Park access control management method and system, computer equipment and storage medium

Abstract

The invention relates to a park access control management method and system, computer equipment and a storage medium. The park access control management method comprises the following steps: acquiring pass data of park access control, wherein the pass data comprises real-time pass data; preprocessing the traffic data; constructing a flow prediction model; inputting the preprocessed real-time traffic data into a traffic prediction model for prediction to obtain a traffic prediction result; and adjusting the opening number of the park access control channels and the pass mode of the park access control based on the flow prediction result. According to the invention, based on the traffic data, the big data prediction model is adopted to accurately predict the traffic of different time periods of the park, the opening number and the traffic mode of the access control channels can be automatically adjusted according to the prediction result, the traffic efficiency can be improved in the peak period, the energy can be saved in the low peak period, the intelligence is high, and the user experience is improved. Therefore, the arrangement and management level of the park is improved.

Description

Technical Field

[0001] This application relates to the technical field of access control management, and particularly to a park access control management method, system, computer device, and storage medium. Background Art

[0002] With the acceleration of the urbanization process, parks have gradually become important economic and social activity centers, and their management and operation face many challenges. During peak hours, the flow of people and vehicles in the park will surge. For example, in a large technology park during the morning rush hour on weekdays, a large number of employees enter the park concentratedly, and the traffic demands of vehicles and people increase sharply in a short period of time. Most traditional access control management systems adopt fixed numbers of opened channels and access modes, and cannot be dynamically adjusted according to real-time traffic, which is not intelligent enough. For instance, during peak hours, only a fixed number of channels are still opened, which will lead to low traffic efficiency, and people and vehicles need to queue for a long time, and may even cause potential safety hazards such as traffic congestion. Moreover, the access control system with a fixed mode maintains the same operating state regardless of the traffic volume, resulting in waste of energy. Summary of the Invention

[0003] Based on this, in view of the problems in the related art, it is necessary to provide a park access control management method, system, computer device, and storage medium to solve the problems of low traffic efficiency, energy waste, and insufficient intelligence in the existing park access control management system.

[0004] To achieve the above object, in a first aspect, this application provides a park access control management method, and the park access control management method includes: Obtain the access data of the park access control, where the access data includes real-time access data; Preprocess the access data; Construct a traffic prediction model; Input the preprocessed real-time access data into the traffic prediction model for prediction to obtain a traffic prediction result; Adjust the number of opened park access control channels and the access mode of the park access control based on the traffic prediction result.

[0005] In some embodiments, the access data further includes historical access data; and constructing the traffic prediction model includes: Construct an LSTM neural network; Train and verify the LSTM neural network based on the preprocessed historical access data to obtain the traffic prediction model.

[0006] In some embodiments, adjusting the number of opened park access control channels and the access mode of the park access control based on the traffic prediction result includes: Set traffic thresholds, where the traffic thresholds include peak-hour thresholds, off-peak-hour thresholds, and special-event thresholds; Obtain the traffic value for the current period based on the traffic prediction result; Based on the traffic value for the current period and the traffic thresholds, adjust the number of opened park access channels and the access mode of the park access control.

[0007] In some embodiments, the access mode of the park access control includes: fast access mode, energy-saving mode, and security mode; the adjusting the number of opened park access channels and the access mode of the park access control based on the traffic value for the current period and the traffic thresholds includes: If the traffic value for the current period is greater than or equal to the peak-hour threshold, increase the number of opened park access channels and / or adjust the access mode of the park access control to the fast access mode; If the traffic value for the current period is less than or equal to the off-peak-hour threshold, decrease the number of opened park access channels and / or adjust the access mode of the park access control to the energy-saving mode; If the traffic value for the current period is greater than or equal to the special-event threshold, increase the number of opened park access channels, adjust the access mode of the park access control to the fast access mode, and / or adjust the access mode of the park access control to the security mode.

[0008] In some embodiments, after adjusting the number of opened park access channels and the access mode of the park access control based on the traffic prediction result, it further includes: Monitor the adjustment effect in real time, and when the adjustment effect is not good, optimize the number of opened park access channels and the access mode of the park access control.

[0009] In some embodiments, the access data further includes historical access data; after adjusting the number of opened park access channels and the access mode of the park access control based on the traffic prediction result, it further includes: Display the access data, the traffic prediction result, the current number of opened park access channels, the current access mode of the park access control, and the current status of the park access control through an interaction interface; Monitor the adjustment effect in real time, and when needed, adjust the number of opened park access channels, adjust the access mode of the park access control, and adjust the issuance of temporary access permissions through the interaction interface; Monitor abnormal behaviors in the park in real time, and when an abnormal behavior occurs in the park, send an alarm message through the interaction interface; Export the historical access data through the interaction interface, analyze the historical access data to obtain the traffic change trend in different time periods; and / or Analyze the traffic prediction results and adjustment results, and generate a data analysis report based on the analysis results.

