CN114495270A - Real-time monitoring and early warning method, device and system and storage medium - Google Patents
Real-time monitoring and early warning method, device and system and storage medium Download PDFInfo
- Publication number
- CN114495270A CN114495270A CN202210047356.5A CN202210047356A CN114495270A CN 114495270 A CN114495270 A CN 114495270A CN 202210047356 A CN202210047356 A CN 202210047356A CN 114495270 A CN114495270 A CN 114495270A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- real
- time
- early warning
- image data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 209
- 238000000034 method Methods 0.000 title claims abstract description 48
- 230000002159 abnormal effect Effects 0.000 claims abstract description 31
- 238000004891 communication Methods 0.000 claims description 25
- 238000012545 processing Methods 0.000 claims description 13
- 238000012790 confirmation Methods 0.000 claims description 10
- 230000000474 nursing effect Effects 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 9
- 238000007635 classification algorithm Methods 0.000 claims description 8
- 238000003062 neural network model Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims 1
- 238000013527 convolutional neural network Methods 0.000 description 9
- 230000015654 memory Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 230000005856 abnormality Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000306 recurrent effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses a real-time monitoring and early warning method, a device, a system and a storage medium. The method comprises the following steps: collecting life image data of a target person in real time; constructing a monitoring model of a monitoring target person according to the life image data; monitoring target personnel in a preset space environment in real time according to the monitoring model to obtain real-time monitoring information; acquiring a real-time monitoring time period when no target person appears in the real-time monitoring information; and if the certain real-time monitoring time period exceeds a preset time period threshold value, generating abnormal monitoring information, sending the abnormal monitoring information to the mobile terminal equipment and carrying out early warning. The embodiment of the application can monitor the target personnel in real time through the shooting terminal, send abnormal conditions to the mobile terminal equipment and perform early warning so as to prompt a user of the mobile equipment to check the conditions of the target personnel in time.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a real-time monitoring and early warning method, device and system and a storage medium.
Background
The intelligent video monitoring is a branch of the application of computer vision technology in the security field.
With the trend of aging of the population in China becoming more and more obvious, the old-age care becomes one of the social focus problems, most of the old people choose to care for the old at home due to the influence of reasons such as economy, conception, medical shortage and the like, children can take care of parents after going on duty, and most of the old people live at home when going on duty, certain hidden dangers still exist, and the cost of the nursing staff is high.
In the related art, a shooting terminal is usually placed at a door of a home to monitor people in a home picture, but the situation of people such as old people and children at home can be known only when watching video playback or looking at a monitoring video picture all the time. Therefore, when the old or children at home are in abnormal conditions, they cannot be treated in time.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a real-time monitoring and early warning method, device, system and storage medium, which can monitor a target person in real time through a shooting terminal, send an abnormal condition to a mobile terminal device and perform early warning.
According to a first aspect embodiment of the present invention, a real-time monitoring and early warning method includes:
collecting life image data of a target person in real time;
constructing a monitoring model of a monitoring target person according to the life image data;
monitoring target personnel in a preset space environment in real time according to the monitoring model to obtain real-time monitoring information;
acquiring a real-time monitoring time period when no target person appears in the real-time monitoring information;
and if the certain real-time monitoring time period exceeds a preset time period threshold value, generating abnormal monitoring information, sending the abnormal monitoring information to the mobile terminal equipment and carrying out early warning.
The real-time monitoring and early warning method provided by the embodiment of the invention at least has the following beneficial effects: collecting life image data of a target person in real time; constructing a monitoring model of a monitoring target person according to the life image data; monitoring target personnel in a preset space environment in real time according to the monitoring model to obtain real-time monitoring information; acquiring a real-time monitoring time period when no target person appears in the real-time monitoring information; and if the certain real-time monitoring time period exceeds a preset time period threshold value, generating abnormal monitoring information, sending the abnormal monitoring information to the mobile terminal equipment and carrying out early warning. Therefore, the target personnel are monitored in real time through the shooting terminal, abnormal conditions are sent to the mobile terminal equipment, and early warning is carried out, so that a user of the mobile equipment is prompted to check the conditions of the target personnel in time.
