A smart city intelligent management system and method based on the Internet of Things

Abstract

This invention relates to the field of urban management technology, specifically to a smart city intelligent management system and method based on the Internet of Things (IoT). The system includes a traffic node timing management module. This module updates the adaptive timing of each traffic node in a priority sequence according to its priority, generating a city-wide adaptive timing information change set. The system then manages the timing of urban traffic nodes based on this change set. This invention not only enables adaptive adjustment of traffic light timing information based on vehicle status at traffic nodes but also considers the correlation between the adjusted timing information of different traffic nodes. It achieves comprehensive analysis of vehicle status at different traffic nodes in the city, ensuring smooth traffic flow and reducing the risk of urban road congestion.

Description

Technical Field

[0001] This invention relates to the field of urban management technology, specifically to a smart city intelligent management system and method based on the Internet of Things. Background Technology

[0002] A smart city is a higher stage of urban informatization where information technologies such as the Internet of Things (IoT), sensor networks, and cloud computing—integrating sensing, acquisition, transmission, and processing—are widely and deeply applied to urban infrastructure and various fields of economic, cultural, and social life. This involves the high integration and in-depth development and utilization of information resources, serving urban planning, construction, and operation management, as well as the needs of governments, businesses, the public, and the lives of urban residents. In the transportation sector, smart cities control the status of traffic lights at various urban traffic nodes to manage vehicle traffic flow, ensuring smooth traffic flow and, to a certain extent, providing a guarantee for people's travel safety.

[0003] However, existing smart city management systems based on the Internet of Things only manage the fixed timing information of traffic lights at urban traffic nodes (simply executing according to the pre-set timing state). They cannot adjust the traffic light timing information according to the vehicle status at traffic nodes (in reality, the vehicle traffic status at traffic nodes is constantly changing, and simply executing according to fixed timing information may lead to road congestion due to dense traffic). Furthermore, they do not consider the correlation and impact between the adjusted timing information of different traffic nodes, thus failing to achieve effective management of the adjusted timing information of urban traffic nodes. Therefore, existing technologies have significant shortcomings. Summary of the Invention

[0004] The purpose of this invention is to provide a smart city management system and method based on the Internet of Things to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, the present invention provides the following technical solution: a smart city intelligent management method based on the Internet of Things, the method comprising the following steps:

[0006] S1. Construct a three-dimensional model of urban roads using BIM technology; treat each traffic light in the urban roads as a traffic node, number each traffic node, and mark the corresponding position of each traffic node in the three-dimensional model of urban roads to obtain an urban traffic model;

[0007] S2. Obtain the standard timing information of traffic lights for each traffic node in the urban traffic model from the preset standard timing information set of urban traffic nodes in the database; collect the driving status information of the surrounding road conditions of the corresponding traffic node once every first preset time by the road condition detection sensor corresponding to the traffic node, and calibrate the standard timing information of the corresponding traffic node according to the timing deviation adjustment coefficient corresponding to the collected driving status information in the preset form of the database, so as to obtain the adaptive timing information corresponding to the corresponding traffic node; summarize the adaptive timing information corresponding to traffic nodes with different numbers at the same time to obtain the urban adaptive timing information set corresponding to the corresponding time.

[0008] S3. Obtain the positional relationship between different traffic nodes in the urban traffic model, analyze the correlation between different traffic nodes, and construct the associated node set corresponding to each traffic node; combine the vehicle traffic data between each traffic node and the corresponding elements in the associated node set in historical data, predict the correlation interference value of the traffic node affected by the comprehensive adaptive timing information of each traffic node in the corresponding associated node set, and generate the adaptive timing influence priority sequence of traffic nodes.

[0009] S4. Update the adaptive timing of each passage node in the priority sequence of adaptive timing impact of passage nodes in descending order of priority, generate the city adaptive timing information change set, and feed it back to the city passage node timing control terminal. Record the feedback time as t1. The city passage node timing control terminal obtains the timing information that each passage node is executing at t1, and controls each passage node to start executing the adaptive timing information corresponding to the passage node with the corresponding number in the city adaptive timing information change set at t1 after executing the timing information that is being executed at t1. The timing information being executed remains unchanged until the city passage node timing control terminal receives the next feedback information.

[0010] Furthermore, the three-dimensional model of the city road is constructed proportionally with the city center point as the reference point;

[0011] Let Ai be the number of the i-th traffic node, and let Bi be the location of the i-th traffic node in the three-dimensional model of the city road.

[0012] Furthermore, the standard timing information set for urban traffic nodes in S2 includes the duration of each traffic light state within the timing time period corresponding to the corresponding traffic node. The duration of the timing time period corresponding to different traffic nodes is different. The duration of the timing time period corresponding to each traffic node is equal to the sum of the durations of each state of the corresponding traffic light. Within the timing time period corresponding to each traffic node, one state of the corresponding traffic light corresponds to a continuous time interval.

[0013] The method for obtaining the city adaptive timing information set corresponding to the time in S2 includes the following steps:

[0014] S21. Obtain the driving status information of the road conditions around the corresponding traffic node from the most recent collection by the road condition detection sensor corresponding to each traffic node. Record the driving status information corresponding to the i-th traffic node as Di. The driving status information includes the density of traffic objects corresponding to different traffic light states within the time interval between two adjacent collections by the corresponding detection sensor. The density of traffic objects represents the ratio of the total number of traffic objects to the corresponding travel time. The traffic objects include pedestrians and vehicles.

[0015] S22. Obtain the standard vehicle condition reference information corresponding to each passage node in the database preset form, and denote the standard vehicle condition reference information corresponding to the i-th passage node as Ci. The standard vehicle condition reference information is the density of passing objects corresponding to the corresponding passage node in the database preset form under different traffic light states.

[0016] S23. Calculate the bias information set of driving status information corresponding to each passage node. Denote the bias information set of driving status information corresponding to the i-th passage node as Pi, where Ci, Di and Pi have the same number of elements.

