A network-disconnection self-recovery scenic spot ticket passage management system

Abstract

This invention relates to the field of management system technology, specifically disclosing a scenic area ticketing and access management system with self-healing capabilities in the event of a network outage. The system includes a cloud server and multiple gate terminals deployed at various access nodes within the scenic area, supporting close-range peer-to-peer communication. The gate terminals are used for full bidirectional communication with the cloud server when online, synchronously acquiring ticketing authorization policies, global blacklist data, and system baseline parameters, and uploading local access records and device operating status. When disconnected from the cloud, a self-healing mechanism is automatically triggered. The gate terminals of this invention support close-range peer-to-peer communication and can be flexibly deployed at all access nodes in the scenic area, including entrances, exits, intersections of attractions within the park, boarding and alighting points for transportation within the park, and points for redemption. In the event of a network outage, they can automatically detect adjacent nodes and build a self-organizing network cluster without requiring additional local servers or other supporting facilities.

Description

Technical Field

[0001] This invention relates to the field of management system technology, specifically to a scenic area ticketing and access management system with self-healing capabilities in the event of a network outage. Background Technology

[0002] With the deepening of digital transformation in the cultural tourism industry, the construction of smart scenic spots has become a core direction for industry development. As a core infrastructure for visitor flow control, ticket verification, and operational scheduling, the ticketing and access management system's operational stability, verification accuracy, and emergency response capabilities directly determine the scenic spot's operational efficiency, service quality, and revenue security. Currently, mainstream scenic spot ticketing and access systems in the industry generally adopt a centralized architecture of "centralized cloud management + front-end gate execution." All ticket authorization verification, anti-reuse conflict detection, and access decisions rely on real-time online interaction between the gate terminal and the cloud server. The front-end gate terminal only undertakes the basic functions of collecting voucher information and executing cloud commands.

[0003] In actual scenic area operations, most scenic areas are located in suburban and mountainous regions with relatively weak network infrastructure. Wide area networks are susceptible to factors such as extreme weather, base station failures, network congestion during holidays with large crowds, and fiber optic cable construction interruptions, leading to prolonged and widespread network outages. Existing ticketing systems based on this centralized architecture suffer from numerous insurmountable technical shortcomings in network outage scenarios: First, the system is heavily reliant on wide area networks, rendering its basic functions paralyzed in the event of a network outage. The core decision-making logic of the existing system is deployed in the cloud. After a network outage, the gate terminals cannot interact with the cloud in real time, making it impossible to perform ticket compliance verification and anti-reuse control. This often results in only two extreme solutions: one is to close the gates and prohibit all passage, which could easily cause large-scale tourist congestion, leading to customer complaints and public opinion risks, severely impacting the normal operation of the scenic area; the other is to downgrade to a no-verification release mode, completely losing ticket control capabilities, resulting in ticket revenue losses and security loopholes such as overcrowding.

[0004] Secondly, existing network outage emergency solutions lack sufficient control capabilities and cannot balance access efficiency and ticketing security. Some existing technologies propose network outage emergency solutions based on locally cached data, but they can only achieve basic credential verification at a single node. They cannot achieve data synchronization and collaborative control between multiple gate nodes, nor can they solve the problem of preventing the reuse of the same ticket credential across multiple nodes, resulting in serious ticketing control vulnerabilities. At the same time, the local verification permissions of existing solutions are fixed preset values ​​and cannot be dynamically adjusted according to the historical operational reliability of the gate equipment, network environment, and node collaboration status. This leads to the dual problems of insufficient authorization for high-reliability devices and excessive authorization for low-reliability devices, making them unsuitable for complex scenic area operation scenarios.

[0005] Third, the lack of a robust data self-healing mechanism after network recovery fails to guarantee global data consistency. Existing emergency network outage solutions can only achieve simple batch uploading of offline access data after network recovery, lacking an intelligent scheduling mechanism for multi-terminal synchronization. When a large number of gate terminals initiate data synchronization simultaneously, it can easily cause cloud network congestion, synchronization interruption, or even data loss. Furthermore, for abnormal data such as time conflicts, frequency conflicts, and status conflicts of the same ticket voucher uploaded from multiple nodes, existing solutions lack a scientific and compliant intelligent arbitration mechanism, relying solely on manual verification and handling, resulting in extremely low efficiency and a high risk of ticket reconciliation errors and tourist disputes, thus failing to guarantee the consistency of global ticket data.

[0006] Fourth, the system's control chain is fragmented, making continuous performance iteration and optimization impossible. The existing technology's online centralized control, network outage emergency response, and network restoration data repair are independent of each other, failing to form a fully closed-loop management architecture. During network outages, equipment operation data and decision execution effects cannot be used to iteratively optimize system control strategies, hindering the continuous improvement of gate equipment reliability and verification decision accuracy, and making it difficult to adapt to the long-term and complex operation and management needs of scenic areas. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a scenic area ticketing and access management system with self-healing capabilities in the event of network outages, thus solving the problems mentioned in the background section.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: a scenic area ticketing and access management system with self-healing after network outage, including a cloud server and multiple gate terminals deployed at various access nodes in the scenic area that support close-range peer-to-peer communication; The gate terminal is used to communicate bidirectionally with the cloud server in the online state, synchronously obtain ticketing authorization policies, global blacklist data and system benchmark parameters, and upload local access records and device operating status; when disconnected from the cloud, it automatically triggers a network disconnection self-healing mechanism, first adaptively switching to single-node autonomous mode or distributed cluster collaborative mode based on the detection results of adjacent nodes, and then completing independent verification / multi-node collaborative verification and release decision of access requests based on locally stored valid data and pre-trained decision logic, and synchronously generating offline access transaction logs with globally unique identifiers; after the network is restored to the cloud, it automatically triggers incremental data synchronization and state consistency repair. The cloud server is used to centrally manage all gate terminals, dynamically distribute policies, maintain global data, and iterate the credit scoring across the entire chain when the network is online. After the network is restored, it performs multi-node hierarchical incremental synchronous scheduling, intelligent arbitration of offline data conflicts, and global data consistency self-healing. Simultaneously, it iterates the arbitration results and policy updates back to the gate terminals, forming a closed-loop management architecture of online centralized management, distributed autonomy when the network is down, and global self-healing when the network is restored.

[0009] Preferably, the gate terminal includes a distributed communication unit, a data synchronization manager, a hierarchical decision engine, a local transaction storage unit, and a device status perception unit; The distributed communication unit includes a wide-area communication module for communicating with the cloud, and a short-range peer-to-peer communication module that supports LoRa and Bluetooth Mesh protocols. It is used for bidirectional data interaction with the cloud in the online state, and to complete node detection of adjacent gate terminals, self-organizing network cluster construction and point-to-point synchronization of passage data in the offline state. The data synchronization manager is used to perform incremental data synchronization with the cloud server based on state differences when the network is connected. During the synchronization process, the granularity and frequency of the synchronization data packets are dynamically adjusted according to the network bandwidth, terminal load and data urgency level. At the same time, the local clock and the cloud global clock are synchronized at the millisecond level. After the network is restored, the offline transaction log is resumed and the consistency verification and repair of the local data are performed. The hierarchical decision engine includes a single-node autonomous decision submodule and a distributed collaborative decision submodule. It is used to activate the corresponding submodule according to the cluster network status in the offline state. Based on the valid dataset last synchronized by the data synchronization manager, it performs multi-dimensional compliance verification, anti-reuse conflict detection and release / interception decisions on the ticket vouchers collected on site. At the same time, it persists the decision results and the original voucher data to the local transaction storage unit. The local transaction storage unit is a non-volatile storage medium with power failure protection, used to persistently store ticketing authorization policies, global blacklists, system benchmark parameters and credit score data issued by the cloud, as well as the full offline access transaction logs generated locally. All stored data is equipped with a global timestamp and digital signature to prevent data tampering. The device status sensing unit is used to collect data on the wide area network connectivity status, the communication status of adjacent nodes, the terminal operating load, the verification module operating status, and the status of the access mechanism in real time, providing a status basis for the mode switching of the hierarchical decision engine and the adjustment of the synchronization strategy of the data synchronization manager.

