Information processing method and device, equipment and storage medium

Abstract

The invention discloses an information processing method and device, equipment and a storage medium, specifically, the embodiment of the invention can be applied to the traffic field, and the method comprises the steps: constructing an intersection relation structure diagram of a target region based on the job and residence flow data and intersection transfer paths between every two sub-regions in the target region, nodes of the intersection relation structure diagram comprise a plurality of intersection nodes corresponding to the intersection transfer paths, edges of the intersection relation structure diagram are used for representing the transfer paths among the intersection nodes, and weights of the edges are used for representing passing demand degrees of the transfer paths; based on an intersection random walk model, traffic probability analysis is carried out on the plurality of intersection nodes to obtain intersection importance indexes corresponding to the plurality of intersection nodes; and carrying out traffic planning processing on the plurality of intersection nodes based on the intersection importance indexes. According to the scheme of the invention, the traffic demand in the target area can be truly and reasonably reflected based on the fixed travel demand of an individual, so that the accuracy of the importance index of the intersection is improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular, to an information processing method, apparatus, device, and storage medium. Background Art

[0002] Junctions play a very important role in traffic congestion control in cities. They are the key nodes connecting different streets and roads. Through reasonable design and traffic management, the operating efficiency of the traffic system can be effectively improved, and traffic congestion can be alleviated. However, there are tens of thousands of junctions in a city. Under the condition of limited resources, it is necessary to select more valuable junctions for dredging or optimization, so as to benefit more people and improve resource utilization.

[0003] In the prior art, the trajectories of passing vehicles are collected through GPS or various sensors (such as cameras), and important junctions are found by tracing the traffic flow, such as junctions with more passing vehicles or congested junctions. However, using vehicle data cannot reflect the real urban traffic demand. Since the number of passengers carried by the vehicle cannot be known through the vehicle trajectory (whether it is GPS or a sensor), the deviation range is 1 to 5 people for a car, while for a bus or shuttle bus, the deviation of the passenger flow may reach nearly a hundred people. For example, using the vehicle trajectory, the junction A with the largest traffic flow can be found. However, if all the vehicles passing through junction A are cars, it may actually affect fewer people than junction B with a smaller traffic flow but passing through buses and shuttle buses, resulting in a deviation in the evaluation of the importance of the junction, and thus affecting traffic planning. Summary of the Invention

[0004] This application provides an information processing method, apparatus, device, and storage medium, which can evaluate the importance of junctions based on the fixed travel needs of individuals on a personal basis, truly and reasonably reflect the traffic demand in the target area, thereby improving the accuracy of the junction importance index, and improving the rationality of traffic planning and resource utilization. The technical solutions of this application are as follows:

[0005] On the one hand, an information processing method is provided, and the method includes:

[0006] Obtain the commuting passenger flow data between every two sub-regions in a plurality of sub-regions in the target area and the junction transfer paths between every two sub-regions;

[0007] Based on the commuting population flow data and the intersection transfer paths, construct an intersection relationship structure diagram of the target area. The nodes of the intersection relationship structure diagram include multiple intersection nodes corresponding to the intersection transfer paths. The edges of the intersection relationship structure diagram are used to represent the transfer paths between the multiple intersection nodes, and the weights of the edges are used to represent the traffic demand degree of the transfer paths. The traffic demand degree is positively correlated with the corresponding numerical size of the commuting population flow data on the transfer paths;

[0008] Based on the intersection random walk model corresponding to the intersection relationship structure diagram, perform a traffic probability analysis on multiple intersection nodes in the intersection relationship structure diagram to obtain the intersection importance indicators corresponding to the multiple intersection nodes;

[0009] Based on the intersection importance indicators, perform traffic planning processing on the multiple intersection nodes.

[0010] On the other hand, an information processing device is provided. The device includes:

[0011] A data acquisition module, configured to acquire the commuting population flow data between every two sub-areas in multiple sub-areas within the target area and the intersection transfer paths between every two sub-areas;

[0012] An intersection relationship structure diagram construction module, configured to construct an intersection relationship structure diagram of the target area based on the commuting population flow data and the intersection transfer paths. The nodes of the intersection relationship structure diagram include multiple intersection nodes corresponding to the intersection transfer paths. The edges of the intersection relationship structure diagram are used to represent the transfer paths between the multiple intersection nodes, and the weights of the edges are used to represent the traffic demand degree of the transfer paths. The traffic demand degree is positively correlated with the corresponding numerical size of the commuting population flow data on the transfer paths;

[0013] A traffic probability analysis module, configured to perform a traffic probability analysis on multiple intersection nodes in the intersection relationship structure diagram based on the intersection random walk model corresponding to the intersection relationship structure diagram to obtain the intersection importance indicators corresponding to the multiple intersection nodes;

[0014] A traffic planning module, configured to perform traffic planning processing on the multiple intersection nodes based on the intersection importance indicators.

[0015] On the other hand, an information processing device is provided. The device includes a processor and a memory. At least one instruction or at least one program segment is stored in the memory, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement the information processing method as described in the first aspect.

[0016] On the other hand, a computer-readable storage medium is provided, in which at least one instruction or at least one program segment is stored, and the at least one instruction or the at least one program segment is loaded and executed by a processor to implement the information processing method as described in the first aspect.

[0017] On the other hand, a computer program product or a computer program is provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the information processing method as described in the first aspect.

[0018] An information processing method, apparatus, device and storage medium provided by this application have the following technical effects:

[0019] Using the technical solution provided by this application, the target area is divided into sub-areas, the commuting population flow data and intersection transfer paths between every two sub-areas are collected, and based on the commuting population flow data and intersection transfer paths, an intersection relationship structure diagram of the target area is constructed. The nodes of the intersection relationship structure diagram include multiple intersection nodes corresponding to the intersection transfer paths. The edges of the intersection relationship structure diagram are used to represent the transfer paths between multiple intersection nodes, and the weights of the edges are used to represent the traffic demand degree of the transfer paths. Then, based on the intersection random walk model corresponding to the intersection relationship structure diagram, the traffic probability of multiple intersection nodes is analyzed, and the intersection importance index corresponding to each of the multiple intersection nodes is obtained. This solution is based on individuals as the basic unit, and the intersection importance is evaluated based on the fixed travel needs of individuals, which truly and reasonably reflects the traffic demand in the target area, thereby improving the accuracy of the intersection importance index and the rationality of traffic planning and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0021] Figure 1 is a schematic diagram of an application environment provided by an embodiment of the present application;

[0022] Figure 2 is a schematic flowchart of an information processing method provided by an embodiment of the present application;

[0023] Figure 3 is a schematic diagram of a target area provided by an embodiment of the present application;

[0024] Figure 4 It is a schematic flowchart of a process for obtaining an intersection transfer path between every two sub - regions provided by an embodiment of the present application;

[0025] Figure 5 is a schematic diagram of an intersection transfer path provided by an embodiment of the present application;

[0026] Figure 6 It is a schematic flowchart of a process for constructing an intersection relationship structure diagram of a target region based on job - residence population flow data and intersection transfer paths in an embodiment of the present application;

[0027] Figure 7 is a schematic diagram of an intersection relationship structure diagram provided by an embodiment of the present application;

[0028] Figure 8 It is a schematic flowchart of a process for determining an intersection random - walk model provided by an embodiment of the present application;

[0029] Figure 9 It is a schematic flowchart of a process for analyzing the passing probabilities of multiple intersection nodes in an intersection relationship structure diagram based on the intersection random - walk model corresponding to the intersection relationship structure diagram, and obtaining intersection importance indicators corresponding to the multiple intersection nodes in an embodiment of the present application;

[0030] Figure 10 It is a schematic flowchart of another information - processing method provided by an embodiment of the present application;

[0031] Figure 11 It is a block diagram of a composition of an information - processing device provided by an embodiment of the present application;

[0032] Figure 12 It is a schematic structural diagram of an information - processing device provided by an embodiment of the present application. Detailed implementation manners

[0033] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0034] It should be noted that the terms "including" and "having" in the specification and claims of the present application and any of their deformations are intended to cover non - exclusive inclusion. For example, a process, method, system, product, or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0035] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0036] Before further elaborating on the embodiments of this application, the nouns and terms

[0037] involved in the embodiments of this application are described. The nouns and terms involved in the embodiments of this application are subject to the following explanations.

