GIS data visual management system and method based on digital twinning

Abstract

The invention discloses a GIS data visual management system and method based on digital twinning, and belongs to the technical field of urban intellectualization, and the method comprises the steps: a data acquisition module collects complexity data (temperature, humidity, accident rate and abrupt slope number) of a route, maintenance difficulty data (construction time, heavy load traffic flow and bridge-tunnel ratio) and vehicle data (traffic flow and vehicle speed) of the route; the line complexity analysis module outputs a line complexity coefficient; the maintenance difficulty analysis module outputs a maintenance difficulty coefficient; the vehicle analysis module fuses the line complexity coefficient, the maintenance difficulty coefficient and the real-time traffic flow to generate a vehicle analysis coefficient; the traffic flow distribution module distributes the target traffic flow based on the vehicle analysis coefficient and the supply point distance index. According to the invention, road network state dynamic evaluation and traffic flow accurate distribution are realized through the multi-factor coupling model, and the urban traffic management efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of urban intelligent technology, and in particular relates to a GIS data visualization management system based on digital twins and a method thereof. Background Art

[0002] In current urban traffic management systems, GIS data visualization is mostly limited to static road network displays and lacks dynamic coupling analysis of complex environmental factors. Traditional methods have difficulty quantifying the synergistic impact of road section complexity (such as climate and topography) and maintenance difficulty (such as construction windows and bridge-tunnel ratios) on traffic flow distribution, resulting in traffic diversion strategies deviating from actual needs. Especially in the event of sudden congestion, the existing system is prone to secondary congestion because it does not consider the attenuation effect of the distance to the supply point (service area) on diversion decisions. Although digital twin technology has been applied to urban modeling, it has not yet formed a closed-loop management system that integrates "environmental complexity assessment-maintenance difficulty quantification-dynamic traffic flow distribution", which restricts the level of intelligent decision-making in urban road networks. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a GIS data visualization management system based on digital twins and a method thereof to solve the above problems.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a GIS data visualization management system based on digital twins and its method, a line network construction module for constructing a line network visualization platform based on BIM and GIS data, and further comprising:

[0005] Data acquisition module, which obtains the number of routes at the current intersection and obtains the complexity data, maintenance difficulty data and vehicle data of each route;

[0006] Route complexity analysis module, which builds a route complexity analysis model based on route complexity data and outputs the route complexity coefficient;

[0007] Route maintenance difficulty analysis module, which builds a maintenance difficulty analysis model based on route maintenance difficulty data and outputs the maintenance difficulty coefficient;

[0008] Route vehicle analysis module, which builds vehicle analysis model data and vehicle analysis coefficient based on route complexity coefficient and vehicle data under maintenance difficulty data;

[0009] The route traffic flow allocation model is constructed based on the current vehicle analysis coefficient of the route and the distance to the nearest supply point to output the target traffic flow of the route section.

[0010] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0011] Further technical solutions: The route complexity data includes the temperature, humidity, number of historical accidents and number of steep slopes in the area where the road section is located; the route maintenance difficulty data includes construction opening hours, heavy-load vehicle flow and the proportion of bridges and tunnels; the vehicle data includes the real-time traffic flow and vehicle speed of the route.

[0012] Further technical solution: The steps of constructing a route traffic flow allocation model based on the current vehicle analysis coefficient of the route and the distance to the nearest supply point to output the target traffic flow of the route section are as follows:

[0013] Import the distance to the nearest supply point into the formula Get the supply point distance index, f(D i ) represents the supply point distance index of the i-th route, D i represents the distance to the supply point on the i-th section, and D0 represents the distance to the reference supply point;

[0014] The supply point distance index is introduced into the diversion weight formula to obtain the diversion weight coefficient. The diversion weight formula is expressed as:

[0015]

[0016] Among them, w i represents the diversion weight coefficient of the i-th line, M i represents the vehicle analysis coefficient of the i-th line, f(D i ) represents the supply point index of the i-th route, n represents the number of intersection routes, j represents the index of the candidate diversion route in the road network, M j represents the vehicle analysis coefficient of the jth line, f(D j ) represents the supply point index of the jth route;

[0017] According to the total traffic flow of the intersection and the diversion weight coefficient, a route traffic flow distribution model is constructed to output the target traffic flow of the route section. The traffic flow distribution model is expressed as:

[0018]

[0019] Among them, Q tar,i represents the target traffic flow of route i, w i represents the diversion weight coefficient of the i-th line, Q total represents the total traffic flow at the intersection section, σ represents the adjustment coefficient, Represents the estimated rate of change of the vehicle coefficient at time t.

[0020] Further technical solution: The steps of constructing the vehicle analysis model data vehicle analysis coefficient based on the route complexity coefficient and the vehicle data under the maintenance difficulty data are as follows:

[0021] Based on the line complexity coefficient and the maintenance difficulty coefficient, a complexity-difficulty correction model is constructed to output a complexity-difficulty correction factor. The complexity-difficulty correction model is expressed as:

[0022] η=1+λRCI+kMDI

[0023] Where η represents the complexity-difficulty correction factor, RCI represents the complexity coefficient of the road section, MDI represents the maintenance difficulty coefficient, λ represents the RCI correction coefficient, and k represents the MDI correction coefficient.