[0010] Preferably, the ticketing authorization strategy includes a dynamic authorization time window with credit gain characteristics, which is dynamically generated and distributed by the cloud server. The dynamic authorization time window is the effective time range within which the gate terminal can independently execute verification decisions after the network is disconnected. When the network is disconnected, the hierarchical decision engine only performs verification decisions on ticket vouchers whose request passage time falls after the start time of the dynamic authorization time window and whose end time is within the preset delay tolerance period; for ticket requests whose request passage time exceeds the above effective range, the hierarchical decision engine directly intercepts them and generates an interception event log with the timeout reason. The length of the dynamic authorization time window The cloud server dynamically adjusts the gate terminal's overall credit score and cluster collaboration status. The basic calculation formula is as follows: ; In the formula, The default base authorization time window length for the system is a fixed baseline value; This is the terminal credit adjustment coefficient. These are the cluster collaboration gain coefficients, all of which are fixed weight values ​​preset by the system; This is the real-time comprehensive credit score for the gate terminal, with a value range of [value range missing]. ; The real-time cluster credit score of the cluster to which the gate terminal belongs, with a value range of [value range missing]. It is used to characterize the data reliability of multi-node collaboration under network outage conditions.

[0011] Preferably, the comprehensive credit score of the gate terminal The calculation is performed iteratively by the cloud server at fixed intervals and then distributed to the corresponding terminals. The calculation formula is as follows: ; In the formula, The weight coefficients for each dimension satisfy... ; For data synchronization compliance, the range of values ​​is... The data is calculated based on the terminal's historical synchronization success rate, synchronization clock deviation, and data integrity rate, and is used to characterize the reliability of data synchronization between the terminal and the cloud. For decision validity, the range of values ​​is... The accuracy of the terminal's offline decision-making is calculated based on the compliance rate, conflict record ratio, and false release / false interception rate of the terminal's historical offline decisions. For equipment operational stability, the value range is... The reliability of the terminal hardware operation is calculated based on the terminal's online rate, hardware failure rate, and verification module recognition accuracy. The cluster credit score The calculation formula is: ; In the formula, This represents the number of valid nodes within the self-organizing network cluster after network outage. For the first in the cluster The comprehensive credit score of each valid node ensures that the cluster credit score is correlated with the individual node credit score, enabling dynamic and coordinated adjustment of the authorization time window.

[0012] Preferably, the workflow of the hierarchical decision engine in the offline state includes a single-node autonomous mode execution process and a distributed cluster collaborative mode execution process. The single-node autonomous mode is activated when no adjacent communicable nodes are detected after a network outage, and its execution flow is as follows: Step S1: A network interruption with the cloud was detected, and no adjacent valid nodes suitable for networking were found. The single-node autonomous mode was activated, and the dynamic authorization time window stored locally was loaded. Delayed tolerance period Global blacklist and valid ticket dataset; Step S2: Collect ticket voucher information through the voucher reading and writing module. Synchronously obtain the request pass time of the local global clock. ; Step S3: Verification Does it meet the requirements? ,in and These are the start and end times of the dynamic authorization time window for local storage, respectively. Step S4: If the verification in step S3 passes, perform voucher legality verification, including voucher validity period verification, blacklist matching, usage count verification, and voucher anti-counterfeiting signature verification; Step S5: Perform local anti-reuse conflict detection to determine the ticketing voucher. Does the last usage time exist in the local transaction log? And satisfy ,in The minimum legal tour interval for this type of ticket is preset by the system; Step S6: If both steps S4 and S5 pass the verification, perform the release operation and update the centralized voucher in the local ticketing data. The remaining number of uses and the last use timestamp are recorded, and an offline access record with a globally unique identifier, digital signature and timestamp is generated and persistently written to the local transaction storage unit. Step S7: If any of the above steps fails, execute the interception operation, generate an interception event log containing the failure reason, credential information, and timestamp, and persist it to the local transaction storage unit. The distributed cluster collaboration mode is activated after detecting at least two communicable adjacent valid nodes and completing the self-organizing network cluster construction following a network outage. Based on the verification process of the single-node autonomous mode, a cross-node anti-reuse collaboration verification is added. The formula for calculating the collaboration verification pass rate is as follows: ; In the formula, For the first in the cluster Each node for the voucher The reuse check result is 1 if there is no reused record and 0 if there is a reused record; only when... ( When the system's preset collaborative decision threshold is reached, the cross-node anti-reuse verification is deemed to have passed before the release operation can be performed.

[0013] Preferably, the cloud server is equipped with a self-healing scheduler, which is used to intelligently plan the order, bandwidth allocation, and synchronization granularity of multi-terminal data synchronization when the gate terminal and the cloud network are reconnected, to avoid network congestion and prioritize the consistency repair of core data. Its scheduling method includes: Step M1: Collect the reported status data of all re-connected gate terminals, including the duration of offline operation. Passage transaction volume processed during offline period Real-time comprehensive credit scoring at the terminal The amount of data in the cluster information and offline transaction logs; Step M2: Based on the reported status data, calculate the data synchronization priority value for each gate terminal to be synchronized. Simultaneously, based on the terminal's offline time and transaction volume, an adaptive synchronization granularity is assigned to each terminal. ; Step M3: According to The terminals are divided into synchronization priority queues in descending order of their values. Higher communication bandwidth and concurrent session resources are allocated to the high priority queues. Data synchronization sessions are established with each terminal in turn. The offline transaction logs are uploaded and verified first, and then the cloud policies and incremental global data are sent out. Step M4: During the synchronization process, monitor the network bandwidth utilization and terminal synchronization status in real time, dynamically adjust the bandwidth allocation and synchronization frequency of each queue, and prioritize the stable execution of synchronization tasks in high-priority queues when network congestion occurs.

[0014] Preferably, the data synchronization priority value The calculation formula is: ; In the formula, The maximum expected offline time defined for the system. This is the maximum offline transaction capacity per terminal preset by the system based on the terminal hardware performance. The weighting coefficients for each influencing factor satisfy the following conditions: ; The adaptive synchronization granularity The calculation formula is: ; In the formula, The system sets the basic synchronization data packet granularity; the longer the offline duration and the larger the offline transaction volume, the smaller the synchronization granularity, enabling the packetized interrupted transmission of large-volume offline data and avoiding network congestion.

[0015] Preferably, the cloud server is equipped with a conflict arbitrator to resolve conflicts when different gate terminals upload the same ticket voucher. When offline access records exhibit time, frequency, or state conflicts, global intelligent arbitration and data consistency repair are performed. The conflict arbitrator incorporates a composite loss arbitration model based on credit scoring and the time dimension. The optimal arbitration result is determined by minimizing the arbitration loss value, and its composite loss function is: ; In the formula, The total arbitration loss is the target value, and the arbitration outcome aims to minimize this total loss. For the time dimension loss, For the loss in the credit dimension, This results in a loss of global consistency. The balancing weight coefficients for each loss item are all preset fixed values ​​by the system; The time dimension loss ,in The timestamp for the pending arbitration passage record. The earliest timestamp in the conflict record set. The maximum time interval in the conflict record set is used to prioritize the acceptance of earlier passage records; The loss in the credit dimension ,in The historical comprehensive credit score of the gate terminal that generated the passage record at the time of record generation is used to prioritize the passage records of terminals with high credit scores. The global consistency loss This is the deviation between the global ticketing status and the original ticketing authorization rules after the record is accepted. The larger the deviation, the higher the loss value. This is used to ensure that the arbitration result complies with the global rules for ticketing authorization.