[0010] In a second aspect, the present application further provides a park access control management system; the park access control management system includes: A data acquisition device for acquiring the access data of the park access control, where the access data includes real-time access data; A preprocessing module for preprocessing the access data; A traffic prediction model construction module for constructing a traffic prediction model; the traffic prediction model predicts based on the preprocessed real-time access data to obtain a traffic prediction result; A control module for adjusting the opening number of the park access control channels and the access mode of the park access control based on the traffic prediction result.

[0011] In some of these embodiments, the data acquisition device is further configured to acquire the abnormal behavior data of the park; it further includes: An analysis module for analyzing the traffic prediction results and adjustment results, and generating a data analysis report based on the analysis results; A display device, including a display module and an adjustment module; the display module is configured to display the access data, the traffic prediction result, the opening number of the current park access control channels, the access mode of the current park access control, the status of the current park access control, the alarm information, and the data analysis report; the adjustment module is used for the management personnel to adjust the opening number of the park access control channels, the access mode of the park access control, and the issuance of temporary access permissions when needed.

[0012] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the park access control management method described in the first aspect are implemented.

[0013] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the park access control management method described in the first aspect are implemented.

[0014] In the above park access control management method, system, computer device, and storage medium, based on the access data, a big data prediction model can be used to accurately predict the traffic flow in different periods of the park, the opening number of the access control channels and the access mode can be automatically adjusted according to the prediction results, the traffic efficiency can be improved during peak hours, and energy can be saved during off-peak hours, with high intelligence, thereby improving the overall management level of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of the park access management method provided in an embodiment of the present application; Figure 2 It is a structural block diagram of the park access management system provided in another embodiment of the present application; Figure 3 It is a structural block diagram of the park access management system provided in yet another embodiment of the present application; Figure 4 It is an internal structural diagram of a computer device provided in yet another embodiment of the present application.

[0017] Explanation of reference numerals: 10, data acquisition device; 20, preprocessing module; 30, traffic prediction model construction module; 40, control module; 50, analysis module; 60, display device; 601, display module; 602, adjustment module. Detailed implementation manners

[0018] In order to make the purpose, technical solutions and advantages of the present application more clear, the following further details the present application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] With the acceleration of the urbanization process, parks have gradually become important economic and social activity centers, and their management and operation face many challenges. During peak hours, the flow of people and vehicles in the park will surge. For example, in large technology parks during the morning rush hour on weekdays, a large number of employees gather to enter the park, and the traffic demands of vehicles and people increase sharply in a short period of time. Most traditional access management systems adopt fixed numbers of opened channels and traffic patterns, and cannot be dynamically adjusted according to real-time traffic, which is not intelligent enough. For example, during peak hours, only a fixed number of channels are still opened, which will lead to low traffic efficiency, and people and vehicles need to queue for a long time, and may even cause safety hazards such as traffic congestion. Moreover, the access control system with a fixed mode maintains the same operating state regardless of the traffic volume, resulting in waste of energy.

[0020] With the rapid development of big data technology, it has become an important research direction to accurately predict traffic flow through a prediction model using massive historical traffic data and real-time data. However, there are many problems in practical applications. For example, the accuracy of the prediction model used is not high, and it may not be able to accurately predict the traffic flow changes in different periods of the park, resulting in the access control system being unable to make reasonable adjustments in a timely manner. In addition, the adjustment of the access control strategy is not flexible enough and cannot be dynamically changed according to multi-dimensional factors such as special activities and security conditions in the park, seriously restricting the intelligentization and high efficiency of park management.

[0021] In one embodiment, please refer to Figure 1 , the present application provides a method for managing access control in a park, and the method for managing access control in the park includes the following steps: S10 to S50.

[0022] S10: Obtain the traffic data of the park access control, and the traffic data includes real-time traffic data.

[0023] S20: Preprocess the traffic data.

[0024] S30: Construct a traffic prediction model.

[0025] S40: Input the preprocessed real-time traffic data into the traffic prediction model for prediction to obtain a traffic prediction result.

[0026] S50: Adjust the opening number of the park access control channels and the access mode of the park access control based on the traffic prediction result.