[0038] 1) The Intelligent Traffic System (ITS), also known as the Intelligent Transportation System, effectively integrates advanced scientific and technological means (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) into transportation, service control, and vehicle manufacturing, strengthening the connection among vehicles, roads, and users, thereby forming a comprehensive transportation system that ensures safety, improves efficiency, enhances the environment, and saves energy.

[0039] 2) Location-Based Service (LBS) uses various types of location technologies to obtain the current location of the location device and provides information resources and basic services to the location device through the mobile Internet.

[0040] 3) OD (Origination-Destination) represents the starting point and the ending point.

[0041] 4) Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a way similar to human intelligence. Artificial intelligence also studies the design principles and implementation methods of various intelligent machines, enabling machines to have the functions of perception, reasoning, and decision-making. Artificial intelligence technology is an interdisciplinary subject with a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, pre-trained model technology, operation / interaction systems, mechatronics, etc. Among them, pre-trained models, also known as large models or foundation models, can be widely applied to downstream tasks in various directions of artificial intelligence after fine-tuning. Artificial intelligence software technology mainly includes several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0042] 5) Machine Learning (ML) is an interdisciplinary subject involving multiple fields such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve their own performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent, and its applications cover all fields of artificial intelligence. Machine learning and deep learning usually include technologies such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, rote learning, random walk, etc. Pre-trained models are the latest development results of deep learning, integrating the above technologies.

[0043] With the research and progress of artificial intelligence technology, artificial intelligence technology has been studied and applied in multiple fields. For example, common ones include smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, driverless, autonomous driving, drones, intelligent transportation, digital twins, virtual humans, robots, artificial intelligence-generated content (AIGC), conversational interactions, intelligent healthcare, intelligent customer service, game AI, etc. It is believed that with the development of technology, artificial intelligence technology will be applied in more fields and play an increasingly important role.

[0044] Please refer to Figure 1 , Figure 1It is a schematic diagram of an application environment provided by an embodiment of the present application. The application environment may include a client 10 and a server 20, and the client 10 and the server 20 may be indirectly connected through a wireless communication method. The server 20 may collect the commuting population flow data between every two sub-regions in a target region through the client 10, and obtain the intersection transfer paths between every two sub-regions. Then, based on the commuting population flow data and the intersection transfer paths, a road intersection relationship structure diagram of the target region is constructed. The nodes of the road intersection relationship structure diagram include multiple intersection nodes corresponding to the intersection transfer paths. The edges of the road intersection relationship structure diagram are used to represent the transfer paths between multiple intersection nodes, and the weights of the edges are used to represent the traffic demand degree of the transfer paths. The traffic demand degree is positively correlated with the corresponding numerical size of the commuting population flow data on the transfer path. Then, based on the road intersection random walk model corresponding to the road intersection relationship structure diagram, the traffic probability analysis of multiple intersection nodes in the road intersection relationship structure diagram is performed to obtain the road intersection importance indicators corresponding to the multiple intersection nodes. Finally, based on the road intersection importance indicators, traffic planning processing is performed on the multiple intersection nodes. It should be noted that Figure 1 is just an example.

[0045] The client may be an entity device of types such as a smart phone, a computer (such as a desktop computer, a tablet computer, a laptop computer), a digital assistant, a smart voice interaction device (such as a smart speaker), a smart wearable device, a vehicle-mounted terminal, etc., or may also be software running on the entity device, such as a computer program. The operating system corresponding to the first client may be an Android system, an iOS system (a mobile operating system developed by Apple Inc.), a Linux system (an operating system), a Microsoft Windows system (Microsoft Windows operating system), etc.

[0046] The server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or may also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The server may include a network communication unit, a processor, a memory, and so on. The server may provide background services for the corresponding client.

[0047] The above-mentioned client 10 and server 20 can be used to construct an information processing system, which can be a distributed system. Taking the blockchain system as an example of the distributed system, it is formed by multiple nodes (any form of computing device connected to the network, such as a server, user terminal) and clients. A peer-to-peer (Peer To Peer) network is formed among the nodes. The peer-to-peer protocol is an application layer protocol running on top of the Transmission Control Protocol (TCP). In the distributed system, any machine such as a server or a terminal can join and become a node. A node includes a hardware layer, an intermediate layer, an operating system layer, and an application layer.

[0048] It should be noted that the information processing method provided in this application can be applied to both the client and the server, and is not limited to the embodiments of the above application environment.

[0049] The following introduces a specific embodiment of an information processing method provided in this application. Figure 2 It is a flowchart of an information processing method provided in an embodiment of this application. This application provides method operation steps as described in the embodiment or flowchart, but based on routine or non-creative labor, it can include more or fewer operation steps. The step order listed in the embodiment is only one way among the execution orders of numerous steps, and does not represent the only execution order. When the actual system or product executes, it can be executed in the order of the method shown in the embodiment or the accompanying drawings, or executed in parallel (for example, in an environment of parallel processors or multi-threaded processing). Specifically, as Figure 2 shown, the method may include:

[0050] S201, obtain the job-residence flow data between every two sub-regions in multiple sub-regions within the target area and the intersection transfer paths between every two sub-regions.

[0051] In the embodiments of this specification, based on the regional management level data, the target level that needs to be traffic-planned currently can be determined, and thus the geographical area corresponding to the target level in the map is used as the target area.

[0052] Schematically, the management levels may include: city, county, township, town, etc. The regional management level data can indicate the geographical areas corresponding to each management level in the map. Exemplarily, the target level can be City A, and thus the geographical area corresponding to City A in the map is used as the target area; the target level can be County B under the jurisdiction of City A, and thus the geographical area corresponding to County B in the map is used as the target area.

[0053] In the embodiments of this specification, multiple sub-regions within the target area can be multiple non-overlapping regions obtained by dividing the target area. Optionally, the areas of the multiple sub-regions are the same.

[0054] In an optional embodiment, to reduce the scale of computation, the target area is partitioned into a grid, obtaining a plurality of grid areas, and the plurality of grids are used as a plurality of sub-areas. Optionally, the side length scale of the grid can be 1 km.

[0055] In the embodiments of this specification, every two of the plurality of sub-areas can be used as a pair of areas, and the commuting population flow data between the pairs of areas is counted, so as to obtain the real traffic travel demand between the pairs of areas.

[0056] In a specific embodiment, the obtaining of the commuting population flow data between every two of the plurality of sub-areas in the target area may include:

[0057] S2011, obtaining the terminal positioning data corresponding to a plurality of object terminals in the target area.

[0058] Specifically, the plurality of object terminals here can be a plurality of user devices in the target area, and the user devices here can be movable devices. In a specific embodiment, the movable devices can be intelligent handheld devices and intelligent wearable devices. Illustratively, the intelligent handheld devices can include: watches, and the intelligent wearable devices can include but are not limited to: smart watches, sports bracelets, etc.