[0016] Preferably, the steps of the conflict arbitrator performing arbitration and global consistency repair include: Step C1: Based on the uploaded offline access records, identify multiple access records with the same ticket voucher ID and that meet the conflict determination rules, and construct a conflict record set; the conflict determination rules include: the time interval between multiple access records of the same voucher is less than a preset minimum legal visit interval. The number of passes exceeded the total number of authorized passes, and the status of the pass conflicted between valid and invalid records on different terminals; Step C2: Extract the timestamp, gate terminal ID that generated the record, and historical comprehensive credit score of the corresponding terminal at the time the record was generated for each passage record in the conflict record set. Verify the original data of the voucher and simultaneously retrieve the original authorization rules and global historical access data of the ticket voucher; Step C3: Based on the composite loss arbitration model, calculate the total arbitration loss value for each record in the conflict record set in sequence. Select The smallest record is the only valid passage record, and the remaining records are marked as conflicting and invalid records, thus completing the arbitration decision; Step C4: Based on the arbitration result, update the globally unique status of the ticket voucher, including the remaining valid usage count, the last valid passage time, and the voucher validity status, and simultaneously complete the consistency repair of the global passage database; Step C5: Based on the arbitration result, iteratively update the comprehensive credit score of the corresponding gate terminal. : The terminal that generates valid records improves its score in the decision-making effectiveness dimension. Terminals that generate invalid conflict records will have their decision validity score reduced. This forms a closed-loop iteration of credit scoring; Step C6: Generate a conflict arbitration report, add the final global status of the voucher, the arbitration result and the updated credit score data to the synchronization queue, and forcefully send it to all relevant gate terminals through the data synchronization channel to complete the consistency repair of local terminal data. At the same time, generate risk control alarm information for abnormal conflict records.

[0017] Preferably, the gate terminal is an intelligent verification terminal deployed at the entrance, exit, internal scenic spot branching points, transportation boarding and alighting points, and consumption verification points of the scenic area, and includes at least an industrial-grade edge processor, a non-volatile memory with power failure protection, a dual-channel communication module, a ticket voucher reading and writing module, and a passage execution mechanism; The industrial-grade edge processor runs all the software modules of the data synchronization manager and hierarchical decision engine, supporting millisecond-level verification decisions and data processing. Its computing power performance is comparable to the system's preset maximum offline transaction capacity per terminal. match; The dual-path communication module includes a 4G / 5G / Ethernet wide-area communication module and a LoRa / Bluetooth Mesh short-range peer-to-peer communication module, which respectively correspond to wide-area communication with the cloud and self-organizing network communication between adjacent nodes in the offline state. The ticketing voucher reading and writing module supports the reading and verification of ID cards, IC cards, paper QR codes, electronic dynamic QR codes, and facial recognition vouchers. Its recognition accuracy and response speed match the system's preset minimum legal browsing interval. In accordance with the time delay requirements for verification decisions; The passage execution mechanism includes a swing gate / wing gate and an audio-visual prompt module, which are used to execute corresponding physical actions and prompts according to the passage / interception instructions of the hierarchical decision engine; The non-volatile memory has a storage capacity that matches the system's preset maximum expected offline time. Basic authorization time window length The matching capability can meet the storage requirements of full transaction logs in an offline state for at least 30 days.

[0018] This invention provides a scenic area ticketing and access management system with self-healing capabilities in the event of network outages, which has the following beneficial effects: 1. This invention innovatively constructs a fully closed-loop management architecture of "online centralized control, distributed autonomy during network outages, and global self-healing upon network recovery." In the online state, centralized control, dynamic policy distribution, and global data maintenance of the entire system are achieved through the cloud. In the offline state, the gate terminal can automatically trigger a self-healing mechanism, adaptively switching between single-node autonomy and distributed cluster collaboration modes based on the detection results of adjacent nodes to complete the verification and release decision of the access request. After network recovery, incremental data synchronization and global state consistency repair are automatically triggered, realizing continuous and stable operation of the system in all scenarios of online, offline, and network recovery. This fundamentally avoids problems such as paralysis of scenic area operations and tourist congestion caused by network interruptions, and greatly improves the risk resistance of the scenic area ticketing system.

[0019] 2. This invention utilizes a dynamic authorization time window mechanism with credit gain characteristics, combining the single-node comprehensive credit score and cluster credit score of the gate terminal to dynamically adjust the effective time range of the local verification decision of the gate terminal after network outage. This provides sufficient authorization guarantees for highly reliable devices while effectively avoiding the management risks of low-reliability devices. At the same time, through the cross-node anti-reuse collaborative verification mechanism in the distributed cluster collaborative mode, multi-node collaborative decision-making is achieved based on node credit weights, completely solving the management loophole of duplicate ticketing between multiple nodes in the traditional network outage solution. Even in network outage scenarios, it can still ensure the accuracy of ticket verification and the security of scenic area revenue.

[0020] 3. This invention utilizes a cloud-based self-healing scheduler to dynamically calculate data synchronization priority based on the offline duration, offline transaction volume, and comprehensive credit score of the gate terminal, and allocates adaptive synchronization granularity. This enables multi-terminal hierarchical incremental synchronization scheduling, effectively avoiding network congestion caused by a large number of terminals synchronizing simultaneously, and ensuring that core data is prioritized for consistency repair. Simultaneously, through a conflict arbitrator with a built-in composite loss arbitration model, it intelligently arbitrates conflicting passage records by comprehensively considering time, credit, and global consistency dimensions, ensuring the compliance and accuracy of the arbitration results. This completely solves the problems of data conflicts, reconciliation errors, and low processing efficiency after network restoration in traditional solutions, achieving consistent self-healing of global ticketing data.

[0021] 4. This invention utilizes a cloud-based, end-to-end credit scoring iteration mechanism to dynamically calculate and distribute the comprehensive credit score of the gate terminal based on three core dimensions: data synchronization compliance, decision-making effectiveness, and equipment operational stability. Simultaneously, it uses the arbitration results of conflicting data after network restoration to iteratively update the corresponding gate terminal's credit score, forming a fully closed-loop optimization chain of "credit scoring - dynamic authorization - decision execution - effect feedback - credit iteration." This continuously improves the decision-making accuracy and operational reliability of the system's autonomous operation during network outages, enabling it to adapt to the complex operating environment and large-scale passenger flow management needs of scenic areas in the long term.

[0022] In summary, the gate terminal of this invention supports short-range peer-to-peer communication and can be flexibly deployed at various access points in scenic areas, such as entrances, exits, intersections of attractions within the park, boarding and alighting points for transportation within the park, and points for consumption verification. In the event of a network outage, it can automatically complete the detection of adjacent nodes and the construction of a self-organizing network cluster without the need for additional deployment of local servers or other supporting facilities, which greatly reduces the deployment cost and maintenance difficulty of the system. It can be widely adapted to the ticketing and access control needs of scenic areas of different sizes, terrains, and operating modes. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the scenic area ticketing and access management system with self-healing function in the event of network outage, as described in this invention. Figure 2 This is a schematic diagram of the gate terminal of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] like Figures 1-2 As shown: The scenic area ticketing and access management system with self-healing function after network outage in this embodiment includes a cloud server and multiple gate terminals deployed at various access nodes in the scenic area that support close-range peer-to-peer communication.

[0026] The gate terminal, when online, establishes a full bidirectional communication link with the cloud server, synchronously acquiring ticketing authorization policies, global blacklist data, and system baseline parameters issued by the cloud, and simultaneously uploading locally generated access records and device operating status data to the cloud. When a wide area network interruption with the cloud server is detected, a self-healing mechanism is automatically triggered. First, adjacent node detection is performed through the near-field communication module. Based on the detection results, it adaptively switches to a single-node autonomous mode or a distributed cluster collaborative mode. Based on locally stored valid data and pre-trained decision logic, it completes independent verification or multi-node collaborative verification of access requests and generates corresponding release / interception decisions, while simultaneously generating offline access transaction logs with globally unique identifiers. After the wide area network with the cloud is detected to be restored, an incremental data synchronization and local state consistency repair process is automatically triggered.

[0027] When online, the cloud server performs centralized management and control of all turnstile terminals in the entire scenic area, issues dynamic ticketing authorization policies, maintains global ticketing data, and iteratively updates the credit score across the entire chain. After the turnstile terminal network is restored, it performs multi-node hierarchical incremental synchronous scheduling, intelligent arbitration of offline data conflicts, and self-healing of global data consistency. Simultaneously, it iteratively sends the arbitration results, updated credit scores, and policy optimization content back to the corresponding turnstile terminals, forming a closed-loop management system for the entire process.

