CN117079430A - Early warning automatic adjustable security monitoring device and method - Google Patents
Early warning automatic adjustable security monitoring device and method Download PDFInfo
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- CN117079430A CN117079430A CN202310826581.3A CN202310826581A CN117079430A CN 117079430 A CN117079430 A CN 117079430A CN 202310826581 A CN202310826581 A CN 202310826581A CN 117079430 A CN117079430 A CN 117079430A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
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Abstract
The invention relates to an early warning automatic adjustable security monitoring device and a method, wherein the monitoring device comprises a background acquisition unit, an object acquisition unit, an environment acquisition unit, a data processing unit, a data transmission unit, a data storage unit, an edge learning unit, an information interaction unit and an alarm unit, can acquire various types of monitoring information, adopts a specific monitoring method to alarm abnormal events in a monitoring area, can automatically learn early warning conditions, trains new early warning conditions suitable for the characteristics of the monitoring area, is more suitable for actual monitoring scenes, continuously improves the accuracy of abnormal event identification and alarm, and reduces the false alarm rate and the alarm failure rate.
Description
Technical Field
The invention belongs to the technical field of security protection, and particularly relates to an automatic and adjustable security protection monitoring device and method for early warning.
Background
The safety problem is a critical thing in social production activities, and is directly related to the property safety of people and even life safety. With the continuous deepening of the social substances civilization and the mental civilization, the whole society is highly vigilant to the safety problem, and the security technology and the security products also obtain favorable development soil in the social development.
The security products are distributed in various industries and fields, and from the large aspect, building talkbacks, intelligent door locks, attendance access control, patrol systems, electronic fences, parking lot management systems and the like belong to the security products, and the intelligent requirements of society on the security products are higher and higher, especially the industries and fields with potential risks. Video is a very popular technology in security products, and with the continuous development of computer technology, the processing technology and processing capability of video images are continuously improved, including remote video monitoring, and the video processing technology and processing capability are widely applied.
In recent years, with the development of artificial intelligence and big data technology, a new development opportunity is provided for traditional video monitoring and even security monitoring products. However, in the prior art, the recognition effect of the intelligent recognition technology is mainly focused on improvement, and a better solution is not available in the aspect of flexibility of early warning conditions.
Disclosure of Invention
The invention solves the technical problems that: in order to overcome the defects in the prior art, the invention provides the security monitoring device and the security monitoring method with the automatic and adjustable early warning function, which can automatically learn the alarm condition and scene in the monitoring process, improve the refinement degree and the accuracy degree of the alarm, reduce the manpower investment to the greatest extent and reduce the cost.
The invention adopts the technical proposal for solving the technical problems that:
the security monitoring device comprises a background acquisition unit, an object acquisition unit, an environment acquisition unit, a data processing unit, a data transmission unit, a data storage unit, an edge learning unit, an information interaction unit and an alarm unit;
the background acquisition unit is used for acquiring static background information of a monitoring area, the object acquisition unit is used for acquiring dynamic change information of the monitoring area, and the environment acquisition unit is used for acquiring environment change information related to sound, light intensity, air quality and temperature and humidity in the monitoring area;
after receiving the data of the background acquisition unit, the object acquisition unit and the environment acquisition unit, the data processing unit analyzes and processes the data according to a preset data processing flow and parameters, identifies safety events and abnormal events, generates abnormal event alarm information, intercepts and stores basic data adopted for identifying the abnormal events, stores identification results, and simultaneously sends the alarm information to the alarm unit through the data transmission unit for alarm;
the data storage unit is used for storing the collected original data, configuration parameters of the monitoring device, result information of event identification processing and input information of the information interaction unit;
the edge learning unit extracts historical data in the data storage unit, learns the historical data, and generates new alarm conditions and scenes on the basis of preset alarm conditions;
the information interaction unit is used as an interface for interaction between the monitoring device and the outside, outputs identification information to the outside and receives feedback information of the outside on the output information.
Preferably, the background acquisition unit adopts a video monitoring mode to acquire.
Preferably, the object acquisition unit adopts one or more of a video monitoring mode and a laser radar.
