An urban road traffic noise dynamic monitoring method and system based on an agent model

Abstract

The application relates to the technical field of urban environment monitoring, and particularly discloses a city road traffic noise dynamic monitoring method and system based on an agent model, which comprises the following steps: constructing a three-dimensional simulation environment based on high-precision map data; acquiring multi-source real-time city traffic flow monitoring data, and constructing a corresponding real-time vehicle feature vector for each vehicle running in the corresponding region of the three-dimensional simulation environment based on the multi-source real-time city traffic flow monitoring data; based on the real-time vehicle feature vector and spatial environment information, simulating and deducing a noise field of the corresponding region of the three-dimensional simulation environment by using an agent model to obtain real-time simulation noise contribution values of the three-dimensional distribution in the corresponding region; and when the real-time simulation noise contribution values are greater than or equal to the standard limit values corresponding to noise constraint specifications, sending early warning information and traffic control suggestions to a traffic control platform, so that real-time and accurate city road traffic noise dynamic monitoring is effectively realized.

Description

Technical Field

[0001] This disclosure generally relates to the field of urban environmental monitoring technology, and specifically to a method and system for dynamic monitoring of urban road traffic noise based on an intelligent agent model. Background Technology

[0002] With the acceleration of urbanization and the continuous growth of motor vehicle ownership, road traffic noise has become a significant source of environmental pollution affecting the quality of life of urban residents. Schools, hospitals, residential areas, and other noise-sensitive areas are key targets for protection. Currently, the monitoring, simulation, and control systems for urban road traffic noise still have significant technical shortcomings, making it difficult to meet the needs of modern urban noise environment management. Summary of the Invention

[0003] In view of the above-mentioned defects or deficiencies in the existing technology, it is desirable to provide a method and system for dynamic monitoring of urban road traffic noise based on an intelligent agent model, so as to effectively realize real-time and accurate dynamic monitoring of urban road traffic noise.

[0004] In a first aspect, embodiments of this application provide a method for dynamic monitoring of urban road traffic noise based on an intelligent agent model, including:

[0005] A three-dimensional simulation environment is constructed based on high-precision map data; the three-dimensional simulation environment integrates spatial environment information and noise constraint specifications within the corresponding area of ​​the three-dimensional simulation environment;

[0006] Acquire multi-source real-time urban traffic flow monitoring data, and based on the multi-source real-time urban traffic flow monitoring data, construct a corresponding real-time vehicle feature vector for each vehicle operating in real time within the corresponding area of ​​the three-dimensional simulation environment.

[0007] Based on the real-time vehicle feature vector and the spatial environment information, the noise field of the corresponding area of ​​the three-dimensional simulation environment is simulated and deduced using an intelligent agent model to obtain the real-time simulated noise contribution value of the three-dimensional distribution in the corresponding area.

[0008] When the real-time simulated noise contribution value is greater than or equal to the standard limit corresponding to the noise constraint specification, an early warning message and traffic control suggestions are sent to the traffic control platform.

[0009] In some embodiments, the step of using an intelligent agent model to simulate and extrapolate the noise field of the corresponding region of the three-dimensional simulated environment based on the real-time vehicle feature vector and the spatial environment information, to obtain the real-time simulated noise contribution value of the three-dimensional distribution within the corresponding region, includes:

[0010] Based on the real-time vehicle feature vector and the spatial environment information, the intelligent agent model is used to update the state features of each spatial node in the corresponding area of ​​the three-dimensional simulation environment to obtain the real-time state features of each spatial node.

[0011] For each of the spatial nodes, noise field analysis is performed based on the real-time state characteristics to obtain the real-time simulated noise contribution value corresponding to the spatial node.

[0012] In some embodiments, the step of updating the state features of each spatial node within the corresponding region of the three-dimensional simulation environment using the intelligent agent model based on the real-time vehicle feature vector and the spatial environment information to obtain the real-time state features of each spatial node includes:

[0013] For each of the spatial nodes, the basic environmental features of the spatial node and the real-time vehicle feature vector corresponding to the spatial node are obtained;

[0014] Based on the basic environmental features and the corresponding real-time vehicle feature vector, the real-time features of the spatial node corresponding to the spatial node are determined.

[0015] Receive real-time features of adjacent nodes corresponding to at least one adjacent spatial node adjacent to the spatial node;

[0016] Based on the real-time features of the spatial node corresponding to the spatial node and the real-time features of the adjacent nodes corresponding to at least one adjacent spatial node, the real-time state features corresponding to the spatial node are determined.

