Method for counting number of people in smart military camp based on edge computing

Abstract

The application discloses a smart military camp population statistics method based on edge computing and belongs to the technical field of power adjustment motors. The smart military camp population statistics method based on edge computing comprises the following steps: S1, deploying a sensor network; S2, setting an edge computing device in the military camp; the edge computing device has processing capacity and storage resources; S3, data acquisition and processing; the data collected by the sensor network is processed and analyzed in real time through the edge device; S4, edge computing and decision-making; the edge device processes and analyzes the collected data, identifies and tracks the movement and position of personnel, calculates the population statistics information in the military camp according to the collected data, and makes decisions to trigger an alarm and dispatch personnel; S5, the edge device transmits the processed data to the cloud for long-term storage and further analysis; and S6, a user interface and a report; a user interface is designed to view real-time population statistics information and historical data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of smart military camp crowd counting methods, more specifically, to a smart military camp crowd counting method based on edge computing. BACKGROUND

[0002] With the development of edge computing and network connection technology and the demand for military camp management and security, the smart military camp crowd counting method uses sensors, cameras, computing devices, and network connections to achieve real-time monitoring and counting of personnel in the military camp. Traditional crowd counting methods usually require manual participation or offline data analysis and cannot provide real-time personnel information. The smart military camp crowd counting method based on edge computing can accurately provide real-time data of the number of personnel in the military camp.

[0003] However, the traditional smart military camp crowd counting method based on edge computing still has many shortcomings: the computing power and storage resources of traditional edge devices are relatively limited and may not be able to handle large-scale data sets and complex statistical algorithms; they are usually fixed hardware devices that are difficult to adjust and expand after deployment; they usually need to rely on network connections to transmit data and communicate with the cloud. In a military environment, network connections may be unstable or subject to interference, affecting data transmission and real-time performance; edge devices may face security and privacy risks when processing and storing data, lack sufficient security measures, and are vulnerable to attacks or data breaches; they cannot accurately identify and track personnel, affecting the accuracy of crowd counting. SUMMARY

[0004] 1. Technical problems to be solved

[0005] The purpose of the present application is to provide a smart military camp crowd counting method based on edge computing to solve the problems raised in the background art:

[0006] The computing power and storage resources of traditional edge devices are relatively limited and may not be able to handle large-scale data sets and complex statistical algorithms; they are usually fixed hardware devices that are difficult to adjust and expand after deployment; they usually need to rely on network connections to transmit data and communicate with the cloud. In a military environment, network connections may be unstable or subject to interference, affecting data transmission and real-time performance; edge devices may face security and privacy risks when processing and storing data, lack sufficient security measures, and are vulnerable to attacks or data breaches; they cannot accurately identify and track personnel, affecting the accuracy of crowd counting.

[0007] 1. Technical solution

[0008] The edge computing-based smart military camp crowd counting method comprises the following steps:

[0009] The edge computing-based smart military camp crowd counting method comprises the following steps:

[0010] S1, deploy a sensor network, deploy a sensor network in the smart military camp, including cameras, thermal sensors and other sensing devices. These sensors can cover key areas in the military camp to monitor the activities and positions of personnel;

[0011] S2, set up edge computing devices in the military camp, such as edge servers or edge nodes. These devices have processing power and storage resources, and can perform local data processing and analysis;

[0012] S3, data collection and processing, the data collected by the sensor network is processed and analyzed by the edge device in real time. For example, cameras can capture images of personnel and use computer vision algorithms for personnel detection and tracking, and thermal sensors can detect body temperature and other information;

[0013] S4, edge computing and decision-making, the edge device processes and analyzes the collected data to identify and track the movement and location of personnel. Based on this data, the edge device can calculate the crowd counting information in the military camp and make decisions, such as triggering alarms, dispatching personnel, etc.;

[0014] S5, data transmission and cloud storage, the edge device can transmit the processed data to the cloud for long-term storage and further analysis. Cloud storage can provide more storage capacity and more powerful computing resources to support more complex statistical analysis and decision-making;

[0015] S6, user interface and report, design a user interface so that military camp managers can view real-time crowd counting information and historical data. The interface can display the distribution of personnel, the trend of the number of people, etc., and generate reports for decision-making reference.