[0028] The specific implementation structure of the turnstile terminal: In this embodiment, the gate terminal is an intelligent verification terminal deployed at the entrance, exit, internal scenic spot branching points, transportation boarding and alighting points, and consumption verification points of the scenic area. At the hardware level, it includes at least an industrial-grade edge processor, a non-volatile memory with power failure protection, a dual-channel communication module, a ticket voucher reading and writing module, and a passage execution mechanism.

[0029] The industrial-grade edge processor uses an ARM Cortex-A series industrial-grade processor with a main frequency of no less than 1.2GHz. It runs all software modules of the data synchronization manager and hierarchical decision engine, supports millisecond-level verification decisions and data processing, and its computing power performance is comparable to the system's preset maximum offline transaction capacity per terminal. Matching, in this embodiment The preset limit is 100,000 transactions.

[0030] The dual-path communication module includes a 4G / 5G / Ethernet wide-area communication module and a LoRa / Bluetooth Mesh short-range peer-to-peer communication module. The wide-area communication module is used to establish a wide-area communication link with the cloud server, and the short-range peer-to-peer communication module is used for node detection of adjacent gate terminals, self-organizing network cluster construction, and point-to-point synchronization of passage data in the event of network outage. The communication distance of the LoRa module is not less than 1km, which is suitable for the wide-range node deployment needs of mountainous and large park-type scenic spots.

[0031] The ticketing voucher reading and writing module supports the reading and verification of ID cards, IC cards, paper QR codes, electronic dynamic QR codes, and facial recognition vouchers, with a recognition accuracy of no less than 99.5% and a single voucher verification response time of no more than 300ms, matching the system's preset minimum legal browsing interval. With regard to the time delay requirements for verification decisions.

[0032] The passage execution mechanism includes a swing gate / wing gate and an audio-visual prompt module, which is used to execute corresponding physical actions and audio-visual prompts according to the release / interception instructions of the hierarchical decision engine. The gate opening and closing response time does not exceed 200ms.

[0033] The non-volatile memory uses industrial-grade eMMC memory chips and has a power-off protection circuit. Its storage capacity is related to the system's preset maximum expected offline time. Basic authorization time window length In this embodiment, the storage capacity is no less than 32GB, which can meet the storage requirements of the full transaction log in the offline state for at least 30 days. All stored data is equipped with a global timestamp and a digital signature generated by the national cryptographic SM2 algorithm to prevent data tampering.

[0034] At the software level, the gate terminal includes a distributed communication unit, a data synchronization manager, a hierarchical decision engine, a local transaction storage unit, and a device status awareness unit.

[0035] Distributed Communication Unit: Connected to the wide area communication module and the near-field peer-to-peer communication module respectively, it is used to maintain the bidirectional data interaction link with the cloud in the online state, and perform encrypted data transmission and heartbeat keep-alive; in the offline state, it broadcasts node probe messages through the near-field peer-to-peer communication module, receives response messages from adjacent nodes, completes the identification of communicable nodes, the construction of self-organizing network clusters, and point-to-point encrypted synchronization of data passing through the cluster.

[0036] Data Synchronization Manager: Connected to the distributed communication unit and local transaction storage unit, it performs incremental data synchronization with the cloud server based on state differences while maintaining network connectivity. It only synchronizes data with state differences from the previous synchronization node, avoiding bandwidth consumption caused by full synchronization. During synchronization, it dynamically adjusts the granularity and frequency of synchronization packets based on real-time network bandwidth, terminal CPU load, and data urgency level. High-urgency data (such as global blacklists and emergency control policies) uses small packets with high frequency synchronization, while low-urgency data (such as historical access records) uses large packets with low frequency synchronization. Simultaneously, it uses the NTP clock synchronization protocol to achieve millisecond-level synchronization between the local clock and the cloud global clock, ensuring a unified time base across the entire system. After network recovery, it performs breakpoint resumption of offline transaction logs, preventing retransmission of already uploaded log segments, and simultaneously performs consistency checks and repairs between local data and cloud global data.

[0037] The hierarchical decision engine comprises a single-node autonomous decision-making submodule and a distributed collaborative decision-making submodule. It connects with the data synchronization manager, local transaction storage unit, ticket voucher read / write module, and access control mechanism. In the event of a network outage, it activates the corresponding submodule based on the cluster network status. Based on the last synchronized valid dataset from the data synchronization manager, it performs multi-dimensional compliance verification and anti-reuse conflict detection on the ticket vouchers collected on-site, generates a pass / interception decision and sends it to the access control mechanism, and simultaneously persists the decision results and original voucher data to the local transaction storage unit.

[0038] Local transaction storage unit: Connected to non-volatile memory driver, it is used to persistently store ticketing authorization policies, global blacklists, system benchmark parameters and credit score data issued by the cloud, as well as the full offline access transaction logs and interception event logs generated locally. All stored data is attached with a global timestamp and digital signature to ensure that the data is tamper-proof and traceable.

[0039] Equipment Status Awareness Unit: Connected to various hardware modules of the gate terminal, hierarchical decision engine, and data synchronization manager, it is used to collect real-time data on wide area network connectivity, communication status of adjacent nodes, terminal CPU / memory operating load, verification module operating status, access mechanism operating status, and power status, providing real-time status basis for mode switching of the hierarchical decision engine and synchronization strategy adjustment of the data synchronization manager.

[0040] Implementation of ticketing authorization strategy and dynamic authorization time window: In this embodiment, the ticketing authorization strategy includes a dynamic authorization time window with credit gain characteristics, which is dynamically generated and distributed by the cloud server. The dynamic authorization time window is the effective time range within which the gate terminal can independently execute verification decisions after the network is disconnected. It is used to limit the local verification permissions of the gate terminal after the network is disconnected, and to balance access security and ticketing security.

[0041] In the offline state, the hierarchical decision engine only performs verification decisions on ticketing vouchers whose request time falls after the start time of the dynamic authorization time window and whose end time is within a preset delay tolerance period. For ticketing requests whose request time exceeds the above-mentioned valid range, the hierarchical decision engine directly intercepts them and generates an interception event log with a timeout reason. In this embodiment, the preset delay tolerance period is... It is set to 24 hours to deal with extreme scenarios of prolonged network outages.

[0042] The length of the dynamic authorization time window The cloud server dynamically adjusts the gate terminal's overall credit score and cluster collaboration status. The basic calculation formula is as follows: ; In the formula, The system's preset basic authorization time window length is a fixed baseline value. In this embodiment... Set to 72 hours; This is the terminal credit adjustment coefficient. The cluster collaboration gain coefficients are all fixed weight values ​​preset by the system. In this embodiment... Take 0.6, Take 0.4; This is the real-time comprehensive credit score for the gate terminal, with a value range of [value range missing]. ; The real-time cluster credit score of the cluster to which the gate terminal belongs, with a value range of [value range missing]. It is used to characterize the data reliability of multi-node collaboration under network outage conditions.

[0043] Using the above formula, gate terminals with higher overall credit scores and better cluster collaboration can obtain a longer dynamic authorization time window and a longer effective period for independent verification after network outage, thus achieving dynamic matching between authorization permissions and device reliability.

[0044] Specific implementation of the credit scoring system: In this embodiment, the comprehensive credit score of the gate terminal The calculation is performed iteratively by the cloud server at a fixed cycle and then distributed to the corresponding terminal. The iteration cycle is set to 24 hours, and the calculation formula is as follows: ; In the formula, , , The weight coefficients for each dimension satisfy... In this embodiment Take 0.3, Take 0.4, We set it to 0.3 to highlight the core weight of decision effectiveness.