[0017] Let Pij be the bias information corresponding to the j-th element in Pi.

[0018] Pij=(Dij / ZDi-Cij / ZCi) / (Cij / ZCi),

[0019] Where Dij represents the density of the passing objects corresponding to the j-th element in Di, ZDi represents the sum of the densities of the passing objects corresponding to all elements in Di, Cij represents the density of the passing objects corresponding to the j-th element in Ci, ZCi represents the sum of the densities of the passing objects corresponding to all elements in Ci, and the traffic light states corresponding to Dij and Cij are the same.

[0020] S24. Obtain the city adaptive timing information set corresponding to the corresponding time. Each element in the city adaptive timing information set corresponds to the adaptive timing information of a traffic node. The adaptive timing information of the i-th traffic node is denoted as Qi, and the traffic light state timing duration corresponding to the j-th element in Qi is denoted as Qij.

[0021] Qij=F(Pij)·Qbij·[∑ j＝1 ji1 Qbij / (∑ j＝1 ji1 F(Pij)·Qbij)],

[0022] Where Qbij represents the timing duration of the j-th element in the standard timing information of the i-th traffic node in the urban traffic node standard timing information set, F(Pij) represents the timing bias adjustment coefficient corresponding to Pij in the database preset form, and ji1 represents the total number of elements in the standard timing information of the i-th traffic node.

[0023] In the process of obtaining the city adaptive timing information set corresponding to the time, this invention takes into account the changes in the density of traffic objects under different traffic light states, and combines the traffic object density preset in the database to accurately obtain the bias adjustment coefficient of each traffic light state, thereby accurately predicting the adaptive timing information of the corresponding traffic nodes (traffic lights).

[0024] Furthermore, the method for constructing the associated node set corresponding to each access node in S3 includes the following steps:

[0025] S301. Obtain the positions of different traffic nodes in the urban traffic model, and denote the i-th traffic node as the reference node;

[0026] Let Ai be the number of the i-th access node, and let Bi be the location point of the i-th access node in the three-dimensional model of the city road.

[0027] S302. Obtain the access node numbers within a circular area centered on the reference node and with a radius equal to the first unit distance in the urban traffic model, and determine the correlation between each access node and the reference node.

[0028] Obtain the planned routes in the urban traffic model for the locations of a given traffic node and a reference node.

[0029] If the planned route does not pass through any other access nodes, the reference node is determined to be associated with the access node corresponding to the planned route; otherwise, the two are determined to be unrelated.

[0030] S303. Summarize all the traffic nodes in the urban traffic model that are related to the reference node to obtain the set of related nodes corresponding to the reference node.

[0031] Furthermore, the method for predicting the association interference value of a passing node in S3, which is comprehensively affected by the adaptive timing information corresponding to each passing node in the corresponding associated node set, includes the following steps:

[0032] S311. Obtain vehicle passage data between each passage node and the corresponding associated node set element in the historical data.

[0033] S312. Obtain the first and second traffic interference values ​​between the reference node Ai and the traffic node corresponding to the kth element in the corresponding associated node set within the second unit time. Record the first traffic interference value as Gik1 and the second traffic interference value as Gik2. Record the traffic node number corresponding to the kth element in the corresponding associated node set of the reference node as Adk.

[0034] The second unit of time is the time interval formed by the two previous data collection points of the road condition detection sensor based on the current time;

[0035] Gik1=Z1 Ai→Adk / u1 Ai→Adk Q1 Ai→Adk ,

[0036] Gik2=Z2 Ai→Adk / u2 Ai→Adk Q2 Ai→Adk ,

[0037] In this invention, the larger the value of Gik1, the greater the impact of the original timing of Ai on Adk; the larger the value of Gik2, the greater the impact of the original timing of Adk on Ai. Generally, the smaller the values ​​of Gik1 and Gik2, the better. However, since the two are interdependent, it is necessary to maintain them in a relatively balanced state.

[0038] Among them, Z1 Ai→Adk This represents the total number of vehicles that move from the reference node to the corresponding access node of Adk within the path between Ai and Adk in the second unit of time. Ai→Adk u1 represents the effective passage duration of the passage node corresponding to Adk within the second unit of time. Ai→Adk Q1 is equal to the duration corresponding to the second unit of time minus the duration of the inaccessible state (corresponding red light state) of vehicles moving from the reference node to the corresponding passage node of Adk. Ai→Adk Q1 represents the road traffic impact coefficient when the reference node moves to the corresponding traffic node of Adk, and Q1 Ai→Adk It was obtained through a database pre-set form query;

[0039] In this invention, Q1 Ai→Adk The value of Q1 in the database is affected by the degree of road damage and the roadside environment (pedestrian traffic, number of entrances and exits of residential areas, etc.). The more complex the road conditions, the higher the value of Q1 in the database. Ai→Adk The larger the preset value;

[0040] Z2 Ai→Adku2 represents the total number of vehicles that move from the corresponding node of Adk to the reference node in the path between Ai and Adk within the second unit of time. Ai→Adk Q2 represents the effective passage duration of the passage node corresponding to Ai within the second unit of time. Ai→Adk This represents the road influence coefficient when the corresponding traveling node (Adk) moves towards the reference node, and Q2 Ai→Adk It was obtained through a database pre-set form query;

[0041] S313. Obtain the association interference value of the reference node Ai, which is comprehensively affected by the adaptive timing information of each passing node in the corresponding associated node set, and denoted as Wi.

[0042] Wi = ∑ k＝1 kim Gik2·(u2 Ai→Adk / ur2 Ai→Adk )-Gik1·(u1 Ai→Adk / ur1 Ai→Adk ),

[0043] The larger the value of Wi in this invention, the greater the influence of the adaptive timing information of each passage node in the corresponding associated node set on the passage of the passage node, that is, the greater the passage pressure of the corresponding passage node, and the more adjustment is needed.

