A smart city cross-regional parking information management method and system

Abstract

The present invention discloses a method and system for managing cross-regional parking information in a smart city, and relates to the technical field of parking management. The method includes: reading parking information elements within a preset area range, and constructing a cross-domain information architecture; traversing the cross-domain information architecture, and constructing an information consensus model; traversing the cross-domain information architecture, and coding and labeling data nodes, and constructing a dual-drive management module; authorizing parking rights based on the user end, and generating parking services; taking the parking rights as constraints, making cross-domain judgments on the parking services; if it is a cross-domain parking service, performing consensus trigger conversion for policy execution; obtaining service execution information, and synchronously updating and storing parking information based on an intercommunication platform. The present invention solves the technical problems in the prior art that parking information in different regions is dispersed, difficult to uniformly manage and integrate, and the user parking experience is poor, and achieves the technical effect of realizing efficient integration and sharing of parking information and improving the user parking experience.

Description

Technical Field

[0001] The present invention relates to the technical field of parking management, and in particular to a method and system for managing cross-regional parking information in a smart city. Background Art

[0002] With the acceleration of urbanization, the number of vehicles in cities has increased dramatically, and parking has become an increasingly serious problem. In the construction of smart cities, cross-regional parking management faces many challenges. Traditional parking management methods are often limited to a single area or parking lot, with isolated information, making it difficult to achieve effective integration and sharing across regions. Parking systems in different regions are independent of each other, and data formats and standards are inconsistent, which makes it difficult to exchange information and form a unified management architecture. In addition, there is a lack of norms for node management of parking data, and it is difficult to ensure the stability of data flow and data security. The definition of user parking rights is not clear enough, and the determination of cross-domain parking services lacks accurate basis, resulting in chaotic management and poor user experience.

[0003] The existing technology has technical problems such as scattered parking information in different areas, difficulty in unified management and integration, and poor user parking experience. Summary of the invention

[0004] The present application provides a method and system for managing cross-regional parking information in a smart city, which is used to solve the technical problems in the prior art of scattered parking information in different regions, difficulty in unified management and integration, and poor user parking experience.

[0005] In view of the above problems, the present application provides a method and system for cross-regional parking information management in a smart city.

[0006] In a first aspect of the present application, a method for managing cross-regional parking information in a smart city is provided, the method comprising:

[0007] Read parking information elements within a preset area, integrate them into an integrated manner, and build a cross-domain information architecture; traverse the cross-domain information architecture, read the information interaction isolation characteristics, perform shared consensus processing, and build an information consensus model, wherein the information consensus model is deployed in the upper layer of the interoperable platform; traverse the cross-domain information architecture, code and label data nodes, and build a dual-drive management module based on data flow and data security; authorize parking rights based on the user end and generate parking services; use the parking rights as a constraint to make cross-domain judgments on the parking services; if it is a cross-domain parking service, determine the business information management strategy based on the dual-drive management module, and perform consensus-triggered conversion of strategy execution based on the information consensus model; obtain business execution information, and synchronously update and store parking information based on the interoperable platform.

[0008] The second aspect of the present application provides a smart city cross-regional parking information management system, the system comprising:

[0009] A cross-domain information architecture construction module, which is used to read parking information elements within a preset area, integrate them, and build a cross-domain information architecture; an information consensus model construction module, which is used to traverse the cross-domain information architecture, read information interaction isolation features, perform shared consensus processing, and build an information consensus model, wherein the information consensus model is deployed in the upper layer of the intercommunication platform; a dual-drive management construction module, which is used to traverse the cross-domain information architecture, code and label data nodes, and build a dual-drive management model based on data flow and data security. management module; a parking service generation module, the parking service generation module is based on the user-side authorization of parking rights and generates parking services; a cross-domain determination module, the cross-domain determination module is used to perform cross-domain determination on the parking service based on the parking rights as a constraint; a policy execution module, the policy execution module is used to determine the business information management policy based on the dual-drive management module if it is a cross-domain parking service, and perform consensus trigger conversion of policy execution based on the information consensus model; a business execution information acquisition module, the business execution information acquisition module is used to obtain business execution information, and synchronize and update and store parking information based on the intercommunication platform.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Read the parking information elements of the preset area and build a cross-domain information architecture; traverse the cross-domain information architecture, read the information interaction isolation characteristics, perform shared consensus processing, and build an information consensus model; traverse the cross-domain information architecture, code and label the data nodes, and build a dual-drive management module; authorize parking rights based on the user end and generate parking services; use the parking rights as a constraint to make cross-domain judgments on the parking services; if it is a cross-domain parking service, determine the business information management strategy based on the dual-drive management module, and perform consensus trigger conversion for strategy execution based on the information consensus model; obtain business execution information, and synchronize and update and store parking information based on the intercommunication platform. The technical effect of realizing efficient integration and sharing of parking information and improving user parking experience has been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic diagram of a method for managing cross-regional parking information in a smart city provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a smart city cross-regional parking information management system provided in an embodiment of the present application.

[0015] Explanation of the accompanying drawings: cross-domain information architecture construction module 10, information consensus model construction module 20, dual-drive management construction module 30, parking business generation module 40, cross-domain determination module 50, strategy execution module 60, business execution information acquisition module 70. DETAILED DESCRIPTION

[0016] The present application provides a smart city cross-regional parking information management method and system, which is used to solve the technical problems in the prior art that parking information in different regions is dispersed, difficult to manage and integrate uniformly, and users have a poor parking experience.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the present application provides a method for managing cross-regional parking information in a smart city, the method comprising:

[0020] Step S100: Read parking information elements within a preset area, integrate them, and build a cross-domain information architecture.

