Scene modal-oriented multi-dimensional visualization and man-machine interaction method

Abstract

The invention provides a scene modal-oriented multi-dimensional visualization and man-machine interaction method, which comprises the following steps of: dividing scene data into a core index dimension, an attribute dimension, a time dimension and an association dimension according to categories, and aiming at different dimension types and combination of the dimension types in scene data information, performing multi-dimensional visualization and man-machine interaction on the core index dimension, the attribute dimension, the time dimension and the association dimension; mapping with visual components in a pre-configured visual component library is carried out, target visual components are determined, and a multi-dimensional visual model is constructed based on all the target visual components; finally, based on the multi-dimensional visualization model, interface layout and interaction control binding are carried out, and a visualization interface is obtained. According to the method, by defining a scene-exclusive dimension system and a data mapping rule, abstract multi-dimensional data is converted into a visual visual component combination, and'one-stop presentation of the multi-dimensional data 'is realized, so that a user is helped to quickly discover deep association among the data, and the user experience is improved. The trouble that a user needs to frequently switch and compare a plurality of independent views when analyzing complex data is thoroughly solved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a multi-dimensional visualization and human-computer interaction method oriented towards scene modalities. Background Technology

[0002] With the rapid development of digital technology, the amount of data generated in various industries is growing exponentially, and the data dimensions are becoming increasingly complex. Taking e-commerce as an example, it is necessary to process multiple dimensions of data such as user ID, behavior type (browsing / adding to cart / placing an order), operation time, product category, and consumption amount. Taking medical scenarios as an example, it is necessary to integrate multiple dimensions of data such as patient basic information, examination indicators, medical records, and medication.

[0003] To extract useful information from this complex data, various data visualization and human-computer interaction tools have emerged in the existing technology, such as Excel-based chart generation functions, business visualization platforms such as Tableau, and Python's Matplotlib / Seaborn visualization library.

[0004] However, existing technologies have strong limitations in visualization dimensions, making it difficult to present complex relationships: existing technologies mostly focus on presenting data in 2-3 dimensions; for example, in e-commerce user behavior analysis, existing tools cannot simultaneously present the relationships between four dimensions: "user age group", "product category", "operation time" and "spending amount", which makes it impossible for users to quickly discover hidden patterns such as "users aged 25-30 prefer to buy high-priced beauty products between 8-10 pm", and they can only switch charts and split dimensions for analysis, which is extremely inefficient. Summary of the Invention

[0005] In view of this, the present invention provides a multi-dimensional visualization and human-computer interaction method oriented to scene modality, so as to solve the problem that the existing technology cannot simultaneously display the deep relationship between multi-dimensional data, and users need to switch views multiple times to explore the multi-dimensional relationship.

[0006] The first aspect of this invention provides a multi-dimensional visualization and human-computer interaction method oriented towards scene modalities, comprising:

[0007] Acquire scene data information; wherein, the scene data information includes dimension types; the dimension types include core indicator dimensions, attribute dimensions, time dimensions, and correlation dimensions;

[0008] For different dimensional types and combinations of dimensional types in the scene data information, mapping is performed with visualization components in the pre-configured visualization component library to determine the target visualization component, and a multi-dimensional visualization model is constructed based on all the target visualization components;

[0009] Based on the aforementioned multi-dimensional visualization model, the interface layout and interactive controls are bound together to obtain the visualization interface.

[0010] Optionally, the scene data information also includes raw data, and after receiving the scene data information, it further includes:

[0011] The original data is preprocessed to obtain standardized data; the preprocessing includes missing value handling, outlier handling, and data standardization.

[0012] Optionally, the step of mapping different dimension types and combinations of dimension types in the scene data information to visualization components in a pre-configured visualization component library to determine the target visualization component includes:

[0013] For the combination of time dimension and core indicator dimension in the scene data information, a time dimension line chart corresponding to the combination of time dimension and core indicator dimension is obtained by matching in the pre-configured visualization component library;

[0014] For the combination of attribute dimensions and core indicator dimensions in the scene data information, a parallel coordinate graph corresponding to the combination of attribute dimensions and core indicator dimensions is obtained by matching in the pre-configured visualization component library;

[0015] For each correlation dimension, the network graph corresponding to the correlation dimension is obtained by matching it in the pre-configured visualization component library.

[0016] Optionally, the scene-modal-oriented multi-dimensional visualization and human-computer interaction method further includes:

[0017] The time-dimensional line chart is used as the main component, and the parallel coordinate graph and network graph are used as secondary components. A correspondence between the main and secondary components is established. When a user clicks on a node in the main component, the data range of the corresponding secondary component is updated synchronously.

[0018] Optionally, the scene-modal-oriented multi-dimensional visualization and human-computer interaction method further includes:

[0019] Capture user interaction information in real time;

[0020] The interaction command is determined based on the user's interaction information, and the corresponding execution logic is performed in response to the interaction command.

[0021] Optionally, the step of determining the interaction instruction based on the user's interaction information and responding to the interaction instruction by performing the execution logic corresponding to the interaction instruction includes:

[0022] If the user's interaction information involves selecting multiple dimensions, then the interaction instruction is determined to be a filtering instruction.

[0023] Iterate through all standardized data, filter out data that meets the multiple dimensions selected by the user, and obtain the target data.

[0024] Optionally, the step of responding to the interaction instruction and performing the execution logic corresponding to the interaction instruction includes:

[0025] If the user's interaction information is clicking a node of the target visualization component, then the interaction instruction is determined to be an association analysis instruction;

[0026] Identify the attribute dimensions and numerical information corresponding to the nodes of the target visualization component, and trigger other target visualization components to synchronously display the data corresponding to the attribute dimensions and numerical information.

[0027] Optionally, the scene data information further includes a hierarchical structure for each dimension, and the execution logic corresponding to the interaction instruction in response to the interaction instruction includes:

[0028] If the user's interaction information is clicking on any dimension of the hierarchy, then the interaction instruction is determined to be a drill-down instruction;

[0029] Load the next level of data according to the hierarchical structure of the level, and synchronize the next level of data to all target visualization components.