[0044] Where kim represents the total number of elements in the set of associated nodes corresponding to the reference node Ai, and ur1 Ai→Adk This indicates the effective passage duration in the adaptive timing information corresponding to the passage node Adk, ur2 Ai→Adk This represents the effective passage duration in the adaptive timing information corresponding to the passage node Ai, Gik2·(u2) Ai→Adk / ur2 Ai→Adk )-Gik1·(u1 Ai→Adk / ur1 Ai→Adk The ) represents the relative interference value of the reference node Ai caused by the adaptive timing information corresponding to the kth passing node in the set of associated nodes.

[0045] The adaptive timing impact priority sequence of the access nodes is obtained by arranging the associated interference values ​​corresponding to each access node in descending order, and the adaptive timing impact priority of each access node is equal to its sequence number.

[0046] Furthermore, the method for generating the city adaptive timing information change set in S4 includes the following steps:

[0047] S41. Obtain the association interference value of each passage node, which is comprehensively affected by the adaptive timing information of each passage node in the corresponding associated node set.

[0048] S42. Filter the passage nodes whose adaptive timing information needs to be changed. The adaptive timing information of the passage nodes whose corresponding associated interference value is greater than or equal to 0 does not need to be changed. The associated interference value of each of the filtered passage nodes is less than 0.

[0049] S43. Obtain the adaptive timing information after the change of each filtered passage node in S42, and denote the duration of the h-th traffic light state in the adaptive timing information after the change of the nth filtered passage node as Rnh.

[0050] Rnh = Int[(Q n h+R1nh) / 2]+rand[0,1],

[0051] Where R1nh represents the duration of the h-th traffic light status in the timing information of the n-th traffic node selected based on the first unit of time before the current time, and Q n h represents the timing duration of the h-th traffic light state in the adaptive timing information corresponding to the nth passing node (Q). n h is the duration of the h-th traffic light status in the adaptive timing information corresponding to the n-th traffic node before the change. Int[] represents the floor function, rand[0,1] represents the random function and the random result is 0 or 1. The number of 1s randomly obtained in rand[0,1] corresponding to the adaptive timing information after the change of each selected traffic node is fixed. The number of 1s randomly obtained in rand[0,1] corresponding to the adaptive timing information after the change of the n-th traffic node is denoted as randn, and randn = Tn - ∑ h＝1 hn Rnh,

[0052] Where Tn represents the timing period corresponding to the nth passage node selected in the database, and hn represents the number of traffic light states in the adaptive timing information after the nth passage node is changed.

[0053] S44. Obtain the city adaptive timing information change set. Each element in the city adaptive timing information change set corresponds to the adaptive timing information of the passage nodes that do not need to be changed in S42 or the adaptive timing information of the passage nodes after the change selected in S43.

[0054] A smart city management system based on the Internet of Things (IoT), the system comprising the following modules:

[0055] The urban traffic model construction module uses BIM technology to construct a three-dimensional model of urban roads; each traffic light in the urban road is taken as a traffic node, and the corresponding position of each traffic node in the three-dimensional model of the urban road is marked to obtain the urban traffic model.

[0056] The timing information adaptive adjustment module acquires the standard timing information of the traffic lights of each traffic node in the urban traffic model from the preset standard timing information in the database, and obtains the standard timing information set of urban traffic nodes. The module collects the driving status information of the surrounding road conditions of the corresponding traffic node once every first preset time through the road condition detection sensor corresponding to the traffic node, and calibrates the standard timing information of the corresponding traffic node according to the timing deviation adjustment coefficient corresponding to the collected driving status information in the preset form in the database, so as to obtain the adaptive timing information corresponding to the corresponding traffic node. The adaptive timing information corresponding to traffic nodes with different numbers at the same time is summarized to obtain the urban adaptive timing information set corresponding to the corresponding time.

[0057] The adaptive information association and impact module acquires the positional relationships between different traffic nodes in the urban traffic model, analyzes the association relationships between different traffic nodes, and constructs a set of associated nodes corresponding to each traffic node. Combining the vehicle traffic data between each traffic node and the elements in the corresponding set of associated nodes in historical data, it predicts the association interference value of the traffic node being affected by the comprehensive adaptive timing information of each traffic node in the corresponding set of associated nodes, and generates a priority sequence of adaptive timing impact of traffic nodes.

[0058] The passage node timing management module updates the adaptive timing of each passage node in the priority sequence of adaptive timing impact in descending order of priority, generates a city adaptive timing information change set, and manages the timing of city passage nodes according to the city adaptive timing information change set.

[0059] Furthermore, the adaptive information association impact module includes an association node set construction module, an association interference value analysis module, and a priority sequence generation module.

[0060] The associated node set construction module obtains the positional relationship between different traffic nodes in the urban traffic model, analyzes the association relationship between different traffic nodes, and constructs the associated node set corresponding to each traffic node;

[0061] The correlation interference value analysis module combines the vehicle traffic data between each traffic node and the corresponding elements in the correlation node set from historical data to predict the correlation interference value of a traffic node affected by the combined adaptive timing information of each traffic node in the corresponding correlation node set.

[0062] The priority sequence generation module combines the prediction results of associated interference values ​​to generate an adaptive timing impact priority sequence for passing nodes.

[0063] Furthermore, when the passage node timing management module manages the timing of urban passage nodes according to the urban adaptive timing information change set, it feeds back the urban adaptive timing information change set to the urban passage node timing control terminal. The feedback time is recorded as t1. The urban passage node timing control terminal obtains the timing information that each passage node is executing at t1, and controls each passage node to start executing the adaptive timing information corresponding to the passage node with the corresponding number in the urban adaptive timing information change set at t1 after executing the timing information that is being executed at t1. The executed timing information remains unchanged until the urban passage node timing control terminal receives the feedback information again.