[0024] A vehicle analysis model is constructed based on the basic vehicle speed, current vehicle speed, and real-time vehicle flow to output a vehicle analysis coefficient. The vehicle analysis model is expressed as:

[0025]

[0026] Among them, M represents the vehicle analysis coefficient, v represents the current vehicle speed, v0 represents the basic vehicle speed, η represents the complexity-difficulty correction factor, Q represents the real-time vehicle flow, Q min Indicates the minimum effective traffic flow.

[0027] Further technical solution: The basic vehicle speed is obtained as follows:

[0028] Based on the real-time traffic volume and free flow speed of the road section, a reference speed model is constructed to output a reference speed. The reference speed model is expressed as:

[0029]

[0030] Among them, v0 represents the reference speed, v f represents the free flow speed (maximum theoretical speed when there is no congestion), Q c represents the benchmark traffic capacity, Q represents the real-time traffic flow, α represents the congestion sensitivity coefficient, and β represents the nonlinear impact index.

[0031] Further technical solution: The steps of constructing a maintenance difficulty analysis model based on the route maintenance difficulty data and outputting the maintenance difficulty coefficient are as follows:

[0032] Perform maximum-minimum normalization on the construction opening time to obtain the construction opening time index;

[0033] The heavy-load vehicle flow rate is ratioed to the maximum allowable heavy-load vehicle flow rate to obtain the heavy-load vehicle flow rate index;

[0034] A maintenance difficulty analysis model is constructed based on the construction opening time index, heavy vehicle flow index, and bridge and tunnel ratio to output the maintenance difficulty coefficient;

[0035] The maintenance difficulty analysis model is expressed as:

[0036] MDI=b1O ind +b2H ind +b3B

[0037] Among them, MDI represents the maintenance difficulty coefficient, O ind represents the construction opening time index, H ind represents the heavy-load traffic index, B represents the proportion of bridges and tunnels, and b i represents the weight and ∑b i =1.

[0038] Further technical solution: The steps of constructing a route complexity analysis model based on route complexity data and outputting a route complexity coefficient are as follows:

[0039] Perform maximum-minimum normalization processing on temperature and humidity to obtain temperature index and humidity index;

[0040] The number of steep slopes and the number of historical accidents are respectively compared with the corresponding maximum values ​​to obtain the steep slope number index and the accident number index;

[0041] Based on the temperature index, humidity index, steep slope index and accident number index, a line complexity analysis model is constructed to output the line complexity coefficient;

[0042] The circuit complexity analysis model is expressed as:

[0043] RCI=a1T ind +a2H ind +a3S ind +a4A ind

[0044] Where RCI represents the circuit complexity coefficient, T ind Represents the temperature index, H ind Represents humidity index, S ind represents the steep slope index, A ind represents the accident number index, a i represents the weight and ∑a i =1.

[0045] A digital twin-based GIS data visualization management method, using the above-mentioned digital twin-based GIS data visualization management system, includes the following steps:

[0046] Build a line network visualization platform based on BIM and GIS data;

[0047] Get the number of routes at the current intersection and obtain the complexity data, maintenance difficulty data, and vehicle data of each route;

[0048] Based on the route complexity data, a route complexity analysis model is constructed to output the route complexity coefficient;

[0049] Based on the route maintenance difficulty data, a maintenance difficulty analysis model is constructed to output the maintenance difficulty coefficient;

[0050] Construct vehicle analysis model data vehicle analysis coefficient based on the route complexity coefficient and vehicle data under maintenance difficulty data;

[0051] Based on the current vehicle analysis coefficient of the route and the distance to the nearest supply point, a route traffic flow allocation model is constructed to output the target traffic flow of the route section.

[0052] The present invention provides a GIS data visualization management system and method based on digital twins, which has the following advantages compared with the existing technology:

[0053] 1. By integrating environmental data (temperature, humidity, accident rate), maintenance data (construction time, heavy traffic), and real-time traffic data, a three-dimensional perception of road network status is achieved. By introducing the supply point distance index and vehicle analysis coefficient, a diversion weight model is constructed to ensure that the target traffic flow distribution responds to both road condition complexity and supply accessibility.

[0054] 2. Propose a "complexity-difficulty correction factor" to dynamically adjust the baseline vehicle speed and improve the sensitivity of the vehicle analysis coefficient to harsh road sections (such as steep slopes and high accident rates). BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0056] 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.