[0030] Optionally, the scene-modal-oriented multi-dimensional visualization and human-computer interaction method further includes:

[0031] According to the set time intervals, updated data is periodically retrieved from the data source; wherein, the updated data includes newly added or changed data;

[0032] The updated data is subjected to anomaly analysis according to preset anomaly judgment rules to obtain anomaly analysis results.

[0033] A second aspect of the present invention provides a multi-dimensional visualization and human-computer interaction device oriented towards scene modalities, comprising:

[0034] A scene data acquisition unit is used to acquire scene data information; wherein, the scene data information includes dimension types; the dimension types include core indicator dimensions, attribute dimensions, time dimensions, and correlation dimensions;

[0035] The multi-dimensional visualization model building unit is used to map different dimensional types and combinations of dimensional types in scene data information to visualization components in a pre-configured visualization component library, determine target visualization components, and build a multi-dimensional visualization model based on all the target visualization components.

[0036] The visualization interface generation unit is used to perform interface layout and binding of interactive controls based on the multi-dimensional visualization model to obtain a visualization interface.

[0037] Optionally, the scene data information also includes raw data, and the scene-modal-oriented multi-dimensional visualization and human-computer interaction device further includes:

[0038] The preprocessing unit is used to preprocess the original data to obtain standardized data; wherein, the preprocessing includes missing value handling, outlier handling and data standardization.

[0039] Optionally, the multi-dimensional visualization model building unit includes:

[0040] The first matching unit is used to match the combination of time dimension and core indicator dimension in the scene data information to obtain the time dimension line chart corresponding to the combination of time dimension and core indicator dimension in the pre-configured visualization component library.

[0041] The second matching unit is used to match the combination of attribute dimensions and core indicator dimensions in the scene data information to obtain the parallel coordinate graph corresponding to the combination of attribute dimensions and core indicator dimensions in the pre-configured visualization component library.

[0042] The third matching unit is used to match the network graph corresponding to the associated dimension in the pre-configured visualization component library for the associated dimension.

[0043] Optionally, the scene-modal-oriented multi-dimensional visualization and human-computer interaction device further includes:

[0044] A unit is created to use a time-dimension line chart as the main component and a parallel coordinate graph and a network graph as secondary components, and to establish the correspondence between the main and secondary components; when a user clicks a node on the main component, the data range of the corresponding secondary component is updated synchronously.

[0045] Optionally, the scene-modal-oriented multi-dimensional visualization and human-computer interaction device further includes:

[0046] An interactive information capture unit is used to capture user interaction information in real time.

[0047] The analysis and response unit is used to determine the interaction instruction based on the user's interaction information, and to respond to the interaction instruction by performing the execution logic corresponding to the interaction instruction.

[0048] Optionally, the analysis response unit includes:

[0049] The filtering instruction determining unit is used to determine the interaction instruction as a filtering instruction if the user's interaction information is to select multiple dimension conditions.

[0050] The data filtering unit is used to traverse all standardized data and filter out data that meets the multiple dimensions selected by the user to obtain the target data.

[0051] Optionally, the analysis response unit includes:

[0052] The association analysis instruction determination unit is used to determine the interaction instruction as an association analysis instruction if the user's interaction information is clicking a node of the target visualization component.

[0053] The information recognition unit is used to identify the attribute dimensions and numerical information corresponding to the nodes of the target visualization component, and to trigger other target visualization components to synchronously display the data corresponding to the attribute dimensions and numerical information.

[0054] Optionally, the scene data information also includes a hierarchical structure for each dimension, and the analysis response unit includes:

[0055] The drill-down instruction determination unit is used to determine that the interaction instruction is a drill-down instruction if the user's interaction information is clicking on any dimension of the level.

[0056] The data loading unit is used to load the next level data of the level according to the hierarchical structure of the level, and synchronize the next level data of the level to all target visualization components.

[0057] Optionally, the scene-modal-oriented multi-dimensional visualization and human-computer interaction device further includes:

[0058] An update data acquisition unit is used to periodically acquire updated data from a data source at set time intervals; wherein the updated data includes newly added or changed data;

[0059] The anomaly analysis unit is used to perform anomaly analysis on the updated data according to preset anomaly judgment rules and obtain anomaly analysis results.

[0060] A third aspect of the present invention provides an electronic device, comprising:

[0061] One or more processors;

[0062] A storage device on which one or more programs are stored;

[0063] When the one or more programs are executed by the one or more processors, the one or more processors implement the scene-oriented modality multi-dimensional visualization and human-computer interaction method as described in any one of the first aspects.

[0064] A fourth aspect of the present invention provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the scene-modal-oriented multi-dimensional visualization and human-computer interaction method as described in any one of the first aspects.

[0065] As can be seen from the above scheme, this invention provides a multi-dimensional visualization and human-computer interaction method oriented towards scene modalities. It categorizes scene data into core indicator dimensions, attribute dimensions, time dimensions, and correlation dimensions. Then, it maps different dimension types and combinations of dimension types in the scene data information to visualization components in a pre-configured visualization component library to determine target visualization components. A multi-dimensional visualization model is then constructed based on all target visualization components. Finally, based on the multi-dimensional visualization model, the interface layout and interactive controls are bound to obtain the visualization interface. This invention, by defining a scene-specific dimension system and data mapping rules, transforms abstract multi-dimensional data into intuitive combinations of visualization components, achieving "one-stop presentation of multi-dimensional data." This helps users quickly discover deep correlations between data, completely solving the problem of users frequently switching and comparing multiple independent views when analyzing complex data. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0067] Figure 1 This is a flowchart illustrating a multi-dimensional visualization and human-computer interaction method for scene-oriented modalities provided in an embodiment of the present invention.

[0068] Figure 2 A flowchart illustrating a method for determining a target visualization component according to another embodiment of the present invention;

[0069] Figure 3 A flowchart illustrating a scene-modal-oriented multi-dimensional visualization and human-computer interaction method provided in another embodiment of the present invention;

[0070] Figure 4 This is a schematic diagram of a scene-oriented multi-dimensional visualization and human-computer interaction device provided in another embodiment of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0073] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this invention are all information and data authorized by the user or fully authorized by all parties.

[0074] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0075] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0076] This invention provides a multi-dimensional visualization and human-computer interaction method oriented towards scene modalities, such as... Figure 1 As shown, the specific steps include:

[0077] S101. Obtain scene data information.

