Consistent encoding of urban spatial data rendering precision and priority update method

Abstract

The application discloses a consistency coded urban space data rendering precision and priority updating method, which comprises the following steps: step 1, layer management of urban space data; step 2, consistency coding of urban space data; step 3, adaptive rendering strategy based on access frequency and regional heat score is adopted, and rendering precision of different space positions is dynamically optimized by analyzing access history and regional heat; and step 4, data of hot spot regions is loaded in advance according to current heat score and historical access frequency, and rendering precision and updating mechanism are adjusted. Through application of the consistency coding, unified management and efficient rendering of multi-source urban space data are realized, and the performance and processing efficiency of data visualization are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban space data visualization, and in particular to a method for updating the rendering accuracy and priority of urban space data with consistent coding. Background Art

[0002] In the construction of smart cities, the multi-source heterogeneity of urban spatial data poses new challenges for data visualization. Urban spatial data not only comes from diverse sources, including basic geographic information, buildings and facilities, road traffic, and remote sensing imagery, but also possesses complex spatial and temporal attributes. Furthermore, the accuracy and timeliness requirements for visualization vary significantly across regions and data types. To effectively support urban planning, management decisions, and public services, achieving real-time, clear visualization in a high-load environment has become a pressing issue.

[0003] Existing urban data visualization solutions are typically based on preset rendering accuracy or resolution, lack the ability to dynamically respond to real-time access needs, and are unable to automatically adjust display accuracy based on user needs and access popularity in different regions. This approach can lead to low rendering efficiency and poor response speed when faced with frequently accessed areas or hotspot data. Furthermore, existing visualization systems generally fail to effectively integrate multidimensional data characteristics such as time, location, and type, making it difficult to achieve consistent data expression in multi-scale and multi-level displays, resulting in inconsistent user understanding of data at different scales.

[0004] Existing technical methods have the following two main shortcomings: 1. There is a lack of a flexible mechanism to integrate the multidimensional coding structure of urban spatial data and improve the user experience through adaptive rendering strategies; 2. When high-precision display or large-scale data access is required, existing systems find it difficult to balance the real-time performance of data loading and rendering accuracy.

[0005] Therefore, it is necessary to design a consistent coding method for updating the rendering accuracy and priority of urban spatial data to overcome the above problems. Summary of the Invention

[0006] To avoid the above problems, a method for updating the rendering accuracy and priority of urban spatial data with consistent coding is proposed. A unified data coding structure is adopted and combined with an adaptive rendering optimization strategy to achieve efficient management and visualization of multi-source heterogeneous urban spatial data.

[0007] The present invention provides a method for updating the rendering accuracy and priority of consistent coded urban spatial data, comprising the following steps:

[0008] Step 1: Manage urban spatial data in layers;

[0009] Step 2: Consistent encoding of urban spatial data;

[0010] In order to standardize the integration and unified management of multi-source heterogeneous data.

[0011] Step 3: Adopting adaptive rendering strategy based on access frequency and regional heat score, dynamically optimizing rendering accuracy of different spatial positions by analyzing access history and regional heat;

[0012] Through adaptive rendering strategy, automatically adjust rendering accuracy according to regional heat analysis and user access frequency, ensure the clarity and response speed of high access frequency area, realize efficient visualization of large-scale urban spatial data. By analyzing historical access records and regional heat score, dynamically adjust the rendering accuracy of different spatial positions, ensure the data clarity of high-frequency access area and the rational use of resources.

[0013] Step 4: According to the current heat score and historical access frequency, load the data of hot area in advance, adjust the rendering accuracy and update mechanism.

[0014] Constructs the pre-loading mechanism, generates the heat score function based on access heat, predicts the high heat area that may be accessed in the future, realizes the pre-loading of data, and improves the real-time response ability.

