A smart community comprehensive management platform based on big data analysis

Abstract

The present invention relates to the field of data processing technology, and in particular to a smart community integrated management platform based on big data analysis, comprising: a communication load analysis module, which obtains load data of the community network by analyzing the types and quantities of various terminals accessing the community base station within a preset time period; a special node positioning module, which identifies special network nodes in the community by dividing special service IP addresses and monitors the network behavior of special network nodes by setting data acquisition rules; a monitoring terminal connection module, which obtains data of each monitoring terminal by connecting to the community monitoring network; and a comprehensive management module, which determines whether there is a security vulnerability in community management by analyzing the matching degree between various terminal access characteristics, special node data flow characteristics and personnel flow characteristics, and outputs an alarm signal.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a smart community comprehensive management platform based on big data analysis. Background Art

[0002] The smart community is a new concept in community management based on information-based, intelligent social management and services. It leverages next-generation information technologies such as the Internet of Things, cloud computing, and mobile internet to achieve modern, intelligent community management. Smart communities integrate multiple information technologies, such as the Internet of Things, big data, and cloud computing. The combined application of these technologies makes community management more intelligent and automated.

[0003] Managing outsiders in a community is a key function of a comprehensive management platform and fundamental to ensuring community safety. Existing management platforms mostly rely on video management, using facial recognition technology to collect facial information of people entering and leaving the community. This information is then compared against a facial database to determine whether a person is allowed in. This management approach only manages entry and exit, but cannot manage the devices carried by entrants, monitor certain behaviors of entrants, or determine if certain behaviors after entry are inconsistent with their registered information. For example, in communities with express delivery and food delivery service points, existing management systems are unable to determine whether entrants are associated with the corresponding service points or whether the service points' network data is functioning properly. These issues not only impact the safety of individuals within the community, but also the security of network resources within the community. Summary of the Invention

[0004] The present invention analyzes the access load information of the community base station to obtain the load type and access time of the community base station, thereby realizing the monitoring of IoT devices in the community. By monitoring the devices in the community, some behaviors of the device carriers are indirectly monitored to determine whether their purpose and behavior of entering the community are related to their service work.

[0005] The technical solution proposed in this invention is: a smart community comprehensive management platform based on big data analysis, including:

[0006] A communication load analysis module, which obtains community network load data by analyzing the types and quantities of various terminals accessing the community base station within a preset time period;

[0007] A special node positioning module, wherein the special node positioning module is configured to be able to identify and connect to special network nodes;

[0008] A monitoring terminal connection module, which connects to the community monitoring network and obtains data from each monitoring terminal;

[0009] The comprehensive management module extracts various terminal access characteristics, special node data traffic characteristics and personnel flow characteristics from the data obtained from the communication load analysis module, the special node positioning module and the monitoring terminal connection module, and analyzes the obtained data to determine whether there are security loopholes in community management and output an alarm signal.

[0010] Preferably, the communication load analysis module obtains the load data of the community network by analyzing the types and quantities of various terminals accessing the community base station within a preset time period, which is achieved through the following steps:

[0011] Obtaining community base station load data within a preset time period at a preset collection frequency; the community base stations include 5G base stations and 4G base stations;

[0012] Extracting the number of mobile network terminals connected to each base station from the base station load data;

[0013] A clustering algorithm is used to identify type 1 network terminals and type 2 network terminals from base station load data; the type 1 network terminals include mobile phones and mobile computers, and the type 2 network terminals include mobile Internet of Things terminals.

[0014] Preferably, the method further includes: forming a network terminal vector by taking the number of mobile network terminals accessed by the 5G base station and the 4G base station obtained at the same collection time point ;in, Respectively indicate time The number of network terminals accessing 5G base stations and the number of network terminals accessing 4G base stations, ,in Respectively represent the starting time and ending time of the preset time period. represents the time variable;

[0015] Obtaining the number of Class II network terminals accessing the community base station, the access time of each Class II network terminal, and the network access permission ID of each Class II network terminal accessing the community base station from the base station load data;

[0016] The network access permission ID and access time of each Class II network terminal constitute the network terminal identification vector ,in, Respectively represent The network access license ID of a Class II network terminal and the time of access to the community base station;

[0017] Combine the network terminal vector and the network terminal identification vector into a long vector , used to monitor the access status of the second type of network terminals.