[0078] The scenario data information includes dimension types; the dimension types include core indicator dimensions, attribute dimensions, time dimensions, and correlation dimensions.

[0079] It is understandable that the business requirements documents may differ for different scenarios. For example, e-commerce scenarios require a "User Behavior Analysis Requirements Specification", while medical scenarios require a "Patient Monitoring Indicator List", etc. There are no restrictions here.

[0080] In this invention, scenario data is divided into "core indicator dimensions" (such as "order amount" in e-commerce scenarios and "heart rate" in medical scenarios), "attribute dimensions" (such as "product category" in e-commerce scenarios and "patient age group" in medical scenarios), "time dimensions" (such as "hour / day / week"), and "association dimensions" (such as "user behavior-product category association" in e-commerce scenarios and "medication type-symptom association" in medical scenarios), without limitation here.

[0081] In another embodiment of the present invention, a corresponding hierarchical structure is set for each dimension. For example, the time dimension is divided into "year → month → day → hour", and the product category dimension is divided into "first-level category → second-level category → third-level category". The number of dimension levels can be set according to the needs of the scenario. The default number of dimension levels is 3-5, and user-defined adjustments are supported. No limitation is made here. Finally, a related dimension library can be generated and saved based on the constructed dimension relationships.

[0082] Optionally, in the actual application of the present invention, the scene data information also includes raw data. After receiving the raw data, the raw data can be preprocessed to obtain standardized data.

[0083] The preprocessing methods include, but are not limited to, handling missing values, handling outliers, and data standardization, etc., which are not limited here.

[0084] Specifically, missing values ​​can be handled using methods such as, but are not limited to, mean imputation and mode imputation; no specific method is specified here. It is understandable that mean imputation is more suitable for numerical data, such as order amounts, while mode imputation is more suitable for categorical data, such as product categories.

[0085] In the practical application of this invention, the filling threshold can be set to a missing rate of <30%. If the missing rate is ≥30%, the data in that dimension will be removed to avoid affecting the accuracy of visualization.

[0086] Outlier handling can be based on, but is not limited to, 3D model. The principle is to identify outliers, i.e., data that exceeds the range of "mean ± 3 × standard deviation". Outliers are marked and prompted to the user (instead of being directly deleted), so that the user can choose to "keep outliers" or "replace with median" to handle outliers.

[0087] Data standardization can be achieved, but is not limited to, converting data of different magnitudes into standardized data within the [0,1] interval, using the Min-Max standardization formula. Where x is the original data, The minimum value of the data in this dimension. This represents the maximum value of the data in this dimension.

[0088] S102. For different dimension types and combinations of dimension types in the scene data information, map them with the visualization components in the pre-configured visualization component library to determine the target visualization component, and build a multi-dimensional visualization model based on all target visualization components.

[0089] The pre-configured visualization component library includes, but is not limited to, line charts, bar charts, heatmaps, parallel coordinate graphs, network diagrams, bubble charts, etc., and there are no restrictions here.

[0090] This invention constructs a multi-dimensional visualization model with multi-dimensional linkage by establishing a mapping relationship between "dimensions and visualization components".

[0091] Optionally, in another embodiment of the present invention, one implementation of the method for determining the target visualization component is as follows: Figure 2 As shown, it includes:

[0092] S201. For the combination of time dimension and core indicator dimension in the scenario data information, the time dimension line chart corresponding to the combination of time dimension and core indicator dimension is obtained by matching in the pre-configured visualization component library.

[0093] The time granularity parameter for the time-dimensional line chart can be set to "hour / day / week / month", with the default granularity being "day". Users can manually switch the granularity, and there is no limitation here.

[0094] In the practical application of this invention, a mapping relationship between the time dimension and the core indicator dimension and the area map can also be established, etc., which is not limited here.

[0095] S202. For the combination of attribute dimensions and core indicator dimensions in the scene data information, the parallel coordinate graph corresponding to the combination of attribute dimensions and core indicator dimensions is obtained by matching in the pre-configured visualization component library.

[0096] The parallel coordinate graph supports up to 8 dimensions to be displayed simultaneously. In the actual application of this invention, a mapping relationship is established between the attribute dimensions and the core indicator dimensions and the heat map. The heat map is suitable for a combination of 2 attribute dimensions and 1 core indicator dimension. The number of axes of the parallel coordinate graph is ≤8 by default. When it exceeds 8 axes, it is automatically split into multiple sub-graphs.

[0097] S203. For the correlation dimension, the network graph corresponding to the correlation dimension is obtained by matching in the pre-configured visualization component library.

[0098] Among them, the node size parameter of the network graph is positively correlated with the core indicator value (e.g., node size = core indicator value / 100, to ensure that the node size is moderate), and the edge thickness parameter is positively correlated with the association strength (association strength = number of times the two dimensions co-occur / total number of times).

[0099] It should be noted that, Figure 2 This is merely an example of the method for determining the target visualization component. It can be executed not only in the order of steps S201 to S203, but also in the order of steps S202, S201, and S203, and of course, in parallel. No limitation is made here.

[0100] Optionally, in another embodiment of the present invention, a multi-component linkage logic design can also be performed. Specifically, but not limited to, the time-dimensional line graph can be used as the main component, the parallel coordinate graph and the network graph can be used as slave components, and a correspondence between the main component and the slave components can be established.

[0101] Specifically, when a user clicks on a node in the main component, the data range of the corresponding slave component in the main component is updated synchronously.

[0102] For example, when a user interacts with the main component (such as clicking a specific time point on a time-dimension line chart), the secondary component automatically updates its data range. For instance, if the "Time-Dimension Line Chart" is set as the main component and the "Product Category Bar Chart" is set as the secondary component, when the user clicks the "May 1, 2024" node on the time-dimension line chart, the product category bar chart automatically updates to "Order Amount of Each Product Category on May 1, 2024," with a response time of ≤0.5 seconds, ensuring smooth interaction.

[0103] Understandably, before designing multi-component linkage logic, it is necessary to complete the matching of components in a single dimension and ensure that each component uses the same data source.

