A method and device for analyzing multidimensional data based on holography

Abstract

The present application relates to the technical field of data analysis, and discloses a method for analyzing multidimensional data based on holography, including: obtaining corresponding multidimensional data to be analyzed, preprocessing the multidimensional data to be analyzed, inputting the preprocessed multidimensional data to be analyzed into a multidimensional relationship model for analysis, and obtaining corresponding preliminary analysis results; determining the corresponding target user, and displaying the preliminary analysis results to the user in a three-dimensional stereoscopic image through real-time dynamic updating based on historical behavior, current context, and holographic projection technology. When the multidimensional data to be analyzed changes, the visual interface corresponding to the preliminary analysis results is updated; obtaining corresponding feedback data, and judging whether to trigger an optimization mechanism based on the feedback data. If triggered, optimization is performed through a preset optimization mechanism based on the feedback data and the preliminary analysis results to obtain the corresponding target analysis results. The present application can realize efficient and accurate analysis of multidimensional data.

Description

Technical Field

[0001] The present application relates to the technical field of data analysis, and in particular to a method and device for holographic multidimensional data analysis. Background Art

[0002] With the advent of the big data era, data volumes are growing exponentially. Traditional one- or two-dimensional data analysis methods are no longer sufficient for complex scenarios. Especially when considering spatial, temporal, and multiple attribute factors simultaneously, efficiently and accurately processing this multidimensional information has become a pressing challenge. While some existing solutions attempt to improve processing power by increasing computing resources, they often overlook the inherent connections between data, resulting in incomplete or inefficient analysis results.

[0003] Furthermore, in fields like medical diagnosis and environmental monitoring, where data authenticity and intuitiveness are paramount, traditional display methods struggle to achieve the desired results. Therefore, developing a data analysis method that comprehensively considers multi-dimensional information and presents it in a more intuitive form is crucial.

[0004] From the above, we can see that how to achieve efficient and accurate analysis of multi-dimensional data still needs to be solved. Summary of the Invention

[0005] In order to achieve efficient and accurate analysis of multi-dimensional data, the present application provides a holographic multi-dimensional data analysis method and device.

[0006] In the first aspect, the present application provides a holographic multidimensional data analysis method, which adopts the following technical solutions:

[0007] A holographic multidimensional data analysis method includes: obtaining corresponding multidimensional data to be analyzed, preprocessing the multidimensional data to be analyzed, retrieving a pre-trained multidimensional relationship model, inputting the preprocessed multidimensional data to be analyzed into the multidimensional relationship model for analysis, and obtaining corresponding preliminary analysis results; wherein the preprocessing includes adopting an adaptive data cleaning algorithm and a dynamic anomaly detection mechanism, and fusing the multidimensional relationship model with a graph neural network, time series analysis, and a hybrid model; determining a corresponding target user, retrieving the target user's corresponding historical behavior and current context, and presenting the preliminary analysis results to the user in a three-dimensional stereoscopic image through real-time dynamic updating based on the historical behavior, current context, and holographic projection technology; when the multidimensional data to be analyzed changes, updating a visualization interface corresponding to the preliminary analysis results through an incremental update mechanism; obtaining corresponding feedback data during the target user's interaction with the visualization interface, determining whether to trigger an optimization mechanism based on the feedback data, and if triggered, performing optimization based on the feedback data and the preliminary analysis results through a preset optimization mechanism to obtain the corresponding target analysis result, wherein the feedback data includes an evaluation of the visualization interface results, a record of the interaction behavior, and improvement suggestions proposed by the user.

[0008] By adopting the above technical solutions, multidimensional data is preprocessed through adaptive data cleaning and dynamic anomaly detection, and efficient and accurate analysis is performed using a multidimensional relational model that integrates graph neural networks, time series analysis, and hybrid models. Combining the target user's historical behavior and current context, the system uses holographic projection technology to dynamically display preliminary analysis results in three-dimensional stereoscopic form in real time, and incrementally updates them based on data changes. Furthermore, the system collects feedback data from user interactions with the visualization interface, triggering optimization mechanisms to further improve the quality of analysis results, ensuring an efficient and accurate analysis process while providing a highly personalized user experience.

[0009] Optionally, after retrieving the historical behavior and current context corresponding to the target user, the method further includes: obtaining the target user's preference settings, extracting corresponding key features for the preference settings, the historical behavior and the current context, wherein the key features include the target user's most frequently accessed data types, most frequently used functions, and preferred time periods; adjusting the display priority of each data in the preliminary analysis results based on the key features; and displaying the preliminary analysis results to the user in a three-dimensional stereoscopic image based on the adjusted display priority.

[0010] By adopting the above technical solution, key features (such as the most frequently accessed data types, the most frequently used functions, and the preferred time periods) are extracted by comprehensively considering the target user's preference settings, historical behaviors, and current context. Based on these features, the display priority of each data in the preliminary analysis results is dynamically adjusted, thereby presenting the analysis results through three-dimensional stereoscopic images in a manner that better meets user needs.

[0011] Optionally, the method also includes: determining different perspective dimensions that need to be analyzed based on the multidimensional data to be analyzed, wherein the different perspective dimensions include time dimension, space dimension, and category dimension; performing multi-perspective analysis on the multidimensional data to be analyzed based on the different perspective dimensions, and obtaining preliminary analysis results corresponding to each perspective, wherein the multi-perspective analysis includes time dimension analysis, space dimension analysis, category dimension analysis, and other dimension analysis; synthesizing the preliminary analysis results from different perspectives to generate a corresponding multi-perspective analysis report, and presenting the multi-perspective analysis results to the user in the form of a three-dimensional stereo image, wherein the multi-perspective analysis report contains key findings and conclusions for each perspective.

[0012] By adopting the above technical solution, multi-dimensional data is analyzed from multiple perspectives, covering multiple dimensions such as time, space, and category. The preliminary analysis results from different perspectives are integrated to generate a comprehensive multi-perspective analysis report, which is presented to users in the form of three-dimensional stereo images, thereby providing deeper and more comprehensive data insights and helping users understand and interpret data from multiple perspectives.

