Smart city big data visual management method

Abstract

The invention relates to the technical field of data visualization, in particular to a smart city big data visualization management method, which comprises the following steps of: based on sensing node and monitoring point position data streams, reconstructing time and space labels, identifying abrupt changes and combining the abrupt changes into candidate blocks, classifying positions and events to generate a structure label set, analyzing interaction frequency to extract a hot spot region, and extracting a hot spot region; and sequencing layer numbers to generate a rendering queue, and identifying a high-frequency interaction area to output a focusing block. According to the method, the loading efficiency is improved by reconstructing a time sequence and a space label, extracting continuous mutation data segments and combining the continuous mutation data segments into a visual area, classifying positions and event types to uniformly map layers, enhancing label definition and space coherence, recognizing a hot spot area in combination with interaction frequency and connection density, and rearranging layer element numbers to optimize a rendering sequence; the attention area is positioned according to user clicking, hovering and rolling paths, interface response intelligence and personalized expression are enhanced, and city data visualization accuracy and interaction adaptation capability are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of data visualization technology, and in particular to a smart city big data visualization management method. Background Art

[0002] The field of data visualization technology involves expressing and presenting data content in a graphical way, making data easier to understand and analyze. The core content of this technology includes data acquisition, data modeling, graphical mapping, and visual interaction. Data visualization technology relies on basic theories such as computer graphics, human-computer interaction, and statistical analysis, and combines the characteristics of different types of data to use charts, maps, network diagrams and other forms to convert and express data. This field has a wide range of applications in government decision-making, urban management, business analysis and other aspects. Especially in large-scale data processing and display, visualization methods are used to improve data readability and information acquisition efficiency, which facilitates decision-making and management execution.

[0003] Among them, the smart city big data visualization management method refers to the multi-source heterogeneous large-scale data involved in the construction of smart cities. Through centralized processing of structured and unstructured data in the fields of urban transportation, environmental monitoring, public safety, government services, etc., it adopts multidimensional data classification, time series data grouping, geographic space tagging and other methods to integrate and organize, combined with fixed visualization templates and theme customization configuration methods, uses a combination of two-dimensional charts and three-dimensional models for graphic mapping, and presents data view content through rule-based view generation methods, thereby completing the unified graphical display and management of various types of urban operation data.

[0004] Existing technologies for processing large-scale urban data rely on fixed templates and static configurations to construct views. These technologies lack in-depth analysis and real-time annotation of dynamic data features, making it difficult to accurately reflect fluctuations in visible areas caused by sudden events or temporal changes. During region mapping, label organization is relatively simple, resulting in a scattered distribution of similar events. This can lead to unclear layer annotation logic and fragmented spatial information. When identifying interactive hotspots, these technologies rely solely on static analysis of structured data, ignoring changes in the actual connection density and behavior frequency between nodes. This makes it difficult to accurately characterize high-frequency interaction areas, hindering users' ability to identify key content. In terms of rendering scheduling, existing layer loading sequences are often executed in a preset order, failing to adjust display logic based on the actual view structure. This can lead to resource waste and interface lag. When analyzing user behavior feedback, common technologies fail to address micro-interactions such as click behavior and hover paths, resulting in insufficient basis for interface optimization and difficulty in accurately aligning view content with user attention. This can lead to problems in smart city visualization management, such as delayed data display, ambiguous identification of key areas, and slow interactive responses, weakening the system's agility and practical value in practical decision support. Summary of the Invention

[0005] The purpose of this invention is to solve the shortcomings of the existing technology and propose a smart city big data visualization management method.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solution: a smart city big data visualization management method, comprising the following steps:

[0007] S1: Based on the data streams from urban sensing nodes and monitoring points, the time series labels and spatial location labels in each data stream are reformatted, the sampling frequency bands and data mutation locations are identified, and data segments that meet the continuous mutation rules are merged and marked as visualization candidate areas to obtain visualization annotation candidate blocks.

[0008] S2: calling the visual annotation candidate blocks, classifying the location identifier and event trigger type attached to each data block, mapping areas of the same type to a map layer, and adding structured labels to the areas within the layer to generate a map layer structure annotation set;

[0009] S3: Based on the interactive data flow records of each sensing node in the map layer structure annotation set, the connection density and trigger frequency between nodes in the layer are determined, nodes with high density and high trigger frequency are combined into interactive hotspots, and hotspot area identifiers are added to the map layer to obtain layer interactive hotspot area blocks;

[0010] S4: Rearrange the layer element numbers covered in the layer interaction hotspot area block, determine the loading order of the rendering tasks according to the number mapping relationship, allocate visualization layers to the differentiated areas, and output the visualization layer priority queue.

[0011] As a further solution of the present invention, the visual annotation candidate block includes mutation duration, spatial position range, and data variation amplitude; the map layer structure annotation set includes area type label, layer index number, and spatial boundary information; the layer interaction hotspot area block includes hotspot area number, interaction correlation strength, and node aggregation center; the visual layer priority queue includes layer loading order, rendering priority level, and visual layering label.

