Underground garage intelligent management system based on block chain

Abstract

The invention relates to the technical field of intelligent management, in particular to an underground garage intelligent management system based on a block chain, which comprises a video anomaly detection module, a block chain encryption storage module, an intelligent risk management module, a parking space demand prediction module, a parking space adjustment control module and a block chain data synchronization module. According to the invention, through real-time image monitoring, the instant identification capability of abnormal behaviors is enhanced, the response efficiency to unauthorized intrusion and speed abnormity is enhanced, the implementation of automatic access control is promoted, the potential security risk is directly reduced, the introduction of the block chain ensures the characteristics of data encryption and non-tampering, and the safety of the system is improved. The security level of monitoring data and the transparency of management are improved, the configuration of safety measures is further optimized through the automation of risk management, the response time is shortened, the utilization rate of a garage is improved through the automation of parking space monitoring, traffic congestion is reduced through effective resource allocation, and the parking space utilization rate is improved. And the overall efficiency and safety of the garage management system are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to an underground garage intelligent management system based on blockchain. Background Art

[0002] Intelligent management technology involves the use of artificial intelligence, the Internet of Things, big data analysis and blockchain to optimize and automate management processes. Through real-time data collection, analysis and decision-making, it improves operational efficiency and accuracy, reduces human errors and costs. In intelligent management systems, blockchain technology is particularly important because it provides data transparency, security and immutability, which is particularly critical for management scenarios that require high security and reliability.

[0003] Among them, the blockchain underground garage intelligent management system involves the use of blockchain technology to optimize the management operations of underground garages, such as parking space monitoring, payment processing, security control and access management. By utilizing the distributed ledger characteristics of blockchain, it ensures the security, transparency and non-tamperability of all transaction records. The system can track parking space usage in real time and automatically process payment transactions during entry and exit. At the same time, it improves the level of security monitoring and prevents unauthorized access and fraud to provide a safer, more efficient and user-friendly garage management environment.

[0004] Existing technologies are prone to delays under peak traffic conditions, affecting the timeliness of decision-making. For example, the lack of real-time parking space adjustment and optimization strategies will result in waste of parking space resources and increased user waiting time, thereby reducing the overall system performance and user satisfaction. In addition, existing technologies fail to make full use of encryption measures, making data vulnerable to tampering and unauthorized access during storage and transmission, especially in environments with extremely high security requirements. These shortcomings expose the limitations of garage management systems in scenarios with high security and high efficiency requirements. Summary of the invention

[0005] In order to solve the problem that the existing technology is prone to delays under peak traffic, affecting the timeliness of decision-making, for example, the lack of real-time parking adjustment and optimization strategies will cause waste of parking resources and increase user waiting time, thereby reducing the overall system performance and user satisfaction. In addition, the existing technology fails to make full use of encryption measures, making data vulnerable to tampering and unauthorized access during storage and transmission, especially in environments with extremely high security requirements. These deficiencies expose the technical problems of the limitations of garage management systems in scenarios with high security and high efficiency requirements. The embodiment of the present invention provides an underground garage intelligent management system based on blockchain. The technical solution is as follows:

[0006] On the one hand, a blockchain-based underground garage intelligent management system is provided, including:

[0007] The video anomaly detection module analyzes the movement trajectories of vehicles and pedestrians based on real-time images collected by the camera, identifies speed anomalies and unauthorized intrusions, activates the underground garage security access control mechanism, performs emergency closure or alarm, and generates abnormal activity records;

[0008] The blockchain encryption storage module uses blockchain technology to encrypt the monitoring data based on the abnormal activity records and the monitored abnormal behaviors, and verifies the integrity and tamper-proofness of the data to generate a data security certificate;

[0009] The intelligent risk management module automatically analyzes and identifies the security risk level based on the data security proof, and automatically determines matching security measures according to the risk assessment, including calling security personnel and activating security facilities, and generating security execution records;

[0010] The parking demand prediction module analyzes the parking space occupancy pattern and time distribution of the underground garage based on the safety execution record and the real-time data of vehicle entry and exit, predicts the busy period, automatically adjusts the parking space allocation logic, and generates parking space configuration information;

[0011] The parking space adjustment control module monitors the real-time position of the vehicle and the parking space status based on the parking space configuration information, adjusts the parking space allocation according to the real-time data, matches the current demand, optimizes the garage traffic flow and parking space layout, and generates parking space optimization results;

[0012] The blockchain data synchronization module maintains data records based on the parking space optimization results, synchronizes real-time data, optimizes data security using blockchain technology, verifies the irreversibility of operation records, and generates data synchronization records.

