An interactive data processing method based on multidimensional data analysis

Abstract

The present invention discloses an interactive data processing method based on multidimensional data analysis, which belongs to the technical field of data interactive processing. It solves the problems in existing methods that when a multidimensional data model processes interactive data, the quality of multidimensional data from multiple sources and heterogeneous structures is low, and the noise of multidimensional data cannot be filtered out, resulting in incomplete capture of effective information of multidimensional data and poor fusion effect. The method includes real-time collection of multidimensional time series data related to user behavior, and a multidimensional data analysis model analyzes and processes the time series data set based on edge computing, and combines a particle swarm algorithm and an ant colony optimization algorithm to visualize and interactively process the data analysis results. The multidimensional data analysis model in the present invention can not only filter and fuse features of multidimensional time series data from multiple sources and heterogeneous structures, but also visualize and interactively process the data analysis results by combining a particle swarm algorithm and an ant colony optimization algorithm, and fuse the user's short-term and long-term behavior preferences to output accurate user behavior prediction results.

Description

Technical Field

[0001] The present invention belongs to the technical field of data interactive processing, and in particular relates to an interactive data processing method based on multidimensional data analysis. Background Art

[0002] In the era of big data, with the rapid development of information technology, the scale of data production has grown exponentially. How to organize, understand and utilize massive amounts of data has become a problem faced by all industries. Real-time data is generally multi-dimensional structured data, semi-structured data and unstructured data, and there is uncertainty, including erroneous, duplicate or incomplete data. Processing this data requires the ability to effectively filter and correct errors to ensure the accuracy and reliability of data interaction.

[0003] Chinese patent CN115658775A discloses a data analysis method, device, electronic device, and computer-readable storage medium. The method includes: collecting IoT customer data based on IoT devices, wherein the IoT customer data includes one or any combination of facial data, diagnostic data, and signal data; performing statistical analysis on the collected IoT customer data to obtain a multidimensional data model; and performing trend forecasting analysis based on the multidimensional data model. However, when processing interactive data using the multidimensional data model in existing methods, the multidimensional data quality of multi-source heterogeneous multidimensional data is low and the multidimensional data noise cannot be filtered out, resulting in incomplete capture of effective information in the multidimensional data and poor fusion effect. To address the above problems, we propose an interactive data processing method based on multidimensional data analysis. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the existing technology and provide an interactive data processing method based on multidimensional data analysis, which solves the problems in the existing methods of multidimensional data models processing interactive data, such as low quality of multi-source heterogeneous multidimensional data and inability to filter out multidimensional data noise, resulting in incomplete capture of effective information of multidimensional data and poor fusion effect.

[0005] In the existing methods, when multidimensional data models process interactive data, the quality of multi-source heterogeneous multidimensional data is low and the noise of multidimensional data cannot be filtered out, resulting in incomplete capture of effective information of multidimensional data and poor fusion effect. To address the above problems, we propose an interactive data processing method based on multidimensional data analysis. In short, when the method is implemented, it first collects multidimensional time series data related to user behavior in real time based on the Internet of Things gateway, integrates and cleans the multidimensional time series data, and pre-builds a multidimensional data analysis model based on deep learning combined with the improved whale algorithm to traverse the interactive database. Then, the multidimensional data analysis model analyzes and processes the time series data set based on edge computing, outputs data analysis results and data flow recommendation links, triggers data interaction instructions, and interactively uploads the data analysis results to the visualization interaction platform based on the flow recommendation link. Finally, the particle swarm algorithm and ant colony optimization algorithm are combined to visualize and interactively process the data analysis results, and output interactive evaluation results. In an embodiment of the present invention, a multidimensional data analysis model is constructed and trained based on deep learning combined with an improved whale algorithm. The multidimensional data analysis model can not only filter and fuse features of multi-source heterogeneous multidimensional time series data, but also visualize and interactively process data analysis results by combining particle swarm optimization and ant colony optimization algorithms, and integrate users' short-term and long-term behavior preferences to output accurate user behavior prediction results.

[0006] The present invention is implemented as follows: an interactive data processing method based on multidimensional data analysis, the interactive data processing method based on multidimensional data analysis comprising:

[0007] Based on the IoT gateway, multi-dimensional time series data related to user behavior is collected in real time, the multi-dimensional time series data is integrated, cleaned, and processed, and the time series data set is output, and the time series data set is uploaded to the interactive database;

[0008] Pre-build a multidimensional data analysis model based on deep learning combined with an improved whale algorithm, traverse the interactive database, load model training samples from the interactive database, train the multidimensional data analysis model with the model training samples, and output a converged multidimensional data analysis model;

[0009] Real-time loading of time-series data sets. The multidimensional data analysis model analyzes and processes the time-series data sets based on edge computing, outputs data analysis results and data flow recommendation links, triggers data interaction instructions, and interactively uploads data analysis results to the visualization interaction platform based on the flow recommendation links.

[0010] In response to data interaction instructions, the visualization interaction platform triggers the visualization interaction logic based on the behavior analysis requirements, combines the particle swarm algorithm and the ant colony optimization algorithm to visualize and interactively process the data analysis results, and outputs the interaction evaluation results.

[0011] Preferably, the method of training a multidimensional data analysis model using model training samples specifically includes:

[0012] Construct a multidimensional data analysis model, using a convolutional neural network as the initial model. The initial model includes an input layer, a convolution module, a pooling layer, and a fully connected layer. Define the loss function of the initial model as the SIou loss function, and set the iterative training rounds and hyperparameters of the initial model.

[0013] Obtain model training samples, divide the model training samples into a training set and a validation set, input the training set, and perform filtering and noise reduction processing on the training set by the initial model to obtain a sample noise reduction set;

[0014] Load the sample denoising set and use it to iteratively train the initial model until convergence;

[0015] Obtain a validation set, use the validation set as input, execute the initial model, output the validation results, and judge whether the error between the validation results and the actual results meets the preset accuracy threshold;

[0016] If the preset accuracy threshold is met, a converged multidimensional data analysis model is output;

[0017] If the preset accuracy threshold is not met, the learning rate of the initial model is fine-tuned using the validation set combined with the fine-tuning mechanism, and the initial model is iteratively trained until convergence.