[0012] As a further solution of the present invention, the step of obtaining the visually labeled candidate blocks is specifically as follows:

[0013] S111: Based on the data streams of urban sensing nodes and monitoring points, extract the time series labels and spatial location labels, identify the sampling frequency band corresponding to the data stream, and obtain the sampling frequency band stability interval value;

[0014] S112: calling the sampling frequency stability interval value, determining the change trend of the continuous sampling segments in the trajectory, identifying the critical point segment of the mutation trend, analyzing the spatiotemporal continuity and intensity of the continuous mutation segments, and obtaining the continuous mutation trend intensity value;

[0015] S113: Based on the continuous mutation trend strength value, select sampling segments that meet the mutation interval length and continuity requirements, analyze the average time interval and position change amplitude, and use the formula:

[0016]

[0017] Calculate the change density value of the visualized mutation block, mark the data segment whose density value is higher than the density benchmark value, and obtain the visualized annotation candidate block;

[0018] Among them, R represents the density value of the visualized mutation block change, T i Represents the time interval value of the i-th sampling point, is the average time interval of the current continuous mutation segments, S i Represents the displacement change value of the spatial area where the i-th sampling point is located, D i is the spatial distance between adjacent points of the i-th sampling point, V i Represents the mutation trend value of the i-th sampling point, and n is the number of sampling points.

[0019] As a further solution of the present invention, the steps of obtaining the map layer structure annotation set are specifically as follows:

[0020] S211: calling the visual annotation candidate block, extracting the location identifier and event trigger type, performing combined judgment and classification based on the coordinate tolerance range and the trigger type keyword, and obtaining a label classification set of similar candidate blocks;

[0021] S212: Analyze the coordinate range of each group of regions based on the classification set of similar candidate block labels, identify the circumscribed boundary area, center point density, and event aggregation number, using the formula:

[0022]

[0023] Calculate the layer area label mapping aggregation value to obtain the map layer aggregation mapping set;

[0024] Among them, L represents the layer area label mapping aggregation value, E a Encode the event label value for the a-th group of regions, is the mean event label of the region, M a is the center point distribution density value of group a, Q a is the event trigger quantity value of group a area, B ais the boundary area value of the a-th group of regions, k is the number of region groups;

[0025] S213: Extracting event types and coordinate attributes of the layer area according to the map layer aggregation mapping set, performing a labeling operation, and generating a map layer structure annotation set.

[0026] As a further solution of the present invention, the step of obtaining the layer interaction hotspot area block is specifically as follows:

[0027] S311: Based on the map layer structure annotation set, analyze the interactive data flow of each perception node, extract the number of connections and triggering frequencies, filter node pairs with high connection and triggering thresholds, and generate a set of strongly connected node pairs;

[0028] S312: Based on the set of strongly connected node pairs, analyze the distribution density and concentration of each node, count the frequency of node appearance and the number of responses, and use the formula

[0029]

[0030] Calculate the density value of the interactive nodes of the layer to form an index set of interactive hotspot areas;

[0031] Among them, H represents the density value of the layer interaction node, F u is the number of responses of node u, is the average number of responses, G u is the frequency of occurrence of node u, X u is the average number of responses of node u, A u is the node spacing of node u, z is the total number of nodes;

[0032] S313: Using the interactive hotspot area index set, identify the hotspot areas on the map layer, record the spatial locations associated with the event types, and obtain the interactive hotspot area blocks of the layer.

[0033] As a further solution of the present invention, the steps for obtaining the visualization layer priority queue are specifically as follows:

[0034] S411: Based on the spatial grid information of the layer covered by the layer interaction hotspot area block, extract the layer element number corresponding to the hotspot area, renumber it according to the area number priority, call the mapping table between area number and element number, perform mapping sorting processing, and obtain the layer element number mapping sequence value;

[0035] S412: According to the layer element number mapping sequence value, the layer element number sequence is compared with the loading weight, and the number interval, weight loading amount and rendering instruction length are extracted using the formula:

[0036]

[0037] Calculate the rendering task loading priority value, sort the layer elements by priority, and establish a rendering sort list;

[0038] Where Y represents the rendering task loading priority value, J1 and J2 are the numbering order values of two adjacent layer elements, W1 and W2 are the layer loading weights of two adjacent layer elements, and R1 and R2 are the rendering instruction length values of two adjacent layer elements.

[0039] S413: Call the rendering sort list, identify the layer area whose number jump is greater than a preset number spacing threshold, extract the corresponding layer number and loading weight, and output the visualization layer priority queue.

[0040] As a further embodiment of the present invention, the method further comprises step S5:

[0041] S5: Based on the visualization rendering area of the priority area in the visualization layer priority queue, analyze the user's click frequency, hovering duration and scrolling path in the smart city visualization interface, mark the area with a trigger frequency higher than the average as the user focus area, and output the intelligent visualization user interaction focus block;

[0042] The intelligent visualization user interaction focus block includes user active hot spots, interaction frequency labels, and focus area identifiers.

