Risk feature identification analysis method for campus security monitoring system

Abstract

The invention discloses a risk feature identification analysis method for a campus security monitoring system, and relates to the technical field of campus risk identification, and the method comprises the steps: collecting the to-be-identified risk data of a campus through a campus security monitoring system, and obtaining the to-be-identified risk features of the campus; performing risk feature identification processing according to the campus risk features to be identified to obtain campus risk features; and obtaining a risk feature identification analysis result according to the campus to-be-identified risk data, the campus to-be-identified risk features and the campus risk features. According to the invention, through deep analysis of the data in the campus security monitoring system, intelligence of campus security monitoring is realized, risk features can be automatically and accurately identified and analyzed, labor intensity and omission risk of manual monitoring are reduced, a false alarm rate is reduced, monitoring efficiency and accuracy and an intelligent level of campus security management are improved, and safety of the campus security monitoring system is improved. And the identification capability of the campus safety risk is enhanced, and the safety of the campus is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of campus risk identification, and in particular to a risk feature identification and analysis method for a campus security monitoring system. Background Art

[0002] As campus safety issues are increasingly valued, how to identify various risks on campus (such as fire, suspicious persons, emergencies, etc.) in real time through security monitoring systems has become an urgent problem to be solved. However, the existing campus security monitoring system mainly manages campus safety through video monitoring, access control management, alarm equipment, etc., but these systems have some problems, such as low monitoring efficiency, inaccurate risk identification, high false alarm rate, etc., and cannot timely and effectively discover and deal with safety hazards on campus. Therefore, a risk feature identification and analysis method for campus security monitoring systems is urgently needed to solve the deficiencies in the existing technology. Summary of the invention

[0003] The purpose of the present invention is to propose a risk feature identification and analysis method for a campus security monitoring system, which can automatically and accurately identify and analyze the risk features in the campus security monitoring system, improve monitoring efficiency and accuracy, reduce the false alarm rate, and enhance the ability to identify campus safety risks, thereby ensuring campus safety.

[0004] To achieve the above object, the present invention provides a risk feature identification and analysis method for a campus security monitoring system, comprising the following steps: S1. Use the campus security monitoring system to collect the risk data to be identified on campus and obtain the risk characteristics to be identified on campus; S2. Perform risk feature identification processing according to the risk features to be identified on the campus to obtain campus risk features; S3. Obtain risk feature identification and analysis results based on the campus risk data to be identified, the campus risk features to be identified and the campus risk features.

[0005] Optionally, using the campus security monitoring system to collect campus risk data to obtain campus risk characteristics to be identified includes: S1-1, using the campus security monitoring system to collect campus map information, campus video monitoring information and campus facility monitoring information; S1-2, obtaining the campus video surveillance information and the campus facility monitoring information as the campus risk data to be identified; S1-3, obtaining campus risk characteristics to be identified based on the campus risk data to be identified and the campus map information; Among them, the campus facility monitoring information includes campus facility operation status data and campus facility operation environment data. The campus facility operation status data includes experimental equipment data, fire-fighting equipment data and sports facilities data. The campus facility operation environment data includes temperature and humidity data and air quality data around the campus facilities. The experimental equipment data are the operation parameters and usage parameters of the experimental equipment. The fire-fighting equipment data are the inspection records and operation status data of the fire-fighting equipment. The sports facilities data are the operation status and usage parameters of the sports facilities.

[0006] Optionally, obtaining the campus risk features to be identified based on the campus risk data to be identified and according to the campus map information includes: S1-3-1, performing separation processing on the campus video surveillance information to obtain image data and audio data of the campus video surveillance information as campus video risk data to be identified; S1-3-2, based on the risk data of the videos to be identified on campus and according to the campus map information, obtaining risk features of the videos to be identified on campus; S1-3-3, based on the campus facility monitoring information and the campus map information, obtaining risk characteristics of the campus facilities to be identified; S1-3-3-1. According to the campus facility monitoring information, the campus map information is used to match the campus facility operation status data with the campus facility operation environment data to obtain a data pair of campus facility monitoring information; S1-3-3-2. Performing time stamp sorting processing on the data pairs of the campus facility monitoring information to obtain continuous data pairs of the campus facility monitoring information; S1-3-3-3, performing linear regression analysis on the continuous data of the campus facility monitoring information to obtain the change trend of the campus facility monitoring information as the risk feature of the campus facility to be identified; S1-3-4, obtaining the risk characteristics of the campus video to be identified and the risk characteristics of the campus facilities to be identified as the risk characteristics of the campus to be identified; The data pair of the campus facility monitoring information is campus facility operation status data and campus facility operation environment data corresponding to the same location information.