[0064] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention can not only adaptively adjust the traffic light timing information according to the vehicle status at traffic nodes, but also take into account the correlation between the timing information after adjustment at different traffic nodes, realize the comprehensive analysis of the vehicle status at different traffic nodes in the city, ensure that vehicles at each traffic node in the city can pass smoothly, reduce the risk of urban road congestion, and realize the effective management of the timing information corresponding to urban traffic nodes. Attached Figure Description

[0065] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0066] Figure 1 This is a flowchart illustrating a smart city intelligent management method based on the Internet of Things according to the present invention.

[0067] Figure 2 This is a schematic diagram of the structure of a smart city intelligent management system based on the Internet of Things according to the present invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Please see Figure 1 This invention provides a technical solution: a smart city intelligent management method based on the Internet of Things, the method comprising the following steps:

[0070] S1. Construct a three-dimensional model of urban roads using BIM technology; treat each traffic light in the urban roads as a traffic node, number each traffic node, and mark the corresponding position of each traffic node in the three-dimensional model of urban roads to obtain an urban traffic model;

[0071] The three-dimensional model of the city roads is constructed proportionally with the city center as the reference point;

[0072] Let Ai be the number of the i-th traffic node, and let Bi be the location of the i-th traffic node in the three-dimensional model of the city road.

[0073] S2. Obtain the standard timing information of traffic lights for each traffic node in the urban traffic model from the preset standard timing information set of urban traffic nodes in the database; collect the driving status information of the surrounding road conditions of the corresponding traffic node once every first preset time by the road condition detection sensor corresponding to the traffic node, and calibrate the standard timing information of the corresponding traffic node according to the timing deviation adjustment coefficient corresponding to the collected driving status information in the preset form of the database, so as to obtain the adaptive timing information corresponding to the corresponding traffic node; summarize the adaptive timing information corresponding to traffic nodes with different numbers at the same time to obtain the urban adaptive timing information set corresponding to the corresponding time.

[0074] The standard timing information set for urban traffic nodes in S2 includes the duration of each traffic light state within the timing time period corresponding to the corresponding traffic node. The duration of the timing time period corresponding to different traffic nodes is different. The duration of the timing time period corresponding to each traffic node is equal to the sum of the durations of each state of the corresponding traffic light. Within the timing time period corresponding to each traffic node, one state of the corresponding traffic light corresponds to a continuous time interval.

[0075] The method for obtaining the city adaptive timing information set corresponding to the time in S2 includes the following steps:

[0076] S21. Obtain the driving status information of the road conditions around the corresponding traffic node from the most recent collection by the road condition detection sensor corresponding to each traffic node. Record the driving status information corresponding to the i-th traffic node as Di. The driving status information includes the density of traffic objects corresponding to different traffic light states within the time interval between two adjacent collections by the corresponding detection sensor. The density of traffic objects represents the ratio of the total number of traffic objects to the corresponding travel time. The traffic objects include pedestrians and vehicles.

[0077] S22. Obtain the standard vehicle condition reference information corresponding to each passage node in the database preset form, and denote the standard vehicle condition reference information corresponding to the i-th passage node as Ci. The standard vehicle condition reference information is the density of passing objects corresponding to the corresponding passage node in the database preset form under different traffic light states.

[0078] S23. Calculate the bias information set of driving status information corresponding to each passage node. Denote the bias information set of driving status information corresponding to the i-th passage node as Pi, where Ci, Di and Pi have the same number of elements.

[0079] Let Pij be the bias information corresponding to the j-th element in Pi.

[0080] Pij=(Dij / ZDi-Cij / ZCi) / (Cij / ZCi),

[0081] Where Dij represents the density of the passing objects corresponding to the j-th element in Di, ZDi represents the sum of the densities of the passing objects corresponding to all elements in Di, Cij represents the density of the passing objects corresponding to the j-th element in Ci, ZCi represents the sum of the densities of the passing objects corresponding to all elements in Ci, and the traffic light states corresponding to Dij and Cij are the same.

[0082] S24. Obtain the city adaptive timing information set corresponding to the corresponding time. Each element in the city adaptive timing information set corresponds to the adaptive timing information of a traffic node. The adaptive timing information of the i-th traffic node is denoted as Qi, and the traffic light state timing duration corresponding to the j-th element in Qi is denoted as Qij.

[0083] Qij=F(Pij)·Qbij·[∑ j＝1 ji1 Qbij / (∑ j＝1 ji1 F(Pij)·Qbij)],

[0084] Where Qbij represents the timing duration of the j-th element in the standard timing information of the i-th traffic node in the urban traffic node standard timing information set, F(Pij) represents the timing bias adjustment coefficient corresponding to Pij in the database preset form, and ji1 represents the total number of elements in the standard timing information of the i-th traffic node.

[0085] S3. Obtain the positional relationship between different traffic nodes in the urban traffic model, analyze the correlation between different traffic nodes, and construct the associated node set corresponding to each traffic node; combine the vehicle traffic data between each traffic node and the corresponding elements in the associated node set in historical data, predict the correlation interference value of the traffic node affected by the comprehensive adaptive timing information of each traffic node in the corresponding associated node set, and generate the adaptive timing influence priority sequence of traffic nodes.

[0086] The method for constructing the associated node set corresponding to each passable node in S3 includes the following steps:

[0087] S301. Obtain the positions of different traffic nodes in the urban traffic model, and denote the i-th traffic node as the reference node;

[0088] Let Ai be the number of the i-th access node, and let Bi be the location point of the i-th access node in the three-dimensional model of the city road.

[0089] S302. Obtain the numbers of each traffic node in the urban traffic model within a circular area centered on the reference node and with a radius of a first unit distance, and determine the correlation between each traffic node and the reference node. The first unit distance is a constant preset in the database.

[0090] Obtain the planned routes in the urban traffic model for the locations of a given traffic node and a reference node.

[0091] If the planned route does not pass through any other access nodes, the reference node is determined to be associated with the access node corresponding to the planned route; otherwise, the two are determined to be unrelated.

[0092] S303. Summarize all the traffic nodes in the urban traffic model that are related to the reference node to obtain the set of related nodes corresponding to the reference node.