[0027] In the method for managing access control in the park of the present application, based on the traffic data, a big data prediction model can be used to accurately predict the traffic flow in different periods of the park. The opening number of the access control channels and the access mode can be automatically adjusted according to the prediction result, improving the traffic efficiency during peak hours and saving energy during off-peak hours, with high intelligence, thus improving the overall management level of the park.

[0028] In step S10, please refer to Figure 1 step S10 of, obtain the traffic data of the park access control, and the traffic data includes real-time traffic data.

[0029] As an example, in step S10, the traffic data of personnel and vehicles can be collected through sensors and cameras installed at positions such as the park entrance. For example, an infrared sensor can be used to detect the passage of personnel, a geomagnetic sensor can be used to sense the entry and exit of vehicles, and a camera can be used to record the detailed information of vehicles and personnel (such as license plate numbers and facial features, etc.) through image recognition technology; these sensors and cameras can transmit the collected data to the central server through, but not limited to, the Internet of Things (IoT) technology to form an original data set, that is, to form traffic data.

[0030] Specifically, in addition to real-time passing data, the passing data may also include historical passing data.

[0031] In step S20, refer to Figure 1 step S20 in, and preprocess the passing data.

[0032] As an example, in step S20, preprocessing the passing data may include the following: data cleaning, data preprocessing, and feature engineering.

[0033] Data cleaning: The passing data (including real-time passing data and historical passing data) may contain noise and outliers. For example, passing records misreported by sensors, recognition errors caused by light problems in cameras, etc. The purpose of data cleaning is to remove these invalid or incorrect data.

[0034] The specific steps of data cleaning may include the following: noise filtering and outlier handling. Noise filtering: Filter data that does not conform to logic by setting thresholds. For example, if a large number of passing records are detected by sensors during non-working hours in the early morning, these passing records will be identified as noise and filtered. Outlier handling: Statistical methods (such as the 3σ principle) can be used to identify outliers. For data outside the normal range, it will be marked as an anomaly and processed. For example, if the vehicle passing volume suddenly surges during a certain period, but historical passing data shows that the traffic flow is usually low during this period, it will be marked as an anomaly and further analyzed.

[0035] Data preprocessing: The cleaned passing data needs to be preprocessed so that the subsequent traffic prediction model can better utilize this data.

[0036] The specific steps of data preprocessing may include the following: data normalization and data completion. Data normalization: Normalize the passing data collected by different sensors so that they are in the same dimension; for example, normalize the vehicle passing volume to between 0 and 1 for subsequent model training. Data completion: For missing data, interpolation methods or prediction methods based on historical passing data can be used for completion; for example, if the sensor data at a certain time point is missing, it can be interpolated and completed according to the data at the previous and subsequent time points.

[0037] Feature engineering: The passing data after data preprocessing needs to be feature-extracted to obtain extracted features (which can include historical extracted features obtained by feature extraction based on historical passing data after data preprocessing and real-time extracted features obtained by feature extraction based on real-time passing data after data preprocessing). Historical extracted features may include time features, special event markers, and historical traffic flow features.

[0038] Specifically, feature engineering may include: time feature extraction, special event marking, and historical traffic feature extraction. Time feature extraction: Extract time features such as hour, week, and month from timestamps to capture the periodic changes in traffic. For example, the period from 8:00 to 9:00 in the morning on weekdays is usually the peak period of personnel passage. Special event marking: Use special events (such as holidays and large-scale events) as features and input them into the model to handle traffic fluctuations under these feature conditions. Historical traffic feature extraction: Calculate statistical features such as the mean and variance of traffic over a past period (such as the past 7 days) as historical traffic features.

[0039] As an example, after preprocessing the access data, before feature extraction of the preprocessed access data, a step of data storage may also be included; specifically, the data storage is as follows: The preprocessed access data will be stored in a high-performance database so that subsequent traffic prediction models can quickly access and use it; the high-performance database can adopt a distributed storage architecture to ensure the high availability and scalability of the preprocessed access data.

[0040] The core tasks of step S10 and step S20 are to collect access data of personnel and vehicles in the park through various sensors and cameras, and clean and preprocess these access data to ensure the accuracy and integrity of the data.

[0041] In a specific example, assume that during the period from 8:00 to 9:00 in the morning on weekdays, a large number of personnel are detected passing through by infrared sensors in the park, but the vehicle traffic volume detected by geomagnetic sensors is relatively low; First, clean the original access data and filter out noise data caused by sensor false alarms (such as access records during non-working hours in the early morning); Then, preprocess the cleaned access data, normalize the traffic volume of personnel and vehicles to between 0 and 1, and interpolate and complete the missing data; Then, the preprocessed access data is stored in a high-performance database to provide a reliable data source for subsequent traffic prediction models; Finally, historical access data for the past 7 days can be loaded from the high-performance database, and time features (such as hour, week) and special event markings can be extracted based on the historical access data. Through this series of steps, the accuracy and integrity of the access data can be ensured, providing a solid foundation for subsequent traffic prediction and access control optimization management.