[0007] Optionally, obtaining risk features of the videos to be identified on campus based on the risk data of the videos to be identified on campus and according to the campus map information includes: S1-3-2-1, using the campus map information to locate the campus video risk data to obtain the location information of the campus video risk data to be identified; S1-3-2-2, performing regional division processing on the campus video risk data to be identified according to the location information of the campus video risk data to be identified to obtain the regional division result of the campus video risk data to be identified; S1-3-2-3, performing time-marking processing based on the regional division result of the campus video risk data to be identified to obtain the timestamp of the regional division result; S1-3-2-4, performing time matching processing on the regional division results of the campus video risk data to be identified according to the timestamp of the regional division results to obtain a pair of campus video risk data to be identified; S1-3-2-5. According to the campus video risk data pair to be identified, the campus map information is used to obtain the location information of the campus image data and the location information of the campus audio data corresponding to the campus video risk data pair to be identified; S1-3-2-6, determine whether the location information of the campus image data is consistent with the location information of the campus audio data, if so, directly execute S1-3-2-7, otherwise, use the location information of the corresponding inconsistent campus image data and the location information of the campus audio data to update the location information of the campus video risk data to be identified, and return to S1-3-2-2; S1-3-2-7. Obtain the timestamp of the campus image data and the timestamp of the campus audio data corresponding to the campus video risk data pair to be identified according to the campus video risk data pair to be identified; S1-3-2-8, determine whether the timestamp of the campus image data is consistent with the timestamp of the campus audio data, if so, obtain the campus video risk data pair to be identified as the campus video risk feature to be identified, otherwise, use the timestamp of the corresponding inconsistent campus image data and the timestamp of the campus audio data to update the timestamp of the area division result, and return to S1-3-2-4; The campus video risk data pair to be identified is the campus video risk data to be identified corresponding to the same location information and the same time period.

[0008] Optionally, performing risk feature identification processing according to the risk features to be identified on the campus to obtain the risk features of the campus includes: S2-1, obtaining campus video risk features according to a campus risk feature recognition model based on the campus video risk features to be identified; S2-2, performing risk feature identification according to the risk features of the campus facilities to be identified to obtain risk features of the campus facilities; S2-2-1. Construct a risk characteristic database of facilities to be identified on historical campuses using the risk characteristics of the facilities to be identified on the campuses and the risk characteristics of the facilities to be identified on the corresponding historical campuses; S2-2-2, using the campus facilities database to obtain risk characteristics of historical campus facilities according to the risk characteristics database of historical campus facilities to be identified; S2-2-3. Obtaining initial campus facility risk characteristics based on the risk characteristics of the campus facilities to be identified and the risk characteristics of historical campus facilities; S2-2-4, determining whether the historical campus facility risk characteristics include the initial campus facility risk characteristics, if so, obtaining the initial campus facility risk characteristics as the campus facility risk characteristics, otherwise, executing S2-2-5; S2-2-5, determine whether the historical campus facility risk feature database contains the initial campus facility risk feature, if so, return to S2-2-3, otherwise, update the campus facility risk feature according to the initial campus facility risk feature, and return to S2-2-1; S2-3, obtaining the campus risk characteristics according to the campus video risk characteristics and the campus facility risk characteristics; Among them, the campus facility database includes basic parameters and operating parameters of campus facilities. The basic parameters of campus facilities are size specifications, rated voltage, rated power, carrying capacity and service life of campus facilities. The operating parameters of campus facilities are operating time, operating environment requirements, maintenance cycle, failure frequency and safety indicators of campus facilities.

[0009] Optionally, obtaining the campus video risk feature according to the campus risk feature identification model based on the campus video risk feature to be identified includes: S2-1-1. Construct a historical campus video risk feature database based on the campus video risk features to be identified and the corresponding historical campus video risk features to be identified; S2-1-2, based on the historical campus video risk feature database to be identified, obtain the first campus video risk feature to be identified as a training set; S2-1-3. Obtain risk features of the videos to be identified in the second campus as a verification set based on the training set; S2-1-4, taking the training set as input and the campus video risk features corresponding to the training set as output, and constructing an initial campus risk feature recognition model based on a support vector machine; S2-1-5, inputting the verification set into the initial campus risk feature recognition model to obtain the campus video risk features corresponding to the verification set; S2-1-6. Determine whether the campus video risk features corresponding to the verification set completely correspond to the campus video risk features corresponding to the training set. If so, obtain the initial campus risk feature identification model as the campus risk feature identification model and execute S2-1-7. Otherwise, update the training set according to the campus video risk features corresponding to the verification set and return to S2-1-2. S2-1-7, inputting the campus risk feature identification model into the campus risk feature identification model to obtain the campus video risk feature; Among them, the campus risk feature identification model includes a deep convolutional neural network, a variational autoencoder, a generative adversarial network, a long short-term memory network and a fusion network based on an attention mechanism.