[0104] This invention constructs a four-dimensional system of "core indicators - attributes - time - correlation" and matches it with multi-dimensional components such as parallel coordinate graphs and network graphs, which can simultaneously present data correlations across 4-8 dimensions. Taking e-commerce scenarios as an example, users can simultaneously view "order amounts (core indicators) of beauty products purchased by users aged 25-30 (attribute dimension) between 8-10 pm (time dimension)" on the same interface, without the need for splitting operations, improving cognitive efficiency by more than 60%.

[0105] S103. Based on the multi-dimensional visualization model, perform interface layout and binding of interactive controls to obtain a visual interface.

[0106] Specifically, the multi-dimensional visualization model constructed in the above steps is transformed into a user-operable visualization interface.

[0107] In the practical application of this invention, existing interface development tools can be used, but are not limited to, for interface layout and binding of interactive controls. The interface development tools can be, but are not limited to, HTML5+CSS3+JavaScript. To ensure cross-browser compatibility, mainstream browsers such as Chrome / Firefox / Edge are supported, but no limitation is made here.

[0108] The interface layout can adopt, but is not limited to, a three-part layout, where the top is the "scene title and operation toolbar" (e.g., 10% of the height), the left is the "dimensional filtering panel" (e.g., 20% of the width), and the right is the "visual component display area" (e.g., 70% of the width).

[0109] The visualization component display area supports a "grid layout". The default number of grid rows is 2-3 and the default number of columns is 2-3. The component size can be adjusted adaptively (minimum width ≥ 200px, minimum height ≥ 150px) to avoid incomplete information display due to components being too small.

[0110] In the actual application of this invention, the page rendering parameters can be, but are not limited to, interface loading time ≤ 3 seconds (when the data volume ≤ 100,000 records). If the data volume is > 100,000 records, a "pagination loading" strategy is adopted, loading 10,000 records each time with a loading interval ≤ 1 second. This is not limited here.

[0111] Once the interface layout is complete, configure the interactive control library, which includes, but is not limited to, controls such as dropdown lists, sliders, checkboxes, and buttons; there are no restrictions here.

[0112] Specifically, interactive controls are bound to dimensions. For example, the "time dimension" is bound to the "date range selector", the "attribute dimension" is bound to the "checkbox group", and the "core metric dimension" is bound to the "drop-down selection box". The control operation response time is ≤0.3 seconds.

[0113] The time range selector supports selecting "today / yesterday / last 7 days / custom date"; the checkbox group supports multiple selections, with a maximum of 10 attribute values; the drop-down selection box supports switching between different core metrics, such as "order amount / order quantity / number of users", which is not limited here.

[0114] Optionally, in another embodiment of the present invention, the present invention also provides a human-computer interaction method corresponding to the multi-dimensional visualization method, realizing the recognition and execution of user interaction commands, and supporting multi-dimensional joint exploration, such as... Figure 3 As shown, it includes:

[0115] S301, Real-time capture of user interaction information.

[0116] User interaction information includes, but is not limited to, interactive operations initiated through mouse clicks, drags, and keyboard shortcuts; no specific restrictions are imposed here.

[0117] S302. Determine the interaction command based on the user's interaction information, and respond to the interaction command by executing the corresponding logic.

[0118] Interactive commands include, but are not limited to, filtering commands, correlation analysis commands, drill-down commands, etc., and are not limited here.

[0119] Specifically, when the user's interaction information involves selecting multiple dimensions, the interaction instruction is determined to be a filtering instruction. Then, all standardized data is traversed, and data that meets the user's selected multiple dimensions is filtered out from all standardized data to obtain the target data.

[0120] The multiple dimensions that can be selected can be chosen by the user in the dimension filter panel, for example: "Time range = May 1-7, 2024" and "Product category = Beauty" and "User age range = 25-30 years old".

[0121] It should be noted that the logical relationship between the filtering conditions in this invention is "AND" by default, but users can manually switch it to "OR". The filtering efficiency in this invention is ≥1000 records / second, thus ensuring fast response speed under large data volumes.

[0122] Specifically, if the user's interaction information is clicking on a node of the target visualization component, the interaction instruction is determined to be a correlation analysis instruction; then, the attribute dimensions and numerical information corresponding to the node of the target visualization component are identified, and other target visualization components are triggered to synchronously display the data corresponding to the attribute dimensions and numerical information.

[0123] For example, when a user clicks on the "Beauty Products" node in the network diagram, the system interprets it as a "relationship analysis instruction," automatically identifies the dimension (product category) and value (beauty) corresponding to the node, and triggers other components to synchronously display data related to the node of the target visualization component.

[0124] In the specific implementation of this invention, all dimension data related to the nodes of the target visualization component are queried based on the associated dimension library. The number of associated results displayed can be set and adjusted by the user (e.g., ≤20 by default, 50 at most, etc.), which is not limited here.

[0125] Specifically, if the user's interaction information is clicking on any dimension of the hierarchy, then the interaction instruction is determined to be a drill-down instruction; then, according to the hierarchical structure of the hierarchy, the next level of data is loaded, and the next level of data is synchronized to all target visualization components.

[0126] For example, when a user double-clicks the time dimension level "May 2024", the system interprets it as a "drill down command" and automatically expands the next level dimension (such as "May 1-31, 2024"), with the drilling depth not exceeding the upper limit of the dimension level.

[0127] The interactive scheme designed in this invention, which combines "multi-dimensional joint screening, cross-dimensional correlation analysis, and hierarchical drill-down," allows users to quickly uncover data details from any dimension. For example, when a user discovers a "sudden increase in orders for a certain type of product," they can simultaneously obtain information such as "the geographical distribution of users who purchased this product," "the change in the number of views of this product during the same period," and "other related purchased products" through a single correlation analysis operation. The operation steps are reduced from 5-8 steps in existing technologies to 1-2 steps, improving exploration efficiency by more than 70%.

[0128] To achieve real-time data updates and anomaly identification, ensuring users are promptly aware of scene changes, in another embodiment of the present invention, a method for multi-dimensional visualization and human-computer interaction oriented towards scene modalities further includes:

[0129] According to the set time interval, update data is periodically retrieved from the data source; the updated data is analyzed for anomalies according to the preset anomaly judgment rules, and the anomaly analysis results are obtained.