[0013] On the other hand, the abnormal activity record includes the security level, behavior type, and alarm status; the data security proof specifically includes the data integrity identification, encryption algorithm type, and timestamp; the security execution record includes risk identification details, execution results, and response time; the parking space configuration information specifically includes predicted parking space demand and space utilization; the parking space optimization results include real-time adjustment data and efficiency evaluation results; the data synchronization record includes synchronization frequency, data integrity check, and security protocol type.

[0014] On the other hand, the video anomaly detection module includes an image acquisition submodule, a behavior analysis submodule, and a security response submodule;

[0015] The image acquisition submodule continuously collects scene images based on the garage camera, identifies the position and movement status of vehicles and pedestrians through real-time video streams, and obtains image data sets;

[0016] The behavior analysis submodule performs movement trajectory analysis based on the image data set, detects abnormal movement speed and potential intrusion behavior, screens unauthorized and abnormal events, and generates behavior anomaly recognition results;

[0017] The security response submodule classifies and processes the identified security threats based on the abnormal behavior identification results, automatically activates the security access control mechanism, including closing the access control or starting the alarm, matches the differentiated security risks, and generates abnormal activity records.

[0018] On the other hand, the blockchain encryption storage module includes a data encryption submodule and an integrity verification submodule;

[0019] The data encryption submodule applies blockchain technology to encrypt data based on the abnormal activity records, verifies the security and privacy of the monitoring data during storage and transmission, and generates an encrypted set of monitoring data;

[0020] The integrity verification submodule verifies the integrity and immutability of the data based on the monitoring data encryption set, uses hash algorithm and blockchain technology, determines the consistency of the data in multiple nodes, and generates data security proof.

[0021] On the other hand, the intelligent risk management module includes a risk identification submodule, a security strategy submodule, and a measure execution submodule;

[0022] The risk identification submodule analyzes the encrypted monitoring data based on the data security proof, identifies potential security threats and abnormal behaviors, including unauthorized access and behavioral deviations, determines the security risk level, and obtains risk level information;

[0023] The security policy submodule evaluates and determines the risk type and degree according to the risk level information, and automatically selects and configures matching security measures according to the risk level to obtain a security response configuration;

[0024] The measure execution submodule executes security operations according to the security response configuration, including calling security personnel and starting security facilities, and generates security execution records.

[0025] On the other hand, the parking demand prediction module includes a data collection submodule, a demand analysis submodule, and a logic adjustment submodule;

[0026] The data collection submodule collects data on vehicle entry and exit times and current parking space occupancy status based on the safety execution record, and tracks vehicle flow through real-time monitoring to obtain traffic dynamic records;

[0027] The demand analysis submodule evaluates the parking space usage pattern based on the traffic dynamic record, calculates the parking space demand trend during peak and off-peak periods, and predicts the parking space demand in the future time period to generate a parking space demand analysis result;

[0028] The logic adjustment submodule automatically adjusts parking space allocation based on the parking space demand analysis result, optimizes parking space allocation logic, matches predicted high-demand time periods, reduces waiting time and congestion in the garage, and generates parking space configuration information.

[0029] On the other hand, by analyzing the parking space usage pattern, the parking space demand trend during peak and off-peak hours is calculated according to the formula:

[0030]

[0031] Calculate the parking space demand R in each time period, where c i represents the vehicle count in the i-th time period, t i represents the duration of the i-th time period, T represents the total observation time, and n represents the number of time periods.

[0032] On the other hand, the parking space adjustment control module includes a position monitoring submodule, a state analysis submodule, and a configuration adjustment submodule;

[0033] The location monitoring submodule captures the location information of vehicles entering and exiting in real time based on the parking space configuration information and combined with ground sensors, continuously monitors the parking position and moving direction of each vehicle, and generates location tracking records;

[0034] The status analysis submodule analyzes the parking space usage status in the garage based on the location tracking record, calculates the current parking space occupancy rate and idle status, evaluates the real-time demand for parking spaces, and generates parking space utilization analysis results;

[0035] The configuration adjustment submodule automatically optimizes parking space allocation and adjusts parking space layout according to the parking space utilization analysis results, matches the current traffic flow changes, optimizes parking space utilization efficiency, and generates parking space optimization results.