[0057] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0058] See also Figure 1 , provided in one embodiment of the present invention, is a GIS data visualization management system based on digital twins, comprising:

[0059] The line network construction module builds a line network visualization platform based on BIM and GIS data (existing technologies will not be described in detail);

[0060] Data acquisition module, which obtains the number of routes at the current intersection and obtains the complexity data, maintenance difficulty data and vehicle data of each route;

[0061] Route complexity analysis module, which builds a route complexity analysis model based on route complexity data and outputs the route complexity coefficient;

[0062] Route maintenance difficulty analysis module, which builds a maintenance difficulty analysis model based on route maintenance difficulty data and outputs the maintenance difficulty coefficient;

[0063] Route vehicle analysis module, which builds vehicle analysis model data and vehicle analysis coefficient based on route complexity coefficient and vehicle data under maintenance difficulty data;

[0064] The route traffic flow allocation model is constructed based on the current vehicle analysis coefficient of the route and the distance to the nearest supply point (service area) to output the target traffic flow of the route section.

[0065] The data acquisition module is used to collect real-time information on the road environment, maintenance status, and traffic flow. This can be achieved using IoT sensors and traffic monitoring equipment, such as temperature and humidity sensors, vehicle-mounted GPS, and cameras. Its purpose is to provide multi-source data support for subsequent analysis. The route complexity analysis module converts environmental parameters into quantitative indicators. Specifically, a weighted fusion algorithm can be used to process temperature, humidity, historical accident counts, and the number of steep slopes. For example, the complexity coefficient can be calculated through normalization and linear combination. Its purpose is to reflect the dynamic impact of the road environment on traffic efficiency. The route maintenance difficulty analysis module assesses the impact of maintenance constraints on traffic capacity. Specifically, normalization and weight distribution methods can be used to process construction opening hours, heavy traffic volume, and the proportion of bridges and tunnels. For example, the maintenance difficulty coefficient can be calculated by combining the construction time compression rate with the heavy traffic volume ratio. Its purpose is to quantify the constraints that maintenance operations impose on traffic flow distribution. The route vehicle analysis module evaluates the efficiency of a road segment by integrating environmental and maintenance factors. Specifically, a coupling model can be used to correlate the complexity coefficient and maintenance difficulty coefficient with real-time vehicle speed and volume. For example, a correction factor can be used to adjust the baseline speed and calculate the vehicle analysis coefficient. This module dynamically characterizes the overall capacity of a road segment. The route traffic allocation model is a decision-making unit for optimizing traffic flow distribution. Specifically, an exponential function and weighted formula can be used to combine the distance to the recharge point and the vehicle analysis coefficient. For example, a decay function can be used to calculate diversion weights and allocate target traffic flows. This model aims to prevent the failure of diversion strategies caused by ignoring the distance to the recharge point.

[0066] Specifically, the data acquisition module collects real-time information on road section temperature, humidity, historical accident data, and construction status, and transmits it to the route complexity analysis module and the maintenance difficulty analysis module. The route complexity analysis module normalizes temperature and humidity, combining the number of steep slopes and the number of historical accidents to generate a complexity coefficient, reflecting the environmental risk level of the road section. The maintenance difficulty analysis module normalizes the construction opening hours and heavy vehicle volume into an index, and combines this with the proportion of bridges and tunnels to generate a maintenance difficulty coefficient, reflecting the degree of traffic restriction caused by maintenance work. The vehicle analysis module receives the complexity coefficient and maintenance difficulty coefficient, adjusts the baseline speed model using a correction factor, and combines real-time traffic volume and speed to generate a vehicle analysis coefficient, dynamically evaluating the traffic efficiency of the road section. The traffic distribution model calculates diversion weights based on the vehicle analysis coefficient and the distance to the supply point. It then generates a target traffic volume based on the total traffic volume at the intersection and dynamically adjusts the load ratio of each road section to balance traffic distribution during sudden congestion.

[0067] Compared to existing technologies, existing solutions rely solely on static road network data or a single traffic parameter to allocate routes, fail to integrate the dynamic impact of environmental complexity and maintenance difficulty on capacity, and ignore the attenuation effect of recharge point distance on diversion strategies. This solution uses digital twin technology to construct a multi-dimensional data fusion model, linking environmental parameters, maintenance constraints, and real-time traffic dynamics. It also introduces an exponential decay function for recharge point distance to optimize weight allocation, enabling traffic allocation strategies to simultaneously adapt to road segment efficiency and recharge point accessibility.

[0068] Through the above technical solution, this application solves the policy deviation problem caused by data isolation in existing systems, and realizes the coordinated analysis of environmental complexity, maintenance difficulty, and traffic dynamics. In the event of sudden congestion, by quantifying the attenuation effect of the distance to the refueling point, it avoids excessive concentration of vehicles on a single backup route, thereby reducing the risk of secondary congestion and improving traffic diversion efficiency.

[0069] Preferably, the route complexity data includes the temperature, humidity, number of historical accidents and number of steep slopes in the area where the road section is located; the route maintenance difficulty data includes the construction opening hours, heavy vehicle flow and proportion of bridges and tunnels; and the vehicle data includes the real-time traffic flow and vehicle speed of the route.