[0018] Preferably, the special node positioning module identifies special network nodes in the community by dividing special service IP addresses, and monitors the network behavior of special network nodes by setting data acquisition rules, which is achieved through the following steps:

[0019] The special node positioning module identifies the Internet access device's connection request and assigns a corresponding IP address based on the service type of the Internet access device; the service types include community service, express delivery, and food delivery;

[0020] Classify express delivery and takeaway services as special services, and classify the IP addresses allocated for special services as special service IPs;

[0021] If the service type of the Internet access device is special service, a special service IP is allocated to the corresponding Internet access device, and the device that uses the special service IP to access the Internet is a special node;

[0022] Establish data acquisition rules for special business IPs and acquire data from special nodes according to a preset acquisition frequency; the data acquisition rules include the type of data allowed to be acquired and the amount of data allowed to be acquired.

[0023] Preferably, the monitoring terminal connection module is connected to the community monitoring network and obtains data from each monitoring terminal through the following steps:

[0024] Connect the monitoring terminal connection module to the community monitoring network;

[0025] The monitoring data is obtained from the community monitoring network according to the standard communication protocol. The monitoring data includes the video monitoring data, card swiping data and face recognition data of each monitoring terminal.

[0026] Preferably, the integrated management module extracts various terminal access features, special node data flow features, and personnel flow features from the data acquired by the communication load analysis module, the special node positioning module, and the monitoring terminal connection module, and is implemented by the following steps:

[0027] Connect the communication load analysis module, the special node positioning module and the monitoring terminal connection module with the comprehensive management module;

[0028] Set the data collection frequency and obtain data from the communication load analysis module, special node positioning module and monitoring terminal connection module according to the data collection frequency;

[0029] The acquired data is divided into load data sets, special node data sets and monitoring data sets.

[0030] Preferably, dividing the acquired data into a load data set, a special node data set, and a monitoring data set includes:

[0031] The load data set is constructed using the network terminal vector, network terminal identification vector and long vector obtained from the communication load analysis module;

[0032] The special node data set is formed by using the special node data obtained from the special node positioning module;

[0033] The monitoring data set is formed by using the monitoring data obtained from the monitoring terminal connection module.

[0034] Preferably, the analysis of the acquired data to determine whether a security vulnerability occurs in community management is achieved through the following steps:

[0035] Obtain load data sets, special node data sets, and monitoring data sets, and perform preprocessing;

[0036] Extract the second-class network terminal access features, extract the special node data flow features, and extract the personnel flow features of the monitoring data to form the second-class network terminal access feature set, the special node data flow feature set, and the personnel feature set;

[0037] Determine whether the access characteristics of Class II network terminals, data traffic characteristics of special nodes, and personnel flow characteristics match, and determine whether management loopholes occur based on the matching results.

[0038] Preferably, the determining whether the second-category network terminal access characteristics, special node data flow characteristics, and personnel flow characteristics match includes:

[0039] Align the second-class network terminal access feature set, special node data flow feature set, and personnel feature set on the time axis;

[0040] Obtain network terminal access characteristics, the network terminal access characteristics include: the number of access terminals of a type , the access time point of the first type of network terminals, the number of second type of network terminals access , the time point at which Class II network terminals access, and the time point corresponding to the maximum number of Class II network terminals accessing , the duration of the maximum number of accesses , the time point for the minimum number of Class II network terminals to access and the time it takes for the minimum number of accesses to increase to the maximum number of accesses , the increase in the number of Class II network terminal access ;

[0041] Get the number of special node traffic, the special node traffic characteristics include: maximum traffic time point , minimum flow time point , the length of time from the lowest flow time point to the maximum flow time point ;