[0018] Preferably, the convolution module consists of a first convolution layer, a second convolution layer, and a third convolution layer. The first convolution layer is frozen and replaced by an iterative filter. The number of channels of the second convolution layer is 256, and the convolution kernel size is 3*3. The third convolution layer is a SimAM module, the number of channels of the third convolution layer is 512, and the convolution kernel size is 3*3. The activation function of the convolution module is a ReLU function. The pooling layer introduces the SimAM attention mechanism, and the pooling layer adopts maximum pooling. The maximum value of the local area of the feature filtered by the convolution is taken to extract the most important feature information. The link discrimination network architecture is introduced into the initial model, and the improved whale algorithm is incorporated into the link discrimination network architecture. The link discrimination network architecture recommends links based on the feature weights of the training samples and the link response ratio discrimination data flow.

[0019] Preferably, the multidimensional data analysis model is based on the edge computing method for analyzing and processing time series data sets, specifically including:

[0020] Obtain a time series data set, perform dimensionality reduction processing on the multi-dimensional time series data in the time series data set, and generate one-dimensional sequence data;

[0021] Among them, the one-dimensional sequence data is expressed as:

[0022]

[0023] Among them, X represents the input representation of multidimensional time series data, i is the dimension of the data, θ t Represents the weight parameter of the data. The weight parameter of the data is defined based on the Huber weight function. x(t) is the output representation of the one-dimensional sequence data.

[0024]

[0025] Among them, θ t represents the weight parameter of the data, m is the number of data types, ε is the preset weight threshold in Huber regression, σ(X) is the residual of multidimensional time series data, Represents the mean of multidimensional time series data;

[0026] Load one-dimensional sequence data and perform Fourier transform on the one-dimensional sequence data based on the Blackman window to obtain a unified two-dimensional time-frequency spectrum;

[0027] An iterative filter is used to filter the two-dimensional time-frequency spectrum and output a filtered data set. The third convolutional layer fuses the filtered data set to obtain a feature fusion set.

[0028] Load the feature fusion set. The pooling layer reduces the features in the feature fusion set based on the SimAM attention mechanism. The sensitivity analysis algorithm is used to consider the impact of the associated data associated with the filtered data. The filtered data in the feature fusion set is mapped to a low-dimensional latent attention space, and the attention vector is collected from the latent attention space.

[0029] Use the attention vector to predict three sets of fusion properties of the filter data: weight inertia coefficient, sensitivity coefficient, and priority coefficient, and output the weight inertia coefficient, sensitivity coefficient, and priority coefficient of the filter data;

[0030] Loading filtered data, where the filtered data includes an attention vector, a weighted inertia coefficient, a sensitivity coefficient, and a priority coefficient. The link discrimination network architecture calculates the link responsiveness of the filtered data based on the improved whale algorithm, and sets at least one set of link responsiveness of the filtered data as a cluster center.

[0031] The link response ratio of the flow link is calculated based on the improved whale algorithm, and the link response ratio is clustered using the fuzzy clustering algorithm. The flow links that meet the cluster center are screened, and at least one group of flow links is integrated and set as the recommended data flow link.

[0032] Output data analysis results and data flow recommendation links, and trigger data interaction instructions.

[0033] Preferably, when Fourier transform processing is performed on one-dimensional sequence data based on the Blackman window:

[0034]

[0035] S t =|Stft t (t,f s ) 2 (4)

[0036] Among them, S t is the output representation of the two-dimensional time-frequency spectrum, t is the sampling time, f s is the sampling frequency, x(t) represents one-dimensional sequence data, is the mean of one-dimensional sequence data, B(x) represents the Blackman window function, Stft t (t,f s ) is a two-dimensional function of one-dimensional sequence data;

[0037]

[0038] Among them, x(t) represents one-dimensional sequence data, is the mean of the one-dimensional sequence data, l and α represent the length and shape parameters of the Blackman window respectively;

[0039] The iterative filter filters the two-dimensional time-frequency spectrum and obtains the filtered data set using the following formula:

[0040]

[0041] Among them, S L t is the output representation of the filtered data set, L is the number of filtering times of the iterative filter, G(w) represents the amplitude response of the iterative filter, arg(·) represents the argument, w(D) represents the iterative filter, and SL is the sector loss of the iterative filter;

[0042] The weight inertia coefficient, sensitivity coefficient, and priority coefficient are determined by the following formula:

[0043]

[0044]

[0045]

[0046] Among them, T R is the attention vector input representation, θ t ,q t , γ represents the weight inertia coefficient, priority coefficient, and sensitivity coefficient, β is the attention space mapping coefficient, η is the sensitivity constant, S1 and S2 are S L tThe filtered data set is based on the first-order mapping coefficients and the second-order mapping coefficients of the sensitivity analysis algorithm.

[0047] Preferably, the method for interactively visualizing data analysis results by combining the particle swarm optimization algorithm and the ant colony optimization algorithm specifically includes:

[0048] Obtain data analysis results, initialize pheromones for the ant colony optimization algorithm, and generate the initial positions of ants;

[0049] Ants are sorted by integrating pheromones and path lengths of an ant colony optimization algorithm to obtain a ranking of at least one group of ants, and pheromone weighting is updated based on the ranking combined with a particle swarm optimization algorithm;

[0050] The weighted update of pheromones based on ranking combined with particle swarm optimization algorithm is expressed as:

[0051] ω i (t+1)=(1-ψ)ω i (t)+c1m1(L best ×Q+Δω i )+c2m2(L best ×Q-Δω i )(10)

[0052] Among them, ω i (t+1) represents the pheromone weighted update result after t+1 weighting, ω i (t) is the current pheromone input, ψ is the ranking weight, Q is the number of ants, Δω i Indicates the update of pheromone, L best represents the optimal path length, c1, c2 represent the particle swarm optimization acceleration factors, m1, m2 are random heuristic factors;

[0053] The ant position is updated in a spiral manner. When the ant position is updated, the fitness function constraint is used to calculate the fitness value of the ant position, and the ant position corresponding to the maximum fitness value is recorded until the maximum number of ant iterations is reached;

[0054] Integrate at least one set of ant positions, output user behavior prediction results based on the ant positions, use visualization tools to process the user behavior prediction results, and output interaction evaluation results.

