CN117354458A - Security visual management system - Google Patents
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Abstract
The invention discloses a security visual management system, which comprises: video camera: the method is used for collecting image information of the face and the vehicle; an intelligent personnel management system: the system is used for uniformly managing access personnel and staff of a company; an intelligent vehicle management system: the system is used for monitoring and managing vehicles in the company parking lot; a storage system: the real-time monitoring video storage device is used for storing and managing real-time monitoring videos of all front-end monitoring cameras; and (3) an application platform: the visual comprehensive management platform integrates video monitoring, alarm management, personnel management and vehicle management; analysis server: carrying out intelligent analysis and processing along with the acquired data; in the invention, the application platform integrates video monitoring, alarm management, personnel management, vehicle management and the like, realizes multi-linkage among multiple systems, supports basic service functions such as real-time monitoring, point position inquiry, video inquiry and playback, image snapshot and the like, and simultaneously supports management functions such as resources, users, rights, video, alarms, logs and the like.
Description
Technical Field
The invention relates to the technical field of security systems, in particular to a security visual management system.
Background
The advent and development of security systems has been made to meet the concerns and needs of society for security issues. With the development and progress of society, criminal activities and security threats are increasing, and people's attention to self security is increasing. Meanwhile, the continuous progress of technology provides technical support and foundation for security systems, and various security devices and technologies are widely applied and developed. Economic development and urbanization process are accelerated, and safety requirements of various places are also increasing. In order to protect personnel and property safety, it is necessary to build a complete security system. In addition, the requirements of law and regulation have prompted the general introduction of security systems in various industries and sites to meet the requirements of law. The security system prevents and deals with potential security risks through various devices and measures such as video monitoring, intrusion alarm, access control and the like, and guarantees social order and public security.
In the prior art, the traditional decentralized management mode is difficult to meet the comprehensive management requirement of the whole security system, and in addition, a user cannot know the running state and the risk condition of the security system conveniently, so that the security visual management system is provided.
Disclosure of Invention
The invention aims to solve the defects that the traditional decentralized management mode is difficult to meet the comprehensive management requirement of the whole security system in the prior art, and a user cannot know the running state and the risk condition of the security system conveniently, and provides the security visual management system.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a security visualization management system, comprising:
video camera: the method is used for collecting image information of the face and the vehicle;
an intelligent personnel management system: the system is used for uniformly managing access personnel and staff of a company;
an intelligent vehicle management system: the system is used for monitoring and managing vehicles in the company parking lot;
a storage system: the real-time monitoring video storage device is used for storing and managing real-time monitoring videos of all front-end monitoring cameras;
and (3) an application platform: the visual comprehensive management platform integrates video monitoring, alarm management, personnel management and vehicle management;
analysis server: carrying out intelligent analysis and processing along with the acquired data;
the intelligent personnel management system compares face images acquired by the camera with a face database in the analysis server, so that personnel identity verification and management are achieved, the intelligent vehicle management system compares vehicle images acquired by the camera with a vehicle database in the analysis server, recognition and management of vehicles are achieved, the storage system can store video data acquired by the camera in storage equipment for intelligent analysis and processing by the analysis server, and data interaction between the application platform and the storage system is convenient for comprehensive management of users.
The technical scheme further comprises the following steps:
the camera comprises an image acquisition module, an image processing module, a feature extraction module and an image deep learning module, wherein the image acquisition module is used for acquiring image or video data of a monitoring area, the image processing module is used for preprocessing the image or video data acquired by the image acquisition module, the feature extraction module is used for extracting face and vehicle feature information from the image processing module, and the image deep learning module is used for identifying and comparing the feature information extracted by the feature extraction module.
The intelligent personnel management system comprises a face recognition module, a face searching module, a personnel information management module, a key personnel management module, a blacklist alarm module and a target track simulation module, wherein the face recognition module is used for recognizing face and processing images, the face searching module is used for supporting personnel to search graphs in a graph mode, namely, face searching is carried out in the personnel information management module according to input face images, the key personnel management module is used for managing and controlling specific key personnel, timely finding and alarming, the blacklist alarm module is used for comparing known blacklist personnel, triggering alarming when the blacklist personnel appears, the target track simulation module is used for generating a target track simulation result according to face images of suspected targets, the face searching module is used for carrying out face searching on the data obtained in the face information management module, obtaining matched face information and returning the search result, the blacklist alarm module is used for triggering alarming when the blacklist personnel is found, and the target track simulation module is used for receiving the target track simulation result transmitted by the face searching module and returning the target track simulation result obtained by the face searching module to the suspected list personnel.