[0013] Optionally, after obtaining the preliminary analysis results, the method further includes: calling pre-set expert rules, wherein the expert rules include the experience and knowledge of domain experts; evaluating the preliminary analysis results according to the rules defined by the expert rules through an inference engine to obtain corresponding evaluation results; comparing the preliminary analysis results with the evaluation results, and if the comparison results are inconsistent, adjusting the preliminary analysis results.

[0014] By adopting the above technical solution, after obtaining the preliminary analysis results, the results are evaluated using pre-set expert rules and reasoning engines, and adjustments are made when the preliminary analysis results are inconsistent with the evaluation results, thereby integrating the experience and knowledge of domain experts and improving the accuracy and reliability of the analysis results.

[0015] Optionally, after obtaining the preliminary analysis results, the method further includes: calling a corresponding anomaly detection algorithm, using the anomaly detection algorithm to detect potential anomalies in the preliminary analysis results, generating a corresponding anomaly detection report, wherein the anomaly detection report marks all detected anomalies; obtaining abnormal feedback from the target user on the anomaly detection report, wherein the abnormal feedback includes the user's confirmation of the abnormal value, correction suggestions, etc.; combining the anomaly detection report with the abnormal feedback, and re-evaluating the anomaly to determine whether there are omissions or misjudgments.

[0016] By adopting the above technical solution, potential outliers are identified and reports are generated through the anomaly detection algorithm, and the feedback from target users on the outliers is re-evaluated to ensure the accuracy and completeness of anomaly detection and reduce omissions and misjudgments.

[0017] Optionally, the method also includes: performing uncertainty assessment on each predicted value in the preliminary analysis results to generate corresponding confidence levels; sorting based on all confidence levels, and highlighting the results corresponding to the results with the highest confidence levels when displayed on the visual interface; and generating a corresponding explanation report for the results corresponding to the results with the lowest confidence levels, wherein the explanation report includes possible causes, influencing factors, and recommended further verification methods.

[0018] By adopting the above technical solution, uncertainty assessment is performed on each predicted value in the preliminary analysis results and a confidence level is generated. High-confidence results are highlighted in the visual interface according to the confidence level, and detailed explanation reports are generated for low-confidence results, thereby helping users better understand the reliability and potential uncertainties of the analysis results.

[0019] In a second aspect, the present application provides a holographic multi-dimensional data analysis device, which adopts the following technical solution:

[0020] A holographic multidimensional data analysis device, comprising:

[0021] A preliminary analysis result acquisition module acquires the corresponding multidimensional data to be analyzed, preprocesses the multidimensional data to be analyzed, retrieves a pre-trained multidimensional relationship model, and inputs the pre-processed multidimensional data to be analyzed into the multidimensional relationship model for analysis to obtain the corresponding preliminary analysis results; wherein, the preprocessing includes the use of an adaptive data cleaning algorithm and a dynamic anomaly detection mechanism, and the multidimensional relationship model integrates graph neural networks, time series analysis, and hybrid models;

[0022] The preliminary analysis result display module determines the corresponding target user, retrieves the historical behavior and current context corresponding to the target user, and uses the historical behavior, current context and holographic projection technology to dynamically update the preliminary analysis results in real time to display them to the user in a three-dimensional image. When the multi-dimensional data to be analyzed changes, the visualization interface corresponding to the preliminary analysis results is updated through an incremental update mechanism;

[0023] The target analysis result acquisition module obtains the corresponding feedback data during the interaction between the target user and the visualization interface, and determines whether to trigger the optimization mechanism based on the feedback data. If triggered, optimization is performed through a preset optimization mechanism based on the feedback data and the preliminary analysis results to obtain the corresponding target analysis results, wherein the feedback data includes the evaluation of the visualization interface results, the interaction behavior record, and the improvement suggestions proposed by the user.

[0024] In a third aspect, the present application provides a holographic multidimensional data analysis method, which adopts the following technical solutions:

[0025] A method for analyzing multidimensional data based on holography includes a processor in which a program of any one of the above-mentioned methods for analyzing multidimensional data based on holography is running.

[0026] In a fourth aspect, the present application provides a storage medium, which adopts the following technical solution:

[0027] A storage medium stores a program according to any one of the above-mentioned methods for analyzing holographic multidimensional data.

[0028] In summary, this application includes at least one of the following beneficial technical effects:

[0029] 1. The holographic multidimensional data analysis method preprocesses multidimensional data through adaptive data cleaning and dynamic anomaly detection, and utilizes a multidimensional relational model that integrates graph neural networks, time series analysis, and hybrid models for efficient and accurate analysis. Based on the target user's historical behavior, current context, and preferences, the system dynamically adjusts the display priority of each data item in the preliminary analysis results and uses holographic projection technology to dynamically display the analysis results in three-dimensional, real-time visualization. Furthermore, the system incorporates expert rules and inference engines to evaluate and adjust preliminary analysis results, ensuring their accuracy and reliability. Furthermore, through anomaly detection algorithms and uncertainty quantification techniques, the quality and credibility of the analysis results are further improved.

[0030] 2. Through multi-perspective analysis technology, this method comprehensively analyzes data from multiple dimensions, including time, space, and category, generating comprehensive multi-perspective analysis reports and presenting them to users in the form of three-dimensional images, providing deeper and more comprehensive data insights. The system also collects feedback from users interacting with the visualization interface, triggering optimization mechanisms to further improve the quality of analysis results, ensuring an efficient and accurate analysis process while providing a highly personalized user experience. These technical approaches work together to make multi-dimensional data analysis not only efficient and accurate, but also more tailored to users' actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The figure is a flow chart showing a method for analyzing holographic multi-dimensional data according to an exemplary embodiment.