[0043] As a further solution of the present invention, the steps for obtaining the intelligent visual user interaction focus block are specifically as follows:

[0044] S511: Based on the visualization layer priority queue, identify the click coordinates, hover duration, and scroll path trajectory in the user interaction log, classify the events into regions according to the timestamp and location fields, and obtain the priority region interaction intensity;

[0045] S512: Based on the interaction intensity of the priority regions, identify the sum of the number of clicks, hovering duration, and number of trajectory segments for each region, compare the region index value with the benchmark value item by item, select regions where any one of the indexes exceeds the benchmark value, record the corresponding index combination, and generate a numerical set of overlapping regions of user interest;

[0046] S513: Calling the area identifier in the user interest overlapping area value set, associating it with the visualization layer rendering field, setting the interactive focus mark bit, and outputting the intelligent visualization user interactive focus block.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by reconstructing the format of time series and spatial labels in the data stream, clarifying the sampling frequency band and data mutation location, and merging data segments with continuous mutation characteristics to form visualization candidate areas, the interference of stray data on the overall presentation effect is effectively avoided. By classifying the candidate data blocks by location identification and event trigger type, similar areas are uniformly mapped to map layers, improving the clarity of label organization and the consistency of spatial distribution. Combined with the connection density and trigger frequency of node interaction data, interactive hotspots are dynamically identified, and the semantic linkage expression between regions is strengthened. A differentiated rendering loading order is constructed with the help of number reordering to achieve reasonable scheduling of visualization rendering resources. Based on the click frequency, hovering duration and scrolling trajectory in user interaction behavior, the focus area is identified, further improving the responsiveness of the interface content and the degree of personalization of the presentation. The overall process is driven by data content and user behavior in a two-way manner, improving the effectiveness of data screening, the aggregation degree of spatial mapping, the accuracy of hotspot identification and the fit of interface interaction, and enhancing the automation level and targeted expression ability of the large-scale urban operation data visualization process. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0050] Figure 2 This is a flowchart for obtaining visually labeled candidate blocks in the present invention;

[0051] Figure 3 This is a flowchart for obtaining a map layer structure annotation set in the present invention;

[0052] Figure 4 This is a flowchart for obtaining the layer interaction hotspot area block in the present invention;

[0053] Figure 5 This is a flowchart for obtaining the visualization layer priority queue in the present invention;

[0054] Figure 6 This is a flowchart for obtaining the intelligent visualization user interaction focus block in the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0057] Example 1

[0058] See also Figure 1 The present invention provides a technical solution: a smart city big data visualization management method, comprising the following steps:

[0059] S1: Based on the data streams from urban sensing nodes and monitoring points, the time series labels and spatial location labels in each data stream are reformatted, the sampling frequency bands and data mutation locations are identified, and data segments that meet the continuous mutation rules are merged and marked as visualization candidate areas to obtain visualization annotation candidate blocks.

[0060] S2: Call the visual annotation candidate blocks, classify the location identifier and event trigger type attached to each data block, map the same type of areas to the map layer, and add structured labels to the areas in the layer to generate a map layer structure annotation set;

[0061] S3: Based on the interactive data flow records of each sensing node in the map layer structure annotation set, the connection density and trigger frequency between nodes in the layer are determined, and nodes with high density and high trigger frequency are combined into interactive hotspots. The hotspot area identifiers are added to the map layer to obtain the layer interactive hotspot area blocks;

[0062] S4: Rearrange the numbers of the layer elements covered in the layer interaction hotspot area block, determine the loading order of the rendering tasks based on the number mapping relationship, allocate visualization layers to the differentiated areas, and output the visualization layer priority queue;

[0063] S5: Based on the visualization rendering area of the priority area in the visualization layer priority queue, the user's click frequency, hovering duration and scrolling path in the smart city visualization interface are analyzed, and the area with a trigger frequency higher than the average is marked as the user focus area, and the intelligent visualization user interaction focus block is output.

[0064] The visual annotation candidate blocks include mutation duration, spatial location range, and data variation amplitude. The map layer structure annotation set includes area type label, layer index number, and spatial boundary information. The layer interaction hotspot area block includes hotspot area number, interaction correlation strength, and node aggregation center. The visual layer priority queue includes layer loading order, rendering priority, and visual layering labels. The intelligent visual user interaction focus block includes user active hot areas, interaction frequency labels, and focus area identifiers.

[0065] See also Figure 2 ,The specific steps for obtaining the visual annotation candidate blocks are:

[0066] S111: Based on the data streams of urban sensing nodes and monitoring points, extract the time series labels and spatial location labels, identify the sampling frequency band corresponding to the data stream, and obtain the sampling frequency band stability interval value;

[0067] First, obtain time series labels and spatial location labels. The data stream reflects the operating status of various areas in the city. For example, in bus monitoring, the time label can indicate the specific time when the vehicle arrives at a station, and the spatial label indicates the specific location of the station. By identifying the sampling frequency band in the data stream, the frequency and pattern of vehicle operation can be analyzed. Combined with the time and space labels, the time and distance required for a vehicle to travel from one station to another can be calculated. By comparing data at different times and locations, traffic scheduling and route planning can be optimized to ensure efficient traffic operation. For example, if a line shows frequent delays during peak hours, it is necessary to increase vehicle deployment or adjust the schedule. Such analysis and calculation help urban transportation departments formulate more effective operation strategies, thereby reducing congestion and improving passenger satisfaction, and obtain the sampling frequency band stability interval value.

[0068] S112: Call the sampling frequency band stability interval value, determine the change trend of the continuous sampling segments in the trajectory, identify the critical point segment of the mutation trend, analyze the spatiotemporal continuity and intensity of the continuous mutation segments, and obtain the continuous mutation trend intensity value;

[0069] By setting specific thresholds to identify important mutation trends, the thresholds are derived based on historical data analysis and represent abnormalities or points that require special attention. For example, in water quality monitoring, if the data from a certain monitoring point suddenly shows a sharp increase in the chemical oxygen demand (COD) concentration, this is an indicator of a pollution event. By comparing the mutation with the set threshold, it is determined whether an alarm is needed. By calculating the spatiotemporal continuity intensity index of continuous mutation sections, the spread speed and scope of the pollution can be determined. For urban environmental protection departments, this information is crucial. They rely on data to respond quickly and take measures to prevent further deterioration of pollution and obtain the continuous mutation trend intensity value.