[0042] In step S30, please refer to Figure 1 step S30 in

[0043] As an example, in step S30, the building of the traffic prediction model may include the following steps: S301~S302.

[0044] S301: Construct an LSTM neural network.

[0045] S302: Train and validate the LSTM neural network based on the pre - processed historical traffic data to obtain the traffic prediction model.

[0046] As an example, in step S301, a long short - term memory (LSTM) neural network can be used as the basic model. LSTM can effectively capture long - term dependencies in time - series data. The specific implementation is as follows: Input layer: The input extraction features can include time features, special event markers, and historical traffic features, etc.

[0047] LSTM layer: Set multiple LSTM cells, and each layer contains multiple neurons, which are used to capture traffic changes at different time scales.

[0048] Output layer: Output the traffic prediction value for a future period of time (such as the next 1 hour).

[0049] Loss function: Use the mean squared error (MSE) as the loss function to measure the difference between the predicted value and the actual value. The specific formula is as follows:

[0050] where, is the actual traffic value of the i - th sample, is the predicted traffic value of the i - th sample, and n is the number of samples.

[0051] As an example, in step S302, training and validating the LSTM neural network based on the pre - processed historical traffic data to obtain the traffic prediction model can include the following: model training and model evaluation.

[0052] Specifically, model training can include: data splitting, training process, validation and parameter tuning. Data splitting: The historical traffic data can be divided into a training set and a validation set. Usually, 80% of the data is used as the training set and 20% of the data is used as the validation set. Training process: Use the training set to train the LSTM neural network, update the parameters of the traffic prediction model through the back - propagation algorithm, and minimize the loss function.

[0053] Specifically, model evaluation can include: evaluation metrics. Evaluation metrics: The mean absolute error (MAE) and the root mean square error (RMSE) can be used as evaluation metrics to measure the prediction accuracy of the traffic prediction model.

[0054] Specifically, the formula for the mean absolute error (MAE) can be as follows:

[0055] Among them, is the actual flow value of the i-th sample, is the predicted flow value of the i-th sample, and n is the number of samples.

[0056] The advantage of MAE is that it is easy to understand and gives the same weight to all types of errors; however, MAE is less sensitive to outliers because it is based on absolute values rather than squared values.

[0057] Specifically, the specific formula for the root mean square error (RMSE) can be as follows:

[0058] Among them, is the actual flow value of the i-th sample, is the predicted flow value of the i-th sample, and n is the number of samples.

[0059] The advantage of RMSE is that it penalizes errors more than MAE because it is based on squared values, which makes RMSE more sensitive to outliers. Therefore, when there are outliers in the database, RMSE may not be the best error metric. However, RMSE is still a very useful metric in many cases, especially when large errors need to be emphasized.

[0060] In practical applications, MAE is often used together with other error metrics (such as the mean squared error MSE or the root mean square error RMSE) to comprehensively evaluate the performance of the flow prediction model.

[0061] As an example, it can also include regularly evaluating the trained flow prediction model and optimizing and updating the flow prediction model according to the evaluation results to ensure the long-term effectiveness of the flow prediction model.

[0062] In a specific example, an LSTM neural network model is constructed. The input features (i.e., the extracted features) include time features, special activity markers, and historical flow features, and the output is the predicted value of the personnel passing volume in the next 1 hour; the LSTM neural network model is trained on the training set, and the parameters of the model are updated through the backpropagation algorithm to minimize the mean squared error; on the test set, the model shows low MAE and RMSE values, indicating high prediction accuracy.

[0063] In step S40, refer to Figure 1 step S40 in, and input the preprocessed real-time passing data into the flow prediction model for prediction to obtain the flow prediction result.

[0064] As an example, in step S40, inputting the preprocessed real-time traffic data into the traffic prediction model for prediction to obtain a traffic prediction result may include: real-time traffic data input and dynamic adjustment. Input the real-time traffic data into the trained traffic prediction model to predict the future traffic. Dynamic adjustment: Dynamically adjust the parameters of the traffic prediction model according to the prediction result to cope with emergencies (such as temporary activities, unexpected events, etc.); for example, when detecting an abnormal increase in traffic, the traffic prediction model will automatically increase the number of neurons in the LSTM layer to improve the prediction accuracy.