[0032] Figure 2 It is a structural block diagram of a holographic multi-dimensional data analysis device according to an exemplary embodiment. DETAILED DESCRIPTION

[0033] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0034] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0035] The present application discloses a method for analyzing multidimensional data based on holography. Figure 1 ,include:

[0036] S100, obtaining corresponding multidimensional data to be analyzed, preprocessing the multidimensional data to be analyzed, retrieving a pre-trained multidimensional relationship model, inputting the preprocessed multidimensional data to be analyzed into the multidimensional relationship model for analysis, and obtaining corresponding preliminary analysis results.

[0037] This involves collecting multidimensional data from various data sources (such as databases, sensors, and log files). This data may include time series data, structured data, and unstructured data. Data from different sources must be integrated to ensure consistency in data format. This may involve operations such as data conversion and alignment, and preliminary checks on data quality to ensure data integrity and consistency. For example, checks may be made for missing values, duplicate records, or inconsistent data.

[0038] When pre-processing the multidimensional data to be analyzed, adaptive data cleaning algorithms and dynamic anomaly detection mechanisms are used. Specifically:

[0039] Adaptive data cleaning algorithm:

[0040] 1. Missing value processing: Use interpolation methods (such as linear interpolation, KNN interpolation), statistical methods (such as mean, median filling) or other machine learning methods (such as predictive models) to fill missing values.

[0041] 2. Outlier detection and correction: Use statistical methods (such as Z-score, IQR) or machine learning methods (such as isolation forest, local outlier factor) to detect outliers, and choose to retain, correct or delete these outliers based on the specific situation.

[0042] 3. Data standardization / normalization: Standardize or normalize the data to make it meet the model input requirements. Common methods include Z-score normalization and Min-Max normalization.

[0043] 4. Data denoising: Use smoothing techniques (such as moving average and exponential smoothing) to reduce noise in the data and improve data quality.

[0044] Dynamic anomaly detection mechanism:

[0045] 1. Real-time monitoring: Monitor new data points in the data stream in real time and detect outliers.

[0046] 2. Dynamic update: Continuously update the anomaly detection model based on new data to ensure that it can adapt to changes in data.

[0047] 3. Feedback mechanism: Combine user feedback and historical data to adjust anomaly detection thresholds and rules to improve detection accuracy.

[0048] Next, we select a multidimensional relational model suitable for the task at hand. This model combines graph neural networks (GNNs), time series analysis (such as LSTM and ARIMA), and hybrid models (such as ensemble learning methods). We then load a pre-trained multidimensional relational model from storage. This model has been trained on a large amount of data and can capture complex dependencies and time series characteristics between data. We also need to set hyperparameters and other configurations for the model to ensure its efficient operation.

[0049] Convert the preprocessed data into the input format required by the model. This may include operations such as feature engineering and data segmentation. The data is then fed into a multidimensional relational model, which analyzes the data and generates preliminary analysis results. Specific analysis tasks may include prediction, classification, and clustering. Preliminary analysis results include predicted values, classification labels, and clustering results.

[0050] S110, determine the corresponding target user, retrieve the historical behavior and current context corresponding to the target user, and display the preliminary analysis results to the user in a three-dimensional stereo image through real-time dynamic update based on the historical behavior, current context and holographic projection technology. When the multi-dimensional data to be analyzed changes, the visual interface corresponding to the preliminary analysis results is updated through the incremental update mechanism.

[0051] Among them, the user currently using the system is determined through user login information, device identifier or other authentication mechanisms, and then the relevant information of the target user is retrieved from the user database or user management system, including basic information (such as name, role), historical behavior data (such as past query records, operation records) and preference settings (such as interface layout preferences, color preferences).

[0052] Collect historical behavior data of target users from data sources such as user activity logs and operation records. This data may include past query records, click behaviors, browsing history, etc. The collected historical behavior data is then cleaned and organized to extract key features, such as the most frequently accessed data types, most frequently used functions, and preferred time periods.

[0053] At the same time, the system obtains the user's current contextual information, such as geographic location, device type, time, and environmental conditions; this contextual information is combined with historical behavior data to generate comprehensive user contextual information. By combining the user's historical behavior and current context, the analysis results are more closely aligned with the user's actual needs and circumstances, improving the personalization and accuracy of the analysis.

[0054] Holographic projection technology converts preliminary analysis results into a format suitable for holographic projection display, such as three-dimensional point clouds, mesh models, etc., and requires designing the layout and style of three-dimensional stereo images to ensure clear presentation of information, for example, using different colors, shapes, and sizes to represent different types of data.

[0055] Holographic projection technology can also be used to render three-dimensional images in real time and dynamically adjust the displayed content based on the user's behavior and context. In addition, user interaction is also possible, including:

[0056] 1. Gesture control: Allows users to rotate, zoom, and pan views through gestures for interaction;

[0057] 2. Voice control: supports voice commands, users can operate through voice commands, such as "zoom in", "rotate", etc.

[0058] 3. Touch operation: On devices that support touch screens, users can interact by touching the screen.

[0059] When the multidimensional data to be analyzed changes, the data source is continuously monitored for changes, detecting new data inflows and verifying data changes through methods such as data version control or hash verification. It is important to note that only the changed data is preprocessed and analyzed. If the data changes significantly, the model may need to be retrained or fine-tuned to adapt to the new data pattern. Simultaneously, the analysis model is rerun based on the changed data to generate new analysis results, which are then updated in real time to the visualization interface, ensuring that users see the latest data and analysis results.

[0060] Through these steps, not only the accuracy and efficiency of data analysis are improved, but also a highly personalized user experience is provided, ensuring that users can understand complex data analysis results in a timely and intuitive manner.

[0061] S120, obtaining corresponding feedback data during the interaction between the target user and the visualization interface, and determining whether to trigger the optimization mechanism based on the feedback data. If triggered, optimizing is performed through a preset optimization mechanism based on the feedback data and the preliminary analysis results to obtain the corresponding target analysis results.