[0021] Specifically, various parking information elements within a pre-set specific area will be read. These elements come from diverse and complex sources and cover many aspects related to parking. When determining the parking cross-domain dimension, three main aspects are identified: physical cross-domain refers to the geographical location of the parking lot, such as across different blocks, urban areas or geographical areas; policy cross-domain involves different policies and regulations for parking formulated by different regions or management departments, such as differences in charging policies, parking time limits, etc.; system cross-domain refers to the differences between different parking management systems, including data formats, information interaction methods, etc. Next, the parking information elements read are comprehensively traversed. Based on the physical cross-domain dimension, the actual geographical location distribution and mutual relationship of the parking lot are analyzed to determine the first topological network, which intuitively shows the connection and distribution of the parking lot in physical space. Based on policy cross-domain, the differences in parking policies in different regions or departments are studied in depth to determine the second topological network, which reflects the impact of policies on the allocation and use of parking resources. Based on system cross-domain, the characteristics and differences of different parking management systems are analyzed to determine the third topological network, which presents the interaction and collaboration between systems. Finally, by fitting these three topological networks, their information and features are organically integrated and combined, and the three aspects of physics, policy and system are comprehensively considered to generate a comprehensive and complete cross-domain information architecture. This architecture can clearly show the cross-domain situation of parking in various dimensions, providing a powerful and comprehensive information foundation for subsequent parking management, planning and services.

[0022] Step S200: traverse the cross-domain information architecture, read the information interaction isolation characteristics, perform shared consensus processing, and build an information consensus model, wherein the information consensus model is deployed in the upper layer of the intercommunication platform.

[0023] Specifically, a comprehensive and detailed traversal of the constructed cross-domain information architecture is conducted. In this traversal process, special attention will be paid to the problems caused by the cross-domain heterogeneity of parking information management. The so-called cross-domain heterogeneity refers to the differences and incompatibilities of parking information in different geographical scopes, policy regulations and system environments. For example, parking lots in different urban areas use completely different data management systems, or parking policies in different regions have significant differences in charging standards, time limits, etc. Based on this cross-domain heterogeneity, the characteristics that lead to information interaction isolation are read, which are manifested as incompatible data formats and inconsistent policy interpretations. In order to solve these isolation problems, an information consensus mechanism is determined. Among them, the first consensus mechanism based on the first topological network (physical cross-domain) mainly solves the information interaction problems caused by the differences in the geographical distribution of parking lots, such as how to unify the location identification and distance calculation methods of parking lots in different regions. The second consensus mechanism based on the second topological network (policy cross-domain) focuses on dealing with information inconsistencies caused by differences in parking policies in different regions, such as formulating unified policy interpretation and implementation standards. The third consensus mechanism based on the third topology network (system cross-domain) is committed to solving the communication and data exchange problems between different parking management systems, such as determining a unified data interface and transmission protocol. By determining these targeted consensus mechanisms, the foundation is laid for the subsequent construction of an information consensus model that can effectively coordinate and integrate various parking information, thereby improving the efficiency and accuracy of parking information management.

[0024] Step S300: traverse the cross-domain information architecture, code and label the data nodes, and build a dual-drive management module based on data flow and data security.

[0025] Specifically, a comprehensive traversal operation is carried out on the constructed cross-domain information architecture. During this traversal process, each data node in the architecture will be accessed one by one. For each data node, coding and labeling are performed. Coding is to assign a unique identifier or code to each data node, and labeling is to add relevant labels and description information to it. These labels and description information include the source, type, importance, update frequency, etc. of the data. In the entire coding and labeling process, data flow and data security are always used as the benchmark. That is to say, the assigned codes and labels should be conducive to the smooth flow of data in the system, ensuring that data can be efficiently and accurately transmitted from one node to another without congestion or loss. At the same time, the security of the data should be fully considered. Through reasonable coding and labeling, sensitive data is specially identified and encrypted, and access rights and control mechanisms are set to prevent unauthorized access, tampering or leakage of data. Based on the above work, a dual-drive management module is finally constructed. On the one hand, this module can effectively drive the smooth transmission and sharing of data, and on the other hand, it can ensure the security and confidentiality of data, thereby realizing efficient and reliable management of cross-domain parking information.

[0026] Step S400: Authorizing parking authority based on the user terminal and generating parking services.

[0027] Specifically, the relevant authorization information of the user side will be obtained first. The user side provides authorization in various ways, such as filling in personal information in the application, selecting the required parking service type, agreeing to the relevant terms of use and privacy policy, etc. The authorization information provided by the user is carefully analyzed and verified to determine the parking permissions allowed to the user, which include the time range of parking (such as a specific time period, cumulative duration), the area range of parking (specific parking lots, blocks or urban areas), the type of parking vehicle (small cars, large trucks, etc.), whether reservations are allowed and the conditions for reservations, etc. After clarifying the user's parking permissions, the corresponding parking services are generated according to these permissions, which involves a series of operations such as creating parking orders, allocating parking spaces, setting billing rules, sending relevant notifications and reminders, etc. The generated parking services will be closely associated with the user's authorization permissions, and will be subsequently processed and executed according to the preset rules and processes to ensure that users can enjoy accurate and convenient parking services within the scope of authorization.

[0028] Step S500: Taking the parking authority as a constraint, a cross-domain determination is performed on the parking service.

[0029] Specifically, the previously determined parking authority is used as an important constraint, which contains key restriction information such as the time, location, and vehicle type of parking allowed. Then, the generated parking business is analyzed and reviewed in detail. It checks various aspects involved in the parking business, including but not limited to the actual geographical location where the user plans to park, the expected start and end time of parking, and the type of vehicle used. By comparing and matching these specific contents of the parking business with the constraints in the parking authority one by one, it is determined whether the parking business belongs to the cross-domain situation. If some elements of the parking business exceed the scope specified by the parking authority, such as the user plans to park in an unauthorized area, or the parking time exceeds the duration allowed by the authority, it will be identified as a cross-domain parking business. On the contrary, if all elements of the parking business fully comply with the constraints of the parking authority, it will be determined as a non-cross-domain parking business, that is, the business is carried out normally within the scope of the user's authorization. This cross-domain determination is of great significance to ensure the standardization and rationality of parking management and to protect the legitimate rights and interests of users and parking lots.

[0030] Step S600: If it is a cross-domain parking business, the business information management strategy is determined based on the dual-drive management module, and the consensus trigger conversion of the strategy execution is performed based on the information consensus model.