[0065] In step S50, refer to Figure 1 step S50 of

[0066] As an example, in step S50, adjusting the opening number of the park access channels and the access mode of the park access based on the traffic prediction result may include the following steps: S501~S503.

[0067] S501: Set traffic thresholds, where the traffic thresholds include peak-hour thresholds, off-peak-hour thresholds, and special-event thresholds.

[0068] S502: Obtain the traffic value of the current period based on the traffic prediction result.

[0069] S503: Adjust the opening number of the park access channels and the access mode of the park access based on the traffic value of the current period and the traffic thresholds.

[0070] As an example, in step S501, the setting of the peak-hour threshold may include: According to the historical traffic data, set the traffic threshold during the peak hour as the peak-hour threshold; it can be determined that the corresponding period is the peak hour when it is predicted that the number of people exceeds 1000 per hour. The setting of the off-peak-hour threshold may include: Set the traffic threshold during the off-peak hour as the off-peak-hour threshold; it can be determined that the corresponding period is the off-peak hour when it is predicted that the number of people is less than 200 per hour. The setting of the special-event threshold may include: For special events (such as large conferences or exhibitions, etc.), set a temporary traffic threshold as the special-event threshold to cope with sudden traffic changes.

[0071] As an example, in step S502, the traffic prediction result can be used as an input to calculate the traffic value of the current period.

[0072] As an example, the access modes of the park access include: fast access mode, energy-saving mode, and security mode. Of course, it can also include the standard access mode (the access mode during normal traffic).

[0073] As an example, in step S503, adjusting the opening number of the park access channels and the access mode of the park access based on the traffic value of the current period and the traffic threshold may include the following: If the traffic value of the current period is greater than or equal to the peak period threshold, increase the opening number of the park access channels, and / or adjust the access mode of the park access to the fast access mode; If the traffic value of the current period is less than or equal to the off-peak period threshold, reduce the opening number of the park access channels, and / or adjust the access mode of the park access to the energy-saving mode; If the traffic value of the current period is greater than or equal to the special event threshold, increase the opening number of the park access channels, adjust the access mode of the park access to the fast access mode, and / or adjust the access mode of the park access to the security mode.

[0074] Specifically, the opening strategy of the park access channels may include the following: Peak period strategy: During the peak period, automatically increase the opening number of the access channels. For example, when it is predicted that the personnel flow exceeds the peak period threshold, all available pedestrian channels can be opened; Off-peak period strategy: During the off-peak period, the opening number of the access channels can be reduced to save energy. For example, when it is predicted that the personnel flow is lower than the off-peak period threshold, only some access channels are opened, and unnecessary equipment is turned off; Special event strategy: When a special event is held in the park, the opening number of the channels can be temporarily increased according to the predicted traffic changes. For example, during a large-scale event, temporary access channels can be opened.

[0075] Specifically, the adjustment of the access mode may include the following: Fast access mode: During the peak period, start the fast access mode. By using technologies such as automatic access card recognition and face recognition, the access time can be reduced. For example, by using face recognition technology, the identity of employees can be automatically recognized without manual card swiping, improving the access efficiency; Energy-saving mode: During the off-peak period, start the energy-saving mode, turn off unnecessary equipment and lighting, and reduce energy consumption. For example, automatically turn off the lighting and monitoring equipment of unoccupied channels to reduce energy consumption; Security mode: When the security situation in the park changes, enable the security mode and strengthen the review of unfamiliar personnel and vehicles. For example, require all visitors to undergo identity verification and increase the issuance of temporary access permissions.

[0076] As an example, in step S503, the opening number of the access channels to be opened can be calculated according to the traffic value of the current period and the traffic threshold; For example, when the traffic value exceeds the peak period threshold, the opening number of the access channels to be opened can be calculated based on the following formula:

[0077] Where N is the opening number of the access channels to be opened, F is the predicted traffic value, and C is the maximum passing capacity of each access channel.

[0078] In a specific example, assume that during the period from 8:00 to 9:00 in the morning on a weekday in a certain park, the predicted number of people flow is 1,200, exceeding the peak period threshold. First, according to the flow prediction result, calculate the number of access control channels to be opened. Assume that the maximum passing capacity of each channel is 200 people, then 6 access control channels need to be opened. Then, enable the fast passage mode, and reduce the passing time through automatic identification of access cards and face recognition technology. At the same time, monitor the passing situation of the access control channels in real time to ensure the effectiveness of the adjustment strategy. During the low peak period, such as from 2:00 to 3:00 in the afternoon, the predicted number of people flow is 150, which is lower than the low peak period threshold. Only 1 access control channel can be opened, and the energy-saving mode can be enabled to turn off unnecessary equipment and lighting to reduce energy consumption. Through this series of steps, the system realizes the dynamic adjustment of the access control channels, improving the passing efficiency and management level.