[0017] In some embodiments, the step of performing noise field analysis based on the real-time state characteristics to obtain the real-time simulated noise contribution value corresponding to the spatial node includes:

[0018] Based on the real-time state characteristics, noise field analysis is performed to obtain the initial simulated noise contribution value corresponding to the spatial node;

[0019] Receive the initial simulated noise contribution value of the adjacent node corresponding to at least one adjacent spatial node adjacent to the spatial node;

[0020] Based on the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes, the real-time simulated noise contribution value corresponding to the spatial node is determined.

[0021] In some embodiments, determining the real-time simulated noise contribution value corresponding to the spatial node based on the initial simulated noise contribution value and the initial simulated noise contribution values ​​of the neighboring nodes includes:

[0022] Based on the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes, identify whether the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes satisfy the continuity of sound pressure, the continuity of sound pressure gradient, and the consistency of dispersion.

[0023] When the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent node satisfy the continuity of sound pressure, the continuity of sound pressure gradient, and the consistency of dispersion, the initial simulated noise contribution value is determined to be the real-time simulated noise contribution value corresponding to the spatial node.

[0024] In some embodiments, it also includes:

[0025] Based on the initial simulated noise contribution value and position of the adjacent nodes, estimate the propagation simulated noise contribution value of the spatial nodes;

[0026] Based on the initial simulated noise contribution value, determine the inferred simulated noise contribution value corresponding to the spatial node;

[0027] If the difference between the propagation simulation noise contribution value and the inferred simulation noise contribution value is less than a preset consistency threshold, then the initial simulation noise contribution value and the initial simulation noise contribution value of the adjacent node are determined to satisfy the discrete divergence consistency.

[0028] Secondly, embodiments of this application provide a dynamic monitoring system for urban road traffic noise based on an intelligent agent model, including:

[0029] An environment construction module is used to construct a three-dimensional simulation environment based on high-precision map data; the three-dimensional simulation environment integrates spatial environment information and noise constraint specifications within the corresponding area of ​​the three-dimensional simulation environment;

[0030] The traffic flow acquisition module is used to acquire multi-source real-time urban traffic flow monitoring data, and based on the multi-source real-time urban traffic flow monitoring data, construct a corresponding real-time vehicle feature vector for each vehicle running in real time within the corresponding area of ​​the three-dimensional simulation environment.

[0031] The noise simulation module is used to simulate and extrapolate the noise field of the corresponding area of ​​the three-dimensional simulation environment based on the real-time vehicle feature vector and the spatial environment information, using an intelligent agent model, to obtain the real-time simulated noise contribution value of the three-dimensional distribution in the corresponding area.

[0032] The early warning module is used to send early warning information and traffic control suggestions to the traffic control platform when the real-time simulated noise contribution value is greater than or equal to the standard limit corresponding to the noise constraint specification.

[0033] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in embodiments of this application.

[0034] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in embodiments of this application.

[0035] Fifthly, embodiments of this application provide a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the method described in embodiments of this application.

[0036] The urban road traffic noise dynamic monitoring method and system based on an intelligent agent model provided in this application constructs a three-dimensional simulation environment based on high-precision map data. The three-dimensional simulation environment integrates spatial environment information and noise constraint specifications within the corresponding area of ​​the three-dimensional simulation environment. It acquires multi-source real-time urban traffic flow monitoring data and, based on this data, constructs a corresponding real-time vehicle feature vector for each vehicle operating in real-time within the corresponding area of ​​the three-dimensional simulation environment. Based on the real-time vehicle feature vector and spatial environment information, it uses an intelligent agent model to simulate and extrapolate the noise field in the corresponding area of ​​the three-dimensional simulation environment, obtaining the real-time simulated noise contribution value distributed in three dimensions within the corresponding area. When the real-time simulated noise contribution value is greater than or equal to the standard limit corresponding to the noise constraint specification, it sends early warning information and traffic control suggestions to the traffic control platform, effectively achieving real-time and accurate dynamic monitoring of urban road traffic noise.

[0037] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0038] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0039] Figure 1 A flowchart illustrating a dynamic monitoring method for urban road traffic noise based on an intelligent agent model, according to an embodiment of this application, is shown.

[0040] Figure 2 This paper shows a schematic diagram of the structure of a dynamic monitoring system for urban road traffic noise based on an intelligent agent model according to an embodiment of this application;

[0041] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device or server according to embodiments of this application is shown. Detailed Implementation

[0042] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0043] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0044] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation instruction steps as shown in the following embodiments or drawings, the method may include more or fewer operation instruction steps based on conventional or non-creative effort. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application. In actual processing or when the device executes the method, it may be executed sequentially or in parallel according to the method shown in the embodiments or drawings.

