CN115550609A - Building Internet of things monitoring system capable of realizing automatic adaptation - Google Patents
Building Internet of things monitoring system capable of realizing automatic adaptation Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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- A—HUMAN NECESSITIES
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/10—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/12—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions
- G08B17/125—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions by using a video camera to detect fire or smoke
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- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
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Abstract
The invention discloses a building Internet of things monitoring system capable of realizing automatic adaptation, relates to the technical field of building monitoring, and aims to solve the problems that the building monitoring is not accurate and the abnormal problem cannot be decided. Realize building thing networking monitored control system of automatic adaptation, including management terminal, video monitoring system, data processing module, the management system and the data record storage system of being incorporated into the power networks, smoke alarm and spraying equipment start-up time, alarm can take place for monitor terminal, can confirm that the control in which region has appeared unusually through alarm information, can look over, the efficiency of looking over when taking place unusually has been improved, confirm the accuracy in unusual region has also been improved, when the condition that naked light or foreign matter sheltered from appears in the detection video picture, can pass through unusual data classification module to the unusual condition, but unusual data classification module intelligent classification also can carry out manual classification, dual classification does benefit to the alarm processing in later stage more.
Description
Technical Field
The invention relates to the technical field of building monitoring, in particular to a building Internet of things monitoring system capable of realizing automatic adaptation.
Background
In order to protect the safety problems between buildings and residents in floors, some monitoring terminals are needed to monitor the buildings, and the following problems still exist in the existing monitoring system:
1. in the management of building personnel in an area, the safety of checking at the background is not guaranteed because the staff do not carry out management limitation.
2. When carrying out video acquisition, video acquisition data is imperfect to and the video acquisition is carried out to the singleness, can't in time know the problem of the condition when taking place conflagration or accident to some region.
3. When abnormal data in the video is detected, further decision can not be made, and the problem that the abnormality in the area can not be properly solved is caused.
Disclosure of Invention
The invention aims to provide a building Internet of things monitoring system capable of realizing automatic adaptation, when a smoke alarm and a spraying device are started, a monitoring terminal can give an alarm, and can confirm which area is abnormal through alarm information, so that the monitoring terminal can check the abnormal area, the checking efficiency during abnormal occurrence is improved, the accuracy of confirming the abnormal area is also improved, when the condition that naked fire or foreign matter is shielded in a video picture is detected, an abnormal condition can be classified intelligently through an abnormal data classification module, the abnormal data classification module can also be used by a human being, double classification is more beneficial to later-stage alarm processing, and the problems in the prior art can be solved.
In order to achieve the purpose, the invention provides the following technical scheme:
a building Internet of things monitoring system capable of realizing automatic adaptation comprises a management terminal, a video monitoring system, a data processing module, a grid-connected management system and a data recording and storing system;
the management terminal is used for a worker to use a work account number, safely log in to a terminal intranet, enter the video monitoring system according to the grade of the work account number, and fail to enter the video monitoring system when the grade of the work account number is unqualified;
the video monitoring system is used for acquiring actively uploaded video data from less than one video acquisition terminal, performing data packaging in a classified manner on the video data in a plurality of different areas and sending the video data to the data processing module;
the data processing module is used for analyzing the video data in different areas and sending the analyzed video abnormal data to the grid-connected management system;
the grid-connected management system is used for establishing a one-way alarm connection line with internal networks such as an alarm mechanism, a medical mechanism and the like in a local area and performing intelligent alarm processing through analyzed abnormal data;
and the data recording and storing system is used for establishing two groups of storage databases which are respectively an abnormal video data storage library and a normal video data storage library.
Preferably, the management terminal includes:
the personnel information input module is used for:
the staff registers the information such as own name, job number, login password and the like;
when a worker inputs information for the first time, more detailed personal information and positions need to be input, and the information needs to be approved by a superior;
a hierarchical information classification module to:
based on position information input by staff in the staff information input module, positions are classified according to the level, and the permission for operating the next step only at a certain position can be set;
a memory module of the type used to:
and the classification module stores data of workers who can perform the next operation based on the hierarchical information.
Preferably, the video surveillance system includes:
a video capture unit to:
the method comprises the steps that video data are obtained through at least one video acquisition terminal in different areas, and each video acquisition terminal in each area is provided with an independent video processor, wherein the interval of the acquired video data can be set as required under the condition that the interval of the acquired video data does not exceed the highest frame rate of monitoring;
a video image processing unit to:
processing the frame number of the acquired at least one video data;
wherein, the processing mode comprises: video image analysis and analysis image processing;
video image analysis for:
constructing a convolutional neural network model, and performing dynamic picture analysis on the convolutional neural network model based on the acquired video to obtain the final MOS value of the acquired video and an image definition threshold value:
analysis image processing for:
determining a weight value of an MOS value and an image definition threshold based on video image analysis, obtaining a final quality score of the collected video based on the weight value, and processing the video data again when the quality score is smaller than a preset threshold until the quality score is larger than or equal to the preset threshold.