The intelligent vehicle management system comprises a vehicle identification module, an intelligent analysis module, an image retrieval module, an overspeed and forbidden snapshot module, a parking lot access management module and a vehicle management module, wherein the vehicle identification module is used for automatically identifying vehicles in a company parking lot through license plate identification technology and associating vehicle information with a database, the intelligent analysis module is used for carrying out intelligent analysis on the identified vehicles so as to effectively manage and monitor the vehicles, the image retrieval module is used for realizing the function of searching pictures through images, a user can upload a vehicle picture to retrieve, the system returns vehicle information similar to the picture, the overspeed and forbidden snapshot module is used for carrying out overspeed and forbidden snapshot on vehicles in the company through monitoring cameras and vehicle identification technology and sending alarm information to related personnel, the parking lot access management module is used for realizing the vehicle identification and management on the access of the parking lot so as to ensure the safety and order of the parking lot, the vehicle management module is used for carrying out the management and the control on specific vehicles, the image retrieval module is used for carrying out the image search, the system is used for sending corresponding characteristic information to the intelligent analysis module to the vehicle identification module to the corresponding to the overspeed and forbidden snapshot module, the system is used for transmitting the characteristic of the corresponding vehicle identification information to the intelligent vehicle identification module to the overspeed and forbidden snapshot module, and the corresponding image information is used for carrying out the statistics and the transmission to the corresponding image analysis module to realize the characteristics of the overspeed and forbidden snapshot, the vehicle identification module transmits the identified vehicle information to the parking lot entrance management module so as to record the entrance and exit records of the vehicle and process transactions such as vehicle payment, and the vehicle identification module transmits the identified vehicle information to the vehicle distribution control module so as to trigger corresponding distribution control early warning and safety measures.
The storage system comprises a front-end monitoring module, a storage management module, a general control center module and a user access module, wherein the front-end monitoring module is used for collecting video stream data generated by a camera, the storage module is used for receiving and storing the video stream data transmitted by the front-end monitoring module, the storage management module is used for managing and scheduling the video data in the storage module, the general control center module is used for centrally managing and controlling the storage system, the user access module is used for providing access and query functions of a user to the storage system, the front-end monitoring module transmits the collected video stream data to the storage module, the storage module receives the video stream data transmitted by the front-end monitoring module and stores the video stream data in storage equipment, the storage management module and the storage module perform data interaction, including operations of storage configuration, scheduling, backup, migration and the like of the video data, the general control center module and the storage management module perform operations of configuration, monitoring, alarming, query and the like of the storage system, and the user access module sends a request to the general control center module to acquire relevant information of the video or the required monitoring data of the storage system.
The application platform comprises a user management module, a data management module, a search module, a data display module, a notification and message module, a data analysis module and an external interface module, wherein the user management module is used for registering, logging in and managing authority of a user, the data management module is used for managing and operating the data in the application platform, the search module is used for providing a user with a search function for the data in the application platform, the data display module is used for displaying the data to the user in a visual mode, the notification and message module is used for sending notification and message to the user, the data analysis module is used for counting and analyzing the data in the application platform, the external interface module is used for carrying out data interaction and integration with an external system, the user management module is used for receiving registration and logging in information input by the user and carrying out interaction with a storage system, the data interaction between the user management module and the storage system is verified, the data management module is used for carrying out data interaction between the user identity and authority, the data keyword input by the user comprises the operations of adding, modifying, deleting and the data, the search module is used for receiving the user, the search keyword input by the user is interacted with the storage system, the data is used for displaying the data in a visual mode, the notification and message is used for sending the notification and the message to the user, the data analysis module is used for carrying out statistics and analysis on the data in the application platform, the data in the application platform is used for carrying out statistics and analysis and the data interaction between the user and the user is used for collecting and the user identity and authority, the user. Including data transmission, reception, synchronization, etc.