[0070] S113: Based on the continuous mutation trend strength value, select the sampling segments that meet the mutation interval length and continuity requirements, analyze the average time interval and position change amplitude, and use the formula:

[0071]

[0072] Calculate the change density value of the visualized mutation block, mark the data segment whose density value is higher than the density benchmark value, and obtain the visualized annotation candidate block;

[0073] Among them, R represents the density value of the visualized mutation block change, T i Represents the time interval value of the i-th sampling point, is the average time interval of the current continuous mutation segments, S i Represents the displacement change value of the spatial area where the i-th sampling point is located, D i is the spatial distance between adjacent points of the i-th sampling point, V i represents the mutation trend value of the i-th sampling point, and n is the number of sampling points;

[0074] In urban monitoring, in order to effectively merge data segments that meet specific spatiotemporal characteristics and calculate the change density value of the mutation block, the specific meaning and acquisition process of each parameter are as follows:

[0075] T i : represents the time interval value of the i-th sampling point, in seconds. For example, in a traffic monitoring system, T i It can be calculated by comparing the timestamps of two adjacent sampling points;

[0076] is the average time interval of the current continuous mutation segments, and the calculation formula is Where n is the number of sampling points;

[0077] S i : represents the displacement change value of the spatial area where the i-th sampling point is located, in meters. In urban environmental monitoring, such as air quality monitoring, S i The straight-line distance between two adjacent sampling points can be calculated using GPS coordinates;

[0078] D i : is the spatial distance between adjacent points of the i-th sampling point, also in meters, and is calculated in the same way as S i same;

[0079] V i : represents the change trend value of the i-th sampling point. This is a dimensionless indicator that can be evaluated by comparing the deviation between the point and the historical data;

[0080] n: the number of sampling points in the current continuous mutation segment;

[0081] To provide a practical calculation example, assume that five consecutive sampling points are selected in a city heat island effect monitoring, and the relevant data are as follows:

[0082] Time interval T = [300, 180, 360, 240, 300] seconds;

[0083] Spatial displacement S = [30, 45, 25, 40, 35] meters;

[0084] Distance between adjacent points D = [32, 50, 28, 42, 38] meters;

[0085] Change trend value V = [0.8, 1.1, 0.7, 0.9, 0.85]

[0086] First calculate

[0087] Then calculate R:

[0088]

[0089] This calculation result R≈12.1 reflects the density of the changing trend of the monitoring points within a given time and space range. A higher R value indicates that there are obvious environmental changes in the monitoring points, which helps urban planners and environmental protection agencies identify hot spots and take appropriate measures to deal with the urban heat island effect.

[0090] See also Figure 3 , the specific steps for obtaining the map layer structure annotation set are:

[0091] S211: Calling the visual annotation candidate blocks, extracting the location identifier and event trigger type, performing combined judgment and classification based on the coordinate tolerance range and the trigger type keyword, and obtaining a label classification set of similar candidate blocks;

[0092] By accurately identifying the location identifier and event trigger type attached to each candidate block, advanced geographic information systems (GIS) are used for data processing. For example, in intelligent transportation, the location identifier can be the vehicle position captured by a traffic camera, and the event trigger type includes a traffic accident or road damage. Through this information, data blocks of the same event type can be automatically classified. For example, if two traffic accidents occur at adjacent locations, the two locations are marked as the same category. Such classification not only helps the traffic management center respond quickly, but can also be used for subsequent data analysis and decision support. For example, by analyzing locations where accidents frequently occur, it can be decided whether it is necessary to add traffic signs or improve road conditions, and obtain a classification set of labels for similar candidate blocks.

[0093] S212: Based on the classification set of similar candidate block labels, analyze the coordinate range of each group of regions, identify the external boundary area, center point density and event aggregation number, and use the formula:

[0094]

[0095] Calculate the layer area label mapping aggregation value to obtain the map layer aggregation mapping set;

[0096] Among them, L represents the layer area label mapping aggregation value, E a Encode the event label value for the a-th group of regions, is the mean event label of the region, M a is the center point distribution density value of group a, Q a is the event trigger quantity value of group a area, B a is the boundary area value of the a-th group of regions, k is the number of region groups;

[0097] In smart city data processing, especially when processing geospatial data related to urban operations, using a specific formula to calculate the aggregation value is a core step. Taking traffic congestion management as an example, data is collected from different sources, such as traffic cameras and GPS data, to form a preliminary set of similar candidate block labels. The following are the specific steps and calculation process for further processing and aggregation of data using the formula:

[0098] Data collection and preprocessing: Collect GPS coordinates and timestamps of traffic congestion events in each area, standardize the data, and ensure consistency of temporal and spatial data, for example, converting time to a unified time zone and converting GPS coordinates to a unified map projection;

[0099] Parameters in the calculation formula:

[0100] E a (Event code value): Assign a code to each type of traffic event (such as minor, moderate, and severe congestion), such as 1 for minor, 2 for moderate, and 3 for severe;

[0101] (Event Label Mean): Calculates the average of all area event codes, providing a benchmark to assess the congestion level of each area;

