CN113838262A - Near field communication method, device and system based on Internet of things - Google Patents

Near field communication method, device and system based on Internet of things Download PDF

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CN113838262A
CN113838262A CN202111157735.1A CN202111157735A CN113838262A CN 113838262 A CN113838262 A CN 113838262A CN 202111157735 A CN202111157735 A CN 202111157735A CN 113838262 A CN113838262 A CN 113838262A
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data
risk
risk threshold
field communication
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CN113838262B (en
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李广普
杨明
孙建成
陈栋
徐忠民
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Industrial and Commercial Bank of China Ltd ICBC
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/0202—Child monitoring systems using a transmitter-receiver system carried by the parent and the child
    • G08B21/0205—Specific application combined with child monitoring using a transmitter-receiver system
    • G08B21/0208—Combination with audio or video communication, e.g. combination with "baby phone" function
    • G—PHYSICS
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    • G—PHYSICS
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    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/0202—Child monitoring systems using a transmitter-receiver system carried by the parent and the child
    • G08B21/0225—Monitoring making use of different thresholds, e.g. for different alarm levels
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/0202—Child monitoring systems using a transmitter-receiver system carried by the parent and the child
    • G08B21/0272—System arrangements wherein the object is to detect exact location of child or item using triangulation other than GPS
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/0202—Child monitoring systems using a transmitter-receiver system carried by the parent and the child
    • G08B21/0277—Communication between units on a local network, e.g. Bluetooth, piconet, zigbee, Wireless Personal Area Networks [WPAN]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a near field communication method, a near field communication device and a near field communication system based on the Internet of things, which relate to the field of safety equipment and can be applied to the financial field and other fields, wherein the method comprises the following steps: detecting collision information received by a user through wearing equipment worn at a preset position of the body of the user; acquiring position information of the wearable device according to the collision information, and constructing an alarm prompt task triggered after a preset period according to the collision information; acquiring video data and audio data of a corresponding area according to the position information, and analyzing the video data and the audio data respectively through a preset video identification algorithm and a preset audio identification algorithm to obtain a first risk threshold and a second risk threshold; and calculating to obtain a risk evaluation value according to the first risk threshold and the second risk threshold, and terminating or maintaining the alarm prompt task according to the risk evaluation value.

Description

Near field communication method, device and system based on Internet of things
Technical Field
The invention relates to the field of safety equipment, can be applied to the financial field and other fields, and particularly relates to a near field communication method, device and system based on the Internet of things.
Background
In real life, there are many scenes that need information interaction, such as bank outlets, vault defense, military or sensitive places. Under these scenarios, it is common practice to directly make a loud call or use an intercom, but this approach suffers from the following drawbacks: 1. lack of timely calling and reminding, and direct calling or intercom interaction mode easily has restriction or reduction effect under factors such as environment, geographical position. 2. Lack swift effectual information interaction device, the interactive mode commonly used among the prior art causes the confusion of information data easily even loses to influence interactive effect. 3. The transaction processing time is prolonged, and the efficiency is not good. 4. When an unexpected emergency occurs and the remote monitoring finds that the emergency is abnormal, the position of a user and the real-time situation of a capture site cannot be accurately judged in time. 5. In special places, such as bank vaults, military confidential places, mobile phones and other common communication signals are shielded, security personnel in such places are likely to be attacked suddenly by thieves, when sudden violent events occur (particularly, the happening time is abnormal working time such as night), security personnel need to take actions quickly to deal with the sudden violent events, and because fewer security personnel are arranged in the places, the security personnel are often busy in dealing with violent conflicts or suddenly attacked in the fields, and are difficult to request for support in time. The conventional communication equipment cannot be combined with a mechanism perfect safety system, the function of the mechanism safety system is fully exerted, and the safety of the mechanism cannot be better protected. 6. If a case happens, the investigator wants to trace to the source to check the monitoring video of the case involved in the case, the camera video with the best angle is difficult to find, the whole monitoring video is required to be watched, the key video segment cannot be found quickly, the video cannot be watched in a jumping mode, otherwise the key video of the video is easy to miss, the checking process consumes time, the tracing investigation work efficiency is low, and the storage space occupied by the video file is also large.