Preferably, the video capture unit comprises:
a data acquisition unit for:
acquiring a building plan and a three-dimensional stereo of a building, inputting the building plan and the three-dimensional stereo into a convolutional neural network for training, and respectively obtaining a first characteristic diagram and a second characteristic diagram corresponding to the building plan and the three-dimensional stereo;
a model construction unit for:
constructing a digital three-dimensional model corresponding to the building based on the first characteristic diagram and the second characteristic diagram, and acquiring user entrance information and building environment information in the building based on a management terminal;
marking positions of different enterprises in the digital three-dimensional model based on the user parking information and the building environment information, and setting simulation monitoring points in the digital three-dimensional model based on a marking result to obtain a target point position table, wherein each monitoring position is at least provided with two monitoring points;
a monitor point adaptation unit to:
determining acquisition tasks of different simulation monitoring points in a target point position table based on the user parking information, and constructing a physical topological network between the simulation monitoring points and a management terminal based on the acquisition tasks;
setting a communication node between the simulation monitoring point and the management terminal based on the physical topology network, and issuing an acquisition task to the corresponding simulation monitoring point based on the communication node;
acquiring a first acquisition behavior characteristic and a second acquisition behavior characteristic of building information of different simulation monitoring points in the same monitoring position based on the issued result, and determining the association degree of the first acquisition behavior characteristic and the second acquisition behavior characteristic;
when the correlation degree reaches a preset threshold value, judging that the setting of the simulation monitoring points in the monitoring position is qualified, and setting the monitoring points based on the same positions of the distribution positions of the simulation monitoring points in the digital three-dimensional model in the actual building;
meanwhile, monitoring whether an acquisition task issued by the management terminal is changed or not in real time, determining a monitoring adjustment strategy when the acquisition task is changed, and automatically adapting the monitoring point based on the monitoring adjustment strategy;
otherwise, judging that the setting of the simulation monitoring points in the monitoring positions is unqualified, adjusting the simulation monitoring points until the correlation degree reaches a preset threshold value, and finishing the setting of the monitoring points in different monitoring positions in the building.
Preferably, the video capture unit includes:
a building monitoring unit for:
taking a video acquisition terminal in each unit building as a unit of a group, and acquiring videos in the unit building by taking a layer as a unit;
a building monitoring unit for:
taking video acquisition of each building as a unit of a group, and carrying out video acquisition on other directions between buildings and in an area;
a parking lot monitoring unit; for:
and carrying out video acquisition on the ground parking lot and the underground parking lot area.
Preferably, the building monitoring unit includes:
unit building fire control monitoring module for:
monitoring fire fighting equipment and fire fighting conditions in a public area of each floor in each unit building;
wherein the fire fighting equipment data includes: the smoke processing module and the spraying processing module can send abnormal data to a next instruction when all or single received data of the smoke processing module and the spraying processing module are abnormal;
an elevator monitoring module to:
monitoring the interior of each elevator in each unit building;
the unit building entrance guard monitoring module is used for:
carrying out video monitoring on an entrance of each unit building;
wherein, entering the unit building mode includes: infrared induction module and entrance guard's card processing module.
Preferably, the building monitoring unit includes:
a utility monitoring module for:
public facilities (fitness equipment, ground carports, road seats and the like) in the area are monitored through a video acquisition terminal;
the regional road monitoring module is used for:
and monitoring all roads in the area through the video acquisition terminal.
Preferably, the parking lot monitoring unit includes:
vehicle import & export monitoring module for:
monitoring an inlet and an outlet of a parking lot in the area through a video acquisition terminal;
a vehicle parking monitoring module for:
and the video acquisition terminal monitors the running or parking condition of the vehicle in the parking lot.
Preferably, the data processing module includes:
a data receiving module to:
receiving the video transmitted by the video monitoring system based on the video;
a data analysis module to:
based on the video information received by the data receiving module, carrying out dynamic picture analysis on the video information;
an abnormal data classification module to:
based on the result of the analysis of the dynamic picture by the data analysis module, carrying out abnormity detection on abnormal pictures in the dynamic picture, carrying out grade classification on the detected abnormal data, and packaging the abnormal data into a plurality of sub-data sets according to the classification;
the abnormality detection is carried out by judging and detecting whether the picture is abnormal or not and analyzing and monitoring data provided by a video monitoring system;
a class two storage module to:
storing the data set based on the abnormal data classification module;
a grid-tie management system comprising:
a classified data receiving module for:
receiving data according to the abnormal data transmitted by the data processing module;
a data verification module to:
based on the received data of the classified data receiving module, further checking the received data, wherein the checking step is accurate to one or more video acquisition terminals in a certain area;
a docketing module for:
storing and recording the checked video data based on the checking result of the data checking module;
an alert module to:
and based on the video data in the filing module, performing one-way alarm connection on internal networks such as alarm mechanisms, medical institutions and the like in the local area through communication equipment, and performing alarm processing on the internal networks.