[0016] When using the edge computing-based smart military camp crowd counting method, data security and privacy protection need to be considered. Ensure that data is encrypted during transmission and storage, and comply with relevant privacy laws and policies. In addition, ensure the reliability and stability of the system, and the ability to handle abnormal situations such as device failure and network interruption.

[0017] Preferably, when setting up an edge server in S2, use high-performance hardware, choose hardware with stronger performance, such as multi-core processors, caches, large-capacity memories, etc., to improve the computing power and response speed of the edge server.

[0018] The edge server is set up in combination with the configuration of the edge server, considering the introduction of special hardware accelerators such as image processors, GPUs (graphics processing units) or FPGAs (field programmable gate arrays) to accelerate computationally intensive tasks such as image processing, machine learning, etc.

[0019] The edge server is set up using virtualization technology to transfer network functions such as firewalls, routers, load balancers, etc. to the edge server to provide more efficient, scalable network services.

[0020] The edge server is set up in combination with the configuration of the edge server, using high-speed solid-state storage (SSD) or NVMe (non-volatile memory express) storage devices to improve data read / write speed and access efficiency.

[0021] The edge server is set up using containerization technology such as Docker to package applications and services into independent containers to achieve more efficient resource utilization and deployment flexibility.

[0022] The edge server is set up by introducing automated resource management and load balancing mechanisms to dynamically adjust the allocation of computing resources on the edge server according to real-time demand and workload conditions to achieve optimal performance and resource utilization.

[0023] The edge server is set up in combination with artificial intelligence and machine learning technology to achieve intelligent decision-making and autonomous processing capabilities on the edge server, enabling it to analyze and respond to edge data in real time and provide more intelligent services.

[0024] The edge server is set up considering the power consumption and heat dissipation of the edge server, using low-power processors and components and optimizing heat dissipation design to reduce energy consumption and improve reliability.

[0025] These innovative configurations can improve the computing and storage capabilities of the edge server, accelerate data processing and decision-making processes, and provide a more reliable, secure and efficient edge computing environment. This will help to realize a wider range of application scenarios, including the number of people in the smart barracks and other military applications.

[0026] Preferably, the S3 data collection and processing uses a wireless sensor network to collect data in the barracks. These sensors can be small, low-power devices that can be flexibly placed in key locations in the barracks. Through wireless communication, they can transmit data to the edge server in real time for processing.

[0027] The data collection and processing port machine learning algorithms to edge devices for local data processing and analysis. This can avoid large amounts of data transmission and cloud computing delays, improving response speed and privacy protection.

[0028] The data collection and processing utilizes AR and VR technologies, combined with sensor data, to provide more intuitive and immersive data display and interaction for military personnel. For example, using AR glasses or head-mounted display devices, personnel can view population statistics and related data through a visual interface.

[0029] The data collection and processing performs data preprocessing on edge devices, including data cleaning, noise reduction, compression, etc. This can reduce the amount of data that needs to be transmitted and stored, and improve the efficiency and accuracy of subsequent processing.

[0030] The data collection and processing utilizes MR technology to superimpose real-time sensor data on the military camp scene, providing more comprehensive information presentation. Through gesture recognition, voice interaction, etc., personnel can interact with data to achieve more intuitive and natural operation and query.

[0031] The data collection and processing realizes data aggregation and collaborative processing between edge devices, integrating and analyzing data from multiple devices. This can take advantage of the distributed nature of edge computing to improve processing capacity and data integration accuracy.

[0032] The data collection and processing utilizes machine vision technology and facial recognition algorithms to automatically detect, identify and track personnel in the military camp. This can achieve more accurate and real-time population statistics and can be associated with other data sources for analysis.