According to some embodiments of the present application, after sending the anomaly monitoring information to the mobile terminal device and performing early warning, the method further includes:
sending the confirmation information to the mobile terminal equipment so that the mobile terminal equipment can communicate with the target terminal equipment of the target personnel according to the confirmation information;
and receiving feedback information sent by the mobile terminal device according to the communication condition with the target terminal device.
According to some embodiments of the present application, after receiving feedback information sent by the mobile terminal device according to a communication situation with the target terminal device, the method further includes:
relieving the early warning for normal communication according to the feedback information;
and acquiring prestored feedback medical care rescue information for communication abnormity according to the feedback information, and sending the feedback medical care rescue information to the mobile terminal equipment.
According to some embodiments of the present application, acquiring live image data of a target person in real time includes:
receiving a nursing mode starting instruction, and entering a preset monitoring mode according to the nursing mode starting instruction;
and monitoring the living image data of the target person in a preset area in real time according to the monitoring mode, wherein the preset area is the area where the target person is located.
According to some embodiments of the present application, a monitoring model for monitoring a target person is constructed according to life image data, including:
identifying and processing the life image data to obtain human shape and posture data of target personnel;
inputting the human-shaped posture data into a preset neural network model for learning, and outputting monitoring parameters;
and constructing a monitoring model of the monitoring target personnel according to the monitoring parameters and the life image data.
According to some embodiments of the present application, the recognizing the living image data to obtain the human-shaped posture data of the target person comprises:
carrying out human shape recognition processing on the life image data to obtain human shape key characteristic data;
and preprocessing the human shape key characteristic data to obtain human shape posture data corresponding to the target person.
According to some embodiments of the present application, a human shape recognition process is performed on the living image data to obtain human shape key feature data, including:
classifying the life image data by using a preset classification algorithm to obtain a plurality of different candidate frame data;
and screening the data of the candidate frames to screen out the human-shaped key characteristic data.
According to the embodiment of the second aspect of the application, a real-time monitoring and early warning device comprises:
the acquisition module is used for acquiring living image data of a target person in real time;
the building module is used for building a monitoring model of a monitoring target person according to the life image data;
the monitoring module is used for monitoring target personnel in a preset space environment in real time according to the monitoring model to obtain real-time monitoring information;
the acquisition module is used for acquiring a real-time monitoring time period when no target person appears in the real-time monitoring information;
and the early warning module is used for generating abnormal monitoring information if a certain real-time monitoring time period exceeds a preset time period threshold value, and sending the abnormal monitoring information to the mobile terminal equipment for early warning.
The real-time monitoring and early warning device provided by the embodiment of the invention at least has the following beneficial effects: collecting life image data of a target person in real time; constructing a monitoring model of a monitoring target person according to the life image data; monitoring target personnel in a preset space environment in real time according to the monitoring model to obtain real-time monitoring information; acquiring a real-time monitoring time period when no target person appears in the real-time monitoring information; and if the certain real-time monitoring time period exceeds a preset time period threshold value, generating abnormal monitoring information, sending the abnormal monitoring information to the mobile terminal equipment and carrying out early warning. Therefore, the target personnel can be monitored in real time through the shooting terminal, abnormal conditions are sent to the mobile terminal equipment, early warning is carried out, and a user of the mobile equipment is prompted to check the conditions of the target personnel in time.
According to the third aspect embodiment of this application, a real-time monitoring and early warning system includes:
a real-time monitoring and early warning device;
shooting a terminal;
a real-time monitoring and early warning device executes:
the invention relates to a real-time monitoring and early warning method.
The storage medium according to the fourth aspect of the present application stores executable instructions, which can be executed by a computer, so that the computer executes a real-time monitoring and early warning method according to the first aspect of the present invention.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The invention is further described with reference to the following figures and examples, in which:
fig. 1 is a first detailed flowchart of a real-time monitoring and early warning method provided by the present invention;
fig. 2 is a second detailed flowchart of the real-time monitoring and early warning method before step S500 in fig. 1;
fig. 3 is a third detailed flowchart illustrating a real-time monitoring and early warning method implemented after step S700 in fig. 2;
FIG. 4 is a flowchart illustrating a specific process of step S100 in FIG. 1;
FIG. 5 is a flowchart illustrating a specific process of step S200 in FIG. 1;
FIG. 6 is a flowchart illustrating a specific process of step S210 in FIG. 5;
fig. 7 is a specific flowchart of step S211 in fig. 6.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present numbers, and the above, below, within, etc. are understood as including the present numbers. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
In the description of the present invention, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
First, several terms referred to in the present application are resolved:
shooting a terminal: also called as a CAMERA (CAMERA or WEBCAM), also called as a computer CAMERA, a computer eye, an electronic eye, etc., is a video input device, and is widely applied to video conferencing, telemedicine, real-time monitoring, etc. Common people can also have image and voice conversations and communications with each other through the camera in the network. In addition, people can also use the method for various popular digital images, video and audio processing and the like.