[0093] The method for predicting the correlation interference value of a passing node in S3, which is comprehensively affected by the adaptive timing information of each passing node in the corresponding associated node set, includes the following steps:

[0094] S311. Obtain vehicle passage data between each passage node and the corresponding associated node set element in the historical data.

[0095] S312. Obtain the first and second traffic interference values ​​between the reference node Ai and the traffic node corresponding to the kth element in the corresponding associated node set within the second unit time. Record the first traffic interference value as Gik1 and the second traffic interference value as Gik2. Record the traffic node number corresponding to the kth element in the corresponding associated node set of the reference node as Adk.

[0096] The second unit of time is the time interval formed by the two previous data collection points of the road condition detection sensor based on the current time;

[0097] Gik1=Z1 Ai→Adk / u1 Ai→Adk Q1 Ai→Adk ,

[0098] Gik2=Z2 Ai→Adk / u2 Ai→Adk Q2 Ai→Adk ,

[0099] Among them, Z1 Ai→Adk This represents the total number of vehicles that move from the reference node to the corresponding access node of Adk within the path between Ai and Adk in the second unit of time. Ai→Adk u1 represents the effective passage duration of the passage node corresponding to Adk within the second unit of time. Ai→Adk Q1 is equal to the duration corresponding to the second unit of time minus the duration of the inaccessible state (corresponding red light state) of vehicles moving from the reference node to the corresponding passage node of Adk. Ai→Adk Q1 represents the road traffic impact coefficient when the reference node moves to the corresponding traffic node of Adk, and Q1 Ai→Adk It was obtained through a database pre-set form query;

[0100] Z2 Ai→Adk u2 represents the total number of vehicles that move from the corresponding node of Adk to the reference node in the path between Ai and Adk within the second unit of time. Ai→Adk Q2 represents the effective passage duration of the passage node corresponding to Ai within the second unit of time. Ai→Adk This represents the road influence coefficient when the corresponding traveling node (Adk) moves towards the reference node, and Q2 Ai→Adk It was obtained through a database pre-set form query;

[0101] S313. Obtain the association interference value of the reference node Ai, which is comprehensively affected by the adaptive timing information of each passing node in the corresponding associated node set, and denoted as Wi.

[0102] Wi = ∑ k＝1 kimGik2·(u2 Ai→Adk / ur2 Ai→Adk )-Gik1·(u1 Ai→Adk / ur1 Ai→Adk ),

[0103] Where kim represents the total number of elements in the set of associated nodes corresponding to the reference node Ai, and ur1 Ai→Adk This indicates the effective passage duration in the adaptive timing information corresponding to the passage node Adk, ur2 Ai→Adk This represents the effective passage duration in the adaptive timing information corresponding to the passage node Ai, Gik2·(u2) Ai→Adk / ur2 Ai→Adk )-Gik1·(u1 Ai→Adk / ur1 Ai→Adk The ) represents the relative interference value of the reference node Ai caused by the adaptive timing information corresponding to the kth passing node in the set of associated nodes.

[0104] In this embodiment, if there are four access nodes: g1, g2, g3, and g4,

[0105] If there are two access nodes, g2 and g3, within the circular area of ​​the first unit distance surrounding access node g1, and access node g3 exists in the planned path from g1 to g2, but no other access nodes exist in the planned path from g1 to g3, then...

[0106] Then the elements in the set of associated nodes of the passable node g1 only include the passable node g3;

[0107] If there are no other passing nodes within the circular area within the first unit distance around passing node g4, then the set of associated nodes of passing node g1 is empty.

[0108] The adaptive timing impact priority sequence of the access nodes is obtained by arranging the associated interference values ​​corresponding to each access node in descending order, and the adaptive timing impact priority of each access node is equal to its sequence number.

[0109] S4. Update the adaptive timing of each passage node in the priority sequence of adaptive timing impact of passage nodes in descending order of priority, generate the city adaptive timing information change set, and feed it back to the city passage node timing control terminal. Record the feedback time as t1. The city passage node timing control terminal obtains the timing information that each passage node is executing at t1, and controls each passage node to start executing the adaptive timing information corresponding to the passage node with the corresponding number in the city adaptive timing information change set at t1 after executing the timing information that is being executed at t1. The timing information being executed remains unchanged until the city passage node timing control terminal receives the next feedback information.

[0110] In this embodiment, if there are two access nodes, they are denoted as g5 and g6 respectively.

[0111] If the adaptive timing information corresponding to g5 in the feedback information of t1 is PSg5, then the adaptive timing information corresponding to g6 in the feedback information of t1 is PSg6.

[0112] If the timing information being executed by g5 at time t1 has just finished and the timing information for the next cycle has not yet started, then the timing information expected to be executed next week is determined to be PSg5.

[0113] If the timing information being executed by g6 at time t1 has not been completed and the timing cycle being executed still needs α seconds, then it is determined that the timing information PSg6 will be executed after α seconds.

[0114] The method for generating the city adaptive timing information change set in S4 includes the following steps:

[0115] S41. Obtain the association interference value of each passage node, which is comprehensively affected by the adaptive timing information of each passage node in the corresponding associated node set.

[0116] In this embodiment, it is assumed that the association influence value corresponding to the passable node that does not have an associated node set is 0.

[0117] S42. Filter the passage nodes whose adaptive timing information needs to be changed. The adaptive timing information of the passage nodes whose corresponding associated interference value is greater than or equal to 0 does not need to be changed. The associated interference value of each of the filtered passage nodes is less than 0.

[0118] S43. Obtain the adaptive timing information after the change of each filtered passage node in S42, and denote the duration of the h-th traffic light state in the adaptive timing information after the change of the nth filtered passage node as Rnh.