[0033] The data collection and processing increases security and privacy protection measures such as data encryption, identity authentication, data desensitization, etc. to ensure data confidentiality and integrity, and comply with relevant privacy laws and policies.

[0034] These innovative methods can improve the efficiency, accuracy and user experience of data collection and processing, and promote the development of smart military.

[0035] Preferably, the S4 edge computing and decision-making also builds a distributed edge computing network, connecting multiple edge devices to form a collaborative network. By sharing computing and storage resources, it achieves more efficient edge data processing and decision-making capabilities.

[0036] The edge computing and decision-making introduces autonomous decision-making and intelligent agent technology on edge devices, enabling them to have certain intelligence and learning capabilities. This allows edge devices to make decisions locally, reducing dependence on the cloud and improving response speed and reliability.

[0037] The edge computing and decision-making introduces stream data processing technology into edge computing to analyze data streams generated by edge devices in real time. Through the stream data processing engine, valuable information can be extracted from data in real time, and decisions can be made quickly.

[0038] The edge computing and decision-making processes involve model training and inference on edge devices, enabling local machine learning and deep learning. By deploying models on edge devices, reliance on the cloud can be reduced, data transmission volume can be decreased, and privacy protection can be improved.

[0039] The edge computing and decision-making process utilizes adaptive resource management technology to dynamically adjust the allocation of computing and storage resources to edge devices based on real-time workload and resource conditions. This optimizes resource utilization and improves performance and energy efficiency.

[0040] The edge computing and decision-making process enables collaborative decision-making between edge devices and the cloud. Edge devices can process local real-time data and make preliminary decisions, while complex decisions can be made in the cloud. Through the collaborative work of the edge and the cloud, a more comprehensive and efficient decision-making process can be achieved.

[0041] The edge computing and decision-making process utilizes machine learning and predictive analytics to analyze and predict data generated by edge devices. Based on these predictions, more accurate and timely decisions can be made, allowing for proactive responses to potential issues.

[0042] The aforementioned edge computing and decision-making system combines edge computing with cloud computing to achieve elastic scheduling and scaling of computing resources. Based on changing needs, the number and configuration of edge devices can be dynamically adjusted to ensure sufficient computing power to support tasks and applications within military facilities.

[0043] Preferably, the user interface design also includes a privacy and security module;

[0044] The privacy and security module clearly displays information about data usage and sharing permissions in the user interface, giving users control over their personal data. For example, it provides a clear explanation of the purpose of data collection, allowing users to choose whether to consent to data collection and use.

[0045] The privacy and security module incorporates anonymization and desensitization techniques into the user interface design to protect the privacy of users' personal information. For example, for data involving personal identity, anonymization or desensitization can be used during display to protect the user's identity and privacy.

[0046] The privacy and security module provides transparent data processing prompts in the user interface, showing users how their data is collected, processed, and stored. This can include information such as the purpose of data processing, data retention period, and data storage location, to enhance users' trust in data security and privacy.

[0047] The privacy security module provides personalized privacy setting options for users, allowing them to adjust the level of data sharing and privacy protection according to their needs and preferences. This can include selective data sharing, preference settings, advertising personalization options, and more.

[0048] The privacy security module introduces security authentication and two-factor authentication in the user interface to ensure the identity and security of the user's data. By providing enhanced authentication mechanisms such as fingerprint recognition, facial recognition, or hardware keys, the security of the user's account is enhanced.

[0049] The privacy security module provides users with data access logs and audit functions, allowing them to track and monitor the access of their data. This can increase the visibility of data usage and timely detect potential security issues.

[0050] The privacy security module provides education and user awareness content about security and privacy through user interface design. For example, users are provided with tips, suggestions and best practices on privacy protection to help them better protect their data and privacy.