[0015] Preferably, step 1 specifically includes: using position coding and type coding to divide urban spatial data into basic geographic information layer, building and facility layer, road layer, remote sensing image and change monitoring layer

[0016] Preferably, the basic geographic information layer contains the topography, rivers and lakes of the city, which is represented by vector data, DEM data or satellite remote sensing image; the building and facility layer mainly covers urban buildings, public facilities and green land, which is the core expression of urban spatial structure and function layout; the road layer includes road network and traffic flow data, reflecting the structure and dynamic condition of urban traffic system; the remote sensing image and change monitoring layer is used for monitoring urban expansion and environmental change, which contains high-resolution remote sensing image and change monitoring data.

[0017] Preferably, in step 2, the urban spatial data is integrated according to the unified coding structure of city coding, position coding, type coding, visualization mode coding, rendering accuracy coding, time coding and color coding.

[0018] Preferably, step 3 specifically includes:

[0019] 3.1 Assign basic rendering accuracy and priority weight to each layer according to different attributes of data layer;

[0020] 3.2 Using the frequency and accuracy data of the encoding, analyze the user access frequency, and construct the thermodynamic scoring function H(x,t) for the position encoding and rendering accuracy encoding:

[0021] H(x,t)=∑ i (a i ×f(x,t)×E(x));

[0022] Where H(x,t) is the heat score corresponding to the position code x and time t; i is the initial weight set based on the region code, indicating the region's priority; i represents different regions, which can be the identifiers of different units such as different plots, buildings, and facilities within a city; f(x, t) is the access frequency of location code x within time t, indicating access popularity; E(x) is the rendering accuracy code of location code x, and the rendering clarity factor is dynamically adjusted based on access popularity. For frequently accessed regions, E(x) is increased to ensure clarity and rendering efficiency.

[0023] 3.3 Automatically adjust the rendering accuracy encoding of high-heat areas based on H(x,t). The access frequency f(x,t) is updated over time. The rendering accuracy is automatically increased or decreased based on the high or low access frequency of the area. The dynamic adjustment formula is:

[0024] E′(x)=E(x)+σ×H(x,t);

[0025] Where E′(x) is the new rendering accuracy encoding value of the position encoding x; σ is the learning rate parameter that controls the rate of rendering accuracy adjustment;

[0026] 3.4 Perform normalization of rendering accuracy. The formula is as follows:

[0027]

[0028] Where, E norm (x) is the normalized rendering precision code. After normalization, the rendering precision code will be adjusted to a specific range of 0 to 9 to represent the final rendering precision; E′(x) is the adjusted rendering precision factor, which represents the rendering precision value after dynamic adjustment or calculation; E min It is the minimum value of the rendering precision encoding, indicating the lowest rendering precision; E max It is the maximum value of the rendering precision code, indicating the highest rendering precision; round() is a rounding function used to adjust the rendering precision value after normalization calculation to the nearest integer; ensuring that the final rendering precision code is an integer value and meets the system's encoding requirements.

[0029] Preferably, in step 4, the advance loading priority formula is:

[0030]

[0031] Where P(x, t) is the pre-load priority corresponding to the position code x and time t, which is used to determine whether to load the data in the area; H(x, t) is the heat score corresponding to the position code x and time t; It is the loading threshold, which means that early loading will only be performed when the heat score exceeds a certain value.

[0032] Preferably, in step 4, the user's behavior is learned through the feedback mechanism to further optimize the regional heat score and rendering accuracy, and the feedback update formula is:

[0033] H′(x,t+1)=H(x,t)+λ×ΔH(x);

[0034] Where H′(x, t+1) is the heat score corresponding to the updated position code x at time t+1; λ is the feedback learning rate; ΔH(x) is the change in heat score, which is obtained by comparing the heat score difference between the current position code x at time t+1 and time t, and is used to dynamically adjust the rendering strategy under the position code.

[0035] Automatically adjust rendering accuracy and update mechanism through continuous feedback from adaptive learning.

[0036] This method abstracts the complexity of urban spatial data into a unified coding structure and dynamically adjusts rendering strategies by analyzing access popularity and historical records to achieve efficient data stratification and display, thus meeting the needs of urban management and the public for real-time, high-precision data visualization. This method utilizes a unified data coding structure combined with an adaptive rendering optimization strategy to achieve efficient management and visualization of multi-source, heterogeneous urban spatial data.