[0119] Rnh = Int[(Q n h+R1nh) / 2]+rand[0,1],

[0120] Where R1nh represents the duration of the h-th traffic light status in the timing information of the n-th traffic node selected based on the first unit of time before the current time, and Q n h represents the timing duration of the h-th traffic light state in the adaptive timing information corresponding to the nth passing node (Q). nh is the duration of the h-th traffic light status in the adaptive timing information corresponding to the n-th traffic node before the change. Int[] represents the floor function, rand[0,1] represents the random function and the random result is 0 or 1. The number of 1s randomly obtained in rand[0,1] corresponding to the adaptive timing information after the change of each selected traffic node is fixed. The number of 1s randomly obtained in rand[0,1] corresponding to the adaptive timing information after the change of the n-th traffic node is denoted as randn, and randn = Tn - ∑ h＝1 hn Rnh,

[0121] Where Tn represents the timing period corresponding to the nth passage node selected in the database, and hn represents the number of traffic light states in the adaptive timing information after the nth passage node is changed.

[0122] S44. Obtain the city adaptive timing information change set. Each element in the city adaptive timing information change set corresponds to the adaptive timing information of the passage nodes that do not need to be changed in S42 or the adaptive timing information of the passage nodes after the change selected in S43.

[0123] like Figure 2 As shown, a smart city management system based on the Internet of Things (IoT) includes the following modules:

[0124] The urban traffic model construction module uses BIM technology to construct a three-dimensional model of urban roads; each traffic light in the urban road is taken as a traffic node, and the corresponding position of each traffic node in the three-dimensional model of the urban road is marked to obtain the urban traffic model.

[0125] The timing information adaptive adjustment module acquires the standard timing information of the traffic lights of each traffic node in the urban traffic model from the preset standard timing information in the database, and obtains the standard timing information set of urban traffic nodes. The module collects the driving status information of the surrounding road conditions of the corresponding traffic node once every first preset time through the road condition detection sensor corresponding to the traffic node, and calibrates the standard timing information of the corresponding traffic node according to the timing deviation adjustment coefficient corresponding to the collected driving status information in the preset form in the database, so as to obtain the adaptive timing information corresponding to the corresponding traffic node. The adaptive timing information corresponding to traffic nodes with different numbers at the same time is summarized to obtain the urban adaptive timing information set corresponding to the corresponding time.

[0126] The adaptive information association and impact module acquires the positional relationships between different traffic nodes in the urban traffic model, analyzes the association relationships between different traffic nodes, and constructs a set of associated nodes corresponding to each traffic node. Combining the vehicle traffic data between each traffic node and the elements in the corresponding set of associated nodes in historical data, it predicts the association interference value of the traffic node being affected by the comprehensive adaptive timing information of each traffic node in the corresponding set of associated nodes, and generates a priority sequence of adaptive timing impact of traffic nodes.

[0127] The passage node timing management module updates the adaptive timing of each passage node in the priority sequence of adaptive timing impact in descending order of priority, generates a city adaptive timing information change set, and manages the timing of city passage nodes according to the city adaptive timing information change set.

[0128] The adaptive information association impact module includes an association node set construction module, an association interference value analysis module, and a priority sequence generation module.

[0129] The associated node set construction module obtains the positional relationship between different traffic nodes in the urban traffic model, analyzes the association relationship between different traffic nodes, and constructs the associated node set corresponding to each traffic node;

[0130] The correlation interference value analysis module combines the vehicle traffic data between each traffic node and the corresponding elements in the correlation node set from historical data to predict the correlation interference value of a traffic node affected by the combined adaptive timing information of each traffic node in the corresponding correlation node set.

[0131] The priority sequence generation module combines the prediction results of associated interference values ​​to generate an adaptive timing impact priority sequence for passing nodes.

[0132] When the passage node timing management module manages the timing of urban passage nodes according to the urban adaptive timing information change set, it feeds back the urban adaptive timing information change set to the urban passage node timing control terminal. The feedback time is recorded as t1. The urban passage node timing control terminal obtains the timing information that each passage node is executing at t1, and controls each passage node to start executing the adaptive timing information corresponding to the passage node with the corresponding number in the urban adaptive timing information change set at t1 after executing the timing information being executed at t1. The executing timing information remains unchanged until the urban passage node timing control terminal receives the feedback information again.

[0133] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0134] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart city management method based on the Internet of Things, characterized in that, The method includes the following steps: S1. Construct a three-dimensional model of urban roads using BIM technology; treat each traffic light in the urban roads as a traffic node, number each traffic node, and mark the corresponding position of each traffic node in the three-dimensional model of urban roads to obtain an urban traffic model; S2. Obtain the standard timing information of traffic lights for each traffic node in the urban traffic model from the preset standard timing information set of urban traffic nodes in the database; collect the driving status information of the surrounding road conditions of the corresponding traffic node once every first preset time by the road condition detection sensor corresponding to the traffic node, and calibrate the standard timing information of the corresponding traffic node according to the timing deviation adjustment coefficient corresponding to the collected driving status information in the preset form of the database, so as to obtain the adaptive timing information corresponding to the corresponding traffic node; summarize the adaptive timing information corresponding to traffic nodes with different numbers at the same time to obtain the urban adaptive timing information set corresponding to the corresponding time. S3. Obtain the positional relationship between different traffic nodes in the urban traffic model, analyze the correlation between different traffic nodes, and construct the associated node set corresponding to each traffic node; combine the vehicle traffic data between each traffic node and the corresponding elements in the associated node set in historical data, predict the correlation interference value of the traffic node affected by the comprehensive adaptive timing information of each traffic node in the corresponding associated node set, and generate the adaptive timing influence priority sequence of traffic nodes. S4. Update the adaptive timing of each passage node in the priority sequence of adaptive timing impact of passage nodes in descending order of priority, generate the city adaptive timing information change set, and feed it back to the city passage node timing control terminal. Record the feedback time as t1. The city passage node timing control terminal obtains the timing information that each passage node is executing at t1, and controls each passage node to start executing the adaptive timing information corresponding to the passage node with the corresponding number in the city adaptive timing information change set at t1 after executing the timing information that is being executed at t1. The timing information being executed remains unchanged until the city passage node timing control terminal receives the next feedback information.