Monitoring the model: in the embodiment of the application, a data model of a monitoring target person is constructed through a neural network model and life image data.
A mobile terminal device: in the embodiments of the present application, the terminal device is a computer, a smart phone, a tablet, or the like that can perform communication.
CNN: convolutional Neural Networks, CNN for short, are a kind of feedforward Neural network.
RNN: recurrent Neural Networks, RNN for short, i.e. the current output of a sequence is also related to the previous output. The concrete expression is that the network will memorize the previous information and apply it to the calculation of the current output, i.e. the input of the hidden layer includes not only the output of the input layer but also the output of the hidden layer at the last moment.
R-CNN network: the Region-based probabilistic Neural Networks or Regions with CNN features, R-CNN for short, is an algorithm combining a candidate Region algorithm SelectSearch and a Convolutional Neural network, and obviously improves the detection speed and precision.
FastRCNN network: the fast-forward target detection method is a two-stage target detection method, FastRCNN is an improvement on R _ CNN, and is the best method for detecting targets based on deep learning R-CNN series.
The embodiments of the present disclosure provide a real-time monitoring and early-warning method, device, system, and storage medium, which are described in detail with reference to the following embodiments, first, a real-time monitoring and early-warning method in the embodiments of the present disclosure is described.
As shown in fig. 1, which is a schematic view of an implementation flow of a real-time monitoring and early-warning method provided in the embodiment of the present application, a real-time monitoring and early-warning method may include, but is not limited to, steps S100 to S500.
S100, collecting life image data of a target person in real time;
s200, constructing a monitoring model of a monitoring target person according to the life image data;
s300, monitoring target personnel in a preset space environment in real time according to the monitoring model to obtain real-time monitoring information;
s400, acquiring a real-time monitoring time period in which the target person does not appear in the real-time monitoring information;
s500, if a certain real-time monitoring time period exceeds a preset time period threshold value, generating abnormal monitoring information, sending the abnormal monitoring information to the mobile terminal equipment, and early warning.
In step S100 of some embodiments, the living image data of the target person is collected in real time, and it is understood that the target person may be a family elder, a child, a family patient, and the like. The life image data is image data of regular life of target people at home shot by the shooting terminal, for example, the old people at home can go out of a bedroom door on time and enter a bathroom door when the old people at home are in 9 am or enter a kitchen for lunch when the old people at home are in 11 am, and the life image data of the target people in the preset space environment is collected in real time through the shooting terminal.
In step S200 of some embodiments, a monitoring model of a monitoring target person is constructed according to the life image data, and the specific implementation steps may be to identify and process the life image data to obtain human-shaped posture data of the target person, input the human-shaped posture data into a preset neural network model for learning, output monitoring parameters, and finally construct the monitoring model of the monitoring target person according to the monitoring parameters and the life image data, so as to monitor the target person in real time to find out a life rule of the target person.
In step S300 of some embodiments, the target person in the preset spatial environment is monitored in real time according to the monitoring model, so as to obtain real-time monitoring information. It can be understood that the preset space environment, for example, a shooting terminal is placed at a corner of a living room, so that all the space environments of the living room can be shot, when people appear in the living room, people can recognize the shape of the people and judge whether a target person exists, and the situation that the guest visits home and is mistakenly considered as the target person can be prevented, so that error early warning is performed. The shooting terminal is used for monitoring target personnel in the preset space environment in real time so as to obtain real-time monitoring information,
the position of the imaging terminal is not limited to a specific position, and the imaging terminal may be placed in a courtyard of a farm house or a corner of a living room. Therefore, the shooting terminal can be placed differently according to different specific live-action scenes.