[0010] Optionally, inputting the campus risk feature recognition model into the campus risk feature recognition model to obtain the campus video risk feature includes: S2-1-7-1. Input the risk features of the campus video to be identified into the deep convolutional neural network to obtain the image features and audio features of the risk features of the campus video to be identified; S2-1-7-2, performing abnormal extraction processing on the image features of the risk features of the campus video to be identified according to the variational autoencoder and the generative adversarial network to obtain abnormal campus image features; S2-1-7-3, performing abnormal extraction processing on the audio features of the risk features of the campus video to be identified according to the long short-term memory network to obtain abnormal audio features of the campus; S2-1-7-4. Based on the abnormal campus image features and the abnormal campus audio features, the campus video risk features are obtained according to the fusion network based on the attention mechanism.

[0011] Optionally, obtaining the campus risk feature according to the campus video risk feature and the campus facility risk feature includes: S2-3-1. Obtain the campus video risk features corresponding to the training set as the historical campus video risk features; S2-3-2, determine whether the campus video risk feature corresponds to the historical campus video risk feature, if so, execute S2-3-3, otherwise, use the campus video risk feature to update the campus video risk feature to be identified, and return to S2-1-1; S2-3-3. Determine whether the campus facility risk characteristics correspond to the historical campus facility risk characteristics. If so, obtain the campus video risk characteristics and campus facility risk characteristics as the campus risk characteristics. Otherwise, use the campus facility risk characteristics to update the campus facility risk characteristics to be identified, and return to S2-2-1.

[0012] Optionally, obtaining risk feature identification analysis results according to the campus risk data to be identified, the campus risk features to be identified, and the campus risk features includes: S3-1. Construct a list of campus risk features to be identified based on the campus risk data to be identified and the campus risk features to be identified; S3-2, determine whether the list of campus risk features to be identified contains the campus risk feature, if so, obtain the location information and timestamp of the campus risk feature using the list of campus risk features to be identified according to the campus risk feature, and execute S3-3, otherwise, update the campus risk feature to be identified using the campus risk feature, and return to S2-1; S3-3, performing location correlation analysis on the campus risk characteristics according to the location information of the campus risk characteristics to obtain the distribution of the campus risk characteristics; S3-4, performing risk trend analysis on the campus risk characteristics according to the timestamp of the campus risk characteristics to obtain a change trend of the campus risk characteristics; S3-5. Based on the distribution of the campus risk characteristics and the changing trend of the campus risk characteristics, a risk report of the campus risk characteristics is obtained as the risk characteristic identification and analysis result.

[0013] Optionally, constructing a list of campus risk features to be identified based on the campus risk data to be identified and the campus risk features to be identified includes: S3-1-1, performing time-stamping processing according to the risk characteristics to be identified on the campus to obtain the timestamp of the risk characteristics to be identified on the campus; S3-1-2, based on the risk features to be identified on the campus, obtaining location information of the risk features to be identified on the campus according to the campus map information; S3-1-3. Obtain the campus risk data to be identified, timestamp and location information corresponding to the campus risk feature to be identified according to the campus risk feature to be identified, the campus risk data to be identified, the timestamp of the campus risk feature to be identified and the location information of the campus risk feature to be identified; S3-1-4. Construct a list of the campus risk characteristics to be identified based on the campus risk characteristics to be identified and the campus risk data to be identified, timestamp and location information corresponding to the campus risk characteristics to be identified.

[0014] Compared with the closest prior art, the present invention has the following beneficial effects: The present invention obtains campus video risk features through a campus risk feature recognition model and can meet the risk feature recognition needs in different scenarios. It has strong adaptability and robustness. The combination of a variational autoencoder and a generative adversarial network can effectively discover abnormal campus image features that are different from normal feature distributions. The long short-term memory network can well process the audio features of the campus video risk features to be identified. By introducing the attention mechanism, the network can pay more attention to feature information that is more important for risk judgment according to specific circumstances. The present invention realizes the intelligence of campus security monitoring by deeply analyzing the data in the campus security monitoring system. It can automatically and accurately identify and analyze risk features, reduce the labor intensity and omission risk of manual monitoring, reduce the false alarm rate, improve the monitoring efficiency and accuracy, and the intelligent level of campus safety management, and enhance the ability to identify campus safety risks, thereby ensuring the safety of the campus. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 The present invention is a flowchart of a risk feature identification and analysis method for a campus security monitoring system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The terms used in the embodiments of the present invention are only used to explain the specific embodiments of the present invention and are not intended to limit the present invention.