[0102] M a (Center point distribution density value): Calculate the traffic incident density at the center point of each area by dividing the total number of incidents by the area (square kilometers);

[0103] Q a (Event trigger count value): Calculates the number of event triggers in each area within a specific time period;

[0104] B a (Boundary coordinate area value): Use GIS tools to calculate the boundary area of each region;

[0105] k (number of regional groups): the number of regions in the classification set;

[0106] Formula calculation example: Assume the following simplified data:

[0107] Area 1: E1=3, M1=50, Q1=100, B1=2km2;

[0108] Area 2: E2 = 2, M2 = 30, Q2 = 80, B2 = 1.5 km2;

[0109] Number of regions k = 2;

[0110] calculate

[0111] The degree of polymerization is calculated using the formula:

[0112]

[0113] The result, L = 24.71, indicates the distribution and aggregation of event types within different regions. A high value indicates a high concentration of event types within a region and / or a small region with a high number of events. This is crucial for urban planning and traffic management decisions, ensuring more accurate data visualization so that policymakers can intuitively understand which areas face severe traffic challenges at a specific time.

[0114] S213: extracting event types and coordinate attributes of the layer area according to the map layer aggregation mapping set, performing labeling operations, and generating a map layer structure annotation set;

[0115] Structured labels are added to each area on the map. The labels contain event types and key data indicators. For example, each traffic congestion point marked on the map shows not only the location, but also the degree of congestion and duration. This is achieved through advanced programming technology and map editing software. In this way, users can intuitively see the detailed information of each area on the map, which not only enhances the information value of the map, but also enables policymakers and urban planners to make more accurate decisions based on the annotations, generating a map layer structure annotation set. This is the final product of the entire data processing process and directly serves all aspects of urban management and planning.

[0116] See also Figure 4 , the specific steps for obtaining the layer interaction hotspot area block are:

[0117] S311: Based on the map layer structure annotation set, analyze the interactive data flow of each sensing node, extract the number of connections and triggering frequencies, filter node pairs with high connection and triggering thresholds, and generate a set of strongly connected node pairs;

[0118] In monitoring and responding to urban traffic flow and public events, the interactive data stream records of sensing nodes are a crucial data source. Real-time data is collected from multiple sensing nodes, which are deployed in key locations in the city, such as transportation hubs and public squares. Each node can capture events and state changes within a specific area. Through specially developed analysis tools, the frequency of interaction and connection density data between nodes are extracted from the huge data set. The process involves counting the signals sent by each node and recording the target node of each signal to determine the frequency of direct interactions between nodes. For example, when dealing with traffic congestion, by analyzing the data streams sent from traffic lights, it is possible to determine which intersections have the most frequent vehicle flows and which become congestion points. By setting thresholds above the average number of connections and trigger frequencies, it is possible to screen out node pairs with particularly frequent interactions. These node pairs are considered potential hotspots. The screening results will directly influence the formulation of urban traffic management and emergency response strategies, generating a collection of strongly connected node pairs.

[0119] S312: Based on the set of strongly connected node pairs, analyze the distribution density and concentration of each node, count the frequency of node occurrence and the number of responses, and use the formula

[0120]

[0121] Calculate the density value of the interactive nodes of the layer to form an index set of interactive hotspot areas;

[0122] Among them, H represents the density value of the layer interaction node, F u is the number of responses of node u, is the average number of responses, G u is the frequency of occurrence of node u, X u is the average number of responses of node u, A u is the node spacing of node u, z is the total number of nodes;

[0123] The density and response intensity between nodes are quantitatively calculated to identify potential hotspots. To achieve this goal, it is necessary to obtain the number of event responses, occurrence frequency, average response number, and spatial distribution spacing of the sensing node per unit area. By statistically analyzing the interactive data stream records, the frequency of each node appearing in the layer unit area (such as per square kilometer) is calculated. The node number and the map grid number are used for pairing indexing, and the layer area is divided into grids with 1 square kilometer as the unit. The number of node appearances in each grid is counted to obtain the node response frequency per unit area F. uThen count the total number of interaction events in which the node participates and divide it by the number of times it is triggered to get the average response number X of the node u Calculate the Euclidean space distance between each node and its connected nodes, use the longitude and latitude coordinates between nodes, convert them into meters through the spatial conversion formula, and finally get the average node spacing A u , the total number of occurrences of the node G u It is formed by the accumulation of all the connection interactions in which the node participates, and the total number of nodes z is the number of unique nodes actually participating in the sample;

[0124] Taking the interactive monitoring of urban intersections as an example, assuming that five nodes are selected, the measured data are as follows:

[0125] Node 1: F1 = 120, G1 = 80, X1 = 1.5, A1 = 300 meters;

[0126] Node 2: F2=95, G2=65, X2=1.8, A2=250 meters;

[0127] Node 3: F3 = 105, G3 = 70, X3 = 1.6, A3 = 270 meters;

[0128] Node 4: F4 = 85, G4 = 60, X4 = 1.7, A4 = 320 meters;

[0129] Node 5: F5 = 110, G5 = 90, X5 = 1.4, A5 = 310 meters;

[0130] Total number of nodes: z = 5;

[0131] Average number of node responses per unit area:

[0132] Substituting into the formula:

[0133] First calculation:

[0134]

[0135] Second calculation:

[0136]

[0137]

[0138] The final aggregation density value is calculated as: H = 10.4 + 0.885 = 11.285;

[0139] The result shows that the interaction density of the five node groups in the layer is 11.285. If the baseline value of the basic interaction density of the layer is set to 10, the current result is higher than the baseline value, and it can be determined that the node set forms an interaction hotspot area index set;

[0140] By using the spatial distribution index A u The square accumulation is used for root normalization and the node response density F is introduced u The average deviation of the hotspot distribution structure is enhanced, which effectively avoids misjudgment caused by a single high-frequency event.