Preferably, the environment acquisition unit adopts one or more of a sound acquisition sensor, a temperature and humidity acquisition sensor, an barometer and a particulate matter sensor.
Preferably, the edge learning unit adopts an independent system to process data, analyzes and processes the history basic data collected by the history alarm, extracts characteristic information of each basic data and specific associated information among each basic data, and finely distinguishes alarm conditions and scene states of a monitoring area so as to predict the occurrence of an abnormal event in advance.
Preferably, the alarm information is divided into two types, namely a preset alarm and a forecast alarm, wherein the preset alarm is generated by the device according to alarm conditions configured in advance, and the forecast alarm is generated by the device according to the edge learning result as a basis after processing the current real-time acquired data.
Preferably, the information interaction unit sends the alarm information to the background management system and the mobile terminal special APP in a wired or wireless mode, and if a false alarm instruction fed back by the background management system or the mobile terminal is not received within a specific time period, the device considers that the abnormal event analysis and the alarm information are valid.
Preferably, the edge learning unit can optimize the basic data learning algorithm of the abnormal event according to the feedback of the alarm result of the abnormal event, and is more suitable for the dynamic change of the object and the environment in the monitoring area.
Preferably, the alarm information is classified into three stages according to the degree of serious emergency, wherein the first stage is highest, the second stage is second lowest, and the third stage is lowest.
The invention also provides a method for carrying out security monitoring by adopting the security monitoring device, which mainly comprises the following steps:
firstly, deploying a monitoring device in a monitoring area, installing a background acquisition unit, an object acquisition unit and an environment acquisition unit at corresponding positions according to the actual condition of the monitoring area, and adjusting the installation angle and the respective monitoring areas;
secondly, configuring basic parameters of the monitoring device, including normal operation parameters, abnormal event identification parameters and alarm conditions of all equipment;
thirdly, the background acquisition unit, the object acquisition unit and the environment acquisition unit acquire basic data in real time and store the basic data in the data storage unit in real time;
a fourth step, the data processing unit receives basic data of the monitoring area in real time, a preset alarm condition is adopted, an abnormal event identification algorithm is utilized to process the basic data, if an abnormal event is identified, alarm information of different grades is generated, basic data in a specific period before and after an alarm time are intercepted from the storage unit, the basic data are stored in a fixed address section of the data storage unit, and a fifth step is executed; if not, executing a sixth step;
fifthly, after the edge learning unit detects that new basic data are stored in the fixed address segment of the storage unit, starting automatic learning of the historical alarm basic data once, extracting the background, the object, the environment and the information of specific association among the background, the object and the environment except the preset condition of the alarm time monitoring area, and generating a prediction alarm condition;
sixthly, the data processing unit carries out secondary processing on the real-time received basic data of the monitoring area according to the prediction alarm condition provided by the edge learning unit body, and identifies whether the monitoring area has potential abnormal events or not, if so, the data processing unit generates different grades of pre-alarm information;
seventh, the data processing unit sends the alarm result to the alarm unit for alarming; the alarm information is sent to the data management system and the mobile terminal, and a feedback instruction for the alarm information is received;
eighth, the data processing unit judges the difference between preset alarm and predictive alarm, and sends an instruction whether to update the alarm condition to the data management system and the mobile terminal, and reminds the background to update the preset alarm strategy.
Preferably, the automatic learning method in the fifth step includes one or more of semi-supervised clustering and semi-supervised neural network.
Preferably, the alarm information generated by using the preset alarm condition in the fourth step is preset alarm information.
Preferably, the alarm information generated using the predicted alarm condition in the sixth step is predicted alarm information.
Compared with the prior art, the invention has the advantages that:
(1) According to the invention, monitoring data of multiple dimensions can be acquired according to the characteristics of the monitoring area, and multiple alarm conditions are set from multiple conditions.
(2) After the monitoring device is deployed, besides alarming according to preset conditions, the actual monitoring scene can be learned, and the refined alarming conditions which are suitable for the monitoring scene and are except the preset conditions are generated.