[0045] It should be noted that the acquisition or use of data in the embodiments of this application requires the user's consent. The relevant data can only be obtained after the user's authorization and permission, and the acquisition or use of the data complies with the laws and regulations of the relevant regions.

[0046] Please refer to Figure 1 , Figure 1 A flowchart illustrating a method for dynamic monitoring of urban road traffic noise based on an intelligent agent model, according to an embodiment of this application, is shown. Figure 1 As shown, the method includes:

[0047] Step 101: Construct a three-dimensional simulation environment based on high-precision map data; the three-dimensional simulation environment integrates spatial environment information and noise constraint specifications within the corresponding area of ​​the three-dimensional simulation environment.

[0048] It should be noted that the 3D simulation environment is a virtual environment for simulating traffic noise based on an intelligent agent model. The 3D simulation environment can access, parse, and process high-precision map data that conforms to relevant national surveying and mapping standards through a data interface. This high-precision map data includes at least road geometric alignment data, 3D outline data of buildings and structures surrounding the road, topographic data, road surface and ancillary facility data, satellite image layer data, and land use and land type data. It should be understood that the 3D simulation environment can also automatically identify and label sensitive targets in the urban sound environment by combining satellite image layers and a pre-set sensitive target feature library.

[0049] In some preferred embodiments, the three-dimensional simulation environment further accesses and parses relevant national and industry standards for acoustic environment quality, motor vehicle noise emission standards, and laws and regulations, such as GB 1495-2002, GB3096-2008, and JT / T 1198-2018, to extract the rules for dividing acoustic environment functional zones, noise limit standards, noise measurement specifications, sensitive target protection requirements, and technical constraints. The extracted standard constraints are then transformed into standardized data operation rules that can be recognized and executed by the intelligent agent model, as well as standard limit reference data that can be presented in a visual interface.

[0050] Step 102: Obtain multi-source real-time urban traffic flow monitoring data. Based on the multi-source real-time urban traffic flow monitoring data, construct a corresponding real-time vehicle feature vector for each vehicle operating in real time within the corresponding area of ​​the three-dimensional simulation environment.

[0051] In other words, by using multi-source real-time urban traffic flow monitoring data, the dynamic characteristics of each vehicle are extracted, and a feature set of all vehicles in the corresponding area covering the three-dimensional simulation environment is constructed, providing excitation source data for subsequent noise field simulation and deduction.

[0052] Specifically, traffic flow monitoring data can be acquired in real time from various types of traffic flow monitoring terminals (such as roadside radar, geomagnetic coils, checkpoint cameras, and floating car GPS, etc.) to form multi-source real-time urban traffic flow monitoring data (such as the instantaneous location of each vehicle i). Instantaneous velocity Model wait).

[0053] It should be understood that after acquiring the raw data of multi-source real-time urban traffic flow monitoring, all accessed multi-source data are uniformly standardized and processed, including time synchronization (aligning all data to the same timestamp), spatial matching (mapping vehicle positions to the road network coordinate system), and missing value handling (using linear interpolation to fill in the missing values). After these data preprocessing methods, multi-source real-time urban traffic flow monitoring data are obtained for the intelligent agent model to perform noise field simulation and deduction.

[0054] For example, the real-time vehicle feature vector corresponding to the i-th vehicle can be represented as:

[0055]

[0056] in, Let be the real-time vehicle feature vector of the i-th vehicle at time t. Let be the three-dimensional spatial coordinates of the i-th vehicle at time t, which can be expanded as follows: , Let be the instantaneous velocity of the i-th vehicle at time t. Let be the three-dimensional acceleration of the i-th vehicle at time t, which can be expanded as , Let i represent the vehicle type, including light, medium, and heavy-duty vehicles. The current state of vehicle i includes constant speed, acceleration, deceleration, idling, etc.

[0057] It should be understood that, in the embodiments of this application, vehicles traveling on urban roads are used as noise sources. By using multi-source real-time urban traffic flow monitoring data, the real-time driving situation of vehicles can be fully understood from multiple angles, so that the intelligent agent model can perform noise field simulation and deduction based on reliable monitoring data, thereby achieving reliable dynamic noise monitoring.

[0058] Step 103: Based on real-time vehicle feature vectors and spatial environment information, use an intelligent agent model to simulate and extrapolate the noise field of the corresponding area of ​​the three-dimensional simulation environment, and obtain the real-time simulated noise contribution value of the three-dimensional distribution in the corresponding area.

[0059] In other words, using real-time vehicle feature vectors as driving factors, the intelligent agent model continuously deduces the laws of noise generation, propagation, diffusion, and attenuation of vehicles as noise sources, and accurately calculates the three-dimensional spatial distribution intensity, propagation path, influence range, and real-time simulated noise contribution value of the noise field in the corresponding area.