2. The smart city management method based on the Internet of Things according to claim 1, characterized in that: The three-dimensional model of the city roads is constructed proportionally with the city center as the reference point; Let Ai be the number of the i-th traffic node, and let Bi be the location of the i-th traffic node in the three-dimensional model of the city road.

3. The smart city management method based on the Internet of Things according to claim 2, characterized in that: The standard timing information set for urban traffic nodes in S2 includes the duration of each traffic light state within the timing time period corresponding to the corresponding traffic node. The duration of the timing time period corresponding to different traffic nodes is different. The duration of the timing time period corresponding to each traffic node is equal to the sum of the durations of each state of the corresponding traffic light. Within the timing time period corresponding to each traffic node, one state of the corresponding traffic light corresponds to a continuous time interval. The method for obtaining the city adaptive timing information set corresponding to the time in S2 includes the following steps: S21. Obtain the driving status information of the road conditions around the corresponding traffic node from the most recent collection of the road condition detection sensor corresponding to each traffic node. Record the driving status information corresponding to the i-th traffic node as Di. The driving status information includes the density of traffic objects corresponding to different traffic light states in the time interval between two adjacent collections of the corresponding detection sensor. The density of traffic objects represents the ratio of the total number of traffic objects to the corresponding travel time. S22. Obtain the standard vehicle condition reference information corresponding to each passage node in the database preset form, and denote the standard vehicle condition reference information corresponding to the i-th passage node as Ci. The standard vehicle condition reference information is the density of passing objects corresponding to the corresponding passage node in the database preset form under different traffic light states. S23. Calculate the bias information set of driving status information corresponding to each passage node. Denote the bias information set of driving status information corresponding to the i-th passage node as Pi, where Ci, Di and Pi have the same number of elements. Let Pij be the bias information corresponding to the j-th element in Pi. Pij=(Dij / ZDi-Cij / ZCi) / (Cij / ZCi), Where Dij represents the density of the passing objects corresponding to the j-th element in Di, ZDi represents the sum of the densities of the passing objects corresponding to all elements in Di, Cij represents the density of the passing objects corresponding to the j-th element in Ci, ZCi represents the sum of the densities of the passing objects corresponding to all elements in Ci, and the traffic light states corresponding to Dij and Cij are the same. S24. Obtain the city adaptive timing information set corresponding to the corresponding time. Each element in the city adaptive timing information set corresponds to the adaptive timing information of a traffic node. The adaptive timing information of the i-th traffic node is denoted as Qi, and the traffic light state timing duration corresponding to the j-th element in Qi is denoted as Qij. Fij=F(Pij)·Qbij·[∑ j＝1 ji1 Qbij / (∑ j＝1 ji1 F(Drink)·Qbij)], Where Qbij represents the timing duration of the j-th element in the standard timing information of the i-th traffic node in the urban traffic node standard timing information set, F(Pij) represents the timing bias adjustment coefficient corresponding to Pij in the database preset form, and ji1 represents the total number of elements in the standard timing information of the i-th traffic node.

4. The smart city management method based on the Internet of Things according to claim 3, characterized in that: The method for constructing the associated node set corresponding to each passable node in S3 includes the following steps: S301. Obtain the positions of different traffic nodes in the urban traffic model, and denote the i-th traffic node as the reference node; Let Ai be the number of the i-th access node, and let Bi be the location point of the i-th access node in the three-dimensional model of the city road. S302. Obtain the access node numbers within a circular area centered on the reference node and with a radius equal to the first unit distance in the urban traffic model, and determine the correlation between each access node and the reference node. Obtain the planned routes in the urban traffic model for the locations of a given traffic node and a reference node. If the planned route does not pass through any other access nodes, the reference node is determined to be associated with the access node corresponding to the planned route; otherwise, the two are determined to be unrelated. S303. Summarize all the traffic nodes in the urban traffic model that are related to the reference node to obtain the set of related nodes corresponding to the reference node.

5. The smart city management method based on the Internet of Things according to claim 4, characterized in that: The method for predicting the correlation interference value of a passing node in S3, which is comprehensively affected by the adaptive timing information of each passing node in the corresponding associated node set, includes the following steps: S311. Obtain vehicle passage data between each passage node and the corresponding associated node set element in the historical data. S312. Obtain the first and second traffic interference values ​​between the reference node Ai and the traffic node corresponding to the kth element in the corresponding associated node set within the second unit time. Record the first traffic interference value as Gik1 and the second traffic interference value as Gik2. Record the traffic node number corresponding to the kth element in the corresponding associated node set of the reference node as Adk. The second unit of time is the time interval formed by the two previous data collection points of the road condition detection sensor based on the current time; Gik1=Z1 Ai→Adk / u1 Ai→Adk ·Q1 Ai→Adk , Gik2=Z2 Ai→Adk / u2 Ai→Adk Q2 Ai→Adk , Among them, Z1 Ai→Adk This represents the total number of vehicles that move from the reference node to the corresponding access node of Adk within the path between Ai and Adk in the second unit of time. Ai→Adk u1 represents the effective passage duration of the passage node corresponding to Adk within the second unit of time. Ai→Adk Q1 is equal to the difference between the duration of the second unit of time and the duration of the inaccessible state for vehicles moving from the reference node to the corresponding access node of Adk. Ai→Adk Q1 represents the road traffic impact coefficient when the reference node moves to the corresponding traffic node of Adk, and Q1 Ai→Adk It was obtained through a database pre-set form query; Z2 Ai→Adk u2 represents the total number of vehicles that move from the corresponding node of Adk to the reference node in the path between Ai and Adk within the second unit of time. Ai→Adk Q2 represents the effective passage duration of the passage node corresponding to Ai within the second unit of time. Ai→Adk This represents the road influence coefficient when the corresponding traveling node (Adk) moves towards the reference node, and Q2 Ai→Adk It was obtained through a database pre-set form query; S313. Obtain the association interference value of the reference node Ai, which is comprehensively affected by the adaptive timing information of each passing node in the corresponding associated node set, and denoted as Wi. Wi=∑ k＝1 kim Gik2·(u2 Ai→Adk / ur2 Ai→Adk )-Gik1·(u1 Ai→Adk / ur1 Ai→Adk ), Where kim represents the total number of elements in the set of associated nodes corresponding to the reference node Ai, and ur1 Ai→Adk This indicates the effective passage duration in the adaptive timing information corresponding to the passage node Adk, ur2 Ai→Adk This represents the effective passage duration in the adaptive timing information corresponding to the passage node Ai, Gik2·(u2) Ai→Adk / ur2 Ai→Adk )-Gik1·(u1 Ai→Adk / ur1 Ai→Adk The ) represents the relative interference value of the reference node Ai caused by the adaptive timing information corresponding to the kth passing node in the set of associated nodes. The adaptive timing impact priority sequence of the access nodes is obtained by arranging the associated interference values ​​corresponding to each access node in descending order, and the adaptive timing impact priority of each access node is equal to its sequence number.