[0019] like Figure 1 As shown, an embodiment of the present invention provides a risk feature identification and analysis method for a campus security monitoring system, comprising the following steps: S1. Use the campus security monitoring system to collect the risk data to be identified on campus and obtain the risk characteristics to be identified on campus; S2. Perform risk feature identification processing according to the risk features to be identified on the campus to obtain campus risk features; S3. Obtain risk feature identification and analysis results based on the campus risk data to be identified, the campus risk features to be identified and the campus risk features.

[0020] S1 specifically includes: S1-1, using the campus security monitoring system to collect campus map information, campus video monitoring information and campus facility monitoring information; S1-2, obtaining the campus video surveillance information and the campus facility monitoring information as the campus risk data to be identified; S1-3, obtaining campus risk characteristics to be identified based on the campus risk data to be identified and the campus map information; The campus facility monitoring information includes campus facility operation status data and campus facility operation environment data.

[0021] In this embodiment, the campus facility operation status data includes experimental equipment data, fire equipment data, and sports facility data, etc. The experimental equipment data is the operation parameters and usage parameters of the experimental equipment, where the operation parameters are the operation time, voltage, current, power, temperature, etc. of the experimental equipment, and the usage parameters include the usage time, usage frequency, etc.; the fire equipment data is the inspection records and operation status data of the fire equipment, where the inspection records include the inspection time, inspection personnel, equipment status, etc. of fire extinguishers, fire hydrants and other fire equipment, and the operation status data is the abnormal data and alarm data of the fire equipment; the sports facility data is the operation status and usage parameters of the sports facilities, the operation status includes parameters such as whether the equipment is operating normally, the operation mode, and the operation time, and the usage parameters include the usage frequency and wear data of the sports equipment.

[0022] The campus facility operating environment data includes temperature and humidity data and air quality data around the campus facilities. The air quality data includes harmful gas concentrations used to detect the concentrations of harmful gases such as formaldehyde, benzene, carbon dioxide, etc. in the air around campus facilities, and particulate matter concentrations used to detect the content of particulate matter such as PM2.5 and PM10 in the air, so as to understand the campus air quality status, avoid excessive particulate matter concentrations that affect the respiratory health of teachers and students, and reduce pollution and damage to some precision instruments and equipment.

[0023] S1-3 specifically includes: S1-3-1, performing separation processing on the campus video surveillance information to obtain image data and audio data of the campus video surveillance information as the campus video risk data to be identified, wherein this embodiment uses the video processing tool FFmpeg to separate and process the campus video surveillance information; S1-3-2, based on the risk data of the videos to be identified on campus and according to the campus map information, obtaining risk features of the videos to be identified on campus; S1-3-3, based on the campus facility monitoring information and the campus map information, obtaining risk characteristics of the campus facilities to be identified; S1-3-3-1. According to the campus facility monitoring information, the campus map information is used to match the campus facility operation status data with the campus facility operation environment data to obtain a data pair of campus facility monitoring information; S1-3-3-2. Performing time stamp sorting processing on the data pairs of the campus facility monitoring information to obtain continuous data pairs of the campus facility monitoring information; S1-3-3-3, performing linear regression analysis on the continuous data of the campus facility monitoring information to obtain the change trend of the campus facility monitoring information as the risk feature of the campus facility to be identified; S1-3-4, obtaining the risk characteristics of the campus video to be identified and the risk characteristics of the campus facilities to be identified as the risk characteristics of the campus to be identified; The data pair of the campus facility monitoring information is campus facility operation status data and campus facility operation environment data corresponding to the same location information.

[0024] S1-3-2 specifically includes: S1-3-2-1, using the campus map information to locate the campus video risk data to obtain the location information of the campus video risk data to be identified; S1-3-2-2, performing regional division processing on the campus video risk data to be identified according to the location information of the campus video risk data to be identified to obtain the regional division result of the campus video risk data to be identified; S1-3-2-3, performing time-marking processing based on the regional division result of the campus video risk data to be identified to obtain the timestamp of the regional division result; S1-3-2-4, performing time matching processing on the regional division results of the campus video risk data to be identified according to the timestamp of the regional division results to obtain a pair of campus video risk data to be identified; S1-3-2-5. According to the campus video risk data pair to be identified, the campus map information is used to obtain the location information of the campus image data and the location information of the campus audio data corresponding to the campus video risk data pair to be identified; S1-3-2-6, determine whether the location information of the campus image data is consistent with the location information of the campus audio data, if so, directly execute S1-3-2-7, otherwise, use the location information of the corresponding inconsistent campus image data and the location information of the campus audio data to update the location information of the campus video risk data to be identified, and return to S1-3-2-2; S1-3-2-7. Obtain the timestamp of the campus image data and the timestamp of the campus audio data corresponding to the campus video risk data pair to be identified according to the campus video risk data pair to be identified; S1-3-2-8, determine whether the timestamp of the campus image data is consistent with the timestamp of the campus audio data, if so, obtain the campus video risk data pair to be identified as the campus video risk feature to be identified, otherwise, use the timestamp of the corresponding inconsistent campus image data and the timestamp of the campus audio data to update the timestamp of the area division result, and return to S1-3-2-4; The campus video risk data pair to be identified is the campus video risk data to be identified corresponding to the same location information and the same time period.