[0037] Compared with the existing technology, the present invention has the following beneficial effects: through the application of consistent coding, unified management and efficient rendering of multi-source urban spatial data are achieved, significantly improving the expressiveness and processing efficiency of data visualization; based on the implementation of adaptive rendering strategy, the rendering accuracy of different areas is ensured to adapt to user needs, and the data response and display effect in high-load scenarios are optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flowchart of a method for updating rendering accuracy and priority of consistently coded urban spatial data according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] like Figure 1 As shown, this embodiment provides a method for updating the rendering accuracy and priority of urban spatial data in consistent coding, including the following steps:

[0041] Step 1: Manage urban spatial data in layers.

[0042] Urban spatial data is characterized by complex hierarchies, rich dimensions, and wide spatiotemporal distribution. Therefore, visualization requires full utilization of a layered mechanism to meet varying precision requirements and visual expression methods. The hierarchical management of urban spatial data can be divided into the following main layers: basic geographic information layer, building and facility layer, road layer, and remote sensing imagery and change monitoring layer.

[0043] The basic geographic information layer contains natural geographic features such as a city's topography, rivers, and lakes, typically represented by vector data, DEM data, or satellite remote sensing imagery. This data is highly stable and is often rendered at low resolution as a background, but clarity is required to support the overlay of other layers of data.

[0044] The Buildings and Facilities layer primarily encompasses urban buildings, public facilities, and green spaces, representing the core of the city's spatial structure and functional layout. Building vector data and 3D model data are commonly used for rendering in this layer. Visualizations typically utilize medium- to high-precision rendering to ensure detailed visualization of buildings at medium to high magnifications.

[0045] The road layer includes data such as road networks and traffic flows, reflecting the structure and dynamics of the urban transportation system. This layer is highly dynamic, with frequently updated data, and is typically visualized using linear and point elements. Low-zoom layers primarily display major roads, while higher-resolution layers include detailed traffic information.

[0046] The Remote Sensing Imagery and Change Monitoring layer is primarily used to monitor urban expansion and environmental change, and contains high-resolution remote sensing imagery and change monitoring data. This layer's data updates quickly and requires high precision. It's often used to display details in multi-scale rendering and is suitable for high-precision image rendering.

[0047] Step 2: Consistently encode urban spatial data.

[0048] Specifically, urban spatial data is integrated into a unified coding structure based on city code, location code, type code, visualization method code, rendering accuracy code, time code, and color code. Each code part is designed with a fixed length to facilitate parsing and data management. The coding format is as follows:

[0049] City (6 bits) | Location (11 bits) | Type (1 bit) | Visualization method (2 bits) | Rendering precision (1 bit) | Time (28 bits) | Color (12 bits)

[0050] The city code is 6 digits and is compiled according to the township and village division standards to ensure regional uniqueness and standardization.

[0051] The location code is 11 bits, of which longitude occupies 6 bits, covers the range of [-180,180], and is expressed in thousandths of a degree; latitude occupies 5 bits, covers the range of [-90,90], and is also expressed in thousandths of a degree.

[0052] The type code is a single-digit number, encoded according to the data layering principle, and is used to distinguish different types of data layers, as shown in the following table.

[0053]

[0054]

[0055] The visualization mode code is two digits. The following is a comparison table of visualization mode codes.

[0056]

[0057] The rendering precision is encoded as a unit length, and the value range is 0 to 9. A higher encoding value indicates a higher rendering precision, while a lower encoding value corresponds to a lower rendering precision.

[0058] Time codes consist of 28 bits and are used to identify specific points in time or time periods, enabling precise description of the temporal dimension of urban spatial data. A single point in time is represented using the date-time format YYYYMMDDhhmmss, with the year, month, day, hour, minute, and second each occupying a specific number of bits. For example, the code 20241028 123045 represents 12:30:45 on October 28, 2024. For time periods, the code consists of a start time and an end time, also using the YYYYMMDDhhmmss format, with the first 14 bits representing the start time and the last 14 bits representing the end time. For example, the code 20241028 120000-20241028 123000 represents the time period from 12:00 to 12:30 on October 28, 2024.