[0042] Obtain a personnel feature set and extract personnel flow features, which include the maximum personnel entry and exit time points within a preset time period. 、 arrive Number of incoming vehicles during the time period 、 arrive The number of inbound personnel during the time period ;

[0043] if , then it is judged that the increase in the number of Class II network terminal access is related to the entry of outsiders, that is, the Class II network terminal access characteristics match the personnel flow characteristics, otherwise it is judged that there is a loophole in the management of outsiders and outputs alarm information 1; among them, and represents a time delay coefficient of one and a time threshold of one;

[0044] if , then it is judged that the increase in the number of Class II network terminal accesses is related to the entry of outsiders, that is, the Class II network terminal access characteristics match the personnel flow characteristics; otherwise, it is judged that there is a loophole in the management of outsiders, and alarm information 1 is output; Among them, and Represent vehicle weight, personnel weight and personnel quantity threshold respectively;

[0045] if , then it is determined that the traffic increase of the special node is related to the increase in the number of Class II network terminal accesses; that is, the network terminal access characteristics match the traffic characteristics of the special node; otherwise, it is determined that there are loopholes in the management of external personnel and network security management, and the second alarm information is output; among them, and Indicates time delay coefficient 2 and time threshold 2.

[0046] Preferably, a prediction model is pre-stored in the integrated management module, and the prediction model uses a random forest algorithm to predict the probability of external personnel management loopholes and network security management loopholes based on network terminal access characteristics, personnel flow characteristics, special node traffic characteristics, alarm information one and alarm information two.

[0047] Beneficial effects of the present invention:

[0048] 1. The platform of the present invention analyzes the type and number of access terminals at base stations within a community, identifies special terminals (Internet of Things devices), and monitors network data at special locations within the community, such as express delivery stations and takeout lockers, by setting up positioning modules at special nodes. By combining the amount of internet data with the number and time of people entering the location, the platform determines the relationship between the increase in internet data volume at the location and the entry of people, and determines whether there are any abnormal internet activities or abnormalities in the number of people entering and leaving the location, thereby achieving comprehensive management of personnel safety and network security.

[0049] 2. The platform of the present invention uses the prediction model preset in the comprehensive management module to analyze the data of the communication load analysis module, the special node positioning module, and the monitoring terminal connection module to predict the probability of security vulnerabilities and output alarm information. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a structural diagram of a smart community comprehensive management platform based on big data analysis in the present invention;

[0051] Figure 2 This is a flow chart of the method for determining security vulnerabilities in community management on the platform of the present invention. DETAILED DESCRIPTION

[0052] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0053] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the elements may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0054] refer to Figure 1 and Figure 2 The technical solution provided by the present invention is: a smart community comprehensive management platform based on big data analysis, including: a communication load analysis module, a special node positioning module, a monitoring terminal connection module and a comprehensive management module.

[0055] Among them, the communication load analysis module is used to obtain access data of community base stations; the special node positioning module is used to identify and connect special network nodes; the monitoring terminal connection module is used to connect to the community monitoring network and obtain data from each monitoring terminal; the comprehensive management module is used to obtain data from the communication load analysis module, the special node positioning module and the monitoring terminal connection module, and by analyzing the obtained data, determine whether there are security vulnerabilities in community management and output an alarm signal.

[0056] The integrated management module includes a processor and a memory connected to the processor, a communication module, a display terminal and an input terminal, and the display terminal and the input terminal are used for inputting data and outputting alarm signals.

[0057] The process by which the communication load analysis module obtains 5G and / or 4G network terminal access data within the community is as follows:

[0058] Obtaining community base station load data within a preset time period at a preset collection frequency; the community base stations include 5G base stations and 4G base stations;

[0059] Extracting the number of mobile network terminals connected to each base station from the base station load data;

[0060] A clustering algorithm is used to identify Class I and Class II network terminals from base station load data; Class I network terminals include mobile phones and mobile computers, and Class II network terminals include mobile IoT terminals. Specifically, identification can be performed by identifying the device's connection frequency, data traffic volume, signal strength, and network access permission information. Mobile phones typically have high and fluctuating data traffic, while IoT devices have low and less volatile data traffic. IoT devices also have low signal strength, while mobile phones have high signal strength.

