Smart city market flow control method and internet of things system

Abstract

The embodiment of the specification provides a smart city market flow control method. The method comprises the following steps: receiving a query demand for an intended place initiated by a user based on a user platform; transmitting the query demand to a management platform based on a service platform, and generating a query instruction based on the management platform; the query instruction contains the regional position of the intended place; obtaining a query result according to the query instruction based on an object platform; the query result includes the current real-time flow of the intended place; adjusting the current real-time flow of the intended place based on an adjustment factor to determine the flow of the intended place at a future time, wherein the adjustment factor is determined based on a recommended index and its confidence degree output by a recommendation model, and the recommendation model determines the recommended index of a related place based on the current flow information of the related place and the relationship with the intended place; when the flow at the future time is greater than a preset threshold, the intended place is subjected to flow control.

Description

[0001] Case Analysis

[0002] This application is a divisional application of Chinese application filed on July 26, 2022, with application number 202210880906.1 entitled "Smart City Public Place Pedestrian Flow Statistics and Control Method and Internet of Things System". Technical Field

[0003] This specification relates to the field of the Internet of Things (IoT), and in particular to a smart city venue traffic management method and IoT system. Background Technology

[0004] With the continuous development of society and the economy, the number of travelers is increasing daily. Traffic congestion often occurs in various locations on the streets, affecting traffic flow and the user's travel experience.

[0005] Therefore, this paper aims to provide a method and IoT system for the statistics and control of pedestrian flow in public places in smart cities. This system leverages IoT and cloud platforms to improve the efficiency of pedestrian flow management. It also enables the statistical analysis of pedestrian flow in these areas and the management and control of pedestrian flow based on the statistics. This helps users plan their trips more effectively, reduces street congestion, and improves the user experience. Summary of the Invention

[0006] One embodiment of this specification provides a method for traffic control in smart city locations. The method includes: receiving a user's query request for a desired location based on a user platform; transmitting the query request to a management platform based on a service platform, and generating a query instruction based on the management platform and sending it to a sensor network platform; the query instruction includes the regional location of the desired location; obtaining a query result from the query instruction obtained by an object platform from the sensor network platform; the query result includes the current real-time traffic of the desired location; adjusting the current real-time traffic of the desired location based on an adjustment factor to determine the future traffic of the desired location, wherein the adjustment factor is determined based on a recommendation index and its confidence level output by a recommendation model, and the recommendation model determines the recommendation index of relevant locations based on the current pedestrian traffic information of relevant locations and their relationship with the desired location; and implementing traffic control for the desired location when the future traffic exceeds a preset threshold.

[0007] One embodiment of this specification provides a smart city location traffic management IoT system. The system includes a user platform, a service platform, a management platform, and an object platform. The user platform receives user-initiated query requests for desired locations. The service platform transmits the query requests to the management platform. The management platform generates a query instruction based on the query request and sends it to the sensor network platform. The query instruction includes the regional location of the desired location. The sensor network platform sends the query instruction to the object platform. The object platform obtains query results based on the query instructions. The query results include the current real-time traffic of the desired location. To obtain the query results, the object platform further adjusts the current real-time traffic of the desired location based on an adjustment factor to determine the future traffic of the desired location. The adjustment factor is determined based on a recommendation index and its confidence level output by a recommendation model. The recommendation model determines the recommendation index of relevant locations based on the current pedestrian traffic information of related locations and their relationship with the desired location. When the future traffic exceeds a preset threshold, traffic management is implemented for the desired location.

[0008] One embodiment of this specification provides a computer-readable storage medium that stores computer instructions that, when executed by a processor, implement a smart city location traffic control method. Attached Figure Description

[0009] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0010] Figure 1 This is a schematic diagram illustrating the application scenario of an IoT system for counting and controlling pedestrian flow in smart city public places, based on some embodiments of this specification.

[0011] Figure 2 This is an exemplary block diagram of an IoT system for counting and controlling pedestrian flow in smart city public places, as shown in some embodiments of this specification.

[0012] Figure 3 This is an exemplary flowchart of a smart city public place pedestrian flow statistics and control method according to some embodiments of this specification;

[0013] Figure 4 These are exemplary schematic diagrams illustrating the determination of relevant locations according to some embodiments of this specification;

[0014] Figure 5 This is an exemplary flowchart of a preset algorithm shown in some embodiments of this specification;

[0015] Figure 6 This is a table of edge betweenness centrality values ​​and total edge betweenness centrality values ​​shown in some embodiments of this specification;

[0016] Figure 7 This is an exemplary flowchart illustrating the determination of a control strategy for an intended location, as shown in some embodiments of this specification. Detailed Implementation

[0017] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0018] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0019] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0020] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0021] Figure 1 This is a schematic diagram illustrating the application scenario of an IoT system for counting and controlling pedestrian flow in smart city public places, based on some embodiments of this specification.

[0022] Application scenario 100 may include server 110, network 120, database 130, terminal device 140, user 150, and location 160. Server 110 may include processing device 112.

[0023] In some embodiments, the application scenario 100 of the public place pedestrian flow statistics and control IoT system can obtain query results for user query needs by implementing the methods and / or processes disclosed in this specification. For example, the processing device can receive a user's query request for a desired place based on the user platform; transmit the query request to the management platform based on the service platform, and generate a query instruction based on the management platform; the management platform can distribute the query instruction to the corresponding sensor network sub-platform of the sensor network platform according to the regional location; the sensor network sub-platform can send the query instruction to the corresponding object platform; the object platform can obtain the query results based on the query instructions; and the object platform can feed back the query results to the user platform through the corresponding sensor network sub-platform, management platform, and service platform respectively.

[0024] Server 110 and terminal device 140 can be connected via network 120, and server 110 can be connected to database 130 via network 120. Server 110 can be used to manage resources and process data and / or information from at least one component of the system or an external data source (e.g., a cloud data center). In some embodiments, server 110 can receive user-initiated queries for desired locations. During processing, server 110 can retrieve data from database 130 or save data to database 130. In some embodiments, server 110 can be a single server or a group of servers. In some embodiments, server 110 can be regional or remote. In some embodiments, server 110 can be implemented on a cloud platform or provided virtually.

[0025] In some embodiments, server 110 may include processing device 112. Processing device 112 may process data and / or information obtained from other devices or system components. The processor may execute program instructions based on this data, information, and / or processing results to perform one or more functions described herein. In some embodiments, processing device 112 may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core multi-chip processing device). By way of example only, processing device 112 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or any combination thereof.

