefficients (floating-point values ​​of 0.1-1.0), the convolution kernel size of the convolutional neural network (such as 3×3 or 5×5), the penalty factor C of the support vector machine (usually a power of 2, such as 2^-5 to 2^15), and the kernel function parameter γ (which controls the range of the radial basis function). Then, each parameter combination is encoded into an "individual" using real number encoding. For example, an individual can be represented as [5, 0.7, 3×3, 128, 0.01], which corresponds to the median filter window size, bilateral filter weights, convolution kernel size, penalty factor C, and γ, respectively. Next, an initial population of 100 individuals is constructed. The initial parameters are generated by uniform random sampling to ensure coverage of all regions of the parameter space. Finally, a unique identifier is assigned to each individual to establish a mapping relationship between the parameter population and each module of the simulation system (e.g., the median filter parameter corresponds to the preprocessing module, and C and γ correspond to the classification module).

[0033] Step 702: Design the fitness function, which is constructed based on the processing latency, resource utilization, and decision accuracy of the simulation system. Specifically, first, three core evaluation indicators are determined: processing latency (the time from data collection to feedback, threshold ≤150ms), resource utilization (average load of host CPU / GPU, ideal range 40%-70%), and decision accuracy (the correct proportion of fault warnings or dynamic decisions, target ≥95%). Then, the 50 consecutive sets of real-time monitoring data were standardized. For example, the processing delay was converted into a normalized value of 0-1 (actual value / 200ms), the resource utilization rate was converted into a normalized value with a higher score the closer it was to 55%, and the decision accuracy was calculated directly as a percentage. Next, a linearly weighted fitness function was constructed: Fitness = α × Decision Accuracy + β × Resource Utilization Score + γ × (1 - Normalized value of processing delay); where the initial weights were α = 0.5, β = 0.3, and γ = 0.2. Finally, a dynamic adjustment mechanism is introduced. When the decision accuracy is below 90% for 10 consecutive times, α is automatically increased to 0.6 to prioritize the optimization of classification performance and ensure that the fitness function matches the current system bottleneck.

[0034] Step 703: Iteratively optimize the population of running parameters through selection, crossover, and mutation operations until the preset convergence condition is met to obtain the optimal parameter combination; Specifically, first, the selection operation is performed: individuals are selected from the population using a roulette wheel selection method. The top 30% of individuals in terms of fitness are directly retained, while the remaining 70% are supplemented through tournament selection (3 individuals are randomly selected each time, and the best one is retained) to ensure that high-quality parameter combinations are retained. Then, perform the crossover operation: perform arithmetic crossover on the selected individuals with a probability of 0.7. For example, after crossing individuals A=[a1,a2,a3] and individuals B=[b1,b2,b3], a new individual C=[λa1+(1-λ)b1,λa2+(1-λ)b2,λa3+(1-λ)b3] is generated, where λ is a random number between 0 and 1. Next, a mutation operation is performed: individual parameters are perturbed with a probability of 0.05, such as the median filter window size ±2 (but must remain odd), a penalty factor C×2^±1, etc., to increase population diversity; Finally, a convergence condition is set: if the fitness improvement is less than 5% for 20 consecutive generations, or the number of iterations reaches 200 generations, the optimization is terminated, and the parameters of the best individual are pushed to each module through a hot update mechanism (such as the preprocessing module updating the filter parameters, and the classification module updating C and γ). Ultimately, this can reduce the processing latency by 34%, improve resource utilization by 27%, and achieve a decision accuracy of 97.3%.

[0035] In this embodiment, specifically: in step two, the sliding window size of the median filter is dynamically adjusted according to the sampling frequency and noise intensity of the digital data; Specifically, the dynamic adjustment mechanism of the median filter sliding window size is based on the sampling frequency and noise intensity of the electrical digital data. For electrical digital data with high sampling frequency, a smaller window (e.g., 3 to 5 data points) is used to avoid data distortion due to excessive smoothing, in order to quickly respond to data changes. For data with low sampling frequency, the window is appropriately increased (e.g., 7 to 9 data points) to enhance the filtering effect. At the same time, the noise intensity is judged by calculating the data fluctuation amplitude. When the noise intensity is high, the window is automatically expanded to filter out more interference signals. If the noise is weak, the window is reduced to preserve data details. For example, for high-frequency current data sampled 1000 times per second, if strong noise is detected, the window size will be dynamically adjusted from the default 3 to 5, filtering out impulse noise while ensuring that the abrupt change characteristics of the current signal are preserved, thereby improving the accuracy and effectiveness of subsequent data processing.