[0062] Collect user feedback on the visualization interface, such as satisfaction ratings, comments, and suggestions. Also record user interactions, such as clicks, drags, and zooms. These actions can reflect user interest in specific data or views. Also collect user suggestions for improvements, including recommendations for analysis results, presentation methods, or functionality. Categorize this feedback into different types, such as positive feedback, negative feedback, and specific suggestions.

[0063] In addition, natural language processing (NLP) technology is used to conduct sentiment analysis on user reviews to determine users' emotional tendencies (such as satisfaction or dissatisfaction); analyze users' interactive behavior records to identify their behavior patterns and preferences; and extract key information from improvement suggestions put forward by users, such as specific improvement points and suggested content.

[0064] By setting thresholds for triggering optimization mechanisms, such as when the proportion of negative reviews exceeds a certain threshold or when users frequently make similar suggestions, the system then determines whether the conditions for triggering the optimization mechanism are met based on pre-set rules. For example, if multiple users make the same improvement suggestion, or if the proportion of negative reviews exceeds a set threshold, the optimization mechanism will be triggered.

[0065] If triggered, the optimization mechanism is selected first: 1. Parameter adjustment: Adjust the model's hyperparameters, such as learning rate, regularization coefficient, etc., based on user feedback; 2. Model retraining: If user feedback indicates that the model has large deviations or deficiencies, retrain the model, which may require more data or a more complex model structure; 3. Rule update: Update business rules or logic based on specific user suggestions to better meet user needs; 4. Interface optimization: Adjust the interface layout, color, font, etc. based on user feedback on the interface to improve user experience.

[0066] Based on user feedback and preliminary analysis results, targeted optimization is performed through a preset optimization mechanism. For example, the optimized model can be used to reanalyze the data to generate new analysis results. The new analysis results are compared with the previous preliminary analysis results to ensure consistency. The optimized target analysis results are then output for visualization.

[0067] Finally, the optimized target analysis results are presented to the user in the form of three-dimensional images through holographic projection technology, and the user can be informed of the updated analysis results through system notifications or emails, and invited to view and provide feedback.

[0068] Based on the execution steps S100 to S120 above, advanced machine learning models (such as neural networks, time series analysis, and hybrid models) are used to conduct in-depth data analysis, extracting complex patterns and trends within the data and generating preliminary analysis results. By integrating historical user behavior and current context, the analysis results are more closely aligned with the user's actual needs. The analysis results are presented through intuitive three-dimensional images, improving user understanding and interaction experience. The display content is dynamically updated in real time, adjusting the display based on user behavior and context to enhance the user experience. The system also understands user satisfaction and needs regarding the analysis results, providing a basis for subsequent optimization. By collecting user feedback, interaction records, and improvement suggestions during the interaction process, the system ensures that the system is responsive to user needs. Analysis results are adjusted promptly based on user feedback to improve user experience and satisfaction. If user feedback contains important improvement suggestions or negative comments, optimization mechanisms are triggered to ensure continuous system improvement and refinement. Pre-defined optimization mechanisms (such as parameter adjustment, model retraining, and rule updates) are used to improve the quality and accuracy of analysis results to better meet user needs.

[0069] After retrieving the target user's corresponding historical behavior and current context, the method further includes:

[0070] S111, obtaining the target user's preference settings, and extracting corresponding key features of the preference settings, historical behaviors, and current context.

[0071] Among them, the target user's preference settings are obtained from the user database or user profile. These preference settings may include interface layout, color preference, font size, display mode, etc.; then the preference setting data is parsed to extract specific configuration information. For example, the user may prefer data display in the form of a chart, or like a specific color theme.

[0072] Key features include the target user's most frequently accessed data types, most frequently used functions, and preferred time periods. Key features extracted from preference settings, historical behaviors, and current context are integrated to form a comprehensive user feature set. Specifically:

[0073] 1. Preference features: Extract key features from user preferences, such as the user's preferred data display method (chart, table), color theme, font size, etc.

[0074] 2. Historical behavior features: Extract key features from users' historical behavior data, such as the most frequently accessed data types (such as sales data, inventory data), the most frequently used functions (such as query, export), and preferred time periods (such as weekdays, weekends).

[0075] 3. Current context features: Extract key features from the user’s current context, such as geographic location, device type (mobile phone, computer), time (daytime, nighttime), etc.

[0076] By extracting and integrating key features, we can better understand user behavior patterns and preferences, providing a basis for subsequent adjustment of the display priority of analysis results.

[0077] S112, adjusting the display priority of each data in the preliminary analysis result based on the key features.

[0078] It should be noted that, in the embodiment of the present application, the priority rule is defined as follows:

[0079] Based on preference: Determine which data types or functions should be displayed first based on the user's preferences. For example, if the user prefers chart format, then chart data will be displayed first.

[0080] Based on historical behavior: Prioritize the data types and features that users access most frequently based on their historical behavior. For example, if a user frequently views sales data, sales data will be displayed first.

[0081] Based on the current context: Dynamically adjust the display priority based on the user's current context. For example, if the user uses the system on a weekday, work-related data may be displayed first; if the user uses the system on a mobile device, data views suitable for small screens may be displayed first.

[0082] Then, by combining the above rules, the display priority of each data is calculated. In the middle, weighted scoring method or other algorithms can be used to determine the final priority. Priority rule definition:

[0083] Based on preferences: Determine which data types or functions should be displayed first based on the user's preferences. For example, if the user prefers charts, chart data will be displayed first.

[0084] Based on historical behavior: Prioritize the data types and features that users access most frequently based on their historical behavior. For example, if a user frequently views sales data, sales data will be displayed first.

[0085] Based on the current context: Display priority is dynamically adjusted based on the user's current context. For example, if a user is using the system during the workday, work-related data may be prioritized; if a user is using the system on a mobile device, data views suitable for small screens may be prioritized.