[0051] The privacy security module establishes a user participation and feedback mechanism, allowing users to participate in the decision-making process of security and privacy protection. Through user feedback collection and consideration of user opinions and needs, the interface design and privacy protection measures are continuously improved,

[0052] These innovative methods can enhance users' awareness and control of security and privacy, and improve users' trust in data processing and sharing. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The overall flowchart of the present application is shown. DETAILED DESCRIPTION

[0054] Embodiment: Please refer to Figure 1 The edge computing-based smart military camp collective number counting method includes the following steps:

[0055] The edge computing-based smart military camp collective number counting method includes the following steps:

[0056] S1 Deploy a sensor network, deploy a sensor network in the smart military camp, including cameras, thermal sensors and other sensing devices, the sensor covers the key areas in the military camp, monitors the activities and positions of personnel;

[0057] S2 Set up edge computing devices in the military camp, including edge servers or edge nodes; the edge computing devices have processing capacity and storage resources for local data processing and analysis;

[0058] S3 Data collection and processing, the data collected by the sensor network is processed and analyzed in real-time by the edge device.

[0059] The camera captures the image of the person and uses computer vision algorithms for person detection and tracking, and the thermal sensor detects the body temperature information of the person.

[0060] S4 Edge computing and decision-making, the edge device processes and analyzes the collected data, identifies and tracks the movement and location of the person, and calculates the number of people in the camp based on the collected data, and makes decisions to trigger alarms and dispatch personnel.

[0061] S5 The edge device transmits the processed data to the cloud for long-term storage and further analysis, and the cloud storage provides larger storage capacity and more powerful computing resources.

[0062] S6 User interface and reporting, design a user interface to view real-time population statistics and historical data; the interface displays the distribution of personnel, population change trends, etc., and generates reports for decision-making reference.

[0063] Specifically, the specific implementation steps of deploying a sensor network are as follows:

[0064] S11 Requirement analysis, clarify the purpose and requirements of the sensor network, determine the parameters to be monitored or measured, such as temperature, humidity, light, etc.

[0065] S12 Select sensors, select appropriate sensor types and specifications based on requirements. Consider the measurement range, accuracy, stability, and reliability of the sensor.

[0066] S13 Install sensors in appropriate locations based on the layout and requirements of the monitoring area. Ensure that the sensors can accurately obtain the required data.

[0067] S14 Network connection, connect the sensor to the network, which can use wired connection (such as Ethernet) or wireless connection (such as Wi-Fi, Bluetooth, LoRaWAN, etc.). Ensure that the sensor can communicate with the data processing device.

[0068] S15 Data processing device, configure and connect the data processing device for receiving, storing, and processing sensor-collected data. It can be a cloud server, local server, or edge computing device, etc.

[0069] S16 Data collection and transmission, set the sampling frequency and transmission method of the sensor to ensure that the sensor collects data regularly and transmits it to the data processing device. It can use the sensor's own data transmission function or through a gateway device for transmission.

[0070] S17 Data analysis and visualization, analyze and process the data collected by the sensor to extract useful information. Use appropriate data analysis tools and algorithms to convert data into visual form, so that users can intuitively understand and use data.

[0071] S17 Security and privacy protection, consider the security and privacy protection measures of data. Use appropriate encryption and access control measures to ensure the security of sensor data during transmission and storage.

[0072] S18 Testing and verification, test and verify the deployed sensor network to ensure the normal operation of the sensor and the accuracy of the data. Check the connection and communication between the sensor and the data processing equipment.

[0073] Maintenance and management: Regularly check and maintain sensor equipment to ensure normal operation. Regularly update software and firmware to fix vulnerabilities and improve performance. Handle sensor failures or replacement in a timely manner.