[0036] On the other hand, by analyzing the location tracking records, the real-time demand for parking spaces is evaluated according to the formula:

[0037]

[0038] Calculate the current parking space occupancy rate, where CR represents the parking space occupancy rate, N occupied Represents the number of currently occupied parking spaces, N total Represents the total number of parking spaces in the garage.

[0039] On the other hand, the blockchain data synchronization module includes a data maintenance submodule and a real-time synchronization submodule;

[0040] The data maintenance submodule updates and maintains the data of the garage management system based on the parking space optimization results, synchronizes the parking space data and real-time status, verifies the timeliness of the data, and generates a data update profile;

[0041] The real-time synchronization submodule uses blockchain technology to synchronize the garage system data in real time based on the data update profile, determines the data consistency between multiple nodes through the distributed ledger, verifies the security and irreversibility of the operation records, and obtains data synchronization records.

[0042] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0043] Real-time image monitoring strengthens the ability to instantly identify abnormal behaviors, enhances the efficiency of responding to unauthorized intrusions and speed anomalies, promotes the implementation of automated access control, and directly reduces potential security risks. The introduction of blockchain ensures data encryption and non-tamperability, improves the security level of monitoring data and the transparency of management, and the automation of risk management further optimizes the configuration of security measures and shortens the response time. The automation of parking space monitoring not only improves the utilization rate of garages, but also reduces traffic congestion by effectively allocating resources, thereby jointly improving the overall efficiency and safety of garage management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] 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.

[0045] Figure 1 is a system flow chart of the present invention;

[0046] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0047] Figure 3 It is a flow chart of the video anomaly detection module of the present invention;

[0048] Figure 4 This is a flowchart of the blockchain encryption storage module of the present invention;

[0049] Figure 5 This is a flow chart of the intelligent risk management module of the present invention;

[0050] Figure 6 This is a flow chart of the parking space demand prediction module of the present invention;

[0051] Figure 7 This is a flow chart of the parking space adjustment control module of the present invention;

[0052] Figure 8 This is a flowchart of the blockchain data synchronization module of the present invention. DETAILED DESCRIPTION

[0053] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0054] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0055] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0056] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0057] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0058] The embodiment of the present invention provides an underground garage intelligent management system based on blockchain, such as Figure 1 As shown, the system includes:

[0059] The video anomaly detection module analyzes the movement trajectories of vehicles and pedestrians based on the real-time images collected by the camera, identifies speed anomalies and unauthorized intrusion behaviors, activates the underground garage security access control mechanism, performs emergency closure or alarm, and generates abnormal activity records; the security access control mechanism refers to the security system used to control and manage entrances and exits; including automatic door control, which uses electronic door locks to control through a control panel or remote access; cameras and access cards, which combine surveillance cameras and access control cards to confirm identity and allow or deny access; license plate recognition, which automatically identifies the license plates of vehicles entering the garage and controls access control based on the license plate number database; emergency door closing mechanism, which automatically triggers the closure of the entrance or exit door when unauthorized intrusion or other security threats are detected.

[0060] The blockchain encryption storage module uses blockchain technology to encrypt monitoring data based on abnormal activity records and abnormal behaviors detected, and verifies the integrity and tamper-proofness of the data to generate data security certificates.

[0061] The intelligent risk management module automatically analyzes and identifies security risk levels based on data security proof, and automatically determines matching security measures based on risk assessment, including calling security personnel and activating security facilities, and generating security execution records; specific security measures and security facilities include: video surveillance, cameras deployed at various key locations in the garage, continuously recording videos to monitor and play back any activities; emergency alarms, including emergency buttons and automatic alarms, which send alerts to security when abnormal behavior or security threats are detected; lighting, automatic sensing lighting is deployed in the garage to improve visibility; obstacles, such as fences or gates, are used to control vehicle access and block entrances and exits in emergencies.