[0059] Specifically, the terminal positioning data can include: the location of the positioning point, the name of the positioning point, and the surrounding information of the positioning point, etc.

[0060] S2012, based on the terminal positioning data, performing commuting area analysis on the plurality of target objects corresponding to the plurality of object terminals, and determining the commuting area distribution data of the plurality of target objects.

[0061] Specifically, the plurality of target objects here can be a plurality of users who live and work in the target area, and the commuting area distribution data can characterize the commuting location distribution of the plurality of users in the target area.

[0062] Specifically, according to the terminal positioning data reported by the user device, the commuting place of the user is identified. Among the intelligent devices that have been connected to the wireless network of the commuting place, a plurality of intelligent devices belonging to the same user are identified, and according to the terminal positioning data of the plurality of intelligent devices, the trajectory points of the same user are obtained. An index is established for the trajectory points and the commuting place, and the data of the indexed trajectory points and commuting place are stored in a database. Combining the positioning data of the intelligent device and the accessed wireless network, the commuting place is confirmed more accurately, improving the confirmation accuracy. Using the identifier of the user as the key, the data of the trajectory points and the commuting place are stored in the database, and finally the data in the database is integrated to obtain the commuting area distribution data.

[0063] In 2013, based on the data of the distribution of employment and residence areas, determine the employment-residence population flow data between every two sub-areas.

[0064] In a specific embodiment, the determining of the employment-residence population flow data between every two sub-areas based on the data of the distribution of employment and residence areas may include:

[0065] 1) Based on the data of the distribution of employment and residence areas, determine the number of employed residents between every two sub-areas;

[0066] 2) Based on the number of employed residents between every two sub-areas, determine the employment-residence population flow data between every two sub-areas.

[0067] In a specific embodiment, the employment-residence population flow data between every two sub-areas may characterize the population flow data with one of the two sub-areas as the residential sub-area and the other as the working sub-area. Specifically, the employment-residence population flow data between every two sub-areas may include: the first population flow data from the residential sub-area to the working sub-area, and the second population flow data from the working sub-area to the residential sub-area.

[0068] In practical applications, the temporal information entropy of the positioning data is about 1, which means that the user has an obvious pattern of moving back and forth between two places (i.e., the place of residence and the place of work). Therefore, if the pair of OD of the place of residence and the place of work is identified, then about 50% of the overall trips can be covered, especially most of the travel demands during the morning and evening rush hours that urgently need traffic management. Therefore, in the embodiments of the present application, the first population flow data from the residential sub-area to the working sub-area and the second population flow data from the working sub-area to the residential sub-area are respectively counted according to the morning rush-hour corresponding work commute scenario and the evening rush-hour corresponding home commute scenario.

[0069] It should be understood that the residential sub - region and the work sub - region here are relative concepts. One of every two sub - regions can be the residential sub - region of a part of the people flow, or the work sub - region of another part of the people flow. Schematically, one of every two sub - regions can be sub - region 1, and the other sub - region can be sub - region 2. The "1" and "2" here are only used to distinguish the two sub - regions. The commuting people flow between sub - region 1 and sub - region 2 can include: the first type of commuting people flow with sub - region 1 as the work sub - region and sub - region 2 as the residential sub - region, and the second type of commuting people flow with sub - region 2 as the work sub - region and sub - region 1 as the residential sub - region. Then, the first people flow data between sub - region 1 and sub - region 2 can include: the people flow data of the first type of commuting people flow from its own residential sub - region (sub - region 2) to its own work sub - region (sub - region 1) and the people flow data of the second type of commuting people flow from its own residential sub - region (sub - region 1) to its own work sub - region (sub - region 2). The second people flow data between sub - region 1 and sub - region 2 can include: the people flow data of the first type of commuting people flow from its own work sub - region (sub - region 1) to its own residential sub - region (sub - region 2) and the people flow data of the second type of commuting people flow from its own work sub - region (sub - region 2) to its own residential sub - region (sub - region 1).

[0070] In an optional embodiment, the people flow data here can be the number of people flow. Correspondingly, determining the commuting people flow data between every two sub - regions based on the number of commuting people between every two sub - regions can include: taking the number of commuting people between every two sub - regions as the number of commuting people flow between every two sub - regions. Schematically, the first people flow number between sub - region 1 and sub - region 2 can include: the number of people flow from its own residential sub - region (sub - region 2) to its own work sub - region (sub - region 1) (i.e., the number of commuting people m living in sub - region 2 and working in sub - region 1) and the number of people flow from its own residential sub - region (sub - region 1) to its own work sub - region (sub - region 2) (i.e., the number of commuting people n living in sub - region 1 and working in sub - region 2). The second people flow number between sub - region 1 and sub - region 2 can include: the number of people flow from its own work sub - region (sub - region 1) to its own residential sub - region (sub - region 2) (i.e., the number of commuting people m) and the number of people flow from its own work sub - region (sub - region 2) to its own residential sub - region (sub - region 1) (i.e., the number of commuting people n).

[0071] Schematically, as Figure 3a shown, the target area can include: 9 sub - regions, namely 9 grids τ1, τ2,..., τ9. The number of commuting people between every two of the 9 grids is respectively counted, and as Figure 3bThe shown job-housing matrix. For example, the number of people living and working between grid τ8 and grid τ9 may include: 50 people living in τ9 and working in τ8, and 120 people living in τ8 and working in τ9. The number of people living and working between grid τ7 and grid τ8 may include: 42 people living in τ7 and working in τ8, and 10 people living in τ8 and working in τ7.

[0072] In an alternative embodiment, when the area of the sub-region is small (for example, the side length scale of the grid is small), the traffic demand degree of the people flow with the place of residence and the place of work in the same sub-region is small. Therefore, the job-housing people flow data of this type may not be statistically analyzed. Correspondingly, as Figure 3b shown, the diagonal data in the job-housing matrix is empty or 0.

[0073] As can be seen from the above embodiments, based on the terminal positioning data, the job-housing areas of multiple target objects corresponding to multiple object terminals are analyzed to determine the job-housing area distribution data of the multiple target objects, and based on the job-housing area distribution data, the job-housing people flow data between every two sub-regions is determined, which can effectively improve the accuracy and authenticity of the job-housing people flow data collection.

[0074] In the embodiments of this specification, the intersection transfer path may be a path obtained based on the one-way transfer relationship between multiple passing intersections corresponding to the target one-way traffic directions of every two sub-regions. Specifically, the target one-way traffic directions may include: the direction from the residential sub-region to the working sub-region, and the direction from the working sub-region to the residential sub-region.

[0075] It should be understood that the target one-way traffic direction here is also a relative concept. Schematically, for the first type of job-housing people flow with sub-region 1 as the working sub-region and sub-region 2 as the residential sub-region, the direction from the residential sub-region to the working sub-region is from sub-region 2 to sub-region 1, and the direction from the working sub-region to the residential sub-region is from sub-region 1 to sub-region 2; for the second type of job-housing people flow with sub-region 2 as the working sub-region and sub-region 1 as the residential sub-region, the direction from the residential sub-region to the working sub-region is from sub-region 1 to sub-region 2, and the direction from the working sub-region to the residential sub-region is from sub-region 2 to sub-region 1.

[0076] In a specific embodiment, as Figure 4 shown, the intersection transfer path between every two sub-regions above is obtained through the following method:

[0077] S401, Plan the passing paths for every two sub-regions to obtain the initial intersection transfer path between every two sub-regions.