(3) The invention can utilize the alarm feedback information outside the device to continuously optimize and correct the learning process and result of the predicted alarm condition, continuously improve the recognition precision of the abnormal event and reduce the false alarm rate and the non-alarm rate.
Drawings
FIG. 1 is a diagram of the components of the security monitoring device of the present invention.
Fig. 2 is a system composition diagram of an embodiment of the present invention.
Fig. 3 is a monitoring method of the device of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention become more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of the invention. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention. 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. The following describes the technical scheme of the present invention in detail with reference to the accompanying drawings, and an embodiment of the present invention is given.
FIG. 1 is a diagram of an early warning automatic adjustable security monitoring device, comprising a background acquisition unit, an object acquisition unit, an environment acquisition unit, a data processing unit, a data transmission unit, a data storage unit, an edge learning unit, an information interaction unit and an alarm unit; the background acquisition unit is used for acquiring static background information of the monitoring area, the object acquisition unit is used for acquiring dynamic change information of the monitoring area, and the environment acquisition unit is used for acquiring environment change information such as sound, light and the like of the monitoring area; after receiving the data of the background acquisition unit, the object acquisition unit and the environment acquisition unit, the data processing unit analyzes and processes the data according to a preset data processing flow and parameters, identifies safety events and abnormal events, generates abnormal event alarm information, intercepts and stores basic data adopted by the identified events individually, and sends the alarm information to the alarm unit through the data transmission unit to alarm while storing the identification result; the data storage unit is used for storing the collected original data, configuration parameters of the monitoring device, result information of event identification processing and input information of the information interaction unit; the edge learning unit extracts historical data in the data storage unit, learns the historical data, and generates new alarm conditions and scenes on the basis of preset alarm conditions; the information interaction unit is used as an interface for interaction between the monitoring device and the outside, outputs identification information to the outside and receives feedback information of the outside on the output information.
Fig. 2 is a diagram of the system components of the present invention applied in a public place security monitoring scenario. The system comprises a video monitoring device 1, a laser radar 2, a sound pick-up 3, a particulate matter sensor 4, a temperature and humidity sensor 5, a network disk 6, a data processor 7, an optical fiber transceiver 8, an edge learner 9, a 4G router 10 and an alarm 11.
The video monitoring 1 mainly collects real-time image pictures in a monitoring area; the laser radar 2 mainly collects the outline and distribution of objects in a monitoring area; the sound pick-up 3 collects sounds in the monitoring area; the particulate matter sensor 4 collects the air quality in the monitoring area; the temperature and humidity sensor 5 collects the temperature and humidity in the monitored area.
In a monitoring area of a public place, a video monitoring device 1, a laser radar 2, a pickup 3, a particulate matter sensor 4 and a temperature and humidity sensor 5 are installed at proper positions, the working angles and working states of the sensors are adjusted, a plurality of sensors can be installed according to different sizes of the monitoring area, and real-time data of the sensors are transmitted to a network disk 6 for storage and transmitted to a data processing device 7 for data processing.
In the network disk 6, monitoring alarm conditions are preset, for example, when people gather, objects move quickly, air is turbid, the temperature is too high, noise is too high in the area, and after the monitoring alarm conditions exceed a set threshold value, an alarm is given.
After receiving the real-time image picture, the data processor 7 processes the image and separates out the image background information in the monitoring picture and the shape and motion trail of the moving objects such as personnel, carts, goods and the like in the picture; after receiving the point cloud data of the laser radar 2, the data processor 7 analyzes the position and the outline of the monitored object and performs superposition comprehensive processing with the video image processing result, so that various object information in the monitored area and real-time change states in the monitored area can be accurately identified even in a severe environment.
The data processor 7 performs spectrum analysis on the audio data collected by the pickup 3 to separate out the sounds of personnel and other objects; analyzing the data of the particulate matter sensor 4 to obtain the concentration difference of the particulate matters in the air in the monitoring area and different positions in real time; and recording and analyzing the data of the temperature and humidity sensor 5 to obtain the temperature and humidity change condition in the monitoring area.