[0062] The parking demand prediction module is based on the safety execution records and the real-time data of vehicle entry and exit. It analyzes the parking space occupancy pattern and time distribution of the underground garage, predicts the busy period, automatically adjusts the parking space allocation logic, and generates parking space configuration information;

[0063] The parking space adjustment control module monitors the real-time position of vehicles and parking space status based on parking space configuration information, adjusts parking space allocation according to real-time data, matches current demand, optimizes garage traffic flow and parking space layout, and generates parking space optimization results;

[0064] The blockchain data synchronization module maintains data records based on the parking space optimization results, synchronizes real-time data, and uses blockchain technology to optimize data security, verify the irreversibility of operation records, and generate data synchronization records.

[0065] Abnormal activity records include security level, behavior type, and alarm status. Data security proof includes data integrity identification, encryption algorithm type, and timestamp. Security execution records include risk identification details, execution results, and response time. Parking space configuration information includes predicted parking demand and space utilization. Parking space optimization results include real-time adjustment data and efficiency evaluation results. Data synchronization records include synchronization frequency, data integrity check, and security protocol type.

[0066] like Figure 2 and Figure 3 As shown, the video anomaly detection module includes an image acquisition submodule, a behavior analysis submodule, and a security response submodule;

[0067] The image acquisition submodule continuously collects scene images based on the garage camera, identifies the position and movement status of vehicles and pedestrians through real-time video streams, and obtains image data sets;

[0068] The collected scene images are frame-decomposed through the video stream parsing algorithm to extract the key information in each frame of the image, and the pixel matrix is ​​converted into a grayscale image for preprocessing, including noise elimination, edge enhancement and contrast adjustment. Subsequently, the target detection algorithm is used to extract the contours of potential vehicles and pedestrians through the sliding window method and partition marking technology. The convolutional neural network (such as the YOLO model) is used for accurate classification and position calibration, and its coordinate data is extracted. The continuous multi-frame data is merged into a mobile trajectory record through the trajectory tracking algorithm. The movement state is obtained by real-time calculation of the displacement difference and velocity vector change, and the image, position, and state information are integrated into an image data set, including an associated data table of each object's category, position, timestamp, and movement state.

[0069] The behavior analysis submodule performs movement trajectory analysis based on the image data set, detects abnormal movement speed and potential intrusion behavior, screens unauthorized and abnormal events, and generates behavior anomaly recognition results;

[0070] The mobile trajectory data is processed in time series segments, and the error is reduced by smoothing the trajectory coordinate points. A predefined abnormal speed threshold is set, and the modulus value of the trajectory speed vector and the threshold are compared. The part exceeding the threshold is marked as an abnormal mobile trajectory. The Euclidean distance between the two trajectory points is calculated and the movement trend is analyzed in combination with the time interval. For potential intrusion behaviors, polygonal boundary conditions for entering the restricted area are set, and the intrusion judgment function is called to check whether there is a boundary crossing. Unauthorized and abnormal events are classified and marked. The classification basis includes speed abnormality, area restriction violation and unidentified target, and an abnormal trajectory and behavior marking table is generated.

[0071] The security response submodule classifies and processes the identified security threats based on the results of behavioral anomaly identification, automatically activates the security access control mechanism, including closing the access control or activating the alarm, matching differentiated security risks, and generating abnormal activity records;

[0072] The security threat information is input into the classification processing mechanism. By evaluating the risk levels of different abnormal events, the access control subsystem is called for high-risk events, the door closing operation is performed through the electronic relay, the alarm system is enabled for specific intrusion behaviors and the audio and video recording is triggered. Different response strategies are triggered based on risk differentiation. For example, low-risk events are saved to the database, and the abnormal activity records are updated at the same time, including a holographic data table of abnormal type, time, location and processing results, to form a traceable abnormal activity record.

[0073] like Figure 2 and Figure 4 As shown, the blockchain encryption storage module includes a data encryption submodule and an integrity verification submodule;

[0074] The data encryption submodule uses blockchain technology to encrypt data based on abnormal activity records, and verifies the security and privacy of monitoring data during storage and transmission, and generates an encrypted set of monitoring data;

[0075] A symmetric encryption algorithm (such as the AES algorithm) is called to encrypt the data. The data is decomposed into blocks of fixed size through a block mode. Each block is encrypted with an independent key. At the same time, a group linking operation is introduced to increase the complexity of the ciphertext. The encrypted data block is then passed through a hash function to generate a unique hash value. The calculated hash value is used as the block identifier in the blockchain and is chained with the hash value of the previous block to ensure the logical order between the data. Finally, the block is broadcast to all participating nodes for storage through an inter-node consensus protocol, completing the distributed storage of recorded data on the blockchain and generating an encrypted set of monitoring data, including encrypted data blocks and corresponding hash value linked lists of all abnormal activity records.