[0045] in, For data synchronization compliance, the range of values ​​is... The data integrity rate is calculated by weighting the synchronization success rate, synchronization clock deviation, and data integrity rate over the past 30 days of the terminal's data history. This value characterizes the reliability of data synchronization between the terminal and the cloud. Higher synchronization success rates, smaller clock deviations, and higher data integrity rates indicate better data integrity. The closer the value is to 1; For decision validity, the range of values ​​is... The accuracy of the terminal's offline decision-making is determined by a weighted average of the compliance rate, conflict record ratio, and false release / false interception rate based on the terminal's historical offline decision-making data. Higher compliance rates, lower conflict record ratios, and lower false operation rates indicate greater accuracy. The closer the value is to 1; For equipment operational stability, the value range is... The reliability of the terminal hardware operation is determined by a weighted average of the terminal's online rate, hardware failure rate, and verification module recognition accuracy over the past 30 days. Higher online rates, lower failure rates, and higher recognition accuracy all contribute to this reliability. The closer the value is to 1.

[0046] The cluster credit score The calculation formula is: ; In the formula, The number of valid nodes in the self-organizing network cluster after network outage. Valid nodes are defined as gate terminals with a historical 30-day online rate of ≥95% and no record of major decision-making errors. For the first in the cluster The comprehensive credit score of each valid node. Through this formula, a strong correlation is formed between the cluster credit score and the individual node credit score, enabling dynamic and coordinated adjustment of the authorization time window. The higher the proportion of high-credit nodes in the cluster, the higher the authorization gain that the cluster as a whole can obtain.

[0047] Implementation of the hierarchical decision engine workflow during network outages: In this embodiment, the workflow of the hierarchical decision engine in the offline state includes the execution process of single-node autonomous mode and the execution process of distributed cluster collaborative mode.

[0048] The single-node autonomous mode is activated when the gate terminal detects a network interruption with the cloud and fails to detect any adjacent, communicable nodes via the proximity communication module. Its execution flow is as follows: Step S1: The device status awareness unit detects a real-time interruption in the cloud wide area network, and the distributed communication unit finds no adjacent valid nodes that can be networked. The hierarchical decision engine activates the single-node autonomous decision submodule and loads the dynamic authorization time window from the local transaction storage unit. Delayed tolerance period Global blacklist and valid ticket dataset; Step S2: Collect ticket voucher information submitted by tourists through the ticket voucher reading and writing module. Synchronously obtain the request pass time of the local global clock. ; Step S3: Verification Does it meet the requirements? ,in and These are the start and end times of the dynamic authorization time window stored locally; if the verification fails, proceed directly to step S7. Step S4: If the verification in step S3 passes, perform the voucher legality verification, which includes voucher validity period verification, global blacklist matching, remaining usage count verification, and voucher anti-counterfeiting signature verification in sequence; if any verification fails, proceed to step S7. Step S5: Perform local anti-reuse conflict detection, traverse the local transaction log, and determine the ticket voucher. Does the last usage time exist in the local transaction log? And satisfy ,in This refers to the minimum legal visit interval for this type of ticket, preset by the system; in this embodiment, the scenic area entrance ticket... Set to 24 hours, single attraction ticket within the park Set to 1 hour, single-trip ticket for transportation within the park. Set the time to 12 hours; if there are usage records that meet the above conditions, it is determined to be a duplicate passage, and the process is skipped to step S7. Step S6: If both steps S4 and S5 pass the verification, the hierarchical decision engine issues a release instruction to the passage execution agency to perform the release operation, and at the same time updates the centralized voucher in the local ticketing data. The remaining number of uses and the last use timestamp are used to generate an offline access record with a globally unique identifier, digital signature, and timestamp, which is persistently written to the local transaction storage unit; the globally unique identifier is generated in the format of "terminal unique ID + millisecond-level timestamp + 16-bit random sequence" to ensure that it is unique throughout the entire system; Step S7: If any of the above steps fails, the hierarchical decision engine issues an interception command to the passage execution agency to perform the interception operation, generate an interception event log containing the reason for failure, credential information, and timestamp, and persist it to the local transaction storage unit.

[0049] Distributed cluster collaboration mode: The distributed cluster collaboration mode is activated after the gate terminal detects a network interruption with the cloud and detects ≥2 communicable adjacent valid nodes through the near-field communication module and completes the construction of the self-organizing network cluster. Based on the verification process of the single-node autonomous mode, a cross-node anti-reuse collaborative verification is added to avoid the same credential from being used repeatedly on multiple nodes in the cluster.

[0050] The formula for calculating the pass rate of collaborative verification is: ; In the formula, For the first in the cluster Each node for the voucher The reuse check result is 1 if there is no reused record and 0 if there is a reused record; only when... In this embodiment, the cross-node anti-reuse verification must pass before the release operation can be performed. The default value is 0.8.

[0051] In practice, after all local verification processes in the single-node autonomous mode are completed and passed, the gate terminal transmits the credentials through the self-organizing network cluster. The information is broadcast to all valid nodes in the cluster. Each node returns its local multiplexing verification result and its own comprehensive credit score. The gate terminal that initiates the verification calculates the collaborative verification pass rate according to the above formula. If the pass rate reaches the threshold, the pass is allowed; otherwise, the pass is blocked and an interception log is generated.

[0052] Implementation of incremental synchronization scheduling after network restoration: In this embodiment, the cloud server is equipped with a self-healing scheduler, which is used to intelligently plan the order, bandwidth allocation, and synchronization granularity of multi-terminal data synchronization when the gate terminal and the cloud network are restored, so as to avoid network congestion caused by a large number of terminals synchronizing at the same time, and to prioritize the consistency repair of core data. The scheduling method includes: Step M1: After the gate terminal restores its network connection with the cloud, it first reports the terminal status data to the cloud. The self-healing scheduler collects the reported status data of all gate terminals that have reconnected, including the terminal's offline working time. Passage transaction volume processed during offline period Real-time comprehensive credit scoring at the terminal The amount of data in the cluster information and offline transaction logs; Step M2: Based on the reported status data, calculate the data synchronization priority value for each gate terminal to be synchronized. Simultaneously, based on the terminal's offline time and transaction volume, an adaptive synchronization granularity is assigned to each terminal. ; Step M3: According to The terminals are divided into synchronization priority queues in descending order of their values. In this embodiment, they are divided into three priority queues: high, medium, and low. 70% of the communication bandwidth and concurrent session resources are allocated to the high priority queue, 20% to the medium priority queue, and 10% to the low priority queue. Encrypted data synchronization sessions are established with each terminal in sequence. The synchronization process follows the principle of "upload first, then download". The offline transaction log is uploaded and its integrity is verified first, and then the cloud strategy and incremental download of global data are executed. Step M4: During the synchronization process, the self-healing scheduler monitors the network bandwidth utilization and terminal synchronization status in real time, and dynamically adjusts the bandwidth allocation and synchronization frequency of each queue. When the network bandwidth utilization exceeds 80%, it is determined to be congested. The synchronization tasks of low-priority queues are suspended, and the synchronization tasks of high- and medium-priority queues are given priority to ensure stable execution.

[0053] Among them, the data synchronization priority value The calculation formula is: ; In the formula, The maximum expected offline time defined by the system is set to 72 hours in this embodiment; The maximum offline transaction capacity of a single terminal is preset by the system based on the terminal hardware performance. In this embodiment, it is set to 100,000 transactions. , , The weighting coefficients for each influencing factor satisfy the following conditions: In this embodiment Take 0.4, Take 0.3, Take 0.3; according to this formula, the longer the offline time, the larger the offline transaction volume, and the higher the terminal credit score, the higher the synchronization priority of the gate terminal, and the more likely it is to complete data synchronization and status repair first.

[0054] The adaptive synchronization granularity The calculation formula is: ; In the formula, The system's preset basic synchronization data packet granularity is set to 100 records / packet in this embodiment. The longer the offline duration and the larger the offline transaction volume, the smaller the synchronization granularity, enabling the packetized interrupted transmission of large-volume offline data and avoiding transmission failures and network congestion caused by excessively large single packet data.