[0025] S2 specifically includes: S2-1, obtaining campus video risk features according to a campus risk feature recognition model based on the campus video risk features to be identified; S2-2, performing risk feature identification according to the risk features of the campus facilities to be identified to obtain risk features of the campus facilities; S2-2-1. Construct a risk characteristic database of facilities to be identified on historical campuses using the risk characteristics of the facilities to be identified on the campuses and the risk characteristics of the facilities to be identified on the corresponding historical campuses; S2-2-2, using the campus facilities database to obtain risk characteristics of historical campus facilities according to the risk characteristics database of historical campus facilities to be identified; S2-2-3. Obtaining initial campus facility risk characteristics based on the risk characteristics of the campus facilities to be identified and the risk characteristics of historical campus facilities; S2-2-4, determining whether the historical campus facility risk characteristics include the initial campus facility risk characteristics, if so, obtaining the initial campus facility risk characteristics as the campus facility risk characteristics, otherwise, executing S2-2-5; S2-2-5, determine whether the historical campus facility risk feature database contains the initial campus facility risk feature, if so, return to S2-2-3, otherwise, update the campus facility risk feature according to the initial campus facility risk feature, and return to S2-2-1; S2-3, obtaining the campus risk characteristics according to the campus video risk characteristics and the campus facility risk characteristics; The campus facility database includes basic parameters and operating parameters of campus facilities.

[0026] In this embodiment, the basic parameters of the campus facilities are the size specifications, rated voltage, rated power, carrying capacity, service life, etc. of the campus facilities, and the operating parameters of the campus facilities are the operating time, operating environment requirements, maintenance cycle, failure frequency, safety indicators, etc. of the campus facilities.

[0027] S2-1 specifically includes: S2-1-1. Construct a historical campus video risk feature database based on the campus video risk features to be identified and the corresponding historical campus video risk features to be identified; S2-1-2, based on the historical campus video risk feature database to be identified, obtain the first campus video risk feature to be identified as a training set; S2-1-3, obtaining risk features of the videos to be identified in the second campus as a verification set according to the training set; S2-1-4, taking the training set as input and the campus video risk features corresponding to the training set as output, and constructing an initial campus risk feature recognition model based on a support vector machine; S2-1-5, inputting the verification set into the initial campus risk feature recognition model to obtain the campus video risk features corresponding to the verification set; S2-1-6. Determine whether the campus video risk features corresponding to the verification set completely correspond to the campus video risk features corresponding to the training set. If so, obtain the initial campus risk feature identification model as the campus risk feature identification model and execute S2-1-7. Otherwise, update the training set according to the campus video risk features corresponding to the verification set and return to S2-1-2. S2-1-7, inputting the campus risk feature identification model into the campus risk feature identification model to obtain the campus video risk feature; Among them, the campus risk feature identification model includes a deep convolutional neural network, a variational autoencoder, a generative adversarial network, a long short-term memory network and a fusion network based on an attention mechanism.

[0028] S2-1-7 specifically includes: S2-1-7-1. Input the risk features of the campus video to be identified into the deep convolutional neural network to obtain the image features and audio features of the risk features of the campus video to be identified. Specifically: The deep convolutional neural network can learn the different characteristic patterns of audio and image in the risk features of the campus video to be identified, and use operations such as convolutional layers and pooling layers to separate the image features and audio features of the risk features of the campus video to be identified; S2-1-7-2, performing abnormal extraction processing on the image features of the risk features of the campus video to be identified according to the variational autoencoder and the generative adversarial network to obtain abnormal campus image features, specifically: The variational autoencoder is used to learn the normal feature distribution corresponding to the image features of the risk features of the campus video to be identified. Through adversarial training with the generative adversarial network, it can effectively discover abnormal campus image features that are different from the normal feature distribution, such as abnormal crowds and abnormal vehicles on campus. S2-1-7-3, performing abnormal extraction processing on the audio features of the risk features of the campus video to be identified according to the long short-term memory network to obtain abnormal campus audio features, specifically: The long short-term memory network can process the time series information corresponding to the audio features of the risk features of the campus video to be identified, remember the long-term dependencies in the audio features, and has a good extraction effect on the abnormal audio features of the campus with time-varying characteristics, such as continuous abnormal noise; S2-1-7-4, based on the abnormal campus image features and the abnormal campus audio features, the campus video risk features are obtained according to the fusion network based on the attention mechanism, specifically: The attention mechanism is introduced to dynamically assign weights to abnormal campus image features and abnormal campus audio features, so that the network can pay more attention to feature information that is more important for risk judgment according to specific circumstances. For example, when judging fire risks, more attention may be paid to the alarm sound in the audio and the delay features in the image. The attention mechanism can highlight the role of these key features in fusion.