[0026] Network 120 can connect the various components of application scenario 100 and / or connect the system to external resources. Network 120 enables communication between the components and with other parts outside the system, facilitating the exchange of data and / or information. In some embodiments, network 120 can be any one or more of wired or wireless networks. For example, network 120 may include cable networks, fiber optic networks, etc., or any combination thereof. Network connections between components can be made using one or more of the above methods. In some embodiments, the network can be a point-to-point, shared, centralized, or other topologies, or a combination of multiple topologies. In some embodiments, network 120 may include one or more network access points. In some embodiments, relevant data of user 150 and location 160 can be transmitted through network 120.

[0027] Database 130 can be used to store data and / or instructions, and database 130 can be directly connected to server 110 or located within server 110. In some embodiments, database 130 can be used to store data related to user 150 and location 160. Database 130 can be implemented in a single central server, multiple servers connected via communication links, or multiple personal devices. In some embodiments, database 130 can be included in server 110, terminal device 140, and other possible system components.

[0028] Terminal device 140 refers to one or more terminal devices or software. In some embodiments, terminal device 140 can serve as a user platform. For example, when the user of the terminal device is a tourist, terminal device 140 can act as a user platform to input the user's query requirements. In some embodiments, terminal device 140 can serve as a management platform. For example, when the user of the terminal device is a crowd control agency, terminal device 140 can act as a management platform to aggregate relevant data and make plans. In some embodiments, the user of terminal device 140 can be one or more users. In some embodiments, terminal device 140 can be one or any combination of other devices with input and / or output functions, such as mobile device 140-1, tablet computer 140-2, and laptop computer 140-3. In some embodiments, terminal device 140 and other possible system components may include processing device 112.

[0029] User 150 can be a user of user terminal 140, and can be a tourist, visitor, crowd control personnel, etc. In some embodiments, a user can issue a query request. For example, a user can query the location of a supermarket, the locations of other supermarkets near a supermarket, the location of a convenience store, etc. In some embodiments, a user can receive information from user terminal 140, such as query results, recommendation information, etc. In some embodiments, the number of users can be one or more.

[0030] Location 160 can be a specific location within a certain area. For example, a location can be any location such as office building 160-1, supermarket 160-2, administrative building 160-3, restaurant, hair salon, station, parking lot, etc. In some embodiments, locations can have functional similarities. For example, shopping malls, convenience stores, and supermarkets are all places where shopping is possible; restaurants, canteens, and food streets are all places where eating is possible, etc. Locations can be classified based on their functional differences and similarities. For example, locations can be categorized as shopping, dining, tourist attractions, medical facilities, etc.

[0031] It should be noted that application scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can make various modifications or variations based on the description in this specification. For example, application scenario 100 may also include an information source. However, these changes and modifications will not depart from the scope of this application.

[0032] An Internet of Things (IoT) system is an information processing system that includes some or all of the following platforms: user platform, service platform, management platform, sensor network platform, and object platform. The user platform is the leader of the entire IoT operation system, used to acquire user needs. User needs are the foundation and prerequisite for the formation of the IoT operation system; all connections between the various platforms are aimed at meeting these needs. The service platform acts as a bridge between the user platform and the management platform, providing input and output services to users. The management platform coordinates and manages the connections and collaboration between various functional platforms (such as the user platform, service platform, sensor network platform, and object platform). It aggregates information from the IoT operation system and provides sensing and control management functions. The sensor network platform connects the management platform and the object platform, functioning as a sensing and communication platform for both sensing and control information. The object platform is the functional platform for generating sensing information and executing control information.

[0033] Information processing in an IoT system can be divided into two flows: processing of sensing information and processing of control information. Control information can be generated based on sensing information. Sensing information processing begins with the object platform acquiring sensing information and transmitting it to the management platform via a sensor network platform. The management platform then transmits the processed sensing information to the service platform, and finally to the user platform. The user analyzes and interprets the sensing information to generate control information. Control information is generated by the user platform and sent to the service platform, which then transmits it to the management platform. The management platform processes the control information and sends it back to the object platform via the sensor network platform, thereby enabling control of the corresponding object.

[0034] In some embodiments, when an IoT system is applied to urban management, it can be referred to as a smart city IoT system.

[0035] Figure 2 This is an exemplary module diagram of an IoT system for counting and controlling pedestrian flow in smart city public places, as shown in some embodiments of this specification.

[0036] like Figure 2 As shown, the IoT system 200 for pedestrian flow statistics and control in smart cities includes a user platform 210, a service platform 220, a management platform 230, a sensor network platform 240, and an object platform 250. In some embodiments, the IoT system 200 for pedestrian flow statistics and control in public places can be part of or implemented by the server 110.

[0037] In some embodiments, the smart city public place pedestrian flow statistics and control IoT system 200 can be applied to various scenarios of pedestrian flow statistics and control. In some embodiments, the public place pedestrian flow statistics and control IoT system 200 can obtain query instructions based on user-initiated query requests for intended locations. Query results are obtained according to the query instructions. In some embodiments, the smart city public place pedestrian flow statistics and control IoT system 200 can determine control strategies for intended locations based on query requests for intended locations and current information about those locations.

[0038] Various scenarios for public place pedestrian flow statistics and control can include scenarios such as pedestrian flow monitoring, municipal construction planning, and urban population distribution prediction. It should be noted that the above scenarios are merely examples and do not limit the specific application scenarios of the smart city public place pedestrian flow statistics and control IoT system 200. Those skilled in the art can apply the smart city public place pedestrian flow statistics and control IoT system 200 to any other suitable scenario based on the content disclosed in this embodiment.

[0039] In some embodiments, the IoT system 200 for smart city public place pedestrian flow statistics and control can be applied to place pedestrian flow monitoring. When applied to place pedestrian flow monitoring, the target platform can be used to collect query requests from potential places and current information of potential places, and determine control strategies for potential places based on the above information.

[0040] In some embodiments, the IoT system 200 for smart city public place pedestrian flow statistics and control can be applied to municipal construction planning. For example, based on the pedestrian flow of a place and the corresponding control strategy, the system determines the user demand for that place in the corresponding area. Based on the user demand, it determines whether to build a new related place nearby.

[0041] In some embodiments, the IoT system 200 for smart city public place pedestrian flow statistics and control can be applied to urban population distribution prediction. For example, the user platform can receive user-initiated query requests for desired locations, the service platform transmits the query requests to the management platform, and generates query instructions based on the management platform; the management platform then distributes the query instructions to the corresponding sensor network sub-platforms of the sensor network platform according to the regional location; the sensor network sub-platforms then send the query instructions to the corresponding object platforms; and the object platforms statistically analyze the query instructions and obtain population distribution information.

[0042] The following will take the application of the IoT system 200 for public place pedestrian flow statistics and control in smart cities to the scenario of monitoring pedestrian flow in public places as an example to give a detailed explanation of the IoT system 200 for public place pedestrian flow statistics and control.