[0060] In a feasible embodiment, based on real-time vehicle feature vectors and spatial environment information, the state features of each spatial node in the corresponding area of ​​the three-dimensional simulation environment are updated using an intelligent agent model to obtain the real-time state features of each spatial node; for each spatial node, noise field analysis is performed based on the real-time state features to obtain the real-time simulation noise contribution value corresponding to the spatial node.

[0061] It should be noted that the 3D simulation environment includes the graph structure within the corresponding area. , where the node set It includes spatially discrete points such as road grid points, building surface points, and locations of sensitive targets, and edge sets. Represents spatial adjacency relationships (constructed based on actual geographical distances in a 3D simulation environment).

[0062] In a feasible embodiment, based on real-time vehicle feature vectors and spatial environment information, an intelligent agent model is used to update the state features of each spatial node within the corresponding area of ​​the three-dimensional simulation environment to obtain the real-time state features of each spatial node. This includes: for each spatial node, obtaining the basic environmental features of the spatial node and the real-time vehicle feature vector corresponding to the spatial node; determining the real-time features of the spatial node corresponding to the spatial node based on the basic environmental features and the corresponding real-time vehicle feature vector; receiving the real-time features of the adjacent nodes corresponding to at least one adjacent spatial node; and determining the real-time state features of the spatial node corresponding to the spatial node based on the real-time features of the spatial node and the real-time features of the adjacent nodes corresponding to at least one adjacent spatial node.

[0063] Among them, the basic environmental characteristics of the current space node This includes, but is not limited to, environmental features of the current spatial node obtained by extracting spatial environment information from the 3D simulation environment, such as building density, terrain elevation, and ground type. The spatial node corresponds to a real-time vehicle feature vector. When there are vehicles within the coverage area of ​​the current spatial node, the value is determined based on the real-time vehicle feature vector of at least one vehicle within the coverage area of ​​the current spatial node; when there are no vehicles within the coverage area of ​​the current spatial node, the value is 0. This is based on basic environmental characteristics. Real-time vehicle feature vectors corresponding to spatial nodes To obtain the real-time features of the spatial nodes corresponding to the spatial nodes. ,in, Encode the location of spatial nodes.

[0064] It should be understood that at any given time, each spatial node updates its own corresponding real-time features. Each spatial node receives the real-time features of its neighboring spatial nodes and then determines the real-time state features of its own spatial node. Specifically, this can be represented as:

[0065]

[0066] in, This represents the real-time state characteristics of spatial node v after its update. This represents the real-time characteristics of spatial node v. Let v be the set of neighboring nodes of spatial node v. Let v be the number of neighboring nodes of spatial node v. Let be the number of neighboring nodes of neighboring node u. , For graph neural networks The weight matrix of the layer, For activation function, This represents the real-time characteristics of the adjacent node u.

[0067] It should be understood that this application uses a graph neural network to fuse the real-time features of spatial nodes with the real-time features of neighboring nodes. After the layer feature information is passed, the features of each spatial node It aggregates information from all vehicle sound sources and environmental boundaries within its spatial neighborhood, providing sufficient spatial context for subsequent noise field simulation. This enables it to accurately characterize the impact of spatial boundaries such as buildings and terrain on noise propagation, and realize the calculation of noise values ​​at any location in three-dimensional space.

[0068] In one feasible embodiment, noise field analysis based on real-time state characteristics is performed to obtain the real-time simulated noise contribution value corresponding to the spatial node, including: performing noise field analysis based on real-time state characteristics to obtain the initial simulated noise contribution value corresponding to the spatial node; receiving the initial simulated noise contribution value of the adjacent node corresponding to at least one adjacent spatial node adjacent to the spatial node; and determining the real-time simulated noise contribution value corresponding to the spatial node based on the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent node.

[0069] In this embodiment, a Physical Information Neural Network (PINN) can be used to analyze the noise field based on the real-time state characteristics of spatial nodes to obtain the initial simulated noise contribution value corresponding to the spatial nodes. Specifically, it can be expressed as follows:

[0070]

[0071] in, This represents the initial simulated noise contribution value corresponding to spatial node v. This represents the real-time state characteristics corresponding to spatial node v.

[0072] It should be noted that the Physical Information Neural Network (PINN) uses acoustic physics equations as preconditions for network training. Through training, the acoustic physics equation constraints are combined with the nonlinear fitting ability of the deep learning model to achieve high-precision and generalizable noise field simulation.

[0073] In a preferred embodiment, to overcome the lack of physical consistency in a purely data-driven model, an acoustic physics equation residual term and rigid boundary conditions applied at the building surface and ground are introduced into the loss function as regularization constraints.