Disclosure of Invention
The invention aims to provide a near-field communication method, a near-field communication device and a near-field communication system based on the Internet of things, which not only provide a conventional communication function, but also organically combine a communication device with a security system of a financial institution through the Internet of things technology, and automatically discriminate through a delay device and an image recognition technology, thereby preventing the false alarm condition caused by the false alarm of the device, fully playing the role of the security system of the financial institution and improving the capability of the institution for dealing with emergent and serious security events.
In order to achieve the above object, the present invention provides a near field communication method based on the internet of things, the method comprising: detecting collision information received by a user through wearing equipment worn at a preset position of the body of the user; acquiring position information of the wearable device according to the collision information, and constructing an alarm prompt task triggered after a preset period according to the collision information; acquiring video data and audio data of a corresponding area according to the position information, and analyzing the video data and the audio data respectively through a preset video identification algorithm and a preset audio identification algorithm to obtain a first risk threshold and a second risk threshold; and calculating to obtain a risk evaluation value according to the first risk threshold and the second risk threshold, and terminating or maintaining the alarm prompt task according to the risk evaluation value.
In the near field communication method based on the internet of things, preferably, the acquiring the position information of the wearable device according to the collision information includes: broadcasting the equipment identification of the current wearable equipment to a positioning base station in a preset area through an ultra-wideband pulse signal according to the collision information; and calculating and obtaining the position information by the positioning base station according to the TDOA and AOA positioning algorithm.
In the near field communication method based on the internet of things, preferably, terminating or maintaining the alarm prompt task according to the risk evaluation value further includes: and when the alarm prompt task is maintained according to the risk evaluation value, storing the video data and the audio data corresponding to the risk evaluation value in a partition mode according to the number and the acquisition time of acquisition equipment.
In the near field communication method based on the internet of things, preferably, the obtaining a first risk threshold value through a predetermined video recognition algorithm analysis according to the video data includes: capturing face data in the video data through a face recognition algorithm according to the video data; comparing the face data with preset face data to obtain a face similarity value, and obtaining target identity information according to the comparison result of the face similarity value and a preset similarity threshold value; analyzing and obtaining depth three-dimensional information of a target person in the video data through a depth vision algorithm, and analyzing the video data through preset behavior type definition and the depth three-dimensional information to obtain behavior data of the target person; and calculating to obtain a first risk threshold value through a preset risk rule according to the target identity information and the behavior data.
In the near field communication method based on the internet of things, preferably, the obtaining a second risk threshold value through a predetermined audio recognition algorithm analysis according to the audio data includes: converting the audio data into text data; performing abnormal semantic recognition on the character text data through a preset abnormal keyword and a preset semantic recognition algorithm to obtain an abnormal semantic recognition result; and obtaining a second risk threshold according to the abnormal semantic recognition result.
In the near field communication method based on the internet of things, preferably, the calculating and obtaining a risk evaluation value according to the first risk threshold and the second risk threshold includes: and calculating to obtain a risk evaluation value according to the weight coefficient corresponding to the video data and the audio data, the first risk threshold and the second risk threshold.
The invention also provides a near field communication device based on the Internet of things, which comprises a wearing carrier and a communication module; the communication module is worn at a preset position of the body of a user through the wearing carrier, wherein the communication module comprises a collision detection module, a positioning module and a time delay module; the collision detection module is used for acquiring collision information received by a user; the positioning module is used for acquiring the equipment identifier of the near field communication device according to the collision information and broadcasting the equipment identifier to a preset area; the time delay module is used for constructing an alarm prompt task triggered after a preset period according to the collision information; and terminating the alarm prompt task according to the received control instruction.
The invention also provides a near field communication system comprising the near field communication device, and the system also comprises a positioning base station, an audio acquisition device, a video acquisition device and a processing device; the positioning base station is used for receiving the equipment identification of the near-field communication device broadcasted by the positioning module through the ultra-wideband pulse signal according to the collision information, and calculating and obtaining position information by using a TDOA and AOA positioning algorithm; the video acquisition device and the audio acquisition device are used for acquiring video data and audio data of corresponding areas according to the position information; the processing device is used for analyzing and obtaining a first risk threshold and a second risk threshold through a preset video identification algorithm and a preset audio identification algorithm according to the video data and the audio data respectively; calculating to obtain a risk evaluation value according to the first risk threshold and the second risk threshold; and generating a control instruction according to the risk evaluation value, and providing the control instruction to the delay module.
In the near field communication system, preferably, the processing device includes a weighting module, and the weighting module is configured to calculate and obtain a risk evaluation value according to a weight coefficient corresponding to the video data and the audio data, the first risk threshold, and the second risk threshold.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the computer program.