[0055] Conflict arbitration and the implementation of global consistency and self-healing: In this embodiment, the cloud server is equipped with a conflict arbitrator to resolve conflicts when different gate terminals upload the same ticket voucher. When offline access records exhibit time, frequency, or state conflicts, global intelligent arbitration and data consistency repair are performed. The conflict arbitrator incorporates a composite loss arbitration model based on credit scoring and the time dimension. The optimal arbitration result is determined by minimizing the arbitration loss value, and its composite loss function is: ; In the formula, The total arbitration loss is the target value, and the arbitration outcome aims to minimize this total loss. For the time dimension loss, For the loss in the credit dimension, This results in a loss of global consistency. , The balancing weight coefficients for each loss term are all preset fixed values ​​by the system. In this embodiment, Take 0.5, Take 0.3.

[0056] Wherein, the time dimension loss ,in The timestamp for the pending arbitration passage record. The earliest timestamp in the conflict record set. The largest time interval in the conflict record set is used. The earlier the passage record, the less the loss in the time dimension, and the more likely it is to be accepted. The loss in the credit dimension ,in The historical comprehensive credit score of the gate terminal that generated the passage record at the time of record generation is used to determine the credit dimension loss of the record generated by the terminal with the higher credit score, and the record is given priority for acceptance. The global consistency loss This is the deviation between the global ticketing status and the original ticketing authorization rules after the record is accepted. The larger the deviation, the higher the loss value. This is used to ensure that the arbitration result complies with the global rules for ticketing authorization.

[0057] The steps for the conflict arbitrator to perform arbitration and global consistency repair include: Step C1: Based on the offline access records uploaded by all gate terminals, identify multiple access records with the same ticket ID and that meet the conflict determination rules, and construct a conflict record set; the conflict determination rules include: the time interval between multiple access records of the same ticket is less than a preset minimum legal visit interval. The number of passes exceeded the total number of authorized passes, and the status of the pass conflicted between valid and invalid records on different terminals; Step C2: Extract the timestamp, gate terminal ID that generated the record, and historical comprehensive credit score of the corresponding terminal at the time the record was generated for each passage record in the conflict record set. Verify the original data of the voucher and simultaneously retrieve the original authorization rules and global historical access data of the ticket voucher; Step C3: Based on the composite loss arbitration model, calculate the total arbitration loss value for each record in the conflict record set in sequence. Select The smallest record is the only valid passage record, and the remaining records are marked as conflicting and invalid records, thus completing the arbitration decision; Step C4: Based on the arbitration result, update the globally unique status of the ticket voucher, including the remaining valid usage count, the last valid passage time, and the voucher validity status, and simultaneously complete the consistency repair of the global passage database; Step C5: Based on the arbitration result, iteratively update the comprehensive credit score of the corresponding gate terminal. : The terminal that generates valid records improves its score in the decision-making effectiveness dimension. Terminals that generate invalid conflict records will have their decision validity score reduced. This forms a closed-loop iteration of credit scoring; Step C6: Generate a conflict arbitration report, add the final global status of the voucher, the arbitration result and the updated credit score data to the synchronization queue, and forcefully send it to all relevant gate terminals through the data synchronization channel to complete the consistency repair of local terminal data. At the same time, for abnormal situations where the same voucher has 3 or more conflict records, generate risk control alarm information and push it to the scenic area operation and management platform. Specific implementation examples: This embodiment uses a 5A-level mountain scenic area as the application scenario. The scenic area covers an area of ​​approximately 120 square kilometers and has two main entrances and two west entrances, eight internal scenic spot branch entrances, and six sightseeing bus boarding and alighting points, totaling 16 access nodes. Each node is equipped with two turnstile terminals as described in this invention, for a total of 32 turnstile terminals throughout the scenic area. All turnstile terminals support 4G wide area communication and LoRa short-range peer-to-peer communication. The cloud server is deployed in the East China region of Alibaba Cloud and is connected to the scenic area's operation and management platform.

[0059] System basic parameter presets In this embodiment, the system's basic parameters are preset as follows: Basic authorization time window Hours, delay tolerance period Hour; Terminal credit adjustment coefficient Cluster cooperative gain coefficient ; Comprehensive credit score weighting , , ; Collaborative verification pass rate threshold ; Minimum legal interval for visiting the main entrance ticket Hours, attraction tickets Hours, sightseeing bus ticket Hour; Maximum expected offline time Maximum offline transaction capacity per hour for a single terminal 10,000 entries; Synchronization Priority Weight , , Basic synchronization granularity Strips / packets; Composite loss function weights , .

[0060] Online operation process: During normal operation of the scenic area, all gate terminals are online and maintain full two-way communication with the cloud server. The cloud server iteratively calculates the comprehensive credit score of all turnstile terminals every day at 2:00 AM. In this embodiment, the main entrance gate terminal has a 100% historical synchronization success rate, no decision-making error records, and a 100% device online rate, with a comprehensive credit score. Comprehensive credit score for turnstile terminals in remote tourist attractions ; The cloud server calculates and distributes dynamic authorization time windows based on the comprehensive credit scores of each gate terminal, including the dynamic authorization time window for the main entrance gate terminal. Hours, approximately 6.5 days; The cloud server updates ticketing authorization policies and global blacklist data incrementally to all gate terminals in real time. The gate terminals upload local access records and equipment operation status to the cloud in real time. When tourists pass through, the gate terminals interact with the cloud in real time to complete ticket verification and release. The average verification response time is 200ms. The data synchronization manager dynamically adjusts the granularity of synchronization data packets based on real-time network bandwidth. During peak periods when network bandwidth is tight, small packets are used for high-frequency synchronization, while large packets are used for low-frequency synchronization during off-peak periods. At the same time, the deviation between the local clock and the global clock in the cloud is kept within 100ms.

[0061] Operation flow in a network outage scenario: Due to the extreme rainstorm, the base station in the mountainous area of ​​the scenic area malfunctioned, causing a wide area network outage between all gate terminals and the cloud server in the entire scenic area, automatically triggering the network outage self-healing mechanism: The four turnstile terminals in the main entrance area detected adjacent, communicable nodes using LoRa modules, completing the construction of a self-organizing network cluster. All four terminals within the cluster are valid nodes, and the cluster's credit score... Switch to distributed cluster collaboration mode; Two turnstile terminals in remote scenic spots switched to single-node autonomous mode because they could not detect adjacent communicable nodes due to terrain obstruction. Tourists holding park entrance tickets request passage at the main entrance gate terminal. The gate terminal first verifies that the requested passage time is within the dynamic authorization time window, and then performs local verification of the credential validity period, blacklist, number of uses, and anti-counterfeiting signature. After all verifications are successful, the credential information is broadcast to the other three terminals in the cluster. Each terminal returns its local reuse verification result, and the collaborative verification pass rate is calculated. Once the cross-node anti-reuse verification passes, the gate will allow passage, generating an offline passage record with a globally unique identifier and writing it to local storage. When a tourist with a used attraction ticket requests passage through a single-node gate terminal at a remote attraction, the gate terminal performs a local anti-reuse detection after completing the time window verification. If it finds that the time interval between the last use time of the ticket and the current request is less than 1 hour, it is determined to be a duplicate passage, and the gate is blocked. An interception event log is generated and written to local storage. During the network outage, all local verification decisions of the gate terminals were executed within the dynamic authorization time window. All passage records and interception logs were persistently stored with timestamps and digital signatures, eliminating the risk of data tampering. No tourist congestion or ticketing out of control occurred in the entire scenic area.