[0043] User platform 210 can be a user-facing service interface. In some embodiments, user platform 210 can receive query requests from users regarding desired locations. In some embodiments, user platform 210 can be configured to return query results to the user. In some embodiments, user platform 210 can send query requests to a service platform. In some embodiments, user platform 210 can receive management policies, query results, etc., sent by the service platform.

[0044] Service platform 220 can be a platform for preliminary processing of query requests. In some embodiments, service platform 220 can transmit query requests to a management platform and generate query instructions based on the management platform, the query instructions including the regional location of the intended location. In some embodiments, service platform 220 can receive control policies, query results, etc., sent by the management platform.

[0045] The management platform 230 can refer to an IoT platform that coordinates and integrates the connections and collaborations between various functional platforms, providing sensing management and control management.

[0046] In some embodiments, the management platform can generate query instructions. In some embodiments, the management platform 230 can distribute the query instructions to the corresponding sensor network sub-platform based on the regional location. In some embodiments, the management platform 230 can receive query requests sent by the service platform.

[0047] The sensor network platform 240 can serve as a connection between the management platform and the object platforms, enabling interaction. In some embodiments, the sensor network platform 240 can receive query instructions sent by the management platform. In some embodiments, the sensor network platform 240 can send query instructions to the corresponding object platform. In some embodiments, the sensor network platform 240 can be configured as a stand-alone structure. A stand-alone structure means that the sensor network platform uses different sensor network sub-platforms (also known as sensor network sub-platforms or sensor network sub-platforms) for data storage, data processing, and / or data transmission for data from different object platforms. For example, each sensor network sub-platform can correspond one-to-one with the object sub-platform (also known as object sub-platform or object sub-platform) of each object platform. The sensor network platform 240 can obtain the intended locations and related location query requirements uploaded by each object sub-platform and upload them to the management platform.

[0048] The object platform 250 can be a functional platform for generating sensing information and ultimately executing control information. The object platform 250 can be used to obtain query results based on query instructions, including current information about the intended location and recommended information about related locations. In some embodiments, the object platform 250 can also feed back the query results to the user platform through corresponding sensor network sub-platforms, management platforms, and service platforms.

[0049] In some embodiments, the object platform 250 can be configured to include multiple object sub-platforms, with different object sub-platforms corresponding to obtaining location information for different regions. For example, the object platform 250 can upload the intended location and related location query requests to the corresponding sensor network platforms respectively.

[0050] In some embodiments, the object platform 250 is further configured to: obtain information on the intended location and related locations in the regional location map according to the query requirements, and generate query results; different nodes in the regional location map represent different locations, and the attributes of the nodes in the regional location map include real-time location information and basic location information; the edges in the regional location map are used to connect two nodes whose mutual relationship meets preset conditions.

[0051] In some embodiments, the object platform 250 is further configured to: divide the regional location map into several sub-maps based on a preset algorithm; determine the target sub-map from the several sub-maps based on query requirements; determine the recommended node based on the target sub-map, and identify the location corresponding to the recommended node as the relevant location.

[0052] In some embodiments, the object platform 250 is further configured to: determine a control strategy for the intended location based on the query requirements of the intended location and the current information of the intended location.

[0053] In some embodiments, the object platform 250 is further configured to: determine the traffic control strategy for the intended location based on the current real-time traffic of the intended location and the number of users querying it.

[0054] In some embodiments, the object platform 250 is further configured to: predict the traffic of the intended venue in the future. When the traffic in the future exceeds a preset threshold, traffic control is implemented for the intended venue.

[0055] In some embodiments, the object platform 250 is further configured to: determine the popularity of a desired location based on the number of users querying the desired location; and adjust the current real-time traffic of the desired location based on the popularity to determine the traffic at future times.

[0056] For detailed information on Object Platform 250, please refer to [link / reference]. Figures 4-7 And its related descriptions.

[0057] For those skilled in the art, once they understand the principles of this system, they may be able to adapt the Smart City Public Place Traffic Flow Statistics and Control IoT System 200 to any other suitable scenario without departing from these principles.

[0058] It should be noted that the above description of the system and its components is for convenience only and should not be construed as limiting this specification to the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various components or construct subsystems connected to other components without departing from these principles. For example, the various components may share a single storage device, or each component may have its own separate storage device. Such modifications are all within the scope of this specification.

[0059] Figure 3 This is an exemplary flowchart illustrating a method for counting and controlling pedestrian flow in smart city public places, based on some embodiments of this specification. Figure 3 As shown. In some embodiments, process 300 may be executed by a processing device.

[0060] Step 310: Receive user-initiated query requests for desired locations based on the user platform.

[0061] The intended location can be a place the user intends to visit. For example, the intended location could be a shopping mall, restaurant, school, administrative center, office building, etc. In some embodiments, the intended location can be a location entered by the user on their terminal, such as the user entering a specific restaurant name on their mobile phone.

[0062] A query request can be the content a user searches for information related to a desired location. For example, a query request might include information such as the location, functions, regulatory status, and foot traffic of a desired location. In some embodiments, query requests can be obtained by the user inputting them into a user terminal, thus using the user terminal as a user platform to obtain query requests.

[0063] Step 320: The query request is transmitted from the service platform to the management platform, and a query instruction is generated based on the management platform; the query instruction includes the regional location of the intended location.

[0064] A query instruction can be content information that can be recognized by the system and extracted from the query request. For example, a query instruction can include information such as the location of the desired place, the query time, and the query method.

[0065] Regional location can be information reflecting the geographical location of the intended location. For example, regional location can be the latitude and longitude of the intended location, coordinates, and relative distance from the current user's location.

[0066] In some embodiments, query requests received from the user platform can be sent to the management platform via the service platform for preliminary processing, forming query instructions that can be recognized by the system. For example, a query instruction can be a matrix or data table composed of information such as the regional location of the intended location, the query time, and the query method.

[0067] Step 330: Based on the regional location, the management platform sends the query command to the corresponding sensor network sub-platform of the sensor network platform.

[0068] In some embodiments, the sensor network platform may include multiple sensor network sub-platforms, and different sensor network sub-platforms may be used to receive query instructions for different regional locations issued by the management platform. The sensor network platform may use different sensor network sub-platforms for data storage, data processing, and / or data transmission for data from different object platforms; wherein, different sensor network sub-platforms correspond to different regional locations.

[0069] Step 340: The query command is sent to the corresponding object platform based on the sensor network sub-platform.

[0070] In some embodiments, an object platform may include multiple object sub-platforms, with different object sub-platforms corresponding to different regional locations. For example, different object sub-platforms may be located in different regional locations, thus establishing a correspondence between sensor network sub-platforms and object sub-platforms located in the same regional area. A sensor network sub-platform can send a query command to its corresponding object sub-platform.

[0071] Step 350: Based on the object platform, obtain the query results according to the query instructions.