[0055] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0056] In an embodiment of the present invention, a multidimensional data analysis model is constructed and trained based on deep learning combined with an improved whale algorithm. The multidimensional data analysis model can not only filter and fuse features of multidimensional time series data from multiple sources and heterogeneity, but also visualize and interactively process data analysis results by combining particle swarm optimization and ant colony optimization algorithms, and fuse users' short-term and long-term behavioral preferences to output accurate user behavior prediction results, thereby overcoming the problems in existing methods of low quality of multidimensional data from multiple sources and heterogeneity and inability to filter out multidimensional data noise when processing interactive data, resulting in incomplete capture of effective information in multidimensional data and poor fusion effect.

[0057] In an embodiment of the present invention, a multidimensional data analysis model and a training method thereof are provided. The multidimensional data analysis model combines a convolutional neural network, a SimAM attention mechanism, and an iterative filter, so that the multidimensional data analysis model has significant advantages in user behavior analysis. It can not only improve feature extraction and representation capabilities, improve model performance and generalization capabilities, but also combine the improved whale algorithm and the link discrimination network architecture to achieve optimal allocation of data flow links. The link discrimination network architecture uses a deep learning model to perform real-time discrimination and optimization of data flow links. This architecture can make full use of the data features of the attention vector, weight inertia coefficient, sensitivity coefficient, and priority coefficient to achieve accurate prediction and adjustment of the link status. The link discrimination network architecture combined with the improved whale algorithm can find the optimal link allocation solution in a wider search space. For the optimal allocation of data flow links, it means that the optimal link can be found faster and the data transmission efficiency can be improved.

[0058] In an embodiment of the present invention, an attention vector is used to comprehensively evaluate user behavior, and the attention vector integrates the weighted inertia coefficient, sensitivity coefficient, and priority coefficient of the filtered data. Through weighted fusion of the weighted inertia coefficient, sensitivity coefficient, and priority coefficient, on the one hand, it can assist the particle swarm algorithm and the ant colony optimization algorithm in visualizing and interactively processing the data analysis results, and accurately realize the user behavior prediction results. On the other hand, it can also assist in screening the flow links that meet the clustering center, thereby providing data flow efficiency and processing speed through edge computing, and reducing the overall load of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of the implementation flow of an interactive data processing method based on multidimensional data analysis provided by the present invention.

[0060] Figure 2 A schematic diagram of the implementation process of a method for training a multidimensional data analysis model through model training samples is shown.

[0061] Figure 3The figure shows a schematic diagram of the implementation process of the multidimensional data analysis model for analyzing and processing time series data sets based on edge computing.

[0062] Figure 4 The figure shows a flowchart of the implementation of the method for interactively processing the visualization of data analysis results by combining the particle swarm optimization algorithm and the ant colony optimization algorithm.

[0063] Figure 5 It is a structural diagram of the interactive data processing system based on multidimensional data analysis provided by the present invention. DETAILED DESCRIPTION

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0065] In the existing methods, when multidimensional data models process interactive data, the quality of multi-source heterogeneous multidimensional data is low and the noise of multidimensional data cannot be filtered out, resulting in incomplete capture of effective information of multidimensional data and poor fusion effect. To address the above problems, we propose an interactive data processing method based on multidimensional data analysis. In short, when the method is implemented, it first collects multidimensional time series data related to user behavior in real time based on the Internet of Things gateway, integrates and cleans the multidimensional time series data, and pre-builds a multidimensional data analysis model based on deep learning combined with the improved whale algorithm to traverse the interactive database. Then, the multidimensional data analysis model analyzes and processes the time series data set based on edge computing, outputs data analysis results and data flow recommendation links, triggers data interaction instructions, and interactively uploads the data analysis results to the visualization interaction platform based on the flow recommendation link. Finally, the particle swarm algorithm and ant colony optimization algorithm are combined to visualize and interactively process the data analysis results, and output interactive evaluation results. In an embodiment of the present invention, a multidimensional data analysis model is constructed and trained based on deep learning combined with an improved whale algorithm. The multidimensional data analysis model can not only filter and fuse features of multidimensional time series data from multiple sources and heterogeneity, but also visualize and interactively process data analysis results by combining particle swarm optimization and ant colony optimization algorithms, and fuse users' short-term and long-term behavioral preferences to output accurate user behavior prediction results, thereby overcoming the problems in existing methods of low quality of multidimensional data from multiple sources and heterogeneity and inability to filter out multidimensional data noise when processing interactive data, resulting in incomplete capture of effective information in multidimensional data and poor fusion effect.

[0066] The embodiment of the present invention provides an interactive data processing method based on multidimensional data analysis. Figure 1 The following is a schematic diagram of the implementation process of the interactive data processing method based on multidimensional data analysis, which specifically includes:

[0067] Step S10: collecting multi-dimensional time series data related to user behavior in real time based on the Internet of Things gateway, integrating and cleaning the multi-dimensional time series data, outputting a time series data set, and uploading the time series data set to an interactive database;

[0068] It should be noted that the IoT gateways are connected to multiple sets of sensors, including but not limited to edge gateways, industrial gateways, wireless gateways, multi-protocol gateways, and fog gateways. Multi-protocol gateways support a variety of communication protocols and standards, allowing devices using different protocols to seamlessly connect and communicate. These gateways are versatile and can handle diverse IoT device ecosystems, simplifying integration and interoperability challenges. Sensors include but are not limited to pressure sensors, vibration sensors, photoelectric sensors, angular displacement sensors, position sensors, biosensors, temperature sensors, and gas sensors. Multidimensional time-series data can be structured, unstructured, or semi-structured interactive behavior images, videos, and logs.