[0062] Post-reconnection self-healing process: After a 28-hour network outage, the base station was repaired, and the entire scenic area's turnstile terminals regained network connectivity with the cloud, automatically triggering the network recovery and self-healing process. All the gate terminals that have been reconnected report status data such as offline duration of 28 hours, offline transaction volume, and terminal credit score to the cloud. The cloud self-healing scheduler calculates the synchronization priority of each terminal. The main entrance gate terminal has a large offline transaction volume and a high credit score, so it has the highest synchronization priority and is placed in the high-priority queue; the remote scenic spot terminals have a small transaction volume and are placed in the medium-priority queue. The self-healing scheduler allocates 70% of the bandwidth to high-priority queues, prioritizing the resumption of offline transaction logs from the main entrance gate terminals. Based on offline duration and transaction volume, it assigns adaptive synchronization granularity to the terminals. To avoid network congestion, use individual packets / packages. The cloud-based conflict arbitrator performs conflict detection on the uploaded offline records and finds that the same sightseeing bus ticket has passage records at two different gate terminals at two different boarding and alighting points, with an interval of only 30 minutes and less than 12 hours. Construct a conflict record set; The arbitrator extracts the timestamps of the two records and the historical credit scores of the corresponding terminals, calculates the total arbitration loss for the two records, and prioritizes the record with the earlier time and higher terminal credit score. The smaller one is deemed a valid record, while the other is marked as an invalid record, thus completing the arbitration. Based on the arbitration result, update the global status of the ticket voucher, iterate in reverse to update the decision validity score of the gate terminal that generated the invalid record, and simultaneously force the arbitration result, the updated voucher status and credit score to the relevant gate terminal to complete the consistency repair of the local data of the terminal. Within one hour of network recovery, all turnstile terminals in the entire scenic area completed incremental data synchronization and status consistency restoration, and there were no reconciliation errors in the global ticketing data, forming a complete closed-loop management system.

[0063] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A scenic area ticketing and access management system with self-healing function in the event of network outages, characterized in that: This includes cloud servers and multiple gate terminals deployed at various access points within the scenic area, supporting close-range peer-to-peer communication; The gate terminal is used to communicate bidirectionally with the cloud server in the online state, synchronously obtain ticketing authorization policies, global blacklist data and system benchmark parameters, and upload local access records and device operating status; when disconnected from the cloud, it automatically triggers a network disconnection self-healing mechanism, first adaptively switching to single-node autonomous mode or distributed cluster collaborative mode based on the detection results of adjacent nodes, and then completing independent verification / multi-node collaborative verification and release decision of access requests based on locally stored valid data and pre-trained decision logic, and synchronously generating offline access transaction logs with globally unique identifiers; after the network is restored to the cloud, it automatically triggers incremental data synchronization and state consistency repair. The cloud server is used for centralized management and control of all gate terminals, dynamic policy distribution, global data maintenance, and end-to-end credit scoring iteration while online; After the network is restored, multi-node hierarchical incremental synchronous scheduling, offline data conflict intelligent arbitration, and global data consistency self-healing are implemented. The arbitration results and policy updates are simultaneously iterated back to the gate terminal, forming a closed-loop management architecture of online centralized control, distributed autonomy during network outages, and global self-healing after network restoration.

2. The scenic area ticketing and access management system with self-healing capability in the event of network outage as described in claim 1, characterized in that, The gate terminal includes a distributed communication unit, a data synchronization manager, a hierarchical decision engine, a local transaction storage unit, and a device status perception unit. The distributed communication unit includes a wide-area communication module for communicating with the cloud, and a short-range peer-to-peer communication module that supports LoRa and Bluetooth Mesh protocols. It is used for bidirectional data interaction with the cloud in the online state, and to complete node detection of adjacent gate terminals, self-organizing network cluster construction and point-to-point synchronization of passage data in the offline state. The data synchronization manager is used to perform incremental data synchronization with the cloud server based on state differences when the network is connected. During the synchronization process, the granularity and frequency of the synchronization data packets are dynamically adjusted according to the network bandwidth, terminal load and data urgency level. At the same time, the local clock and the cloud global clock are synchronized at the millisecond level. After the network is restored, the offline transaction log is resumed and the consistency verification and repair of the local data are performed. The hierarchical decision engine includes a single-node autonomous decision submodule and a distributed collaborative decision submodule. It is used to activate the corresponding submodule according to the cluster network status in the offline state. Based on the valid dataset last synchronized by the data synchronization manager, it performs multi-dimensional compliance verification, anti-reuse conflict detection and release / interception decisions on the ticket vouchers collected on site. At the same time, it persists the decision results and the original voucher data to the local transaction storage unit. The local transaction storage unit is a non-volatile storage medium with power failure protection, used to persistently store ticketing authorization policies, global blacklists, system benchmark parameters and credit score data issued by the cloud, as well as the full offline access transaction logs generated locally. All stored data is equipped with a global timestamp and digital signature to prevent data tampering. The device status sensing unit is used to collect data on the wide area network connectivity status, the communication status of adjacent nodes, the terminal operating load, the verification module operating status, and the status of the access mechanism in real time, providing a status basis for the mode switching of the hierarchical decision engine and the adjustment of the synchronization strategy of the data synchronization manager.

3. The scenic area ticketing and access management system with self-healing capability in the event of network outage as described in claim 2, characterized in that, The ticketing authorization strategy includes a dynamic authorization time window with credit gain characteristics, which is dynamically generated and distributed by the cloud server. The dynamic authorization time window is the effective time range within which the gate terminal can independently execute verification decisions after the network is disconnected. When the network is disconnected, the hierarchical decision engine only performs verification decisions on ticket vouchers whose request passage time falls after the start time of the dynamic authorization time window and whose end time is within the preset delay tolerance period; for ticket requests whose request passage time exceeds the above effective range, the hierarchical decision engine directly intercepts them and generates an interception event log with the timeout reason. The length of the dynamic authorization time window The cloud server dynamically adjusts the gate terminal's overall credit score and cluster collaboration status. The basic calculation formula is as follows: ； In the formula, The default base authorization time window length for the system is a fixed baseline value; This is the terminal credit adjustment coefficient. These are the cluster collaboration gain coefficients, all of which are fixed weight values ​​preset by the system; This is the real-time comprehensive credit score for the gate terminal, with a value range of [value range missing]. ; The real-time cluster credit score of the cluster to which the gate terminal belongs, with a value range of [value range missing]. It is used to characterize the data reliability of multi-node collaboration under network outage conditions.

4. The scenic area ticketing and access management system with self-healing capability in the event of network outage as described in claim 3, characterized in that, The comprehensive credit score of the gate terminal The calculation is performed iteratively by the cloud server at fixed intervals and then distributed to the corresponding terminals. The calculation formula is as follows: ； In the formula, The weight coefficients for each dimension satisfy... ; For data synchronization compliance, the range of values ​​is... The data is calculated based on the terminal's historical synchronization success rate, synchronization clock deviation, and data integrity rate, and is used to characterize the reliability of data synchronization between the terminal and the cloud. For decision validity, the range of values ​​is... The accuracy of the terminal's offline decision-making is calculated based on the compliance rate, conflict record ratio, and false release / false interception rate of the terminal's historical offline decisions. For equipment operational stability, the value range is... The reliability of the terminal hardware operation is calculated based on the terminal's online rate, hardware failure rate, and verification module recognition accuracy. The cluster credit score The calculation formula is: ； In the formula, This represents the number of valid nodes within the self-organizing network cluster after network outage. For the first in the cluster The comprehensive credit score of each valid node ensures that the cluster credit score is correlated with the individual node credit score, enabling dynamic and coordinated adjustment of the authorization time window.