[0141] S313: Using the interactive hotspot area index set, identify the hotspot area on the map layer, record the spatial location associated with the event type, and obtain the interactive hotspot area block of the layer;

[0142] Hotspot areas are specifically identified on the corresponding layers of the city map, the data is integrated into the map visualization platform, and geographic information system (GIS) technology is used to accurately depict the location of each hotspot area on the map. For example, if a node pair set shows that there is a high frequency of passenger interaction near a bus stop, the area will be marked as a traffic hotspot on the map, thereby assisting urban transportation planners to adjust bus routes or increase the frequency to optimize services. In this way, the layer interaction hotspot area blocks are finally generated, which not only provides a visual display of information, but also serves as a decision support tool to help city managers make more effective responses and planning decisions in complex urban environments.

[0143] See also Figure 5 , the steps for obtaining the visualization layer priority queue are as follows:

[0144] S411: Based on the spatial grid information of the layer covered by the layer interaction hotspot area block, extract the layer element number corresponding to the hotspot area, renumber it according to the area number priority, call the mapping table between area number and element number, perform mapping sorting processing, and obtain the layer element number mapping sequence value;

[0145] The operation of rearranging the element numbers of the layer interaction hotspot area blocks involves recalculating and prioritizing the existing data. The number of each layer element is extracted, and the loading order of the rendering tasks of each element is determined according to the mapping relationship of the numbers. This process requires calling a data processing algorithm to ensure the accuracy and efficiency of the number rearrangement. By associating the layer element numbers with their spatial position information, the element rendering order is optimized. Such processing not only optimizes the data loading speed, but also improves the rendering efficiency. In practice, it can include using sorting algorithms such as quick sort or heap sort to process a large number of element numbers. The optimized loading order directly affects the response time of the user interface and the operating efficiency of the system, and obtains the layer element number mapping sequence value to ensure efficient processing and smooth user experience.

[0146] S412: Map the sequence value of the layer element number to the layer element number sequence and the loading weight, extract the number interval, weight loading amount and rendering instruction length, using the formula:

[0147]

[0148] Calculate the rendering task loading priority value, sort the layer elements by priority, and establish a rendering sort list;

[0149] Where Y represents the rendering task loading priority value, J1 and J2 are the numbering order values of two adjacent layer elements, W1 and W2 are the layer loading weights of two adjacent layer elements, and R1 and R2 are the rendering instruction length values of two adjacent layer elements.

[0150] Based on the number mapping relationship of layer elements and layer loading properties, the loading priority value they should bear in the rendering task is calculated to form a sorting basis, ensuring that the visualization system has a rational structure and orderly scheduling rendering mechanism in the smart city scenario;

[0151] J1, J2: are the numbering sequence values of two adjacent layer elements, directly extracted from the element number rearrangement list, and the unit is a dimensionless natural number;

[0152] W1, W2: The weights of the two element layers, normalized according to the frequency of event triggering in the region, ranging from 0 to 1, unitless;

[0153] R1, R2: The length of layer rendering instructions, indicating the number of commands required to call for each layer element in graphics rendering, in bars;

[0154] Example of actual parameter values (derived from the urban traffic monitoring layer): Layer elements 1 and 2, numbered J1=12 and J2=8, have their load weights normalized by the number of triggers to W1=0.75 and W2=0.6. For example, corresponding to 75 and 60 events, respectively, the number of rendering commands is R1=50 instructions and R2=40 instructions;

[0155] Substitute into the formula for calculation:

[0156] The result shows that the rendering task loading priority value between the current number pair is 0.0695. If the loading priority threshold is set to 0.05, this value is higher than the baseline, indicating that this layer element pair needs to be processed and rendered first;

[0157] The difference between numbers and the product of loading weights are combined to avoid interference of a single order or weight in the sorting logic, and the complexity of the rendering instruction structure is introduced to normalize the denominator, realizing a priority coordination sorting mechanism based on multi-dimensional input factors, effectively improving the accuracy of layer loading scheduling and dynamic layering judgment capabilities.

[0158] S413: Calling the rendering sort list, identifying the layer area whose number jump is greater than a preset number spacing threshold, extracting the corresponding layer number and loading weight, and outputting the visualization layer priority queue;

[0159] Visual layering of differentiated areas involves the management and priority adjustment of multiple data layers. Layer elements are dynamically rendered through programming languages such as JavaScript and frameworks, and their position in the visualization queue is adjusted according to the importance of the elements and the frequency of user interaction. The use of layered rendering technology can effectively manage the display of large-scale data and ensure the priority display of key information, which not only improves the response speed but also improves the user's interactive experience. For example, the display priority of data layers can be dynamically adjusted according to the user's browsing history and preferences. The implementation of this method helps to achieve a more personalized and responsive user interface design, outputting a visualization layer priority queue customized according to actual application needs and data characteristics, reflecting the ability to intelligently manage and optimize the display of urban big data.