Preferably, the first and second liquid crystal materials are,
preferably, the video surveillance system includes:
a moving object tracking unit for:
the method comprises the following steps of obtaining video data collected by a video collecting terminal, analyzing the video data, judging whether a moving target exists in a current monitoring area, and controlling the video collecting terminal to track the real-time position of the moving target when the moving target exists, wherein the specific steps comprise:
a first calculation unit to:
calculating the weight of each pixel point in the video image collected by the video collecting terminal according to the following formula:
wherein the content of the first and second substances,
representing the weight of each pixel point in the collected video image; alpha represents a normalized constant and has a value range of (0,1); i represents the number of the current pixel points in the collected video image and the value range is [1,n ]](ii) a n represents the total number of the current pixel points in the collected video image; x is the number of i Representing the position of the ith pixel point in the video image; k (-) represents a kernel function, and the closer the pixel point is to the moving target, the larger the value is; i | · | | represents a norm; | x i The | | represents the vector length between the ith pixel point in the video image and the target central pixel point in the moving target; f (x) i ) Representing the ith imageColor feature vectors of the pixel points in the video image; omega represents a preset feature vector; gamma [ f (x) i )-ω]Represents a pulse function, and when f (x) i ) When the value is consistent with omega, the value is 1; τ represents a color probability distribution of a candidate region in the captured video image;
a probability distribution representing color characteristics of the captured video image;
a second calculation unit to:
calculating the real-time position of the moving object in the monitoring area according to the following formula:
wherein eta represents the real-time position of the moving object in the monitoring area; rho represents an error factor, and the value range is (0.02,0.05);
representing the weight of the ith pixel point; | x i -y | | represents the vector length between the position of the ith pixel point and the target center pixel point in the previous frame of moving object; g (·) = -k' (·);
a video data acquisition unit for:
comparing a first position of a moving object in a current video frame image with a second position in a previous video frame image;
if the first position of a target central pixel point in a moving target in a current video frame image is different from the second position of the target central pixel point in a previous video frame image, judging that the position of the moving target in a monitoring area is changed, and performing video image acquisition on the moving target at the current position;
meanwhile, the real-time position of a target center pixel point in a moving target in the current frame image is used as the target center of the next frame video image, iteration is carried out, and the tracking of the moving target is completed;
otherwise, judging that the position of the moving target in the monitoring area is not changed, and ignoring the current video frame image until the moving target leaves the monitoring area.
Compared with the prior art, the invention has the following beneficial effects:
1. the building Internet of things monitoring system capable of realizing automatic adaptation can set positions above a certain level through the hierarchy information classification module to be monitored and checked, can effectively guarantee the safety of checking personnel, avoids the possibility that non-workers mistakenly read the monitoring, and optimizes the video checking picture in each area because the video acquisition terminals in each area are provided with the independent video processors.
2. The invention provides a building Internet of things monitoring system capable of realizing automatic adaptation, wherein a building monitoring unit is subdivided into each unit building and each floor, each elevator in each floor is monitored, the building monitoring unit is subdivided into fitness equipment in a public area, road conditions in the area, parking sheds on the ground, garden facilities and seats in the area, a parking lot monitoring unit monitors the vehicle entrance, the vehicle exit and the vehicle parking condition, in addition to monitoring of dynamic pictures in the building monitoring unit, the possibility of whether a fire condition occurs or not can be timely detected through a smoke processing module and a spray processing module, when a smoke alarm and the spray equipment are started, a monitoring terminal can give an alarm, when a worker receives the alarm, the worker can confirm which area is monitored abnormally through alarm information, check the alarm, the checking efficiency when the abnormal occurs is improved, and the accuracy of confirming the abnormal area is also improved.
3. The building Internet of things monitoring system capable of realizing automatic adaptation provided by the invention has the advantages that when the condition that naked fire or foreign matter is shielded in a video picture is detected, abnormal conditions can be classified intelligently or manually, double classification is more beneficial to later-stage alarm processing, the abnormal conditions in the data are further confirmed by the data checking module, the video acquisition terminal and the abnormal data in an abnormal area are subjected to case-recording storage on the internal network of the monitoring terminal after confirmation, and then the alarm processing is performed by the alarm module, so that the efficiency of processing the abnormal conditions can be improved more quickly.
4. The invention provides a building Internet of things monitoring system capable of realizing automatic adaptation, which is characterized in that a digital stereo model of a building is built, and simulation monitoring points are set in the digital stereo model, so that the reasonability of the settings of the monitoring points at different monitoring positions in the building is effectively confirmed, the monitoring effects of the different monitoring positions are guaranteed, meanwhile, a collection task issued by a management terminal is monitored in real time, and when the collection task is changed, the collection behaviors of the monitoring points are automatically adapted in time, the efficiency and the accuracy of adapting monitoring equipment are improved, the monitoring effect of the building is guaranteed, and the safety coefficient of the building is improved.
5. The building Internet of things monitoring system capable of realizing automatic adaptation provided by the invention tracks the moving target in the monitoring area in real time, and when the position of the moving target changes, the video image corresponding to the current position is adopted in time, so that the behavior and the action of the moving target in the monitoring area can be known in time, the building monitoring effect is improved, and the building safety is guaranteed.
Drawings
FIG. 1 is a schematic view of the overall process of the monitoring terminal according to the present invention;
FIG. 2 is a schematic diagram of a management terminal module according to the present invention;
FIG. 3 is a block diagram of a video surveillance system according to the present invention;
FIG. 4 is a schematic view of a video capture unit module according to the present invention;
FIG. 5 is a schematic view of a building monitoring unit module of the present invention;
FIG. 6 is a schematic view of a building monitoring unit module according to the present invention;
FIG. 7 is a schematic view of a parking lot monitoring unit module according to the present invention;
FIG. 8 is a block diagram of a data processing system according to the present invention;
fig. 9 is a schematic diagram of a grid-connected management system module according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the problem that in the prior art, in the management of building staff in an area, a worker is not restricted to perform management, so that the safety of checking in the background is not guaranteed, please refer to fig. 1 to 2, the following technical solutions are provided in this embodiment:
the utility model provides a can realize building thing networking monitored control system of automatic adaptation which characterized in that: the system comprises a management terminal, a video monitoring system, a data processing module, a grid-connected management system and a data recording and storing system; the management terminal is used for a worker to use a work account number, safely log in to a terminal intranet, enter the video monitoring system according to the grade of the work account number, and fail to enter the video monitoring system when the grade of the work account number is unqualified; the video monitoring system is used for acquiring actively uploaded video data from less than one video acquisition terminal, performing data packaging in a classified manner on the video data in a plurality of different areas, and sending the video data to the data processing module; the data processing module is used for analyzing the video data in different areas and sending the analyzed video abnormal data to the grid-connected management system; the grid-connected management system is used for establishing a one-way alarm connection line with internal networks such as an alarm mechanism, a medical mechanism and the like in a local area and performing intelligent alarm processing through analyzed abnormal data; and the data recording and storing system is used for establishing two groups of storage databases which are respectively an abnormal video data storage library and a normal video data storage library.