[0029] S2-3 specifically includes: S2-3-1. Obtain the campus video risk features corresponding to the training set as the historical campus video risk features; S2-3-2, determine whether the campus video risk feature corresponds to the historical campus video risk feature, if so, execute S2-3-3, otherwise, use the campus video risk feature to update the campus video risk feature to be identified, and return to S2-1-1; S2-3-3. Determine whether the campus facility risk characteristics correspond to the historical campus facility risk characteristics. If so, obtain the campus video risk characteristics and campus facility risk characteristics as the campus risk characteristics. Otherwise, use the campus facility risk characteristics to update the campus facility risk characteristics to be identified, and return to S2-2-1.

[0030] S3 specifically includes: S3-1. Construct a list of campus risk features to be identified based on the campus risk data to be identified and the campus risk features to be identified; S3-2, determine whether the list of campus risk features to be identified contains the campus risk feature, if so, obtain the location information and timestamp of the campus risk feature using the list of campus risk features to be identified according to the campus risk feature, and execute S3-3, otherwise, update the campus risk feature to be identified using the campus risk feature, and return to S2-1; S3-3, performing location correlation analysis on the campus risk characteristics according to the location information of the campus risk characteristics to obtain the distribution of the campus risk characteristics; S3-4, performing risk trend analysis on the campus risk characteristics according to the timestamp of the campus risk characteristics to obtain a change trend of the campus risk characteristics; S3-5. Based on the distribution of the campus risk characteristics and the changing trend of the campus risk characteristics, a risk report of the campus risk characteristics is obtained as the risk characteristic identification and analysis result.

[0031] S3-1 specifically includes: S3-1-1, performing time-stamping processing according to the risk feature to be identified on the campus to obtain the timestamp of the risk feature to be identified on the campus; S3-1-2, based on the risk features to be identified on the campus, obtaining location information of the risk features to be identified on the campus according to the campus map information; S3-1-3. Obtain the campus risk data to be identified, timestamp and location information corresponding to the campus risk feature to be identified according to the campus risk feature to be identified, the campus risk data to be identified, the timestamp of the campus risk feature to be identified and the location information of the campus risk feature to be identified; S3-1-4. Construct a list of the campus risk characteristics to be identified based on the campus risk characteristics to be identified and the campus risk data to be identified, timestamp and location information corresponding to the campus risk characteristics to be identified.

[0032] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0033] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0034] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0035] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A risk feature identification and analysis method for a campus security monitoring system, characterized in that: The specific steps include: S1. Use the campus security monitoring system to collect the risk data to be identified on campus and obtain the risk characteristics to be identified on campus; S2. Perform risk feature identification processing according to the risk features to be identified on the campus to obtain campus risk features; S3. Obtain risk feature identification and analysis results based on the campus risk data to be identified, the campus risk features to be identified and the campus risk features.

2. The risk feature identification and analysis method for a campus security monitoring system according to claim 1 is characterized in that: Using the campus security monitoring system to collect campus risk data to obtain the characteristics of campus risk to be identified includes: S1-1, using the campus security monitoring system to collect campus map information, campus video monitoring information and campus facility monitoring information; S1-2, obtaining the campus video surveillance information and the campus facility monitoring information as the campus risk data to be identified; S1-3, obtaining campus risk characteristics to be identified based on the campus risk data to be identified and the campus map information; Among them, the campus facility monitoring information includes campus facility operation status data and campus facility operation environment data. The campus facility operation status data includes experimental equipment data, fire-fighting equipment data and sports facilities data. The campus facility operation environment data includes temperature and humidity data and air quality data around the campus facilities. The experimental equipment data are the operation parameters and usage parameters of the experimental equipment. The fire-fighting equipment data are the inspection records and operation status data of the fire-fighting equipment. The sports facilities data are the operation status and usage parameters of the sports facilities.