[0160] See also Figure 6 ,The specific steps for obtaining the intelligent visualization user interaction focus block are:

[0161] S511: Based on the visual layer priority queue, identify the click coordinates, hover duration, and scroll path trajectory in the user interaction log, classify the events into regions according to the timestamp and location fields, and obtain the priority region interaction intensity;

[0162] Collect user interaction data on smart city interfaces from logs, including click coordinates, hover duration, and scroll path trajectories. Data collection relies on the accurate recording of event timestamps and region location fields. For example, in a specific user study, considering a smart city navigation system, users' click behavior on the map can indicate their interest or demand for specific areas. By analyzing the frequency of users' clicks and hover duration in specific priority areas, we can effectively identify user behavior patterns and locations where demand is concentrated. The data is used to build a detailed user interaction profile. Each event is marked and archived for subsequent data processing and analysis. This archiving work requires not only recording the event itself, but also paying attention to the event context, such as the specific location and time of the click, as well as the user's behavior before and after the click. Associating this information with the region location field can reveal users' usage habits and preferences for smart city functions, providing an empirical basis for the design and optimization of smart cities. The archived data needs to be processed using statistical methods, for example, calculating the average number of clicks and hover duration for each area. The statistical results can reflect the degree of user attention to different areas, providing a basis for further analysis of how to improve the layout of the city navigation interface and obtaining the interaction intensity of priority areas.

[0163] S512: Based on the interaction intensity of the priority regions, identify the sum of the number of clicks, hovering duration, and number of trajectory segments for each region, compare the region index value with the benchmark value item by item, select regions where any one of the indexes exceeds the benchmark value, record the corresponding index combination, and generate a numerical set of overlapping regions of user interest;

[0164] First, we need to calculate the total number of clicks, hovering duration, and number of trajectory overlap segments in each area. This calculation is achieved by summing the corresponding data in each area. For example, in smart parking, it can be calculated based on the frequency of parking lot use and the user's movement trajectory within the parking lot. By summing the data, we can get the total interaction intensity of each area, and then calculate the mean according to the number of areas. The mean represents the average interaction behavior of ordinary users in the area. The data of each area is compared with the mean using a numerical comparison method. For each area, if any of its click count, hovering duration, or number of trajectory overlap segments exceeds the corresponding mean, the area is considered to be an area of user interest overlap. This comparison is based on quantitative threshold settings, which are based on the average value obtained from the analysis of a large amount of previously collected data. In order to ensure the accuracy of the data and the scientific nature of the threshold settings, a large amount of data verification and model testing is required. The areas screened out by this method have a higher user interaction frequency. This information is extremely important for further optimizing the interaction design of smart cities and generating a numerical set of user interest overlap areas.

[0165] S513: Calling the area identifier in the user interest overlapping area value set, associating it with the visualization layer rendering field, setting the interactive focus flag, and outputting the intelligent visualization user interactive focus block;

[0166] Matching with the rendering mark in the visualization layer, for example, in smart traffic management, the frequent operation of users on a certain traffic hub indicates that there are problems with unreasonable design or insufficient information at that location. By marking the areas with high frequency of user interaction, it can be intuitively displayed on the dynamic map of urban traffic management. This not only helps administrators identify and solve problems, but also provides ordinary users with more intuitive traffic information. Special visual marks need to be set on the main layer, such as darkening or flashing colors to highlight the area. The setting of such visual marks should be adjusted according to the user's interaction data to ensure that it can effectively attract the attention of users and administrators, and finally output the intelligent visualization user interaction focus block. The results are obtained through comprehensive analysis of user data and the application of visualization technology. Its purpose is to improve user experience and management efficiency and ensure that various services of the smart city can be effectively used and optimized.

[0167] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A smart city big data visualization management method, characterized in that: The following steps are involved: S1: Based on the data streams from urban sensing nodes and monitoring points, the time series labels and spatial location labels in each data stream are reformatted, the sampling frequency bands and data mutation locations are identified, and data segments that meet the continuous mutation rules are merged and marked as visualization candidate areas to obtain visualization annotation candidate blocks. S2: calling the visual annotation candidate blocks, classifying the location identifier and event trigger type attached to each data block, mapping areas of the same type to a map layer, and adding structured labels to the areas within the layer to generate a map layer structure annotation set; S3: Based on the interactive data flow records of each sensing node in the map layer structure annotation set, the connection density and trigger frequency between nodes in the layer are determined, nodes with high density and high trigger frequency are combined into interactive hotspots, and hotspot area identifiers are added to the map layer to obtain layer interactive hotspot area blocks; S4: Rearrange the layer element numbers covered in the layer interaction hotspot area block, determine the loading order of the rendering tasks according to the number mapping relationship, allocate visualization layers to the differentiated areas, and output the visualization layer priority queue.

2. The smart city big data visualization management method according to claim 1 is characterized in that: The visualization annotation candidate block includes mutation duration, spatial location range, and data variation amplitude; the map layer structure annotation set includes area type label, layer index number, and spatial boundary information; the layer interaction hotspot area block includes hotspot area number, interaction correlation strength, and node aggregation center; the visualization layer priority queue includes layer loading order, rendering priority level, and visualization layer label.