[0074] When setting up edge servers in S2, use high-performance hardware, including multi-core processors, caches, and large-capacity memory to choose hardware configurations with stronger performance;

[0075] When setting up edge servers, introduce special hardware accelerators such as image processors, GPUs (graphics processing units) or FPGAs (field programmable gate arrays) based on the configuration of edge servers, such as image processors, GPUs (graphics processing units) or FPGAs (field programmable gate arrays);

[0076] When setting up edge servers, use virtualization technology to transfer network functions (firewalls, routers, load balancers, etc.) to edge servers;

[0077] When setting up edge servers, use high-speed solid-state storage (SSD) storage devices;

[0078] When setting up edge servers, use containerization technology Docker to package applications and services into independent containers;

[0079] When setting up edge servers, introduce automated resource management and load balancing mechanisms to dynamically adjust the allocation of computing resources on edge servers according to real-time demand and workload;

[0080] When setting up edge servers, combine artificial intelligence and machine learning technologies to make intelligent decisions and autonomous processing capabilities on edge servers, analyze and respond to edge data in real time;

[0081] When setting up edge servers, consider the power consumption and heat dissipation of edge servers, use low-power processors and components, and optimize the heat dissipation design.

[0082] S3 Data Collection and Processing uses a wireless sensor network to collect data in the military camp. The wireless sensor network is a small, low-power device that is flexibly arranged in key locations in the military camp. Through wireless communication, the wireless sensor network transmits data to the edge server in real time for processing;

[0083] Data Collection and Processing transplants machine learning algorithms to edge devices for local data processing and analysis;

[0084] Data Collection and Processing utilizes AR and VR technology, combined with sensor data, uses AR glasses or head-mounted display devices to view population statistics and related data through a visual interface;

[0085] Data Collection and Processing performs data preprocessing on edge devices, including data cleaning, noise reduction, and compression;

[0086] Data Collection and Processing utilizes MR technology to superimpose real-time sensor data on the military camp scene, providing information presentation, and interacting with data through gesture recognition, voice interaction, etc;

[0087] Data Collection and Processing realizes data aggregation and collaborative processing between edge devices, integrates and analyzes data from multiple devices, and utilizes the distributed characteristics of edge computing to improve processing capacity and data integration accuracy;

[0088] Data Collection and Processing utilizes machine vision technology and facial recognition algorithms to automatically detect, identify, and track personnel in the military camp, and conducts correlation analysis with other data sources;

[0089] Data Collection and Processing increases security and privacy protection measures, including data encryption, identity authentication, and data desensitization, to ensure data confidentiality and integrity, and complies with relevant privacy laws and policies.

[0090] S4 Edge Computing and Decision Making builds a distributed edge computing network by connecting multiple edge devices to form a collaborative network;

[0091] Edge Computing and Decision Making introduces autonomous decision-making and intelligent agent technology on edge devices, enabling them to have certain intelligence and learning capabilities, allowing edge devices to make decisions locally;

[0092] Edge Computing and Decision Making introduces stream data processing technology into edge computing, analyzes data streams generated by edge devices in real time, extracts valuable information from data through a stream data processing engine, and makes decisions quickly.

[0093] Edge Computing and Decision Making performs model training and inference on edge devices, enabling local machine learning and deep learning;

[0094] Edge computing and decision-making utilize adaptive resource management techniques to dynamically adjust the allocation of computing and storage resources on edge devices based on real-time workload and resource conditions.

[0095] Edge computing and decision-making enable collaborative decision-making between edge devices and the cloud, with edge devices handling local real-time data and making preliminary decisions, while complex decisions are made in the cloud.

[0096] Edge computing and decision-making utilize machine learning and predictive analysis techniques to analyze and predict the data generated by edge devices, resulting in predictive results, and making decisions based on these results.

[0097] Edge computing and decision-making combine edge computing with cloud computing to achieve flexible computing resource scheduling and expansion, dynamically adjusting the number and configuration of edge devices according to demand changes.