The analysis server comprises a data receiving module, a data storage module, a data deep learning module and a data interaction module, wherein the data receiving module is used for receiving transmitted image or video data, the data storage module is used for storing the received image or video data in storage equipment for subsequent analysis and inquiry, the data deep learning module is used for intelligently analyzing and processing the received image or video data by utilizing a feedforward neural network, and the data interaction module is used for carrying out data interaction with other systems or platforms, such as carrying out data sharing and communication with an intelligent personnel management system, an intelligent vehicle management system and the like.
The feature extraction module performs feature extraction by applying PCA, and sets the data after the data are converted into M samples { X } 1 ,X 2 ,...,X M (v) each sample has N-dimensional featuresEach feature X j All have respective characteristic values;
firstly, the method comprises the steps of decentralizing all the features, namely removing the mean value, calculating the mean value of each feature, and then subtracting the mean value of each feature for all the samples, wherein the mean values are respectivelyAfter decentralization, covariance matrix is obtainedWherein the diagonal lines are respectively the features X 1 And X 2 Variance of (2), off-diagonalCovariance, cov (X) 1 ,X 1 ) The calculation formula of (2) is +.>Obtaining covariance matrix C of M samples under the N-dimensional characteristic;
after the covariance matrix is obtained, the characteristic value and the corresponding characteristic vector thereof are obtained according to a characteristic equation Cmu=lambda mu, wherein lambda is the characteristic value, mu is the corresponding characteristic vector thereof, the largest first k characteristic values and the corresponding characteristic vectors are selected for projection, the projection is the dimension reduction process, the original characteristic is reduced from high dimension to low dimension, a large amount of redundant information is removed after dimension reduction, at least more than 85% of the original information is reserved, and the processed data is convenient to transmit.
The image deep learning module performs information learning and processing through a convolutional neural network:
input data represents: assuming that the input data is a multi-dimensional array, and is marked as X, wherein the dimension of X is (batch size, channel number, height and width) and represents the number of input samples, the number of channels, the image height and the image width respectively;
convolution operation: the input data is subjected to sliding window convolution operation through convolution check, characteristics in the input data are extracted, the dimension of a convolution kernel is assumed to be (the number of channels, the height of the convolution kernel and the width of the convolution kernel), the dimension is marked as W, and the convolution operation can be expressed by the following formula: z [ i, j ] = sum (sum (X [: i: i+h, j: j+w ]. Times.W)) +b, where Z is the output of the convolution operation, represents the feature map, i and j represent the location of the feature map, h and W represent the height and width of the convolution kernel, respectively, and b is the bias term;
activation function: after convolution operation, non-linear transformation is usually performed on the feature map by using ReLU, so that the expression capability of the network is increased;
pooling operation: the average value is selected in the local perception area to serve as a pooling result, so that the space dimension of the feature map is reduced, and important feature information is reserved;
full connection operation: after a plurality of rolling and pooling operations, the feature map is unfolded into a one-dimensional vector, classification or regression tasks are carried out through a full-connection layer, each neuron in the full-connection layer is connected with all neurons of the previous layer, and a mapping relation between input and output is established through learning weights and bias;
loss function: in CNN, a cross entropy loss function is used to measure the gap between the prediction result of the model and the real label;
back propagation algorithm: calculating the gradient of the loss function to the model parameters by using a back propagation algorithm, and updating the model parameters by using gradient descent to continuously optimize the performance of the model;
the image deep learning module can automatically learn and extract features from input data through a convolutional neural network, and processing and analyzing images and other multidimensional data are realized.
The invention has the following beneficial effects:
1. in the invention, the application platform is used for realizing the unified security resource management of the whole network, the unified management of the systems such as video monitoring, vehicle management, access control management, alarm management and the like is realized, the remote parameter configuration, the remote control and the like are realized, the application platform is used for realizing the unified user and authority management of the whole network, and the monitoring and management requirements of multiple users of the system are met.
2. In the invention, the front-end camera uses the face recognition comparison terminal with the deep learning capability, and the rear end also has the analysis server with the deep learning capability, so that the efficiency and reliability of the system in the aspect of intelligent management and control are improved.
3. In the invention, the application platform integrates video monitoring, alarm management, personnel management, vehicle management and the like, realizes multi-linkage among multiple systems, supports basic service functions such as real-time monitoring, point position inquiry, video inquiry and playback, image snapshot and the like, and simultaneously supports management functions such as resources, users, rights, video, alarms, logs and the like.