In step S400 of some embodiments, a real-time monitoring time period during which the target person does not appear in the real-time monitoring information is acquired. It can be understood that a real-time video set shot by the shooting terminal is obtained, and a real-time monitoring time period in which the target person is not monitored in real-time monitoring of the preset space environment is counted and analyzed.
In step S500 of some embodiments, if a certain real-time monitoring time period exceeds a preset time period threshold, generating abnormal monitoring information, and sending the abnormal monitoring information to the mobile terminal device and performing early warning. It can be understood that the preset time period threshold is, for example, 1 hour, when a target person in the preset space environment is monitored in real time, the old at home generally appears in the monitoring area of the shooting terminal at about 9 hours, and the shooting terminal collects the life image data, but at 8 hours to 10 hours in a certain day, the picture of the old at home still does not appear in the real-time monitoring information, and then abnormal monitoring information is generated, and is sent to the mobile terminal device for early warning.
It should be noted that the mobile terminal device may be an intelligent mobile terminal device such as a mobile phone, a computer, a tablet, and the like.
Further, the early warning is that abnormal monitoring information is displayed on the mobile terminal device and the shooting terminal sends out an alarm early warning.
In some embodiments, referring to fig. 2, after performing step S500, a real-time monitoring and early warning method may further include, but is not limited to, steps S600 to S700.
S600, sending the confirmation information to the mobile terminal equipment so that the mobile terminal equipment can communicate with the target terminal equipment of the target personnel according to the confirmation information;
and S700, receiving feedback information sent by the mobile terminal device according to the communication condition with the target terminal device.
In step S600 of some embodiments, the confirmation information is sent to the mobile terminal device, so that the mobile terminal device communicates with the target terminal device of the target person according to the confirmation information. It can be understood that the confirmation information refers to information for confirming that the received abnormality monitoring information is confirmed by a family member holding the mobile terminal device to confirm whether an abnormality exists or whether an unexpected early warning is caused by other things, and the target terminal is an intelligent terminal device of the target person, such as a smart phone, a smart watch, and the like.
Further, the user corresponding to the mobile terminal device communicates with the target terminal device of the target person according to the confirmation information to determine whether the target person has abnormal things.
In step S700 of some embodiments, feedback information sent by the mobile terminal device according to the communication situation with the target terminal device is received. It can be understood that the shooting terminal receives feedback information of communication conditions of the family members and the target person in communication sent by the mobile terminal device.
It should be noted that the communication situation may be that the family members corresponding to the mobile terminal device successfully communicate with the target person corresponding to the target terminal device, and the safety of the target person is ensured without any abnormality; the family members corresponding to the mobile terminal device may not be able to successfully communicate with the target person corresponding to the target terminal device, and if the family members are not always in contact with the target person, it may be determined that the target person is abnormal.
The feedback information is information returned to the shooting terminal by the mobile terminal device according to the communication condition, and is used for removing the early warning or further giving an alarm.
In some embodiments, referring to fig. 3, after step S700 is performed, a real-time monitoring and early warning method may further include, but is not limited to, steps S800 to S900.
S800, relieving early warning for normal communication according to the feedback information;
and S900, acquiring prestored feedback medical care rescue information for communication abnormity according to the feedback information, and sending the feedback medical care rescue information to the mobile terminal equipment.
In step S800 of some embodiments, the warning is released for the communication according to the feedback information. It can be understood that if the shooting terminal receives the feedback information that the communication is normal, the early warning is released, including stopping the early warning sent by the shooting terminal.
In step S900 of some embodiments, pre-stored feedback medical care rescue information is acquired for the communication abnormality according to the feedback information, and the feedback medical care rescue information is sent to the mobile terminal device. It can be understood that if feedback information is communication anomaly, just acquire the feedback medical care rescue information of storage in shooting the terminal, this feedback medical care rescue information, the communication mode of the urgent contact person of family's place community is being taken notes, directly send this urgent contact way to mobile terminal equipment, can report to the police through mobile terminal equipment, also can directly send this anomaly information to the community personnel that the rescue information corresponds are doctorsed and nurses to the feedback, thereby can be timely to old man or children at home, patient etc. salvage in time, in order to avoid causing life danger because of delaying.