[0086] Priority calculation: Calculate the display priority of each piece of data based on the above rules. Weighted scoring or other algorithms can be used to determine the final priority. Dynamically adjust the display priority of each piece of data in the analysis results based on user preferences, historical behavior, and current context, ensuring that users see the most relevant and important information.

[0087] S113 : Displaying the preliminary analysis result to the user in a three-dimensional image based on the adjusted display priority.

[0088] Among them, the data in the preliminary analysis results are reorganized according to the adjusted display priority and prepared for the three-dimensional stereoscopic image display of holographic projection technology. It is necessary to design the layout and style of the three-dimensional stereoscopic image to ensure that high-priority data is more visually prominent, for example, using different colors, sizes and positions to represent data of different priorities.

[0089] Through the above steps S111 to S113, not only the accuracy and reliability of the analysis results are improved, but also a highly personalized user experience is provided, ensuring that users can quickly and intuitively understand complex data analysis results.

[0090] The method also includes:

[0091] S114: Determine different perspective dimensions that need to be analyzed based on the multidimensional data to be analyzed.

[0092] Among them, different perspective dimensions that need to be analyzed are determined based on data characteristics and business needs. Common dimensions include:

[0093] Time dimension: Analyze the changing trends of data over time, identify cyclical patterns, seasonal changes, etc.

[0094] Spatial dimension: Analyze the distribution of data in different geographical locations and identify regional differences and hot spots.

[0095] Category dimension: Analyze the data characteristics of different categories and identify the differences and associations between categories.

[0096] Other dimensions: Other dimensions can be introduced according to specific needs, such as user behavior dimension, product performance dimension, etc.

[0097] S115 , performing multi-perspective analysis on the multi-dimensional data to be analyzed based on different perspective dimensions, and obtaining preliminary analysis results corresponding to each perspective.

[0098] Specifically, time dimension analysis: extract time series data, arrange them in chronological order, use time series analysis methods (such as ARIMA, LSTM) to identify trends, periodicity and seasonality of the data, detect outliers in the time series, identify abnormal events or emergencies, and then make future trend predictions based on historical data to provide decision support.

[0099] Spatial dimension analysis: Extract data containing geographic location information, use geographic information system (GIS) technology to draw the spatial distribution map of the data, identify hot spots through heat maps or other visualization tools, and finally use clustering algorithms (such as K-means and DBSCAN) to identify geographic areas with similar characteristics.

[0100] Category dimension analysis: Extract data containing category information, use classification algorithms (such as decision trees and random forests) to classify the data, use association rule mining algorithms (such as Apriori and FP-Growth) to discover the association relationship between categories, and finally compare the differences between different categories to identify key features.

[0101] Other dimension analysis:

[0102] User behavior dimension: Analyze user operation records, click streams and other data to identify user behavior patterns.

[0103] Product performance dimension: Analyze product performance indicators, such as response time, failure rate, etc., to evaluate product quality.

[0104] By comprehensively analyzing data from multiple perspectives, complex relationships and patterns in the data are revealed, providing deeper data insights.

[0105] S116 , synthesizing the preliminary analysis results of different perspectives to generate a corresponding multi-perspective analysis report, and presenting the multi-perspective analysis results to the user in the form of a three-dimensional image.

[0106] Initial analysis results from each perspective are aggregated to form a comprehensive data set. The results from different perspectives are then compared to identify consistency and differences. Key findings from each perspective are then combined for comprehensive analysis to draw overall conclusions. The report details key findings from each perspective, including trends, patterns, and anomalies. Based on the comprehensive analysis, specific conclusions and recommendations are then presented to guide business decisions. Charts, graphs, and other visualization tools can be used to intuitively present the analysis results.

[0107] The multi-view analysis results are converted into a 3D model suitable for holographic projection display. Holographic projection technology is used to render the 3D image in real time, and the displayed content is dynamically adjusted based on user behavior and context. When the data changes, the 3D image is updated in real time through an incremental update mechanism to ensure that users see the latest analysis results.

[0108] These steps not only improve the accuracy and reliability of analysis results, but also provide a highly personalized user experience, ensuring that users can quickly and intuitively understand complex data analysis results.

[0109] After obtaining the preliminary analysis results, the method further includes:

[0110] S101, retrieve pre-set expert rules.

[0111] Domain experts, based on their experience and knowledge, define a set of rules. These rules can be based on business logic, industry standards, or best practices for specific scenarios. These expert rules are then stored in a rule base. The rule base can be a database, file system, or other storage medium. After obtaining preliminary analysis results, relevant expert rules are retrieved from the rule base. This may involve selecting an appropriate rule set based on the data type, analysis task, or user needs.

[0112] S102, evaluating the preliminary analysis results according to the rules defined by the expert rules through the inference engine to obtain corresponding evaluation results.

[0113] Among them, the reasoning engine first parses the expert rules retrieved from the rule library and understands the specific content and logic of each rule. The reasoning engine matches the preliminary analysis results with each rule to check whether the rule conditions are met. If the conditions are met, the corresponding rule action is executed. The reasoning engine generates an evaluation result based on the application of the rules. It should be pointed out here that the evaluation result may include verification of the preliminary analysis results, correction suggestions or new analysis conclusions.

[0114] S103, comparing the preliminary analysis result with the evaluation result. If the comparison results are inconsistent, adjusting the preliminary analysis result.

[0115] Among them, the preliminary analysis results are compared item by item with the evaluation results generated by the inference engine, which may involve numerical comparison, pattern matching or other forms of comparison methods; the differences between the preliminary analysis results and the evaluation results are identified, which may include numerical deviations, classification errors, omissions or misjudgments; all differences found are recorded to provide a basis for subsequent adjustments.

[0116] By comparing preliminary analysis results with evaluation results, potential problems and inconsistencies can be identified to ensure the accuracy and consistency of analysis results.