4. According to the invention, comprehensive supervision of the whole community is realized, whole network scheduling, management and intelligent application are realized, and a set of security visual comprehensive supervision system with high definition, networking, intellectualization and high integration is provided.
Drawings
FIG. 1 is a system block diagram of a security visual management system provided by the invention;
FIG. 2 is a system block diagram of a camera in the present invention;
FIG. 3 is a system block diagram of a storage system according to the present invention;
FIG. 4 is a system block diagram of an intelligent vehicle management system of the present invention;
FIG. 5 is a system block diagram of the intelligent personnel management system of the present invention;
FIG. 6 is a system block diagram of an application platform in the present invention;
fig. 7 is a system block diagram of an analysis server in the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1-7, the security visual management system provided by the invention comprises:
video camera: the method is used for collecting image information of the face and the vehicle;
an intelligent personnel management system: the system is used for uniformly managing access personnel and staff of a company;
an intelligent vehicle management system: the system is used for monitoring and managing vehicles in the company parking lot;
a storage system: the real-time monitoring video storage device is used for storing and managing real-time monitoring videos of all front-end monitoring cameras;
and (3) an application platform: the visual comprehensive management platform integrates video monitoring, alarm management, personnel management and vehicle management;
analysis server: carrying out intelligent analysis and processing along with the acquired data;
the intelligent personnel management system compares the face image acquired by the camera with a face database in the analysis server, so that the identity verification and management of personnel are realized, the intelligent vehicle management system compares the vehicle image acquired by the camera with the vehicle database in the analysis server, the identification and management of vehicles are realized, the storage system can store video data acquired by the camera in the storage device for intelligent analysis and processing by the analysis server, the application platform and the storage system can interact data, and the comprehensive management of users is facilitated.
The camera comprises an image acquisition module, an image processing module, a feature extraction module and an image deep learning module, wherein the image acquisition module is used for acquiring image or video data of a monitoring area, the image processing module is used for preprocessing the image or video data acquired by the image acquisition module, the feature extraction module is used for extracting face and vehicle feature information from the image processing module, and the image deep learning module is used for identifying and comparing the feature information extracted by the feature extraction module.
The intelligent personnel management system comprises a face recognition module, a face searching module, a personnel information management module, a key personnel distribution control module, a blacklist alarm module and a target track simulation module, wherein the face recognition module is used for face recognition and image processing, the face searching module is used for supporting personnel to search the figure in a picture, namely, face searching is carried out in the personnel information management module according to an input face image, the key personnel distribution control module is used for distributing and controlling specific key personnel, timely finding and alarming, the blacklist alarm module is used for comparing known blacklist personnel, when the blacklist personnel appears, alarming is triggered, the target track simulation module is used for generating a target track simulation result according to the face image of a suspected target, the face searching module carries out face searching on data obtained in the face recognition module in the personnel information management module, obtains matched face information, and returns the search result, the blacklist alarm module obtains the information of the known blacklist personnel, when the blacklist personnel is found, the alarming is triggered, the target track simulation module receives the face image of the suspected target transmitted by the face searching module, obtains the target track simulation result of the suspected target, and returns the target track simulation result to a user.
The intelligent vehicle management system comprises a vehicle identification module, an intelligent analysis module, an image retrieval module, an overspeed and forbidden snapshot module, a parking lot entrance management module and a vehicle management module, wherein the vehicle identification module is used for automatically identifying vehicles in a company parking lot through license plate identification technology and associating vehicle information with a database, the intelligent analysis module is used for intelligently analyzing the identified vehicles so as to effectively manage and monitor the vehicles, the image retrieval module is used for realizing the function of searching a map through an image searching technology, a user can upload a vehicle picture for retrieval, the system can return vehicle information similar to the picture, the overspeed and forbidden snapshot module is used for conducting overspeed and forbidden snapshot on vehicles in the company, and sending alarm information to related personnel through a monitoring camera and the vehicle identification technology, the parking lot entrance management module is used for realizing the identification and management of the vehicles at the entrance so as to ensure the safety and the order of the parking lot, the vehicle entrance management module is used for conducting the distribution control on specific vehicles, once the vehicles are identified, the system can send early warning and trigger corresponding safety measures, the identification modules can send the corresponding safety measures to the vehicle to the intelligent analysis module and trigger the corresponding safety measures to the vehicle, the characteristics of the intelligent vehicle information can be recorded and the characteristics of the vehicle can be recorded, the characteristics of the vehicle can be recorded and the characteristics can be transmitted to the vehicle information can be recorded and the vehicle information can be stored by the intelligent vehicle management module through the intelligent vehicle management module, the vehicle identification module transmits the identified vehicle information to the vehicle distribution control module so as to trigger corresponding distribution control early warning and safety measures.