[0078] In practical applications, since there is a high similarity in the group selection of commuting paths, the one-way commuting path between every two sub-regions can be considered unique. In the embodiments of the present application, the path planning algorithm in the prior art can be used to plan the passing paths for every two sub-regions, and the initial intersection transfer paths between every two sub-regions can be obtained.

[0079] Specifically, the initial intersection transfer path can be a path composed of multiple passing intersections sorted in order based on the target one-way passing direction corresponding to every two sub-regions. Among them, there is a one-way transfer path between two adjacent passing intersections. Exemplarily, using θ to represent intersections, taking the grid τ1 and the grid τ2 in Figure 3a as an example, the initial intersection transfer path p from the grid τ1 to the grid τ2 1→2 is represented as Here, the multiple passing intersections sorted in order can be respectively: and Among them, and there is a one-way transfer path between them, and there is a one-way transfer path between them, and there is a one-way transfer path between them.

[0080] S402. Based on the distance data between two target intersections, determine the local correlation index between the two target intersections. The two target intersections are any two non-adjacent intersections in the initial intersection transfer path.

[0081] Specifically, the distance data between the two target intersections here can be the straight-line distance data of the two target intersections on the map. Schematically, the straight-line distance data can include: Euclidean distance.

[0082] Specifically, the local correlation index can characterize the local traffic correlation degree between the two target intersections. Generally, the local correlation index is negatively correlated with the distance data. The smaller the value corresponding to the distance data, the larger the local correlation index.

[0083] S403. When the local correlation index meets the preset correlation condition, based on the passing order of the two target intersections in the initial intersection transfer path, add the one-way transfer path between the two target intersections in the initial intersection transfer path to obtain the intersection transfer path.

[0084] In a specific embodiment, the preset correlation condition can be preset in combination with the traffic correlation requirements in practical applications. Schematically, the preset correlation condition can be that the local correlation index is greater than the preset index threshold.

[0085] In an alternative embodiment, since the local association index is negatively correlated with the distance data, the fact that the above local association index is greater than the preset index threshold can also be understood as the distance data being less than the preset distance threshold.

[0086] Exemplarily, for the above-mentioned initial intersection transfer path p 1→2 , non-adjacent intersections and the straight-line distance between and are both greater than the threshold c, but and the straight-line distance between 1→2 is less than c. Therefore, in the initial intersection transfer path p to a one-way transfer path is added, resulting in the intersection transfer path P as shown in Figure 5a . 1→2 .

[0087] As can be seen from the above embodiments, for each pair of sub-regions, a travel path is planned to obtain the initial intersection transfer path between each pair of sub-regions. Based on the distance data between two target intersections, the local association index between the two target intersections is determined. When the local association index meets the preset association condition, based on the passing order of the two target intersections in the initial intersection transfer path, a one-way transfer path between the two target intersections is added to the initial intersection transfer path to obtain the intersection transfer path, which can effectively improve the rationality and authenticity of the intersection transfer path in representing the commuting intersection association relationship between two sub-regions.

[0088] S202. Based on the commuting population flow data and the intersection transfer path, construct a road intersection relationship structure diagram of the target area. The nodes of the road intersection relationship structure diagram include multiple intersection nodes corresponding to the intersection transfer path. The edges of the road intersection relationship structure diagram are used to represent the transfer paths between multiple intersection nodes, and the weights of the edges are used to represent the traffic demand degree of the transfer paths. The traffic demand degree is positively correlated with the corresponding numerical size of the commuting population flow data on the transfer path.

[0089] In a specific embodiment, as shown in Figure 6 , the above-mentioned construction of the road intersection relationship structure diagram of the target area based on the commuting population flow data and the intersection transfer path may include:

[0090] S601. Using the intersections in the intersection transfer path between each pair of sub-regions as intersection nodes and the one-way transfer paths in the intersection transfer path as directed edges, construct an intersection relationship sub-graph corresponding to each pair of sub-regions. The weight of the directed edge is determined based on the commuting population flow data between each pair of sub-regions.

[0091] In a specific embodiment, the magnitude of the directed edge weight is positively correlated with the corresponding numerical magnitude of the commuting population flow data between every two sub-regions. Specifically, the number of commuting population flows on the one-way transfer path between every two sub-regions can be used as the directed edge weight in the intersection relationship sub-graph corresponding to every two sub-regions.

[0092] S602. Merge the intersection relationship sub-graphs corresponding to every two sub-regions among multiple sub-regions to obtain an intersection relationship structure diagram.

[0093] As can be seen from the above embodiments, taking the intersections in the intersection transfer paths between every two sub-regions as intersection nodes, and taking the one-way transfer paths in the intersection transfer paths as directed edges, constructing the intersection relationship sub-graph corresponding to every two sub-regions, and merging the intersection relationship sub-graphs corresponding to every two sub-regions among multiple sub-regions to obtain an intersection relationship structure diagram can improve the accuracy of the intersection relationship structure diagram in representing the intersection relationship of the target region on the basis of improving the accuracy of the intersection relationship sub-graph in representing the intersection relationship of the corresponding two sub-regions.

[0094] In a specific embodiment, the above-mentioned merging process of the intersection relationship sub-graphs corresponding to every two sub-regions among multiple sub-regions to obtain an intersection relationship structure diagram may include:

[0095] S603. Merge the same intersection nodes in the intersection relationship sub-graphs corresponding to every two sub-regions to obtain multiple intersection nodes in the intersection relationship structure diagram;

[0096] S604. Perform weight merging on the same directed edges in the intersection relationship sub-graphs corresponding to every two sub-regions to obtain the edges and the weights of the edges in the intersection relationship structure diagram.

[0097] Exemplarily, in the morning commute scenario, the number of commuting population flows from grid τ1 to grid τ2 is 50, and the intersection transfer path P from grid τ1 to grid τ2 1→2 such as Figure 5a to obtain as Figure 7a shown in the sub- Figure 1 , the number of commuting population flows from grid τ1 to grid τ3 is 20, and the intersection transfer path P from grid τ1 to grid τ3 1→3 such as Figure 5b shown to obtain as Figure 7b shown in the sub- Figure 2 . Merge sub- Figure 1 and sub- Figure 2 by performing the merging process of S603 to S604 to obtain the intersection relationship structure diagram as Figure 7c shown.

[0098] As can be seen from the above embodiments, by merging the sub-graphs of intersection relationships corresponding to every two sub-regions among multiple sub-regions to obtain an intersection relationship structure diagram, an intersection relationship structure diagram of the target region with higher accuracy can be obtained on the basis of comprehensively considering the intersection relationships between sub-regions.

[0099] S203. Based on the intersection random walk model corresponding to the intersection relationship structure diagram, perform a traffic probability analysis on multiple intersection nodes in the intersection relationship structure diagram to obtain intersection importance indicators corresponding to the multiple intersection nodes.

[0100] In the embodiments of this specification, the intersection importance indicator is used to characterize the importance degree of the corresponding intersection node in the traffic travel of the target region.

[0101] Specifically, after constructing the relationship between intersections into a structure diagram, the problem of finding key intersections can be regarded as a problem of detecting the traffic probability of intersection nodes (that is, when there are N intersection nodes, which intersection node to choose for passage, and the sum of the traffic probabilities of the N intersection nodes is 1). Obviously, the intersection nodes with higher traffic probabilities will affect more intersections and the commuting behaviors of more people. In the embodiments of this application, the traffic probability of each intersection node is measured using the random walk feature, so as to obtain the intersection importance indicator corresponding to each intersection node.