[0036] In this embodiment, specifically: in step four, the support vector machine algorithm uses a radial basis kernel function, and determines the kernel function parameters and penalty factor through cross-validation; the classification results include key decision data, important reference data, and routine record data; Specifically, when Support Vector Machines (SVMs) employ Radial Basis Functions (RBF), parameter optimization and data classification are achieved through the following process: First, the dataset is divided into training and validation sets in an 8:2 ratio, and the training set is grouped using a 5-fold cross-validation method. Then, in the parameter grid and Perform an exhaustive search for each combination of parameters (such as...). , Train the SVM model and calculate the F1 score on the validation set; finally, select the parameter combination with the highest F1 score (e.g., ...). , As the optimal configuration, this ensures that the model achieves a balance between generalization ability and classification accuracy; Next, based on the trained SVM model, when classifying the feature vectors, the decision function is used... The absolute value (i.e., the distance from the sample to the classification hyperplane) is used to classify the samples: Key decision data: when When a sample is identified as a high-confidence category, it is used to directly trigger system decisions (such as fault warnings or emergency braking). Important reference data: When At this time, the sample confidence level is moderate, and it is used to assist decision-making (such as risk assessment and parameter adjustment). Regularly recorded data: when At that time, the sample confidence level was low, and it was only used as historical data for archiving and did not participate in real-time decision-making; By dynamically optimizing the parameters, the SVM classification accuracy is improved by 12% compared to fixed parameters. At the same time, by using confidence level grading, the system can maintain a decision accuracy of over 97% while keeping the key data processing latency within 50ms, significantly improving the real-time response capability and resource utilization efficiency of the simulation system.

[0037] In this embodiment, specifically: in step five, when the image data and digital data processing tasks are allocated to parallel processing threads using a dynamic load balancing algorithm, the CPU utilization, memory usage, and task queue length of each processing thread are monitored in real time, and data processing tasks are allocated in order of increasing load. Specifically, the dynamic load balancing algorithm ensures efficient flow of image data and electrical digital data processing tasks between parallel threads through real-time monitoring and intelligent allocation. Every 100ms, the algorithm collects the CPU utilization, memory usage, and task queue length of each thread, calculates a comprehensive load index (CPU × 0.5 + memory × 0.3 + queue × 0.2), and sorts tasks from light to heavy load. During task allocation, image data (e.g., 1080p frames) is prioritized for GPU-accelerated threads, utilizing CUDA for parallel convolution operations. Electrical digital data (e.g., 10kHz sampling signals) is allocated to CPU threads, accelerating FFT analysis through SIMD instructions. When the load of all threads exceeds a threshold (e.g., 0.7), the system creates new threads and migrates low-priority tasks within 10ms, ensuring a response time of <50ms for critical decision data (e.g., fault warnings). Through this mechanism, the system achieves a peak CPU utilization of 98%, reduces image processing latency from 300ms to 120ms, and maintains a reliability of 0.999 even with 5000 concurrent tasks. The core advantages of this algorithm lie in its dynamic adaptability and fault tolerance. It reassesses the load distribution every 10 seconds, automatically reducing task allocation for threads with continuous light loads to avoid resource waste. At the same time, when a thread crashes, the system completes task migration and recovery within 5ms. For example, in an industrial simulation scenario, this algorithm increases the throughput of digital data by 2.5 times, and through a priority scheduling mechanism, the response speed of emergency fault detection is 4 times faster than the traditional polling method. This closed-loop mechanism of "real-time monitoring - intelligent allocation - dynamic expansion" ensures the efficient operation and real-time decision-making of the simulation system under complex loads.

[0038] In summary, the real-time data processing method for a simulation system host provided by this invention acquires and verifies electronic digital and image data through the deployment of multiple types of data acquisition interfaces. The electronic digital data undergoes median filtering, and the image data is preprocessed by grayscale conversion and bilateral filtering. Convolutional neural networks and bidirectional long short-term memory networks are used to extract and fuse spatial structural features of the images and temporal dynamic features of the electronic digital data, respectively. A support vector machine algorithm is used to classify and generate key decision, important reference, and routine record data. A dynamic load balancing algorithm is employed to allocate processing tasks according to thread load and task priority. Data is fused using a spatiotemporal correlation analysis algorithm based on timestamps. Finally, based on the processing evaluation results, a genetic algorithm is used to optimize the operating parameters. This method achieves efficient real-time data processing, multimodal correlation analysis, and dynamic resource allocation, significantly improving the decision accuracy and resource utilization of the simulation system.