[0031] Specifically, first, the information management strategy applicable to the cross-domain business is determined based on the dual-drive management module constructed earlier. The dual-drive management module plays a key role in ensuring smooth data flow and data security. At this time, the corresponding strategy will be formulated according to the characteristics and needs of the cross-domain business, such as the amount of data involved, the transmission path, the security level and other factors, including the selection of data encryption method, the designation of transmission channel, the setting of backup and recovery mechanism, etc. At the same time, the information consensus model constructed earlier is used to perform consensus trigger conversion for policy execution. Since cross-domain business involves different regions, policies and systems, the information consensus model can ensure that all parties reach a consensus on the understanding and execution of the strategy. In this process, the strategy needs to be converted and adapted to adapt to different environments and conditions. For example, the unified strategy can be fine-tuned according to the policy requirements of different regions, or the strategy can be converted into specific instructions that different systems can understand and execute. Through the above operations of determining the management strategy based on the dual-drive management module and performing consensus trigger conversion based on the information consensus model, the information flow in the cross-domain parking business can be effectively coordinated and managed to ensure the smooth progress of the business, while ensuring the security of the data and the collaboration of all parties.

[0032] Step S700: Acquire business execution information, and synchronously update and store parking information based on the intercommunication platform.

[0033] Specifically, we actively obtain the execution information of parking services. These execution information comes from a wide range of sources, including data collected by on-site equipment (such as sensors, cameras, etc.) in parking lots, feedback from user-side applications, and records of management systems. The execution information obtained is rich and diverse, covering detailed data such as the actual entry and exit time of vehicles, parking locations, parking duration, fee settlement, and whether there are any violations. After obtaining these execution information, they will be synchronized and updated on the intercommunication platform in a timely manner, which means that no matter other related systems, departments or users, they can obtain the latest and most accurate parking business execution status at the first time. At the same time, this information will be properly stored, and the storage method will take into account the security, integrity and queryability of the data, so as to facilitate subsequent data analysis, auditing, tracing, and provide reference for future decision-making. Through timely synchronization and proper storage, the accuracy and timeliness of parking information can be guaranteed, providing strong support for the efficient operation and continuous optimization of the entire parking management system.

[0034] In a possible implementation, step S100 further includes:

[0035] Step S110: Determine parking cross-domain dimensions, wherein the parking cross-domain dimensions include physical cross-domain, policy cross-domain and system cross-domain.

[0036] Step S120: traverse the parking information elements, determine a first topology network based on physical cross-domain, determine a second topology network based on policy cross-domain, and determine a third topology network based on system cross-domain.

[0037] Step S130: Fitting the first topology network, the second topology network, and the third topology network to generate the cross-domain information architecture.

[0038] Specifically, the first task is to clearly define the various dimensions involved in parking cross-domain. In terms of physical cross-domain, the focus is on the distribution and extension of parking lots in actual space, which includes parking lots spanning different administrative regions, such as extending from one district of a city to another, or spanning different functional areas, such as from a commercial area to a residential area. At the same time, attention should also be paid to factors such as the physical distance between parking lots, connecting channels, and surrounding road facilities, which will affect the convenience and efficiency of parking. Policy cross-domain focuses on the differences in parking policies formulated by different regions or management departments, which is reflected in the inconsistency of parking fee standards, some places charge by the hour, and some charge by the day; different parking time restrictions, some areas allow long-term parking, and some have strict restrictions; there are also parking policies for special vehicles, such as whether there are preferential policies for new energy vehicles. System cross-domain focuses on the diversity and differences of the parking management systems adopted, including differences in data storage and processing methods, user information management modes, parking space reservation and allocation mechanisms, etc. For example, some systems use traditional database management, while others are based on cloud services; some systems support online payment, while others may rely more on cash payment, etc.

[0039] Start to check and analyze the acquired parking information elements one by one. These information elements contain a large amount of complex and diverse data, such as the specific location coordinates of the parking lot, the number and status of parking spaces, the details of the charging policy, the technical parameters of the management system used, etc. During the traversal process, the first topological network is determined based on physical cross-domain, which means focusing on the distribution and relationship of parking lots in geographical space. For example, by analyzing the distance between each parking lot, the situation of connecting roads, the layout of surrounding buildings and facilities, etc., a network structure that can reflect the physical location and connection of parking lots is constructed. This first topological network can intuitively show the physical proximity and accessibility between parking lots. The second topological network is determined based on policy cross-domain. At this time, the information on parking policies will be studied in depth, such as the regulations on parking fee standards in different regions, parking restriction policies in different time periods, parking preferential policies for special vehicles, etc. By integrating and analyzing these policy information, a network reflecting policy differences and associations is constructed. This second topological network can help understand and grasp the impact of policies in different regions on the allocation and use of parking resources. The third topology network is determined based on system cross-domain, and the characteristics and differences of the parking management systems used in various parking lots are analyzed in detail, including data processing methods, system architecture, user interaction interface, and interface with external systems. Based on these system-level characteristics, a network structure that can reflect the interaction and coordination between different systems is constructed. This third topology network helps to evaluate the compatibility between systems and the smoothness of information flow. By determining these three topology networks based on physical cross-domain, policy cross-domain, and system cross-domain, a clear and specific structural foundation is provided for the subsequent comprehensive analysis and management of parking cross-domain issues.