[0074] In a preferred embodiment, an agent model instance can be assigned to each spatial node in the 3D simulation environment to calculate the initial simulated noise contribution value of that spatial node. This eliminates the central computing bottleneck; each agent model instance only needs to communicate with its local neighbors, making it suitable for real-time calculation of large-scale road networks. When traffic flow changes, only the input features of the affected spatial nodes need to be updated, and the agent model instances re-infer locally without requiring global recalculation. Furthermore, the decoder of each agent model instance is constrained by acoustic equations, ensuring that the overall field of the 3D simulation environment conforms to physical laws.

[0075] It should be understood that in urban road traffic noise fields, sound pressure (i.e., noise contribution value), as a continuous function of spatial location, should satisfy the spatial continuity condition in areas without reflection sources or strong discontinuities. This means that the sound pressure value at adjacent spatial nodes should change continuously, and the sound pressure gradient (i.e., the rate of change of the sound field) should also satisfy the corresponding physical constraints.

[0076] In the noise field simulation based on the collaboration of agent model instances, each agent model instance independently calculates its location. Initial simulated noise contribution value at the location Because the calculations of each agent model instance are independent, and numerical errors or local model biases may exist, discontinuities in sound pressure or sound pressure gradients may occur between the simulation results of adjacent agent model instances (such as sound pressure jumps, abrupt changes in gradient direction, etc.). This discontinuity violates the basic laws of acoustic physics and must be detected and corrected through a collaborative verification mechanism among agents.

[0077] Specifically, based on the initial simulated noise contribution value and the initial simulated noise contribution value of adjacent nodes, it is determined whether the initial simulated noise contribution value and the initial simulated noise contribution value of adjacent nodes satisfy the continuity of sound pressure, the continuity of sound pressure gradient, and the consistency of discrete dispersion; when the initial simulated noise contribution value and the initial simulated noise contribution value of adjacent nodes satisfy the continuity of sound pressure, the continuity of sound pressure gradient, and the consistency of discrete dispersion, the initial simulated noise contribution value is determined to be the real-time simulated noise contribution value corresponding to the spatial node.

[0078] Among these, sound pressure continuity refers to the difference between the initial simulated noise contribution values ​​of two adjacent spatial nodes being less than the allowable error determined by the spatial resolution. Sound pressure gradient continuity refers to the smooth change in the gradient direction and magnitude of the sound pressure gradient between adjacent nodes. Discrete divergence consistency refers to the difference between the gradient divergence of sound pressure between adjacent nodes and the average divergence calculated by their neighbors being less than a preset physical tolerance.

[0079] In a feasible embodiment, the propagation simulation noise contribution value of the spatial node is estimated based on the initial simulated noise contribution value and position of the adjacent nodes; the inferred simulated noise contribution value of the spatial node is determined based on the initial simulated noise contribution value; if the difference between the propagation simulation noise contribution value and the inferred simulated noise contribution value is less than a preset consistency threshold, then the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes are determined to satisfy the dispersion consistency.

[0080] Specifically, based on the initial simulated noise contribution values ​​and positions of adjacent nodes, the second derivative of the spatial node is estimated as the simulated noise contribution value for propagation. This can be expressed as:

[0081]

[0082] in, This represents the contribution value of the propagation simulation noise corresponding to spatial node v. The initial simulated noise contribution value of the neighboring nodes of the adjacent spatial node u. The initial simulated noise contribution value for spatial node v. The gradient of the initial simulated noise contribution value for spatial node v. Let v be the position coordinates of the spatial node v. Let be the position coordinates of the adjacent spatial node u, and let be the set of adjacent nodes of the spatial node v.

[0083] Then, the initial simulated noise contribution value is differentiated to obtain the second-order reciprocal true value of the initial simulated noise contribution value. If the propagation simulation noise contribution value With the truth value of the second reciprocal If the difference is less than the preset consistency threshold, then the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes are determined to satisfy the discrete divergence consistency.

[0084] It should be understood that by curve fitting the real-time simulated noise contribution value of each spatial node in the corresponding area of ​​the three-dimensional simulation environment, the real-time simulated noise contribution value of the three-dimensional distribution in the corresponding area can be obtained.

[0085] Therefore, this application embodiment utilizes intelligent agent model instances configured independently with each spatial node to analyze the initial simulated noise contribution value of each spatial node, and based on spatial consistency verification, effectively realizes the accurate simulation of the noise contribution value of each spatial node, providing a reliable data foundation for dynamic monitoring of urban road traffic noise.

[0086] Step 104: When the real-time simulated noise contribution value is greater than or equal to the standard limit value corresponding to the noise constraint specification, send early warning information and traffic control suggestions to the traffic control platform.