The storage system comprises a front-end monitoring module, a storage management module, a master control center module and a user access module, wherein the front-end monitoring module is used for collecting video stream data generated by a camera, the storage module is used for receiving and storing the video stream data transmitted by the front-end monitoring module, the storage management module is used for managing and scheduling the video data in the storage module, the master control center module is used for centrally managing and controlling the storage system, the user access module is used for providing access and query functions of a user to the storage system, the front-end monitoring module transmits the collected video stream data to the storage module, the storage module receives the video stream data transmitted by the front-end monitoring module and stores the video stream data in storage equipment, the storage management module and the storage module conduct data interaction, including operations such as storage configuration, scheduling, backup and migration of the video data, the master control center module conducts data interaction with the storage management module, including operations such as configuration, monitoring, alarming and query of the storage system, and the user access module sends a request to the master control center module to acquire required monitoring video data or related information of the storage system.
The application platform comprises a user management module, a data management module, a search module, a data display module, a notification and message module, a data analysis module and an external interface module, wherein the user management module is used for registering, logging in and managing authority of a user, the data management module is used for managing and operating data in the application platform, the search module is used for providing a search function of the user on the data in the application platform, the data display module is used for displaying the data to the user in a visual mode, the notification and message module is used for sending notification and message to the user, the data analysis module is used for counting and analyzing the data in the application platform, the external interface module is used for carrying out data interaction and integration with an external system, the user management module receives registration and logging in information and interacts with a storage system, the user identity and authority are verified, the data management module carries out data interaction with the storage system and comprises operations such as adding, modifying and deleting the data, the search module receives search key words input by the user and interacts with the storage system, the data returns a search result meeting the conditions, the data display module acquires the data from the storage system, processes and displays the data, the data to the map and sends the notification and message to the user, the map and the information to the user, the corresponding data is generated, the data is displayed and the map and the data is displayed by the map and the map is displayed, the map and the data is sent to the corresponding data is displayed and the data to the user interface and the user interface.
The analysis server comprises a data receiving module, a data storage module, a data deep learning module and a data interaction module, wherein the data receiving module is used for receiving transmitted image or video data, the data storage module is used for storing the received image or video data in storage equipment for subsequent analysis and inquiry, the data deep learning module is used for intelligently analyzing and processing the received image or video data by utilizing a feedforward neural network, and the data interaction module is used for carrying out data interaction with other systems or platforms, such as carrying out data sharing and communication with an intelligent personnel management system, an intelligent vehicle management system and the like.
The feature extraction module is used for carrying out feature extraction by applying PCA, and the data after the data are set as M samples { X } 1 ,X 2 ,...,X M (v) each sample has N-dimensional featuresEach feature X j All have respective characteristic values;
firstly, the method comprises the steps of decentralizing all the features, namely removing the mean value, calculating the mean value of each feature, and then subtracting the mean value of each feature for all the samples, wherein the mean values are respectivelyAfter decentralization, covariance matrix is obtainedWherein the diagonal lines are respectively the features X 1 And X 2 Is covariance on the off-diagonal,cov(X 1 ,X 1 ) The calculation formula of (2) is +.>Obtaining covariance matrix C of M samples under the N-dimensional characteristic;
after the covariance matrix is obtained, the characteristic value and the corresponding characteristic vector thereof are obtained according to a characteristic equation Cmu=lambda mu, wherein lambda is the characteristic value, mu is the corresponding characteristic vector thereof, the largest first k characteristic values and the corresponding characteristic vectors are selected for projection, the projection is the dimension reduction process, the original characteristic is reduced from high dimension to low dimension, a large amount of redundant information is removed after dimension reduction, at least more than 85% of the original information is reserved, and the processed data is convenient to transmit.