[0079] As an example, after step S50, the following steps may also be included: Monitor the adjustment effect in real time, and when the adjustment effect is not good, optimize the number of opened access control channels in the park and the passing mode of the park access control.

[0080] Specifically, when the adjustment effect is not good, optimize the adjustment of the number of opened access control channels in the park and the passing mode of the park access control according to the real-time passing data.

[0081] As an example, after step S50, the following steps may also be included: Display the passing data, the flow prediction result, the current number of opened access control channels in the park, the current passing mode of the park access control, and the current status of the park access control through an interactive interface; Monitor the adjustment effect in real time, and when needed, adjust the number of opened access control channels in the park, adjust the passing mode of the park access control, and adjust the issuance of temporary passing permissions through the interactive interface; Monitor the abnormal behaviors in the park in real time, and when an abnormal behavior occurs in the park, send an alarm message through the interactive interface; Export the historical passing data through the interactive interface, analyze the historical passing data to obtain the flow change trend in different time periods; and / or Analyze the flow prediction result and the adjustment result, and generate a data analysis report based on the analysis result.

[0082] Specifically, a friendly human-computer interaction interface can be provided to monitor the traffic prediction results and access control adjustment strategies (i.e., the adjustment methods for the number of opened access control channels and the access modes) in real time, support the management staff to manually adjust the access control strategies, and visually understand the park access conditions and security conditions through the visual interface to make scientific and reasonable decisions. The core task of this step is to provide an intuitive and easy-to-use interaction interface, enabling the management staff to monitor the traffic prediction results and access control adjustment strategies in real time and make manual adjustments according to the actual situation.

[0083] More specifically, the interaction interface can be used to display the following content of real-time traffic monitoring: Display the real-time passing traffic of each entrance of the current park on the interaction interface, including the real-time passing number of people and the real-time passing number of vehicles, which can be visually displayed by visual methods such as bar charts and line charts to show the traffic changes in different time periods; for example, the bar chart can display the real-time passing traffic of each park entrance, and the line chart can show the change trend of the traffic in the past hour. Traffic prediction curve: The traffic prediction curve for the next 1 hour can be displayed on the interaction interface to help the management staff understand the traffic change trend in advance; the prediction curve can be dynamically adjusted based on historical passing data and real-time passing data to ensure its accuracy; for example, the prediction curve can display the predicted passing volume of each park entrance in the next 1 hour to help the management staff make decisions in advance. Access control channel status: Display the number of opened access control channels and their status (such as "opened", "closed", "under maintenance") of the currently opened access control channels on the interaction interface, and visually display the current status of each access control channel through color coding (such as green for opened and red for closed). By displaying the number of opened access control channels and their status of each park entrance on the interaction interface, it can help the management staff quickly understand the operation situation of the access control.

[0084] More specifically, the interaction interface can be used to display the following content of the manual adjustment function: Opening / closing of access control channels: The management staff can manually (referring to the management staff manually touching the adjustment module of the interaction interface) open or close a certain access control channel through the interaction interface. For example, when it is detected that the traffic at a certain entrance suddenly increases, the management staff can open additional access control channels through the interaction interface to improve the passing efficiency. Adjustment of access mode: The management staff can adjust the access mode through the interaction interface. For example, when a security incident is detected, the management staff can enable the security mode through the interaction interface to strengthen the review of all personnel and vehicles. Issuance of temporary access permissions: The management staff can increase the issuance of access permissions through the interaction interface. For example, when a large-scale event is held in the park, the management staff can increase the issuance of temporary access permissions through the interaction interface to cope with the increase in visitor traffic.

[0085] More specifically, the interactive interface can be used to display the following real-time feedback: Abnormal behavior alarm: When abnormal behavior is detected (such as people gathering, vehicles parked illegally, etc.), the interactive interface will pop up an alarm prompt and display the specific information of the abnormal behavior. For example, the interactive interface can display the time and location of the abnormal behavior occurrence, as well as relevant pictures or video screenshots, to help the management personnel make a quick response. Policy execution feedback: Real-time feedback on the execution of access control policies to ensure the effectiveness of policy adjustments. For example, the interactive interface can display information such as the passage efficiency of each channel and the review status of visitors, to help the management personnel evaluate the execution effect of the adjusted policy.