In some embodiments, as shown with reference to fig. 4, step S100 may also include, but is not limited to, steps S110 to S120.
S110, receiving a nursing mode starting instruction, and entering a preset monitoring mode according to the nursing mode starting instruction;
and S120, monitoring the living image data of the target person in a preset area in real time according to the monitoring mode, wherein the preset area is the area where the target person is located.
In step S110 of some embodiments, a care mode start instruction is received, and a preset monitoring mode is entered according to the care mode start instruction. It can be understood that, the shooting terminal receives the operation from the user, and the processor of the shooting terminal sends a nursing mode starting instruction, and then the shooting terminal enters the monitoring mode according to the nursing mode starting instruction.
It should be noted that the shooting terminal has a plurality of modes, each mode executes different functions, and each mode can be turned on and off at any time according to an operation command of a user.
In step S120 of some embodiments, the life image data of the target person in a preset area is monitored in real time according to the monitoring mode, where the preset area is an area where the target person is located. It can be understood that, after the shooting terminal enters the shooting mode, the living image data of the target person in the preset area is monitored in real time according to the monitoring mode, and it should be noted that the preset area is an area where the target person is usually located, such as a living room, a courtyard, and the like.
In some embodiments, as shown with reference to fig. 5, step S200 may also include, but is not limited to, steps S210-S230.
S210, identifying the living image data to obtain human shape and posture data of a target person;
s220, inputting the human-shaped posture data into a preset neural network model for learning, and outputting monitoring parameters;
and S230, constructing a monitoring model of the monitoring target personnel according to the monitoring parameters and the life image data.
In step S210 of some embodiments, the living image data is subjected to recognition processing, and human-shaped posture data of the target person is obtained. It is understood that the specific implementation steps can be as follows: the human shape recognition processing is firstly carried out on the living image data to obtain human shape key characteristic data, and then the human shape key characteristic data is preprocessed to obtain human shape posture data corresponding to the target person.
In step S220 of some embodiments, the human-shaped pose data is input into a preset neural network model for learning, and monitoring parameters are output. It is understood that the human-shaped pose data obtained in step S210 is input into a preset neural network model for machine learning, so as to output monitoring parameters.
It should be noted that the neural network model may be a preset deep learning model, and the implementation of the algorithm uses fast-RCNN to develop an algorithm library for a third party, which is not further described in the embodiment of the present application.
In step S230 of some embodiments, a monitoring model of the monitoring target person is constructed according to the monitoring parameters and the life image data. It can be understood that the monitoring model of the monitoring target person is constructed together with the monitoring parameters learned by the machine learning in the step S220 and the life image data, so as to monitor the target person according to the monitoring model, and the monitoring and early warning of the shooting terminal is more accurate.
In some embodiments, as shown with reference to fig. 6, step S210 may also include, but is not limited to, steps S211 to S212.
S211, carrying out human shape recognition processing on the living image data to obtain human shape key characteristic data;
s212, preprocessing the human shape key characteristic data to obtain human shape posture data corresponding to the target person.
In step S211 of some embodiments, a human shape recognition process is performed on the live image data to obtain human shape key feature data. It can be understood that the specific implementation steps may be that, a preset classification algorithm is used to classify the life image data to obtain a plurality of different candidate frame data, and then the plurality of candidate frame data are subjected to screening processing to screen out the human-shape key feature data.
In step S212 of some embodiments, the human-form key feature data is preprocessed to obtain human-form pose data corresponding to the target person. It is understood that the human form key feature data obtained through step S211 is preprocessed to obtain human form posture data corresponding to the target person. The preprocessing can be cleaning processing of the human shape key characteristic data, and the human shape attitude data can be selected according to the human shape attitude coordinate.
In some embodiments, as shown with reference to fig. 7, step S211 may also include, but is not limited to, steps S213 to S214.
S213, classifying the life image data by using a preset classification algorithm to obtain a plurality of different candidate frame data;
and S214, screening the data of the candidate frames to screen out the human-shaped key characteristic data.