The data processor analyzes and processes each basic data, compares the basic data with preset alarm conditions, generates preset alarm information after the alarm conditions are met, controls the alarm to alarm through the optical fiber transceiver, sends the alarm information to a special APP in a background data management system and a mobile terminal through the 4G router 10, intercepts a section of basic data before and after the effective alarm time, and stores the intercepted basic data in an abnormal event basic data storage space opened up in the network disk 6.
After receiving the identification of the basic data of the stored new abnormal event, the edge learner 9 reads the basic data of the historical abnormal event in a specific period from the network disk 6, performs learning training, and performs feature mining on real-time features of monitoring picture change, monitoring object profile, monitoring object distribution, air quality, temperature and humidity and specific association relation among the data when the occurrence of the past abnormal event is identified, so as to generate a prediction alarm condition which is possibly abnormal event except the preset alarm condition.
In the edge learner 9, a specific data analysis mining algorithm, for example, a semi-supervised clustering or semi-supervised neural network algorithm is adopted to analyze the occurrence condition of the potential abnormal event, for example, after the air quality is lower than a specific value, the movement speed of personnel or objects in the monitoring area reaches above a corresponding value; for example, the object profile is moved at a high speed after being larger than a specific value; such as a person performing a specific relationship between the aggregate and the audible change value in the monitored area for a long time.
After the data processor is compared with the preset alarm conditions, if no abnormal events exist in the aspects of personnel, objects, air quality, temperature, noise and the like in the monitored area, the alarm conditions which are generated by the edge learner 9 and possibly have the abnormal events are called, the analysis results of the personnel, the objects, the air quality, the temperature, the noise and the like in the monitored area are subjected to secondary comprehensive analysis, the situation in the future monitored area is predicted, the potential abnormal events are identified, predicted alarm information is generated, and the alarm information is sent to a special APP in a background data management system and a mobile terminal through the 4G router 10, wherein the alarm information can comprise real-time image screenshots of the monitored area.
After receiving preset alarm information and predicted alarm information, the background data management system and the mobile terminal prompt the feedback of the alarm information, and if the alarm is false alarm, the feedback of the alarm is invalid; if the alarm is normal, the feedback alarm is effective; if no feedback is made within 1 hour, the data processing system defaults to alarm valid.
When the predictive alarm is confirmed to be effective for more than 2 times, the data processing system sends corresponding predictive alarm conditions to a background data management system and a mobile terminal APP through the 4G router 10 to prompt whether to update the preset early warning conditions; if the alarm information is updated to the preset early warning condition, the alarm is controlled to alarm in real time except that the alarm information is sent out through the 4G router 10 after the condition is met next time for alarming.
The present invention is not described in detail in part as common general knowledge in the art.
The object of the present invention is fully effectively achieved by the above-described embodiments. Those skilled in the art will appreciate that the present invention includes, but is not limited to, those illustrated in the drawings and described in the foregoing detailed description. While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
Claims (10)
1. The security monitoring device with the automatic and adjustable early warning function is characterized by comprising a background acquisition unit, an object acquisition unit, an environment acquisition unit, a data processing unit, a data transmission unit, a data storage unit, an edge learning unit, an information interaction unit and an alarm unit;
the background acquisition unit is used for acquiring static background information of a monitoring area, the object acquisition unit is used for acquiring dynamic change information of the monitoring area, and the environment acquisition unit is used for acquiring environment change information related to sound, light intensity, air quality and temperature and humidity in the monitoring area;
after receiving the data of the background acquisition unit, the object acquisition unit and the environment acquisition unit, the data processing unit analyzes and processes the data according to a preset data processing flow and parameters, identifies safety events and abnormal events, generates abnormal event alarm information, intercepts and stores basic data adopted for identifying the abnormal events, stores the identification result, and simultaneously sends the alarm information to the alarm unit through the data transmission unit for alarm;
the data storage unit is used for storing the collected original data, configuration parameters of the monitoring device, result information of event identification processing and input information of the information interaction unit;
the edge learning unit extracts historical data in the data storage unit, learns the historical data, and generates new alarm conditions and scenes on the basis of preset alarm conditions;
the information interaction unit is used as an interface for interaction between the monitoring device and the outside, outputs identification information to the outside and receives feedback information of the outside on the output information.