3. The smart city big data visualization management method according to claim 1 is characterized in that: The steps for obtaining the visual annotation candidate blocks are specifically as follows: S111: Based on the data streams of urban sensing nodes and monitoring points, extract the time series labels and spatial location labels, identify the sampling frequency band corresponding to the data stream, and obtain the sampling frequency band stability interval value; S112: calling the sampling frequency stability interval value, determining the change trend of the continuous sampling segments in the trajectory, identifying the critical point segment of the mutation trend, analyzing the spatiotemporal continuity and intensity of the continuous mutation segments, and obtaining the continuous mutation trend intensity value; S113: Based on the continuous mutation trend strength value, select sampling segments that meet the mutation interval length and continuity requirements, analyze the average time interval and position change amplitude, and use the formula: Calculate the change density value of the visualized mutation block, mark the data segment whose density value is higher than the density benchmark value, and obtain the visualized annotation candidate block; Among them, R represents the density value of the visualized mutation block change, T i represents the time interval value of the i-th sampling point, T is the average time interval of the current continuous mutation segment, S i Represents the displacement change value of the spatial area where the i-th sampling point is located, D i is the spatial distance between adjacent points of the i-th sampling point, V i Represents the mutation trend value of the i-th sampling point, and n is the number of sampling points.

4. The smart city big data visualization management method according to claim 3 is characterized in that: The steps for obtaining the map layer structure annotation set are specifically as follows: S211: calling the visual annotation candidate block, extracting the location identifier and event trigger type, performing combined judgment and classification based on the coordinate tolerance range and the trigger type keyword, and obtaining a label classification set of similar candidate blocks; S212: Analyze the coordinate range of each group of regions based on the classification set of similar candidate block labels, identify the circumscribed boundary area, center point density, and event aggregation number, using the formula: Calculate the layer area label mapping aggregation value to obtain the map layer aggregation mapping set; Among them, L represents the layer area label mapping aggregation value, E a is the event label encoding value of the a-th group region, E is the mean event label of the region, M a is the center point distribution density value of group a, Q a is the event trigger quantity value of group a area, B a is the boundary area value of the a-th group of regions, k is the number of region groups; S213: Extracting event types and coordinate attributes of the layer area according to the map layer aggregation mapping set, performing a labeling operation, and generating a map layer structure annotation set.

5. The smart city big data visualization management method according to claim 4 is characterized in that: The steps for obtaining the layer interaction hotspot area block are specifically as follows: S311: Based on the map layer structure annotation set, analyze the interactive data flow of each perception node, extract the number of connections and triggering frequencies, filter node pairs with high connection and triggering thresholds, and generate a set of strongly connected node pairs; S312: Based on the set of strongly connected node pairs, analyze the distribution density and concentration of each node, count the frequency of node appearance and the number of responses, and use the formula Calculate the density value of the interactive nodes of the layer to form an index set of interactive hotspot areas; Among them, H represents the density value of the layer interaction node, F u is the number of responses of node u, F is the average number of responses, G u is the frequency of occurrence of node u, X u is the average number of responses of node u, A u is the node spacing of node u, z is the total number of nodes; S313: Using the interactive hotspot area index set, identify the hotspot areas on the map layer, record the spatial locations associated with the event types, and obtain the interactive hotspot area blocks of the layer.

6. The smart city big data visualization management method according to claim 5 is characterized in that: The steps for obtaining the visualization layer priority queue are specifically as follows: S411: Based on the spatial grid information of the layer covered by the layer interaction hotspot area block, extract the layer element number corresponding to the hotspot area, renumber it according to the area number priority, call the mapping table between area number and element number, perform mapping sorting processing, and obtain the layer element number mapping sequence value; S412: According to the layer element number mapping sequence value, the layer element number sequence is compared with the loading weight, and the number interval, weight loading amount and rendering instruction length are extracted using the formula: Calculate the rendering task loading priority value, sort the layer elements by priority, and establish a rendering sort list; Where Y represents the rendering task loading priority value, J1 and J2 are the numbering order values of two adjacent layer elements, W1 and W2 are the layer loading weights of two adjacent layer elements, and R1 and R2 are the rendering instruction length values of two adjacent layer elements. S413: Call the rendering sort list, identify the layer area whose number jump is greater than a preset number spacing threshold, extract the corresponding layer number and loading weight, and output the visualization layer priority queue.

7. The smart city big data visualization management method according to claim 1, characterized in that: The method further comprises step S5: S5: Based on the visualization rendering area of the priority area in the visualization layer priority queue, analyze the user's click frequency, hovering duration and scrolling path in the smart city visualization interface, mark the area with a trigger frequency higher than the average as the user focus area, and output the intelligent visualization user interaction focus block; The intelligent visualization user interaction focus block includes user active hot spots, interaction frequency labels, and focus area identifiers.

8. The smart city big data visualization management method according to claim 7 is characterized in that: The steps for obtaining the intelligent visual user interaction focus block are specifically as follows: S511: Based on the visualization layer priority queue, identify the click coordinates, hover duration, and scroll path trajectory in the user interaction log, classify the events into regions according to the timestamp and location fields, and obtain the priority region interaction intensity; S512: Based on the interaction intensity of the priority regions, identify the sum of the number of clicks, hovering duration, and number of trajectory segments for each region, compare the region index value with the benchmark value item by item, select regions where any one of the indexes exceeds the benchmark value, record the corresponding index combination, and generate a numerical set of overlapping regions of user interest; S513: Calling the area identifier in the user interest overlapping area value set, associating it with the visualization layer rendering field, setting the interactive focus mark bit, and outputting the intelligent visualization user interactive focus block.

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