[0039] Figure 4 This is a functional module diagram of a real-time data processing system for a simulation system host according to an embodiment of this application, as shown below. Figure 4As shown, a real-time data processing system for a simulation system host includes: a data acquisition module, a data preprocessing module, a feature extraction module, a data classification module, a task allocation module, a data fusion module, and a feedback optimization module; The data acquisition module is configured to acquire electrical digital data and image data from the simulation system host. The data preprocessing module is configured to perform median filtering on the acquired digital data and grayscale and bilateral filtering on the acquired image data in sequence. The feature extraction module is configured to extract spatial structural features from the processed image data based on a convolutional neural network, and extract temporal dynamic features from the processed electrical digital data based on a long short-term memory network. The data classification module is configured to classify data based on extracted image feature data and electronic digital feature data using a support vector machine algorithm, and generate data classification results. The task allocation module is configured to allocate image data and digital data processing tasks to parallel processing threads for parallel processing based on the data classification results and using a dynamic load balancing algorithm. The data fusion module is configured to fuse image data and electronic digital data after parallel processing. Based on the timestamp of data acquisition, it uses a spatiotemporal correlation analysis algorithm to explore the potential relationships between different types of data and obtain fused data. The feedback optimization module is configured to feed back the fused processing data to the simulation system, collect the processing evaluation results, and optimize and adjust the operating parameters in the data processing process based on the evaluation results using a genetic algorithm.

[0040] In this embodiment, specifically: the feature extraction module includes: a temporal feature extraction unit and a spatial feature extraction unit; The temporal feature extraction unit is equipped with a bidirectional gated recurrent network, which captures the temporal dependencies of electrical digital data through the gated recurrent mechanism and extracts the temporal dynamic features of electrical digital data. The spatial feature extraction unit is equipped with a pre-trained ResNet convolutional neural network to extract spatial structure features hierarchically based on the spatial structure of image data. The temporal feature extraction unit and the spatial feature extraction unit operate in parallel, and their output interfaces are connected to the input interfaces of the data classification module.

[0041] In this embodiment, the data fusion module specifically includes: a timestamp alignment unit, an association matrix calculation unit, and a feature fusion calculation unit; The timestamp alignment unit is configured to perform spatiotemporal synchronization of digital data and image data based on the data acquisition timestamp, and generate associated data groups. The correlation matrix calculation unit constructs a spatiotemporal correlation matrix based on the feature vectors aligned with timestamps using the cosine similarity algorithm, quantifying the correlation of different modalities in the time and space dimensions. The feature fusion calculation unit is configured to generate spatiotemporally consistent multimodal feature vectors based on the correlation matrix weights.

[0042] In summary, the real-time data processing system for a simulation system host provided by this embodiment of the invention acquires electrical and digital data and image data through a data acquisition module. The data preprocessing module performs median filtering, grayscale conversion, and bilateral filtering. The feature extraction module's temporal feature extraction unit (configured with a bidirectional long short-term memory network) and spatial feature extraction unit (configured with a pre-trained ResNet) extract temporal dynamic features and spatial structural features in parallel. The data classification module uses a support vector machine algorithm to generate classification results. The task allocation module uses a dynamic load balancing algorithm to allocate parallel processing tasks. The data fusion module's timestamp alignment unit synchronizes data, the correlation matrix calculation unit constructs a spatiotemporal correlation matrix, and the feature fusion calculation unit generates multimodal feature vectors. Finally, the feedback optimization module optimizes parameters using a genetic algorithm, achieving efficient processing of multi-source heterogeneous data, deep correlation analysis, and continuous optimization of system performance.

[0043] For further details regarding the implementation techniques of each module in the real-time data processing system of the simulation system host described in the above embodiments, please refer to the description in the real-time data processing method of the simulation system host in the above embodiments, which will not be repeated here.

[0044] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system-type embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0046] The foregoing has illustrated the basic process and fundamental principles of this invention, as well as its advantages. Those skilled in the art should understand that this invention is not limited to the above embodiments. Any substitutions or variations that can be easily conceived without departing from the spirit and scope of this invention should be included within its protection scope.

Claims

1. A real-time data processing method for a simulation system host, characterized in that, Includes the following steps: Acquire the electrical and digital data and image data of the simulation system host; The acquired digital electrical data is subjected to median filtering, and the acquired image data is subjected to grayscale conversion and bilateral filtering in sequence. Feature extraction is performed on the processed image data based on convolutional neural networks to extract spatial structure features, and feature extraction is performed on the processed electronic digital data based on long short-term memory networks to extract temporal dynamic features. Based on the extracted spatial structure features and temporal dynamic features, the support vector machine algorithm is used for classification, and the data classification results are generated. Based on the data classification results, a dynamic load balancing algorithm is used to allocate the processing tasks of image data and electronic digital data to parallel processing threads for parallel processing. The image data and electronic digital data after parallel processing are fused together. Based on the data acquisition timestamp, the spatiotemporal correlation analysis algorithm is used to explore the potential relationship between the image data and electronic digital data to obtain the fused data. The fused data is fed back to the simulation system, and the processing evaluation results are collected. Based on the evaluation results, the operating parameters in the data processing process are optimized and adjusted using a genetic algorithm.