[0087] In other words, the system obtains the real-time simulated noise contribution value from each spatial node and determines in real time whether the noise simulation result exceeds the standard limit in the noise constraint specification integrated in the three-dimensional simulation environment; if it exceeds the standard, it sends an early warning message to the traffic control platform and automatically pushes accurately matched traffic control suggestions.

[0088] It should be understood that the standard limits in the noise constraint specifications integrated into the 3D simulation environment are automatically adjusted and integrated into each spatial node based on its attribute characteristics (such as noise sensitivity, whether it is a sensitive target, etc.) during the construction of the 3D simulation environment. This ensures that the standard limits of each spatial node, while meeting the noise constraint specifications, also conform to the specific attributes of each spatial node. Once the intelligent agent model instance corresponding to each spatial node calculates the real-time simulated noise contribution value, it can determine whether the noise exceeds the limit based on the integrated standard limits. This allows for timely sending of early warning information to the traffic control platform and automatic push of accurately matched traffic control suggestions, eliminating the need for identification and judgment of each spatial node after each noise simulation, thus greatly improving the efficiency of noise identification.

[0089] Therefore, the urban road traffic noise dynamic monitoring method based on an intelligent agent model provided in this application constructs a three-dimensional simulation environment based on high-precision map data. The three-dimensional simulation environment integrates spatial environment information and noise constraint specifications within the corresponding area of ​​the three-dimensional simulation environment. It acquires multi-source real-time urban traffic flow monitoring data and, based on the multi-source real-time urban traffic flow monitoring data, constructs a corresponding real-time vehicle feature vector for each vehicle operating in real-time within the corresponding area of ​​the three-dimensional simulation environment. Based on the real-time vehicle feature vector and spatial environment information, it uses an intelligent agent model to simulate and extrapolate the noise field in the corresponding area of ​​the three-dimensional simulation environment, obtaining the real-time simulated noise contribution value distributed in three dimensions within the corresponding area. When the real-time simulated noise contribution value is greater than or equal to the standard limit corresponding to the noise constraint specification, it sends early warning information and traffic control suggestions to the traffic control platform, effectively achieving real-time and accurate dynamic monitoring of urban road traffic noise.

[0090] It should be noted that although the operation of the method of the present invention is described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed in order to achieve the desired result.

[0091] Figure 2 A schematic diagram of the structure of a dynamic monitoring system for urban road traffic noise based on an intelligent agent model provided in an embodiment of this application is shown.

[0092] like Figure 2 As shown, the urban road traffic noise dynamic monitoring system 10 based on the intelligent agent model includes:

[0093] The environment construction module 11 is used to construct a three-dimensional simulation environment based on high-precision map data; the three-dimensional simulation environment integrates spatial environment information and noise constraint specifications within the corresponding area of ​​the three-dimensional simulation environment;

[0094] Traffic flow acquisition module 12 is used to acquire multi-source real-time urban traffic flow monitoring data, and based on the multi-source real-time urban traffic flow monitoring data, construct a corresponding real-time vehicle feature vector for each vehicle running in real time within the corresponding area of ​​the three-dimensional simulation environment.

[0095] The noise simulation module 13 is used to simulate and extrapolate the noise field of the corresponding area of ​​the three-dimensional simulation environment based on the real-time vehicle feature vector and the spatial environment information, using an intelligent agent model, to obtain the real-time simulated noise contribution value of the three-dimensional distribution in the corresponding area.

[0096] The early warning module 14 is used to send early warning information and traffic control suggestions to the traffic control platform when the real-time simulated noise contribution value is greater than or equal to the standard limit value corresponding to the noise constraint specification.

[0097] In some embodiments, the noise simulation module 13 is specifically used for:

[0098] Based on the real-time vehicle feature vector and the spatial environment information, the intelligent agent model is used to update the state features of each spatial node in the corresponding area of ​​the three-dimensional simulation environment to obtain the real-time state features of each spatial node.

[0099] For each of the spatial nodes, noise field analysis is performed based on the real-time state characteristics to obtain the real-time simulated noise contribution value corresponding to the spatial node.

[0100] In some embodiments, the noise simulation module 13 is specifically used for:

[0101] For each of the spatial nodes, the basic environmental features of the spatial node and the real-time vehicle feature vector corresponding to the spatial node are obtained;

[0102] Based on the basic environmental features and the corresponding real-time vehicle feature vector, the real-time features of the spatial node corresponding to the spatial node are determined.