The image deep learning module performs information learning and processing through a convolutional neural network:
input data represents: assuming that the input data is a multi-dimensional array, and is marked as X, wherein the dimension of X is (batch size, channel number, height and width) and represents the number of input samples, the number of channels, the image height and the image width respectively;
convolution operation: the input data is subjected to sliding window convolution operation through convolution check, characteristics in the input data are extracted, the dimension of a convolution kernel is assumed to be (the number of channels, the height of the convolution kernel and the width of the convolution kernel), the dimension is marked as W, and the convolution operation can be expressed by the following formula: z [ i, j ] = sum (sum (X [: i: i+h, j: j+w ]. Times.W)) +b, where Z is the output of the convolution operation, represents the feature map, i and j represent the location of the feature map, h and W represent the height and width of the convolution kernel, respectively, and b is the bias term;
activation function: after convolution operation, non-linear transformation is usually performed on the feature map by using ReLU, so that the expression capability of the network is increased;
pooling operation: the average value is selected in the local perception area to serve as a pooling result, so that the space dimension of the feature map is reduced, and important feature information is reserved;
full connection operation: after a plurality of rolling and pooling operations, the feature map is unfolded into a one-dimensional vector, classification or regression tasks are carried out through a full-connection layer, each neuron in the full-connection layer is connected with all neurons of the previous layer, and a mapping relation between input and output is established through learning weights and bias;
loss function: in CNN, a cross entropy loss function is used to measure the gap between the prediction result of the model and the real label;
back propagation algorithm: calculating the gradient of the loss function to the model parameters by using a back propagation algorithm, and updating the model parameters by using gradient descent to continuously optimize the performance of the model;
the image deep learning module can automatically learn and extract features from input data through a convolutional neural network, and processing and analyzing images and other multidimensional data are realized.
In the embodiment of the invention, when personnel come in and go out of a company and personnel of the company appear in a monitoring area in the company, a camera acquires image or video data of the monitoring area, an image deep learning module identifies and compares the characteristic information extracted by a characteristic extraction module, intelligent processing is carried out on the characteristic information, face searching is carried out in a personnel information management module, when important personnel are identified, a key personnel control module is triggered, the key personnel control module is used for controlling specific important personnel, timely finding and alarming are carried out, when blacklist personnel appear, a blacklist alarming module is triggered to alarm, and a target track simulation result is generated through a target track simulation module, and the key personnel control module is used for controlling and capturing in advance;
for vehicles entering and exiting a company parking lot, automatically identifying the vehicles in the company parking lot through images or video data of the camera parking lot and associating vehicle information with a database through an intelligent vehicle management system, when overspeed and forbidden parking of the vehicles occur, the intelligent vehicle management system can take illegal shots of overspeed and forbidden parking of the vehicles in the company through a monitoring camera and vehicle identification technology and send alarm information to related personnel, and meanwhile, the intelligent vehicle management system can identify and manage the vehicles at an entrance and an exit of the parking lot so as to ensure safety and order of the parking lot, when a specific vehicle is identified, the specific vehicle is controlled, and once the specific vehicle is identified, the system sends early warning and triggers corresponding safety measures;
the user establishes a user account number of the user through the application platform, and logs in the user account number to search related information through the application platform, wherein the information can be presented in the forms of charts, tables, maps and the like.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A security visualization management system, comprising:
video camera: the method is used for collecting image information of the face and the vehicle;
an intelligent personnel management system: the system is used for uniformly managing access personnel and staff of a company;
an intelligent vehicle management system: the system is used for monitoring and managing vehicles in the company parking lot;
a storage system: the real-time monitoring video storage device is used for storing and managing real-time monitoring videos of all front-end monitoring cameras;
and (3) an application platform: the visual comprehensive management platform integrates video monitoring, alarm management, personnel management and vehicle management;
analysis server: carrying out intelligent analysis and processing along with the acquired data;
the intelligent personal management system is characterized in that the intelligent personal management system compares face images acquired by the camera with a face database in the analysis server, so that personal identity verification and management are realized, the intelligent vehicle management system compares vehicle images acquired by the camera with a vehicle database in the analysis server, recognition and management of vehicles are realized, the storage system can store video data acquired by the camera in the storage device for intelligent analysis and processing by the analysis server, and the application platform and the storage system can interact data, so that comprehensive management of users is facilitated.