[0061] The purpose of obtaining access data for Class I network terminals is to determine how outsiders entering a community use community base stations. This data supports the adjustment and management of community base station capacity. By comparing the number of accessed Class I network terminals with the number of accessed Class II network terminals, it can be determined whether the service personnel entering the community are performing work related to their service content. For example, a courier typically carries an IoT device for express delivery and a mobile phone. If the increase in the number of IoT access devices matches the number of mobile phones connected at the same time (within an allowable error range), and the data communication time periods of the IoT devices and mobile phones substantially overlap, the entrant is deemed to be engaged in courier delivery work. If the difference between the mobile phone's data communication time and the IoT device's data communication time exceeds 30 minutes, the entrant is deemed to have completed their service work or is not performing any service work and is still within the community, which may pose a security risk.

[0062] The purpose of acquiring Class II network terminal access data points is to determine how special groups of people entering the community use community base stations. These special groups include couriers and salespeople. Couriers use IoT devices to track deliveries, while salespeople use POS machines and other IoT devices to conduct transactions. These outsiders frequently enter and exit the community and are familiar with management personnel, which can easily lead to management paralysis and management loopholes.

[0063] Therefore, it is necessary to focus on monitoring the access data of the second type of network terminals, specifically:

[0064] The number of mobile network terminals connected to the 5G base station and the 4G base station obtained at the same collection time point constitutes the network terminal vector ;in, Respectively indicate time The number of network terminals accessing 5G base stations and the number of network terminals accessing 4G base stations, ,in Respectively represent the starting time and ending time of the preset time period. represents the time variable;

[0065] Obtaining the number of Class II network terminals accessing the community base station, the access time of each Class II network terminal, and the network access permission ID of each Class II network terminal accessing the community base station from the base station load data;

[0066] The network access permission ID and access time of each Class II network terminal constitute the network terminal identification vector ,in, Respectively represent The network access license ID of a Class II network terminal and the time of access to the community base station;

[0067] Combine the network terminal vector and the network terminal identification vector into a long vector Through this vector, the ID information and access time information of IoT devices accessing 4G base stations and 5G base stations in the community can be monitored. By counting, the number of IoT devices entering the community within a period of time can be obtained. The number of IoT devices can be used to indirectly obtain the number of couriers and sales personnel entering the community.

[0068] There are usually fixed express delivery and receiving points, express delivery lockers and takeaway lockers in the community. The entry and exit registration equipment and express delivery lockers in the delivery and receiving points need to be connected to the Internet (via wired or wireless connection). When the courier enters, the Internet access data of these locations will increase (entry and exit processing and communication message sending are required). Therefore, by monitoring the Internet communication data of these locations, it is possible to determine whether the changes in the Internet communication data are related to the entry and exit of couriers and takeaway deliverymen.

[0069] The specific process is as follows:

[0070] The Internet access device's network connection request is identified through the special node positioning module, and the corresponding IP address is allocated according to the business type of the Internet access device; the business types include community service business, express delivery business, and takeaway business; express delivery business and takeaway business are classified as special business, and the IP address allocated for special business is classified as special business IP; if the business type of the Internet access device is special business, a special business IP is allocated to the corresponding Internet access device, and the device that uses the special business IP to access the Internet is a special node; establish data acquisition rules with the special business IP, and acquire data of the special node according to the preset collection frequency; the data acquisition rules include the type of data allowed to be acquired and the amount of data allowed to be acquired.

[0071] The monitoring terminal connection module obtains surveillance footage of community entrances and exits and public areas, and obtains image information of people and vehicles entering and leaving from the images. The target recognition algorithm YOLO can identify the corresponding personnel and vehicle information, and based on the recognition results, statistics on the number of people entering, especially couriers, courier vehicles, takeaway personnel, etc.