[0059] Color encoding uses 12-bit values ​​to record color information in RGBA format, with each color channel occupying three bits. From left to right, the encoding is for the red, green, blue, and transparency channel values, ensuring that the color depth of each channel is in the range of 0 to 255, represented by three bits. The red channel records the intensity of the red hue, the green and blue channels record the corresponding hue intensities, and the transparency channel represents the transparency effect, with the larger the value, the more opaque. For example, the encoding "255000000255" represents pure red, with an RGB value of 255,0,0 and a transparency channel of 255. This 12-bit color encoding ensures accurate color representation, allowing urban spatial data to have clear visual effects and transparency control when rendered.

[0060] Step 3: Adopt an adaptive rendering strategy based on access frequency and regional heat score, and dynamically optimize the rendering accuracy of different spatial locations by analyzing access history and regional heat.

[0061] This step involves self-learning optimization, first assigning a base rendering accuracy and priority weight to each layer based on the different attributes of the data layer. When calculating the heat score, the layer priority is combined with the access frequency and rendering accuracy encoding to achieve differentiated rendering optimization.

[0062] The basic geographic information layer has a higher priority, with a weight of a = 1.8. The buildings and facilities layer has a medium priority, with a weight of a = 1.5. The roads layer has a medium priority, with a weight of a = 1.2. The remote sensing imagery and change monitoring layer has a lower priority, with a weight of a = 1.0.

[0063] Dynamically optimize rendering strategies through a self-learning mechanism, enabling adaptive adjustments to rendering needs in different regions. Leveraging encoded frequency and accuracy data, we analyze user access frequency and generate access heatmaps. We periodically analyze these models to automatically adjust rendering accuracy and loading patterns, optimizing data response in different regions.

[0064] The heat score function H(x,t) is constructed by encoding the position and rendering accuracy, and the rendering resources of the frequently accessed areas are automatically adjusted:

[0065] H(x,t)=∑ i (a i ×f(x,t)×E(x));

[0066] Where H(x,t) is the heat score corresponding to the position code x and time t; iis the initial weight set according to the coding of the region, representing the priority of the region, i represents different regions, which can be the identification of different units such as different plots, buildings, facilities, etc. in a city region; f(x, t) is the access frequency of the location code x at time t, representing the access heat; E(x) is the rendering accuracy code of the location code x, the rendering clarity factor dynamically adjusted by the access heat, for high-frequency access regions, E(x) increases to ensure clarity and rendering efficiency.

[0067] According to H(x, t), the rendering accuracy code of the high-heat region is automatically adjusted, the access frequency f(x, t) is updated over time, and the rendering accuracy is automatically improved or reduced according to the high and low access frequency of the region, and the dynamic adjustment formula is:

[0068] E'(x) = E(x) + σ × H(x, t);

[0069] In the formula, E'(x) is the new rendering accuracy code value of the location code x; σ is the learning rate parameter, which controls the rate of rendering accuracy adjustment.

[0070] High-frequency region H(x, t) is high, E(x) is adapted to the demand; low-frequency region H(x, t) is low, E(x) is reduced, thereby saving resources.

[0071] In order to ensure that the rendering accuracy code maintains a single character length (0-9), rendering accuracy normalization processing is performed, and the normalization formula is used:

[0072]

[0073] In the formula, E norm (x) is the normalized rendering accuracy code, after normalization processing, the rendering accuracy code will be adjusted to a specific range of 0 to 9, which is used to represent the final rendering accuracy; E'(x) is the adjusted rendering accuracy factor, which represents the rendering accuracy value after dynamic adjustment or calculation; E min is the minimum value of the rendering accuracy code, representing the lowest rendering accuracy; E max is the maximum value of the rendering accuracy code, representing the highest rendering accuracy; round() is a rounding function, used to adjust the rendering accuracy value after normalization calculation to the nearest integer; to ensure that the final rendering accuracy code is an integer value and meets the coding requirements of the system.

[0074] Suppose E min = 0 and E max = 20, then:

[0075]

[0076] The normalized rendering accuracy code E norm(x)=6, used to update the rendering precision bit in the encoding.