[0069] For example, multidimensional time-series data can include web page interaction data such as number of user clicks, page dwell time, and scroll depth. It can also include in-app operation records, such as button clicks, menu selections, and feature usage frequency. The interaction database consists of storage tables, indexes, views, and triggers, and can be a NoSQL database.

[0070] In this embodiment, the multi-dimensional time series data can be integrated and cleaned by deleting outliers and processing missing values.

[0071] Step S20: pre-build a multidimensional data analysis model based on deep learning combined with an improved whale algorithm, traverse the interactive database, load model training samples from the interactive database, train the multidimensional data analysis model using the model training samples, and output a converged multidimensional data analysis model;

[0072] In this embodiment, when loading model training samples from the interactive database, the batch export tool mongoexport can be used to export the samples in batches. The data of the model training samples come from a wide range of sources and can be collected from public websites, software (Gaussian, VASP, etc.) calculations, and literature.

[0073] Step S30, real-time loading of time series data sets, multi-dimensional data analysis model based on edge computing to analyze and process the time series data sets, output data analysis results and data flow recommendation links, trigger data interaction instructions, and interactively upload data analysis results to the visualization interaction platform based on the flow recommendation links.

[0074] Step S40: In response to the data interaction instruction, the visualization interaction platform triggers the visualization interaction logic based on the behavior analysis requirements, combines the particle swarm algorithm and the ant colony optimization algorithm to process the data analysis results visually, and outputs the interaction evaluation results.

[0075] In this embodiment, the visual interaction platform triggers the visual interaction logic based on behavioral analysis requirements. These requirements can include user preference requirements, user interest analysis requirements, user threat detection requirements analysis, and user demand expectation analysis. Through in-depth analysis of user behavior, product problems and deficiencies can be identified, providing a basis for product iteration. Furthermore, through analysis of user behavior data, user interests, preferences, and purchasing behaviors can be understood, thereby achieving precision marketing, improving marketing effectiveness, and realizing targeted marketing.

[0076] In an embodiment of the present invention, a multidimensional data analysis model is constructed and trained based on deep learning combined with an improved whale algorithm. The multidimensional data analysis model can not only filter and fuse features of multidimensional time series data from multiple sources and heterogeneity, but also visualize and interactively process data analysis results by combining particle swarm optimization and ant colony optimization algorithms, and fuse users' short-term and long-term behavioral preferences to output accurate user behavior prediction results, thereby overcoming the problems in existing methods of low quality of multidimensional data from multiple sources and heterogeneity and inability to filter out multidimensional data noise when processing interactive data, resulting in incomplete capture of effective information in multidimensional data and poor fusion effect.

[0077] The embodiment of the present invention provides a method for training a multidimensional data analysis model through model training samples. Figure 2 The following is a schematic diagram of the implementation process of a method for training a multidimensional data analysis model using model training samples. The method for training a multidimensional data analysis model using model training samples specifically includes:

[0078] Step S101: construct a multidimensional data analysis model, using a convolutional neural network as the initial model of the multidimensional data analysis model. The initial model includes an input layer, a convolution module, a pooling layer, and a fully connected layer. The loss function of the initial model is defined as the SIou loss function, and the iterative training rounds and hyperparameters of the initial model are set.

[0079] It should be noted that the training rounds of the initial model are 200-500 times, the hyperparameter learning rate is 0.002, and the Adam optimizer is used to adaptively adjust the hyperparameters.

[0080] In this embodiment, the convolution module consists of a first convolution layer, a second convolution layer, and a third convolution layer. The first convolution layer is frozen and replaced by an iterative filter. The number of channels of the second convolution layer is 256, and the convolution kernel size is 3*3. The third convolution layer is a SimAM module, the number of channels of the third convolution layer is 512, and the convolution kernel size is 3*3. The activation function of the convolution module is a ReLU function. The pooling layer introduces the SimAM attention mechanism, and the pooling layer adopts maximum pooling. The maximum value of the local area of the feature filtered by the convolution is taken to extract the most important feature information. The link discrimination network architecture is introduced into the initial model, and the improved whale algorithm is incorporated into the link discrimination network architecture. The link discrimination network architecture recommends links based on the feature weights of the training samples and the link response ratio.

[0081] It should be noted that the input layer is connected to the convolution module, the convolution module is connected to the pooling layer, a link discrimination network architecture is set between the pooling layer and the fully connected layer, and the link discrimination network architecture is connected to the fully connected layer. The activation function of the fully connected layer can be a Sigmoid function, which is more sensitive to outliers (that is, data points where the error between the predicted value and the actual value is much larger than other errors), thereby ensuring that the model will be more stable and accurate at the end of training.

[0082] Step S102, obtain model training samples, divide the model training samples into a training set and a validation set, input the training set, and perform filtering and noise reduction processing on the training set by the initial model to obtain a sample noise reduction set; in this embodiment, the ratio of the training set to the validation set can be 9:1.

[0083] Step S103: Load the sample denoising set, and iteratively train the initial model using the sample denoising set until convergence;

[0084] Step S104: Obtain a validation set, use the validation set as input, execute the initial model, and output the validation results;

[0085] Step S105, determining whether the error between the verification result and the actual result meets a preset accuracy threshold;

[0086] Step S106: if the preset accuracy threshold is met, output a converged multidimensional data analysis model;

[0087] Step S107: If the preset accuracy threshold is not met, the learning rate of the initial model is fine-tuned using the validation set combined with the fine-tuning mechanism, and the process returns to step S103 to continue iteratively training the initial model until convergence.

[0088] In this embodiment, the preset accuracy threshold can be 0.9-0.95. After the model is trained to convergence, statistical analysis software can be used to perform leave-one-out cross-validation calculation on the model, and the cross-validation correlation coefficient is calculated to be 0.876, ensuring that the cross-validation correlation coefficient value is greater than 0.5, proving that the model is robust.