[0070] Preferably, the steps of constructing a route complexity analysis model based on route complexity data and outputting a route complexity coefficient are:

[0071] Perform maximum-minimum normalization processing on temperature and humidity to obtain temperature index and humidity index;

[0072] The number of steep slopes and the number of historical accidents are respectively compared with the corresponding maximum values ​​to obtain the steep slope number index and the accident number index;

[0073] Based on the temperature index, humidity index, steep slope index and accident number index, a line complexity analysis model is constructed to output the line complexity coefficient;

[0074] The circuit complexity analysis model is expressed as:

[0075] RCI=a1T ind +a2H ind +a3S ind +a4A ind

[0076] Where RCI represents the circuit complexity coefficient, T ind Represents the temperature index, H ind Represents humidity index, S ind represents the steep slope index, A ind represents the accident number index, a i represents the weight and ∑a i =1.

[0077] Specifically, temperature and humidity data are normalized into dimensionless indices, allowing for a quantitative comparison of the impact of extreme climate conditions on road traffic. The ratio of the number of steep slopes to the number of historical accidents converts discrete statistical values ​​into standardized risk indicators. For example, if a road section has eight steep slopes and the system's preset maximum allowable value is 10, its steep slope index is 0.8. When these indices are weighted and summed to generate a route complexity coefficient, the weighting can be dynamically adjusted based on seasonal variations. For example, the temperature index can be weighted higher in winter to reflect the risk of icing. In the route complexity analysis model, when a road section has a temperature index of 0.6, a humidity index of 0.4, a steep slope index of 0.8, and an accident index of 0.5, and the weights are 0.3, 0.2, 0.3, and 0.2, the calculated RCI value is 0.6. This coefficient is input into the subsequent traffic allocation model as a quantitative indicator, linking environmental factors with traffic decision-making.

[0078] Compared with existing technologies, traditional methods typically consider single factors such as temperature or slope independently and fail to dynamically scale historical accident data. For example, existing technologies may directly use absolute temperature values ​​in calculations, resulting in poor comparability between high-latitude and low-latitude data. This solution establishes a unified evaluation system for multi-dimensional environmental parameters through normalization and ratio processing. At the same time, the adjustable weight coefficients enable the model to adapt to different regional characteristics. For example, in mountainous road management, the weight of the steep slope index can be increased to accurately reflect the complexity of the terrain, while the weight distribution of the humidity index can be strengthened in rainy areas.

[0079] Through the above technical solution, this application solves the problem that traditional GIS systems cannot dynamically quantify the impact of multi-dimensional environmental factors on the complexity of road sections. By establishing standardized climate, terrain and safety risk evaluation indicators, the quantitative integration of complex environmental parameters is achieved. This technical solution enables traffic allocation decisions to respond in real time to dynamic changes such as temperature anomalies and steep slope distribution. For example, when heavy rain causes a sudden increase in the humidity index, the system can automatically reduce the target traffic flow allocation for the line, thereby preventing traffic congestion caused by slippery roads. At the same time, the introduction of the historical accident number index enables the system to identify high-risk sections and optimize traffic routes in advance, effectively improving the proactive prevention capabilities of urban road network management.

[0080] Preferably, the steps of constructing a maintenance difficulty analysis model based on route maintenance difficulty data and outputting a maintenance difficulty coefficient are as follows:

[0081] Perform maximum-minimum normalization on the construction opening time to obtain the construction opening time index;

[0082] The heavy-load vehicle flow rate is ratioed to the maximum allowable heavy-load vehicle flow rate to obtain the heavy-load vehicle flow rate index;

[0083] A maintenance difficulty analysis model is constructed based on the construction opening time index, heavy vehicle flow index, and bridge and tunnel ratio to output the maintenance difficulty coefficient;

[0084] The maintenance difficulty analysis model is expressed as:

[0085] MDI=b1O ind +b2H ind +b3B

[0086] Among them, MDI represents the maintenance difficulty coefficient, O ind represents the construction opening time index, H ind represents the heavy-load traffic index, B represents the proportion of bridges and tunnels, and b i represents the weight and ∑b i =1.

[0087] Among them, the construction open time index refers to mapping the original construction time data to a standardized value in the range of 0-1 through the maximum-minimum normalization method. This processing method can eliminate the dimensional differences in construction time of different sections and make the data horizontally comparable. The heavy-load vehicle flow index refers to the ratio of the heavy-load vehicle flow of the current section to the maximum value allowed by the design. This ratio processing can convert the absolute flow into a relative load rate to avoid evaluation deviations due to differences in the bearing capacity of the section. The bridge-tunnel ratio refers to the ratio of the length of the bridge and tunnel to the total length of the section. This parameter directly uses the original proportion data because it itself has characterized the objective impact of the complexity of the section structure on maintenance operations. Weight coefficient b iRefers to the contribution distribution parameters of each influencing factor, which can be determined by expert scoring method or principal component analysis method. i =1 ensures that the maintenance difficulty coefficient output by the model has normalized characteristics.