[0117] Based on the specific circumstances of the discrepancy, an adjustment strategy is developed, which may include numerical corrections, classification adjustments, supplementation of missing data, or re-running of parts of the analysis process. The preliminary analysis results are corrected according to the developed adjustment strategy. This may involve manual or automatic adjustments, depending on the nature and complexity of the discrepancy. The adjusted analysis results are verified to ensure that they comply with expert rules and expected goals. The adjusted analysis results are updated in the system and prepared for further presentation or report generation.

[0118] Based on the above steps S101 to S103, by calling the pre-set expert rules, the reasoning engine is used to automatically evaluate the preliminary analysis results, and the preliminary analysis results are compared with the evaluation results to identify potential problems and inconsistencies. The preliminary analysis results are then adjusted according to the comparison results to ensure the accuracy and reliability of the final analysis results. At the same time, it integrates the knowledge and experience of domain experts and complies with industry standards and best practices.

[0119] After obtaining the preliminary analysis results, the method further includes:

[0120] S104: Retrieve a corresponding anomaly detection algorithm, use the anomaly detection algorithm to detect potential anomalies in the preliminary analysis results, and generate a corresponding anomaly detection report. The anomaly detection report marks all detected anomalies.

[0121] Among them, select an appropriate anomaly detection algorithm based on data characteristics and analysis requirements. Common anomaly detection algorithms include statistical methods (such as Z-score, IQR), machine learning methods (such as isolation forest, local outlier factor LOF, one-class support vector machine), etc.; according to the characteristics of the dataset and historical experience, set the parameters of the anomaly detection algorithm. For example, for statistical-based methods, you may need to set a threshold; for machine learning-based methods, you may need to adjust the model's hyperparameters. You can call the selected anomaly detection algorithm from a pre-built algorithm library and prepare the input data.

[0122] Next, the data from the preliminary analysis results is fed into an anomaly detection algorithm. This algorithm runs to identify potential outliers in the data. The algorithm determines which data points deviate from the normal range based on the established rules or models. An anomaly detection report is then generated, containing all detected outliers and related information, such as the specific value, location, and degree of abnormality. All detected outliers are marked in the report so that users can quickly identify and address them.

[0123] S105: Obtain abnormal feedback from the target user on the abnormality detection report.

[0124] Feedback on anomaly detection reports from target users is collected through the system interface, emails, questionnaires, and other means. The feedback may include confirmation of the outlier, correction suggestions, requests for further explanation, etc. Natural language processing (NLP) technology or other methods are then used to parse the user feedback text and extract key information. For example, it can be identified whether the user confirms that a certain outlier is a true anomaly or whether the user has made specific correction suggestions. User feedback can be categorized as confirmation of anomaly, denial of anomaly, or correction suggestion.

[0125] S106 , combining the anomaly detection report with the anomaly feedback, and re-evaluating the anomaly value to determine whether there are omissions or misjudgments.

[0126] Among them, the user's anomaly feedback is combined with the anomaly detection report to form a comprehensive dataset. This may involve updating the tags in the anomaly detection report to add user confirmation or correction information.

[0127] During the reassessment process, if the user confirms that an outlier is a true anomaly, the anomaly marker is retained. If the user denies that an outlier is an anomaly, the anomaly marker is removed and the user's reasoning is recorded. If the user provides specific correction suggestions, the anomaly detection algorithm's parameters or rules are adjusted based on the suggestions and the algorithm is rerun. The adjusted anomaly detection results are verified to ensure that the new results meet the user's feedback and expectations. After that, an updated anomaly detection report is generated and the final results are fed back to the user.

[0128] Based on executing the above steps S104 to S106, a detailed anomaly detection report is generated by calling a suitable anomaly detection algorithm, collecting and combining professional feedback from target users, re-evaluating and adjusting the anomaly values, thereby improving the accuracy of anomaly detection, integrating the user's professional knowledge and experience, and ensuring that the anomaly detection results are more reliable and practical.

[0129] It should be noted here that the preliminary analysis results include multiple predicted values, so the method also includes:

[0130] S107, performing uncertainty assessment on each predicted value in the preliminary analysis results and generating corresponding confidence levels.

[0131] Select an appropriate uncertainty quantification method based on the data characteristics and analysis requirements. Common methods include Bayesian methods, Monte Carlo sampling, and Bootstrap methods. If Bayesian methods are used, the model must be trained using Bayesian techniques to obtain the posterior distribution. If Monte Carlo sampling or Bootstrap methods are used, multiple sampling attempts are required to estimate the distribution of the predicted values.

[0132] For each predicted value, calculate its confidence level. For example, in Bayesian methods, you can calculate the posterior probability distribution of the predicted value and extract a confidence interval or confidence level from it. In Monte Carlo sampling or bootstrap methods, you can calculate the standard deviation or percentile of the predicted value from the results of multiple sampling. Then record the confidence level of each predicted value to form a confidence list or matrix.

[0133] By performing uncertainty assessment on each predicted value and generating corresponding confidence levels, we provide users with quantitative indicators of the reliability of the prediction results, helping them understand the uncertainty of the prediction results.

[0134] S108 , sorting based on all confidence levels, and highlighting the results with higher confidence levels when displayed on the visual interface.

[0135] All predictions are sorted from high to low by confidence, with high-confidence predictions placed first and low-confidence predictions placed last. A visualization interface is designed to highlight high-confidence results. For example, different colors (such as green for high confidence and red for low confidence), font sizes, and icons can be used to distinguish results of different confidence levels. Furthermore, the visualization interface is updated in real time during user interaction with the system to ensure that the latest confidence ranking results are always displayed. By highlighting high-confidence results in the visualization interface, users can quickly identify and focus on the most reliable results, improving decision-making efficiency.

[0136] S109: Generate a corresponding explanation report for the result with the lower confidence ranking.