[0130] The updated data includes newly added or changed data (not full data). The set time interval can be set according to the needs of the scenario. For example, traffic flow monitoring can be set to "5 seconds / time", while low-frequency scenarios, such as monthly sales analysis for e-commerce, can be set to "30 minutes / time". User-defined adjustments are supported, with an adjustment range of "1 second to 24 hours / time", which is not limited here.

[0131] In practical applications of this invention, a real-time connection needs to be established with the data source of the target scenario, such as connecting to an order database in an e-commerce scenario or connecting to a monitor data interface in a medical scenario; this is not limited to these methods. This invention supports, but is not limited to, connection methods such as WebSocket / HTTP polling; these are not limited to these methods.

[0132] In the practical application of this invention, updated data can be identified based on data timestamps, but is not limited to, thereby reducing the amount of data transmitted and ensuring that the update process does not affect the smoothness of the interface.

[0133] It should be noted that the preset anomaly detection rules include, but are not limited to, threshold-based anomaly detection rules and trend-based anomaly detection rules, which are not limited here.

[0134] Threshold-based anomaly detection rules can determine an anomaly when a core indicator exceeds a preset threshold range. For example, in e-commerce scenarios, the threshold for "sudden drop in order volume" is set to "a decrease of ≥50% compared to the average of the previous hour", and in medical scenarios, the threshold for "abnormal heart rate" is set to ">120 beats / minute or <60 beats / minute". The threshold can be customized by the user and is not limited here.

[0135] The rule for judging trend anomalies is to determine an anomaly when the trend of the core indicator changes abruptly. For example, if the absolute value of the slope of the line chart is greater than 0.8, and the slope calculation period is set to "3 update periods", there is no limit here, to avoid misjudging anomalies due to short-term fluctuations.

[0136] In the actual application of this invention, when an anomaly occurs, the user can be proactively notified through a dual approach of "pop-up notification + color marking". Specifically, the pop-up displays the anomaly dimension, anomaly value, and anomaly time; at the same time, the anomaly data point is marked in red in the visualization component, with a notification delay time of ≤1 second, to ensure that the user does not miss key information. This is not limited here.

[0137] This invention automatically updates data and proactively alerts users to anomalies through the above-mentioned solution, effectively avoiding decision-making errors caused by information lag. Taking urban traffic scenarios as an example, when the traffic flow on a certain road segment exceeds the threshold, the system can complete the data update within 5 seconds and remind the user through pop-up windows and color markings. The user's decision response time is shortened from the "minute level" of the existing technology to the "second level".

[0138] The present invention will be further explained using the "e-commerce platform user behavior analysis scenario" as an example. The "e-commerce platform user behavior analysis scenario" aims to help e-commerce operators explore the relationship between user behavior (browsing / adding to cart / placing orders) and product categories, time, and user attributes through multi-dimensional visualization and interactive operations, monitor user behavior trends in real time, and optimize operational strategies.

[0139] The data source for the current scenario can be, but is not limited to, the user behavior database of the e-commerce platform. It includes, but is not limited to, seven types of raw data: user ID, behavior type (browsing / adding to cart / placing an order), operation time, product ID, product primary / secondary category, user age group, and user region. The data format is JSON, and the real-time update frequency requirement is "10 seconds / time".

[0140] First, the dimensions for this scenario are categorized as follows: Core metrics: Number of user actions (e.g., number of views / add-to-cart / orders); Attribute dimensions: Primary product category (clothing / beauty / food / home appliances), Secondary product category (e.g., beauty category includes "lipstick / face cream / eyeshadow"), User age group (18-24 years old / 25-30 years old / 31-40 years old / 40 years old and above), User region (North China / East China / South China / West China / Central China); Time dimension: The hierarchy is set to "year → month → day → hour", and the time granularity is "hour" by default; Association dimension: User behavior type - product category association (e.g., "ordering behavior - beauty products" association, "adding to cart behavior - clothing products" association).

[0141] Then, data preprocessing is performed, including but not limited to: handling missing values, handling outliers, and data standardization.

[0142] The specific process for handling missing values ​​can be as follows: if the missing rate of "user age group" in the original data is 15% (<30%), use "mode imputation" (mode is 25-30 years old); "product secondary category" has no missing values ​​and does not need to be processed.

[0143] The outlier handling process can be based on 3D. The principle is to identify outliers in the "number of orders" (more than 50 orders per hour, with a mean of 12 and a standard deviation of 10, and mean + 3 × standard deviation = 42), mark the outliers, and prompt the operations staff to "confirm whether it is a fraudulent order behavior".

[0144] The data standardization process can convert "number of user actions" (ranging from 1 to 50 times) into data in the range [0,1]. For example, "number of orders placed 20 times" after standardization is ((20-1) / (50-1)=0.388).

[0145] After completing the data preprocessing, a multi-dimensional visualization model for e-commerce scenarios is constructed. Specifically,

[0146] For the time dimension and core metric dimension: Match a "line chart" with the time granularity set to "hours". The X-axis represents the operation time (0-23 hours), and the Y-axis represents the number of user actions (browsing / adding to cart / placing an order). Switching between different behavior types is supported. For the attribute dimension (product primary category + user age group) and core metric dimension: Match a "parallel coordinate graph" with four axes (product primary category, user age group, number of browsing times, number of order times), with axis colors of blue, green, orange, and red respectively. For the association dimension (user behavior type - product category association): Match a "network graph" with nodes representing "behavior type + product primary category" (e.g., "placing an order - beauty" "adding to cart - clothing"). Node size = number of associations / 5 (number of associations ranges from 10-200, node size ranges from 2-40px), and edge thickness = association strength × 2 (association strength ranges from 0.1-0.8, edge thickness ranges from 0.2-1.6px).

[0147] Then, a multi-component linkage logic design was implemented, with the "time-dimensional line chart" as the main component and the "parallel coordinate graph" and "network graph" as secondary components. When a user clicks the node "May 20, 2024, 20:00" on the line chart, the parallel coordinate graph and network graph are automatically updated to reflect the user behavior data for "May 20, 2024, 20:00", with a linkage response time of 0.3 seconds.