[0088] Specifically, the construction opening time is converted into an index in the range of 0-1 through maximum-minimum normalization. For example, the construction time of a certain section of road in the road network is the shortest 2 hours and the longest is 8 hours. When the actual construction time is 5 hours, the calculated index value is 0.5. The heavy-load traffic index is calculated by the ratio of the actual traffic flow to the design maximum value. For example, the maximum allowable heavy-load traffic flow of a certain section of road is 200 vehicles / hour. When the real-time traffic flow is 150 vehicles / hour, the index value is 0.75. The proportion of bridges and tunnels directly uses the original data. For example, if a section of road with a total length of 10 kilometers includes 3 kilometers of bridges and 1 kilometer of tunnels, the proportion is 40%. Finally, the three indices are substituted into the linear model MDI=b1O ind +b2H ind +b3B performs a weighted calculation, where the weight coefficient can be adjusted according to the actual road network characteristics. Through this model, the maintenance difficulty of different road sections can be converted into a unified numerical value, providing a quantitative basis for subsequent traffic flow allocation.

[0089] Compared with existing technologies, traditional methods usually consider construction time or traffic flow indicators separately, and fail to establish a quantitative model for multi-factor coupling. In existing technologies, the proportion of bridges and tunnels is often simplified to a binary judgment (whether bridges and tunnels are included), while this solution accurately characterizes the complexity of the structure through continuous proportional data. Existing technologies lack an assessment of the relative load of heavy-duty traffic flow. This solution achieves horizontal comparability of the carrying capacity of road sections through ratio processing. In existing technologies, construction time evaluation mostly uses absolute value comparison. This solution eliminates dimensional differences through normalization processing, making the calculation results more adaptable to dynamic road network environments.

[0090] Through the above technical solution, this application achieves standardized processing and integrated calculation of three types of heterogeneous data: construction time, heavy traffic volume, and bridge and tunnel structure. This solves the problem of difficulty in quantifying and integrating multi-source data in traditional maintenance difficulty assessment. The maintenance difficulty coefficient output by the linear weighted model accurately reflects the actual difficulty of maintenance operations on different road sections, providing data support for avoiding high-maintenance difficulty sections in dynamic traffic allocation models, thereby reducing the risk of secondary congestion caused by maintenance operations.

[0091] Preferably, the steps of constructing the vehicle analysis model data vehicle analysis coefficient based on the route complexity coefficient and the vehicle data under the maintenance difficulty data are:

[0092] Based on the real-time traffic volume and free flow speed of the road section, a reference speed model is constructed to output a reference speed. The reference speed model is expressed as:

[0093]

[0094] Among them, v0 represents the reference speed, v f represents the free flow speed (maximum theoretical speed when there is no congestion), Q c represents the benchmark traffic capacity, Q represents the real-time traffic volume, α represents the congestion sensitivity coefficient, and β represents the nonlinear impact index;

[0095] Based on the line complexity coefficient and the maintenance difficulty coefficient, a complexity-difficulty correction model is constructed to output a complexity-difficulty correction factor. The complexity-difficulty correction model is expressed as:

[0096] η=1+λRCI+kMDI

[0097] Where η represents the complexity-difficulty correction factor, RCI represents the complexity coefficient of the road section, MDI represents the maintenance difficulty coefficient, λ represents the RCI correction coefficient, and k represents the MDI correction coefficient.

[0098] A vehicle analysis model is constructed based on the basic vehicle speed, current vehicle speed, and real-time vehicle flow to output a vehicle analysis coefficient. The vehicle analysis model is expressed as:

[0099]

[0100] Among them, M represents the vehicle analysis coefficient, v represents the current vehicle speed, v0 represents the basic vehicle speed, η represents the complexity-difficulty correction factor, Q represents the real-time vehicle flow, Q min Indicates the minimum effective traffic flow.

[0101] The free-flow speed in the baseline speed model refers to the theoretical maximum speed of vehicles in an uncongested state and is used to establish a benchmark for the relationship between traffic volume and speed attenuation. The baseline capacity refers to the maximum traffic volume on a road section under standard conditions. Specifically, it can be determined by multiplying the number of lanes specified in the road design specifications by the capacity of a single lane. This serves as a reference for the impact of real-time traffic volume. The congestion sensitivity coefficient refers to the sensitivity of traffic volume to speed and is used to adjust the attenuation of traffic volume against the baseline speed. The nonlinear impact index refers to the nonlinear effect of traffic volume growth on speed and is used to reflect the critical effect of a sudden speed drop during a traffic surge. The route complexity coefficient in the complexity-difficulty correction factor is a comprehensive quantitative value of the environmental factors affecting a road section. Specifically, it can be calculated by weighting temperature, humidity, number of accidents, and number of steep slopes, and is used to represent the degree of environmental restriction on vehicle traffic. The maintenance difficulty coefficient reflects the restrictive effect of maintenance conditions on traffic flow. The minimum effective traffic flow refers to the threshold that triggers the effective contribution of traffic flow to the analysis coefficient. Specifically, it can be set by the 5% quantile traffic flow value in the historical traffic flow data of the road section to filter out random fluctuation interference under low traffic flow.