[0098] The user interface design also adds a privacy and security module;

[0099] The privacy and security module explicitly displays information about data usage and sharing permissions in the user interface, providing users with control over their personal data, and providing clear explanations of data collection purposes, allowing users to choose whether to agree to data collection and use;

[0100] The privacy and security module incorporates anonymization and desensitization techniques in the user interface design to protect personal information privacy, and for data involving personal identity, uses anonymization and desensitization processing when displaying to protect identity and privacy;

[0101] The privacy and security module provides transparent data processing prompts in the user interface, showing how its data is collected, processed, and stored, including data processing purposes, data retention period, and data storage location information;

[0102] The privacy and security module provides personalized privacy settings options for users, allowing them to adjust data sharing and privacy protection levels according to their needs and preferences, including selective data sharing, preference settings, and personalized advertising options;

[0103] The privacy and security module introduces security authentication and two-factor authentication in the user interface, providing enhanced identity verification mechanisms, including fingerprint recognition, facial recognition, and hardware keys;

[0104] The privacy and security module provides data access logs and audit functions to track and monitor data access, increasing data usage visibility and promptly identifying potential security issues;

[0105] The privacy and security module provides security and privacy education and user awareness enhancement content through user interface design, providing users with privacy protection tips, suggestions, and best practices;

[0106] The privacy security module establishes a user participation and feedback mechanism, participates in the decision-making process of security and privacy protection, collects and considers the opinions and needs of users through user feedback, and continuously improves the interface design and privacy protection measures.

[0107] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above examples, and the above examples and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for counting the number of people in a smart military camp based on edge computing, characterized in that, The method for counting the number of people in a smart military camp based on edge computing includes the following steps: S1 deploys a sensor network, including cameras, thermal sensors and other sensing devices, in a smart military camp. The sensors cover key areas of the camp to monitor personnel activities and locations. S2 deploys edge computing devices in military camps, including edge servers or edge nodes; the edge computing devices have processing capabilities and storage resources to perform local data processing and analysis. S3 data acquisition and processing: Data acquired by the sensor network is processed and analyzed in real time through edge devices; The camera captures images of people and uses computer vision algorithms to detect and track them, while the thermal sensor detects the people's body temperature. S4 edge computing and decision-making: Edge devices process and analyze the collected data, identify and track personnel movement and location, and calculate the personnel statistics in the military camp based on the collected data, and make decisions such as triggering alarms and dispatching personnel. The edge device described in S5 transmits the processed data to the cloud for long-term storage and further analysis. The cloud storage provides greater storage capacity and more powerful computing resources. The S6 user interface and reports are designed to provide a view of real-time headcount statistics and historical data. The interface displays the distribution of people, headcount trends, and generates reports for decision-making reference.

2. The method for counting the number of people in a smart military camp based on edge computing according to claim 1, characterized in that, When setting up the edge server in S2, high-performance hardware is used, and a more powerful hardware configuration including multi-core processors, high-speed cache, and large-capacity memory is selected. When setting up the edge server, in conjunction with the configuration of the edge server, specialized hardware accelerators, including image processors, GPUs (graphics processors) or FPGAs (field-programmable gate arrays), are introduced. The setting up of the edge server utilizes virtualization technology to transfer network functions (firewalls, routers, load balancers, etc.) to the edge server; The edge server is configured using a high-speed solid-state storage (SSD) device. The edge server is set up using Docker containerization technology, which packages the application and services into independent containers. The aforementioned setting up of edge servers introduces an automated resource management and load balancing mechanism, which dynamically adjusts the allocation of computing resources for edge servers based on real-time needs and workload. The setting of the edge server combines artificial intelligence and machine learning technologies, enabling intelligent decision-making and autonomous processing capabilities on the edge server, and real-time analysis and response to edge data. When setting up the edge server, the power consumption and heat dissipation of the edge server are taken into consideration, low-power processors and components are used, and the heat dissipation design is optimized.