[0040] The first topological network, the second topological network and the third topological network determined previously are fitted and merged. For the first topological network (based on physical cross-domain), each parking lot node is given some weights that reflect its physical characteristics, such as the size of the parking lot area, the number of parking spaces that can be accommodated, and the distance from the surrounding important landmarks or roads, while the weight of the edge connecting two parking lot nodes is set according to the actual geographical distance between them. The closer the distance, the greater the weight of the edge. For the second topological network (based on policy cross-domain), the weight assigned to the node can be the strictness of the parking policy, such as the level of charging standards, the maximum time limit for parking, etc. The weight of the edge is determined based on the degree of policy differences in the areas where the two parking lots are located. The greater the policy difference, the smaller the weight of the edge. For the third topological network (based on system cross-domain), the weight of the node can be the complexity of the parking management system, the strength of the data processing capability, etc. The weight of the edge is set according to the difficulty of interaction between the two parking management systems. The more difficult the interaction, the smaller the weight of the edge. Next, the Hungarian algorithm in the graph matching algorithm is used for matching. First, an initial matching matrix is ​​created, which records the possible matching relationships between the nodes in the three topological networks. Then, for each possible matching combination, the total weight corresponding to this matching is calculated. This total weight is calculated by comprehensively considering the weights of the nodes and edges in the three topological networks. During the calculation process, if it is found that two parking lots are very close in physical distance, but the parking policies in their areas are very different, or the parking management systems they use are very incompatible, then the matching weight between the two parking lots will be reduced accordingly. The matching relationship between the nodes is constantly adjusted, the total weight is recalculated, and this process is repeated until a matching solution is found that maximizes the total weight or meets the pre-set optimization conditions. The optimal matching solution finally obtained is the result of successfully fitting the three topological networks, thereby generating a cross-domain information architecture that can fully reflect the cross-domain situation of physics, policies, and systems.

[0041] In a possible implementation, step S200 further includes:

[0042] Step S210: traverse the cross-domain information architecture, and read the information interaction isolation feature according to the cross-domain heterogeneity of parking information management.

[0043] Step S220: Based on the information interaction isolation feature, determine an information consensus mechanism, wherein the information consensus mechanism includes a first consensus mechanism based on the first topology network, a second consensus mechanism based on the second topology network, and a third consensus mechanism based on the third topology network.

[0044] Step S230: Based on the information consensus mechanism, data-driven modeling is performed to generate an information consensus model.

[0045] Specifically, a comprehensive and in-depth inspection of the cross-domain information architecture that has been built integrates parking information from different regions, following different policies and based on different systems. During the traversal process, problems will be found based on the cross-domain heterogeneity in parking information management. Cross-domain heterogeneity is reflected in many aspects. For example, parking lots in different regions use completely different data formats to record parking information, some in text form, and some in specific encoding formats; under different policy environments, the calculation method of parking time, the setting of charging standards, and the treatment of special vehicles may be very different; different parking management systems also have differences in information storage structure, data update frequency, and interaction with external systems. Based on these cross-domain heterogeneities, read the features that cause obstacles or isolation between different parts of the information. For example, it is found that the data of some parking lots cannot communicate directly with the systems in other regions, or there are logical conflicts when integrating parking information in different policy areas, or the data generated by some systems are incompatible in format when integrated with the overall architecture. Through careful traversal and cross-domain heterogeneity, these information interaction isolation features can be accurately identified and read, laying the foundation for subsequently solving information interaction barriers and establishing a consensus mechanism.

[0046] The first consensus mechanism is determined for the information interaction isolation characteristics based on the first topology network (physical cross-domain). This includes establishing a unified geographic coordinate system so that parking lots in different physical locations can be accurately identified and located under the same standard, and formulating standardized distance measurement methods to ensure consistency when evaluating the physical distance or accessibility between parking lots. For the characteristics based on the second topology network (policy cross-domain), the second consensus mechanism will focus on solving the problems caused by policy differences, such as formulating a set of universal fee conversion rules, so that different fee standards can be converted into a unified calculation method regardless of the policy area. At the same time, a unified parking time measurement standard is clarified to avoid confusion caused by different policies. Based on the characteristics of the third topology network (system cross-domain), the third consensus mechanism will be committed to eliminating system differences, which involves stipulating a unified data storage and transmission format, requiring each parking management system to store and send data in the same format. Establish a standard interface protocol so that different systems can communicate and exchange data according to unified rules. By clarifying these three consensus mechanisms for different topological networks, we can specifically solve the problem of information interaction isolation caused by physical cross-domain, policy cross-domain and system cross-domain, thus laying the foundation for efficient, accurate and consistent parking information management.

[0047] For the consensus mechanism based on the first topological network (physical cross-domain), the K-Means clustering algorithm was used to obtain the geographic coordinate data of many parking lots, including longitude and latitude. First, several initial cluster center points were randomly selected, and the Euclidean distance from each parking lot coordinate to these center points was calculated. According to the distance, the parking lot is divided into the cluster represented by the nearest center point. Then, the new center point of each cluster is recalculated and determined by the mean. This process of assigning and recalculating the center point is repeated until the center point no longer changes significantly or the predetermined number of iterations is reached. In this way, through the clustering results, geographically similar parking lots can be classified into one category. For the same type of parking lots, a unified physical distance measurement standard is established, thereby forming an information consensus mechanism for physical cross-domain. For the consensus mechanism based on the second topological network (policy cross-domain), the linear regression algorithm is used. By collecting parking fee standard data in different regions and related policy factors, such as parking time, regional type, etc., a linear equation is constructed with the fee standard as the dependent variable and the policy factor as the independent variable. Then, a large amount of historical data is used for training, and the regression coefficient is continuously adjusted through methods such as the least squares method, so that the model can predict the charging standards as accurately as possible. When faced with new policy factors, the trained model can be used to obtain reasonable charging predictions, and then a unified charging conversion rule and policy interpretation standard are formulated to form a cross-domain policy information consensus mechanism. For the consensus mechanism based on the third topology network (system cross-domain), data standardization and normalization methods are adopted. There are data in various formats and units in different parking management systems. For example, time has different recording methods such as hours, minutes, and seconds, and the amount uses different monetary units and precision. The time data is uniformly converted to the same unit, such as all recorded in seconds. For the amount data, it is uniformly converted to a specific monetary unit and the same number of decimal places is specified. Through such standardization, the differences in data formats and units between different systems are eliminated, so that data can be processed and compared under a unified framework, and a system cross-domain information consensus mechanism is established. After completing the above algorithm application and model establishment for each topology network, these models are integrated and optimized to form a comprehensive information consensus model.

[0048] In a possible implementation, step S300 further includes:

[0049] Step S310: Setting a tag mode for parking information.