[0102] Specifically, the benchmark speed model dynamically reflects the attenuation law of vehicle speed under different congestion levels by performing nonlinear mapping on the ratio of real-time traffic volume to benchmark capacity. For example, when the real-time traffic volume is close to the benchmark capacity, the β value in the exponential term will aggravate the downward trend of vehicle speed, simulating the sudden drop in traffic efficiency under saturated traffic conditions. The complexity-difficulty correction model linearly superimposes the line complexity coefficient and the maintenance difficulty coefficient, and adjusts the contribution weights of the two through the λ and k correction coefficients to quantify the superimposed impact of environmental and maintenance factors on traffic flow. For example, in sections with a high proportion of bridges and tunnels, the increase in the maintenance difficulty coefficient MDI will directly increase the value of the correction factor η, thereby reducing the benchmark value of the theoretical speed in the vehicle analysis model. The vehicle analysis model dynamically characterizes the degree of deviation of the actual speed from the theoretical value under environmental constraints by calculating the ratio of the current speed to the corrected benchmark speed. At the same time, the exponential function term By setting the minimum effective traffic flow Q min , suppress the interference of noise signals under low traffic volume on the analysis coefficient. For example, when the real-time traffic volume of a certain road section is lower than Q min When , the exponential function approaches zero, making the vehicle analysis coefficient M mainly dependent on the vehicle speed ratio term, avoiding the interference of invalid data.

[0103] Compared with existing technologies, traditional methods typically use fixed thresholds to determine the traffic status of road sections, without considering the dynamic correction of traffic flow analysis due to environmental complexity and maintenance difficulty. For example, existing technologies often use a single traffic volume or speed as the basis for diversion, ignoring the implicit limitations of environmental factors such as steep slopes, bridges and tunnels on vehicle traffic capacity. In addition, existing models are susceptible to random fluctuations in low traffic volume scenarios, resulting in frequent adjustments to diversion decisions. This solution introduces a complexity-difficulty correction factor, embedding environmental and maintenance data into the speed benchmark calculation, so that the vehicle analysis coefficient can reflect the coupled influence of multiple factors; at the same time, the minimum effective traffic volume threshold is used to filter out noise data, thereby improving the stability of the analysis results.

[0104] Through the above technical solution, this application realizes the dynamic coupling analysis of environmental factors, maintenance conditions and real-time traffic data, solving the problem of insufficient quantification of multi-factor influence in traditional methods. For example, in the scenario where the road surface is slippery due to heavy rain, the increase of the route complexity coefficient RCI reduces the theoretical reference speed through the correction factor η, so that the vehicle analysis coefficient M can accurately reflect the negative impact of the slippery road surface on the actual traffic efficiency. At the same time, the minimum effective traffic flow Q min The setting avoids the interference of data fluctuations caused by occasional vehicle braking on the analysis results during low traffic volume at night, ensuring the reliability of traffic flow allocation decisions.

[0105] Preferably, the steps of constructing a route traffic flow allocation model based on the current vehicle analysis coefficient of the route and the distance to the nearest supply point (service area) to output the target traffic flow of the route section are:

[0106] Import the distance to the nearest supply point into the formula Get the supply point distance index, f(D i ) represents the supply point distance index of the i-th route, D i represents the distance to the supply point on the i-th section, and D0 represents the distance to the reference supply point;

[0107] The supply point distance index is introduced into the diversion weight formula to obtain the diversion weight coefficient. The diversion weight formula is expressed as:

[0108]

[0109] Among them, w i represents the diversion weight coefficient of the i-th line, M i represents the vehicle analysis coefficient of the i-th line, f(D i ) represents the supply point index of the i-th route, n represents the number of intersection routes, j represents the index of the candidate diversion route in the road network, M j represents the vehicle analysis coefficient of the jth line, f(D j ) represents the supply point index of the jth route;

[0110] According to the total traffic flow of the intersection and the diversion weight coefficient, a route traffic flow distribution model is constructed to output the target traffic flow of the route section. The traffic flow distribution model is expressed as:

[0111]

[0112] Among them, Q tar,i represents the target traffic flow of route i, w i represents the diversion weight coefficient of the i-th line, Q total represents the total traffic flow at the intersection section, σ represents the adjustment coefficient, represents the estimated rate of change of the vehicle coefficient at time t and

[0113] The supply point distance index refers to the exponential function that quantifies the attenuation effect of the supply point distance on vehicle flow diversion. The specific formula can be used: Implementation, where D0 is the reference distance, for example, it can be set to 1.5 times the average supply distance of the road section. This function makes the diversion priority of the road section with a longer distance lower, thereby suppressing the secondary congestion caused by the inconvenience of supply.

[0114] The diversion weight coefficient refers to the dynamic weight parameter of the comprehensive vehicle traffic capacity and supply accessibility, which can be specifically obtained through the normalization formula Calculation, for example, the vehicle analysis coefficient M i Distance index f(D i ) are multiplied and normalized to ensure that the weights reflect both the traffic status of the road section and the accessibility of the supply point.