In step S213 of some embodiments, the life image data is classified by using a preset classification algorithm, so as to obtain a plurality of different candidate frame data. It can be understood that the preset classification algorithm may be a face recognition classification algorithm, the living image data is classified according to the face recognition classification algorithm, and the photo with the human figure appearing in each frame of image is framed, so as to obtain a plurality of candidate frame data in different periods, and each candidate frame identifies the human face appearing in the picture, so as to observe the target person more clearly.
In step S214 of some embodiments, a screening process is performed on the plurality of candidate frame data to screen out human-shaped key feature data. It can be understood that, screening processing is performed on the plurality of candidate frame data according to preset screening conditions, that is, for example, the old and the children are distinguished, parents of the children, the children and the like are identified at present, corresponding human shape key feature data are respectively obtained, screening processing can also be performed according to gender, for example, screening is performed, the human shape key feature data of men and the human shape key feature data of women are used for enabling the recognition of the shooting terminal to be more accurate, and therefore the monitoring and early warning accuracy in the monitoring mode is improved.
In addition, this application embodiment still provides a real time monitoring early warning device, includes:
the acquisition module is used for acquiring living image data of a target person in real time;
the building module is used for building a monitoring model of a monitoring target person according to the life image data;
the monitoring module is used for monitoring target personnel in a preset space environment in real time according to the monitoring model to obtain real-time monitoring information;
the acquisition module is used for acquiring a real-time monitoring time period when no target person appears in the real-time monitoring information;
and the early warning module is used for generating abnormal monitoring information if a certain real-time monitoring time period exceeds a preset time period threshold value, and sending the abnormal monitoring information to the mobile terminal equipment for early warning.
According to the real-time monitoring and early warning device provided by the embodiment of the disclosure, through the implementation of the real-time monitoring and early warning method, the target person can be monitored in real time through the shooting terminal, and the abnormal condition is sent to the mobile terminal device for early warning, so that a user of the mobile device is prompted to check the condition of the target person in time.
The specific implementation of the real-time monitoring and early warning apparatus in this embodiment is substantially the same as the specific implementation of the real-time monitoring and early warning method, and is not described herein again.
In addition, this application embodiment still provides a real time monitoring early warning system, includes:
a real-time monitoring and early warning device;
shooting a terminal;
a real-time monitoring and early warning device executes:
the invention relates to a real-time monitoring and early warning method.
In addition, the embodiment of the present invention further provides a storage medium storing executable instructions, where the executable instructions can be executed by a computer, so that the computer executes the real-time monitoring and early warning method according to the first aspect of the present invention.
The memory, as a non-transitory storage medium, may be used to store non-transitory software programs as well as non-transitory computer-executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present disclosure are for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation to the technical solutions provided in the embodiments of the present disclosure, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present disclosure are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and therefore do not limit the scope of the claims of the embodiments of the present disclosure. Any modifications, equivalents and improvements within the scope and spirit of the embodiments of the present disclosure should be considered within the scope of the claims of the embodiments of the present disclosure by those skilled in the art.
Claims (10)
1. A real-time monitoring and early warning method is applied to a shooting terminal and comprises the following steps:
collecting life image data of a target person in real time;
constructing a monitoring model for monitoring the target person according to the life image data;
monitoring the target personnel in a preset space environment in real time according to the monitoring model to obtain real-time monitoring information;
acquiring a real-time monitoring time period in which the target person does not appear in the real-time monitoring information;
and if a certain real-time monitoring time period exceeds a preset time period threshold value, generating abnormal monitoring information, and sending the abnormal monitoring information to the mobile terminal equipment for early warning.
2. The real-time monitoring and early warning method according to claim 1, wherein after the abnormal monitoring information is sent to a mobile terminal device and early warning is performed, the method further comprises:
sending confirmation information to the mobile terminal equipment so that the mobile terminal equipment can communicate with target terminal equipment of the target personnel according to the confirmation information;
and receiving feedback information sent by the mobile terminal device according to the communication condition with the target terminal device.
3. The real-time monitoring and early warning method according to claim 2, wherein after the receiving the feedback information sent by the mobile terminal device according to the communication condition with the target terminal device, the method further comprises:
relieving the early warning for normal communication according to the feedback information;
and acquiring prestored feedback medical care rescue information for communication abnormity according to the feedback information, and sending the feedback medical care rescue information to the mobile terminal equipment.