[0102] In the embodiments of this specification, various intersection random walk models in the prior art can be used to perform a traffic probability analysis on multiple intersection nodes in the intersection relationship structure diagram. In a specific embodiment, as Figure 8 shown, the intersection random walk model can be obtained through the following method:

[0103] S205. Based on the intersection relationship structure diagram, perform an intersection node transfer analysis to determine the basic transfer probability data and the random transfer probability data. The basic transfer probability data is used to characterize the one-way transfer probability between any intersection node among multiple intersection nodes and its outgoing intersection nodes, and the random transfer probability data is used to characterize the one-way transfer probability between any two intersection nodes among multiple intersection nodes.

[0104] In a specific embodiment, based on the one-way transfer paths between multiple intersection nodes in the intersection relationship structure diagram and the weights of the directed edges corresponding to the one-way transfer paths, perform a node transfer analysis on the multiple intersection nodes to obtain the basic transfer probability data and the random transfer probability data.

[0105] In a specific embodiment, the basic transfer probability data and the random transfer probability data may be in the form of matrices. Exemplarily, there are N intersection nodes in the intersection relationship structure diagram, and both the basic transfer probability data and the random transfer probability data can be represented as N×N matrices. The elements in the basic transfer probability data can be represented as: Where W represents the directed edge weight, a ∈ [1, N], b ∈ [1, N], and G represents the set of outgoing intersection nodes (i.e., the directly pointed intersection nodes) of intersection node a; the elements in the random transition probability data can be expressed as: Where a ∈ [1, N], b ∈ [1, N].

[0106] S206. Based on a preset probability adjustment factor, perform weighted processing on the basic transition probability data and the random transition probability data to determine the intersection random walk model.

[0107] Specifically, the preset probability adjustment factor can be used to adjust the traffic transition probability of intersection nodes. In an optional embodiment, the value range of the preset probability adjustment factor is 0 to 1.

[0108] Schematically, the preset probability adjustment factor is denoted as d, the basic transition probability data is denoted as P, and the random transition probability data is denoted as Q. Then the intersection random walk model can be expressed as: d×P + (1 - d)×Q. At this time, d represents the probability of continuing to move to the outgoing intersection nodes of a certain intersection node when reaching that intersection node, and (1 - d) represents the probability of randomly moving to other intersection nodes when reaching a certain intersection node.

[0109] As can be seen from the above embodiments, according to the one-way transfer paths and the one-way transfer path corresponding directed edge weights between multiple intersection nodes in the intersection relationship structure diagram, perform node transfer analysis on multiple intersection nodes, determine the basic transition probability data and the random transition probability data, and based on the preset probability adjustment factor, adjust the traffic transition probability of intersection nodes to determine the intersection random walk model, which can effectively improve the applicability of the intersection random walk model to the intersection transfer situation in the target area, thereby improving the practicality of the subsequent traffic probability analysis situation.

[0110] In a specific embodiment, as Figure 9 shown, for the above intersection random walk model corresponding to the intersection relationship structure diagram, performing traffic probability analysis on multiple intersection nodes in the intersection relationship structure diagram, the obtained intersection importance indicators corresponding to multiple intersection nodes may include:

[0111] S901. Determine the initial traffic probability data corresponding to multiple intersection nodes.

[0112] Specifically, the initial traffic probability data corresponding to multiple intersection nodes can represent the initial set values of the respective initial traffic probabilities of multiple intersection nodes. In an optional embodiment, in the case of N intersection nodes, the value of the initial traffic probability data of each intersection node can be 1 / N.

[0113] S902. Randomly walk through multiple intersection nodes based on the initial passing probability data and the intersection random walk model to obtain updated passing probability data corresponding to the multiple intersection nodes.

[0114] S903. Based on the updated passing probability data, repeatedly execute the step of randomly walking through multiple intersection nodes based on the initial passing probability data and the intersection random walk model to obtain updated passing probability data corresponding to the multiple intersection nodes until a preset iteration termination condition is reached.

[0115] In an optional embodiment, the preset iteration termination condition may be that the difference between the current passing probability data and the previous passing probability data is less than a preset difference.

[0116] S904. Conduct importance analysis on multiple intersection nodes based on the updated passing probability data obtained when reaching the preset iteration termination condition to obtain intersection importance indicators.

[0117] In a specific embodiment, the numerical value of the intersection importance indicator corresponding to each intersection node is positively correlated with the numerical value of the updated passing probability data corresponding to each intersection node obtained when reaching the preset iteration termination condition.

[0118] In a specific embodiment, the intersection importance indicator corresponding to the i-th intersection node among N intersection nodes can be expressed by the following formula:

[0119]

[0120] Where L in (θ i ) represents the intersection nodes connected to θ i , L out (θ j ) represents the intersection nodes connected out of θ j , d is a preset probability adjustment factor. Therefore, the intersection importance indicator corresponding to the i-th intersection node can be the weighted sum of the intersection importance indicators of all intersection nodes connected to θ i .

[0121] As can be seen from the above embodiments, by performing passing probability analysis on multiple intersection nodes in the intersection relationship structure diagram based on the intersection random walk model corresponding to the intersection relationship structure diagram to obtain intersection importance indicators corresponding to the multiple intersection nodes, the accuracy of the intersection importance indicators can be effectively improved.

[0122] S204. Perform traffic planning processing on multiple intersection nodes based on the intersection importance indicators.

[0123] Specifically, based on the intersection importance index, multiple intersection nodes can be sorted to obtain the target node order, and traffic planning processing can be performed on the multiple intersection nodes according to the target node order. The traffic planning processing here can include, but is not limited to: selecting important intersections for optimization and management, selecting important intersections to invest in Internet of Things devices, etc.

[0124] In an alternative embodiment, terminal location data corresponding to multiple object terminals in the target area can be collected every preset collection period, so as to update the commuting population flow data between every two sub-regions, thereby updating the intersection importance index in the target area and improving the accuracy and real-time performance of traffic planning processing.

[0125] In an alternative embodiment, as Figure 10 shown, every two of the above-mentioned sub-regions may include: a work sub-region and a residential sub-region. The above-mentioned commuting population flow data may include: first population flow data from the residential sub-region to the work sub-region and second population flow data from the work sub-region to the residential sub-region. The above-mentioned intersection transfer path may include: a first target path from the residential sub-region to the work sub-region and a second target path from the work sub-region to the residential sub-region. Taking the intersections in the intersection transfer path between every two sub-regions as intersection nodes and the one-way transfer paths in the intersection transfer path as directed edges, constructing the intersection relationship sub-graph corresponding to every two sub-regions may include:

[0126] S6011, taking the intersections in the first target path as intersection nodes and the one-way transfer paths in the first target path as directed edges, constructing the first sub-graph corresponding to every two sub-regions. The weight of the directed edge of the first sub-graph is determined based on the first population flow data;

[0127] S6012, taking the intersections in the second target path as intersection nodes and the intersection transfer paths in the second target path as directed edges, constructing the second sub-graph corresponding to every two sub-regions. The weight of the directed edge of the second sub-graph is determined based on the second population flow data;

[0128] S6013, taking the first sub-graph and the second sub-graph as the intersection relationship sub-graph;

[0129] Correspondingly, the above-mentioned merging process of the intersection relationship sub-graphs corresponding to every two sub-regions among multiple sub-regions to obtain the intersection relationship structure diagram may include:

[0130] S6021, merging the first sub-graphs corresponding to every two sub-regions among multiple sub-regions to obtain the first intersection relationship structure diagram;

[0131] S6022, merging the second sub-graphs corresponding to every two sub-regions among multiple sub-regions to obtain the second intersection relationship structure diagram;

[0132] S6023. Use the first intersection relationship structure diagram and the second intersection relationship structure diagram as the intersection relationship structure diagram.