[0115] The estimated change rate of vehicle coefficient refers to the change trend of vehicle analysis coefficient over time, which can be realized by differential calculation within a sliding time window. For example, M is calculated at intervals of 5 minutes. i The instantaneous rate of change of is used to dynamically adjust the target traffic flow to respond to sudden traffic state fluctuations.

[0116] Specifically, the traffic flow distribution model operates through the following steps: First, the exponential decay value is calculated based on the distance between each road segment and the nearest supply point. The longer the distance, the smaller the exponential value. For example, when a road segment is 3 kilometers away from the supply point and D0 = 2 kilometers, its exponential value is f(D i )=0.223, indicating that the diversion weight of this section needs to be reduced due to the inconvenience of supply. Secondly, the vehicle analysis coefficient is multiplied by the supply point index to generate the comprehensive score of the candidate route. For example, for a section M i =0.8 and f(D i )=0.5, then its score is 0.4. The higher the score, the more suitable the road section is for diversion. Then, the scores of all candidate routes are normalized to obtain the diversion weight, and the basic traffic flow is allocated based on the total traffic flow. Finally, the real-time change rate of the vehicle analysis coefficient is introduced. For example, when a certain road section M i When it rises by 15% within 10 minutes, the target traffic flow is dynamically increased by adjusting the coefficient σ, so as to deal with potential congestion risks in advance.

[0117] Compared to existing technologies, traditional traffic allocation methods rely solely on static road network data or real-time traffic flow, failing to consider the impact of distance to refueling points on vehicle range and driver route selection. For example, existing systems may allocate excessively high traffic to long refueling routes, causing vehicles to be stranded due to inability to refuel in a timely manner. This solution quantifies the inhibitory effect of distance on traffic diversion through an exponential decay function and dynamically adjusts the allocation strategy based on the rate of change of vehicle states, addressing the imbalanced allocation caused by relying solely on traffic flow data.

[0118] Through the above technical solutions, this application achieves dynamic optimization of traffic flow distribution in sudden congestion scenarios: the refueling point distance index is used to suppress the diversion weight of long-distance routes, preventing vehicles from being stranded due to refueling difficulties; the target traffic flow is adjusted in real time by the change rate of the vehicle analysis coefficient, allowing the distribution strategy to quickly respond to traffic state fluctuations. For example, when a sudden accident on a highway causes congestion, the system prioritizes directing traffic to alternative sections with better traffic capacity that are closer to the refueling point. At the same time, the allocation ratio of each section is dynamically adjusted based on real-time traffic flow changes, effectively reducing the probability of secondary congestion.

[0119] A digital twin-based GIS data visualization management method, using the above-mentioned digital twin-based GIS data visualization management system, includes the following steps:

[0120] Build a line network visualization platform based on BIM and GIS data;

[0121] Get the number of routes at the current intersection and obtain the complexity data, maintenance difficulty data, and vehicle data of each route;

[0122] Based on the route complexity data, a route complexity analysis model is constructed to output the route complexity coefficient;

[0123] Based on the route maintenance difficulty data, a maintenance difficulty analysis model is constructed to output the maintenance difficulty coefficient;

[0124] Construct vehicle analysis model data vehicle analysis coefficient based on the route complexity coefficient and vehicle data under maintenance difficulty data;

[0125] Based on the current vehicle analysis coefficient of the route and the distance to the nearest supply point (service area), a route traffic allocation model is constructed to output the target traffic flow of the route section.

[0126] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0127] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A GIS data visualization management system based on digital twins, including a line network construction module that constructs a line network visualization platform based on BIM and GIS data, characterized in that: Also includes: Data acquisition module, which obtains the number of routes at the current intersection and obtains the complexity data, maintenance difficulty data and vehicle data of each route; Route complexity analysis module, which builds a route complexity analysis model based on route complexity data and outputs the route complexity coefficient; Route maintenance difficulty analysis module, which builds a maintenance difficulty analysis model based on route maintenance difficulty data and outputs the maintenance difficulty coefficient; Route vehicle analysis module, which builds vehicle analysis model data and vehicle analysis coefficient based on route complexity coefficient and vehicle data under maintenance difficulty data; The route traffic flow allocation model is constructed based on the current vehicle analysis coefficient of the route and the distance to the nearest supply point to output the target traffic flow of the route section.

2. The GIS data visualization management system based on digital twin according to claim 1 is characterized in that: The route complexity data includes the temperature, humidity, number of historical accidents and number of steep slopes in the area where the road section is located; the route maintenance difficulty data includes the construction opening hours, heavy vehicle flow and the proportion of bridges and tunnels; and the vehicle data includes the real-time traffic flow and speed of the route.