[0076] The integrity verification submodule uses hash algorithms and blockchain technology based on the monitoring data encryption set to verify data integrity and immutability, determine the consistency of data on multiple nodes, and generate data security proof;

[0077] First, the hash value of each block is recalculated, and the data consistency is verified by comparing the original hash value stored on the blockchain with the recalculated hash value. Then, synchronous verification is performed among multiple nodes, and the distributed ledger protocol is called to check whether the storage content of the block in each node is the same. If the block content of a node is inconsistent with that of other nodes, the node is marked as an abnormal node and a warning message is output. Then, the tamper-proof nature of the blockchain is used to verify the risk of data tampering. The integrity of the link is determined by re-checking the hash association relationship between the previous and next blocks in the chain structure, and the verified blocks are marked as complete data, and finally a data security proof is generated.

[0078] like Figure 2 and Figure 5 As shown, the intelligent risk management module includes a risk identification submodule, a security strategy submodule, and a measure execution submodule;

[0079] The risk identification submodule analyzes the encrypted monitoring data based on data security proof, identifies potential security threats and abnormal behaviors, including unauthorized access and behavioral deviations, determines the security risk level, and obtains risk level information;

[0080] The encrypted monitoring data is classified and organized according to the data source and time series, and the relevant access logs and behavior records are extracted. The key fields in the logs, such as user identity, operation content and access time, are analyzed. Combined with the definition of behavioral deviation, the behavioral threshold range is set, and unauthorized access behavior is identified by comparing the access frequency, operation type and regional restrictions. For behavioral deviations, the changing trend of the behavioral trajectory is calculated, and the degree of deviation from the historical behavior model is compared. If the degree of deviation exceeds the preset threshold, it is marked as abnormal behavior. Finally, the identified security threats and abnormal behaviors are assigned risk weights respectively, and the comprehensive risk score is calculated. The corresponding security risk level is determined according to the risk classification standard, and risk level information containing specific risk events is generated.

[0081] The security policy submodule evaluates and determines the risk type and degree based on the risk level information, and automatically selects and configures matching security measures based on the risk level to obtain the security response configuration;

[0082] Analyze the type and degree of each risk event, determine the list of available security measures by reading the mapping table between risk level and security measures, call the security measure selection algorithm based on the specific scenario of the event, select the blockade area according to the risk level of high, medium and low, and parameterize the selected measures into security response configuration, which includes the trigger conditions, execution nodes and execution priority of the specific operation. At the same time, check for conflicts with historical security policy records. If there is a conflict, reallocate the execution priority to complete the matching security response configuration.

[0083] The measure execution submodule executes security operations according to the security response configuration, including calling security personnel and starting security facilities, and generating security execution records;

[0084] The event details are sent to the security personnel's mobile terminal, including the event time, location, risk level and recommended measures. Security facilities are controlled through the network interface. For example, the access control closing command is transmitted to the access control controller through the RS485 interface, and the trigger alarm signal starts the alarm device through the relay. At the same time, the surveillance camera is called to record the event scene. The recorded operation steps and event execution status are stored as an execution log. The execution log includes information such as operation time, execution device, execution result and abnormal feedback.

[0085] like Figure 2 and Figure 6 As shown, the parking space demand prediction module includes a data collection submodule, a demand analysis submodule, and a logic adjustment submodule;

[0086] The data collection submodule collects data on vehicle entry and exit times and current parking space occupancy status based on safety execution records, and tracks vehicle flow through real-time monitoring to obtain traffic dynamic records;

[0087] Extract the timestamp of vehicle entry and exit records and the corresponding parking space occupancy status data, use data cleaning steps to remove abnormal records, such as duplicate or missing entries, analyze the relationship between vehicle entry and exit events and parking space status changes, organize the data into a chronological parking space status table, continuously capture vehicle position changes, establish a vehicle trajectory data structure with vehicle number as the index, calculate the time interval of each entry and exit action, generate a time series of vehicle flow paths, and store the results as a traffic dynamic record table. The traffic dynamic record includes complete data on each vehicle's entry and exit time, parking space usage, position changes and flow trends.