[0072] Search results can be information related to the desired location. For example, search results can include current information about the desired location and recommendations for related locations.

[0073] The current information can be real-time information about the intended location. For example, the current information may include real-time pedestrian flow information, whether access is currently restricted, and traffic restrictions on roads surrounding the intended location.

[0074] The relevant location can be a place with similar characteristics to the intended location. For example, the relevant location can be a place with similar functions; such as a pedestrian street or supermarket when the intended location is a shopping mall, or an outpatient department or pharmacy when the intended location is a hospital. Another example is that the relevant location can be a place with a similar geographical location; for example, other nearby parking lots when the intended location is a parking lot. In some embodiments, the spatial distance between the relevant location and the intended location can be within a preset range.

[0075] Recommendation information can be information that recommends relevant locations to users. For example, recommendation information can be any form of information, such as text, voice, or images, or a combination thereof.

[0076] In some embodiments, query results can be further determined based on sensors, manual input, or calculations using preset rules. For example, the platform can obtain real-time pedestrian flow information from pedestrian flow monitoring sensors as query results; alternatively, the platform can use the responses of human customer service representatives to query requests as query results; furthermore, the platform can determine query results by comparing the location of various venues with the desired venue area, and by classifying venues based on their functions. For example, it can determine related venues for the desired venue by comparing coordinates, latitude and longitude, or determine related venues related to the functions of the desired venue by using a preset venue function classification table.

[0077] Step 360: Based on the object platform, the query results are fed back to the user platform through the corresponding sensor network sub-platform, management platform, and service platform of the sensor network platform.

[0078] The process of sending query results from the object platform back to the user platform is the reverse of the process of transmitting user needs and query instructions, and will not be elaborated here.

[0079] In some embodiments, process 300 may further include step 370, whereby the object platform determines a control strategy for the intended location based on the query requirements of the intended location and the current information of the intended location.

[0080] The step numbering of step 370 is for illustrative purposes only and does not imply a limitation on the order of the steps. For example, step 370 may be performed between steps 350 and 360. In some embodiments, step 370 in process 300 may be optional, i.e., step 370 may be omitted.

[0081] Control strategies can be plans to restrict the flow of people, limit movement, and restrict entry and exit at a target location. For example, control strategies can include information such as the time frame for control, the area to be controlled, the arrangement of personnel for control operations, and the plan for diverting crowds.

[0082] In some embodiments, the control strategy can be determined based on system presets. For example, when the current pedestrian flow information of the intended location exceeds the system's preset pedestrian flow threshold, the control strategy is determined to be measures such as limiting pedestrian flow or pedestrian diversion. The target platform can obtain the control strategy generated by the system based on the corresponding sensor network sub-platform.

[0083] In some embodiments, control policies can be determined based on user settings. For example, users can set specific control policies such as traffic restrictions and access restrictions based on emergencies such as road construction or epidemics. The target platform can obtain manually input control policies based on the corresponding sensor network sub-platform. For further explanation on determining control policies for intended locations, please refer to [link to relevant documentation]. Figure 7 And related content.

[0084] The public place pedestrian flow statistics and control method described in some embodiments of this specification can recommend relevant places and control strategies to users based on the current pedestrian flow situation of a certain place. Through intelligent recommendations based on pedestrian flow, user needs can be met as much as possible while avoiding users going to places with high pedestrian flow, thereby improving the user experience.

[0085] It should be noted that the above description of process 300 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 300 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification. For example, process 300 may also include a pretreatment step.

[0086] Figure 4This is an exemplary schematic diagram illustrating the determination of relevant locations according to some embodiments of this specification.

[0087] like Figure 4 As shown, the relevant locations in the query results can be determined through the following process 400: Based on the query requirements 410, the object platform obtains information on the intended locations and related locations in the regional location map 420 and generates query results 430.

[0088] In some embodiments, the query request may include a desired location. The object platform can extract information related to the desired location from the query request and find the corresponding node in the regional location map. For example, a user's query request may include the name, location coordinates, and function type of the desired location. Based on this information, the object platform finds the node corresponding to the desired location related to the aforementioned information in the regional location map.

[0089] In some embodiments, the object platform can determine the nodes corresponding to relevant locations based on the nodes corresponding to the intended locations. In some embodiments, the object platform determines the nodes corresponding to relevant locations based on the attributes of the nodes corresponding to the intended locations. For example, the nodes corresponding to relevant locations can be determined based on the location, type, etc. of the nodes. In some embodiments, relevant locations (corresponding nodes) can be determined through steps S1 to S3 described below.

[0090] In some embodiments, the object platform can generate query results. For example, the object platform can input the attributes of the nodes corresponding to the intended location and related locations into a preset query result table as the query results.

[0091] The regional location map 420 can represent the relationships between all locations within a certain region and the characteristics of each location itself. The size of the region can be set according to the user's query requirements for locations; for example, a region can be a city, a district / county, a township, etc. Locations in the regional location map can be represented based on nodes in the map. The characteristics of each location can be obtained based on node attributes. The relationships between locations can be obtained based on edges in the regional location map. In some embodiments, the regional location map can be further obtained by importing existing map data as base data and using methods such as manual labeling.

[0092] A regional location map can include multiple nodes, with different nodes representing different locations. For example, node 1 could represent a parking lot within the regional location map, while node 2 could represent a hotel within the regional location map.

[0093] In some embodiments, each node may include node attributes. Node attributes can be various parameters of the node. For example, node attributes may include real-time location information, basic location information, etc., for the location corresponding to the node.

[0094] Real-time venue information can be information that changes dynamically based on the real-time situation of the venue. For example, real-time venue information may include at least one of the following: current pedestrian flow and current control information.

[0095] Current pedestrian traffic can be the number of visitors to the venue at the current moment or within a time period close to the current moment. In some embodiments, current pedestrian traffic can be detected by sensors installed at the venue or on relevant road sections.

[0096] Current control information can be information on the management of pedestrian flow at the venue at the current time or within a time period close to the current time. For example, current control information can include information on pedestrian flow restrictions, flow control, or full openness. In some embodiments, current control information can be obtained through user input or via a network.

[0097] Basic location information can be information that does not change over time and is relatively fixed. For example, basic location information may include at least one of the following: location type (i.e., the function of the location) and location (such as coordinates or latitude and longitude).

[0098] The type of venue can be its functional type. For example, venue types can include dining, shopping, schools, accommodation, medical facilities, etc. The location of a venue can be its specific geographical location. For example, the location can be its latitude, longitude, coordinates, etc.

[0099] Edge 424 in the regional location graph is used to connect two nodes whose relationship meets preset conditions. Edge attributes represent the relationships between different nodes in the regional location graph. Edge attributes can include edge weights, which reflect the correlation between the two nodes to be connected or connected. For example, edge weights can be values ​​such as 1, 2, or 10; the larger the edge weight, the weaker the correlation between the two nodes.