[0072] The integrated management module receives data from the load analysis module, the special node positioning module, and the monitoring terminal connection module. After analyzing and processing the data, it outputs the corresponding alarm information through the display terminal.

[0073] The specific process is as follows:

[0074] Connect the communication load analysis module, special node positioning module and monitoring terminal connection module to the comprehensive management module; set the data collection frequency, and obtain data from the communication load analysis module, special node positioning module and monitoring terminal connection module according to the data collection frequency; divide the obtained data into load data set, special node data set and monitoring data set.

[0075] The network terminal vector, network terminal identification vector and long vector obtained from the communication load analysis module are used to form a load data set; the special node data obtained from the special node positioning module are used to form a special node data set; and the monitoring data obtained from the monitoring terminal connection module are used to form a monitoring data set.

[0076] Obtain the load data set, special node data set, and monitoring data set and perform preprocessing; extract the second-category network terminal access characteristics, extract the special node data flow characteristics, and extract the personnel flow characteristics of the monitoring data to form the second-category network terminal access feature set, special node data flow feature set, and personnel feature set; determine whether the second-category network terminal access characteristics, special node data flow characteristics, and personnel flow characteristics match, and determine whether there is a management loophole based on the matching results.

[0077] Align the second-class network terminal access feature set, special node data flow feature set, and personnel feature set on the time axis;

[0078] Obtain network terminal access characteristics, the network terminal access characteristics include: the number of access terminals of a type , the access time point of the first-class network terminal, the duration of the first-class network terminal accessing the community base station, the duration of the second-class network terminal accessing the community base station, the number of second-class network terminals accessing the community base station , the time point at which Class II network terminals access, and the time point corresponding to the maximum number of Class II network terminals accessing , the duration of the maximum number of accesses , the time point for the minimum number of Class II network terminals to access The time it takes for the minimum number of Class II network terminals to increase to the maximum number of terminals , the increase in the number of Class II network terminal access .

[0079] Get the number of special node traffic, the special node traffic characteristics include: maximum traffic time point , minimum flow time point , the length of time from the lowest flow time point to the maximum flow time point .

[0080] Obtain a personnel feature set and extract personnel flow features, which include the maximum personnel entry and exit time points within a preset time period. 、 arrive Number of incoming vehicles during the time period 、 arrive The number of inbound personnel during the time period .

[0081] if , then it is judged that the increase in the number of Class II network terminal access is related to the entry of outsiders, that is, the Class II network terminal access characteristics match the personnel flow characteristics, otherwise it is judged that there is a loophole in the management of outsiders and outputs alarm information 1; among them, and Indicates a time delay coefficient of one and a time threshold of one.

[0082] if , then it is judged that the increase in the number of Class II network terminal accesses is related to the entry of outsiders, that is, the access characteristics of Class II network terminals match the characteristics of personnel flow; otherwise, it is judged that there is a loophole in the management of outsiders, that is, the increase in the number of IoT device accesses is much greater than the number of couriers and sales personnel entering, and the comprehensive management module will output alarm information 1 to notify the management personnel to handle it; among them, and They represent vehicle weight, personnel weight and personnel quantity threshold respectively.

[0083] if , then it is judged that the increase in traffic of special nodes is related to the increase in the number of Class II network terminal access; that is, the network terminal access characteristics match the traffic characteristics of special nodes; otherwise, it is judged that there are loopholes in the management of external personnel and network security management, that is, the network traffic has increased abnormally, and there may be abnormal network activities. The integrated management module will output alarm information 2 to notify the management personnel to handle it; among them, and Indicates time delay coefficient 2 and time threshold 2.

[0084] In the comprehensive management module, a prediction model based on the random forest algorithm is set up. Through the trained prediction model, the network terminal access characteristics, personnel flow characteristics, special node traffic characteristics, and the relationship between alarm information 1 and alarm information 2 are analyzed to predict the probability of external personnel management loopholes. and the probability of network security management vulnerabilities , respectively, with the set personnel management threshold and network security thresholds Compare, if It is judged that there is a personnel management loophole. If , it is judged that there is a network security management loophole.