A management terminal, comprising: the personnel information input module is used for: the staff registers the information such as own name, job number, login password and the like; when a worker inputs information for the first time, more detailed personal information and positions need to be input, and the information needs to be approved by a superior level; a hierarchical information classification module to: based on position information input by staff in the staff information input module, positions are classified according to the level, and the permission for operating the next step only at a certain position can be set; a memory module of the type used to: and the classification module stores data of workers who can perform the next operation based on the hierarchical information.
Specifically, when building supervisory equipment in the region is to be checked, whether the checking personnel are the staff in the region is to be confirmed firstly, and simultaneously, the staff can not be monitored and checked by any staff, and the staff above a certain level can be set by the hierarchical information classification module to monitor and check, so that the safety of the checking personnel can be effectively guaranteed, and the possibility that non-staff mistakenly watches the monitoring is avoided.
In order to solve the problems in the prior art that, when video data is acquired, the quality of a dynamic picture is poor, the frame rate is incorrect, and the sharpness threshold of the dynamic picture cannot be processed, please refer to fig. 3, this embodiment provides the following technical solutions:
a video surveillance system comprising: a video capture unit to: the method comprises the steps that video data are obtained through at least one video acquisition terminal in different areas, and each video acquisition terminal in each area is provided with an independent video processor, wherein the interval of the acquired video data can be set as required under the condition that the interval of the acquired video data does not exceed the highest frame rate of monitoring; a video image processing unit to: processing the frame number of the acquired at least one video data; wherein, the processing mode comprises: video image analysis and analysis image processing; video image analysis for: constructing a convolutional neural network model, and performing dynamic picture analysis on the convolutional neural network model based on the acquired video to obtain the final MOS value of the acquired video and an image definition threshold value: analysis image processing for: determining a weight value of an MOS value and an image definition threshold value based on video image analysis, obtaining a final quality score of the collected video based on the weight value, and processing the video data again when the quality score is smaller than a preset threshold value until the quality score is larger than or equal to the preset threshold value.
Specifically, the video acquisition unit acquires video data of video acquisition terminals in the areas, each video acquisition terminal in each area is provided with an independent video processor, the image quality of video viewing in each area is optimized, and the video image processing unit analyzes and analyzes video images of video images in each video processor.
In order to solve the problems that in the prior art, when video acquisition is performed, video acquisition data is incomplete, and only video acquisition is performed singly, and the situation cannot be known in time when a fire or an accident occurs in a certain area, please refer to fig. 4 to 7, the embodiment provides the following technical solutions:
a video capture unit comprising: a building monitoring unit for: taking a video acquisition terminal in each unit building as a unit of a group, and acquiring videos in the unit building by taking a layer as a unit; a building monitoring unit for: taking video acquisition of each building as a unit of a group, and carrying out video acquisition on other directions between buildings and in an area; a parking lot monitoring unit; for: and carrying out video acquisition on the ground parking lot and the underground parking lot area.
Building monitoring unit includes: unit building fire control monitoring module for: monitoring fire fighting equipment and fire fighting conditions in a public area of each floor in each unit building; wherein the fire fighting equipment data includes: the smoke processing module and the spraying processing module can send abnormal data to the next instruction when all or single received data of the smoke processing module and the spraying processing module are abnormal; an elevator monitoring module to: monitoring the interior of each elevator in each unit building; the unit building entrance guard monitoring module is used for: carrying out video monitoring on an entrance of each unit building; wherein, entering the unit building mode includes: infrared induction module and entrance guard's card processing module.
A building monitoring unit comprising: a utility monitoring module for: public facilities (fitness equipment, ground carports, road seats and the like) in the area are monitored through a video acquisition terminal; the regional road monitoring module is used for: and monitoring all roads in the area through the video acquisition terminal.
Parking lot monitoring unit, including: vehicle import & export monitoring module for: monitoring an inlet and an outlet of a parking lot in the area through a video acquisition terminal; a vehicle parking monitoring module for: and the video acquisition terminal monitors the running or parking condition of the vehicle in the parking lot.
Specifically, the building monitoring unit and the parking lot monitoring unit can monitor and collect all areas in the area in an all-round mode, the building monitoring unit is subdivided into each unit building and each floor, monitoring of each elevator in each floor is achieved, the building monitoring unit is subdivided into fitness equipment in a public area, monitoring of road conditions in the area, parking sheds on the ground and garden facilities and seats in the area, monitoring of vehicle entrance positions, vehicle exit positions and vehicle parking conditions is achieved through the parking lot monitoring unit, monitoring of dynamic pictures is achieved in the building monitoring unit, possibility of whether fire occurs or not can be timely detected through the smoke processing module and the spraying processing module, when the smoke alarm and the spraying equipment are started, the monitoring module can give an alarm, when the received alarm is given, a worker can recognize that the monitoring in which area is abnormal through alarm information, checking can be achieved, checking efficiency when the abnormal occurs is improved, and accuracy of confirming the abnormal areas is also improved.