[0100] In some embodiments, the preset conditions that need to be met between two connected nodes may be related to the edge weights between the two nodes. For example, the preset condition may be that the edge weight is not higher than a preset threshold.

[0101] In some embodiments, edge weights can be determined by the difference in location type and spatial distance between two corresponding nodes.

[0102] Location type difference can be the difference in type between two locations corresponding to two nodes. In some embodiments, location type difference can be represented by a location type difference value in the range of 0 to 5. The larger the location type difference value, the greater the difference in functional category between the two locations. For example, the location type difference value between different shopping malls can be 0, the location type difference value between a shopping mall and a convenience store can be 1, the location type difference value between a shopping mall and a parking lot can be 5, and so on.

[0103] Spatial distance can be the straight-line distance between locations. In some embodiments, spatial distance can be represented by a spatial score ranging from 0 to 5, with a higher score indicating a closer distance between the two locations. For example, two locations with a spatial distance of 0 to 200 meters may have a spatial score of 5, while two locations with a spatial distance of more than 2000 meters may have a spatial score of 1.

[0104] In some embodiments, the edge weight between two nodes can be calculated using the location type difference value and the spatial score. For example, the edge weight between nodes A and B can be calculated using the following formula (1):

[0105]

[0106] in, Let be the edge weight of the edge between nodes A and B. Let be the distance score between node A and node B. This represents the difference in location type between node A and node B.

[0107] In some embodiments, the distance score and the difference in location type can be weighted for calculation, that is, the edge weight of the edge between two nodes can be the weighted sum of the distance score and the difference in location type between the two nodes. For example, the edge weight of the edge between nodes A and B can be calculated using the following formula (2):

[0108]

[0109] in, The weights for distance scores, The weights for the differences in location types are m1 + m2 = 1.

[0110] In some embodiments, the weights of the distance score and the location type difference value can be determined manually, for example, based on user query requirements.

[0111] In some embodiments, the preset conditions that the two connected nodes must meet may be that the difference in the location type of the two connected nodes is less than a preset difference value; or that the spatial distance between the two connected nodes is less than a distance threshold.

[0112] As an example only, the preset conditions could be that the difference in location type between the two connected nodes is less than 2; or the spatial distance between the two connected nodes is less than 200m; or the spatial score is greater than 4, etc.

[0113] In some embodiments, relevant locations can be determined through the following process: determining candidate locations based on relevance and edge weight; and determining relevant locations from the candidate locations based on query requirements.

[0114] Relevance can be the degree of similarity between nodes. For example, relevance can be the degree of relevance between nodes based on their location (i.e., location) or type (i.e., type of place). In some embodiments, relevance can be divided into different levels. For example, relevance can include different levels such as primary relevance and secondary relevance. Primary relevance can be the relevance level with the highest degree of similarity between nodes. Secondary relevance can be the relevance level with a relatively high degree of similarity between nodes. For example, as... Figure 4 As shown, nodes A and B are connected by only one edge, and they can be a first-level association; nodes A and D are connected by at least two edges, and they can be a second-level association; ...

[0115] Candidate locations can be locations whose relevance and edge weights meet preset conditions. For example, preset conditions could be that the relevance with the target location is level one or level two and the edge weight corresponding to the edge with the target location is no greater than 4.

[0116] In some embodiments, the relevant location (i.e., the corresponding node) can also be determined through the following steps:

[0117] S1, based on a preset algorithm, divides the regional site map into several sub-maps.

[0118] A preset algorithm can be an algorithm that divides a regional location map according to certain rules. Based on the preset algorithm, nodes in the regional location map can be clustered based on certain characteristics. For a detailed explanation of the preset algorithm, please refer to [link to relevant documentation]. Figure 5 And its related descriptions.

[0119] A subgraph can be a subgraph consisting of nodes with similar characteristics and the edges between them in a regional location graph. For example, a subgraph can be a set of nodes and edges in a regional location graph, and it can include at least one node. For example, based on a preset algorithm, it can be used to... Figure 4 The area map 420 is divided into two sub-maps, such as sub-map 421 and sub-map 425.

[0120] S2, based on the query requirements, determines the target subgraph from several subgraphs.

[0121] A target subgraph can be a subgraph containing intended locations. In some embodiments, a target subgraph may include target nodes (i.e., intended locations). In some embodiments, an object platform may use the subgraph containing the intended location as the target subgraph. For example, if Figure 4 If the target node is node 422, then subgraph 421 can be determined as the target subgraph.

[0122] S3. Based on the target subgraph, determine the recommended nodes and identify the locations corresponding to the recommended nodes as relevant locations.

[0123] Recommended node 423 can be a node that meets preset requirements, such as a node similar to target node 422. In some embodiments, it can be a node located in the same subgraph as the target node (e.g., Figure 4 One or more nodes within the target subgraph 421 are selected as recommended nodes.

[0124] The regional location maps described in some embodiments of this specification enable the visualization of location functions and locations, facilitating the identification of relevant locations. Furthermore, the accuracy of the identification process is improved by determining candidate locations based on the correlation between nodes corresponding to locations and edge weights.

[0125] Figure 5 This is an exemplary flowchart of a preset algorithm shown according to some embodiments of this specification. In some embodiments, process 500 may be executed by an object platform.

[0126] Step 510: Take any one of the n nodes in the area map as the reference node and determine the shortest path from the reference node to the other nodes.

[0127] The reference node can be any one of all the nodes included in the regional site map. In some embodiments, during a regional site map partitioning, each node (n in total) in the regional site map can be used as a reference node for calculation.

[0128] The shortest path can be the shortest edge connecting the base node to other nodes. For example, in Figure 4In the diagram, when point A is the reference node, the shortest path from A to B is edge AB; the shortest path from A to C is edge AC; the shortest path from A to D is edge ABD (i.e., AB+BD) and edge ACD (i.e., AC+CD); the shortest path from A to E is edge ABDE and edge ACDE; the shortest path from A to F is edge ABDEF and edge ACDEF; the shortest path from A to G is edge ABDEG and edge ACDEG; the shortest path from A to H is edge ABDH and edge ACDEH; the shortest path from A to J is edge ABDEFJ, edge ACDEFJ, edge ABDEGJ, edge ACDEGJ, edge ABDEHJ, and edge ACDEHJ. For the shortest paths when other nodes are reference nodes, refer to the section on shortest paths when point A is the reference node.

[0129] Step 520: Calculate the edge betweenness centrality value of all edges in the region location graph based on the shortest path.

[0130] Edge Betweenness Centrality (EBC) value can be a parameter that characterizes the proportion of shortest paths from a given point to other nodes in a region map that pass through a certain edge.