[0086] More specifically, the interactive interface can be used to display the following data export and analysis: Historical data export: The management personnel can export historical passage data through the interactive interface for further analysis. For example, export the historical passage data of the past week to analyze the traffic change trend in different time periods. Data analysis report: Automatically generate a data analysis report and display it on the interactive interface to help the management personnel understand the passage and security status of the park. For example, the data analysis report can include information such as the accuracy of traffic prediction results and the execution effect of access control policies, to help the management personnel make scientific and reasonable decisions.

[0087] In a specific example, assume that in a certain park during the period from 8:00 to 9:00 in the morning on a weekday, it is predicted that the personnel flow will increase significantly. First, display the real-time traffic monitoring and traffic prediction curve on the interactive interface to help the management personnel understand the traffic change trend in advance. When it is detected that the traffic at a certain entrance suddenly increases, the management personnel can manually open additional access control channels through the interactive interface to improve the passage efficiency. At the same time, real-time feedback on the execution of access control policies is provided to ensure the effectiveness of policy adjustments. When abnormal behavior (such as people gathering) is detected, the interactive interface will pop up an alarm prompt and display the specific information of the abnormal behavior to help the management personnel make a quick response. Through this series of steps, the system realizes the dynamic adjustment and real-time monitoring of access control policies, improving the management efficiency and security of the park.

[0088] It should be understood that although Figure 1 the steps in the flowchart of Figure 1At least a part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed and completed at the same moment, but can be executed at different moments. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0089] In another embodiment, please refer to Figure 2 , the present application also provides a campus access control management system. The campus access control management system may include: a data acquisition device 10, a preprocessing module 20, a traffic prediction model construction module 30, and a control module 40. Among them, the data acquisition device 10 is used to acquire the access data of the campus access control, and the access data includes real-time access data. The preprocessing module 20 is used to preprocess the access data. The traffic prediction model construction module 30 is used to construct a traffic prediction model. The traffic prediction model predicts based on the preprocessed real-time access data to obtain a traffic prediction result. The control module 40 is used to adjust the opening number of the campus access control channels and the access mode of the campus access control based on the traffic prediction result.

[0090] In the campus access control management system of the present application, by setting the data acquisition device 10, the preprocessing module 20, the traffic prediction model construction module 30, and the control module 40, it is possible to accurately predict the traffic in different periods of the campus based on the access data by using a big data prediction model, and automatically adjust the opening number of the access control channels and the access mode according to the prediction result, which can improve the access efficiency during peak hours and save energy during off-peak hours, and has a high degree of intelligence, thereby improving the overall management level of the campus.

[0091] As an example, please refer to Figure 3 , the data acquisition device 10 is further used to acquire the abnormal behavior data of the campus. The campus access control management system may further include: an analysis module 50 and a display device 60. Among them, the analysis module 50 is used to analyze the traffic prediction result and the adjustment result, and generate a data analysis report based on the analysis result. The display device 60 includes a display module 601 and an adjustment module 602. The display module 601 is used to display the access data, the traffic prediction result, the current opening number of the campus access control channels, the current access mode of the campus access control, the current state of the campus access control, the alarm information, and the data analysis report. The adjustment module 602 is used to allow the management personnel to adjust the opening number of the campus access control channels, the access mode of the campus access control, and the issuance of temporary access permissions when needed. Specifically, the display device 60 may include an interactive interface for human-computer interaction. The display module 601 may be the display screen of the interactive interface, and the adjustment module 602 may be the touch buttons on the interactive interface, etc.

[0092] As an example, the park access control management system of the present application can be used to execute, such as Figure 1 the park access control management method in the related embodiments.

[0093] For the specific limitations of the park access control management system, reference can be made to the limitations on the park access control management method in the foregoing text, which will not be elaborated here. Each module in the above park access control management system can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above respective modules.

[0094] In another embodiment, the present application further provides a computer device, which can be a server, and its internal structure diagram can be as Figure 4 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as real-time access data, historical access data, traffic prediction results, and access control adjustment strategies. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it is used to implement, such as Figure 1 the park access control management method described in the related embodiments.

[0095] In another embodiment, the present application further provides a computer device, which can be a terminal, and its internal structure diagram can be as Figure 4 shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it is used to implement, such as Figure 1and a method for managing access control in a park as described in the related embodiments. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0096] Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0097] In another embodiment, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of Figure 1 a method for managing access control in a park as described in and the related embodiments.