[0148] After completing the construction of the multi-dimensional visualization model, the interface layout is designed and rendered, specifically using a three-part layout. For example: the top toolbar (50px high) displays the title "E-commerce User Behavior Analysis Platform," and on the right is a dropdown menu for "Data Update Frequency" (default 10 seconds / time) and an "Abnormal Prompt Switch" button. The left-side filter panel (200px wide) includes a "Time Range Selector" (supports selection of "Today / Yesterday / Last 7 Days / Custom"), a "Product Category Checkbox Group" (Apparel / Beauty / Food / Home Appliances, supports multiple selections), and a "User Age Group Checkbox Group" (4 age groups including 18-24 years old, supports multiple selections). The right-side display area (width 1100px) uses a 2-row, 2-column grid layout. The top left is a time-dimension line chart (width 500px, height 300px), the top right is a parallel coordinate graph (width 500px, height 300px), the bottom left is a network diagram (width 500px, height 300px), and the bottom right is a "Core Indicator Summary Card" (displaying the total number of views / add-to-cart / orders for the day). The interface loading time is 2.5 seconds.

[0149] After completing the interface layout design and rendering, interactive controls were bound. Specifically, the "Time Dimension" was bound to a "Date Range Selector," supporting the selection of any time period from "May 1, 2024 to May 31, 2024." The "Product Category" was bound to a checkbox group, with all selected by default, but allowing users to deselect specific product categories (e.g., deselecting the "Home Appliances" category would display only apparel / beauty / food data). The "Core Metrics" were bound to a dropdown list, supporting switching between "Number of Views / Number of Added to Cart / Number of Orders," with a switching response time of 0.2 seconds.

[0150] After completing the interface layout design and rendering, and binding of interactive controls, an interactive visual interface was obtained. When a user selects "Time Range = May 20-21, 2024", "Product Category = Beauty", and "User Age Range = 25-30 years old" in the filter panel, the system parses it as a "multi-dimensional joint filter instruction", with the logical relationship being "AND". The interactive operation executes multi-dimensional filtering: the system traverses all user behavior data from May 20-21, 2024, retaining data where "Product Category = Beauty and User Age Range = 25-30 years old", with a filtering efficiency of 1200 records / second. After filtering, the interface components are updated synchronously, with an update time of 0.4 seconds.

[0151] When a user clicks the "Place Order - Beauty" node in the network diagram, the system interprets it as a "correlation analysis command," identifying the target dimensions as "behavior type = placing an order, product category = beauty." The system queries all data associated with "Place Order - Beauty," displaying "the geographical distribution of users aged 25-30 who purchased beauty products on May 20-21" and "other related purchased products (such as beauty tools)." 20 correlation results are displayed, with the interface updating in 0.3 seconds.

[0152] When a user double-clicks the "May 20, 2024" node on the time-dimension line chart, the system interprets it as a "drill-down command," expanding to display hourly data for "May 20, 2024, 0:00-23:00." The system loads the hourly user behavior data for "May 20, 2024, 0:00-23:00," the X-axis of the line chart switches from "date" to "hour," and other components are simultaneously updated to hourly data. The drill-down update takes 0.5 seconds.

[0153] This invention performs real-time data monitoring and acquisition, with an update frequency of 10 seconds per update. It establishes a connection with the e-commerce user behavior database using WebSocket, and each update only retrieves "new user behavior data added within the last 10 seconds" (approximately 50-100 incremental data entries per update), avoiding full data transmission. For example, at 20:00:10 on May 20, 2024, the system retrieves 32 new user behavior data entries (20 browsing, 8 adding to cart, and 4 placing orders) from 20:00:00 to 20:00:10 and automatically loads them into the visualization component.

[0154] This invention also pre-sets anomaly judgment rules, such as threshold-type anomalies and trend-type anomalies. The threshold-type anomaly judgment rule can be to set the "order count anomaly threshold" to "a decrease of ≥50% compared to the average of the previous hour." For example, if the average number of orders in the previous hour (19:00-20:00) was 30 times / 10 seconds, and the number of orders within a certain 10-second period was ≤15, it is judged as an anomaly. The trend-type anomaly judgment rule can be to set the "order count trend anomaly slope threshold" to 0.8. If the number of orders decreases from 30 times / 10 seconds to 5 times / 10 seconds within three consecutive update cycles (30 seconds), the slope is (5-30) / 3=-8.3, and its absolute value >0.8, it is judged as an anomaly.

[0155] This invention will also provide proactive alerts when an anomaly occurs. For example, at 20:00:40 on May 20, 2024, the system detected that "number of orders placed = 12 times / 10 seconds" (a decrease of 60% compared to the average of the previous hour). A pop-up window immediately appears with the message "[Anomaly Alert] The number of orders placed at 20:00:40 is abnormal. The current number is 12, a decrease of 60% compared to the average of the previous hour." At the same time, the order count data point at that time is marked in red on the line graph, with a delay of 0.8 seconds.

[0156] Through the operations described in the above embodiments, e-commerce operators can achieve the following effects:

[0157] 1. Multi-dimensional correlation exploration: By simultaneously viewing behavioral data of "25-30 year old users purchasing beauty products between 8-10 pm" on the same interface, we discovered the pattern that "this group has the highest conversion rate when placing orders between 8 pm and 9 pm", providing data support for operational activities (such as limited-time discounts on beauty products at 8 pm).

[0158] 2. Efficient interactive operation: Through a single correlation analysis operation, the geographic distribution of users associated with "ordering beauty" can be quickly obtained (60% of whom are from East China), eliminating the need for multiple filtering steps and improving operational efficiency by 75% compared to existing technologies.

[0159] 3. Real-time situation monitoring: The anomaly of "a sudden drop in the number of orders at 20:00:40" was detected in a timely manner. After investigation, it was found to be "a brief lag in the payment system". The operations staff immediately contacted the technical team to fix it and avoid order loss. The decision response time was shortened to the second level compared with the existing technology.

[0160] As shown in the above scheme, this invention provides a multi-dimensional visualization and human-computer interaction method oriented towards scene modalities. It categorizes scene data into core indicator dimensions, attribute dimensions, time dimensions, and correlation dimensions. Then, it maps different dimension types and combinations of dimension types in the scene data information to visualization components in a pre-configured visualization component library to determine target visualization components. A multi-dimensional visualization model is then constructed based on all target visualization components. Finally, based on the multi-dimensional visualization model, the interface layout and interactive controls are bound to obtain the visualization interface. This invention, by defining a scene-specific dimension system and data mapping rules, transforms abstract multi-dimensional data into intuitive combinations of visualization components, achieving "one-stop presentation of multi-dimensional data." This helps users quickly discover deep correlations between data, completely solving the problem of users frequently switching and comparing multiple independent views when analyzing complex data. This invention can be widely applied to general scenarios such as financial data analysis, medical data monitoring, e-commerce user behavior analysis, and urban public service status management, aiming to improve users' efficiency in understanding and exploring multi-dimensional data in complex scenarios through visualization and interaction technologies.

[0161] Another embodiment of the present invention provides a multi-dimensional visualization and human-computer interaction device oriented towards scene modalities, such as Figure 4 As shown, it specifically includes:

[0162] Scene data acquisition unit 401 is used to acquire scene data information.

[0163] The scenario data information includes dimension types; the dimension types include core indicator dimensions, attribute dimensions, time dimensions, and correlation dimensions.

[0164] Optionally, in another embodiment of the present invention, the scene data information further includes raw data. One implementation of a multi-dimensional visualization and human-computer interaction device for scene modalities further includes:

[0165] The preprocessing unit is used to preprocess the raw data to obtain standardized data.

[0166] Preprocessing includes handling missing values, handling outliers, and data standardization.

[0167] The multi-dimensional visualization model building unit 402 is used to map different dimensional types and combinations of dimensional types in scene data information to visualization components in the pre-configured visualization component library, determine the target visualization components, and build a multi-dimensional visualization model based on all target visualization components.

[0168] Optionally, in another embodiment of the present invention, one implementation of the multi-dimensional visualization model construction unit 402 includes:

[0169] The first matching unit is used to match the combination of time dimension and core indicator dimension in the scene data information to obtain the corresponding time dimension line chart in the pre-configured visualization component library.

[0170] The second matching unit is used to match the combination of attribute dimensions and core indicator dimensions in the scene data information to obtain the parallel coordinate graph corresponding to the combination of attribute dimensions and core indicator dimensions in the pre-configured visualization component library.

[0171] The third matching unit is used to match the network graph corresponding to the associated dimension from the pre-configured visualization component library for the associated dimension.

[0172] For details on the specific operation of the units disclosed in the above embodiments of the present invention, please refer to the corresponding method embodiments, such as... Figure 2 As shown, it will not be elaborated further here.

[0173] Optionally, in another embodiment of the present invention, one implementation of a scene-modal-oriented multi-dimensional visualization and human-computer interaction device further includes:

[0174] Create a unit to use the time-dimensional line chart as the main component and the parallel coordinate chart and network diagram as secondary components, and establish the correspondence between the main component and the secondary components.

[0175] Specifically, when a user clicks on a node in the main component, the data range of the corresponding slave component in the main component is updated synchronously.

[0176] The specific working process of the units disclosed in the above embodiments of the present invention can be found in the corresponding method embodiments, and will not be repeated here.

[0177] The visualization interface generation unit 403 is used to perform interface layout and binding of interactive controls based on a multi-dimensional visualization model to obtain a visualization interface.

[0178] For details on the specific operation of the units disclosed in the above embodiments of the present invention, please refer to the corresponding method embodiments, such as... Figure 1 As shown, it will not be elaborated further here.

[0179] Optionally, in another embodiment of the present invention, one implementation of a scene-modal-oriented multi-dimensional visualization and human-computer interaction device further includes:

[0180] The interactive information capture unit is used to capture user interaction information in real time.

[0181] The analysis and response unit is used to determine the interaction command based on the user's interaction information and to execute the corresponding logic in response to the interaction command.

[0182] For details on the specific operation of the units disclosed in the above embodiments of the present invention, please refer to the corresponding method embodiments, such as... Figure 3 As shown, it will not be elaborated further here.

[0183] Optionally, in another embodiment of the present invention, one implementation of the analysis response unit includes:

[0184] The filtering instruction determination unit is used to determine the interaction instruction as a filtering instruction if the user's interaction information involves selecting multiple dimensions.

[0185] The data filtering unit is used to traverse all standardized data and filter out data that meets the multiple dimensions selected by the user to obtain the target data.

[0186] The specific working process of the units disclosed in the above embodiments of the present invention can be found in the corresponding method embodiments, and will not be repeated here.

[0187] Optionally, in another embodiment of the present invention, one implementation of the analysis response unit includes:

[0188] The association analysis instruction determination unit is used to determine the interaction instruction as an association analysis instruction if the user's interaction information is clicking on a node of the target visualization component.

[0189] The information recognition unit is used to identify the attribute dimensions and numerical information corresponding to the nodes of the target visualization component, and to trigger other target visualization components to synchronously display the data corresponding to the attribute dimensions and numerical information.

[0190] The specific working process of the units disclosed in the above embodiments of the present invention can be found in the corresponding method embodiments, and will not be repeated here.

[0191] Optionally, in another embodiment of the present invention, the scene data information further includes a hierarchical structure for each dimension, and one implementation of the analysis response unit includes:

[0192] The drill-down instruction determination unit is used to determine the interaction instruction as a drill-down instruction if the user's interaction information is clicking on any dimension of the hierarchy.

[0193] The data loading unit is used to load the next level of data according to the hierarchical structure of the hierarchy, and to synchronize the next level of data to all target visualization components.

[0194] The specific working process of the units disclosed in the above embodiments of the present invention can be found in the corresponding method embodiments, and will not be repeated here.

[0195] Optionally, in another embodiment of the present invention, one implementation of a scene-modal-oriented multi-dimensional visualization and human-computer interaction device further includes:

[0196] The data acquisition unit is used to periodically acquire updated data from the data source at set time intervals.

[0197] Updated data includes newly added or changed data.

[0198] The anomaly analysis unit is used to perform anomaly analysis on the updated data according to preset anomaly judgment rules and obtain anomaly analysis results.

[0199] The specific working process of the units disclosed in the above embodiments of the present invention can be found in the corresponding method embodiments, and will not be repeated here.