[0089] In an embodiment of the present invention, a multidimensional data analysis model and a training method thereof are provided. The multidimensional data analysis model combines a convolutional neural network, a SimAM attention mechanism, and an iterative filter, so that the multidimensional data analysis model has significant advantages in user behavior analysis. It can not only improve feature extraction and representation capabilities, improve model performance and generalization capabilities, but also combine the improved whale algorithm and the link discrimination network architecture to achieve optimal allocation of data flow links. The link discrimination network architecture uses a deep learning model to perform real-time discrimination and optimization of data flow links. This architecture can make full use of the data features of the attention vector, weight inertia coefficient, sensitivity coefficient, and priority coefficient to achieve accurate prediction and adjustment of the link status. The link discrimination network architecture combined with the improved whale algorithm can find the optimal link allocation solution in a wider search space. For the optimal allocation of data flow links, it means that the optimal link can be found faster and the data transmission efficiency can be improved.

[0090] The embodiment of the present invention provides a method for analyzing and processing time series data sets based on edge computing using a multidimensional data analysis model. Figure 3 The following is a schematic diagram of the implementation process of a multidimensional data analysis model based on edge computing for analyzing and processing a time series data set. The method of analyzing and processing a time series data set based on edge computing for a multidimensional data analysis model specifically includes:

[0091] Step S201: obtaining a time series data set, performing dimensionality reduction processing on the multi-dimensional time series data in the time series data set, and generating one-dimensional sequence data;

[0092] Among them, the one-dimensional sequence data is expressed as:

[0093]

[0094] Where X represents the input representation of multidimensional time series data, i is the dimension of the data, which can be 1-10, θ t Represents the weight parameter of the data. The weight parameter of the data is defined based on the Huber weight function. x(t) is the output representation of the one-dimensional sequence data.

[0095]

[0096] Among them, θ trepresents the weight parameter of the data, m is the number of data types. In this embodiment, the number of data types is 4-15, ε is the weight threshold preset in Huber regression, and the weight threshold is set to 0.1-0.6, σ(X) is the residual of the multidimensional time series data, and X represents the mean of the multidimensional time series data;

[0097] In this embodiment of the present invention, dimensionality reduction processing is performed on multidimensional time-series data to generate one-dimensional sequence data, overcoming the high computational complexity and low efficiency that may be encountered with direct processing. Dimensionality reduction can map data from a high-dimensional space to a low-dimensional space, thereby reducing the data's dimensionality and improving the efficiency of data processing and analysis. The Huber weighting function, incorporated into the dimensionality reduction process, ensures that the reduced data still reflects the main trends and characteristics of the original data.

[0098] Step S202, loading one-dimensional sequence data, performing Fourier transform processing on the one-dimensional sequence data based on the Blackman window, and obtaining a unified two-dimensional time-frequency spectrum;

[0099] It's important to note that the Blackman window has certain filtering properties that allow it to simultaneously perform Fourier transforms and remove noise from signals, making it extremely useful for analyzing noisy signals. It is applicable in a wide range of scenarios, including audio processing, image processing, and other fields. Its broad application prospects make this method highly practical.

[0100] Step S203: filtering the two-dimensional time-frequency spectrum using an iterative filter to output a filtered data set, and fusing the filtered data set using a third convolutional layer to obtain a feature fusion set;

[0101] In this embodiment, when Fourier transform processing is performed on one-dimensional sequence data based on the Blackman window:

[0102]

[0103] S t =|Stft t (t,f s ) 2 (4)

[0104] Among them, S t is the output representation of the two-dimensional time-frequency spectrum, t is the sampling time, f s is the sampling frequency, x(t) represents one-dimensional sequence data, is the mean of one-dimensional sequence data, B(x) represents the Blackman window function, Stft t (t,f s ) is a two-dimensional function of one-dimensional sequence data;

[0105]

[0106] Among them, x(t) represents one-dimensional sequence data, is the mean of the one-dimensional sequence data, l and α represent the length and shape parameters of the Blackman window respectively.

[0107] Step S204: Load the feature fusion set. The pooling layer reduces the features in the feature fusion set based on the SimAM attention mechanism. A sensitivity analysis algorithm is used to consider the influence of associated data associated with the filtered data. The filtered data in the feature fusion set is mapped to a low-dimensional latent attention space, and attention vectors are collected from the latent attention space.

[0108] In this embodiment, the sensitivity analysis algorithm can be a global sensitivity analysis and a local sensitivity analysis algorithm. When the sensitivity analysis algorithm is used to consider the influence of the associated data associated with the filtered data, the global sensitivity analysis focuses on the influence of the associated data associated with the filtered data on the entire input space, while the local sensitivity analysis focuses on the influence of the associated data associated with the filtered data in a specific area. When the feature fusion concentrates the filtered data and maps it to a low-dimensional potential attention space, a deep learning model (such as an autoencoder, a variational autoencoder, etc.) can be used to map the fused features to a low-dimensional potential attention space. This latent space can capture the inherent structure and pattern in the data and provide support for subsequent attention mechanisms. During the mapping process, the model parameters can be optimized by minimizing the reconstruction error or maximizing the mutual information and other objective functions.

[0109] Step S205, use the attention vector to predict three sets of fusion properties of the filter data: weighted inertia coefficient, sensitivity coefficient, and priority coefficient, and output the weighted inertia coefficient, sensitivity coefficient, and priority coefficient of the filter data.

[0110] In an embodiment of the present invention, an attention vector is used to comprehensively evaluate user behavior, and the attention vector integrates the weighted inertia coefficient, sensitivity coefficient, and priority coefficient of the filtered data. Through weighted fusion of the weighted inertia coefficient, sensitivity coefficient, and priority coefficient, on the one hand, it can assist the particle swarm algorithm and the ant colony optimization algorithm in visualizing and interactively processing the data analysis results, and accurately realize the user behavior prediction results. On the other hand, it can also assist in screening the flow links that meet the clustering center, thereby providing data flow efficiency and processing speed through edge computing, and reducing the overall load of the system.