In order to solve the problem that, in the prior art, after a video is captured, only a single check is performed on video information, and an abnormal judgment cannot be made on a dynamic picture in the video, please refer to fig. 8, the embodiment provides the following technical scheme:
a data processing module comprising: a data receiving module to: receiving the video transmitted by the video monitoring system based on the video; a data analysis module to: based on the video information received by the data receiving module, carrying out dynamic picture analysis on the video information; an abnormal data classification module to: based on the result of the analysis of the dynamic picture by the data analysis module, carrying out abnormity detection on abnormal pictures in the dynamic picture, carrying out grade classification on the detected abnormal data, and packaging the abnormal data into a plurality of sub-data sets according to the classification; the abnormality detection is carried out by judging and detecting whether the picture is abnormal or not and analyzing and monitoring data provided by a video monitoring system; a class two storage module to: and storing the data set based on the abnormal data classification module.
Specifically, the data analysis module receives the video data and then carries out abnormity analysis on dynamic pictures in the video, when the condition that open fire or foreign matters are shielded in the video pictures is detected, the abnormal conditions can be classified intelligently and can be also carried out by artificial human beings, and the double classification is more beneficial to the alarm processing in the later period.
In order to solve the problem in the prior art that when abnormal data in a video is detected, no further decision can be made, and thus the abnormality in a region cannot be properly solved, please refer to fig. 9, the embodiment provides the following technical solutions:
a grid-tie management system comprising: a classified data receiving module for: receiving data according to the abnormal data transmitted by the data processing module; a data verification module to: based on the received data of the classified data receiving module, further checking the received data, wherein the checking step is accurate to one or more video acquisition terminals in a certain area; a docketing module for: storing and recording the checked video data based on the checking result of the data checking module; an alert module to: and based on the video data in the filing module, performing one-way alarm connection on internal networks such as alarm mechanisms, medical mechanisms and the like in the local area through communication equipment, and performing alarm processing on the internal networks.
Specifically, the abnormal condition in the data is further confirmed through the data checking module, the video acquisition terminal and the abnormal data in the abnormal area are subjected to the intranet of the monitoring terminal for record storage after the abnormal condition is confirmed, and then the alarm module is used for carrying out alarm processing on the abnormal data, so that the abnormal processing efficiency can be improved more quickly.
This embodiment provides, video acquisition unit includes:
a data acquisition unit for:
acquiring a building plan and a three-dimensional stereo of a building, inputting the building plan and the three-dimensional stereo into a convolutional neural network for training, and respectively obtaining a first characteristic diagram and a second characteristic diagram corresponding to the building plan and the three-dimensional stereo;
a model construction unit for:
constructing a digital three-dimensional model corresponding to the building based on the first characteristic diagram and the second characteristic diagram, and acquiring user entrance information and building environment information in the building based on a management terminal;
marking positions of different enterprises in the digital three-dimensional model based on the user parking information and the building environment information, and setting simulation monitoring points in the digital three-dimensional model based on a marking result to obtain a target point position table, wherein each monitoring position is at least provided with two monitoring points;
a monitor point adaptation unit to:
determining acquisition tasks of different simulation monitoring points in a target point position table based on the user parking information, and constructing a physical topological network between the simulation monitoring points and a management terminal based on the acquisition tasks;
setting a communication node between the simulation monitoring point and the management terminal based on the physical topology network, and issuing the acquisition task to the corresponding simulation monitoring point based on the communication node;
acquiring a first acquisition behavior characteristic and a second acquisition behavior characteristic of building information of different simulation monitoring points in the same monitoring position based on the issued result, and determining the association degree of the first acquisition behavior characteristic and the second acquisition behavior characteristic;
when the correlation degree reaches a preset threshold value, judging that the setting of the simulation monitoring points in the monitoring position is qualified, and setting the monitoring points based on the same positions of the distribution positions of the simulation monitoring points in the digital three-dimensional model in the actual building;
meanwhile, monitoring whether the collection task issued by the management terminal is changed or not in real time, determining a monitoring adjustment strategy when the collection task is changed, and automatically adapting the monitoring point based on the monitoring adjustment strategy;
otherwise, judging that the setting of the simulation monitoring points in the monitoring positions is unqualified, adjusting the simulation monitoring points until the correlation degree reaches a preset threshold value, and finishing the setting of the monitoring points in different monitoring positions in the building.
In this embodiment, the first profile may be a graph in the building plane that characterizes the building distribution.
In this embodiment, the second characteristic map may be a map of the three-dimensional perspective view that characterizes the building structure.
In this embodiment, the digitized stereo model may be a model constructed from building characteristics for determining the location of the monitoring points.
In this embodiment, the user parking information may be the type of the user parked and the floor where a different user is parked, etc.
In this embodiment, the building environment information may be the location of the building, the conditions around the floors in the building, and the like.
In this embodiment, the analog monitoring point may be a monitoring point set in the digital stereo model according to the user parking information and the building environment information, so as to facilitate determining a suitable monitoring point position.
In this embodiment, the target point location table is used to record the location information of the monitoring points at different monitoring locations in the building.
In this embodiment, the collection task may be a type of monitoring data that needs to be collected by the monitoring point, and specifically may be a face image of a person, a person entering or exiting condition, and the like.