2. The visual management system of claim 1, wherein the camera comprises an image acquisition module, an image processing module, a feature extraction module and an image deep learning module, the image acquisition module is used for acquiring image or video data of a monitoring area, the image processing module is used for preprocessing the image or video data acquired by the image acquisition module, the feature extraction module is used for extracting face and vehicle feature information from the image processing module, and the image deep learning module is used for identifying and comparing the feature information extracted by the feature extraction module.
3. The security visual management system according to claim 1, wherein the intelligent personnel management system comprises a face recognition module, a face search module, a personnel information management module, a key personnel management module, a blacklist alarm module and a target track simulation module, wherein the face recognition module is used for face recognition and image processing, the face search module is used for supporting personnel to search graphs in a graph, namely, according to an input face image, face search is carried out in the personnel information management module, the key personnel management module is used for carrying out management on specific key personnel, finding and alarming in time, the blacklist alarm module is used for comparing known blacklist personnel, triggering alarm when the blacklist personnel appears, the target track simulation module is used for generating a target track simulation result according to the face image of a suspected target, the face search module is used for carrying out face search on data obtained in the face recognition module in the personnel information management module, obtaining matched face information and returning the search result, the blacklist alarm module is used for obtaining the information of the known blacklist personnel, and when the blacklist personnel is found, the target track simulation result is received, and the target track simulation result is returned to the target track simulation module.
4. The visual security management system according to claim 1, wherein the intelligent vehicle management system comprises a vehicle identification module, an intelligent analysis module, an image retrieval module, an overspeed, forbidden and stopped snapshot module, a parking lot entrance management module and a vehicle distribution control module, wherein the vehicle identification module is used for automatically identifying vehicles in a company parking lot through license plate identification technology and associating vehicle information with a database, the intelligent analysis module is used for carrying out intelligent analysis on the identified vehicles so as to effectively manage and monitor the vehicles, the image retrieval module is used for realizing the function of searching a map through an image search technology, a user can upload a vehicle picture to retrieve, the system can return vehicle information similar to the picture, the overspeed and forbidden and stopped snapshot module is used for carrying out overspeed and forbidden and stopped illegal and taking a snapshot of vehicles in the company and sending alarm information to related personnel, the entrance and exit management module is used for realizing the identification and management of vehicles at the entrance and exit of the company, the intelligent analysis module is used for carrying out the safety and ordered distribution of the identified vehicles, the image retrieval module is used for carrying out the function of searching the vehicle picture to the vehicle identification module, the vehicle identification module is used for carrying out statistics and the statistics on the characteristics of the vehicle identification module and the vehicle identification system, and the vehicle identification module is used for triggering the characteristics of the vehicle identification module to realize the vehicle identification and the vehicle analysis, the vehicle identification module is used for transmitting the identified vehicle information to the parking lot entrance management module so as to record the entrance and exit records of the vehicle and process transactions such as vehicle payment and the like, and transmitting the identified vehicle information to the vehicle distribution control module so as to trigger corresponding distribution control early warning and safety measures.
5. The security visual management system according to claim 1, wherein the storage system comprises a front-end monitoring module, a storage management module, a master control center module and a user access module, the front-end monitoring module is used for collecting video stream data generated by a camera, the storage module is used for receiving and storing the video stream data transmitted by the front-end monitoring module, the storage management module is used for managing and scheduling the video data in the storage module, the master control center module is used for centrally managing and controlling the storage system, the user access module is used for providing access and query functions for users to the storage system, the front-end monitoring module transmits the collected video stream data to the storage module, the storage module receives the video stream data transmitted by the front-end monitoring module and stores the video stream data in a storage device, the storage management module and the storage module conduct data interaction, including operations such as storage configuration, scheduling, backup and migration of the video data, the master control center module and the storage management module conduct data interaction, including configuration, monitoring, operation and query of the storage system, and the master control center module need to acquire the video information related to the storage system, and the user access and the master control center module need to access to the video information.