[0111] The iterative filter filters the two-dimensional time-frequency spectrum and obtains the filtered data set using the following formula:

[0112]

[0113] Among them, S L tis the output representation of the filtered data set, L is the number of filtering times of the iterative filter, G(w) represents the amplitude response of the iterative filter, arg(·) represents the argument, w(D) represents the iterative filter, and SL is the sector loss of the iterative filter;

[0114] The weight inertia coefficient, sensitivity coefficient, and priority coefficient are determined by the following formula:

[0115]

[0116]

[0117]

[0118] Among them, T R is the attention vector input representation, θ t ,q t , γ represents the weight inertia coefficient, priority coefficient, and sensitivity coefficient respectively, β is the attention space mapping coefficient. In this embodiment, the attention space mapping coefficient can be 0.1-0.5, η is the sensitivity constant. In this embodiment, the sensitivity constant is 1-5, S1, S2 are S L t The filtered data set is based on the first-order mapping coefficients and the second-order mapping coefficients of the sensitivity analysis algorithm.

[0119] In this embodiment, the first-order and second-order mapping coefficients are first-order and second-order effect indices based on the sensitivity analysis algorithm. The first-order mapping coefficient measures the contribution of a single parameter to the variance of the output variable. It reflects the proportion of the change in the output variance within a range of parameter changes. The second-order mapping coefficient measures the contribution of two input variables and their combination to the variance of the output variable.

[0120] Step S206: Loading filtered data, where the filtered data includes an attention vector, a weighted inertia coefficient, a sensitivity coefficient, and a priority coefficient. The link discrimination network architecture calculates the link responsiveness of the filtered data based on an improved whale algorithm, and sets at least one set of link responsiveness of the filtered data as a cluster center.

[0121] It's important to note that a flow link typically consists of the previous hop node where the data originates, the current node where the data resides, and the next hop node to which the data is sent. During data transmission, these nodes together form a complete flow link, the entire transmission path from source to destination. The clarity of this link directly impacts data transmission efficiency and security.

[0122] Step S207: Calculate the link response ratio of the flow link based on the improved whale algorithm, cluster the link response ratio using the fuzzy clustering algorithm, select the flow link that meets the cluster center, integrate at least one group of flow links, and set the flow link as the recommended data flow link;

[0123] The fuzzy clustering algorithm can be the Fuzzy C-Means (FCM) algorithm. When clustering link response ratio data using the fuzzy clustering algorithm, parameters such as the number of clusters, the fuzzy factor, the permissible error for iteration, and the maximum number of iterations are set. Through iterative calculations, the algorithm outputs a membership matrix and cluster center for each data point (i.e., each link). The membership matrix then determines the degree to which each link belongs to a cluster center. A higher membership indicates a closer link to the corresponding cluster center. A threshold (e.g., 0.8) is set, and links with a membership above this threshold are considered to belong to that cluster center. This threshold can be adjusted based on actual needs. This allows the selection of flow links that match a specific cluster center.

[0124] Step S208: output the data analysis results and the recommended data flow link, and trigger the data interaction instruction.

[0125] The embodiment of the present invention provides a method for interactively processing data analysis results by combining particle swarm optimization and ant colony optimization. Figure 4 The following is a schematic diagram of the implementation process of a method for visualizing and interactively processing data analysis results by combining a particle swarm algorithm and an ant colony optimization algorithm. The method for visualizing and interactively processing data analysis results by combining a particle swarm algorithm and an ant colony optimization algorithm specifically includes:

[0126] Step S301, obtaining data analysis results, initializing pheromones of the ant colony optimization algorithm, and generating initial ant positions;

[0127] Step S302, sorting the ants by integrating the pheromone and path length of the ant colony optimization algorithm to obtain a ranking of at least one group of the ants, and updating the pheromone weights based on the ranking combined with the particle swarm optimization algorithm;

[0128] The weighted update of pheromones based on ranking combined with particle swarm optimization algorithm is expressed as:

[0129] ω i (t+1)=(1-ψ)ω i (t)+c1m1(L best ×Q+Δω i )+c2m2(L best ×Q-Δω i )(10)

[0130] Among them, ω i(t+1) represents the pheromone weighted update result after t+1 weighting, ω i (t) represents the current pheromone input, ψ represents the ranking weight value. In this embodiment, the ranking weight value can be calculated by subjective weighting, objective weighting, and combined weighting. Q is the number of ants, Δω i Indicates the update of pheromone, L best represents the optimal path length, c1, c2 represent the particle swarm optimization acceleration factors, m1, m2 are random heuristic factors, and the random heuristic factor value range is 0-1;

[0131] Step S303: Update the ant positions in a spiral manner. When the ant positions are updated, the fitness function constraint is used to calculate the fitness value of the ant positions, and the ant position corresponding to the maximum fitness value is recorded until the maximum number of ant iterations is reached.

[0132] Step S304: Integrate at least one set of ant positions, output user behavior prediction results based on the ant positions, process the user behavior prediction results using a visualization tool, and output an interaction evaluation result.

[0133] It should be noted that visualization tools can be Tableau, Power BI, and Python libraries. Tableau provides a rich variety of chart types and interactive features, allowing users to customize and explore data. With Tableau, companies can create intuitive dashboards and reports that reflect user shopping behavior trends, key indicators, and potential problems.

[0134] In this embodiment, a particle swarm optimization algorithm and an ant colony optimization algorithm are combined to perform interactive visualization of data analysis results and output user behavior prediction results. This method has the advantages of strong optimization capabilities, a balance between global search and local development, high adaptability and flexibility, intuitive interactive visualization, high prediction accuracy, and real-time and dynamic updates. These advantages make this method promising for broad application in multiple fields.