In this embodiment, the physical topology network may be a distributed control link between the management terminal and different monitoring points, which is constructed according to the collection tasks of the different monitoring points, and may issue corresponding collection instructions according to the types of the collection tasks of the different monitoring points, so as to implement effective collection of the monitoring data.
In this embodiment, the first collection behavior feature may be a collection task performed by one of the monitoring devices in the same monitoring location.
In this embodiment, the second collection behavior feature may be a collection task performed by another monitoring device in the same monitoring location.
In this embodiment, the degree of association is a degree for characterizing whether the first acquisition behavior feature and the second acquisition behavior feature are the same.
In this embodiment, the preset threshold is set in advance, and is used to measure whether the correlation degree between the first acquisition behavior feature and the second acquisition behavior feature meets the minimum requirement of consistency.
In this embodiment, the monitoring adjustment policy may adjust the type of the collected data of the monitoring point according to different collection tasks, so as to complete automatic adaptation.
The working principle of the technical scheme is as follows: the method comprises the steps of analyzing a building plan view and a three-dimensional stereo view of a building, accurately and effectively constructing a digital stereo model corresponding to the building, marking user conditions of different positions in the building according to user entrance information and environment information of the building, setting corresponding simulation monitoring points according to marking results, determining physical topological relations between the different simulation monitoring points and a management terminal, accurately and effectively setting communication nodes, analyzing collected behavior characteristics of the different monitoring points in the same monitoring position, effectively verifying the positions of the monitoring points by the Western Ann, monitoring a collection task in real time, and automatically adapting the collected behaviors of the different monitoring points in time when the collection task changes.
The beneficial effects of the above technical scheme are: through the digital three-dimensional model who founds the building to set for the simulation monitoring point in the digital three-dimensional model, the realization is effectively confirmed the rationality that different monitoring position monitoring points set up in the building, the monitoring effect to different monitoring positions has been ensured, and simultaneously, the collection task of issuing management terminal carries out real-time supervision, and when the collection task changes, in time carry out automatic adaptation to the collection action of monitoring point, the efficiency and the degree of accuracy to the supervisory equipment adaptation have been improved, the monitoring effect to the building has been ensured, the factor of safety of building has been improved.
The present embodiment provides a video monitoring system, including:
a moving object tracking unit for:
the method comprises the following steps of obtaining video data collected by a video collecting terminal, analyzing the video data, judging whether a moving target exists in a current monitoring area, and controlling the video collecting terminal to track the real-time position of the moving target when the moving target exists, wherein the specific steps comprise:
a first calculation unit to:
calculating the weight of each pixel point in the video image collected by the video collecting terminal according to the following formula:
wherein the content of the first and second substances,
representing the weight of each pixel point in the collected video image; alpha represents a normalized constant and has a value range of (0,1); i represents the number of the current pixel points in the collected video image and the value range is [1,n ]](ii) a n represents the total number of the current pixel points in the collected video image; x is a radical of a fluorine atom i Representing the position of the ith pixel point in the video image; k (·) represents a kernel function, and the closer the pixel point is to the moving target, the larger the value is; | | · | | represents a norm; | x i The | | represents the vector length between the ith pixel point in the video image and the target central pixel point in the moving target; f (x) i ) Representing the color characteristic vector of the ith pixel point in the video image; omega represents a preset feature vector; gamma [ f (x) i )-ω]Represents a pulse function, and when f (x) i ) When the value is consistent with omega, the value is 1; τ represents a color probability distribution of a candidate region in the captured video image;
a probability distribution representing color characteristics of the captured video image;
a second calculation unit to:
calculating the real-time position of the moving object in the monitoring area according to the following formula:
wherein eta represents the real-time position of the moving target in the monitoring area; rho represents an error factor, and the value range is (0.02,0.05);
representing the weight of the ith pixel point; | x i -y | | represents the vector length between the position of the ith pixel point and the target center pixel point in the previous frame of moving object; g (·) = -k' (·);
a video data acquisition unit for:
comparing a first position of a moving object in a current video frame image with a second position in a last frame video image;
if the first position of a target central pixel point in a moving target in the current video frame image is different from the second position of the target central pixel point in the previous frame video image, judging that the position of the moving target in the monitoring area is changed, and carrying out video image acquisition on the moving target at the current position;
meanwhile, the real-time position of a target center pixel point in a moving target in the current frame image is used as the target center of the next frame video image, iteration is carried out, and the tracking of the moving target is completed;
otherwise, judging that the position of the moving target in the monitoring area is not changed, and ignoring the current video frame image until the moving target leaves the monitoring area.
In this embodiment, the moving object may be a person or object or the like that moves within the monitored area.
In this embodiment, the kernel support vector machine maps the input space to the high-dimensional feature space through some non-linear transformation.
In this embodiment, the target center pixel point in the moving target may be the center of the pixel point in the video image of the moving target.
In this embodiment, the color feature vector may represent the moving object and the situation that the unique colors of different pixel points in the background image are in the video image.
In this embodiment, the preset feature vector is set in advance, and the set may be a distribution of colors in the background image, which is not changed.
In this embodiment, the first position refers to a position of a target center pixel point of the moving object in the current video frame image.
In this embodiment, the second position refers to a position of a target center pixel point of the moving object in the previous video frame image.
In this embodiment, the target center may be a reference for tracking, when the position of the target center pixel point of the moving target in the adjacent video frame image changes, the position of the target center pixel point of the moving target in the current video frame image.