[0103] Receive real-time features of adjacent nodes corresponding to at least one adjacent spatial node adjacent to the spatial node;

[0104] Based on the real-time features of the spatial node corresponding to the spatial node and the real-time features of the adjacent nodes corresponding to at least one adjacent spatial node, the real-time state features corresponding to the spatial node are determined.

[0105] In some embodiments, the noise simulation module 13 is specifically used for:

[0106] Based on the real-time state characteristics, noise field analysis is performed to obtain the initial simulated noise contribution value corresponding to the spatial node;

[0107] Receive the initial simulated noise contribution value of the adjacent node corresponding to at least one adjacent spatial node adjacent to the spatial node;

[0108] Based on the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes, the real-time simulated noise contribution value corresponding to the spatial node is determined.

[0109] In some embodiments, the noise simulation module 13 is specifically used for:

[0110] Identify whether the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes satisfy the requirements of sound pressure continuity, sound pressure gradient continuity and discrete dispersion consistency;

[0111] When the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent node satisfy the continuity of sound pressure, the continuity of sound pressure gradient, and the consistency of dispersion, the initial simulated noise contribution value is determined to be the real-time simulated noise contribution value corresponding to the spatial node.

[0112] In some embodiments, the noise simulation module 13 is specifically used for:

[0113] Based on the initial simulated noise contribution value and position of the adjacent nodes, estimate the propagation simulated noise contribution value of the spatial nodes;

[0114] Based on the initial simulated noise contribution value, determine the inferred simulated noise contribution value corresponding to the spatial node;

[0115] If the difference between the propagation simulation noise contribution value and the inferred simulation noise contribution value is less than a preset consistency threshold, then the initial simulation noise contribution value and the initial simulation noise contribution value of the adjacent node are determined to satisfy the discrete divergence consistency.

[0116] It should be understood that the modules or modules described in the Urban Road Traffic Noise Dynamic Monitoring System 10 based on the intelligent agent model are similar to those in the reference system. Figure 1The steps in the described method correspond accordingly. Therefore, the operations and features described above for the method are also applicable to the agent-based urban road traffic noise dynamic monitoring system 10 and its included modules, and will not be repeated here. The agent-based urban road traffic noise dynamic monitoring system 10 can be pre-implemented in the browser or other secure applications of an electronic device, or it can be loaded into the browser or its secure applications of an electronic device through download or other means. The corresponding modules in the agent-based urban road traffic noise dynamic monitoring system 10 can cooperate with the modules in the electronic device to implement the solution of the embodiments of this application.

[0117] The division of modules or units mentioned in the detailed description above is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0118] The following is for reference. Figure 3 , Figure 3 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application is shown.

[0119] like Figure 3 As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage section 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the system's operating instructions. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0120] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0121] Specifically, according to embodiments of this application, the flowchart above refers to... Figure 2 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program contains program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the system of this application.

[0122] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operational instructions of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two connected blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified functions or operational instructions, or using a combination of dedicated hardware and computer instructions.

[0124] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be housed in a processor; for example, a processor can be described as including an environment construction module, a traffic flow acquisition module, a noise simulation module, and an early warning module. The names of these units or modules do not necessarily limit the specific unit or module itself. For example, the environment construction module can also be described as "constructing a three-dimensional simulation environment based on high-precision map data; the three-dimensional simulation environment integrates spatial environmental information and noise constraint specifications within the corresponding area of ​​the three-dimensional simulation environment."

[0125] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not assembled into the electronic device. The computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the agent-based dynamic monitoring method for urban road traffic noise described in this application.

[0126] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for dynamic monitoring of urban road traffic noise based on an intelligent agent model, characterized in that, include: A three-dimensional simulation environment is constructed based on high-precision map data; the three-dimensional simulation environment integrates spatial environment information and noise constraint specifications within the corresponding area of ​​the three-dimensional simulation environment; Acquire multi-source real-time urban traffic flow monitoring data, and based on the multi-source real-time urban traffic flow monitoring data, construct a corresponding real-time vehicle feature vector for each vehicle operating in real time within the corresponding area of ​​the three-dimensional simulation environment. Based on the real-time vehicle feature vector and the spatial environment information, the noise field of the corresponding area of ​​the three-dimensional simulation environment is simulated and deduced using an intelligent agent model to obtain the real-time simulated noise contribution value of the three-dimensional distribution in the corresponding area. When the real-time simulated noise contribution value is greater than or equal to the standard limit corresponding to the noise constraint specification, an early warning message and traffic control suggestions are sent to the traffic control platform.