4. The real-time monitoring and early warning method according to claim 3, wherein the real-time collecting of the life image data of the target person comprises:
receiving a nursing mode starting instruction, and entering a preset monitoring mode according to the nursing mode starting instruction;
and monitoring the life image data of the target person in a preset area in real time according to the monitoring mode, wherein the preset area is the area where the target person is located.
5. The real-time monitoring and early warning method according to any one of claims 1 to 4, wherein the constructing a monitoring model for monitoring the target person according to the life image data comprises:
identifying the life image data to obtain human shape and posture data of the target person;
inputting the human-shaped attitude data into a preset neural network model for learning, and outputting monitoring parameters;
and constructing the monitoring model for monitoring the target personnel according to the monitoring parameters and the life image data.
6. The real-time monitoring and early warning method according to claim 5, wherein the identifying the life image data to obtain the human-shaped posture data of the target person comprises:
carrying out human shape recognition processing on the life image data to obtain human shape key characteristic data;
and preprocessing the human shape key characteristic data to obtain the human shape attitude data corresponding to the target personnel.
7. The real-time monitoring and early warning method according to claim 6, wherein the human shape recognition processing is performed on the life image data to obtain human shape key feature data, and the method comprises the following steps:
classifying the life image data by using a preset classification algorithm to obtain a plurality of different candidate frame data;
and screening the plurality of candidate frame data to screen out the human-shaped key characteristic data.
8. A real-time monitoring and early warning device, which is characterized in that the device comprises:
the acquisition module is used for acquiring living image data of a target person in real time;
the construction module is used for constructing a monitoring model for monitoring the target personnel according to the life image data;
the monitoring module is used for monitoring the target personnel in a preset space environment in real time according to the monitoring model to obtain real-time monitoring information;
the acquisition module is used for acquiring a real-time monitoring time period in which the target person does not appear in the real-time monitoring information;
and the early warning module is used for generating abnormal monitoring information if a certain real-time monitoring time period exceeds a preset time period threshold value, and sending the abnormal monitoring information to the mobile terminal equipment for early warning.
9. A real-time monitoring and forewarning system, the system comprising:
a real-time monitoring and early warning device;
shooting a terminal;
the real-time monitoring and early warning device executes the following steps:
a real-time monitoring and forewarning method as claimed in any one of claims 1 to 7.
10. A storage medium having stored thereon executable instructions executable by a computer to cause the computer to perform:
a real-time monitoring and forewarning method as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210047356.5A CN114495270A (en) | 2022-01-17 | 2022-01-17 | Real-time monitoring and early warning method, device and system and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210047356.5A CN114495270A (en) | 2022-01-17 | 2022-01-17 | Real-time monitoring and early warning method, device and system and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114495270A true CN114495270A (en) | 2022-05-13 |
Family
ID=81512403
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210047356.5A Pending CN114495270A (en) | 2022-01-17 | 2022-01-17 | Real-time monitoring and early warning method, device and system and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114495270A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115148002A (en) * | 2022-07-04 | 2022-10-04 | 上海易同科技股份有限公司 | Anti-suicide early warning method, device, equipment and storage medium based on Bluetooth positioning |
| CN115240302A (en) * | 2022-07-18 | 2022-10-25 | 珠海格力电器股份有限公司 | Method and device for monitoring indoor safety environment, electronic equipment and storage medium |
| CN115599051A (en) * | 2022-09-23 | 2023-01-13 | 北京珞安科技有限责任公司（Cn） | Light industry on duty configuration safety judgment system and method |