[0131] For example, when point A is the reference node, the edge betweenness centrality value of edge AB can be: the sum of the proportions of paths passing through edge AB in the shortest paths from node A to other nodes (X) among all paths from A to other nodes X. For example, from point A to point J, there are six paths: ABDEFJ, ACDEFJ, ABDEGJ, ACDEGJ, ABDEHJ, and ACDEHJ. Among them, three paths, ABDEFJ, ABDEGJ, and ABDEHJ, pass through edge AB, so the value is 3 / 6. From point A to point F, there are two paths: ABDEF and ACDEF. The ABDEF path passes through edge AB, so the value is 1 / 2. Similarly, from point A to point H, the value is 1 / 2. From point A to point G, the value is also 1 / 2. From point A to point E, there are two paths: ABDE and ACDE. The ABDE path passes through edge AB, so the value is 1 / 2. From point A to point D, there are two paths: ABD and ACD. The ABD path passes through edge AB, so the value is 1 / 2. From point A to point B, there is only one path, AB, so the value is 1. From point A to point C, there is only one path, AC, which does not pass through edge AB, so the value is 0. Therefore, the betweenness centrality value of side AB can be 1 / 2 + 1 / 2 + 1 / 2 + 1 / 2 + 1 / 2 + 1 / 2 + 0 = 4.

[0132] With A as the reference node, the calculation method for the edge betweenness centrality values ​​of other edges is the same as that for edge AB. The edge betweenness centrality values ​​are: AC 4, BD 3, CD 3, DE 5, EF 4 / 3, EH 4 / 3, EG 4 / 3, FJ 1 / 3, HG 1 / 3, GJ 1 / 3, BC 0, and FH 0.

[0133] Step 530: Take each node of the regional site graph as a reference node in turn, and repeat the above operation to calculate the edge betweenness centrality value of each edge in the regional site graph when each node is taken as a reference node.

[0134] For example, taking point H as the reference node, the shortest paths from point H to other nodes are obtained: HF, HE, HJ, HED, HEG, HJG, HEDB, HEDC, HEDBA, HEDCA. The edge betweenness centrality values ​​of each edge are calculated: FH = 1, EH = 11 / 2, HJ = 3 / 2, DE = 4, EG = 1 / 2, GJ = 1 / 2, BD = 3 / 2, CD = 3 / 2, AB = 1 / 2, AC = 1 / 2, BC = 0, EF = 0, FJ = 0.

[0135] For calculations using other nodes as the reference node, please refer to the calculation instructions above, which will not be repeated here.

[0136] Step 540: Obtain the n edge betweenness centrality values ​​of each edge in the region location graph obtained based on the aforementioned operations.

[0137] For example, in sequence Figure 4 After calculating the edge betweenness centrality value for each edge by taking nodes A through J as reference nodes in sequence, each edge can obtain 9 edge betweenness centrality values.

[0138] In some embodiments, such as Figure 6 As shown, the results obtained from the above calculations can be compiled into Table 600 to obtain the edge betweenness centrality value of each edge when each node is used as the reference node.

[0139] Step 550: Sum the n edge betweenness centrality values ​​of each edge to obtain the total edge betweenness centrality value of each edge.

[0140] The total value of edge betweenness centrality can be the sum of the edge betweenness centrality values.

[0141] In some embodiments, the total edge betweenness centrality can be obtained by directly adding the individual edge betweenness centrality values. For example, as... Figure 6 As shown, the total edge betweenness centrality of edge AB can be expressed as the sum of the betweenness centrality values ​​of its nine edges. Similarly, the total edge betweenness centrality of each of the other edges can be calculated separately.

[0142] In some embodiments, the total edge betweenness centrality can be obtained by weighted summation of the individual edge betweenness centrality values.

[0143] In some embodiments, the object platform can determine a second weight for each edge betweenness centrality value among the n edge betweenness centrality values ​​of each edge, based on user needs; and use the weighted sum of the n edge betweenness centrality values ​​of an edge as the total edge betweenness centrality value of that edge, based on the second weight.

[0144] The second weight can be the degree of contribution (importance) of each edge betweenness centrality value to the total edge betweenness centrality value. In some embodiments, the second weight can be determined by user needs. For example, when user needs include a higher degree of attention to real-time pedestrian flow information, the difference in pedestrian flow between the two nodes corresponding to each edge can be used as the weight of that edge. The larger the difference, the smaller the corresponding weight.

[0145] By assigning weights to each edge betweenness centrality value based on user needs, the impact of user needs on the results can be amplified when calculating the total edge betweenness centrality value, making the subsequent regional site map division more in line with user experience.

[0146] Step 560: Obtain the edge betweenness centrality score of each edge based on the total edge betweenness centrality of each edge.

[0147] The edge betweenness centrality score can be a parameter obtained by evaluating the total edge betweenness centrality value. In some embodiments, the edge betweenness centrality score can be the product of the total edge betweenness centrality value and a scoring coefficient, which can be set manually or use a default value. For example, the scoring coefficient can be 0.5. Figure 6 As shown, the total edge betweenness centrality of edge AB is 8, so the edge betweenness centrality score of edge AB can be 4.

[0148] In some embodiments, the recommendation information for relevant locations in step 350 further includes a recommendation index. The recommendation index is determined by processing the current pedestrian flow information and the relationship with the intended location for each node in the target subgraph using a recommendation model.

[0149] Recommendation models can be models that determine recommendation indices. Recommendation models can also be machine learning models; for example, a recommendation model could be a convolutional neural network model.

[0150] The input to the recommendation model can include the current foot traffic information of each node and its relationship with the intended location; the output can include the recommendation index for each node.

[0151] The relationship with the intended location can be any data related to the intended location. For example, the relationship with the intended location could be the edge weights of the edges connecting the corresponding node in the target subgraph to the node corresponding to the intended location, or the edge betweenness centrality scores of the edges connecting the corresponding node in the target subgraph to the node corresponding to the intended location.

[0152] A recommendation index can be a parameter reflecting the system's degree of recommendation for a particular relevant node. For example, the recommendation index can be a specific numerical value such as 6 or 9, or a recommendation level such as highly recommended, strongly recommended, or not recommended. In some embodiments, the recommendation index can be an integer between 0 and 10.

[0153] In some embodiments, the recommendation model can be trained based on a large number of labeled training samples. In some embodiments, the training samples can be historical pedestrian traffic information of each node and its relationship with intended locations, and the labels can be the corresponding recommendation indices. Labels can be obtained through manual annotation.

[0154] By determining the recommendation index through a recommendation model, the degree of recommendation for nodes can be quantified, unnecessary costs associated with manual recommendations can be reduced, and recommendation accuracy can be improved.