[0133] Correspondingly, the above intersection random walk model may include: a first random walk model corresponding to the first intersection relationship structure diagram and a second random walk model corresponding to the second intersection relationship structure diagram. The above analysis of the passing probabilities of multiple intersection nodes in the intersection relationship structure diagram based on the intersection random walk model corresponding to the intersection relationship structure diagram to obtain the intersection importance indicators corresponding to the multiple intersection nodes may include:

[0134] S2031. Based on the first random walk model, analyze the passing probabilities of multiple first intersection nodes in the first intersection relationship structure diagram to obtain the first intersection importance indicators corresponding to the multiple first intersection nodes;

[0135] S2032. Based on the second random walk model, analyze the passing probabilities of multiple second intersection nodes in the second intersection relationship structure diagram to obtain the second intersection importance indicators corresponding to the multiple second intersection nodes;

[0136] S2033. Based on the first intersection importance indicators corresponding to the multiple first intersection nodes and the second intersection importance indicators corresponding to the multiple second intersection nodes, obtain the intersection importance indicators corresponding to the multiple intersection nodes.

[0137] As can be seen from the above embodiments, by separately counting the pedestrian flow data and intersection transfer paths according to the morning rush hour corresponding to the morning commute scenario and the evening rush hour corresponding to the evening commute scenario, constructing the intersection relationship structure diagrams for the morning commute scenario and the evening commute scenario respectively, and thus obtaining the intersection importance indicators for the morning commute scenario and the evening commute scenario respectively, it is possible to improve the rationality and efficiency of traffic planning on the basis of enhancing the time-period specificity of intersection importance.

[0138] Using the technical solution provided by the present application, divide the target area into sub-areas, collect the commuting pedestrian flow data and intersection transfer paths between every two sub-areas, and based on the commuting pedestrian flow data and intersection transfer paths, construct the intersection relationship structure diagram of the target area. The nodes of this intersection relationship structure diagram include multiple intersection nodes corresponding to the intersection transfer paths. The edges of this intersection relationship structure diagram are used to represent the transfer paths between multiple intersection nodes, and the weights of the edges are used to represent the passing demand degrees of the transfer paths. Then, based on the intersection random walk model corresponding to this intersection relationship structure diagram, analyze the passing probabilities of multiple intersection nodes to obtain the intersection importance indicators corresponding to each of the multiple intersection nodes. This solution takes individuals as the basic unit and evaluates the intersection importance based on the fixed travel demands of individuals, truly and reasonably reflecting the traffic demands within the target area, thereby improving the accuracy of the intersection importance indicators and the rationality and resource utilization rate of traffic planning.

[0139] An embodiment of the present application further provides an information processing device, as Figure 11 shown. The information processing device may include:

[0140] A data acquisition module 1110, configured to acquire the commuting population flow data between every two sub-regions in multiple sub-regions within a target region and the intersection transfer paths between every two sub-regions;

[0141] An intersection relationship structure diagram construction module 1120, configured to construct an intersection relationship structure diagram of the target region based on the commuting population flow data and the intersection transfer paths. The nodes of the intersection relationship structure diagram include multiple intersection nodes corresponding to the intersection transfer paths, and the edges of the intersection relationship structure diagram are used to represent the transfer paths between multiple intersection nodes. The weight of the edge is used to represent the traffic demand degree of the transfer path, and the traffic demand degree is positively correlated with the corresponding numerical size of the commuting population flow data on the transfer path;

[0142] A traffic probability analysis module 1130, configured to perform a traffic probability analysis on multiple intersection nodes in the intersection relationship structure diagram based on the intersection random walk model corresponding to the intersection relationship structure diagram, and obtain the intersection importance indicators corresponding to the multiple intersection nodes;

[0143] A traffic planning module 1140, configured to perform traffic planning processing on multiple intersection nodes based on the intersection importance indicators.

[0144] In a specific embodiment, the above data acquisition module 1110 may include:

[0145] A terminal positioning data acquisition unit, configured to acquire the terminal positioning data corresponding to multiple object terminals within the target region;

[0146] A commuting and living area analysis unit, configured to perform a commuting and living area analysis on multiple target objects corresponding to multiple object terminals based on the terminal positioning data, and determine the commuting and living area distribution data of the multiple target objects;

[0147] A commuting population flow data determination unit, configured to determine the commuting population flow data between every two sub-regions based on the commuting and living area distribution data.

[0148] In a specific embodiment, the above data acquisition module 1110 may include:

[0149] A traffic path planning unit, configured to perform a traffic path planning on every two sub-regions to obtain the initial intersection transfer paths between every two sub-regions;

[0150] A local association index analysis unit, configured to determine the local association index between two target intersections based on the distance data between the two target intersections, where the two target intersections are any two non-adjacent intersections in the initial intersection transfer paths;

[0151] A one-way transfer path adding unit, configured to, when a local association index meets a preset association condition, add a one-way transfer path between two target road intersections in an initial road intersection transfer path based on the passing order of the two target road intersections in the initial road intersection transfer path, so as to obtain a road intersection transfer path.

[0152] In a specific embodiment, the above-mentioned road intersection relationship structure diagram construction module 1120 may include:

[0153] A road intersection relationship sub-diagram construction unit, configured to use the road intersections in the road intersection transfer path between every two sub-regions as road intersection nodes, use the one-way transfer paths in the road intersection transfer path as directed edges, construct a road intersection relationship sub-diagram corresponding to every two sub-regions, and the weight of the directed edge is determined based on the employment-residence population flow data between every two sub-regions;

[0154] A sub-diagram merging unit, configured to perform a merging process on the road intersection relationship sub-diagrams corresponding to every two sub-regions among multiple sub-regions to obtain a road intersection relationship structure diagram.

[0155] In a specific embodiment, the above-mentioned sub-diagram merging unit may include:

[0156] A node merging unit, configured to perform a merging process on the same road intersection nodes in the road intersection relationship sub-diagrams corresponding to every two sub-regions to obtain multiple road intersection nodes in the road intersection relationship structure diagram;

[0157] A directed edge merging unit, configured to perform a weight merging on the same directed edges in the road intersection relationship sub-diagrams corresponding to every two sub-regions to obtain the edges and the weights of the edges in the road intersection relationship structure diagram.

[0158] In a specific embodiment, the above-mentioned device may further include:

[0159] A road intersection node transfer analysis module, configured to perform a road intersection node transfer analysis based on the road intersection relationship structure diagram to determine basic transfer probability data and random transfer probability data, where the basic transfer probability data is used to represent the one-way transfer probability between any road intersection node and the road intersection nodes connected out by itself among multiple road intersection nodes, and the random transfer probability data is used to represent the one-way transfer probability between any two road intersection nodes among multiple road intersection nodes;

[0160] A road intersection random walk model determination module, configured to perform a weighted process on the basic transfer probability data and the random transfer probability data based on a preset probability adjustment factor to determine a road intersection random walk model.

[0161] In a specific embodiment, the above-mentioned passing probability analysis module 1130 may include:

[0162] An initial passing probability data determination unit, configured to determine initial passing probability data corresponding to multiple road intersection nodes;

[0163] A random walk unit, configured to perform random walks on multiple intersection nodes based on initial passing probability data and an intersection random walk model, to obtain updated passing probability data corresponding to the multiple intersection nodes;

[0164] An iterative execution unit, configured to, based on the updated passing probability data, repeatedly execute the step of performing random walks on multiple intersection nodes based on the initial passing probability data and the intersection random walk model to obtain updated passing probability data corresponding to the multiple intersection nodes, until a preset iteration termination condition is reached;

[0165] An intersection node importance analysis unit, configured to perform importance analysis on multiple intersection nodes based on the updated passing probability data obtained when the preset iteration termination condition is reached, to obtain intersection importance indicators.