[0098] In another embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of Figure 1 a method for managing access control in a park as described in and the related embodiments.

[0099] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0100] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features of the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0101] The above embodiments only represent several implementation manners of the present application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for managing access control in a park, characterized in that, Including: Obtaining the access data of the park entrance and exit, where the access data includes real-time access data; Preprocessing the access data; Constructing a traffic prediction model; Inputting the preprocessed real-time access data into the traffic prediction model for prediction to obtain a traffic prediction result; Adjusting the opening number of the park entrance and exit channels and the access mode of the park entrance and exit based on the traffic prediction result.

2. The method according to claim 1, wherein The access data further includes historical access data; the constructing of the traffic prediction model includes: Constructing an LSTM neural network; Training and validating the LSTM neural network based on the preprocessed historical access data to obtain the traffic prediction model.

3. The method according to claim 1, wherein Adjusting the opening number of the park entrance and exit channels and the access mode of the park entrance and exit based on the traffic prediction result includes: Setting traffic thresholds, where the traffic thresholds include peak period thresholds, off-peak period thresholds, and special event thresholds; Obtaining the traffic value of the current period based on the traffic prediction result; Adjusting the opening number of the park entrance and exit channels and the access mode of the park entrance and exit based on the traffic value of the current period and the traffic thresholds.

4. The method according to claim 3, characterized in that, The access mode of the park entrance and exit includes: fast access mode, energy-saving mode, and security mode; the adjusting the opening number of the park entrance and exit channels and the access mode of the park entrance and exit based on the traffic value of the current period and the traffic thresholds includes: If the traffic value of the current period is greater than or equal to the peak period threshold, increasing the opening number of the park entrance and exit channels and / or adjusting the access mode of the park entrance and exit to the fast access mode; If the traffic value of the current period is less than or equal to the off-peak period threshold, reducing the opening number of the park entrance and exit channels and / or adjusting the access mode of the park entrance and exit to the energy-saving mode; If the traffic value of the current period is greater than or equal to the special event threshold, increasing the opening number of the park entrance and exit channels, adjusting the access mode of the park entrance and exit to the fast access mode, and / or adjusting the access mode of the park entrance and exit to the security mode.

5. The method according to claim 1, wherein After adjusting the opening number of the park entrance and exit channels and the access mode of the park entrance and exit based on the traffic prediction result, it further includes: Real-time monitoring of the adjustment effect, and when the adjustment effect is not good, optimizing the opening number of the park entrance and exit channels and the access mode of the park entrance and exit.

6. The method according to any one of claims 1 to 5, characterized in that, The access data further includes historical access data; after adjusting the opening number of the park entrance and exit channels and the access mode of the park entrance and exit based on the traffic prediction result, it further includes: Displaying the access data, the traffic prediction result, the current opening number of the park entrance and exit channels, the current access mode of the park entrance and exit, and the current status of the park entrance and exit through an interactive interface; Real-time monitoring of the adjustment effect, and when needed, adjusting the opening number of the park entrance and exit channels, adjusting the access mode of the park entrance and exit, and adjusting the issuance of temporary access permissions through the interactive interface; Real-time monitoring of abnormal behaviors in the park, and when an abnormal behavior occurs in the park, sending an alarm message through the interactive interface; Exporting the historical access data through the interactive interface, analyzing the historical access data to obtain the traffic change trend in different time periods; and / or Analyze the traffic prediction results and adjustment results, and generate a data analysis report based on the analysis results.

7. A park access control management system, characterized in that Including: A data acquisition device for acquiring access data of the park entrance, and the access data includes real-time access data; A preprocessing module for preprocessing the access data; A traffic prediction model construction module for constructing a traffic prediction model; the traffic prediction model predicts based on the preprocessed real-time access data to obtain traffic prediction results; A control module for adjusting the opening number of the park entrance channels and the access mode of the park entrance based on the traffic prediction results.

8. The system according to claim 7, wherein The data acquisition device is further used to acquire abnormal behavior data of the park; further including: An analysis module for analyzing the traffic prediction results and adjustment results, and generating a data analysis report based on the analysis results; A display device, including a display module and an adjustment module; the display module is used to display the access data, the traffic prediction results, the opening number of the current park entrance channels, the access mode of the current park entrance, the status of the current park entrance, alarm information, and the data analysis report; the adjustment module is used for managers to adjust the opening number of the park entrance channels, the access mode of the park entrance, and the granting of temporary access permissions when needed.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the park entrance management method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the park entrance management method according to any one of claims 1 to 6 are implemented.

Images