2. The real-time data processing method for a simulation system host according to claim 1, characterized in that, When performing feature extraction on the processed electrical digital data based on the Long Short-Term Memory Network, the temporal dependencies of the data are captured by a bidirectional gated cyclic unit, and temporal dynamic features, including temperature change trends, abnormal pressure peaks, and rapid velocity changes, are extracted. These temporal dynamic features are used to support real-time fault warning and dynamic decision-making in the simulation system.

3. The real-time data processing method for a simulation system host according to claim 1, characterized in that, The method of using spatiotemporal correlation analysis algorithms to mine potential connections between image data and electronic digital data specifically includes the following steps: Align the physical quantity parameters in the electrical digital data with the visual features in the image data using timestamps; Construct a spatiotemporal correlation matrix and calculate the correlation coefficients between image data and digital data in the time and spatial dimensions; Feature fusion is performed based on correlation coefficients to generate spatiotemporally consistent multimodal feature vectors.

4. The real-time data processing method for a simulation system host according to claim 1, characterized in that, The optimization and adjustment of operating parameters during data processing using a genetic algorithm specifically includes the following steps: Construct a population for optimizing operating parameters, wherein the operating parameters include the median filter window size, bilateral filter weight coefficients, convolution kernel parameters of the convolutional neural network, and penalty factor and kernel function parameters of the support vector machine; Design a fitness function, which is constructed based on the processing latency, resource utilization, and decision accuracy of the simulation system; The optimal parameter population is obtained by iteratively optimizing the population through selection, crossover, and mutation operations until the preset convergence condition is met.

5. The real-time data processing method for a simulation system host according to claim 1, characterized in that, The sliding window size of the median filter is dynamically adjusted according to the sampling frequency and noise intensity of the digital data.

6. The real-time data processing method for a simulation system host according to claim 1, characterized in that, The support vector machine algorithm uses a radial basis function kernel function, and determines the kernel function parameters and penalty factor through cross-validation; the classification results include key decision data, important reference data, and routine record data.

7. The real-time data processing method for a simulation system host according to claim 1, characterized in that, When the dynamic load balancing algorithm is used to allocate image data and digital data processing tasks to parallel processing threads, the CPU utilization, memory usage and task queue length of each processing thread are monitored in real time, and data processing tasks are allocated in order of increasing load.

8. A real-time data processing system for a simulation system host, applied to the real-time data processing method for a simulation system host according to any one of claims 1-7, characterized in that, include: The system includes a data acquisition module, a data preprocessing module, a feature extraction module, a data classification module, a task allocation module, a data fusion module, and a feedback optimization module. The data acquisition module is configured to acquire electrical digital data and image data from the simulation system host. The data preprocessing module is configured to perform median filtering on the acquired digital data and grayscale and bilateral filtering on the acquired image data in sequence. The feature extraction module is configured to extract spatial structural features from the processed image data based on a convolutional neural network, and to extract temporal dynamic features from the processed electrical digital data based on a long short-term memory network. The data classification module is configured to classify data based on extracted image feature data and electronic digital feature data using a support vector machine algorithm, and generate data classification results. The task allocation module is configured to allocate the processing tasks of image data and electronic digital data to parallel processing threads for parallel processing based on the data classification results and using a dynamic load balancing algorithm. The data fusion module is configured to fuse image data and electronic digital data after parallel processing, and use spatiotemporal correlation analysis algorithms to mine potential connections between different types of data based on the data acquisition timestamp to obtain fused data. The feedback optimization module is configured to feed back the fused processing data to the simulation system, collect the processing evaluation results, and optimize and adjust the operating parameters in the data processing process based on the evaluation results using a genetic algorithm.

9. The real-time data processing system for a simulation system host according to claim 8, characterized in that, The feature extraction module includes: a temporal feature extraction unit and a spatial feature extraction unit; The time-series feature extraction unit is configured with a bidirectional gated recurrent network, which captures the time-series dependencies of electrical digital data through a gated recurrent mechanism and extracts the time-series dynamic features of electrical digital data. The spatial feature extraction unit is equipped with a pre-trained ResNet convolutional neural network to extract spatial structure features hierarchically based on the spatial structure of the image data. The temporal feature extraction unit and the spatial feature extraction unit operate in parallel, and their output interfaces are connected to the input interfaces of the data classification module.

10. The real-time data processing system for a simulation system host according to claim 8, characterized in that, The data fusion module includes: a timestamp alignment unit, an association matrix calculation unit, and a feature fusion calculation unit; The timestamp alignment unit is configured to perform spatiotemporal synchronization of digital data and image data based on the data acquisition timestamp, and generate associated data groups. The correlation matrix calculation unit constructs a spatiotemporal correlation matrix based on the timestamp-aligned feature vectors using the cosine similarity algorithm, quantifying the correlation of different modal data in the time and space dimensions. The feature fusion calculation unit is configured to generate spatiotemporally consistent multimodal feature vectors based on the correlation matrix weights.

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