The present invention also provides a computer-readable storage medium storing a computer program for executing the above method by a computer.
The invention has the beneficial technical effects that: the system not only provides a conventional communication function, but also organically combines a communication device with a security system of a financial mechanism through the Internet of things technology, and automatically discriminates through a delay device and an image recognition technology, so that the false alarm condition caused by false alarm of the device is prevented, the effect of the security system of the financial mechanism is fully exerted, and the capability of the mechanism for dealing with emergent and serious security incidents is improved. When abnormal conditions occur, the monitoring system can rapidly record to form short videos, and the system classifies and stores the short video files according to the serial numbers of the near field communication devices. Because the system can accurately judge the abnormal condition, only record and store the monitoring video of the abnormal condition, and carry out numbering and filing according to the number of the near field communication device and the date and time of the abnormal condition, a researcher can quickly read the short video of the abnormal condition according to the number of the near field communication device and the occurrence time of the abnormal event, and daily normal invalid video is automatically filtered by the system and skipped without special storage, thereby greatly reducing the storage pressure of the video monitoring system and improving the investigation efficiency.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a schematic flow chart illustrating a near field communication method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a position location principle according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating obtaining a first risk threshold by using video data according to an embodiment of the present invention;
FIG. 4 is a flow chart illustrating behavior data acquisition using video data according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a training process of an optical flow branch according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating bridging of optical flow branches and RGB branches according to an embodiment of the present invention;
FIG. 7 is a flowchart illustrating obtaining a second risk threshold using audio data according to an embodiment of the invention;
fig. 8A to 8B are schematic structural diagrams of a near field communication device according to an embodiment of the present invention;
fig. 9 is a schematic circuit diagram of a near field communication device according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a near field communication system according to an embodiment of the present invention;
fig. 11 is a schematic flow chart illustrating active communication according to an embodiment of the present invention;
fig. 12 is a schematic flow chart illustrating a passive communication according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, unless otherwise specified, the embodiments and features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions and, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than here.
Referring to fig. 1, the present invention provides a near field communication method based on the internet of things, the method including:
s101, detecting collision information received by a user through wearing equipment worn at a preset position of the body of the user;
s102, acquiring position information of the wearable device according to the collision information, and constructing an alarm prompt task triggered after a preset period according to the collision information;
s103, acquiring video data and audio data of a corresponding area according to the position information, and analyzing the video data and the audio data respectively through a preset video identification algorithm and a preset audio identification algorithm to obtain a first risk threshold and a second risk threshold;
s104, calculating according to the first risk threshold and the second risk threshold to obtain a risk evaluation value, and terminating or maintaining the alarm prompt task according to the risk evaluation value.
Therefore, the near-field communication method based on the Internet of things organically combines the device with a security system of a financial institution through the Internet of things technology, automatically discriminates through the image recognition technology, prevents false alarm conditions caused by equipment misinformation, fully plays the role of the security system of the financial institution, and improves the capability of important financial places such as bank outlets, vaults and the like to deal with emergent and serious security events.
In an embodiment of the present invention, acquiring the position information of the wearable device according to the collision information includes: broadcasting the equipment identification of the current wearable equipment to a positioning base station in a preset area through an ultra-wideband pulse signal according to the collision information; and calculating and obtaining the position information by the positioning base station according to the TDOA and AOA positioning algorithm. Specifically, positional information contains three-dimensional coordinate data, and this locate mode is totally different with traditional RFID, bluetooth, WIFI data locate mode, and above several kinds of traditional modes can only fix a position in certain regional scope, also can't give accurate position on three-dimensional space, for example WIFI can only reach about 2 meters at the precision of indoor location, can't accomplish accurate location. The positioning method provided by the invention adopts UWB (ultra wide band) pulse signals, and the ultra wide band positioning technology is a brand new technology which is greatly different from the traditional communication positioning technology. The three-dimensional space coordinate acquisition system can accurately acquire three-dimensional space coordinates, assist a bank safety system to accurately adjust a camera to acquire monitoring images and audio data, greatly improve monitoring accuracy and avoid frequent false alarm.