| CN116935285A (en) * | 2023-08-02 | 2023-10-24 | 北京汉唐自远技术股份有限公司 | Conference video detection method and system based on image recognition |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012190294A (en) * | 2011-03-11 | 2012-10-04 | Sinanen-Shoji Inc | Watching system for single person of single household |
| CN106485877A (en) * | 2015-08-28 | 2017-03-08 | 杭州萤石网络有限公司 | The monitoring method of intelligence and system |
| CN111369765A (en) * | 2020-04-13 | 2020-07-03 | 无锡青起长升智能科技有限公司 | Intelligent home nursing method and device based on Internet of things technology |
| CN111932828A (en) * | 2019-11-05 | 2020-11-13 | 上海中侨健康智能科技有限公司 | Intelligent old-age care monitoring and early warning system based on digital twin technology |
| CN113052029A (en) * | 2021-03-12 | 2021-06-29 | 天天惠民(北京)智能物流科技有限公司 | Abnormal behavior supervision method and device based on action recognition and storage medium |
-
2022
- 2022-01-17 CN CN202210047356.5A patent/CN114495270A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012190294A (en) * | 2011-03-11 | 2012-10-04 | Sinanen-Shoji Inc | Watching system for single person of single household |
| CN106485877A (en) * | 2015-08-28 | 2017-03-08 | 杭州萤石网络有限公司 | The monitoring method of intelligence and system |
| CN111932828A (en) * | 2019-11-05 | 2020-11-13 | 上海中侨健康智能科技有限公司 | Intelligent old-age care monitoring and early warning system based on digital twin technology |
| CN111369765A (en) * | 2020-04-13 | 2020-07-03 | 无锡青起长升智能科技有限公司 | Intelligent home nursing method and device based on Internet of things technology |
| CN113052029A (en) * | 2021-03-12 | 2021-06-29 | 天天惠民(北京)智能物流科技有限公司 | Abnormal behavior supervision method and device based on action recognition and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115148002A (en) * | 2022-07-04 | 2022-10-04 | 上海易同科技股份有限公司 | Anti-suicide early warning method, device, equipment and storage medium based on Bluetooth positioning |
| CN115240302A (en) * | 2022-07-18 | 2022-10-25 | 珠海格力电器股份有限公司 | Method and device for monitoring indoor safety environment, electronic equipment and storage medium |
| CN115599051A (en) * | 2022-09-23 | 2023-01-13 | 北京珞安科技有限责任公司（Cn） | Light industry on duty configuration safety judgment system and method |
| CN116935285A (en) * | 2023-08-02 | 2023-10-24 | 北京汉唐自远技术股份有限公司 | Conference video detection method and system based on image recognition |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114495270A (en) | Real-time monitoring and early warning method, device and system and storage medium | |
| CN112364696A (en) | Method and system for improving family safety by using family monitoring video | |
| WO2011132387A1 (en) | Collating device | |
| JP2004021495A (en) | Monitoring system and monitoring method | |
| KR100822476B1 (en) | Remote emergency monitoring system and method | |
| US11631306B2 (en) | Methods and system for monitoring an environment | |
| KR20190052190A (en) | PROTECTION SYSTEM FOR VULNERABLE CLASSES USING Internet Of Things AND METHOD THEREFOR | |
| JP7138619B2 (en) | Monitoring terminal and monitoring method | |
| CN110246292B (en) | Household video monitoring method, device and storage medium | |
| CN111243142A (en) | Failure processing method and device for intelligent door lock and storage medium | |
| CN111227789A (en) | Human health monitoring method and device | |
| JP5088463B2 (en) | Monitoring system | |
| KR102233679B1 (en) | Apparatus and method for detecting invader and fire for energy storage system | |
| JP2002304651A (en) | Device and method for managing entering/leaving room, program for executing the same method and recording medium with the same execution program recorded thereon | |
| CN114188027A (en) | Health data processing method, device, terminal and medium based on intelligent equipment | |
| CN113393628A (en) | Intelligent monitoring alarm method, system, intelligent terminal and storage medium | |
| CN112989993A (en) | Recording method, recording system, and computer-readable storage medium | |
| CN114455414B (en) | Elevator passenger overturn alarming method and device and electronic equipment | |
| US20230306711A1 (en) | Monitoring system, camera, analyzing device, and ai model generating method | |
| CN110148234A (en) | Campus brush face picks exchange method, storage medium and system | |
| CN112804492B (en) | Communication prompting method and device for electronic peepholes | |
| CN113660455B (en) | Method, system and terminal for fall detection based on DVS data | |
| CN112580390B (en) | Security monitoring method and device based on intelligent sound box, sound box and medium | |
| JP2018173913A (en) | Image processing system, information processing device, and program | |
| JP7198052B2 (en) | image surveillance system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |