Information display method, device, equipment and storage medium

Abstract

The present application relates to the field of data visualization technology and discloses an information display method, apparatus, device and storage medium, including: determining the deployment parameters of the sensor array based on the spatial feature data of the target monitoring area; dividing the target monitoring area into multiple dynamic display units according to the deployment parameters, and determining the data display value of each dynamic display unit; generating visual display data based on the data display value, and mapping the visual display data to each dynamic display unit. By dynamically calculating the sensor deployment parameters based on the spatial feature data and dividing the dynamic display units, the problem of uneven resource allocation in the traditional fixed layout is solved, and the coverage accuracy and data display efficiency of the monitoring area are effectively improved. Through the dynamic calculation and mapping of the data display value, the precise matching of monitoring information and visual display is achieved, and the real-time response capability of the system is enhanced.

Description

Technical Field

[0001] The present application relates to the field of data visualization technology, and in particular to an information display method, device, equipment and storage medium. Background Art

[0002] Existing environmental monitoring information display systems typically employ fixed sensor layouts, making it difficult to dynamically adjust display strategies based on the spatial characteristics of the monitored area. Data display units are often divided into static partitions, resulting in poor alignment between visual display data and dynamic monitoring needs. Anomaly detection and adaptive adjustment capabilities are weak, and there is a lack of differentiated processing for multidimensional data features (such as real-time data, historical data, and spatiotemporal correlations). This results in inefficient information display and makes it difficult to provide users with accurate decision support. Summary of the Invention

[0003] The main purpose of this application is to provide an information display method, device, equipment and storage medium, aiming to solve the technical problem that the existing environmental monitoring information display system lacks differentiated processing of multidimensional data features, resulting in low information display efficiency and difficulty in providing users with accurate decision support.

[0004] To achieve the above objectives, the present application proposes an information display method, which includes:

[0005] Determine the deployment parameters of the sensor array based on the spatial characteristic data of the target monitoring area;

[0006] Dividing the target monitoring area into a plurality of dynamic display units according to the deployment parameters, and determining a data display value for each of the dynamic display units;

[0007] Visual display data is generated based on the data display value, and the visual display data is mapped to each of the dynamic display units.

[0008] Optionally, the step of dividing the target monitoring area into a plurality of dynamic display units according to the deployment parameters and determining a data display value of each dynamic display unit includes:

[0009] Constructing a three-dimensional monitoring topology map according to the deployment parameters, and dividing the core display area and the edge display area according to the three-dimensional monitoring topology map;

[0010] Dividing the target monitoring area into a plurality of dynamic display units based on the core display area and the edge display area;

[0011] Determine the display type and update frequency of each dynamic display unit, and assign a corresponding data display value to each dynamic display unit based on the display type and the update frequency.

[0012] Optionally, the step of constructing a three-dimensional monitoring topology map according to the deployment parameters and dividing a core display area and an edge display area according to the three-dimensional monitoring topology map includes:

[0013] generating a spatial grid model based on the deployment parameters, wherein each grid node in the spatial grid model includes a sensor type identifier and a monitoring direction vector;

[0014] Calculating the coverage density coefficient according to the angle between the monitoring direction vector and the normal vector of the environment interface, and generating a three-dimensional monitoring topology map using a spatial interpolation algorithm;

[0015] The continuous areas in the three-dimensional monitoring topology map where the data capture efficiency index exceeds a preset efficiency threshold are used as core display areas, and the remaining areas are marked as edge display areas.

[0016] Optionally, the step of generating visual display data based on the data display value and mapping the visual display data to each of the dynamic display units includes:

[0017] Acquire a data feature type of the data display value, where the data feature type includes real-time data and historical data;

[0018] When the data display value is real-time data, extracting spatiotemporal characteristic parameters of the real-time data, and generating dynamic heat map data based on the spatiotemporal characteristic parameters;

[0019] When the data display value is historical data, identifying the time span and data dimension of the historical data;

[0020] Generate a spatiotemporal correlation graph according to the time span and the data dimension;

[0021] The dynamic heat map data and the spatiotemporal correlation map are mapped to each of the dynamic display units as visual display data.

[0022] Optionally, after the step of generating visual display data based on the data display value and mapping the visual display data to each of the dynamic display units, the method further includes:

[0023] Obtaining real-time data fluctuation characteristics of the target monitoring area;

[0024] Identify the duration of data anomalies and the frequency of anomaly triggering of the dynamic display unit according to the real-time data fluctuation characteristics;

[0025] Calculating an abnormal attention index for each of the dynamic display units based on the duration of the data anomaly and the frequency of the abnormal triggering;

[0026] When it is detected that the abnormal attention index of the dynamic display unit exceeds a preset index threshold, the data display strategy of the dynamic display unit is adjusted.

[0027] Optionally, after the step of generating visual display data based on the data display value and mapping the visual display data to each of the dynamic display units, the method further includes:

[0028] When an environmental interference source is detected within a preset monitoring range, a quantitative evaluation is performed on the monitoring blind area caused by the environmental interference source to obtain a blind area influence range coefficient;

[0029] When the continuous interference time of the environmental interference source exceeds the preset interference threshold and the blind spot influence range coefficient exceeds the permitted range, the key monitoring indicators in the monitoring blind spot are identified;

[0030] If the key monitoring indicator is identified to exist, the monitoring weight of the key monitoring indicator is dynamically allocated to adjacent display units.

[0031] Optionally, if the key monitoring indicator is identified, the step of dynamically allocating the monitoring weight of the key monitoring indicator to adjacent display units includes:

[0032] If a key monitoring indicator is identified, the security level and data correlation of the key monitoring indicator are obtained;

[0033] Clustering and grouping the key monitoring indicators based on the data correlation, and calculating the minimum sampling frequency required for each group of indicators;

[0034] Selecting adjacent display units that meet the minimum sampling frequency within a preset monitoring range based on a load balancing strategy;

[0035] In descending order of the security levels, the task loads of the key monitoring indicators are sequentially distributed to the corresponding adjacent display units.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes an information display device, which includes:

[0037] A parameter acquisition module is used to calculate the deployment parameters of the sensor array based on the spatial feature data of the target monitoring area;

[0038] a data acquisition module, configured to divide the target monitoring area into a plurality of dynamic display units according to the deployment parameters, and determine a data display value for each of the dynamic display units;

[0039] A data display module is configured to generate visual display data based on the data display value, and map the visual display data to each of the dynamic display units.

[0040] In addition, to achieve the above-mentioned purpose, the present application also proposes an information display device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the information display method described above.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the information display method described above are implemented.

[0042] This application discloses determining the deployment parameters of a sensor array based on the spatial feature data of a target monitoring area; dividing the target monitoring area into multiple dynamic display units according to the deployment parameters, and determining the data display value of each dynamic display unit; generating visual display data based on the data display value, and mapping the visual display data to each dynamic display unit. By dynamically calculating sensor deployment parameters based on spatial feature data and dividing the dynamic display units, the problem of uneven resource allocation in traditional fixed layouts is solved, effectively improving the coverage accuracy and data display efficiency of the monitoring area. Through the dynamic calculation and mapping of data display values, a precise match between monitoring information and visual display is achieved, enhancing the real-time response capability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 This is a flowchart of the first embodiment of the information display method of this application;

[0046] Figure 2 This is a flow chart of the second embodiment of the information display method of this application;

[0047] Figure 3 This is a flowchart of the third embodiment of the information display method of this application;

[0048] Figure 4 This is an anti-cheating platform interface display diagram for this application information display method;

[0049] Figure 5 This is a flowchart of the fourth embodiment of the information display method of this application;

[0050] Figure 6 This is a schematic diagram of the module structure of the information display device according to an embodiment of the present application;

[0051] Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the information display method in the embodiment of the present application.

[0052] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] In existing environmental monitoring information display systems, sensors are typically deployed in a fixed layout, making it difficult to dynamically adjust display strategies based on the spatial characteristics of the monitored area. Traditional methods often use static partitioning to divide data display units, leading to problems such as insufficient data display accuracy in key areas and wasted resources in marginal areas. Furthermore, the visual display of data is poorly aligned with dynamic monitoring needs, and its anomaly detection and adaptive adjustment capabilities are weak. This makes it impossible to effectively optimize the weight distribution and visualization effects of display units, especially in scenarios with environmental interference or data fluctuations. Existing technologies lack differentiated processing of multidimensional data features (real-time / historical data, spatiotemporal correlations), resulting in inefficient information display and difficulty in providing users with accurate decision support.

[0056] This application provides a data visualization information display method, which dynamically calculates sensor deployment parameters and divides display units through spatial feature data, realizes the optimal configuration of monitoring resources, significantly improves the pertinence and display efficiency of data collection, and at the same time enhances the system's adaptability to complex environments, meeting the needs of refined monitoring and intelligent decision-making in complex scenarios.

[0057] It should be noted that the execution subject of this embodiment can be a computing service device with data collection, network communication, and program execution functions, such as a monitoring system, or an electronic device capable of implementing the above functions. The following uses a data visualization system as an example to illustrate this embodiment and the following embodiments.

[0058] Based on this, the embodiment of the present application provides an information display method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the information display method of this application.

[0059] In this embodiment, the information display method includes:

[0060] Step S10: Determine the deployment parameters of the sensor array based on the spatial feature data of the target monitoring area.

[0061] It should be noted that the target monitoring area is the specific spatial range where data collection and monitoring are required. It has clear geographic or three-dimensional spatial boundaries. Its spatial characteristics include attributes such as terrain undulation, obstacle distribution, and the distribution of monitoring requirements (e.g., anomaly areas). Spatial feature data is a multidimensional dataset describing the physical characteristics of the target monitoring area, such as geographic coordinates, environmental interfaces, and monitoring direction requirements. A sensor array is a collaborative monitoring network composed of multiple sensor types (e.g., optical cameras, infrared thermal imagers, gas concentration sensors, vibration sensors), or a single sensor integrating multiple detection functions, arranged in a specific spatial topology. Its deployment must ensure coverage integrity, data complementarity, and redundancy and fault tolerance. Deployment parameters are a set of core indicators that determine the spatial configuration of sensors. Specifically, they include: 1. Location parameters: the three-dimensional coordinates of sensor nodes; 2. Orientation parameters: the sensor's pitch and azimuth angles; 3. Density parameters: the number of sensors per unit area; and 4. Collaboration parameters: the data fusion weights and timing synchronization rules between multiple sensors.

[0062] It should be understood that the deployment parameters of the sensor array can be determined based on previous monitoring experience and standards and specifications in related fields. Alternatively, machine learning algorithms can be used to learn from large amounts of historical monitoring data and corresponding spatial feature data, thereby establishing a mapping relationship between spatial feature data and sensor deployment parameters. A mathematical model can also be constructed based on the spatial feature data to describe the sensor's monitoring range and data collection performance. Optimization algorithms can then be used to determine deployment parameters such as the number, location, and orientation of sensors to achieve comprehensive coverage of the target monitoring area or meet specific monitoring requirements.

[0063] Step S20: dividing the target monitoring area into a plurality of dynamic display units according to the deployment parameters, and determining a data display value for each of the dynamic display units.

[0064] It should be noted that dynamic display units are spatial regions dynamically divided based on sensor deployment parameters and real-time monitoring data. Each unit has its own independent display strategy (such as update frequency and visualization method). The data display value is a quantitative indicator of the priority level of monitoring data within the dynamic display unit. It is calculated based on the importance of the data type, update timeliness, and spatial correlation.

[0065] In one example, based on the sensor density in the deployment parameters, the Delaunay triangulation algorithm is used to divide the monitoring area into basic grid cells, where the grid vertices correspond to the sensor node locations. , where A is the area and N is the number of sensors. Then calculate the effective monitoring time ratio in each grid , according to the dynamic threshold, the core or edge area is identified. (μ is the mean, σ is the standard deviation), marked as core display unit, otherwise it is edge unit. The quadtree subdivision strategy is applied in the core unit, that is, when the difference between adjacent sensor data is When the grid is divided into four equal parts, the density of the grid will be increased to 4 times of the original density after encryption to ensure the accuracy of capturing abnormal data. is the preset difference threshold, and Represents the monitoring data of adjacent sensors.

[0066] For real-time data streams, Kalman filtering can be used to fuse multi-sensor data or multiple data from a single sensor to obtain data trend confidence scores. For historical data, a temporal convolutional network can be used to extract cross-period features and generate data trend confidence scores. .

[0067] Data display value It can be calculated by weighting the following parameters:

[0068]

[0069] in, is the weight coefficient (the default is 0.5, 0.3, 0.2, which must be satisfied ); is the confidence level of historical data trend. The data update interval, is the maximum allowed update interval; is the distance from the cell center to the core area, is the radius of the monitoring area.

[0070] If a data mutation is detected within a cell (such as ), triggering a temporary increase in the display value:

[0071]

[0072] in, is the corrected data display value, 、 It is the monitoring data of the current and previous moments. and are the theoretical maximum and minimum values ​​of the monitoring indicators.

[0073] Step S30: Generate visual display data based on the data display value, and map the visual display data to each of the dynamic display units.

[0074] It should be noted that visual display of data is data presented in intuitive forms such as graphics and images, such as heat maps, line charts, bar charts, spatiotemporal correlation maps, etc., to facilitate users to quickly understand and analyze data.

[0075] It should be understood that generating data for visualization requires first determining whether the displayed data represents real-time or historical data. Real-time data reflects the current monitoring situation, while historical data is a record of data from a past period of time. Generating different visualization content based on different data feature types and mapping them to corresponding dynamic display units allows for personalized display effects to meet the needs of different users.

[0076] As you can understand, for real-time data, we can extract spatiotemporal characteristic parameters of the data stream, such as the data's changing trends and fluctuations over time and space. Based on these spatiotemporal characteristic parameters, we adjust the data display values ​​to highlight the data's changing characteristics. Finally, we generate dynamic heatmap data. Heatmaps can intuitively display the spatial distribution and changes of data. Different colors represent different data value ranges, and through dynamic changes, they can reflect the evolution of data over time.

[0077] It's understandable that for historical data, the time span and data dimensions of the data profile can be identified. The time span refers to the period of time covered by the data, while the data dimensions include the different attributes or characteristics of the data. Based on the time span and data dimensions, a spatiotemporal correlation map is generated. The map can display the correlations between different time points and different data dimensions, helping users discover potential patterns and trends in the data.

[0078] In this embodiment, the deployment parameters of the sensor array are determined based on the spatial characteristic data of the target monitoring area; the target monitoring area is divided into multiple dynamic display units according to the deployment parameters, and the data display value of each dynamic display unit is determined; visual display data is generated based on the data display value, and the visual display data is mapped to each dynamic display unit. By dynamically calculating sensor deployment parameters based on spatial characteristic data and dividing the dynamic display units, the uneven resource allocation problem of traditional fixed layouts is solved, effectively improving the coverage accuracy and data display efficiency of the monitoring area. Through the dynamic calculation and mapping of data display values, a precise match between monitoring information and visual display is achieved, enhancing the real-time responsiveness of the system.

[0079] Reference Figure 2 , Figure 2 This is a flow chart of the second embodiment of the information display method of this application. Based on the above-mentioned first embodiment, the second embodiment of the information display method of this application is proposed.

[0080] In the second embodiment, step S20 includes:

[0081] Step S201: construct a three-dimensional monitoring topology map according to the deployment parameters, and divide the three-dimensional monitoring topology map into a core display area and an edge display area.

[0082] It should be noted that the 3D monitoring topology is a spatial relationship network model constructed based on sensor deployment parameters (location, orientation, and density). It represents the coverage range, signal propagation paths, and environmental interaction characteristics of sensor nodes, including sensor location, inter-sensor data communication links or signal coverage overlap, and link quality. The core display area is a key monitoring subspace with the highest monitoring priority, significantly higher data capture efficiency, and higher update frequency than other areas. These areas correspond to high-risk sources, critical facilities, or areas prone to data mutation. The edge display area is a secondary monitoring subspace with lower monitoring needs and sparse sensor coverage.

[0083] Furthermore, in order to calculate the coverage density coefficient based on the angle between the monitoring direction vector and the normal vector of the environment interface, and to generate a three-dimensional monitoring topology map by combining spatial interpolation, the sensor coverage efficiency can be more accurately evaluated, monitoring blind spots can be avoided, and resource allocation can be optimized. Step S201 may include:

[0084] A spatial grid model is generated based on the deployment parameters, wherein each grid node in the spatial grid model includes a sensor type identifier and a monitoring direction vector; a coverage density coefficient is calculated based on the angle between the monitoring direction vector and the normal vector of the environmental interface, and a three-dimensional monitoring topology map is generated using a spatial interpolation algorithm; continuous areas in the three-dimensional monitoring topology map whose data capture efficiency index exceeds a preset efficiency threshold are defined as core display areas, and the remaining areas are marked as edge display areas.

[0085] It can be understood that the spatial grid model is a three-dimensional discretized spatial representation model constructed from sensor deployment parameters (such as location, orientation, and type). Each grid node represents a spatial sampling point. The sensor type identifier indicates the sensor category to which the node belongs, and the monitoring direction vector is the unit vector of the sensor's primary monitoring direction. The coverage density coefficient quantifies the effective monitoring capability of the sensor for spatial nodes. The environment interface normal vector is a unit vector that describes the geometric orientation of the monitoring area boundary.

[0086] It should be noted that the spatial interpolation algorithm estimates data at unknown locations based on data from known grid nodes, generating a continuous three-dimensional monitoring topology map. The data capture efficiency index reflects the sensor's ability to capture data in a specific area, with higher values ​​indicating better data capture. The preset efficiency threshold is a pre-set critical value for the data capture efficiency index, used to demarcate core and edge display areas.

[0087] It should be understood that by considering the angle between the monitoring direction vector and the normal vector of the environmental interface, the sensor coverage can be more accurately assessed, thereby generating a more accurate 3D monitoring topology map. The appropriate spatial interpolation algorithm and region division method can be selected based on the actual situation; different algorithms and methods may produce different results. The environment of the target monitoring area may change, such as sensor repositioning or changes in the environmental interface. Therefore, the spatial grid model and 3D monitoring topology map need to be regularly updated.

[0088] In one example, the target monitoring area is divided into several small grid cells according to deployment parameters, and a spatial grid model is constructed. Each grid node is assigned a sensor type identifier and a monitoring direction vector. For each grid node in the spatial grid model, the angle between its monitoring direction vector and the normal vector of the environmental interface is calculated. The size of this angle affects the sensor's coverage of the area; smaller angles generally provide better coverage. The coverage density coefficient is calculated based on the size of this angle; a larger coverage density coefficient indicates greater sensor coverage of the area.

[0089] Cover density coefficient CDC, calculation formula:

[0090]

[0091] in, Normal vector of the environment interface. The wall normal vector (0, 0, 1) indicates vertical upwards. Represents the monitoring direction vector. , it indicates a direction conflict, such as the camera is facing the wall; , then the monitoring direction matches the environmental interface, and only effective monitoring direction. is the Euclidean distance from the sensor to the spatial node.

[0092] Using the spatial interpolation algorithm, the coverage density coefficient of the unknown location is estimated based on the coverage density coefficient of the known grid nodes, thereby generating a continuous three-dimensional monitoring topology map. This application generates a continuous spatial field from discrete node data based on Kriging interpolation. The formula is as follows:

[0093]

[0094] in, is the grid node coordinate of the spatial grid model corresponding to the coverage density coefficient CDC, is the weight coefficient, which is calculated by the semivariogram function, and its corresponding weight coefficient interpolation variance is the smallest.

[0095] In the generated 3D monitoring topology, a preset efficiency threshold is set. The continuous area where the data capture efficiency index exceeds the threshold is designated as the core display area, and the remaining area is marked as the edge display area. The Data Capture Efficiency Index (DCEI) combines coverage density and data quality. The calculation process is as follows:

[0096]

[0097] in, is the data delay time, The maximum allowable delay is set to 0.7. The preset efficiency threshold is set to 0.7. The continuous areas in the 3D monitoring topology map with a data capture efficiency index exceeding 0.7 are designated as core display areas, such as areas on a truck scale. The remaining areas are marked as edge display areas, such as areas where vehicles enter and exit.

[0098] Step S202 : dividing the target monitoring area into a plurality of dynamic display units based on the core display area and the edge display area.

[0099] It's understood that within the core display area, the target monitoring area can be divided based on sensor coverage density, such as the overlapping area of ​​5 sensors as a unit. Alternatively, it can be divided by functional area, such as the different workstations on a truck scale. The edge display area can be divided into a grid, such as 3m x 3m square units.

[0100] It should be understood that when the data fluctuation in a certain area exceeds a threshold, the dynamic display units can be automatically merged or split. When the difference in the update frequency of adjacent unit data is too large, the boundaries of the dynamic display units can be adjusted.

[0101] Step S203 : determining the display type and update frequency of each dynamic display unit, and assigning a corresponding data display value to each dynamic display unit based on the display type and update frequency.

[0102] It's important to note that the display type refers to the specific form in which data is visualized, such as tables, line charts, bar charts, heat maps, and dashboards. Different display types are suitable for different data characteristics and presentation requirements. The update frequency refers to the interval at which data in a dynamic display unit is updated, for example, updates every second, every minute, or every hour.

[0103] Understandably, for data with high real-time requirements, frequently changing data, and a need to demonstrate changing trends, such as equipment operating parameters (temperature, speed, etc.) on a production line, a line chart can be used to clearly show how data changes over time. For data that changes relatively slowly and focuses more on the overall situation, a table format can clearly list various data information, such as the inventory quantity of various materials in a warehouse.

[0104] It should be understood that in the core display area, the update frequency of data with extremely high real-time requirements, such as weighing data of a truck scale, can be set to once per second or every few seconds.

[0105] In this embodiment, a three-dimensional monitoring topology is constructed based on the deployment parameters, and the topology is divided into a core display area and an edge display area based on the topology. The target monitoring area is then divided into multiple dynamic display units based on the core display area and the edge display area. The display type and update frequency of each dynamic display unit are determined, and a corresponding data display value is assigned to each dynamic display unit based on the data type and update frequency. By constructing a three-dimensional monitoring topology and dividing the core and edge areas, a differentiated data display strategy is implemented, prioritizing high-frequency and high-precision data updates in the core area, maximizing the information value of key areas given limited resources.

[0106] Reference Figure 3 , Figure 3This is a flow chart of the third embodiment of the information display method of this application. Based on the above-mentioned second embodiment, the third embodiment of the information display method of this application is proposed.

[0107] In the third embodiment, step S30 includes:

[0108] Step S301: Acquire the data feature type of the data display value, where the data feature type includes real-time data and historical data.

[0109] It should be noted that data feature types refer to the temporal dimension and structural characteristics of the data within a dynamic display unit. Real-time data is an instantaneous, unaggregated stream of data collected directly from sensors or devices, while historical data is aggregated or statistical data that has been stored and processed.

[0110] It's understood that the data feature type for obtaining the data display value can be determined by comparing the data timestamp with the current time. Data closer to the current time is real-time data (such as weighing data just collected by a sensor); data older and recorded in the past is historical data. The data feature type can also be determined by the update frequency.

[0111] Step S302: When the data display value is real-time data, extract the spatiotemporal characteristic parameters of the real-time data, and generate dynamic heat map data based on the spatiotemporal characteristic parameters.

[0112] It can be understood that spatiotemporal feature parameters refer to key indicators of time and space dimensions extracted from real-time data streams, such as data refresh rate, data distribution density, etc.

[0113] Specifically, the data collection interval, frequency of change, and time series trends are determined. The spatial location of the data (e.g., latitude and longitude, regional grid coordinates), as well as the correlation between adjacent spatial points, is clarified. Time series data is filtered (e.g., moving average filtering) to eliminate high-frequency noise. If missing data exists, it is interpolated using a time series prediction algorithm. For example, if a sensor's real-time temperature data occasionally exhibits unusual jumps, it can be smoothed using a moving average filter. Spatial interpolation algorithms (inverse distance weighted interpolation) are used to fill in missing values. Areas with significant spatial variation are weighted to highlight them. The adjusted data is mapped to a color space (e.g., blue for normal values ​​and red for abnormal values), and the display is dynamically updated chronologically. For example, a heat map is refreshed every second to visually display the spatial distribution of real-time data and its evolution over time, helping users quickly identify hotspots (e.g., weighing areas and abnormal areas) and their dynamic trends.

[0114] Step S303: When the data display value is historical data, identify the time span and data dimension of the historical data.

[0115] It's important to note that a Historical Data Profile (HDP) refers to an aggregated dataset extracted from a historical database with a well-defined time span and dimensional structure. The time span is the time range covered by the historical data stream, and the data dimensions are the independent variables or attributes contained in the historical data.

[0116] As you can understand, time span identification can extract the earliest and latest timestamps in historical data, determine the start and end times of data coverage, and calculate the interval between them. Data dimension identification can analyze data structure and count the number of independent attributes or features, such as product IDs and measurement data.

[0117] Step S304: Generate a spatiotemporal correlation graph based on the time span and the data dimension.

[0118] Specifically, generating a spatiotemporal correlation map requires data preprocessing. This involves organizing historical data, labeling each data point with a clear timestamp, and clarifying the attributes of each dimension (such as region and numerical indicators). The data is divided into time granularities, with each time unit serving as a time node. Regions, attribute values, and other data dimensions are defined as spatial nodes. Time nodes are connected to corresponding region nodes. Using graph databases (such as Neo4j) or visualization tools (such as Gephi and Echarts), an intuitive spatiotemporal correlation map is generated based on the definitions of nodes and edges. Node types are distinguished by color and size, and edge relationships are represented by line styles. This clearly demonstrates the interconnections and evolution of historical data across time, space, and attribute dimensions.

[0119] Step S305 : Mapping the dynamic heat map data and the spatiotemporal correlation map as visual display data to each of the dynamic display units.

[0120] In one example, reference Figure 4 , Figure 4 This is an interface diagram for an anti-cheating platform for this application's information display method. The diagram displays core data for equipment monitoring and production management: the upper section displays alarm information indicating current abnormalities such as instrument disconnection (8 level 1), sensor communication (3 level 1), sensor humidity (4 level 2), sensor tilt (5 level 2), and sensor temperature (5 level 2). The middle section displays weighing statistics, showing 24 completed weighing operations, along with a vehicle weighing diagram. The right side displays monitoring information and identified feature maps. The equipment maintains a zero maintenance record and an average lifespan of 100% optimal. The lower bar chart quantifies the unit weight distribution of eight categories, with each category peaking at 2,000 kg, forming a stepped distribution pattern from high to low. The overall data visualization is intuitive, making it easy to quickly grasp the system's operating status and production indicators.

[0121] In the third embodiment, after step S30, the method further includes:

[0122] Step S401: Acquire the real-time data fluctuation characteristics of the target monitoring area.

[0123] It should be noted that the real-time data fluctuation characteristics refer to the laws and characteristics of the data values ​​changing over time in the data stream collected by sensors in the target monitoring area in real time, including fluctuation amplitude, frequency, periodicity, anomalies, etc.

[0124] It should be understood that by using statistical and mathematical methods to study data characteristics such as trends, seasonality, and cyclicality over time, it is possible to identify points or periods in the data that do not conform to expected patterns or deviate from normal behavior. Furthermore, judgment criteria can be dynamically adjusted based on the statistical characteristics of real-time data (such as mean and standard deviation) to distinguish normal from abnormal fluctuations.

[0125] Step S402 : Identify the duration of data anomaly and the frequency of anomaly triggering of the dynamic display unit according to the real-time data fluctuation characteristics.

[0126] It should be noted that the abnormal duration is the time span from the start to the end of the data abnormality state of the dynamic display unit. The abnormal trigger frequency is the number of times the dynamic display unit data abnormality occurs within a unit of time.

[0127] It is understandable that the type of anomaly, such as a sudden anomaly or a continuous anomaly, can be determined by the duration of the data anomaly and the frequency of the anomaly triggering.

[0128] Step S403 : calculating an abnormal attention index of each of the dynamic display units based on the duration of the data abnormality and the abnormal triggering frequency.

[0129] It should be noted that the Anomaly Attention Index (AAI) is a comprehensive indicator that can quantitatively evaluate the severity of anomalies in dynamic display units and is used for priority sorting and resource scheduling.

[0130] In one example, the abnormal attention index is used to quantify the severity of the abnormality of the dynamic display unit, combining the duration of the abnormality and the trigger frequency. The formula is as follows:

[0131]

[0132] in, is the cumulative abnormal duration of dynamic display unit i; is the abnormal trigger frequency of unit i; is the maximum duration of all units in the system; The maximum trigger frequency of all units in the system. and is the weight coefficient, satisfying , the corresponding weight information can be selected from the database according to the exception type. Indicates that a time decay factor is applied to historical abnormal data, with recent abnormalities having a greater impact. t is the interval between the current time and the time when the abnormality occurred. is the attenuation coefficient.

[0133] Step S404: When it is detected that the abnormal attention index of the dynamic display unit exceeds a preset index threshold, the data display strategy of the dynamic display unit is adjusted.

[0134] It's important to note that a data display strategy encompasses a collection of rules, including data visualization method, update frequency, and priority. When the abnormal attention index of a dynamically displayed unit exceeds a preset threshold, a strategy adjustment is triggered. The abnormal unit is highlighted, an alarm tone is automatically played, and a cause analysis diagram for the abnormal unit is displayed.

[0135] In this example, dynamic heat maps and spatiotemporal correlation maps are generated based on the different characteristics of real-time and historical data. This not only meets real-time requirements but also provides in-depth analysis of data evolution patterns, enhancing the multidimensionality of visualization and user decision support. Furthermore, an abnormal attention index is calculated based on the fluctuation characteristics of real-time data, and the display strategy is dynamically adjusted, enabling proactive identification and response to data anomalies, enhancing the system's reliability and early warning capabilities.

[0136] Reference Figure 5 , Figure 5 This is a flow chart of the fourth embodiment of the information display method of this application. Based on the above-mentioned third embodiment, the fourth embodiment of the information display method of this application is proposed.

[0137] In the fourth embodiment, after step S30, the method further includes:

[0138] Step S501: When it is detected that there is an environmental interference source within a preset monitoring range, a monitoring blind area caused by the environmental interference source is quantitatively evaluated to obtain a blind area influence range coefficient.

[0139] It's important to note that environmental interference sources are physical or chemical factors, such as temperature and humidity, that negatively impact sensor monitoring performance. Monitoring blind spots are areas where sensors cannot effectively collect data due to interference sources. The blind zone impact factor (BZF) is a metric that quantitatively assesses the impact of interference sources on the monitoring area.

[0140] It is understandable that sensors can diagnose and determine the source of environmental interference by identifying sudden drops in signal strength or abnormal noise interference, and can also locate the interference source in combination with non-sensor data such as video surveillance and infrared thermal imaging.

[0141] It should be understood that monitoring blind spots can be quantitatively assessed based on the impact ranges of different interference sources. Alternatively, a pre-trained CNN or random forest model can be used to input the interference source type and environmental parameters (such as humidity and temperature) to directly output the blind spot impact range coefficient. Of course, to avoid misjudgments, a signal attenuation model can also be used to determine the boundaries of the area around the interference source where the signal falls below the threshold. The calculated blind spot radius can then be used to determine the blind spot area, thereby deriving the blind spot impact range coefficient.

[0142] In one example, when a blind spot is physically blocked, the sensor signal propagation path can be simulated to mark the area blocked by the obstacle. The blind spot area formula is as follows:

[0143]

[0144] in, is the theoretical coverage of sensor k, is the obstacle projected area.

[0145] For the calculation of electromagnetic interference blind areas, the coverage range can be dynamically narrowed according to the signal-to-noise ratio threshold, as follows:

[0146]

[0147] in, is the sensor transmission power, is the interference noise intensity, is the maximum signal-to-noise ratio threshold. The blind spot impact range coefficient can be expressed as the ratio of the blind spot area to the preset monitoring range area.

[0148] Step S502: When the continuous interference time of the environmental interference source exceeds a preset interference threshold and the blind spot influence range coefficient exceeds a permitted range, key monitoring indicators in the monitoring blind spot are identified.

[0149] It should be noted that key monitoring indicators are monitoring parameters that have a significant impact on system operation, safety or decision-making.

[0150] Step S503: If the key monitoring indicator is identified to exist, the monitoring weight of the key monitoring indicator is dynamically allocated to adjacent display units.

[0151] It should be noted that the monitoring weight represents the monitoring task priority or computing resource ratio allocated to the adjacent unit. The adjacent display unit is a dynamic display unit that is spatially adjacent to the monitoring blind zone.

[0152] Furthermore, in order to cluster and group key indicators based on their security levels and data relevance, and distribute task loads through load balancing strategies, thereby ensuring the monitoring accuracy of high-priority indicators and improving the overall stability and resource utilization of the system, step S503 may include:

[0153] If a key monitoring indicator is identified, the security level and data correlation of the key monitoring indicator are obtained; the key monitoring indicators are clustered and grouped based on the data correlation, and the minimum sampling frequency required for each group of indicators is calculated; adjacent display units that meet the minimum sampling frequency are selected within the preset monitoring range based on the load balancing strategy; and the task load of the key monitoring indicator is distributed to the corresponding adjacent display units in order from high to low security levels.

[0154] It should be noted that the safety level is the potential harm to the system caused by abnormalities in key monitoring indicators. The data correlation indicator is the degree of dependence on other indicators or system functions. The minimum sampling frequency is the minimum data collection frequency required for each set of indicators. The load balancing strategy is used to balance the computing and communication loads of adjacent units when assigning tasks to avoid overload.

[0155] Specifically, key indicators are classified into different safety levels based on industry standards or corporate specifications. Data correlations corresponding to key monitoring indicators are also selected based on pre-set rules. K-Means or hierarchical clustering is used to group strongly correlated indicators. The minimum sampling frequency for each group of indicators is the frequency of the highest-frequency indicator within the group. Neighboring units of units whose current load is below the threshold and closest to the blind spot are assigned tasks from highest to lowest safety level. Finally, cross-validation (such as comparing data from adjacent units with historical data) is used to ensure the accuracy of the assigned data.

[0156] In one example, in a truck scale monitoring scenario, when electromagnetic interference causes sensor failure in a certain area, the system first identifies key indicators (weight, pressure) and evaluates their safety level and data correlation, clustering the strongly correlated weight (Level 1) and pressure (Level 2) into a high-risk group with a minimum sampling frequency of 1 time / second, and temperature (Level 3) into a medium-risk group (0.2 times / second). Then, based on the load balancing strategy, the adjacent unit B (current load 55%) closest to the blind spot and with a lower load is selected to take on 60% of the high-risk group tasks, and unit A (current load 60%) takes on 40%. At the same time, priority is given to allocation according to safety level, ensuring that the continuity of weight data is improved from 40% to 95.2%, and the unit loads are controlled at 75.1% and 85.3% respectively. While ensuring the monitoring of key indicators, system overload is avoided, improving stability by 35.4% and reducing the false alarm rate by 42.1% compared with traditional methods.

[0157] In this embodiment, when an environmental interference source is detected within a preset monitoring range, the monitoring blind spot caused by the interference source is quantitatively evaluated to obtain a blind spot impact range coefficient. When the duration of the environmental interference source's interference exceeds a preset threshold and the impact range coefficient exceeds the permitted range, key monitoring indicators in the monitoring blind spot are identified. If the key monitoring indicator is identified, the monitoring weight of the indicator is dynamically allocated to adjacent display units. Quantitatively assessing the impact of the blind spot when an environmental interference source is present and dynamically allocating monitoring weights to adjacent units effectively compensates for the monitoring gap caused by the interference and ensures the continuity and integrity of key indicators.

[0158] It should be noted that the above examples are only used to understand this application and do not constitute a limitation on the information display method of this application. More simple transformations based on this technical concept are all within the scope of protection of this application.

[0159] This application also provides an information display device, please refer to Figure 6 , the information display device includes:

[0160] A parameter acquisition module 10 is used to calculate the deployment parameters of the sensor array based on the spatial feature data of the target monitoring area;

[0161] A data acquisition module 20 is configured to divide the target monitoring area into a plurality of dynamic display units according to the deployment parameters, and determine a data display value for each of the dynamic display units;

[0162] The data display module 30 is configured to generate visual display data based on the data display value, and map the visual display data to each of the dynamic display units.

[0163] The information display device provided in this application, which utilizes the information display method of the aforementioned embodiment, can address the technical issues in existing environmental monitoring information display systems, such as a lack of differentiated processing of multidimensional data features, resulting in inefficient information display and difficulty in providing accurate decision support to users. Compared to the prior art, the beneficial effects of the information display device provided in this application are the same as those of the information display method provided in the aforementioned embodiment, and the other technical features of the information display device are the same as those disclosed in the aforementioned embodiment method, and are not further described here.

[0164] The present application provides an information display device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the information display method in the above-mentioned embodiment one.

[0165] Reference below Figure 7 It shows a schematic diagram of the structure of an information display device suitable for implementing the embodiments of the present application. The information display device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The information display device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0166] like Figure 7 As shown, the information display device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the information display device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape or hard disk; and communication devices 1009. The communication device 1009 can allow the information display device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows an information display device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0167] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0168] The information display device provided in this application, which utilizes the information display method in the above-described embodiment, can address the technical issues in existing environmental monitoring information display systems, such as the lack of differentiated processing of multidimensional data features, resulting in inefficient information display and difficulty in providing accurate decision support to users. Compared to the prior art, the beneficial effects of the information display device provided in this application are the same as those of the information display method provided in the above-described embodiment, and the other technical features of the information display device are the same as those disclosed in the method in the previous embodiment, and are not further described here.

[0169] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0170] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0171] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, wherein the computer-readable program instructions are used to execute the information display method in the above-mentioned embodiment.

[0172] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media 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, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0173] The computer-readable storage medium may be included in the information display device, or may exist independently without being assembled into the information display device.

[0174] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the information display device, the information display device executes the information display method described above.

[0175] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0177] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0178] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned information display method. This computer-readable storage medium can address the technical issues in existing environmental monitoring information display systems, which lack differentiated processing of multidimensional data features, resulting in inefficient information display and difficulty in providing accurate decision support for users. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the information display method provided in the aforementioned embodiments and are not further elaborated here.

[0179] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An information display method, characterized in that: The information display method includes: Determining the deployment parameters of the sensor array based on the spatial characteristic data of the target monitoring area, wherein the deployment parameters include position parameters, direction parameters, density parameters, and coordination parameters; Dividing the target monitoring area into a plurality of dynamic display units according to the deployment parameters, and determining a data display value for each of the dynamic display units; generating visual display data based on the data display value, and mapping the visual display data to each of the dynamic display units; The step of dividing the target monitoring area into a plurality of dynamic display units according to the deployment parameters and determining a data display value for each of the dynamic display units includes: Constructing a three-dimensional monitoring topology map according to the deployment parameters, and dividing the core display area and the edge display area according to the three-dimensional monitoring topology map; Dividing the target monitoring area into a plurality of dynamic display units based on the core display area and the edge display area; Determining the display type and update frequency of each dynamic display unit, and assigning a corresponding data display value to each dynamic display unit based on the display type and the update frequency, wherein the data display value represents a quantitative indicator of the degree to which the monitoring data in the dynamic display unit needs to be presented first, and is calculated based on the importance of the data type, the timeliness of the update, and the spatial correlation; The step of constructing a three-dimensional monitoring topology map according to the deployment parameters and dividing the core display area and the edge display area according to the three-dimensional monitoring topology map includes: generating a spatial grid model based on the deployment parameters, wherein each grid node in the spatial grid model includes a sensor type identifier and a monitoring direction vector; Calculating the coverage density coefficient according to the angle between the monitoring direction vector and the normal vector of the environment interface, and generating a three-dimensional monitoring topology map using a spatial interpolation algorithm; The continuous area in the three-dimensional monitoring topology map where the data capture efficiency index exceeds a preset efficiency threshold is marked as a core display area, and the remaining area is marked as an edge display area; After the step of generating visual display data based on the data display value and mapping the visual display data to each of the dynamic display units, the method further includes: When an environmental interference source is detected within a preset monitoring range, a quantitative evaluation is performed on the monitoring blind area caused by the environmental interference source to obtain a blind area influence range coefficient; When the continuous interference time of the environmental interference source exceeds the preset interference threshold and the blind spot influence range coefficient exceeds the permitted range, the key monitoring indicators in the monitoring blind spot are identified; If the key monitoring indicator is identified, the monitoring weight of the key monitoring indicator is dynamically allocated to adjacent display units; If the key monitoring indicator is identified, the step of dynamically allocating the monitoring weight of the key monitoring indicator to adjacent display units includes: If a key monitoring indicator is identified, the security level and data correlation of the key monitoring indicator are obtained; Clustering and grouping the key monitoring indicators based on the data correlation, and calculating the minimum sampling frequency required for each group of indicators; Selecting adjacent display units that meet the minimum sampling frequency within a preset monitoring range based on a load balancing strategy; In descending order of the security levels, the task loads of the key monitoring indicators are sequentially distributed to the corresponding adjacent display units.

2. The information display method according to claim 1, wherein: The step of generating visual display data based on the data display value and mapping the visual display data to each of the dynamic display units includes: Acquire a data feature type of the data display value, where the data feature type includes real-time data and historical data; When the data display value is real-time data, extracting spatiotemporal characteristic parameters of the real-time data, and generating dynamic heat map data based on the spatiotemporal characteristic parameters; When the data display value is historical data, identifying the time span and data dimension of the historical data; Generate a spatiotemporal correlation graph according to the time span and the data dimension; The dynamic heat map data and the spatiotemporal correlation map are mapped to each of the dynamic display units as visual display data.

3. The information display method according to claim 1 or 2, characterized in that: After the step of generating visual display data based on the data display value and mapping the visual display data to each of the dynamic display units, the method further includes: Obtaining real-time data fluctuation characteristics of the target monitoring area; Identify the duration of data anomalies and the frequency of anomaly triggering of the dynamic display unit according to the real-time data fluctuation characteristics; Calculating an abnormal attention index for each of the dynamic display units based on the duration of the data anomaly and the frequency of the abnormal triggering; When it is detected that the abnormal attention index of the dynamic display unit exceeds a preset index threshold, the data display strategy of the dynamic display unit is adjusted.

4. An information display device, characterized in that: The device comprises: A parameter acquisition module is used to calculate the deployment parameters of the sensor array based on the spatial feature data of the target monitoring area, wherein the deployment parameters include position parameters, direction parameters, density parameters and coordination parameters; a data acquisition module, configured to divide the target monitoring area into a plurality of dynamic display units according to the deployment parameters, and determine a data display value for each of the dynamic display units; a data display module, configured to generate visual display data based on the data display value, and map the visual display data to each of the dynamic display units; The data acquisition module is further configured to construct a three-dimensional monitoring topology map based on the deployment parameters, and divide the three-dimensional monitoring topology map into a core display area and an edge display area according to the three-dimensional monitoring topology map; divide the target monitoring area into a plurality of dynamic display units based on the core display area and the edge display area; determine a display type and an update frequency for each of the dynamic display units, and assign a corresponding data display value to each of the dynamic display units based on the display type and the update frequency, wherein the data display value represents a quantitative indicator of the degree to which the monitoring data within the dynamic display unit needs to be presented in a priority manner, and is calculated based on the importance of the data type, the timeliness of the update, and the spatial correlation; The data acquisition module is further configured to generate a spatial grid model based on the deployment parameters, wherein each grid node in the spatial grid model includes a sensor type identifier and a monitoring direction vector; calculate a coverage density coefficient based on the angle between the monitoring direction vector and the normal vector of the environment interface, and generate a three-dimensional monitoring topology map using a spatial interpolation algorithm; define continuous areas in the three-dimensional monitoring topology map where the data capture efficiency index exceeds a preset efficiency threshold as core display areas, and mark the remaining areas as edge display areas; The data display module is further configured to, when detecting the presence of an environmental interference source within a preset monitoring range, quantitatively evaluate the monitoring blind area caused by the environmental interference source to obtain a blind area impact range coefficient; when the continuous interference time of the environmental interference source exceeds a preset interference threshold and the blind area impact range coefficient exceeds a permitted range, identify the key monitoring indicators in the monitoring blind area; if the key monitoring indicators are identified, dynamically allocate the monitoring weights of the key monitoring indicators to adjacent display units; The data display module is further configured to obtain the security level and data correlation of the key monitoring indicators if a key monitoring indicator is identified; cluster and group the key monitoring indicators based on the data correlation, and calculate the minimum sampling frequency required for each group of indicators; select adjacent display units that meet the minimum sampling frequency within a preset monitoring range based on a load balancing strategy; and distribute the task loads of the key monitoring indicators to the corresponding adjacent display units in descending order of the security level.

5. An information display device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the information presentation method according to any one of claims 1 to 3.

6. A storage medium, characterized in that The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the information display method according to any one of claims 1 to 3 are implemented.

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