[0088] The demand analysis submodule evaluates parking space usage patterns based on traffic dynamic records, calculates parking space demand trends during peak and off-peak hours, and predicts parking space demand in future time periods to generate parking space demand analysis results;

[0089] By analyzing the parking space usage pattern, the parking space demand trend during peak and off-peak hours is calculated according to the formula:

[0090]

[0091] Calculate the parking space demand R in each time period, where c i represents the vehicle count in the i-th time period, t i represents the duration of the i-th time period (hours), T represents the total observation time (hours), and n represents the number of time periods;

[0092] First, based on traffic dynamic records, the number of vehicles entering the parking lot in each time period is collected. i , and record the duration t of each time period i For example, the morning rush hour is from 7:00AM to 9:00AM, a total of 2 hours. If 100 vehicles enter during this period, then c i =100, t i =2, the total time T depends on the length of the observation period. For example, if a working day is 12 hours, the parking demand rate R reflects the average occupancy of parking spaces during the time, which is calculated as follows:

[0093] There are three observation time periods in a day. The vehicle count and duration of each time period are: c1 = 100, t1 = 2, c2 = 50, t2 = 2, c3 = 150, t3 = 3, and the total time T = 12 hours. The calculation of the parking demand rate R is:

[0094]

[0095] The result shows that during the observation period, the average demand rate of parking spaces was 62.5%, indicating that on average 62.5% of parking spaces were occupied every hour, which helps to evaluate the parking space usage pattern and further guide the optimization of parking space allocation and management strategies.

[0096] The logic adjustment submodule automatically adjusts parking space allocation based on the parking space demand analysis results, optimizes parking space allocation logic, matches predicted high-demand periods, reduces waiting time and congestion in the garage, and generates parking space configuration information;

[0097] Call the parking space allocation rule library, match the priority allocation strategy for high-demand periods, adjust the parking space allocation logic to dynamic mode, set the allocation priority conditions to vehicle type, expected stay time, and parking space distance, and screen the parking space allocation plans that meet the conditions. The optimized allocation logic rearranges the parking space numbers according to demand, adjusts the parking order and entrance diversion, and updates the parking space configuration table. The parking space configuration information includes the parking space number, assigned vehicle, scheduled time, and adjustment priority. Finally, the optimized parking space configuration information is generated for the actual operation of garage management.

[0098] like Figure 2 and Figure 7 As shown, the parking space adjustment control module includes a position monitoring submodule, a state analysis submodule, and a configuration adjustment submodule;

[0099] The location monitoring submodule is based on the parking space configuration information and combines ground sensors to capture the location information of vehicles entering and exiting in real time, continuously monitor the parking position and movement direction of each vehicle, and generate location tracking records;

[0100] Combined with ground sensors, the system collects the location information of vehicles entering and exiting in real time, records the vehicle's movement path, parking position and direction changes through sensors, analyzes the complete trajectory of the vehicle from the entrance to the target parking space, uses a coordinate point sequence to mark the vehicle's direction of travel, analyzes the accuracy of the parking position and vehicle status changes, continuously monitors whether the vehicle deviates from the designated area, records the time point and parking space number of each parking and leaving action, integrates the vehicle number, time series, parking area and movement trajectory data, and generates a location tracking record.

[0101] The status analysis submodule analyzes the usage status of parking spaces in the garage based on location tracking records, calculates the current occupancy rate and idle status of parking spaces, evaluates the real-time demand for parking spaces, and generates parking space utilization analysis results;

[0102] By analyzing the location tracking records, the real-time demand for parking spaces is evaluated according to the formula:

[0103]

[0104] Calculate the current parking space occupancy rate, where CR represents the parking space occupancy rate, Noccupied Represents the number of currently occupied parking spaces, N total Represents the total number of parking spaces in the garage;

[0105] First, extract the parking space data of vehicles from the location tracking records and count the number of parking spaces actually occupied at a specific time N. occupied For example, if the record shows that 150 of the 250 parking spaces are occupied, then N occupied =150, total number of parking spaces N total is a fixed property of the garage, with a total of 250 parking spaces, so N total =250, the parking space occupancy rate CR is calculated as follows:

[0106]

[0107] The result shows that at the moment of observation, the parking space occupancy rate of the garage is 60%, which reflects the usage density of the garage and helps to evaluate the parking space utilization efficiency and further parking space allocation strategy.