[0137] Among them, the prediction values ​​with lower confidence are screened out from the sorted results, and an explanation report is generated:

[0138] Possible causes: Analyze the possible reasons for low confidence, such as poor data quality, inappropriate model parameters, interference from external factors, etc.; Influencing factors: Identify the main factors affecting confidence, such as missing data, outliers, model overfitting or underfitting, etc.; Further verification methods: Propose recommended further verification methods, such as increasing the data sample size, adjusting model parameters, introducing more features, performing cross-validation, etc.

[0139] Finally, the report will be formatted to make it easy to read and understand. The report can contain charts, text descriptions, and specific steps for recommendations.

[0140] By evaluating the uncertainty of each predicted value and generating a confidence level, high-reliability results are highlighted in the visual interface based on confidence ranking, while providing detailed explanatory reports (including causes, influencing factors, and improvement suggestions) for low-confidence results. This improves the transparency, reliability, and interpretability of the prediction results, helping users better understand and optimize the analysis results.

[0141] The present application discloses a holographic multi-dimensional data analysis device, referring to Figure 2 , devices include but are not limited to:

[0142] The preliminary analysis result acquisition module 200 acquires the corresponding multidimensional data to be analyzed, preprocesses the multidimensional data to be analyzed, retrieves a pre-trained multidimensional relationship model, and inputs the pre-processed multidimensional data to be analyzed into the multidimensional relationship model for analysis to obtain the corresponding preliminary analysis results; wherein the preprocessing includes the use of an adaptive data cleaning algorithm and a dynamic anomaly detection mechanism, and the integration of the multidimensional relationship model with a graph neural network, time series analysis, and a hybrid model;

[0143] The preliminary analysis result display module 210 determines the corresponding target user, retrieves the target user's corresponding historical behavior and current context, and displays the preliminary analysis results to the user in a three-dimensional stereo image through real-time dynamic updates based on the historical behavior, current context, and holographic projection technology. When the multi-dimensional data to be analyzed changes, the visualization interface corresponding to the preliminary analysis results is updated through an incremental update mechanism;

[0144] The target analysis result acquisition module 220 obtains the corresponding feedback data during the interaction between the target user and the visualization interface, and determines whether to trigger the optimization mechanism based on the feedback data. If triggered, optimization is performed through a preset optimization mechanism based on the feedback data and the preliminary analysis results to obtain the corresponding target analysis results, wherein the feedback data includes the evaluation of the visualization interface results, the interaction behavior record, and the improvement suggestions proposed by the user.

[0145] Furthermore, the device includes but is not limited to:

[0146] The key feature extraction module obtains the target user's preferences and uses the preferences, historical behavior, and current context to extract corresponding key features. Key features include the target user's most frequently accessed data types, most frequently used functions, and preferred time periods.

[0147] Display priority adjustment module, which is used to adjust the display priority of each data in the preliminary analysis results based on key features;

[0148] The preliminary analysis results are displayed to the user in a three-dimensional stereoscopic image based on the adjusted display priority.

[0149] Furthermore, the device includes but is not limited to:

[0150] A different perspective dimension determination module is used to determine the different perspective dimensions that need to be analyzed based on the multidimensional data to be analyzed, where the different perspective dimensions include time dimension, space dimension, and category dimension;

[0151] The multi-perspective analysis module is used to perform multi-perspective analysis on the multi-dimensional data to be analyzed based on different perspective dimensions, and obtain preliminary analysis results corresponding to each perspective. The multi-perspective analysis includes time dimension analysis, space dimension analysis, category dimension analysis, and other dimension analysis;

[0152] The multi-perspective analysis report generation module synthesizes the preliminary analysis results from different perspectives to generate a corresponding multi-perspective analysis report, which is presented to the user in the form of a three-dimensional image. The multi-perspective analysis report includes the key findings and conclusions of each perspective.

[0153] Furthermore, the device includes but is not limited to:

[0154] An expert rule retrieval module is used to retrieve pre-set expert rules, wherein the expert rules include the experience and knowledge of domain experts;

[0155] The evaluation result acquisition module evaluates the preliminary analysis results according to the rules defined by the expert rules through the inference engine to obtain the corresponding evaluation results;

[0156] The result comparison result is used to compare the preliminary analysis results with the evaluation results. If the comparison results are inconsistent, the preliminary analysis results will be adjusted.

[0157] Furthermore, the device includes but is not limited to:

[0158] The anomaly detection report generation module calls the corresponding anomaly detection algorithm and uses the anomaly detection algorithm to detect potential anomalies in the preliminary analysis results to generate the corresponding anomaly detection report. The anomaly detection report marks all detected anomalies.

[0159] The abnormal feedback acquisition module is used to obtain abnormal feedback from the target user on the abnormal detection report, wherein the abnormal feedback includes the user's confirmation of the abnormal value and correction suggestions;

[0160] The anomaly judgment module combines the anomaly detection report with the anomaly feedback and re-evaluates the anomaly value to determine whether there are omissions or misjudgments.

[0161] Furthermore, the device includes but is not limited to:

[0162] The confidence generation module performs uncertainty assessment on each predicted value in the preliminary analysis results to generate corresponding confidence levels;

[0163] The sorting module is used to sort based on the total confidence level. When displayed on the visual interface, the results with the highest confidence level are highlighted.

[0164] The explanation report generation module is used to generate a corresponding explanation report for the result corresponding to the lower confidence ranking, wherein the explanation report includes possible causes, influencing factors and recommended further verification methods.

[0165] An embodiment of the present application further discloses a method for analyzing multidimensional data based on holography, comprising a processor in which a program according to any one of the above-mentioned methods for analyzing multidimensional data based on holography is running.

[0166] An embodiment of the present application further discloses a storage medium storing a program according to any one of the above-mentioned holographic multi-dimensional data analysis methods.