5. A scenic area ticketing and access management system with self-healing capabilities in the event of network outage, as described in claim 4, is characterized in that... The workflow of the hierarchical decision engine in the offline state includes the execution process of single-node autonomous mode and the execution process of distributed cluster collaborative mode. The single-node autonomous mode is activated when no adjacent communicable nodes are detected after a network outage, and its execution flow is as follows: Step S1: A network interruption with the cloud was detected, and no adjacent valid nodes suitable for networking were found. The single-node autonomous mode was activated, and the dynamic authorization time window stored locally was loaded. Delayed tolerance period Global blacklist and valid ticket dataset; Step S2: Collect ticket voucher information through the voucher reading and writing module. Synchronously obtain the request pass time of the local global clock. ; Step S3: Verification Does it meet the requirements? ,in and These are the start and end times of the dynamic authorization time window for local storage, respectively. Step S4: If the verification in step S3 passes, perform voucher legality verification, including voucher validity period verification, blacklist matching, usage count verification, and voucher anti-counterfeiting signature verification; Step S5: Perform local anti-reuse conflict detection to determine the ticketing voucher. Does the last usage time exist in the local transaction log? And satisfy ,in The minimum legal tour interval for this type of ticket is preset by the system; Step S6: If both steps S4 and S5 pass the verification, perform the release operation and update the centralized voucher in the local ticketing data. The remaining number of uses and the last use timestamp are recorded, and an offline access record with a globally unique identifier, digital signature and timestamp is generated and persistently written to the local transaction storage unit. Step S7: If any of the above steps fails, execute the interception operation, generate an interception event log containing the failure reason, credential information, and timestamp, and persist it to the local transaction storage unit. The distributed cluster collaboration mode is activated after detecting at least two communicable adjacent valid nodes and completing the self-organizing network cluster construction following a network outage. Based on the verification process of the single-node autonomous mode, a cross-node anti-reuse collaboration verification is added. The formula for calculating the collaboration verification pass rate is as follows: ； In the formula, For the first in the cluster Each node for the voucher The reuse check result is 1 if there is no reused record and 0 if there is a reused record; only when... Only when the cross-node anti-reuse verification is passed can the release operation be performed.

6. A scenic area ticketing and access management system with self-healing capabilities in the event of network outage, as described in claim 5, is characterized in that... The cloud server is equipped with a self-healing scheduler, which intelligently plans the order, bandwidth allocation, and synchronization granularity of multi-terminal data synchronization when the gate terminal and the cloud network are reconnected, avoiding network congestion and prioritizing the consistency restoration of core data. Its scheduling method includes: Step M1: Collect the reported status data of all re-connected gate terminals, including the duration of offline operation. Passage transaction volume processed during offline period Real-time comprehensive credit scoring at the terminal The amount of data in the cluster information and offline transaction logs; Step M2: Based on the reported status data, calculate the data synchronization priority value for each gate terminal to be synchronized. Simultaneously, based on the terminal's offline time and transaction volume, an adaptive synchronization granularity is assigned to each terminal. ; Step M3: According to The terminals are divided into synchronization priority queues in descending order of their values. Higher communication bandwidth and concurrent session resources are allocated to the high priority queues. Data synchronization sessions are established with each terminal in turn. The offline transaction logs are uploaded and verified first, and then the cloud policies and global data are incrementally distributed. Step M4: During the synchronization process, monitor the network bandwidth utilization and terminal synchronization status in real time, dynamically adjust the bandwidth allocation and synchronization frequency of each queue, and prioritize the stable execution of synchronization tasks in high-priority queues when network congestion occurs.

7. A scenic area ticketing and access management system with self-healing capabilities in the event of network outage, as described in claim 6, is characterized in that... The data synchronization priority value The calculation formula is: ； In the formula, The maximum expected offline time defined for the system. This is the maximum offline transaction capacity per terminal preset by the system based on the terminal hardware performance. The weighting coefficients for each influencing factor satisfy the following conditions: ; The adaptive synchronization granularity The calculation formula is: ； In the formula, The system sets the basic synchronization data packet granularity; the longer the offline duration and the larger the offline transaction volume, the smaller the synchronization granularity, enabling the packetized interrupted transmission of large-volume offline data and avoiding network congestion.

8. A scenic area ticketing and access management system with self-healing capabilities in the event of network outage, as described in claim 7, is characterized in that... The cloud server is equipped with a conflict arbitrator to resolve conflicts when different turnstile terminals upload the same ticket voucher. When offline access records exhibit time, frequency, or state conflicts, global intelligent arbitration and data consistency repair are performed. The conflict arbitrator incorporates a composite loss arbitration model based on credit scoring and the time dimension. The optimal arbitration result is determined by minimizing the arbitration loss value. Its composite loss function is: ； In the formula, The total arbitration loss is the target value, and the arbitration outcome aims to minimize this total loss. For the time dimension loss, For the loss in the credit dimension, This results in a loss of global consistency. The balancing weight coefficients for each loss item are all preset fixed values ​​by the system; The time dimension loss ,in The timestamp for the pending arbitration passage record. The earliest timestamp in the conflict record set. The maximum time interval in the conflict record set is used to prioritize the acceptance of earlier passage records; The loss in the credit dimension ,in The historical comprehensive credit score of the gate terminal that generated the passage record at the time of record generation is used to prioritize the passage records of terminals with high credit scores. The global consistency loss This is the deviation between the global ticketing status and the original ticketing authorization rules after the record is accepted. The larger the deviation, the higher the loss value. This is used to ensure that the arbitration result complies with the global rules for ticketing authorization.

9. A scenic area ticketing and access management system with self-healing capability in the event of network outage, as described in claim 8, is characterized in that... The steps for the conflict arbitrator to perform arbitration and global consistency repair include: Step C1: Based on the uploaded offline access records, identify multiple access records with the same ticket voucher ID and that meet the conflict determination rules, and construct a conflict record set; the conflict determination rules include: the time interval between multiple access records of the same voucher is less than a preset minimum legal visit interval. The number of passes exceeded the total number of authorized passes, and the status of the pass conflicted between valid and invalid records on different terminals; Step C2: Extract the timestamp, gate terminal ID that generated the record, and historical comprehensive credit score of the corresponding terminal at the time the record was generated for each passage record in the conflict record set. Verify the original data of the voucher and simultaneously retrieve the original authorization rules and global historical access data of the ticket voucher; Step C3: Based on the composite loss arbitration model, calculate the total arbitration loss value for each record in the conflict record set in sequence. Select The smallest record is the only valid passage record, and the remaining records are marked as conflicting and invalid records, thus completing the arbitration decision; Step C4: Based on the arbitration result, update the globally unique status of the ticket voucher, including the remaining valid usage count, the last valid passage time, and the voucher validity status, and simultaneously complete the consistency repair of the global passage database; Step C5: Based on the arbitration result, iteratively update the comprehensive credit score of the corresponding gate terminal. : The terminal that generates valid records improves its score in the decision-making effectiveness dimension. Terminals that generate invalid conflict records will have their decision validity score reduced. This forms a closed-loop iteration of credit scoring; Step C6: Generate a conflict arbitration report, add the final global status of the voucher, the arbitration result and the updated credit score data to the synchronization queue, and forcefully send it to all relevant gate terminals through the data synchronization channel to complete the consistency repair of local terminal data. At the same time, generate risk control alarm information for abnormal conflict records.

10. A scenic area ticketing and access management system with self-healing capabilities in the event of network outage, as described in claim 9, is characterized in that... The gate terminal is an intelligent verification terminal deployed at scenic area entrances, exits, internal scenic spot junctions, transportation boarding and alighting points, and consumption verification points. It includes at least an industrial-grade edge processor, a non-volatile memory with power failure protection, a dual-channel communication module, a ticket voucher reading and writing module, and a passage execution mechanism. The industrial-grade edge processor runs all the software modules of the data synchronization manager and hierarchical decision engine, supporting millisecond-level verification decisions and data processing. Its computing power performance is comparable to the system's preset maximum offline transaction capacity per terminal. match; The dual-path communication module includes a 4G / 5G / Ethernet wide-area communication module and a LoRa / Bluetooth Mesh short-range peer-to-peer communication module, which respectively correspond to wide-area communication with the cloud and self-organizing network communication between adjacent nodes in the offline state. The ticketing voucher reading and writing module supports the reading and verification of ID cards, IC cards, paper QR codes, electronic dynamic QR codes, and facial recognition vouchers. Its recognition accuracy and response speed match the system's preset minimum legal browsing interval. In accordance with the time delay requirements for verification decisions; The passage execution mechanism includes a swing gate / wing gate and an audio-visual prompt module, which are used to execute corresponding physical actions and prompts according to the passage / interception instructions of the hierarchical decision engine; The non-volatile memory has a storage capacity that matches the system's preset maximum expected offline time. Basic authorization time window length The matching capability can meet the storage requirements of full transaction logs in an offline state for at least 30 days.

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