[0155] Step 570: Divide the regional site map based on the edge betweenness centrality score of each edge.

[0156] In some embodiments, the partitioning process can be based on the edge betweenness centrality score. For example, the edge with the highest edge betweenness centrality score can be used as the dividing edge, and the region map including the two nodes corresponding to that edge can be divided into two subgraphs. For example, based on the aforementioned calculation... Figure 4 The edge betweenness centrality scores of each edge in the region are as follows: FH = 1, EH = 34 / 6, HJ = 19 / 6, DE = 20, EG = 14 / 3, GJ = 11 / 3, BD = 9, CD = 9, AB = 4, AC = 4, BC = 1, EF = 34 / 6, and FJ = 19 / 6. Therefore, in region location graph 420, edge DE has the highest edge betweenness centrality score, meaning it can be divided using edge DE as the dividing edge, resulting in subgraphs 421 and 425.

[0157] In some embodiments, edges whose edge betweenness centrality scores exceed a threshold value can be partitioned, and the region map including the two nodes corresponding to each edge can be divided into multiple subgraphs. The threshold value can be determined based on user settings.

[0158] In some embodiments, the target subgraph can be further divided according to the aforementioned method based on the division results of the aforementioned steps to obtain better division results, and based on the final division results, the locations corresponding to other nodes in the same subgraph as the target node are selected as recommended locations.

[0159] In some embodiments, the aforementioned partitioning steps can be repeated until each subgraph contains only one node. If one partition corresponds to one stage, and after the original graph is partitioned y times, each subgraph contains only one node, then the entire partitioning process has y stages. The modularity (represented by the letter Q in the formula) value corresponding to each stage can be calculated, and the partitioning result corresponding to the stage with the largest modularity value (Q value) is taken as the optimal partitioning result. In the subgraph obtained by this partitioning result, other nodes located in the same subgraph as the target node are taken as recommended locations.

[0160] The modularity value can be understood as the difference between a network and a random network under a certain clustering partition. Since the random network does not have a subgraph structure, the larger the difference, the better the subgraph partitioning result. The modularity value (Q value) can be obtained based on the following formula (3):

[0161]

[0162] in, The number of edges in the original graph. and It is any two nodes in the graph. This indicates whether an edge exists between two nodes (a value of 1 if it exists, and a value of 0 otherwise). Represents the degree of nodes v and w. This indicates whether two nodes are in the same subgraph (a value of 1 if they are in the same subgraph, and a value of 0 otherwise). In some embodiments, the theoretical range of the Q value is (-0.5, 1).

[0163] By using the preset algorithms described in some embodiments of this specification, subgraphs with a higher degree of node correlation can be obtained. When a user initiates a query, the results can be directly retrieved from the subgraph with a higher degree of correlation, avoiding the huge computational load caused by querying the entire area map and improving query efficiency.

[0164] Figure 7 This is an exemplary flowchart illustrating the determination of a control strategy for an intended location, according to some embodiments of this specification. In some embodiments, process 700 may be executed by an object platform.

[0165] In some embodiments, the object platform may determine the traffic control strategy 730 for the intended venue based on the current foot traffic 710 and the number of users querying the venue 720.

[0166] The number of users querying, 720, can be the number of users who submitted query requests within a certain time period. In some embodiments, the number of users querying within a certain time period may be less than or equal to the number of query requests. For example, the same user may submit multiple query requests within that time period. By determining the number of users querying, this number can be used as a basis for determining the current or future foot traffic at the intended location.

[0167] Traffic flow control strategies can be solutions for managing pedestrian flow. For example, when the pedestrian flow in a certain place exceeds the threshold, traffic flow control strategies may include limiting the flow of people in that place, diverting people to other places, and increasing the number of control personnel and resources; when the pedestrian flow in a certain place is less than the threshold, traffic flow control strategies may include fully opening the place and reducing the number of control personnel and resources.

[0168] In some embodiments, traffic control strategies can be determined based on preset traffic thresholds. For example, when the sum of the current traffic volume and the number of users querying the system is greater than the preset traffic threshold, the traffic control strategy can be measures such as limiting or diverting traffic; when the sum of the current traffic volume and the number of users querying the system is less than the traffic threshold, the traffic control strategy can be measures such as opening up the system.

[0169] In some embodiments, the decision to redirect traffic to other locations is determined based on the edge weights of the edges connecting locations corresponding to other nodes in the subgraph to the intended location. For example, if there are no relevant locations in the target subgraph corresponding to the intended location, no traffic redirection is performed; if there are at least one relevant location in the target subgraph corresponding to the intended location, traffic is redirected to that relevant location. The redirection method can be to redirect traffic to multiple relevant locations simultaneously, or to prioritize redirecting traffic to relevant locations corresponding to nodes connected by edges with lower edge weights. The redirection method can also be to send recommendation information about locations corresponding to nodes whose edge weights are less than a preset threshold to the user client corresponding to the user initiating the query.

[0170] By diverting pedestrian traffic as described above, local control of pedestrian flow can be achieved, avoiding unnecessary dangers caused by crowds gathering while meeting user needs.

[0171] In some embodiments, the method for statistically analyzing and controlling pedestrian traffic in public places may further include determining whether to implement traffic control measures at a given location based on predicted future traffic volume. For example, traffic control measures can be implemented at a location if the predicted future traffic volume exceeds a preset threshold.

[0172] Future traffic flow can be the foot traffic at a desired location at a future time. In some embodiments, future traffic flow can be determined based on the popularity of the desired location. For specific instructions on traffic control for desired locations at a future time, please refer to the section on determining and implementing traffic control strategies.

[0173] In some embodiments, the object platform can determine the popularity of a location based on the number of users querying the location.

[0174] Popularity can be interpreted as the degree of user preference for a desired location. Popularity can be determined in several ways. For example, it can be based on the number of times a location is listed as a desired location in user queries, user reviews of the location (such as the number of positive online reviews), or real-time foot traffic information. In some embodiments, popularity can be represented by a specific value, such as a value from 1 to 5, with higher values ​​indicating greater popularity.

[0175] In some embodiments, the object platform can adjust the current real-time traffic of an intended location based on its popularity to determine traffic at future times.

[0176] The future flow rate can be calculated using the following formula (4):

[0177]

[0178] in, For future time flow, This represents the current real-time traffic. It is a constant (it can be any value between 60% and 100%). This represents the popularity level.

[0179] In some embodiments, an adjustment factor can be determined based on the confidence level of the recommendation model, and the flow rate in future times can be adjusted by the adjustment factor to obtain the adjusted flow rate in future times.

[0180] Recommendation models may also include a confidence score. The confidence score can be a parameter reflecting the reliability of the recommendation index output by the recommendation model. In some embodiments, the confidence score can be expressed as a percentage from 0 to 100%, with higher confidence scores indicating higher reliability of the recommendation index output by the recommendation model.