[0085] In the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the method of the present application are performed. It should be noted that the computer-readable medium mentioned above in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wire segments, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a propagated data signal, either in baseband or as part of a carrier wave, embodying computer-readable program code. Such a propagated data signal may take various forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical fiber cable, RF, etc., or any suitable combination thereof.

[0086] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or portion of code that contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as combinations of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions.

[0087] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be subject to any changes or modifications.

Claims

1. A smart community comprehensive management platform based on big data analysis, characterized by: include: A communication load analysis module, which obtains community network load data by analyzing the types and quantities of various terminals accessing the community base station within a preset time period; Special node positioning module, which identifies special network nodes in the community by dividing special service IP addresses and monitors the network behavior of special network nodes by setting data acquisition rules; A monitoring terminal connection module, which obtains data from each monitoring terminal by connecting to the community monitoring network; The comprehensive management module extracts various terminal access characteristics, special node data flow characteristics and personnel flow characteristics from the data obtained from the communication load analysis module, the special node positioning module and the monitoring terminal connection module, analyzes the matching degree between the various terminal access characteristics, special node data flow characteristics and personnel flow characteristics, determines whether there are security loopholes in community management, and outputs an alarm signal.

2. The smart community integrated management platform based on big data analysis according to claim 1 is characterized in that: The communication load analysis module obtains the load data of the community network by analyzing the types and quantities of various terminals accessing the community base station within a preset time period, and is implemented through the following steps: Obtaining community base station load data within a preset time period at a preset collection frequency; the community base stations include 5G base stations and 4G base stations; Extracting the number of mobile network terminals connected to each base station from the base station load data; A clustering algorithm is used to identify type 1 network terminals and type 2 network terminals from base station load data; the type 1 network terminals include mobile phones and mobile computers, and the type 2 network terminals include mobile Internet of Things terminals.

3. A smart community comprehensive management platform based on big data analysis according to claim 2, It is characterized in that Also includes: The number of mobile network terminals connected to the 5G base station and the 4G base station obtained at the same collection time point constitutes the network terminal vector ;in, Respectively indicate time The number of network terminals accessing 5G base stations and the number of network terminals accessing 4G base stations, ,in Respectively represent the starting time and ending time of the preset time period. represents the time variable; Obtaining the number of Class II network terminals accessing the community base station, the access time of each Class II network terminal, and the network access permission ID of each Class II network terminal accessing the community base station from the base station load data; The network access permission ID and access time of each Class II network terminal constitute the network terminal identification vector ,in, Respectively represent The network access license ID of a Class II network terminal and the time of access to the community base station; Combine the network terminal vector and the network terminal identification vector into a long vector , used to monitor the access status of the second type of network terminals.

4. The smart community integrated management platform based on big data analysis according to claim 3 is characterized in that: The special node positioning module identifies special network nodes in the community by dividing special service IP addresses, and monitors the network behavior of special network nodes by setting data acquisition rules. This is achieved through the following steps: The special node positioning module identifies the Internet access device's connection request and assigns a corresponding IP address based on the service type of the Internet access device; the service types include community service, express delivery, and food delivery; Classify express delivery and takeaway services as special services, and classify the IP addresses allocated for special services as special service IPs; If the service type of the Internet access device is special service, a special service IP is allocated to the corresponding Internet access device, and the device that uses the special service IP to access the Internet is a special node; Establish data acquisition rules for special business IPs and acquire data from special nodes according to a preset acquisition frequency; the data acquisition rules include the type of data allowed to be acquired and the amount of data allowed to be acquired.

5. The smart community integrated management platform based on big data analysis according to claim 4 is characterized in that: The monitoring terminal connection module connects to the community monitoring network and obtains data from each monitoring terminal through the following steps: Connect the monitoring terminal connection module to the community monitoring network; The monitoring data is obtained from the community monitoring network according to the standard communication protocol. The monitoring data includes the video monitoring data, card swiping data and face recognition data of each monitoring terminal.