3. The GIS data visualization management system based on digital twin according to claim 2 is characterized in that: The steps for constructing a route traffic flow allocation model based on the current vehicle analysis coefficient of the route and the distance to the nearest supply point to output the target traffic flow of the route section are as follows: Import the distance to the nearest supply point into the formula Get the supply point distance index, f(D i ) represents the supply point distance index of the i-th route, D i represents the distance to the supply point on the i-th section, and D0 represents the distance to the reference supply point; The supply point distance index is introduced into the diversion weight formula to obtain the diversion weight coefficient. The diversion weight formula is expressed as: Among them, w i represents the diversion weight coefficient of the i-th line, M i represents the vehicle analysis coefficient of the i-th line, f(D i ) represents the supply point index of the i-th route, n represents the number of intersection routes, j represents the index of the candidate diversion route in the road network, M j represents the vehicle analysis coefficient of the jth line, f(D j ) represents the supply point index of the jth route; According to the total traffic flow of the intersection and the diversion weight coefficient, a route traffic flow distribution model is constructed to output the target traffic flow of the route section. The traffic flow distribution model is expressed as: Among them, Q tar,i represents the target traffic flow of the i-th route, w i represents the diversion weight coefficient of the i-th line, Q total represents the total traffic flow at the intersection section, σ represents the adjustment coefficient, Represents the estimated rate of change of the vehicle coefficient at time t.

4. The GIS data visualization management system based on digital twin according to claim 2 or 3 is characterized in that: The steps for constructing the vehicle analysis model data vehicle analysis coefficient based on the route complexity coefficient and the vehicle data under the maintenance difficulty data are as follows: Based on the line complexity coefficient and the maintenance difficulty coefficient, a complexity-difficulty correction model is constructed to output a complexity-difficulty correction factor. The complexity-difficulty correction model is expressed as: η=1+λRCI+kMDI Where η represents the complexity-difficulty correction factor, RCI represents the complexity coefficient of the road section, MDI represents the maintenance difficulty coefficient, λ represents the RCI correction coefficient, and k represents the MDI correction coefficient. A vehicle analysis model is constructed based on the basic vehicle speed, current vehicle speed, and real-time vehicle flow to output a vehicle analysis coefficient. The vehicle analysis model is expressed as: Among them, M represents the vehicle analysis coefficient, v represents the current vehicle speed, v0 represents the basic vehicle speed, η represents the complexity-difficulty correction factor, Q represents the real-time vehicle flow, Q min Indicates the minimum effective traffic flow.

5. The digital twin-based GIS data visualization management system according to claim 4 is characterized in that: Based on the real-time traffic volume and free flow speed of the road section, a reference speed model is constructed to output a reference speed. The reference speed model is expressed as: Among them, v0 represents the reference speed, v f represents the free flow speed (maximum theoretical speed when there is no congestion), Q c represents the benchmark traffic capacity, Q represents the real-time traffic flow, α represents the congestion sensitivity coefficient, and β represents the nonlinear impact index.

6. The digital twin-based GIS data visualization management system according to claim 4, characterized in that: The steps to construct a maintenance difficulty analysis model based on route maintenance difficulty data and output the maintenance difficulty coefficient are as follows: Perform maximum-minimum normalization on the construction opening time to obtain the construction opening time index; The heavy-load vehicle flow rate is ratioed to the maximum allowable heavy-load vehicle flow rate to obtain the heavy-load vehicle flow rate index; A maintenance difficulty analysis model is constructed based on the construction opening time index, heavy vehicle flow index, and bridge and tunnel ratio to output the maintenance difficulty coefficient; The maintenance difficulty analysis model is expressed as: MDI=b1O ind +b2H ind +b3B Among them, MDI represents the maintenance difficulty coefficient, O ind represents the construction opening time index, H ind represents the heavy-load traffic index, B represents the proportion of bridges and tunnels, and b i represents the weight and ∑b i =1.

7. The GIS data visualization management system based on digital twin according to claim 4 is characterized in that: The steps to construct a route complexity analysis model based on route complexity data and output the route complexity coefficient are as follows: Perform maximum-minimum normalization processing on temperature and humidity to obtain temperature index and humidity index; The number of steep slopes and the number of historical accidents are respectively compared with the corresponding maximum values ​​to obtain the steep slope number index and the accident number index; Based on the temperature index, humidity index, steep slope index and accident number index, a line complexity analysis model is constructed to output the line complexity coefficient; The circuit complexity analysis model is expressed as: RCI=a1T ind +a2H ind +a3S ind +a4A ind Where RCI represents the circuit complexity coefficient, T ind Represents the temperature index, H ind Represents humidity index, S ind represents the steep slope index, A ind represents the accident number index, a i represents the weight and ∑a i =1.

8. A digital twin-based GIS data visualization management method, using the digital twin-based GIS data visualization management system according to claim 1, characterized in that: The following steps are involved: Build a line network visualization platform based on BIM and GIS data; Get the number of routes at the current intersection and obtain the complexity data, maintenance difficulty data, and vehicle data of each route; Based on the route complexity data, a route complexity analysis model is constructed to output the route complexity coefficient; Based on the route maintenance difficulty data, a maintenance difficulty analysis model is constructed to output the maintenance difficulty coefficient; Construct vehicle analysis model data vehicle analysis coefficient based on the route complexity coefficient and vehicle data under maintenance difficulty data; Based on the current vehicle analysis coefficient of the route and the distance to the nearest supply point, a route traffic flow allocation model is constructed to output the target traffic flow of the route section.

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