[0181] The adjustment factor can be a parameter used to correct the flow rate at future times. In some embodiments, the adjustment factor can be calculated using the following formula (5):

[0182]

[0183] in, To adjust the factor, The recommendation index is the output of the recommendation model. This represents the confidence level of the recommendation model.

[0184] The adjusted future flow rate can be obtained using the following formula (6):

[0185]

[0186] in, This represents the adjusted future flow rate. By adjusting the future flow rate using an adjustment factor, we can obtain a forecast that more closely reflects reality.

[0187] By using the methods described in some embodiments of this specification to determine the traffic control strategy for a target location, the traffic control strategy can be adjusted in response to changes in pedestrian traffic to meet the real-time control needs of dynamic changes in pedestrian traffic. In addition, by predicting future pedestrian traffic based on popularity, future control can be planned in advance, improving the user's current and future travel experience.

[0188] Some embodiments of this specification also disclose a computer-readable storage medium that stores computer instructions that, when executed by a processor, implement a method for counting and controlling pedestrian flow in public places.

[0189] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0190] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0191] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0192] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0193] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0194] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0195] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for managing visitor flow in smart city venues, characterized in that, The method includes: Based on the user platform receiving user-initiated query requests for desired venues; The service platform transmits the query request to the management platform, and the management platform generates a query instruction and sends it to the sensor network platform; the query instruction includes the regional location of the intended location. Based on the query instruction obtained from the sensor network platform by the object platform, the query result is obtained; the query result includes the current real-time traffic of the intended location; The popularity of a place is determined based on the number of users who query the place of interest. The current real-time traffic of the intended venue is adjusted based on its popularity to determine the future traffic of the intended venue; wherein the formula for determining the future traffic of the intended venue is as follows: In the formula, For the flow rate at the stated future time, The current real-time traffic, It is a constant whose value ranges from 60% to 100%. The value representing the popularity; The adjustment factor is determined based on the recommendation index and its confidence level output by the recommendation model. The recommendation model determines the recommendation index of the relevant venue based on the current foot traffic information of the relevant venue and its relationship with the intended venue; wherein: The relationship between the relevant locations and the intended locations includes the edge weights and edge betweenness centrality scores of the edges connecting the nodes corresponding to the relevant locations and the nodes corresponding to the intended locations in the target subgraph; wherein, the target subgraph is a subgraph containing the relevant locations among multiple subgraphs, and the multiple subgraphs and the edge betweenness centrality scores are determined based on a preset algorithm for dividing the regional location graph; The regional location map represents the interrelationships between all locations within the region and the characteristics of each location itself. The regional location map includes multiple nodes, with different nodes representing different locations. The edges in the regional location map are used to connect two nodes whose interrelationships satisfy preset conditions. The formula for determining the adjustment factor is as follows: In the formula, The adjustment factor is... The recommendation index output by the recommendation model. The confidence level of the recommendation model; Based on the adjustment factor, the flow rate at future times is adjusted to determine the adjusted future flow rate; wherein, the formula for determining the adjusted future flow rate is as follows: In the formula, The adjusted future flow rate; When the adjusted future traffic flow exceeds a preset threshold, traffic control is applied to the intended location.

2. The method according to claim 1, characterized in that, Traffic control for the intended venues includes: Traffic control is implemented for the intended venues based on traffic control strategies, which include: limiting the flow of people to the intended venues, diverting people to related venues, and increasing the number of control personnel and resources.

3. The method according to claim 1, characterized in that, The attributes of the nodes in the area map include real-time location information and basic location information. The real-time location information includes at least one of current pedestrian flow and current control information. The basic location information includes at least one of location type and location. The preset conditions are that the difference in location type between the two connected nodes is less than a preset difference value, or the spatial distance between the two connected nodes is less than a distance threshold.

4. A smart city venue traffic management IoT system, characterized in that, The system includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform; The user platform is used to receive user-initiated query requests for desired locations; The service platform is used to transmit the query request to the management platform; The management platform generates a query instruction based on the query request and sends it to the sensor network platform. The query instruction includes the regional location of the intended location; The sensor network platform is used to send the query command to the object platform; The object platform is used to obtain query results according to the query instruction; the query results include the current real-time traffic of the intended location; in order to obtain the query results, the object platform is further used to: The popularity of a place is determined based on the number of users who query the place of interest. The current real-time traffic of the intended venue is adjusted based on its popularity to determine the future traffic of the intended venue; wherein the formula for determining the future traffic of the intended venue is as follows: In the formula, For the flow rate at the stated future time, The current real-time traffic, It is a constant whose value ranges from 60% to 100%. The value representing the popularity; The adjustment factor is determined based on the recommendation index and its confidence level output by the recommendation model. The recommendation model determines the recommendation index of the relevant venue based on the current foot traffic information of the relevant venue and its relationship with the intended venue; wherein: The relationship between the relevant locations and the intended locations includes the edge weights and edge betweenness centrality scores of the edges connecting the nodes corresponding to the relevant locations and the nodes corresponding to the intended locations in the target subgraph; wherein, the target subgraph is a subgraph containing the relevant locations among multiple subgraphs, and the multiple subgraphs and the edge betweenness centrality scores are determined based on a preset algorithm for dividing the regional location graph; The regional location map represents the interrelationships between all locations within the region and the characteristics of each location itself. The regional location map includes multiple nodes, with different nodes representing different locations. The edges in the regional location map are used to connect two nodes whose interrelationships satisfy preset conditions. The formula for determining the adjustment factor is as follows: In the formula, The adjustment factor is... The recommendation index output by the recommendation model. The confidence level of the recommendation model; Based on the adjustment factor, the flow rate at future times is adjusted to determine the adjusted future flow rate; wherein, the formula for determining the adjusted future flow rate is as follows: In the formula, The adjusted future flow rate; When the adjusted future traffic flow exceeds a preset threshold, traffic control is applied to the intended location.

5. The system according to claim 4, characterized in that, In order to control traffic to the intended venues, the target platform is further used for: Traffic control is implemented for the intended venues based on traffic control strategies, which include: limiting the flow of people to the intended venues, diverting people to related venues, and increasing the number of control personnel and resources.

6. The system according to claim 4, characterized in that, Different nodes in the regional location map represent different locations. The attributes of the nodes in the regional location map include real-time location information and basic location information. The real-time location information includes at least one of current pedestrian flow and current control information. The basic location information includes at least one of location type and location. The edges in the area map are used to connect two nodes whose relationship meets preset conditions. The preset conditions are that the difference in the location type of the two connected nodes is less than a preset difference value, or the spatial distance between the two connected nodes is less than a distance threshold.

7. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1-3.

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