6. The smart community integrated management platform based on big data analysis according to claim 5 is characterized in that: The integrated management module extracts various terminal access features, special node data flow features, and personnel flow features from the data obtained from the communication load analysis module, the special node positioning module, and the monitoring terminal connection module, and is implemented through the following steps: Connect the communication load analysis module, the special node positioning module and the monitoring terminal connection module with the comprehensive management module; Set the data collection frequency and obtain data from the communication load analysis module, special node positioning module and monitoring terminal connection module according to the data collection frequency; The acquired data is divided into load data sets, special node data sets and monitoring data sets.

7. The smart community integrated management platform based on big data analysis according to claim 6 is characterized in that: The data acquisition is divided into a load data set, a special node data set, and a monitoring data set, including: The load data set is constructed using the network terminal vector, network terminal identification vector and long vector obtained from the communication load analysis module; The special node data set is formed by using the special node data obtained from the special node positioning module; The monitoring data set is formed by using the monitoring data obtained from the monitoring terminal connection module.

8. The smart community integrated management platform based on big data analysis according to claim 7 is characterized in that: The analysis of the acquired data to determine whether there are security vulnerabilities in community management is achieved through the following steps: Obtain load data sets, special node data sets, and monitoring data sets, and perform preprocessing; Extract the second-class network terminal access features, extract the special node data flow features, and extract the personnel flow features of the monitoring data to form the second-class network terminal access feature set, the special node data flow feature set, and the personnel feature set; Determine whether the access characteristics of Class II network terminals, data traffic characteristics of special nodes, and personnel flow characteristics match, and determine whether management loopholes occur based on the matching results.

9. The smart community integrated management platform based on big data analysis according to claim 8 is characterized in that: The determining whether the second-category network terminal access characteristics, special node data flow characteristics, and personnel flow characteristics match includes: Align the second-class network terminal access feature set, special node data flow feature set, and personnel feature set on the time axis; Obtain network terminal access characteristics, the network terminal access characteristics include: the number of access terminals of a type , the access time point of the first type of network terminals, the number of second type of network terminals access , the time point at which Class II network terminals access, and the time point corresponding to the maximum number of Class II network terminals accessing , the duration of the maximum number of accesses , the time point for the minimum number of Class II network terminals to access and the time it takes for the minimum number of accesses to increase to the maximum number of accesses , the increase in the number of Class II network terminal access ; Get the number of special node traffic, the special node traffic characteristics include: maximum traffic time point , minimum flow time point , the length of time from the lowest flow time point to the maximum flow time point ; Obtain a personnel feature set and extract personnel flow features, which include the maximum personnel entry and exit time points within a preset time period. 、 arrive Number of incoming vehicles during the time period 、 arrive The number of inbound personnel during the time period ; if , then it is judged that the increase in the number of Class II network terminal access is related to the entry of outsiders, that is, the Class II network terminal access characteristics match the personnel flow characteristics, otherwise it is judged that there is a loophole in the management of outsiders and outputs alarm information 1; among them, and represents a time delay coefficient of one and a time threshold of one; if , then it is judged that the increase in the number of Class II network terminal accesses is related to the entry of outsiders, that is, the Class II network terminal access characteristics match the personnel flow characteristics; otherwise, it is judged that there is a loophole in the management of outsiders, and alarm information 1 is output; Among them, and Represent vehicle weight, personnel weight and personnel quantity threshold respectively; if , then it is determined that the traffic increase of the special node is related to the increase in the number of Class II network terminal accesses; that is, the network terminal access characteristics match the traffic characteristics of the special node; otherwise, it is determined that there are loopholes in the management of external personnel and network security management, and the second alarm information is output; among them, and Indicates time delay coefficient 2 and time threshold 2.

10. The smart community integrated management platform based on big data analysis according to claim 9 is characterized in that: A prediction model is pre-stored in the comprehensive management module. The prediction model uses a random forest algorithm to predict the probability of external personnel management loopholes and network security management loopholes based on network terminal access characteristics, personnel flow characteristics, special node traffic characteristics, alarm information one and alarm information two.

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