[0108] The configuration adjustment submodule automatically optimizes parking space allocation and adjusts parking space layout according to the parking space utilization analysis results, matches the current traffic flow changes, optimizes parking space utilization efficiency, and generates parking space optimization results;

[0109] Read the current parking space occupancy and idle status data, re-plan the parking space allocation plan, dynamically allocate idle parking spaces, give priority to providing parking locations for vehicles near the entrance area, adjust the parking space layout according to the real-time traffic flow, update the parking space distribution strategy, record the reallocated parking space numbers and corresponding vehicle information, generate a parking space configuration table containing the adjusted parking space numbers and distribution plan, and obtain the parking space optimization result.

[0110] like Figure 2 and Figure 8 As shown, the blockchain data synchronization module includes a data maintenance submodule and a real-time synchronization submodule;

[0111] The data maintenance submodule updates and maintains the data of the garage management system based on the parking space optimization results, synchronizes the parking space data and real-time status, verifies the timeliness of the data, and generates a data update overview;

[0112] Extract the adjusted parking space number, allocation status and usage data, maintain the parking space information in the garage management system, match the parking space number with the real-time occupancy status, check the consistency between the parking space record and the actual status, and call the data table in the update system if any inconsistency is found, record the difference information before and after the update, verify the timestamp of each data to confirm the timeliness of the data, check the integrity of all parking space data, remove duplicate records and repair missing entries, and finally generate a data update overview, including the updated parking space status table, correction records and timeliness verification results.

[0113] The real-time synchronization submodule uses blockchain technology to synchronize garage system data in real time based on the data update profile, determines the data consistency between multiple nodes through distributed ledgers, verifies the security and irreversibility of operation records, and obtains data synchronization records;

[0114] Read the updated parking space status table, store the data in a distributed manner, process the parking space status data in blocks and generate blocks, record the hash value of each block in a chain structure, call the consensus protocol to verify the consistency of data between nodes, check whether there are unsynchronized or erroneous nodes, sign the operation records to ensure the irreversibility of the data, and if an abnormal node is found, mark it as invalid and notify the system for investigation, broadcast the verified update records to all participating nodes and confirm that the write is successful, and finally generate data synchronization records, including the status of each node, data verification results, and block storage records.

[0115] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0116] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0117] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0118] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0120] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0121] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0123] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0124] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent management system for underground garages based on blockchain, characterized in that: The system comprises: The video anomaly detection module analyzes the movement trajectories of vehicles and pedestrians based on real-time images collected by the camera, identifies speed anomalies and unauthorized intrusions, activates the underground garage security access control mechanism, performs emergency closure or alarm, and generates abnormal activity records; The blockchain encryption storage module uses blockchain technology to encrypt the monitoring data based on the abnormal activity records and the monitored abnormal behaviors, and verifies the integrity and tamper-proofness of the data to generate a data security certificate; The intelligent risk management module automatically analyzes and identifies the security risk level based on the data security proof, and automatically determines matching security measures according to the risk assessment, including calling security personnel and starting security facilities, and generating security execution records; The parking demand prediction module analyzes the parking space occupancy pattern and time distribution of the underground garage based on the safety execution record and the real-time data of vehicle entry and exit, predicts the busy period, automatically adjusts the parking space allocation logic, and generates parking space configuration information; The parking space adjustment control module monitors the real-time position of the vehicle and the parking space status based on the parking space configuration information, adjusts the parking space allocation according to the real-time data, matches the current demand, optimizes the garage traffic flow and parking space layout, and generates parking space optimization results; The blockchain data synchronization module maintains data records based on the parking space optimization results, synchronizes real-time data, and uses blockchain technology to optimize data security, verify the irreversibility of operation records, and generate data synchronization records.

2. The blockchain-based underground garage intelligent management system according to claim 1 is characterized in that: The abnormal activity record includes the security level, behavior type, and alarm status. The data security proof specifically includes the data integrity identification, encryption algorithm type, and timestamp. The security execution record includes risk identification details, execution results, and response time. The parking space configuration information specifically includes the predicted parking space demand and space utilization. The parking space optimization results include real-time adjustment data and efficiency evaluation results. The data synchronization record includes synchronization frequency, data integrity check, and security protocol type.