In the above embodiment, the TDOA and AOA positioning algorithms are used to analyze the device identifier, so that the multipath resolution is strong, the accuracy is high, X, Y, Z three-dimensional coordinates of the wearable device can be obtained, and the positioning accuracy can reach centimeter level. Based on the three-dimensional space coordinate accurately obtained, the camera can be accurately adjusted to obtain effective and clear monitoring images and audio data according to the coordinate, abnormal conditions are automatically identified through image identification and voice identification, the alarm system is quickly closed before the time delay is finished, the monitoring accuracy is greatly improved, and the condition of false alarm is avoided frequently. Specifically, in actual work, as shown in fig. 2, anchor nodes and bridge nodes in known positions arranged in advance may be used to communicate with newly joined blind nodes, and a triangulation location or perhaps "fingerprint" location method is used to determine the position of the location device identifier, where the location method employs N location base stations (N > ═ 3), and the TDOA and AOA location algorithms are used to analyze the tag position, so that the multipath resolution is strong, the accuracy is high, and the location accuracy can reach the centimeter level. The TDOA location algorithm is a method that uses time difference of arrival to perform location. The positioning module, such as a positioning tag, can constantly send information to the periphery, and the information contains the ID value of the tag. When the safety protection system is deployed nearby and at least three base stations receive the information, the current position of the tag can be calculated according to the time difference of the information reaching the three base stations. The safety protection system acquires X, Y, Z three-dimensional coordinates of the shield equipment, and when the positioning base station is installed, the height difference of the Z axis needs to be particularly pulled to ensure the accuracy on the Z axis. Therefore, the audio and video acquisition equipment, such as a high-definition monitoring camera, can accurately adjust the direction and the focal length of the camera according to the calculated X, Y, Z three-dimensional coordinates, and more clearly and accurately capture the position of the shield and the image information of the surrounding environment.
In an embodiment of the present invention, terminating or maintaining the alarm prompting task according to the risk evaluation value further includes: and when the alarm prompt task is maintained according to the risk evaluation value, storing the video data and the audio data corresponding to the risk evaluation value in a partition mode according to the number and the acquisition time of acquisition equipment. Specifically, in actual work, when the alarm prompting task is maintained according to the risk evaluation value, it represents that a risk or an abnormal situation does exist in the current area, and at this time, related evidence needs to be retained, for this purpose, short video data files in which an abnormal situation occurs (for example, an alarm is triggered manually by a device or a device is squeezed, and a collision sensor is triggered due to a severe collision) are stored in a classified manner, the files are stored in partitions (blocks or folders) according to device numbers, the video files are uniformly placed in an independent region (block or folder), the region (block or folder) is named by the device number, and the short video data files are named according to the following rules, as shown in table 1 below:
TABLE 1
Figure BDA0003288911400000061
Figure BDA0003288911400000071
Referring to fig. 3, in an embodiment of the present invention, the obtaining the first risk threshold by analyzing the video data through a predetermined video recognition algorithm includes:
s301, capturing face data in the video data through a face recognition algorithm according to the video data;
s302, comparing the face data with preset face data to obtain a face similarity value, and obtaining target identity information according to the comparison result of the face similarity value and a preset similarity threshold value;
s303, analyzing and obtaining depth three-dimensional information of a target person in the video data through a depth vision algorithm, and analyzing the video data through preset behavior type definition and the depth three-dimensional information to obtain behavior data of the target person;
s304, calculating according to the target identity information and the behavior data through a preset risk rule to obtain a first risk threshold.
In the embodiment, the face image is captured from the image data, the face recognition is performed according to the similarity between the pre-stored face image and the captured face image, the depth three-dimensional information of the target person is acquired from the image based on a depth vision algorithm, and the abnormal behavior analysis is performed according to the depth three-dimensional information of the target person. Specifically, the image analysis module calculates the similarity between the captured face image and a pre-stored face image, if the similarity between the captured face image and a pre-stored image reaches a preset similarity threshold, the face recognition is judged to be successful, and the information of the captured face image is determined according to the pre-stored image information. And then based on a depth vision algorithm, acquiring depth three-dimensional information (target height information, accuracy of target behavior analysis and multi-target detection, target position information, multi-target detection, especially target distance detection, target depth information and judgment of multi-target position distance) of the target personnel from the image, and realizing abnormal behavior analysis such as border crossing, entering/leaving area, area intrusion, loitering, personnel focusing, quick movement, illegal parking, object leaving/taking and the like. For example, at this time, the time is night non-business hours, but a face which is not on the white list appears in the monitoring picture or video, namely, the suspicious state is judged, and the remote security monitoring agent equipment is notified to carry out follow-up processing. If the picture shows that the communication device is in a static state or no suspicious people exist nearby the user, the communication device is judged to be in a false alarm.