3. The risk feature identification and analysis method for a campus security monitoring system according to claim 2 is characterized in that: Acquiring campus risk features to be identified based on the campus risk data to be identified and according to the campus map information includes: S1-3-1, performing separation processing on the campus video surveillance information to obtain image data and audio data of the campus video surveillance information as campus video risk data to be identified; S1-3-2, based on the risk data of the videos to be identified on campus and according to the campus map information, obtaining risk features of the videos to be identified on campus; S1-3-3, based on the campus facility monitoring information and the campus map information, obtaining risk characteristics of the campus facilities to be identified; S1-3-3-1. According to the campus facility monitoring information, the campus map information is used to match the campus facility operation status data with the campus facility operation environment data to obtain a data pair of campus facility monitoring information; S1-3-3-2. Performing time stamp sorting processing on the data pairs of the campus facility monitoring information to obtain continuous data pairs of the campus facility monitoring information; S1-3-3-3, performing linear regression analysis on the continuous data of the campus facility monitoring information to obtain the change trend of the campus facility monitoring information as the risk feature of the campus facility to be identified; S1-3-4, obtaining the risk characteristics of the campus video to be identified and the risk characteristics of the campus facilities to be identified as the risk characteristics of the campus to be identified; The data pair of the campus facility monitoring information is campus facility operation status data and campus facility operation environment data corresponding to the same location information.

4. The risk feature identification and analysis method for a campus security monitoring system according to claim 3 is characterized in that: Acquiring risk features of the videos to be identified on campus based on the risk data of the videos to be identified on campus and according to the campus map information includes: S1-3-2-1, using the campus map information to locate the campus video risk data to obtain the location information of the campus video risk data to be identified; S1-3-2-2, performing regional division processing on the campus video risk data to be identified according to the location information of the campus video risk data to be identified to obtain the regional division result of the campus video risk data to be identified; S1-3-2-3, performing time-marking processing based on the regional division result of the campus video risk data to be identified to obtain the timestamp of the regional division result; S1-3-2-4, performing time matching processing on the regional division results of the campus video risk data to be identified according to the timestamp of the regional division results to obtain a pair of campus video risk data to be identified; S1-3-2-5. According to the campus video risk data pair to be identified, the campus map information is used to obtain the location information of the campus image data and the location information of the campus audio data corresponding to the campus video risk data pair to be identified; S1-3-2-6, determine whether the location information of the campus image data is consistent with the location information of the campus audio data, if so, directly execute S1-3-2-7, otherwise, use the location information of the corresponding inconsistent campus image data and the location information of the campus audio data to update the location information of the campus video risk data to be identified, and return to S1-3-2-2; S1-3-2-7. Obtain the timestamp of the campus image data and the timestamp of the campus audio data corresponding to the campus video risk data pair to be identified according to the campus video risk data pair to be identified; S1-3-2-8, determine whether the timestamp of the campus image data is consistent with the timestamp of the campus audio data, if so, obtain the campus video risk data pair to be identified as the campus video risk feature to be identified, otherwise, use the timestamp of the corresponding inconsistent campus image data and the timestamp of the campus audio data to update the timestamp of the area division result, and return to S1-3-2-4; The campus video risk data pair to be identified is the campus video risk data to be identified corresponding to the same location information and the same time period.

5. The risk feature identification and analysis method for a campus security monitoring system according to claim 4 is characterized in that: Performing risk feature identification processing according to the risk features to be identified on the campus to obtain the risk features of the campus includes: S2-1, obtaining campus video risk features according to a campus risk feature recognition model based on the campus video risk features to be identified; S2-2, performing risk feature identification according to the risk features of the campus facilities to be identified to obtain risk features of the campus facilities; S2-2-1. Construct a risk characteristic database of facilities to be identified on historical campuses using the risk characteristics of the facilities to be identified on the campuses and the risk characteristics of the facilities to be identified on the corresponding historical campuses; S2-2-2, using the campus facilities database to obtain risk characteristics of historical campus facilities according to the risk characteristics database of historical campus facilities to be identified; S2-2-3. Obtaining initial campus facility risk characteristics based on the risk characteristics of the campus facilities to be identified and the risk characteristics of historical campus facilities; S2-2-4, determining whether the historical campus facility risk characteristics include the initial campus facility risk characteristics, if so, obtaining the initial campus facility risk characteristics as the campus facility risk characteristics, otherwise, executing S2-2-5; S2-2-5, determine whether the historical campus facility risk feature database contains the initial campus facility risk feature, if so, return to S2-2-3, otherwise, update the campus facility risk feature according to the initial campus facility risk feature, and return to S2-2-1; S2-3, obtaining the campus risk characteristics according to the campus video risk characteristics and the campus facility risk characteristics; Among them, the campus facility database includes basic parameters and operating parameters of campus facilities. The basic parameters of campus facilities are size specifications, rated voltage, rated power, carrying capacity and service life of campus facilities. The operating parameters of campus facilities are operating time, operating environment requirements, maintenance cycle, failure frequency and safety indicators of campus facilities.