The working principle of the technical scheme is as follows: the method comprises the steps of calculating the weight of each pixel point in a video image collected by a video collection terminal, calculating the real-time position of a fish-hole target in a monitoring area according to the calculated weight of each pixel point, iterating the real-time position, tracking a moving target in the monitoring area, and timely taking the video image of the current position of the moving target when the position of the moving target changes.
The beneficial effects of the above technical scheme are: by tracking the moving target in the monitoring area in real time and taking the video image corresponding to the current position in time when the position of the moving target changes, the behavior of the moving target in the monitoring area can be known conveniently and timely, so that the monitoring effect on the building is improved, and the safety of the building is guaranteed.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The utility model provides a can realize building thing networking monitored control system of automatic adaptation which characterized in that: the system comprises a management terminal, a video monitoring system, a data processing module, a grid-connected management system and a data recording and storing system;
the management terminal is used for a worker to use a work account number, safely log in to a terminal intranet, enter the video monitoring system according to the grade of the work account number, and the video monitoring system cannot enter the terminal intranet when the grade of the work account number is unqualified;
the video monitoring system is used for acquiring actively uploaded video data from less than one video acquisition terminal, performing data packaging in a classified manner on the video data in a plurality of different areas, and sending the video data to the data processing module;
the data processing module is used for analyzing the video data in different areas and sending the analyzed video abnormal data to the grid-connected management system;
the grid-connected management system is used for establishing a one-way alarm connection with internal networks such as alarm mechanisms, medical institutions and the like in a local area and carrying out intelligent alarm processing through analyzed abnormal data;
and the data recording and storing system is used for establishing two groups of storage databases, namely an abnormal video data storage library and a normal video data storage library.
2. The building internet of things monitoring system capable of achieving automatic adaptation according to claim 1, wherein the management terminal comprises:
the personnel information input module is used for:
the staff registers own name, job number and login password;
when a worker inputs information for the first time, more detailed personal information and positions need to be input, and the information needs to be approved by a superior;
a hierarchical information classification module to:
classifying the positions according to the levels of the grades based on the position information input by the staff in the staff information input module, and setting the permission that the next step of operation is performed only at a certain position;
a memory module of the type used to:
and the classification module stores data of workers who perform the next operation based on the hierarchical information.
3. The building internet of things monitoring system capable of achieving automatic adaptation according to claim 1, wherein: a video surveillance system comprising:
a video capture unit to:
the method comprises the steps that video data are obtained through at least one video acquisition terminal in different areas, and each video acquisition terminal in each area is provided with an independent video processor, wherein the interval of the acquired video data is set as required under the condition that the interval of the acquired video data does not exceed the highest frame rate of monitoring;
a video image processing unit for:
processing the frame number of the acquired at least one video data;
wherein, the processing mode comprises: video image analysis and analysis image processing;
video image analysis for:
constructing a convolutional neural network model, and performing dynamic picture analysis on the convolutional neural network model based on the acquired video to obtain the final MOS value of the acquired video and an image definition threshold value:
analysis image processing for:
determining a weight value of an MOS value and an image definition threshold based on video image analysis, obtaining a final quality score of the collected video based on the weight value, and processing the video data again when the quality score is smaller than a preset threshold until the quality score is larger than or equal to the preset threshold.
4. The building internet of things monitoring system capable of achieving automatic adaptation according to claim 3, wherein: a video capture unit comprising:
a data acquisition unit to:
acquiring a building plan and a three-dimensional stereo of a building, inputting the building plan and the three-dimensional stereo into a convolutional neural network for training, and respectively obtaining a first characteristic diagram and a second characteristic diagram corresponding to the building plan and the three-dimensional stereo;
a model construction unit for:
constructing a digital three-dimensional model corresponding to the building based on the first characteristic diagram and the second characteristic diagram, and acquiring user entrance information and building environment information in the building based on a management terminal;
marking positions of different enterprises in the digital three-dimensional model based on the user parking information and the building environment information, and setting simulation monitoring points in the digital three-dimensional model based on marking results to obtain a target point position table, wherein each monitoring position is provided with at least two monitoring points;
a monitor point adaptation unit to:
determining acquisition tasks of different simulation monitoring points in a target point position table based on the user parking information, and constructing a physical topological network between the simulation monitoring points and a management terminal based on the acquisition tasks;
setting a communication node between the simulation monitoring point and the management terminal based on the physical topology network, and issuing the acquisition task to the corresponding simulation monitoring point based on the communication node;
acquiring a first acquisition behavior characteristic and a second acquisition behavior characteristic of building information of different simulation monitoring points in the same monitoring position based on the issued result, and determining the association degree of the first acquisition behavior characteristic and the second acquisition behavior characteristic;
when the correlation degree reaches a preset threshold value, judging that the setting of the simulation monitoring points in the monitoring position is qualified, and setting the monitoring points based on the same positions of the distribution positions of the simulation monitoring points in the digital three-dimensional model in the actual building;
meanwhile, monitoring whether an acquisition task issued by the management terminal is changed or not in real time, determining a monitoring adjustment strategy when the acquisition task is changed, and automatically adapting the monitoring point based on the monitoring adjustment strategy;
otherwise, judging that the setting of the simulation monitoring points in the monitoring positions is unqualified, adjusting the simulation monitoring points until the correlation degree reaches a preset threshold value, and finishing the setting of the monitoring points in different monitoring positions in the building.