[0050] Step S320: traverse the cross-domain information architecture, determine the data flow type and key information architecture nodes, and assign codes and labels based on the label mode.

[0051] Step S330: Use label transfer and label change to assist in interactive tracking of the parking data stream trajectory.

[0052] Specifically, a systematic and clear labeling model is designed for parking information. First, the classification dimension of the label needs to be determined. For example, it can be divided according to the area to which the parking lot belongs, such as commercial area, residential area, office area, etc.; it can also be based on the parking time period, such as daytime, nighttime, peak hours, non-peak hours, etc.; it can also be based on the type of vehicle, such as small cars, SUVs, trucks, etc. Then, set specific label values ​​for each classification dimension. When setting the labeling model, ensure that the label is concise, unique, and easy to understand, so as to facilitate subsequent identification, processing, and analysis. At the same time, the scalability of the labeling model should be taken into account so that new labels can be easily added when there are new classification needs in the future.

[0053] Starting from the beginning of the architecture, we will gradually advance and identify and clarify the type of data flow we encounter. For example, the instantaneous data of each car entering the parking lot captured in real time is a real-time inflow data flow; the data calculated by summing up the parking fees of parking lots in each area every day is a periodic summary data flow; and the data such as vehicle violation records transmitted between different parking management systems are cross-system interactive data flows. In addition to determining the type of data flow, we will also find out the nodes that play a key role in the entire architecture, such as the central server that undertakes core data processing and a large amount of storage work; the exchange center that can connect and integrate data from parking lots in multiple different areas; the interface node responsible for data exchange with external parties such as urban traffic management systems, etc. These are all key information architecture nodes. According to the previously set label mode, the data flow and nodes are coded and labeled. If the information is cross-domain, an additional specific identification bit is added to the label. For example, "IC" is used to indicate cross-domain information. Assuming that the parking data now indicates that a parking lot has no vacant parking spaces and is in a full state, the label may be set to "FULL-STATUS". For example, if the data is about parking fee settlement transmitted from city A to city B, the label can be "AB-COST-TRANSFER". Or if it is data reflecting that parking spaces are always tight in a specific area of ​​a parking lot during a specific time period, the label may be "TIGHT-SECTION-TIME". Through such specific, detailed and targeted coding and labeling operations, each data flow and key node has a label that can accurately reflect its own characteristics and status, which makes the data flow and the important role of key nodes in the entire cross-domain information architecture clear and graspable at a glance, providing a solid and reliable foundation for subsequent data analysis, rational allocation of resources and making relevant decisions.

[0054] Utilize transfers and changes of previously assigned tags to help track parking numbers

[0055] According to the flow trajectory of data flow and the interaction between them, when parking data is transferred from one location to another in the cross-domain information architecture, the attached labels will also be transferred. By monitoring the transfer path of the label, we can clearly understand how the data flows between different nodes, systems and areas. For example, the label of the parking start data of a vehicle is "Start-Zone A". When this data is transferred to the node that processes the charge, the label changes to "Charging-Zone A", and then when it is transferred to the data storage node, it becomes "Stored-Zone A". By tracking the changes of these labels, we can know exactly where the data flows at every step in the processing process. At the same time, the change of labels can also reflect the change of data status. For example, the label "Empty-Slot" that originally represents an empty parking space is changed to "Occupied-Slot" after a vehicle is parked. By paying close attention to the transfer and change of labels, we can fully, real-time and accurately grasp the dynamics of the parking data flow, including the source of the data, the path it passes through, the processing steps and the change of status. This helps to timely discover anomalies or problems in data flow, optimize the data processing process, and improve the efficiency and reliability of the parking management system.

[0056] In a possible implementation, step S600 further includes:

[0057] Step S610: The dual-drive management module includes a data flow management unit and a security management unit.

[0058] Step S620: For the cross-domain parking service, determine a first information strategy and a second information strategy based on the data flow management unit and the security management unit.

[0059] Step S630: Coordinate the first information policy and the second information policy to determine the business information management policy.

[0060] Specifically, the dual-drive management module is set up to effectively manage and control related businesses. Among them, the data flow management unit mainly focuses on the flow process of data. It is responsible for monitoring the generation, collection, transmission, processing and storage of data. For example, it determines the frequency and method of collecting data from different parking lots, plans the transmission path of data in the network to reduce delays and improve efficiency, decides how to process large amounts of data quickly and accurately, and plans appropriate storage space to save data. The security management unit focuses on data security protection. It involves formulating and implementing access control policies to ensure that only authorized personnel can access and operate specific data, and uses encryption technology to encrypt sensitive data to prevent data from being stolen or tampered with during transmission and storage. At the same time, it is responsible for security auditing, monitoring and recording data operation activities, so as to promptly discover and respond to any potential security threats or violations. In short, the data flow management unit and the security management unit together constitute the dual-drive management module, which play a role in the two key aspects of efficient data processing and security protection, so as to achieve effective management and stable operation of related businesses.

[0061] Specific information strategies are formulated specifically for cross-domain parking services. Based on the data flow management unit, the first information strategy is determined, which includes the following aspects: data collection strategy, which determines the frequency of data collection from parking lots in different regions, such as hourly, daily or real-time collection, and the specific content of the collection, such as vehicle entry time, departure time, parking space usage, etc. Data transmission strategy, which plans the transmission method and path of data between different regions and systems. For example, use a high-speed dedicated line network to ensure fast data transmission, or use a distributed storage architecture to disperse data storage pressure. Data processing strategy, which determines how to quickly process and analyze the large amount of collected cross-domain parking data, and uses cloud computing platforms or high-performance servers to improve processing speed. Based on the security management unit, the second information strategy is determined, which includes: access control strategy, which clarifies the access rights level of different users or systems to cross-domain parking data, such as administrators have full access rights, while ordinary users can only view part of the data. Encryption strategy, which selects a suitable encryption algorithm to encrypt sensitive parking data, such as user payment information, vehicle information, etc., to ensure the confidentiality of data during transmission and storage. Backup and recovery strategy, formulate regular data backup plan, and quick recovery plan when data is damaged or lost, to ensure business continuity. By determining these two targeted information strategies based on data flow management unit and security management unit respectively, it can provide comprehensive and effective information management guidance for cross-domain parking business.