[0135] The embodiment of the present invention provides an interactive data processing system based on multidimensional data analysis. Figure 5 The structure diagram of the interactive data processing system based on multidimensional data analysis is shown. The interactive data processing system based on multidimensional data analysis specifically includes:

[0136] The data collection module 100 collects multi-dimensional time series data related to user behavior in real time based on the Internet of Things gateway, integrates and cleans the multi-dimensional time series data, outputs a time series data set, and uploads the time series data set to the interactive database;

[0137] Model building module 200, which pre-builds a multidimensional data analysis model based on deep learning combined with an improved whale algorithm, traverses the interactive database, loads model training samples from the interactive database, trains the multidimensional data analysis model using the model training samples, and outputs a converged multidimensional data analysis model;

[0138] The multidimensional analysis module 300 is used to load time-series data sets in real time. The multidimensional data analysis model analyzes and processes the time-series data sets based on edge computing, outputs data analysis results and data flow recommendation links, triggers data interaction instructions, and interactively uploads data analysis results to the visualization interaction platform based on the flow recommendation links.

[0139] The interaction processing module 400 responds to the data interaction instruction. The visualization interaction platform triggers the visualization interaction logic based on the behavior analysis requirements, combines the particle swarm algorithm and the ant colony optimization algorithm to visualize the data analysis results, and outputs the interaction evaluation results.

[0140] In this embodiment, data acquisition module 100, model building module 200, multidimensional analysis module 300, and interactive processing module 400 utilize DTU or 5G communication to achieve interactive data transmission. It is understood that the interactive data processing system based on multidimensional data analysis provided in this embodiment of the present invention corresponds to the interactive data processing method based on multidimensional data analysis described above. For explanations, examples, and beneficial effects of the relevant content, reference can be made to the corresponding content in the interactive data processing method based on multidimensional data analysis, and will not be repeated here.

[0141] On the other hand, an embodiment of the present invention further provides a computer device, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the method of any one of the above embodiments is implemented.

[0142] As a non-volatile computer-readable storage medium, the memory can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the program instructions / modules corresponding to the interactive data processing method based on multidimensional data analysis in the embodiment of the present application. The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of the interactive data processing method based on multidimensional data analysis, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0143] The processor executes various functional applications and data processing of the server by running non-volatile software programs, instructions and modules stored in the memory, that is, realizes the interactive data processing method based on multidimensional data analysis of the above method embodiment.

[0144] In summary, the present invention provides an interactive data processing method based on multidimensional data analysis. In an embodiment of the present invention, a multidimensional data analysis model is constructed and trained based on deep learning combined with an improved whale algorithm. The multidimensional data analysis model can not only filter and fuse features of multi-source heterogeneous multidimensional time series data, but also visualize and interactively process the data analysis results by combining particle swarm optimization and ant colony optimization algorithms, and fuse users' short-term and long-term behavioral preferences to output accurate user behavior prediction results, thereby overcoming the problems in existing methods of low quality of multi-source heterogeneous multidimensional data and inability to filter out multidimensional data noise when processing interactive data with multidimensional data models, resulting in incomplete capture of effective information of multidimensional data and poor fusion effect.

[0145] It should be noted that for the aforementioned embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0146] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the scope of protection of the invention. Obviously, the embodiments described are only some embodiments of the present invention, rather than all embodiments. Based on these embodiments, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in this field can still combine, add, delete or make other adjustments to the features in the various embodiments of the present invention according to the circumstances without conflict, without making creative work, so as to obtain different other technical solutions that do not deviate from the concept of the present invention in essence, and these technical solutions also fall within the scope of protection of the present invention.

Claims

1. An interactive data processing method based on multidimensional data analysis, characterized in that: The interactive data processing method based on multidimensional data analysis includes: Based on the IoT gateway, multi-dimensional time series data related to user behavior is collected in real time, the multi-dimensional time series data is integrated, cleaned, and processed, and the time series data set is output, and the time series data set is uploaded to the interactive database; Pre-build a multidimensional data analysis model based on deep learning combined with an improved whale algorithm, traverse the interactive database, load model training samples from the interactive database, train the multidimensional data analysis model with the model training samples, and output a converged multidimensional data analysis model; Among them, the convolutional neural network is used as the initial model of the multidimensional data analysis model, and the initial model includes an input layer, a convolution module, a pooling layer and a fully connected layer; the convolution module consists of a first convolution layer, a second convolution layer, and a third convolution layer. The first convolution layer is frozen and replaced by an iterative filter. The number of channels of the second convolution layer is 256, and the convolution kernel size is 3*3. The third convolution layer is a SimAM module, the number of channels of the third convolution layer is 512, and the convolution kernel size is 3*3. The activation function of the convolution module is a ReLU function, and the pooling layer introduces the SimAM attention mechanism. The pooling layer adopts maximum pooling, takes the maximum value of the local area of the feature filtered by the convolution, and extracts the most important feature information. The link discrimination network architecture is introduced in the initial model, and the improved whale algorithm is incorporated into the link discrimination network architecture. The link discrimination network architecture discriminates the data flow and recommends links based on the training sample feature weights and the link response ratio. Real-time loading of time-series data sets. The multidimensional data analysis model analyzes and processes the time-series data sets based on edge computing, outputs data analysis results and data flow recommendation links, triggers data interaction instructions, and interactively uploads data analysis results to the visualization interaction platform based on the flow recommendation links. The multidimensional data analysis model is based on the edge computing method for analyzing and processing time series data sets, specifically including: Loading filtered data, where the filtered data includes an attention vector, a weighted inertia coefficient, a sensitivity coefficient, and a priority coefficient. The link discrimination network architecture calculates the link responsiveness of the filtered data based on the improved whale algorithm, and sets at least one set of link responsiveness of the filtered data as a cluster center. The link response ratio of the flow link is calculated based on the improved whale algorithm, and the link response ratio is clustered using the fuzzy clustering algorithm. The flow links that meet the cluster center are screened, and at least one group of flow links is integrated and set as the recommended data flow link. Output data analysis results and data flow recommendation links, and trigger data interaction instructions.