5. The building internet of things monitoring system capable of realizing automatic adaptation as claimed in claim 3, characterized in that: a video capture unit comprising:
the building monitoring unit is used for:
taking a video acquisition terminal in each unit building as a unit of a group, and acquiring videos in the unit building by taking a layer as a unit;
a building monitoring unit for:
taking video acquisition of each building as a unit of a group, and carrying out video acquisition on other directions between buildings and in an area;
a parking lot monitoring unit; for:
and carrying out video acquisition on the ground parking lot and the underground parking lot.
6. The building internet of things monitoring system capable of achieving automatic adaptation according to claim 5, wherein: building monitoring unit includes:
unit building fire control monitoring module for:
monitoring fire fighting equipment and fire fighting conditions in a public area of each floor in each unit building;
wherein the fire fighting equipment data includes: the smoke processing module and the spraying processing module can send abnormal data to the next instruction when all or single received data of the smoke processing module and the spraying processing module are abnormal;
an elevator monitoring module for:
monitoring the interior of each elevator in each unit building;
the unit building entrance guard monitoring module is used for:
carrying out video monitoring on an entrance of each unit building;
wherein, entering the unit building mode includes: infrared induction module and entrance guard's card processing module.
7. The building internet of things monitoring system capable of achieving automatic adaptation according to claim 5, wherein: a building monitoring unit comprising:
a utility monitoring module for:
monitoring public facilities in the area through a video acquisition terminal;
the regional road monitoring module is used for:
and monitoring all roads in the area through the video acquisition terminal.
8. The building internet of things monitoring system capable of achieving automatic adaptation according to claim 1, wherein: parking lot monitoring unit, including:
vehicle import & export monitoring module for:
monitoring an inlet and an outlet of a parking lot in the area through a video acquisition terminal;
a vehicle parking monitoring module for:
and the video acquisition terminal monitors the running or parking condition of the vehicle in the parking lot.
9. The building internet of things monitoring system capable of achieving automatic adaptation according to claim 1, wherein: a data processing module comprising:
a data receiving module to:
receiving the video transmitted by the video monitoring system based on the video;
a data analysis module to:
based on the video information received by the data receiving module, carrying out dynamic picture analysis on the video information;
an abnormal data classification module to:
based on the result of the dynamic picture analysis by the data analysis module, carrying out abnormity detection on abnormal pictures in the dynamic picture, carrying out grade classification on the detected abnormal data, and packaging the abnormal data into a plurality of sub-data sets according to the classification;
the abnormality detection is carried out by judging and detecting whether the picture is abnormal or not and analyzing and monitoring data provided by a video monitoring system;
a class two storage module to:
storing the data set based on the abnormal data classification module;
a grid-tie management system comprising:
a classified data receiving module for:
receiving data according to the abnormal data transmitted by the data processing module;
a data verification module to:
based on the received data of the classified data receiving module, further checking the received data, wherein the checking step is accurate to one or more video acquisition terminals in a certain area;
a docketing module for:
storing and recording the checked video data based on the checking result of the data checking module;
an alert module to:
and on the basis of the video data in the filing module, performing one-way alarm connection on an alarm mechanism and a medical mechanism in the local area through communication equipment, and performing alarm processing on the alarm mechanism and the medical mechanism.
10. The building internet of things monitoring system capable of realizing automatic adaptation according to claim 1, characterized in that: a video surveillance system comprising:
a moving object tracking unit to:
the method comprises the following steps of obtaining video data collected by a video collecting terminal, analyzing the video data, judging whether a moving target exists in a current monitoring area, and controlling the video collecting terminal to track the real-time position of the moving target when the moving target exists, wherein the specific steps comprise:
a first calculation unit to:
calculating the weight of each pixel point in the video image collected by the video collecting terminal according to the following formula:
wherein the content of the first and second substances,
representing the weight of each pixel point in the collected video image; alpha represents a normalized constant and has a value range of (0,1); i represents the number of the current pixel points in the collected video image and the value range is [1,n ]](ii) a n represents collectionThe total number of current pixel points in the video image; x is the number of i Representing the position of the ith pixel point in the video image; k (·) represents a kernel function, and the closer the pixel point is to the moving target, the larger the value is; | | · | | represents a norm; | x i The | | represents the vector length between the ith pixel point in the video image and the target central pixel point in the moving target; f (x) i ) Representing the color characteristic vector of the ith pixel point in the video image; omega represents a preset feature vector; gamma [ f (x) i )-ω]Represents a pulse function, and when f (x) i ) When the value is consistent with omega, the value is 1; τ represents a color probability distribution of a candidate region in the captured video image;
a probability distribution representing a color characteristic of the captured video image;
a second calculation unit to:
calculating the real-time position of the moving object in the monitoring area according to the following formula:
wherein eta represents the real-time position of the moving target in the monitoring area; rho represents an error factor, and the value range is (0.02,0.05);
representing the weight of the ith pixel point; | x i -y | | represents the vector length between the position of the ith pixel point and the target center pixel point in the previous frame of moving object; g (·) = -k' (·);
a video data acquisition unit for:
comparing a first position of a moving object in a current video frame image with a second position in a previous video frame image;
if the first position of a target central pixel point in a moving target in a current video frame image is different from the second position of the target central pixel point in a previous video frame image, judging that the position of the moving target in a monitoring area is changed, and performing video image acquisition on the moving target at the current position;
meanwhile, the real-time position of a target center pixel point in a moving target in the current frame image is used as the target center of the next frame video image, iteration is carried out, and the tracking of the moving target is completed;
otherwise, judging that the position of the moving target in the monitoring area is not changed, and ignoring the current video frame image until the moving target leaves the monitoring area.
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