[0062] In this step, the focus is on coordinating the first information strategy (based on the data flow management unit) and the second information strategy (based on the security management unit) determined earlier to form a unified and complete business information management strategy. The coordination process is not a simple merger, but to find a balance and the best combination between the two. For example, in order to improve the data processing speed, the first information strategy tends to adopt a more efficient but less secure data transmission method, while the second information strategy emphasizes data encryption and strict access control, which will affect the transmission speed to a certain extent. When coordinating, it is necessary to comprehensively consider the needs of the business and the risk tolerance. If the cross-domain parking business has extremely high requirements for data real-time, then the data transmission method can be appropriately optimized on the basis of ensuring a certain security level to speed up the processing speed. However, if the security of the data is critical, it is necessary to make certain compromises in speed and adopt more stringent encryption and access control measures. Through repeated evaluation and adjustment, the first information strategy and the second information strategy complement and support each other, ensuring that data can be transmitted and processed quickly and smoothly in a cross-domain environment to meet the efficient operation requirements of the business, and fully guaranteeing the security of the data to prevent any potential security threats. The finalized business information management strategy should achieve the best balance between data flow efficiency and data security, effectively guide the information management of cross-domain parking business, and promote the stable development and continuous optimization of the business.

[0063] In a possible implementation, step S610 further includes:

[0064] Step S610: traverse the parking information elements to determine the sensitive information category.

[0065] Step S620: Determine a differential security mode for the sensitive information class, wherein the differential security mode balances information value and information security and processes information with differential relaxation.

[0066] Step S630: Identify the cross-domain information architecture and determine a channel security mode for the channel network.

[0067] Step S640: coordinate the differential security mode and the channel security mode to perform data security management.

[0068] Specifically, all parking-related information elements are checked and evaluated one by one. Parking information elements include many aspects, such as the owner's contact number, the vehicle's license plate number, the specific parking location (accurate to the parking space number), the start and end time of parking, the payment method, whether a coupon or discount code is used, etc. In the process of traversing these elements, determine which ones belong to the sensitive information category based on relevant laws and regulations, industry standards, and an assessment of the potential risks of the information. Generally speaking, information such as ID card numbers and bank card numbers that can be directly related to personal finances and identities will be clearly identified as sensitive information. Through such a comprehensive and detailed traversal and judgment, the sensitive information categories in parking information can be accurately divided, laying the foundation for subsequent targeted security protection measures.

[0069] When faced with the identified sensitive information categories, this step aims to establish a differential security model for them. The core of this differential security model is to cleverly balance the value and security of information. First, the value of each type of sensitive information will be evaluated. The value of information depends on factors such as its importance to business operations, the economic impact it brings, or the degree of impact on user rights. For example, information such as the owner's bank card number that directly involves funds is evaluated as extremely high in value. Information such as the start time of parking is relatively low in value, but still has certain significance. At the same time, the security requirements of each type of sensitive information are analyzed. This includes considering the risk level caused by information leakage, such as personal privacy leakage, property loss, legal liability, etc. Then, according to the value and security evaluation results of the information, different differential relaxations are set for information processing. For sensitive information with high value and extremely high security requirements, such as bank card numbers, the most stringent security measures will be adopted, including advanced encryption algorithms, multiple authentication, strict access control, etc. The differential relaxation is extremely low, and almost no security compromise is allowed. For sensitive information of relatively low value but still requiring certain protection, such as the start time of parking, relatively loose but still effective security measures will be adopted, such as conventional encryption, appropriate access permission settings, etc. The differential relaxation is relatively high, which allows a moderate trade-off between security and processing efficiency to a certain extent. In this way, the differential security mode can flexibly and reasonably configure security resources and measures according to the characteristics of different sensitive information categories, which not only ensures the high security of important information, but also improves the efficiency and cost-effectiveness of overall information processing to a certain extent.

[0070] First of all, we need to accurately identify and deeply understand the entire cross-domain information architecture. The cross-domain information architecture covers the structure and methods of parking information exchange and integration between different regions and different systems. The channel network part needs to be analyzed in detail. The channel network is a channel for data transmission, including wired networks, wireless networks, dedicated lines and other forms. When determining the channel security mode, multiple factors need to be considered. For example, the stability and reliability of the channel, assessing whether it is susceptible to interference or interruption; the bandwidth and transmission speed of the channel, which will affect the implementation effect of security measures such as encryption; the security and credibility of the terminal equipment connected to the channel. Based on these factors, the appropriate channel security mode can be determined. Possible modes include but are not limited to the following: encrypted transmission, encrypting the data transmitted in the channel to ensure that even if the data is intercepted, it is difficult to interpret. Identity authentication, strict identity authentication of devices and users connected to the channel to prevent unauthorized access. Traffic monitoring, real-time monitoring of data traffic in the channel, timely detection of abnormal traffic fluctuations, indicating attacks or data leaks. Firewall setting, setting firewalls at the entrance and exit of the channel to prevent illegal access and malicious data from entering. By comprehensively considering the above factors and determining the appropriate channel security mode, the data transmission security of the channel network part in the cross-domain information architecture can be effectively guaranteed.