[0167] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A holographic multidimensional data analysis method, characterized in that: include: Obtain corresponding multidimensional data to be analyzed, preprocess the multidimensional data to be analyzed, retrieve a pre-trained multidimensional relationship model, input the preprocessed multidimensional data to be analyzed into the multidimensional relationship model for analysis, and obtain corresponding preliminary analysis results; wherein the preprocessing includes adopting an adaptive data cleaning algorithm and a dynamic anomaly detection mechanism, and integrating the multidimensional relationship model with a graph neural network, time series analysis, and a hybrid model; Determine the corresponding target user, retrieve the historical behavior and current context corresponding to the target user, and present the preliminary analysis results to the user in a three-dimensional stereoscopic image through real-time dynamic updating based on the historical behavior, current context, and holographic projection technology. When the multi-dimensional data to be analyzed changes, the visualization interface corresponding to the preliminary analysis results is updated through an incremental update mechanism; In the process of presenting the preliminary analysis results to the user in the form of a three-dimensional image, it also includes: determining different perspective dimensions that need to be analyzed based on the multi-dimensional data to be analyzed, wherein the different perspective dimensions include time dimension, space dimension, and category dimension; performing multi-perspective analysis on the multi-dimensional data to be analyzed based on the different perspective dimensions to obtain preliminary analysis results corresponding to each perspective, wherein the multi-perspective analysis includes time dimension analysis, space dimension analysis, and category dimension analysis, wherein the time dimension is used to analyze the trend of data changes over time and identify periodic patterns and seasonal changes; the space dimension is used to analyze the distribution of data in different geographical locations to identify regional differences and hot spots; the category dimension is used to analyze data features of different categories to identify differences and associations between categories; the preliminary analysis results from different perspectives are synthesized to generate a corresponding multi-perspective analysis report, and the multi-perspective analysis results are presented to the user in the form of a three-dimensional image, wherein the multi-perspective analysis report contains key findings and conclusions for each perspective; Perform uncertainty assessment on each predicted value in the preliminary analysis results and generate corresponding confidence levels. Sorting is performed based on all confidence levels, and results with higher confidence levels are highlighted in the visualization interface. For results with lower confidence levels, generate corresponding explanation reports, which include possible causes, influencing factors, and recommended further verification methods. Obtain feedback data corresponding to the target user's interaction with the visualization interface, and determine whether to trigger an optimization mechanism based on the feedback data. If triggered, perform optimization through a preset optimization mechanism based on the feedback data and the preliminary analysis results to obtain corresponding target analysis results, wherein the feedback data includes evaluations of visualization interface results, interactive behavior records, and improvement suggestions proposed by users.

2. The holographic multidimensional data analysis method according to claim 1, characterized in that: After retrieving the historical behavior and current context corresponding to the target user, the method further includes: Obtaining the target user's preference settings, and extracting corresponding key features from the preference settings, the historical behavior, and the current context, wherein the key features include the target user's most frequently accessed data types, most frequently used functions, and preferred time periods; Adjusting the display priority of each data in the preliminary analysis result based on the key features; The preliminary analysis result is presented to the user in a three-dimensional stereoscopic image based on the adjusted display priority.

3. The holographic multidimensional data analysis method according to claim 1, characterized in that: After obtaining the preliminary analysis results, the method further includes: Retrieve pre-set expert rules, where the expert rules include the experience and knowledge of domain experts; Evaluate the preliminary analysis results according to the rules defined by the expert rules through an inference engine to obtain corresponding evaluation results; The preliminary analysis results are compared with the evaluation results. If the comparison results are inconsistent, the preliminary analysis results are adjusted.

4. The holographic multidimensional data analysis method according to claim 3, characterized in that: After obtaining the preliminary analysis results, the method further includes: Call the corresponding anomaly detection algorithm, use the anomaly detection algorithm to detect potential anomalies in the preliminary analysis results, and generate a corresponding anomaly detection report. The anomaly detection report marks all detected anomalies; Obtaining abnormal feedback from the target user on the abnormal detection report, wherein the abnormal feedback includes the user's confirmation of the abnormal value and correction suggestions; The anomaly detection report is combined with the anomaly feedback, and the anomaly values ​​are re-evaluated to determine whether there are any omissions or misjudgments.

5. A device for executing the holographic multidimensional data analysis method according to any one of claims 1 to 4, characterized in that: include: A preliminary analysis result acquisition module acquires the corresponding multidimensional data to be analyzed, preprocesses the multidimensional data to be analyzed, retrieves a pre-trained multidimensional relationship model, and inputs the pre-processed multidimensional data to be analyzed into the multidimensional relationship model for analysis to obtain the corresponding preliminary analysis results; wherein, the preprocessing includes the use of an adaptive data cleaning algorithm and a dynamic anomaly detection mechanism, and the multidimensional relationship model integrates graph neural networks, time series analysis, and hybrid models; The preliminary analysis result display module determines the corresponding target user, retrieves the historical behavior and current context corresponding to the target user, and uses the historical behavior, current context and holographic projection technology to dynamically update the preliminary analysis results in real time to display them to the user in a three-dimensional image. When the multi-dimensional data to be analyzed changes, the visualization interface corresponding to the preliminary analysis results is updated through an incremental update mechanism; The target analysis result acquisition module obtains the corresponding feedback data during the interaction between the target user and the visualization interface, and determines whether to trigger the optimization mechanism based on the feedback data. If triggered, optimization is performed through a preset optimization mechanism based on the feedback data and the preliminary analysis results to obtain the corresponding target analysis results, wherein the feedback data includes the evaluation of the visualization interface results, the interaction behavior record, and the improvement suggestions proposed by the user.

6. The device according to claim 5, characterized in that The device also includes: A key feature extraction module obtains the target user's preference settings and uses the preference settings, the historical behavior, and the current context to extract corresponding key features, wherein the key features include the target user's most frequently accessed data types, most frequently used functions, and preferred time periods; A display priority adjustment module, for adjusting the display priority of each data in the preliminary analysis result based on the key features; The preliminary analysis result is presented to the user in a three-dimensional stereoscopic image based on the adjusted display priority.

7. A storage medium, characterized in that: A program based on the holographic multi-dimensional data analysis method as described in any one of claims 1 to 4 is stored.

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