6. The risk feature identification and analysis method for a campus security monitoring system according to claim 5 is characterized in that: Acquiring campus video risk features according to the campus risk feature recognition model based on the campus video risk features to be identified includes: S2-1-1. Construct a historical campus video risk feature database based on the campus video risk features to be identified and the corresponding historical campus video risk features to be identified; S2-1-2, based on the historical campus video risk feature database to be identified, obtain the first campus video risk feature to be identified as a training set; S2-1-3, obtaining risk features of the videos to be identified in the second campus as a verification set according to the training set; S2-1-4, taking the training set as input and the campus video risk features corresponding to the training set as output, and constructing an initial campus risk feature recognition model based on a support vector machine; S2-1-5, inputting the verification set into the initial campus risk feature recognition model to obtain the campus video risk features corresponding to the verification set; S2-1-6. Determine whether the campus video risk features corresponding to the verification set completely correspond to the campus video risk features corresponding to the training set. If so, obtain the initial campus risk feature identification model as the campus risk feature identification model and execute S2-1-7. Otherwise, update the training set according to the campus video risk features corresponding to the verification set and return to S2-1-2. S2-1-7, inputting the campus risk feature identification model into the campus risk feature identification model to obtain the campus video risk feature; Among them, the campus risk feature identification model includes a deep convolutional neural network, a variational autoencoder, a generative adversarial network, a long short-term memory network and a fusion network based on an attention mechanism.

7. The risk feature identification and analysis method for a campus security monitoring system according to claim 6 is characterized in that: Inputting the campus risk feature recognition model into the campus risk feature recognition model to obtain the campus video risk feature includes: S2-1-7-1. Input the risk features of the campus video to be identified into the deep convolutional neural network to obtain the image features and audio features of the risk features of the campus video to be identified; S2-1-7-2, performing abnormal extraction processing on the image features of the risk features of the campus video to be identified according to the variational autoencoder and the generative adversarial network to obtain abnormal campus image features; S2-1-7-3, performing abnormal extraction processing on the audio features of the risk features of the campus video to be identified according to the long short-term memory network to obtain abnormal audio features of the campus; S2-1-7-4. Based on the abnormal campus image features and the abnormal campus audio features, the campus video risk features are obtained according to the fusion network based on the attention mechanism.

8. The risk feature identification and analysis method for a campus security monitoring system according to claim 6 is characterized in that: Acquiring the campus risk feature according to the campus video risk feature and the campus facility risk feature includes: S2-3-1. Obtain the campus video risk features corresponding to the training set as the historical campus video risk features; S2-3-2, determine whether the campus video risk feature corresponds to the historical campus video risk feature, if so, execute S2-3-3, otherwise, use the campus video risk feature to update the campus video risk feature to be identified, and return to S2-1-1; S2-3-3. Determine whether the campus facility risk characteristics correspond to the historical campus facility risk characteristics. If so, obtain the campus video risk characteristics and campus facility risk characteristics as the campus risk characteristics. Otherwise, use the campus facility risk characteristics to update the campus facility risk characteristics to be identified, and return to S2-2-1.

9. The risk feature identification and analysis method for a campus security monitoring system according to claim 2 is characterized in that: Obtaining risk feature identification analysis results according to the campus risk data to be identified, the campus risk features to be identified and the campus risk features includes: S3-1. Construct a list of campus risk features to be identified based on the campus risk data to be identified and the campus risk features to be identified; S3-2, determine whether the list of campus risk features to be identified contains the campus risk feature, if so, obtain the location information and timestamp of the campus risk feature using the list of campus risk features to be identified according to the campus risk feature, and execute S3-3, otherwise, update the campus risk feature to be identified using the campus risk feature, and return to S2-1; S3-3, performing location correlation analysis on the campus risk characteristics according to the location information of the campus risk characteristics to obtain the distribution of the campus risk characteristics; S3-4, performing risk trend analysis on the campus risk characteristics according to the timestamp of the campus risk characteristics to obtain a change trend of the campus risk characteristics; S3-5. Based on the distribution of the campus risk characteristics and the changing trend of the campus risk characteristics, a risk report of the campus risk characteristics is obtained as the risk characteristic identification and analysis result.

10. A risk feature identification and analysis method for a campus security monitoring system according to claim 9, characterized in that: Constructing a list of campus risk features to be identified based on the campus risk data to be identified and the campus risk features to be identified includes: S3-1-1, performing time-stamping processing according to the risk feature to be identified on the campus to obtain the timestamp of the risk feature to be identified on the campus; S3-1-2, based on the risk features to be identified on the campus, obtaining location information of the risk features to be identified on the campus according to the campus map information; S3-1-3. Obtain the campus risk data to be identified, timestamp and location information corresponding to the campus risk feature to be identified according to the campus risk feature to be identified, the campus risk data to be identified, the timestamp of the campus risk feature to be identified and the location information of the campus risk feature to be identified; S3-1-4. Construct a list of the campus risk characteristics to be identified based on the campus risk characteristics to be identified and the campus risk data to be identified, timestamp and location information corresponding to the campus risk characteristics to be identified.

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