6. The security visual management system of claim 5, wherein the application platform comprises a user management module for registration, login, and rights management of a user, a data management module for managing and operating data in the application platform, a search module for providing a search function of the user for the data in the application platform, a data display module for displaying the data to the user in a visual manner, a notification and message module for sending notification and messages to the user, a data analysis module for counting and analyzing the data in the application platform, and an external interface module, the system is used for carrying out data interaction and integration with an external system, the user management module receives registration and login information input by a user and interacts with the storage system to verify the identity and authority of the user, the data management module carries out data interaction with the storage system and comprises operations of adding, modifying, deleting and the like of data, the search module receives search keywords input by the user and interacts with the storage system to return a search result meeting the conditions, the data display module acquires data from the storage system and processes and displays the data, the data display module presents the data to the user in the forms of a chart, a table, a map and the like, the notification and message module receives a request of the user or an event triggered by the system to generate corresponding notification and message and sends the notification and message to the user, the data analysis module acquires data from the storage system to carry out data processing and analysis to generate a statistical result, and presenting the data to a user or other modules, wherein the external interface module exchanges data with an interface of an external system, and the operations comprise data transmission, data receiving, data synchronization and the like.
7. The security visualization management system of claim 1, wherein the analysis server comprises a data receiving module, a data storage module, a data deep learning module and a data interaction module, wherein the data receiving module is used for receiving transmitted image or video data, the data storage module is used for storing the received image or video data in a storage device for subsequent analysis and inquiry, the data deep learning module is used for intelligently analyzing and processing the received image or video data by utilizing a feedforward neural network, and the data interaction module is used for carrying out data interaction with other systems or platforms, such as carrying out data sharing and communication with an intelligent personnel management system, an intelligent vehicle management system and the like.
8. The security visual management system according to claim 6, wherein the feature extraction module is configured to perform feature extraction by applying PCA, and set the data after the data processing as M samples { X } 1 ,X 2 ,...,X M (v) each sample has N-dimensional featuresEach feature X j All have respective characteristic values;
firstly, the method comprises the steps of decentralizing all the features, namely removing the mean value, calculating the mean value of each feature, and then subtracting the mean value of each feature for all the samples, wherein the mean values are respectivelyAfter decentralization, covariance matrix is obtainedWherein the diagonal lines are respectively the features X 1 And X 2 Is covariance on the off-diagonal, cov (X 1 ,X 1 ) The calculation formula of (2) is +.>Obtaining covariance matrix C of M samples under the N-dimensional characteristic;
after the covariance matrix is obtained, the characteristic value and the corresponding characteristic vector thereof are obtained according to a characteristic equation Cmu=lambda mu, wherein lambda is the characteristic value, mu is the corresponding characteristic vector thereof, the largest first k characteristic values and the corresponding characteristic vectors are selected for projection, the projection is the dimension reduction process, the original characteristic is reduced from high dimension to low dimension, a large amount of redundant information is removed after dimension reduction, at least more than 85% of the original information is reserved, and the processed data is convenient to transmit.
9. The visual management system of claim 3, wherein the image deep learning module learns and processes information through a convolutional neural network:
input data represents: assuming that the input data is a multi-dimensional array, and is marked as X, wherein the dimension of X is (batch size, channel number, height and width) and represents the number of input samples, the number of channels, the image height and the image width respectively;
convolution operation: the input data is subjected to sliding window convolution operation through convolution check, characteristics in the input data are extracted, the dimension of a convolution kernel is assumed to be (the number of channels, the height of the convolution kernel and the width of the convolution kernel), the dimension is marked as W, and the convolution operation can be expressed by the following formula: z [ i, j ] = sum (sum (X [: i: i+h, j: j+w ]. Times.W)) +b, where Z is the output of the convolution operation, represents the feature map, i and j represent the location of the feature map, h and W represent the height and width of the convolution kernel, respectively, and b is the bias term;
activation function: after convolution operation, non-linear transformation is usually performed on the feature map by using ReLU, so that the expression capability of the network is increased;
pooling operation: the average value is selected in the local perception area to serve as a pooling result, so that the space dimension of the feature map is reduced, and important feature information is reserved;
full connection operation: after a plurality of rolling and pooling operations, the feature map is unfolded into a one-dimensional vector, classification or regression tasks are carried out through a full-connection layer, each neuron in the full-connection layer is connected with all neurons of the previous layer, and a mapping relation between input and output is established through learning weights and bias;
loss function: in CNN, a cross entropy loss function is used to measure the gap between the prediction result of the model and the real label;
back propagation algorithm: calculating the gradient of the loss function to the model parameters by using a back propagation algorithm, and updating the model parameters by using gradient descent to continuously optimize the performance of the model;
the image deep learning module can automatically learn and extract features from input data through a convolutional neural network, and processing and analyzing images and other multidimensional data are realized.
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