[0200] As can be seen from the above solution, this invention provides a multi-dimensional visualization and human-computer interaction device oriented towards scene modalities. It categorizes scene data into core indicator dimensions, attribute dimensions, time dimensions, and correlation dimensions. Then, it maps different dimension types and combinations of dimension types in the scene data information to visualization components in a pre-configured visualization component library to determine target visualization components. A multi-dimensional visualization model is then constructed based on all target visualization components. Finally, based on the multi-dimensional visualization model, the interface layout and interactive controls are bound to obtain the visualization interface. This invention, by defining a scene-specific dimension system and data mapping rules, transforms abstract multi-dimensional data into intuitive combinations of visualization components, achieving "one-stop presentation of multi-dimensional data." This helps users quickly discover deep correlations between data, completely solving the problem of users frequently switching and comparing multiple independent views when analyzing complex data. This invention can be widely applied to general scenarios such as financial data analysis, medical data monitoring, e-commerce user behavior analysis, and urban public service status management, aiming to improve users' efficiency in understanding and exploring multi-dimensional data in complex scenarios through visualization and interaction technologies.

[0201] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0202] Another embodiment of the present invention provides an electronic device, comprising:

[0203] One or more processors.

[0204] A storage device on which one or more programs are stored.

[0205] When the one or more programs are executed by the one or more processors, the one or more processors implement the scene-oriented modality-based multi-dimensional visualization and human-computer interaction method as described in the above embodiments.

[0206] Another embodiment of the present invention provides a computer storage medium storing a computer program thereon, wherein when the computer program is executed by a processor, it implements the scene-oriented modality-based multi-dimensional visualization and human-computer interaction method as described in the above embodiments.

[0207] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0208] It should be noted that the computer-readable medium described above in this invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0209] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0210] Another embodiment of the present invention provides a computer program product, which, when executed, is used to perform the above-described scene-oriented modality-based multi-dimensional visualization and human-computer interaction method.

[0211] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, it performs the functions defined in the methods of the embodiments of the present invention.

[0212] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in this invention is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely exemplary forms for implementing the invention.

[0213] While several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0214] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention is not limited to the specific combination of the above-described technical features, but also includes other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with technical features of the present invention (but not limited to) that have similar functions.

Claims

1. A multi-dimensional visualization and human-computer interaction method oriented towards scene modalities, characterized in that, include: Acquire scene data information; wherein, the scene data information includes dimension types; the dimension types include core indicator dimensions, attribute dimensions, time dimensions, and correlation dimensions; For different dimensional types and combinations of dimensional types in the scene data information, mapping is performed with visualization components in the pre-configured visualization component library to determine the target visualization component, and a multi-dimensional visualization model is constructed based on all the target visualization components; Based on the aforementioned multi-dimensional visualization model, the interface layout and interactive controls are bound together to obtain the visualization interface.

2. The multi-dimensional visualization and human-computer interaction method oriented towards scene modalities according to claim 1, characterized in that, The scene data information also includes raw data, and after receiving the scene data information, it further includes: The original data is preprocessed to obtain standardized data; the preprocessing includes missing value handling, outlier handling, and data standardization.

3. The multi-dimensional visualization and human-computer interaction method oriented towards scene modalities according to claim 1, characterized in that, The process of mapping different dimension types and combinations of dimension types in the scene data information to visualization components in a pre-configured visualization component library to determine the target visualization component includes: For the combination of time dimension and core indicator dimension in the scene data information, a time dimension line chart corresponding to the combination of time dimension and core indicator dimension is obtained by matching in the pre-configured visualization component library; For the combination of attribute dimensions and core indicator dimensions in the scene data information, a parallel coordinate graph corresponding to the combination of attribute dimensions and core indicator dimensions is obtained by matching in the pre-configured visualization component library; For each correlation dimension, the network graph corresponding to the correlation dimension is obtained by matching it in the pre-configured visualization component library.

4. The scene-modal-oriented multi-dimensional visualization and human-computer interaction method according to claim 3, characterized in that, Also includes: The time-dimensional line chart is used as the main component, and the parallel coordinate graph and network graph are used as secondary components. A correspondence between the main and secondary components is established. When a user clicks on a node in the main component, the data range of the corresponding secondary component is updated synchronously.

5. The scene-modal-oriented multi-dimensional visualization and human-computer interaction method according to claim 2, characterized in that, Also includes: Capture user interaction information in real time; The interaction command is determined based on the user's interaction information, and the corresponding execution logic is performed in response to the interaction command.

6. The scene-modal-oriented multi-dimensional visualization and human-computer interaction method according to claim 5, characterized in that, The step of determining the interaction command based on the user's interaction information and responding to the interaction command by performing the execution logic corresponding to the interaction command includes: If the user's interaction information involves selecting multiple dimensions, then the interaction instruction is determined to be a filtering instruction. Iterate through all standardized data, filter out data that meets the multiple dimensions selected by the user, and obtain the target data.

7. The scene-modal-oriented multi-dimensional visualization and human-computer interaction method according to claim 5, characterized in that, The execution logic corresponding to the interaction instruction in response to the interaction instruction includes: If the user's interaction information is clicking a node of the target visualization component, then the interaction instruction is determined to be an association analysis instruction; Identify the attribute dimensions and numerical information corresponding to the nodes of the target visualization component, and trigger other target visualization components to synchronously display the data corresponding to the attribute dimensions and numerical information.

8. The scene-modal-oriented multi-dimensional visualization and human-computer interaction method according to claim 5, characterized in that, The scene data information also includes a hierarchical structure for each dimension, and the execution logic corresponding to the interaction command in response to the interaction command includes: If the user's interaction information is clicking on any dimension of the hierarchy, then the interaction instruction is determined to be a drill-down instruction; Load the next level of data according to the hierarchical structure of the level, and synchronize the next level of data to all target visualization components.

9. The scene-modal-oriented multi-dimensional visualization and human-computer interaction method according to claim 1, characterized in that, Also includes: According to the set time intervals, updated data is periodically retrieved from the data source; wherein, the updated data includes newly added or changed data; The updated data is subjected to anomaly analysis according to preset anomaly judgment rules to obtain anomaly analysis results.

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