[0071] The key point is to coordinate the differential security mode and channel security mode identified above to achieve comprehensive and efficient data security management. An effective communication and coordination mechanism between the two security modes is established, which involves creating a unified security management platform or framework in which the differential security mode and channel security mode can interact and cooperate with each other. For example, when sensitive information is ready to be transmitted through a channel, the differential security mode will determine the corresponding encryption level and access control policy based on the category and importance of the information. At the same time, the channel security mode will select the appropriate data transmission method and encryption algorithm based on the characteristics of the channel and the current security status. In the actual data processing process, collaborative work is reflected in each link. When data is generated or collected, the differential security mode first classifies and evaluates it to determine its sensitivity. Then, before the data enters the channel for transmission, the channel security mode will further process the data securely according to the current network environment and transmission requirements to ensure its security during transmission. In the data storage process, the differential security mode will determine the storage location and encryption method based on the importance of the data, while the channel security mode will ensure the security of the connection between the storage device and the external network. Through continuous monitoring and feedback, the two modes can dynamically adjust and optimize security policies according to actual conditions. If a security threat is found in the channel transmission, the channel security mode will immediately take measures, such as interrupting the transmission or launching an emergency plan, and notify the differential security mode to adjust the processing strategy for related sensitive information. By coordinating the differential security mode and the channel security mode, a seamless and comprehensive data security management system can be formed to protect the security and integrity of cross-domain parking information to the greatest extent, reduce the risk of data leakage and tampering, and ensure the normal and safe operation of the business.

[0072] In a possible implementation, step S700 further includes:

[0073] Step S710: If it is the parking peak phase, generate parking guidance information based on the parking service to provide parking guidance to the user.

[0074] Specifically, the relevant data of parking is continuously monitored to determine whether it has entered the peak parking stage. The peak parking stage usually refers to a state in which the parking demand reaches a high level within a specific time period, and the parking space utilization rate of the parking lot is close to or reaches saturation. When it is determined that the current parking peak stage is in progress, parking guidance information will be generated based on the current parking business situation. This process will comprehensively consider various factors and data, such as collecting the real-time parking space occupancy of each parking lot, including the number of parked vehicles and the number of remaining empty parking spaces, and obtaining detailed information such as the location information, charging standards, and opening hours of the parking lot. In addition, the traffic conditions of the surrounding roads, such as whether there is congestion, road construction, etc., as well as the user's historical parking preferences and behavior data, will also be considered. Based on these rich data and factors, the generated parking guidance information may be presented to users in various forms. Text prompts can be pushed through mobile phone applications, such as "There are 10 vacancies in the nearby parking lot A, the charge is 5 yuan per hour, the current road is relatively smooth, it is recommended to go there." It can also be directly marked on the electronic map The location of the parking lot with vacancies and provide navigation routes, or in the form of voice broadcasts, provide real-time parking guidance for users during driving. The detailed and targeted parking guidance information generated in this way can effectively help users make quick decisions during peak parking periods, save time in finding parking spaces, improve parking efficiency and convenience, and also help alleviate traffic congestion and pressure during peak parking periods.

[0075] Embodiment 2

[0076] Based on the same inventive concept as the method for managing cross-regional parking information in a smart city in the aforementioned embodiment, Figure 2 As shown, the present application provides a smart city cross-regional parking information management system, and the system and method embodiments in the present application embodiments are based on the same inventive concept. The system includes:

[0077] The cross-domain information architecture construction module 10 is used to read parking information elements within a preset area, perform integrated integration, and construct a cross-domain information architecture.

[0078] The information consensus model construction module 20 is used to traverse the cross-domain information architecture, read the information interaction isolation characteristics, perform shared consensus processing, and construct an information consensus model, wherein the information consensus model is deployed in the upper layer of the intercommunication platform.

[0079] The dual-drive management construction module 30 is used to traverse the cross-domain information architecture, perform coding and labeling of data nodes, and construct a dual-drive management module based on data flow and data security.

[0080] The parking service generation module 40 generates parking services based on the parking authority authorized by the user.

[0081] The cross-domain determination module 50 is used to perform a cross-domain determination on the parking service based on the parking authority as a constraint.

[0082] The strategy execution module 60 is used to determine the business information management strategy based on the dual-drive management module if it is a cross-domain parking business, and to perform consensus-triggered conversion of strategy execution based on the information consensus model.

[0083] The business execution information acquisition module 70 is used to acquire business execution information and synchronously update and store parking information based on the intercommunication platform.

[0084] Furthermore, the cross-domain information architecture construction module 10 also includes:

[0085] A parking cross-domain dimension determination unit, the parking cross-domain dimension determination unit is used to determine the parking cross-domain dimension, wherein the parking cross-domain dimension includes physical cross-domain, policy cross-domain and system cross-domain; a topology network determination unit, the topology network determination unit is used to traverse the parking information elements, determine the first topology network based on physical cross-domain, determine the second topology network based on policy cross-domain, and determine the third topology network based on system cross-domain; a cross-domain information architecture generation unit, the cross-domain information architecture generation unit is used to fit the first topology network, the second topology network and the third topology network to generate the cross-domain information architecture.

[0086] Furthermore, the information consensus model building module 20 also includes:

[0087] An information interaction isolation feature reading unit, the information interaction isolation feature reading unit is used to traverse the cross-domain information architecture and read the information interaction isolation feature according to the cross-domain heterogeneity of parking information management; an information consensus mechanism determination unit, the information consensus mechanism determination unit determines the information consensus mechanism based on the information interaction isolation feature, the information consensus mechanism includes a first consensus mechanism based on the first topological network, a second consensus mechanism based on the second topological network and a third consensus mechanism based on the third topological network; an information consensus model generation unit, the information consensus model generation unit performs data-driven modeling based on the information consensus mechanism to generate an information consensus model.

[0088] Furthermore, the dual-drive management building module 30 also includes:

[0089] A label mode setting unit, the label mode setting unit is used to set the label mode for parking information; a coding and labeling unit, the coding and labeling unit is used to traverse the cross-domain information architecture, determine the data flow type and key information architecture nodes, and perform coding and labeling based on the label mode; a trajectory interaction tracking unit, the trajectory interaction tracking unit assists in trajectory interaction tracking of parking data streams through label transfer and label change.

[0090] Furthermore, the policy execution module 60 also includes:

[0091] The dual-drive management module includes a data flow management unit and a security management unit; an information policy determination unit, which is used to determine the first information policy and the second information policy for the cross-domain parking business based on the data flow management unit and the security management unit; and a business information management policy determination unit, which is used to coordinate the first information policy and the second information policy to determine the business information management policy.