2. The method for dynamic monitoring of urban road traffic noise based on an intelligent agent model according to claim 1, characterized in that, The step of using an intelligent agent model to simulate and extrapolate the noise field of the corresponding region of the three-dimensional simulated environment based on the real-time vehicle feature vector and the spatial environment information, to obtain the real-time simulated noise contribution value of the three-dimensional distribution within the corresponding region, includes: Based on the real-time vehicle feature vector and the spatial environment information, the intelligent agent model is used to update the state features of each spatial node in the corresponding area of ​​the three-dimensional simulation environment to obtain the real-time state features of each spatial node. For each of the spatial nodes, noise field analysis is performed based on the real-time state characteristics to obtain the real-time simulated noise contribution value corresponding to the spatial node.

3. The method for dynamic monitoring of urban road traffic noise based on an intelligent agent model according to claim 2, characterized in that, Based on the real-time vehicle feature vector and the spatial environment information, the intelligent agent model is used to update the state features of each spatial node within the corresponding area of ​​the three-dimensional simulation environment to obtain the real-time state features of each spatial node, including: For each of the spatial nodes, the basic environmental features of the spatial node and the real-time vehicle feature vector corresponding to the spatial node are obtained; Based on the basic environmental features and the corresponding real-time vehicle feature vector, the real-time features of the spatial node corresponding to the spatial node are determined. Receive real-time features of adjacent nodes corresponding to at least one adjacent spatial node adjacent to the spatial node; Based on the real-time features of the spatial node corresponding to the spatial node and the real-time features of the adjacent nodes corresponding to at least one adjacent spatial node, the real-time state features corresponding to the spatial node are determined.

4. The method for dynamic monitoring of urban road traffic noise based on an intelligent agent model according to claim 2, characterized in that, The step of performing noise field analysis based on the real-time state characteristics to obtain the real-time simulated noise contribution value corresponding to the spatial node includes: Based on the real-time state characteristics, noise field analysis is performed to obtain the initial simulated noise contribution value corresponding to the spatial node; Receive the initial simulated noise contribution value of the adjacent node corresponding to at least one adjacent spatial node adjacent to the spatial node; Based on the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes, the real-time simulated noise contribution value corresponding to the spatial node is determined.

5. The method for dynamic monitoring of urban road traffic noise based on an intelligent agent model according to claim 4, characterized in that, The step of determining the real-time simulated noise contribution value corresponding to the spatial node based on the initial simulated noise contribution value and the initial simulated noise contribution values ​​of the adjacent nodes includes: Based on the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes, identify whether the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent nodes satisfy the continuity of sound pressure, the continuity of sound pressure gradient, and the consistency of dispersion. When the initial simulated noise contribution value and the initial simulated noise contribution value of the adjacent node satisfy the continuity of sound pressure, the continuity of sound pressure gradient, and the consistency of dispersion, the initial simulated noise contribution value is determined to be the real-time simulated noise contribution value corresponding to the spatial node.

6. The method for dynamic monitoring of urban road traffic noise based on an intelligent agent model according to claim 5, characterized in that, Also includes: Based on the initial simulated noise contribution value and position of the adjacent nodes, estimate the propagation simulated noise contribution value of the spatial nodes; Based on the initial simulated noise contribution value, determine the inferred simulated noise contribution value corresponding to the spatial node; If the difference between the propagation simulation noise contribution value and the inferred simulation noise contribution value is less than a preset consistency threshold, then the initial simulation noise contribution value and the initial simulation noise contribution value of the adjacent node are determined to satisfy the discrete divergence consistency.

7. A dynamic monitoring system for urban road traffic noise based on an intelligent agent model, characterized in that, include: An environment construction module is used to construct a three-dimensional simulation environment based on high-precision map data; the three-dimensional simulation environment integrates spatial environment information and noise constraint specifications within the corresponding area of ​​the three-dimensional simulation environment; The traffic flow acquisition module is used to acquire multi-source real-time urban traffic flow monitoring data, and based on the multi-source real-time urban traffic flow monitoring data, construct a corresponding real-time vehicle feature vector for each vehicle running in real time within the corresponding area of ​​the three-dimensional simulation environment. The noise simulation module is used to simulate and extrapolate the noise field of the corresponding area of ​​the three-dimensional simulation environment based on the real-time vehicle feature vector and the spatial environment information, using an intelligent agent model, to obtain the real-time simulated noise contribution value of the three-dimensional distribution in the corresponding area. The early warning module is used to send early warning information and traffic control suggestions to the traffic control platform when the real-time simulated noise contribution value is greater than or equal to the standard limit corresponding to the noise constraint specification.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for dynamic monitoring of urban road traffic noise based on an intelligent agent model as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the dynamic monitoring method for urban road traffic noise based on an agent model as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for dynamic monitoring of urban road traffic noise based on an intelligent agent model, as described in any one of claims 1-6.

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