[0077] Step 4: Based on the current popularity score and historical access frequency, load the data of the hotspot area in advance and adjust the rendering accuracy and update mechanism.

[0078] This step updates the model and pre-loading logic using an adaptive learning algorithm based on access records. Based on historical access frequency and the frequency of changes in spatial data, it automatically predicts the area the user is about to visit and pre-loads data. The pre-loading priority formula is:

[0079]

[0080] Where P(x, t) is the pre-load priority corresponding to the position code x and time t, which is used to determine whether to load the data in the area; H(x, t) is the heat score corresponding to the position code x and time t; It is the loading threshold, which means that early loading will only be performed when the heat score exceeds a certain value.

[0081] When H(x, t) increases (high-frequency access area), P(x, t) also increases, that is, the priority is improved, triggering the early loading of data, reducing user waiting time, and ensuring real-time rendering.

[0082] Through the feedback mechanism, we learn from user behavior and further optimize the regional heat score and rendering accuracy. The feedback update formula is:

[0083] H′(x,t+1)=H(x,t)+λ×ΔH(x);

[0084] Where H′(x, t+1) is the heat score corresponding to the updated position code x at time t+1; λ is the feedback learning rate; ΔH(x) is the change in heat score, which is obtained by comparing the heat score difference between the current position code x at time t+1 and time t, and is used to dynamically adjust the rendering strategy under the position code.

[0085] Automatically adjust rendering accuracy and update mechanism through continuous feedback from adaptive learning.

[0086] For example: the existing code is: 420100–11420030450–2–21–

[0087] 4-2023101500000000000000000000–255000000000

[0088] Through this coded information, we can determine that this is data about a building in Wuhan City. The specific analysis is as follows:

[0089] Location code (12 bits): coded as 11420030450, indicating a specific area in the city.

[0090] Type code (1 bit): Coded as 2, representing building-related data information.

[0091] Rendering precision encoding (1 bit): The initial encoding is 4.

[0092] Time variable: set to the current time point t=10t=10t=10.

[0093] According to the data layering model, the initial weight of the building and facility layer is set to α = 1.5, and the visit frequency f(x, t) = 15 represents the frequency of visits to the area within a specific time period. The rendering precision factor E(x) = 4 (consistent with the initial rendering precision encoding) needs to be calculated and adjusted. The thermal scoring function is:

[0094] H(11420030450,10)=1.5×15×4=90;

[0095] The results show that at time t = 10, the heat score of region 11420030450 is 90, indicating a high heat value. Based on the current heat score, the rendering accuracy encoding needs to be dynamically adjusted. Assuming a learning rate of δ = 0.1, the adaptive adjustment formula is used:

[0096] E'(x)=4+0.1×90=4+9=13;

[0097] Next, normalization is performed and E is set min =0 and E max =20, then:

[0098]

[0099] Normalized rendering accuracy encoding E norm (x) = 6, used to update the rendering precision in the encoding. Through adaptive learning, the rendering precision factor E(x) is adjusted to 6, corresponding to higher rendering accuracy. This process automatically adapts to thermal requirements, ensuring clearer data display in high-access areas.

[0100] In addition, using the early loading priority formula, assuming the loading threshold Calculate P(x,t):

[0101]

[0102] The results show that if the advance loading priority is close to 1, the data in the area will be automatically loaded to ensure real-time performance and rendering effect during access.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for updating the rendering accuracy and priority of urban spatial data with consistent coding, characterized in that: The steps include: Step 1: Manage urban spatial data in layers; Step 2: Consistently encode urban spatial data; Step 3: Adopt an adaptive rendering strategy based on access frequency and regional heat score, and dynamically optimize the rendering accuracy of different spatial locations by analyzing access history and regional heat. Step 4: Based on the current popularity score and historical access frequency, preload the data of the hotspot area and adjust the rendering accuracy and update mechanism; Step 3 includes: 3.1 Assign basic rendering accuracy and priority weight to each layer based on the different attributes of the data layer; 3.2 Using the frequency and accuracy data of the encoding, analyze the user access frequency, and construct the thermodynamic scoring function H(x,t) for the position encoding and rendering accuracy encoding: H(x,t)=∑ i (a i ×f(x,t)×E(x)); Where H(x,t) is the heat score corresponding to the position code x and time t; i is the initial weight set according to the region's encoding, indicating the region's priority; i represents different regions; f(x, t) is the access frequency of position code x within time t, indicating access popularity; E(x) is the rendering accuracy encoding of position code x, and is a rendering clarity factor dynamically adjusted by access popularity. For frequently accessed regions, E(x) increases to ensure clarity and rendering efficiency.