[0092] Furthermore, the policy execution module 60 also includes:

[0093] A sensitive information determination unit, the sensitive information determination unit is used to traverse the parking information elements and determine the sensitive information class; a differential security mode determination unit, the differential security mode determination unit is used to determine the differential security mode for the sensitive information class, wherein the differential security mode balances the information value and information security and performs information processing with differential relaxation; a channel security mode determination unit, the channel security mode determination unit is used to identify the cross-domain information architecture and determine the channel security mode for the channel network; a data security management unit, the data security management unit is used to coordinate the differential security mode and the channel security mode to perform data security management.

[0094] Furthermore, the service execution information acquisition module 70 further includes:

[0095] A parking guidance unit is used to generate parking guidance information based on the parking service to provide parking guidance to users if it is a parking peak period.

[0096] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0097] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0098] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for managing cross-regional parking information in a smart city, characterized in that: The method comprises: Read parking information elements within a preset area, integrate them, and build a cross-domain information architecture; Traversing the cross-domain information architecture, reading the information interaction isolation features, performing shared consensus processing, and building an information consensus model, wherein the information consensus model is deployed in the upper layer of the intercommunication platform; Traverse the cross-domain information architecture, code and label data nodes, and build a dual-drive management module based on data flow and data security; Authorize parking rights based on the user end and generate parking services; Taking the parking authority as a constraint, performing cross-domain determination on the parking service; If it is a cross-domain parking business, the business information management strategy is determined based on the dual-drive management module, and the consensus trigger conversion of the strategy execution is performed based on the information consensus model; Obtain business execution information, and synchronize and update and store parking information based on the interoperable platform; The construction of a cross-domain information architecture includes: Determining parking cross-domain dimensions, wherein the parking cross-domain dimensions include physical cross-domain, policy cross-domain and system cross-domain; Traversing the parking information elements, determining a first topology network based on physical cross-domain, determining a second topology network based on policy cross-domain, and determining a third topology network based on system cross-domain; Fitting the first topological network, the second topological network and the third topological network to generate the cross-domain information architecture; The information consensus model is constructed, including: Traversing the cross-domain information architecture, and reading information interaction isolation features according to the cross-domain heterogeneity of parking information management; Based on the information interaction isolation feature, determining an information consensus mechanism, wherein the information consensus mechanism includes a first consensus mechanism based on the first topology network, a second consensus mechanism based on the second topology network, and a third consensus mechanism based on the third topology network; Based on the information consensus mechanism, data-driven modeling is performed to generate an information consensus model; Traversing the cross-domain information architecture and labeling data nodes, including: Set label mode for parking information; Traversing the cross-domain information architecture, determining the data flow type and key information architecture nodes, and performing coding and labeling based on the labeling mode; Assist in interactive tracking of parking data streams through label transfer and label change; Determining a business information management strategy based on the dual-driver management module includes: The dual-drive management module includes a data flow management unit and a security management unit; For the cross-domain parking service, determining a first information strategy and a second information strategy based on the data flow management unit and the security management unit; The first information strategy and the second information strategy are coordinated to determine the business information management strategy.

2. A smart city cross-region parking information management method as claimed in claim 1, characterized in that: The dual-drive management module includes a security management unit, including: Traversing the parking information elements to determine sensitive information categories; Determine a differential security mode for the sensitive information class, wherein the differential security mode balances information value and information security and processes information with differential relaxation; Identify the cross-domain information architecture and determine a channel security mode for the channel network; The differential security mode and the channel security mode are coordinated to perform data security management.

3. A smart city cross-region parking information management method as claimed in claim 1, characterized in that: The method further comprises: If it is the parking peak period, parking guidance information is generated based on the parking service to provide parking guidance to users.

4. A smart city cross-regional parking information management system, characterized in that: A method for managing cross-regional parking information in a smart city according to any one of claims 1 to 3, the system comprising: A cross-domain information architecture construction module, which is used to read parking information elements within a preset area, perform integrated integration, and construct a cross-domain information architecture; An information consensus model construction module, which is used to traverse the cross-domain information architecture, read the information interaction isolation characteristics, perform shared consensus processing, and build an information consensus model, wherein the information consensus model is deployed in the upper layer of the intercommunication platform; A dual-drive management construction module, which is used to traverse the cross-domain information architecture, perform coding and labeling of data nodes, and construct a dual-drive management module based on data flow and data security; A parking service generation module, which generates parking services based on the parking authority authorized by the user terminal; A cross-domain determination module, the cross-domain determination module is used to perform a cross-domain determination on the parking service based on the parking authority as a constraint; A strategy execution module, wherein the strategy execution module is used to determine the business information management strategy based on the dual-drive management module and perform consensus trigger conversion of strategy execution based on the information consensus model if it is a cross-domain parking business; A business execution information acquisition module, which is used to acquire business execution information and synchronously update and store parking information based on the intercommunication platform; The cross-domain information architecture construction module further includes: A parking cross-domain dimension determination unit, the parking cross-domain dimension determination unit is used to determine the parking cross-domain dimension, wherein the parking cross-domain dimension includes physical cross-domain, policy cross-domain and system cross-domain; a topology network determination unit, the topology network determination unit is used to traverse the parking information elements, determine a first topology network based on physical cross-domain, determine a second topology network based on policy cross-domain, and determine a third topology network based on system cross-domain; a cross-domain information architecture generation unit, the cross-domain information architecture generation unit is used to fit the first topology network, the second topology network and the third topology network to generate the cross-domain information architecture; The information consensus model building module also includes: An information interaction isolation feature reading unit, the information interaction isolation feature reading unit is used to traverse the cross-domain information architecture and read the information interaction isolation feature according to the cross-domain heterogeneity of parking information management; an information consensus mechanism determination unit, the information consensus mechanism determination unit determines the information consensus mechanism based on the information interaction isolation feature, the information consensus mechanism includes a first consensus mechanism based on the first topological network, a second consensus mechanism based on the second topological network and a third consensus mechanism based on the third topological network; an information consensus model generation unit, the information consensus model generation unit performs data-driven modeling based on the information consensus mechanism to generate an information consensus model.

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