6. The smart city management method based on the Internet of Things according to claim 1, characterized in that: The method for generating the city adaptive timing information change set in S4 includes the following steps: S41. Obtain the association interference value of each passage node, which is comprehensively affected by the adaptive timing information of each passage node in the corresponding associated node set. S42. Filter the passage nodes whose adaptive timing information needs to be changed. The adaptive timing information of the passage nodes whose corresponding associated interference value is greater than or equal to 0 does not need to be changed. The associated interference value of each of the filtered passage nodes is less than 0. S43. Obtain the adaptive timing information after the change of each filtered passage node in S42, and denote the duration of the h-th traffic light state in the adaptive timing information after the change of the nth filtered passage node as Rnh. Rnh=Int[(Q n h+R1nh) / 2]+rand[0,1], Where R1nh represents the duration of the h-th traffic light status in the timing information of the n-th traffic node selected based on the first unit of time before the current time, and Q n h represents the timing duration of the h-th traffic light state in the adaptive timing information corresponding to the nth selected traffic node, Int[] represents the floor function, rand[0,1] represents a random function with a random result of 0 or 1, and the number of 1s randomly obtained in rand[0,1] corresponding to the adaptive timing information after the change of each selected traffic node is fixed. The number of 1s randomly obtained in rand[0,1] corresponding to the adaptive timing information after the change of the nth selected traffic node is denoted as randn, and randn = Tn - ∑ h＝1 hn Rnh, Where Tn represents the timing period corresponding to the nth passage node selected in the database, and hn represents the number of traffic light states in the adaptive timing information after the nth passage node is changed. S44. Obtain the city adaptive timing information change set. Each element in the city adaptive timing information change set corresponds to the adaptive timing information of the passage nodes that do not need to be changed in S42 or the adaptive timing information of the passage nodes after the change selected in S43.

7. A smart city intelligent management system based on the Internet of Things, characterized in that, The system includes the following modules: The urban traffic model construction module uses BIM technology to construct a three-dimensional model of urban roads; each traffic light in the urban road is taken as a traffic node, and the corresponding position of each traffic node in the three-dimensional model of the urban road is marked to obtain the urban traffic model. The timing information adaptive adjustment module acquires the standard timing information of the traffic lights of each traffic node in the urban traffic model from the preset standard timing information in the database, and obtains the standard timing information set of urban traffic nodes. The module collects the driving status information of the surrounding road conditions of the corresponding traffic node once every first preset time through the road condition detection sensor corresponding to the traffic node, and calibrates the standard timing information of the corresponding traffic node according to the timing deviation adjustment coefficient corresponding to the collected driving status information in the preset form in the database, so as to obtain the adaptive timing information corresponding to the corresponding traffic node. The adaptive timing information corresponding to traffic nodes with different numbers at the same time is summarized to obtain the urban adaptive timing information set corresponding to the corresponding time. The adaptive information association and impact module acquires the positional relationships between different traffic nodes in the urban traffic model, analyzes the association relationships between different traffic nodes, and constructs a set of associated nodes corresponding to each traffic node. Combining the vehicle traffic data between each traffic node and the elements in the corresponding set of associated nodes in historical data, it predicts the association interference value of the traffic node being affected by the comprehensive adaptive timing information of each traffic node in the corresponding set of associated nodes, and generates a priority sequence of adaptive timing impact of traffic nodes. The passage node timing management module updates the adaptive timing of each passage node in the priority sequence of adaptive timing impact in descending order of priority, generates a city adaptive timing information change set, and manages the timing of city passage nodes according to the city adaptive timing information change set.

8. A smart city intelligent management system based on the Internet of Things according to claim 7, characterized in that: The adaptive information association impact module includes an association node set construction module, an association interference value analysis module, and a priority sequence generation module. The associated node set construction module obtains the positional relationship between different traffic nodes in the urban traffic model, analyzes the association relationship between different traffic nodes, and constructs the associated node set corresponding to each traffic node; The correlation interference value analysis module combines the vehicle traffic data between each traffic node and the corresponding elements in the correlation node set from historical data to predict the correlation interference value of a traffic node affected by the combined adaptive timing information of each traffic node in the corresponding correlation node set. The priority sequence generation module combines the prediction results of associated interference values ​​to generate an adaptive timing impact priority sequence for passing nodes.

9. A smart city intelligent management system based on the Internet of Things according to claim 7, characterized in that: When the passage node timing management module manages the timing of urban passage nodes according to the urban adaptive timing information change set, it feeds back the urban adaptive timing information change set to the urban passage node timing control terminal. The feedback time is recorded as t1. The urban passage node timing control terminal obtains the timing information that each passage node is executing at t1, and controls each passage node to start executing the adaptive timing information corresponding to the passage node with the corresponding number in the urban adaptive timing information change set at t1 after executing the timing information being executed at t1. The executing timing information remains unchanged until the urban passage node timing control terminal receives the feedback information again.

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