2. The automatic early warning and adjustable security monitoring device according to claim 1, wherein the background collection unit is used for collection in a video monitoring mode, the object collection unit is one or more of video monitoring and laser radar, and the environment collection unit is one or more of a sound collection sensor, a temperature and humidity collection sensor, an barometer and a particulate matter sensor.
3. The automatic early warning and adjustable security monitoring device according to claim 1, wherein the edge learning unit adopts an independent system to conduct data processing, analyzes and processes history basic data collected by history warning, extracts characteristic information of each basic data and specific associated information among each basic data, and finely distinguishes warning conditions and scene states of a monitoring area so as to predict occurrence of abnormal events in advance.
4. The automatic early warning and adjustable security monitoring device according to claim 1, wherein the alarm information is divided into two types, namely a preset alarm and a predictive alarm, wherein the preset alarm is generated by the device according to an alarm condition configured in advance, and the predictive alarm is generated by the device according to an edge learning result as a basis after processing current real-time acquired data.
5. The automatic early warning and adjustable security monitoring device according to claim 1, wherein the information interaction unit sends alarm information to the background management system and the mobile terminal special APP in a wired or wireless mode, and if a false alarm instruction fed back by the background management system or the mobile terminal is not received in a specific time period, the device considers that the abnormal event analysis and the alarm information are valid.
6. The automatic early warning and adjustable security monitoring device according to claim 1, wherein the edge learning unit can optimize a learning algorithm of basic information of an abnormal event according to feedback of an alarm result of the abnormal event, and is more suitable for dynamic changes of objects and environments in a monitoring area.
7. A method for security monitoring using the security monitoring device of claim 1, comprising the steps of:
firstly, deploying a monitoring device in a monitoring area, installing a background acquisition unit, an object acquisition unit and an environment acquisition unit at corresponding positions according to the actual condition of the monitoring area, and adjusting the installation angle and the respective monitoring areas;
secondly, configuring basic parameters of the monitoring device, including normal operation parameters, abnormal event identification parameters and alarm conditions of all equipment;
thirdly, the background acquisition unit, the object acquisition unit and the environment acquisition unit acquire basic data in real time and store the basic data in the data storage unit in real time;
a fourth step, the data processing unit receives basic data of the monitoring area in real time, a preset alarm condition is adopted, an abnormal event identification algorithm is utilized to process the basic data, if an abnormal event is identified, alarm information of different grades is generated, basic data in a specific period before and after an alarm time are intercepted from the storage unit, the basic data are stored in a fixed address section of the data storage unit, and a fifth step is executed; if not, executing a sixth step;
fifthly, after the edge learning unit detects that new basic data are stored in the fixed address segment of the storage unit, starting automatic learning of the historical alarm basic data once, extracting the background, the object, the environment and the information of specific association among the background, the object and the environment except the preset condition of the alarm time monitoring area, and generating a prediction alarm condition;
sixthly, the data processing unit carries out secondary processing on the real-time received basic data of the monitoring area according to the prediction alarm condition provided by the edge learning unit body, and identifies whether the monitoring area has potential abnormal events or not, if so, the data processing unit generates different grades of pre-alarm information;
seventh, the data processing unit sends the alarm result to the alarm unit for alarming; the alarm information is sent to the data management system and the mobile terminal, and a feedback instruction for the alarm information is received;
eighth, the data processing unit judges the difference between preset alarm and predictive alarm, and sends an instruction whether to update the alarm condition to the data management system and the mobile terminal, and reminds the background to update the preset alarm strategy.
8. The method of claim 7, wherein the automatic learning method in the fifth step comprises one or more of semi-supervised clustering and semi-supervised neural network.
9. The method according to claim 7, wherein the alarm information generated by using the preset alarm condition in the fourth step is preset alarm information.
10. The method according to claim 7, wherein the alarm information generated by using the predicted alarm condition in the sixth step is predicted alarm information.
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