2. The interactive data processing method based on multidimensional data analysis according to claim 1, characterized in that: The interactive data processing method based on multidimensional data analysis further includes: In response to data interaction instructions, the visualization interaction platform triggers the visualization interaction logic based on the behavior analysis requirements, combines the particle swarm algorithm and the ant colony optimization algorithm to visualize and interactively process the data analysis results, and outputs the interaction evaluation results.

3. The interactive data processing method based on multidimensional data analysis according to claim 1, characterized in that: The method of training a multidimensional data analysis model using model training samples specifically includes: Construct a multidimensional data analysis model, using a convolutional neural network as the initial model. The initial model includes an input layer, a convolution module, a pooling layer, and a fully connected layer. Define the loss function of the initial model as the SIou loss function, and set the iterative training rounds and hyperparameters of the initial model. Obtain model training samples, divide the model training samples into a training set and a validation set, input the training set, and perform filtering and noise reduction processing on the training set by the initial model to obtain a sample noise reduction set; Load the sample denoising set and use it to iteratively train the initial model until convergence; Obtain a validation set, use the validation set as input, execute the initial model, output the validation results, and judge whether the error between the validation results and the actual results meets the preset accuracy threshold; If the preset accuracy threshold is met, a converged multidimensional data analysis model is output; If the preset accuracy threshold is not met, the learning rate of the initial model is fine-tuned using the validation set combined with the fine-tuning mechanism, and the initial model is iteratively trained until convergence.

4. The interactive data processing method based on multidimensional data analysis according to claim 3, characterized in that: The multidimensional data analysis model is based on the edge computing method for analyzing and processing time series data sets, specifically including: Obtain a time series data set, perform dimensionality reduction processing on the multi-dimensional time series data in the time series data set, and generate one-dimensional sequence data; Among them, the one-dimensional sequence data is expressed as: （1） in, Represents the input representation of multidimensional time series data, is the dimension of the data, Represents the weight parameter of the data, which is defined based on the Huber weight function. is the output representation of one-dimensional sequence data; （2） in, represents the weight parameter of the data, is the number of data types, is the preset weight threshold in Huber regression, is the residual of multidimensional time series data, Represents the mean of multidimensional time series data; Load one-dimensional sequence data and perform Fourier transform on the one-dimensional sequence data based on the Blackman window to obtain a unified two-dimensional time-frequency spectrum; An iterative filter is used to filter the two-dimensional time-frequency spectrum and output a filtered data set. The third convolutional layer fuses the filtered data set to obtain a feature fusion set. Load the feature fusion set. The pooling layer reduces the features in the feature fusion set based on the SimAM attention mechanism. The sensitivity analysis algorithm is used to consider the impact of the associated data associated with the filtered data. The filtered data in the feature fusion set is mapped to a low-dimensional latent attention space, and the attention vector is collected from the latent attention space. Use the attention vector to predict three sets of fusion properties of the filtered data: weight inertia coefficient, sensitivity coefficient, and priority coefficient, and output the weight inertia coefficient, sensitivity coefficient, and priority coefficient of the filtered data.

5. The interactive data processing method based on multidimensional data analysis according to claim 4, characterized in that: When performing Fourier transform processing on one-dimensional sequence data based on the Blackman window: （3） （4） in, is the output representation of the two-dimensional time-spectrum graph, is the sampling time, is the sampling frequency, represents one-dimensional sequence data, is the mean of the one-dimensional sequence data, represents the Blackman window function, is a two-dimensional function of one-dimensional sequence data; （5） in, represents one-dimensional sequence data, is the mean of the one-dimensional sequence data, represent the length and shape parameters of the Blackman window, respectively.

6. The interactive data processing method based on multidimensional data analysis according to claim 5, characterized in that: The iterative filter filters the two-dimensional time-frequency spectrum and obtains the filtered data set using the following formula: （6） in, is the output representation of the filtered dataset, is the number of filtering iterations, represents the magnitude response of the iterative filter, Indicates the angle of argument, represents an iterative filter, is the fan-shaped loss of the iterative filter; The weight inertia coefficient, sensitivity coefficient, and priority coefficient are determined by the following formula: （7） （8） （9） in, is the attention vector input representation, Represent weight inertia coefficient, priority coefficient, and sensitivity coefficient respectively. is the attention space mapping coefficient, is the sensitivity constant, They are The filtered data set is based on the first-order mapping coefficients and the second-order mapping coefficients of the sensitivity analysis algorithm.

7. The interactive data processing method based on multidimensional data analysis according to claim 2, characterized in that: The method for interactively visualizing data analysis results by combining the particle swarm optimization algorithm and the ant colony optimization algorithm specifically includes: Obtain data analysis results, initialize pheromones for the ant colony optimization algorithm, and generate the initial positions of ants; Ants are sorted by integrating pheromones and path lengths of an ant colony optimization algorithm to obtain a ranking of at least one group of ants, and pheromone weighting is updated based on the ranking combined with a particle swarm optimization algorithm; The weighted update of pheromones based on ranking combined with particle swarm optimization algorithm is expressed as: （10） in, express The pheromone weighted update result after the second weighting, is the current pheromone input representation, Represents the ranking weight value, is the number of ants, Indicates the update of pheromone, represents the optimal path length, They represent the particle swarm optimization acceleration factor, is the random heuristic factor.

8. The interactive data processing method based on multidimensional data analysis according to claim 7, characterized in that: The method for interactively processing the data analysis results by combining the particle swarm optimization algorithm and the ant colony optimization algorithm specifically includes: The ant position is updated in a spiral manner. When the ant position is updated, the fitness function constraint is used to calculate the fitness value of the ant position, and the ant position corresponding to the maximum fitness value is recorded until the maximum number of ant iterations is reached; Integrate at least one set of ant positions, output user behavior prediction results based on the ant positions, use visualization tools to process the user behavior prediction results, and output interaction evaluation results.

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