[0166] In an optional embodiment, every two of the above-mentioned sub-regions may include: a working sub-region and a residential sub-region, the above-mentioned commuting population flow data may include: first population flow data from the residential sub-region to the working sub-region and second population flow data from the working sub-region to the residential sub-region, the above-mentioned intersection transfer paths may include: a first target path from the residential sub-region to the working sub-region and a second target path from the working sub-region to the residential sub-region, and the above-mentioned intersection relationship sub-graph construction unit may include:

[0167] A first sub-graph construction unit, configured to use the intersections in the first target path as intersection nodes and the one-way transfer paths in the first target path as directed edges to construct a first sub-graph corresponding to every two sub-regions, and the weight of the directed edges of the first sub-graph is determined based on the first population flow data;

[0168] A second sub-graph construction unit, configured to use the intersections in the second target path as intersection nodes and the intersection transfer paths in the second target path as directed edges to construct a second sub-graph corresponding to every two sub-regions, and the weight of the directed edges of the second sub-graph is determined based on the second population flow data;

[0169] An intersection relationship sub-graph determination unit, configured to use the first sub-graph and the second sub-graph as the intersection relationship sub-graph;

[0170] Correspondingly, the above-mentioned sub-graph merging unit may include:

[0171] A first sub-graph merging unit, configured to perform a merging process on the first sub-graphs corresponding to every two sub-regions among multiple sub-regions to obtain a first intersection relationship structure diagram;

[0172] A second sub-graph merging unit, configured to perform a merging process on the second sub-graphs corresponding to every two sub-regions among multiple sub-regions to obtain a second intersection relationship structure diagram;

[0173] An intersection relationship structure diagram determination unit, configured to use the first intersection relationship structure diagram and the second intersection relationship structure diagram as the intersection relationship structure diagram.

[0174] In an alternative embodiment, the above intersection random walk model may include: a first random walk model corresponding to the first intersection relationship structure diagram and a second random walk model corresponding to the second intersection relationship structure diagram. The above traffic probability analysis module 1130 may include:

[0175] A first intersection importance index unit, configured to perform traffic probability analysis on a plurality of first intersection nodes in the first intersection relationship structure diagram based on the first random walk model, and obtain first intersection importance indexes corresponding to the plurality of first intersection nodes;

[0176] A second intersection importance index unit, configured to perform traffic probability analysis on a plurality of second intersection nodes in the second intersection relationship structure diagram based on the second random walk model, and obtain second intersection importance indexes corresponding to the plurality of second intersection nodes;

[0177] An intersection importance index determination unit, configured to obtain intersection importance indexes corresponding to a plurality of intersection nodes based on the first intersection importance indexes corresponding to the plurality of first intersection nodes and the second intersection importance indexes corresponding to the plurality of second intersection nodes.

[0178] It should be noted that the devices in the device embodiments and the method embodiments are based on the same inventive concept.

[0179] An embodiment of the present application provides an information processing device, which includes a processor and a memory. At least one instruction or at least one program segment is stored in the memory, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement the information processing method provided in the above method embodiment.

[0180] Furthermore, Figure 12 A hardware structure diagram of an information processing device for implementing the information processing method provided in the embodiment of the present application is shown. The information processing device may participate in forming or include the information processing device provided in the embodiment of the present application. As Figure 12As shown, the information processing device 120 may include one or more processors 1202 (shown as 1202a, 1202b, ……, 1202n in the figure) (the processor 1202 may include, but is not limited to, processing devices such as a microprocessor MCU or a programmable logic device FPGA), a memory 1204 for storing data, and a transmission device 1206 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 12 the structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the information processing device 120 may further include more or fewer components than those Figure 12 shown in, or have a different configuration from that Figure 12 shown.

[0181] It should be noted that the above one or more processors 1202 and / or other data processing circuits can generally be referred to as "data processing circuits" herein. The data processing circuit can be embodied in software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of the other elements in the information processing device 120 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is used for processor control (such as the selection of a variable resistor terminal path connected to an interface).

[0182] The memory 1204 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the information processing method described in the embodiments of the present application. The processor 1202 executes various functional applications and data processing by running the software programs and modules stored in the memory 1204, that is, implements the above-mentioned information processing method. The memory 1204 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 1204 may further include a memory remotely disposed relative to the processor 1202, and these remote memories can be connected to the information processing device 120 through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0183] The transmission device 1206 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by the communication provider of the information processing device 120. In one example, the transmission device 1206 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one embodiment, the transmission device 1206 may be a Radio Frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0184] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables the user to interact with the user interface of the information processing device 120 (or mobile device).

[0185] Embodiments of the present application also provide a computer-readable storage medium, which can be disposed in the information processing device to store at least one instruction or at least one segment of program related to the information processing method in the method embodiment. The at least one instruction or the at least one segment of program is loaded and executed by the processor to implement the information processing method provided in the above method embodiment.

[0186] Optionally, in this embodiment, the above storage medium may be located in at least one of multiple network servers in a computer network. Optionally, in this embodiment, the above storage medium may include, but is not limited to: various media that can store program codes such as USB flash drives, read-only memories (ROM), random access memories (RAM), mobile hard disks, magnetic disks, or optical discs.

[0187] Embodiments of the present application also provide a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the information processing method provided in the method embodiment. The computer program product or the computer program can be applied to fields such as video calls and short videos to perform tasks such as assisting video recommendations, and effectively and quickly measure the current video quality.

[0188] It should be noted that the above-mentioned sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. Moreover, the above-mentioned specific embodiments of the present application have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0189] Each embodiment in the present application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device and equipment embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiments.

[0190] Those of ordinary skill in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware, or can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc.

[0191] In the embodiments of the present application, the term "module" or "unit" refers to a computer program with a predetermined function or a part of a computer program, which works together with other relevant parts to achieve a predetermined goal, and can be fully or partially implemented by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of the overall module or unit that includes the function of the module or unit.

[0192] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An information processing method, characterized in that The method includes: Obtaining the commuting population flow data between every two sub-regions in multiple sub-regions within the target area and the intersection transfer paths between every two sub-regions; Based on the commuting population flow data and the intersection transfer paths, constructing an intersection relationship structure diagram of the target area. The nodes of the intersection relationship structure diagram include multiple intersection nodes corresponding to the intersection transfer paths. The edges of the intersection relationship structure diagram are used to represent the transfer paths between the multiple intersection nodes, and the weights of the edges are used to represent the traffic demand degree of the transfer paths. The traffic demand degree is positively correlated with the corresponding numerical size of the commuting population flow data on the transfer paths; Based on the intersection random walk model corresponding to the intersection relationship structure diagram, performing a traffic probability analysis on multiple intersection nodes in the intersection relationship structure diagram to obtain the intersection importance indicators corresponding to the multiple intersection nodes; Based on the intersection importance indicators, performing traffic planning processing on the multiple intersection nodes.

2. The method according to claim 1, wherein The intersection transfer paths between every two sub-regions are obtained by the following method: Performing a traffic path planning on every two sub-regions to obtain the initial intersection transfer paths between every two sub-regions; Based on the distance data between two target intersections, determining the local correlation index between the two target intersections. The two target intersections are any two non-adjacent intersections in the initial intersection transfer paths; When the local correlation index meets the preset correlation condition, based on the passing order of the two target intersections in the initial intersection transfer paths, adding a one-way transfer path between the two target intersections in the initial intersection transfer paths to obtain the intersection transfer paths.