2. The method for updating rendering accuracy and priority of consistent coded urban spatial data as claimed in claim 1, characterized in that: Step 1 specifically includes: using location coding and type coding to stratify urban spatial data into basic geographic information layer, building and facility layer, road layer, remote sensing image and change monitoring layer.

3. The method for updating rendering accuracy and priority of consistent coded urban spatial data as claimed in claim 2, characterized in that: The basic geographic information layer includes the city's topography, rivers, and lakes, represented by vector data, DEM data, or satellite remote sensing images; the building and facilities layer mainly covers urban buildings, public facilities, and green spaces, and is the core expression of the city's spatial structure and functional layout; the road layer includes road networks and traffic flow data, reflecting the structure and dynamic status of the urban transportation system; the remote sensing imagery and change monitoring layer is used to monitor urban expansion and environmental changes, and contains high-resolution remote sensing images and change monitoring data.

4. The method for updating rendering accuracy and priority of consistent coded urban spatial data as claimed in claim 2, characterized in that: In step 2, the urban spatial data are integrated according to a unified coding structure of city code, location code, type code, visualization method code, rendering accuracy code, time code and color code.

5. The method for updating rendering accuracy and priority of consistent coded urban spatial data as claimed in claim 4, characterized in that: Step 3 also includes: 3.3 Automatically adjust the rendering accuracy encoding of high-heat areas based on H(x,t). The access frequency f(x,t) is updated over time. The rendering accuracy is automatically increased or decreased based on the high or low access frequency of the area. The dynamic adjustment formula is: E′(x)=E(x)+σ×H(x,t); Where E′(x) is the new rendering accuracy encoding value of the position encoding x; σ is the learning rate parameter that controls the rate of rendering accuracy adjustment; 3.4 Perform normalization of rendering accuracy. The formula is as follows: Where, E norm (x) is the normalized rendering precision code. After normalization, the rendering precision code will be adjusted to a specific range of 0 to 9 to represent the final rendering precision; E′(x) is the adjusted rendering precision factor, which represents the rendering precision value after dynamic adjustment or calculation; E min It is the minimum value of the rendering precision encoding, indicating the lowest rendering precision; E max It is the maximum value of the rendering precision code, indicating the highest rendering precision; round() is a rounding function used to adjust the rendering precision value after normalization calculation to the nearest integer; ensuring that the final rendering precision code is an integer value and meets the system's encoding requirements.

6. The method for updating rendering accuracy and priority of consistently coded urban spatial data as claimed in claim 5, characterized in that: In step 4, the early loading priority formula is: Where P(x, t) is the pre-load priority corresponding to the position code x and time t, which is used to determine whether to load the data in the area; H(x, t) is the heat score corresponding to the position code x and time t; θ is the loading threshold, which means that early loading will only be performed when the heat score exceeds a certain value.

7. The method for updating rendering accuracy and priority of consistent coded urban spatial data according to claim 6, characterized in that: In step 4, the user behavior is learned through the feedback mechanism to further optimize the regional heat score and rendering accuracy. The feedback update formula is: H′(x,t+1)=H(x,t)+λ×ΔH(x); Where H′(x, t+1) is the heat score corresponding to the updated position code x at time t+1; λ is the feedback learning rate; ΔH(x) is the change in heat score, which is obtained by comparing the heat score difference between the current position code x at time t+1 and time t, and is used to dynamically adjust the rendering strategy under the position code. Automatically adjust rendering accuracy and update mechanism through continuous feedback from adaptive learning.

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