3. The method for counting the number of people in a smart military camp based on edge computing according to claim 1, characterized in that, The S3 data acquisition and processing uses a wireless sensor network to collect data in the military camp. The wireless sensor network is a small, low-power device that can be flexibly deployed in key locations in the military camp. Through wireless communication, the wireless sensor network transmits data to an edge server for processing in real time. The data acquisition and processing involves porting machine learning algorithms to edge devices for local data processing and analysis; The data acquisition and processing utilizes AR and VR technologies, combined with sensor data, and uses AR glasses or head-mounted display devices to view population statistics and related data through a visual interface; The data acquisition and processing are performed on edge devices, including data cleaning, noise reduction, and compression. The data acquisition and processing utilizes MR technology to overlay real-time sensor data onto the military camp scene, providing information presentation and allowing interaction with the data through gesture recognition, voice interaction, and other methods. The data acquisition and processing achieves data aggregation and collaborative processing among edge devices, integrating and analyzing data from multiple devices, and leveraging the distributed characteristics of edge computing to improve processing capabilities and the accuracy of data synthesis. The data acquisition and processing utilizes machine vision technology and facial recognition algorithms to automatically detect, identify, and track personnel in military camps, and perform correlation analysis with other data sources; The data collection and processing incorporates enhanced security and privacy protection measures, including data encryption, identity authentication, and data anonymization, to ensure data confidentiality and integrity, and to comply with relevant privacy laws and policies.

4. The method for counting the number of people in a smart military camp based on edge computing according to claim 1, characterized in that, The S4 edge computing and decision-making constructs a distributed edge computing network, connecting multiple edge devices to form a collaborative network; The edge computing and decision-making introduces autonomous decision-making and intelligent agent technologies into edge devices, enabling them to have a certain degree of intelligence and learning capabilities, and allowing edge devices to make decisions locally. The edge computing and decision edge introduce streaming data processing technology into edge computing, analyzes the data streams generated by edge devices in real time, extracts valuable information from the data in real time through the streaming data processing engine, and makes decisions quickly. The edge computing and decision-making processes perform model training and inference on edge devices, enabling local machine learning and deep learning. The edge computing and decision-making utilizes adaptive resource management technology to dynamically adjust the allocation of computing and storage resources for edge devices based on real-time workload and resource status. The edge computing and decision-making enables collaborative decision-making between edge devices and the cloud. The edge devices process local real-time data and make preliminary decisions, while complex decisions are made in the cloud. The edge computing and decision-making utilizes machine learning and predictive analytics to analyze and predict data generated by edge devices, obtain prediction results, and make decisions based on the prediction results. The edge computing and decision-making system combines edge computing with cloud computing to achieve elastic scheduling and expansion of computing resources, dynamically adjusting the number and configuration of edge devices according to changes in demand.

5. The method for counting the number of people in a smart military camp based on edge computing according to claim 1, characterized in that, The user interface design also includes a privacy and security module; The privacy and security module clearly displays information on data usage and sharing permissions in the user interface, provides users with control over their personal data, provides a clear explanation of the purpose of data collection, and allows users to choose whether to agree to data collection and use. The privacy and security module incorporates anonymization and desensitization technologies into the user interface design to protect the privacy of personal information. For data involving personal identity, anonymization and desensitization are used when displaying it to protect identity and privacy. The privacy and security module provides transparent data processing prompts in the user interface, showing how the data is collected, processed, and stored, including information on the purpose of data processing, data retention period, and data storage location; The privacy and security module provides users with personalized privacy settings options, allowing them to adjust the level of data sharing and privacy protection according to their needs and preferences, including selective data sharing, preference settings, and personalized advertising options; The privacy and security module introduces security authentication and two-factor authentication into the user interface, providing enhanced authentication mechanisms, including fingerprint recognition, facial recognition, and hardware keys; The privacy and security module provides data access logs and auditing functions to track and monitor data access, increase visibility of data usage, and promptly identify potential security issues. The privacy and security module provides educational and user awareness-raising content on security and privacy through user interface design, offering users tips, suggestions, and best practices regarding privacy protection; The privacy and security module establishes a user participation and feedback mechanism, allowing users to participate in the decision-making process for security and privacy protection. It collects and considers user opinions and needs through user feedback, and continuously improves interface design and privacy protection measures.

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