3. The method according to claim 1, characterized in that, The obtaining of the commuting population flow data between every two sub-regions in multiple sub-regions within the target area includes: Obtaining the terminal location data corresponding to multiple object terminals within the target area; Based on the terminal location data, performing a commuting area analysis on multiple target objects corresponding to the multiple object terminals to determine the commuting area distribution data of the multiple target objects; Based on the commuting area distribution data, determining the commuting population flow data between every two sub-regions.

4. The method according to claim 1, wherein The constructing of the intersection relationship structure diagram of the target area based on the commuting population flow data and the intersection transfer paths includes: Taking the intersections in the intersection transfer paths between every two sub-regions as intersection nodes and taking the one-way transfer paths in the intersection transfer paths as directed edges to construct an intersection relationship sub-diagram corresponding to every two sub-regions. The weight of the directed edge is determined based on the commuting population flow data between every two sub-regions; Performing a merging process on the intersection relationship sub-diagrams corresponding to every two sub-regions among the multiple sub-regions to obtain the intersection relationship structure diagram.

5. The method according to claim 4, wherein Each two sub-regions include: a working sub-region and a residential sub-region. The commuting flow data includes: first flow data from the residential sub-region to the working sub-region and second flow data from the working sub-region to the residential sub-region. The intersection transfer paths include: a first target path from the residential sub-region to the working sub-region and a second target path from the working sub-region to the residential sub-region. Constructing an intersection relationship sub-graph corresponding to each two sub-regions with the intersections in the intersection transfer paths between each two sub-regions as intersection nodes and the one-way transfer paths in the intersection transfer paths as directed edges includes: Constructing a first sub-graph corresponding to each two sub-regions with the intersections in the first target path as intersection nodes and the one-way transfer paths in the first target path as directed edges, and the weights of the directed edges of the first sub-graph are determined based on the first flow data; Constructing a second sub-graph corresponding to each two sub-regions with the intersections in the second target path as intersection nodes and the intersection transfer paths in the second target path as directed edges, and the weights of the directed edges of the second sub-graph are determined based on the second flow data; Taking the first sub-graph and the second sub-graph as the intersection relationship sub-graph; Correspondingly, the merging process of the intersection relationship sub-graphs corresponding to each two sub-regions among the multiple sub-regions to obtain the intersection relationship structure diagram includes: Merging the first sub-graphs corresponding to each two sub-regions among the multiple sub-regions to obtain a first intersection relationship structure diagram; Merging the second sub-graphs corresponding to each two sub-regions among the multiple sub-regions to obtain a second intersection relationship structure diagram; Taking the first intersection relationship structure diagram and the second intersection relationship structure diagram as the intersection relationship structure diagram.

6. The method according to claim 5, wherein The intersection random walk model includes: a first random walk model corresponding to the first intersection relationship structure diagram and a second random walk model corresponding to the second intersection relationship structure diagram. Based on the intersection random walk model corresponding to the intersection relationship structure diagram, analyzing the passing probabilities of multiple intersection nodes in the intersection relationship structure diagram to obtain the intersection importance indicators corresponding to the multiple intersection nodes includes: Based on the first random walk model, analyzing the passing probabilities of multiple first intersection nodes in the first intersection relationship structure diagram to obtain the first intersection importance indicators corresponding to the multiple first intersection nodes; Based on the second random walk model, analyzing the passing probabilities of multiple second intersection nodes in the second intersection relationship structure diagram to obtain the second intersection importance indicators corresponding to the multiple second intersection nodes; Based on the first intersection importance indicators corresponding to the multiple first intersection nodes and the second intersection importance indicators corresponding to the multiple second intersection nodes, obtaining the intersection importance indicators corresponding to the multiple intersection nodes.

7. The method according to claim 4, characterized in that The merging process of the intersection relationship sub-graphs corresponding to each two sub-regions among the multiple sub-regions to obtain the intersection relationship structure diagram includes: Merge the same intersection nodes in the intersection relationship subgraphs corresponding to every two of the sub-regions to obtain multiple intersection nodes in the intersection relationship structure diagram; Merge the weights of the same directed edges in the intersection relationship subgraphs corresponding to every two of the sub-regions to obtain the edges in the intersection relationship structure diagram and the weights of the edges.

8. The method according to claim 1, characterized in that, Before performing the passing probability analysis on multiple intersection nodes in the intersection relationship structure diagram based on the intersection random walk model corresponding to the intersection relationship structure diagram to obtain the intersection importance indicators corresponding to the multiple intersection nodes, the method further includes: Perform intersection node transfer analysis based on the intersection relationship structure diagram to determine basic transfer probability data and random transfer probability data, where the basic transfer probability data is used to represent the one-way transfer probability between any intersection node among the multiple intersection nodes and the intersection nodes connected out by itself, and the random transfer probability data is used to represent the one-way transfer probability between any two intersection nodes among the multiple intersection nodes; Based on a preset probability adjustment factor, perform weighted processing on the basic transfer probability data and the random transfer probability data to determine the intersection random walk model.

9. The method according to claim 1, characterized in that, The performing the passing probability analysis on multiple intersection nodes in the intersection relationship structure diagram based on the intersection random walk model corresponding to the intersection relationship structure diagram to obtain the intersection importance indicators corresponding to the multiple intersection nodes includes: Determine the initial passing probability data corresponding to the multiple intersection nodes; Based on the initial passing probability data and the intersection random walk model, perform random walk on the multiple intersection nodes to obtain the updated passing probability data corresponding to the multiple intersection nodes; Based on the updated passing probability data, repeat the step of performing random walk on the multiple intersection nodes based on the initial passing probability data and the intersection random walk model to obtain the updated passing probability data corresponding to the multiple intersection nodes until a preset iteration termination condition is reached; Based on the updated passing probability data obtained by reaching the preset iteration termination condition, perform importance analysis on the multiple intersection nodes to obtain the intersection importance indicators.

10. An information processing apparatus, characterized in that, The device includes: A data acquisition module, configured to acquire the commuting population flow data between every two of multiple sub-regions in a target region and the intersection transfer paths between every two of the sub-regions; An intersection relationship structure diagram construction module, configured to construct an intersection relationship structure diagram of the target region based on the commuting population flow data and the intersection transfer paths, where the nodes of the intersection relationship structure diagram include multiple intersection nodes corresponding to the intersection transfer paths, the edges of the intersection relationship structure diagram are used to represent the transfer paths between the multiple intersection nodes, and the weights of the edges are used to represent the passing demand degrees of the transfer paths, and the passing demand degree is positively correlated with the numerical size of the commuting population flow data on the transfer paths; A passing probability analysis module, configured to perform passing probability analysis on multiple intersection nodes in the intersection relationship structure diagram based on the intersection random walk model corresponding to the intersection relationship structure diagram to obtain the intersection importance indicators corresponding to the multiple intersection nodes; A traffic planning module for performing traffic planning processing on the multiple intersection nodes based on the intersection importance index.

11. An information processing apparatus, characterized in that, The device includes a processor and a memory, and at least one instruction or at least one program segment is stored in the memory, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement the information processing method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, At least one instruction or at least one program segment is stored in the storage medium, and the at least one instruction or the at least one program segment is loaded and executed by a processor to implement the information processing method according to any one of claims 1 to 9.

13. A computer program product, characterized in that, The computer program product includes at least one instruction or at least one program segment, and the at least one instruction or the at least one program segment is loaded and executed by a processor to implement the information processing method according to any one of claims 1 to 9.

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