3. The blockchain-based underground garage intelligent management system according to claim 1 is characterized in that: The video anomaly detection module comprises: The image acquisition submodule continuously collects scene images based on the garage camera, identifies the position and movement status of vehicles and pedestrians through real-time video streams, and obtains image data sets; The behavior analysis submodule performs movement trajectory analysis based on the image data set, detects abnormal movement speed and potential intrusion behavior, screens unauthorized and abnormal events, and generates behavior anomaly recognition results; The security response submodule classifies and processes the identified security threats based on the abnormal behavior identification results, automatically activates the security access control mechanism, including closing the access control or starting the alarm, matches the differentiated security risks, and generates abnormal activity records.

4. The blockchain-based underground garage intelligent management system according to claim 1 is characterized in that: The blockchain encryption storage module includes: The data encryption submodule applies blockchain technology to encrypt data based on the abnormal activity records, verifies the security and privacy of the monitoring data during storage and transmission, and generates an encrypted set of monitoring data; The integrity verification submodule verifies the integrity and immutability of the data based on the monitoring data encryption set, uses hash algorithm and blockchain technology, determines the consistency of the data in multiple nodes, and generates data security proof.

5. The blockchain-based underground garage intelligent management system according to claim 1 is characterized in that: The intelligent risk management module includes: The risk identification submodule analyzes the encrypted monitoring data based on the data security proof, identifies potential security threats and abnormal behaviors, including unauthorized access and behavioral deviations, determines the security risk level, and obtains risk level information; The security policy submodule evaluates and determines the risk type and degree according to the risk level information, and automatically selects and configures matching security measures according to the risk level to obtain a security response configuration; The measure execution submodule executes security operations according to the security response configuration, including calling security personnel and starting security facilities, and generates security execution records.

6. The blockchain-based underground garage intelligent management system according to claim 1 is characterized in that: The parking space demand prediction module includes: The data collection submodule collects data on vehicle entry and exit times and current parking space occupancy status based on the safety execution record, and tracks vehicle flow through real-time monitoring to obtain traffic dynamic records; The demand analysis submodule evaluates the parking space usage pattern based on the traffic dynamic record, calculates the parking space demand trend during peak and off-peak periods, and predicts the parking space demand in the future time period to generate a parking space demand analysis result; The logic adjustment submodule automatically adjusts parking space allocation based on the parking space demand analysis result, optimizes parking space allocation logic, matches predicted high-demand time periods, reduces waiting time and congestion in the garage, and generates parking space configuration information.

7. The blockchain-based underground garage intelligent management system according to claim 6 is characterized in that: By analyzing the parking space usage pattern, the parking space demand trend during peak and off-peak hours is calculated according to the formula: Calculate the parking space demand R in each time period, where c i represents the vehicle count in the i-th time period, t i represents the duration of the i-th time period, T represents the total observation time, and n represents the number of time periods.

8. The blockchain-based underground garage intelligent management system according to claim 1 is characterized in that: The parking space adjustment control module includes: The location monitoring submodule captures the location information of vehicles entering and exiting in real time based on the parking space configuration information and combined with ground sensors, continuously monitors the parking position and moving direction of each vehicle, and generates location tracking records; The status analysis submodule analyzes the parking space usage status in the garage based on the location tracking record, calculates the current parking space occupancy rate and idle status, evaluates the real-time demand for parking spaces, and generates parking space utilization analysis results; The configuration adjustment submodule automatically optimizes parking space allocation and adjusts parking space layout according to the parking space utilization analysis results, matches the current traffic flow changes, optimizes parking space utilization efficiency, and generates parking space optimization results.

9. The blockchain-based underground garage intelligent management system according to claim 8 is characterized in that: By analyzing the location tracking records, the real-time demand for parking spaces is evaluated according to the formula: Calculate the current parking space occupancy rate, where CR represents the parking space occupancy rate, N occupied Represents the number of currently occupied parking spaces, N total Represents the total number of parking spaces in the garage.

10. The blockchain-based underground garage intelligent management system according to claim 1 is characterized in that: The blockchain data synchronization module includes: The data maintenance submodule updates and maintains the data of the garage management system based on the parking space optimization results, synchronizes the parking space data and real-time status, verifies the timeliness of the data, and generates a data update profile; The real-time synchronization submodule uses blockchain technology to synchronize the garage system data in real time based on the data update profile, determines the data consistency between multiple nodes through the distributed ledger, verifies the security and irreversibility of the operation records, and obtains data synchronization records.

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