Referring to fig. 4, in the above embodiment, the obtaining of the behavior data of the target person by analyzing the video image data according to the preset behavior category definition and the depth three-dimensional information may include:
s401, extracting and collecting the video image data according to preset behavior category definition to obtain source video data, and extracting image frame data according to the source video data in a classification mode according to behavior intervals;
s402, calculating image frame data through a full variational 1 normal form algorithm to obtain optical flow image frame data, and training by adopting cross entropy as a loss function according to the optical flow image frame data to obtain an optical flow branch model;
s403, establishing a bridging relation between the optical flow branches and the image frame branches according to preset weight parameters of the optical flow branches, and training a preset image classification model through image frame data according to the bridging relation to obtain an image branch model;
s404, acquiring behavior data of a target person according to the image frame data, the optical flow branch model and the image branch model.
In actual work, the flow of fig. 4 may include three parts, namely, preprocessing the acquired video data, defining behavior categories, searching for source videos, acquiring data, and storing in a classified manner; and determining a video key behavior interval, determining a frame rate, an image position and an image resolution, and extracting and storing an image frame of the trimmed video. And cleaning and training video light stream data, including light stream branch training, light stream feature transfer and RGB training. And respectively inputting the RGB image and the optical flow image into the trained RGB branch and optical flow branch to obtain behavior classification.
The specific implementation modes of optical flow branch training, optical flow feature transfer and RGB training are as follows: firstly, the optical flow branch training utilizes a full variation 1 model algorithm to calculate RGB image frames of a source video, and optical flow image frames in the horizontal direction and the vertical direction are respectively obtained from two adjacent frames. And 3-dimensional convolution and pooling expansion are carried out on the pre-trained optical flow image classification model according to the expansion 3D operation. The corresponding horizontal optical flow graph and vertical optical flow graph are input into the model for training, and cross entropy is used as a loss function to obtain trained optical flow branches, which can be specifically referred to as fig. 5.
Secondly, the optical flow feature transfer firstly fixes the trained optical flow branch weight parameters, and then establishes the bridge connection between the optical flow branches and the RGB branches to realize the transfer of the optical flow feature information, which can be referred to as the graph shown in FIG. 6; adding intermediate layer features, namely optical flow features, obtained by branching the optical flow of the same video, into intermediate layer features of RGB branches in a distillation calculation mode for splicing to obtain intermediate layer features with increased dimensionality, and then training according to a normal path; performing gradient reduction on the weighting loss function, and optimizing training parameters in multiple rounds to obtain trained RGB branches; and integrating the optical flow branch and the RGB branch through a full connection layer to obtain final output. The transmission comprises two bridging lines, the first bridging line is transmitted in the 3D convolution layer process, and the bridging lines are viewed according to the concreteThe experimental results of the frequency set were selected from 9 lines a to i, denoted Feature1flowTo Feature1RGBTransferring;
finally, performing RGB training, selecting a pre-trained RGB image classification model, performing 3-dimensional convolution and pooling expansion according to expansion 3D operation, and inputting RGB image frames; connecting the optical flow branches, calculating the characteristics of the RGB branches and the optical flow branches by using a full-connection classifier, and outputting classification probability; and constructing a loss function, wherein the loss function consists of three parts, namely the 2 norm of the first transfer line, the 2 norm of the second transfer line and the cross entropy of the final classification.
losstotal＝α1L1+β1L2+γ1L3
＝α1||Feature1RGB-Feature1flow||2+β1||Feature2RGB-Feature2flow||2+γ1CrossEntropy(p，y)；
In the above equation, the loss function includes three terms, the first term represents the portion of the first transfer line, L1 is the 2 norm of the difference between the RGB features and the optical flow features at this stage, α1Is L1 corresponds to a weight; the second term represents the second portion of the transmission line, L2 being the 2 norm, β, of the difference between the two characteristics at this stage1Is L2 corresponds to a weight; the third term L3 is the cross entropy of the final classification, γ1Is L3 corresponds to a weight; feature1RGBIs a first piece of RGB information; feature1flowIs a first piece of optical flow information; feature2RGBFeature2 as a second piece of RGB informationflowIs the second optical flow information.
Referring to fig. 7, in an embodiment of the present invention, the obtaining the second risk threshold by analyzing the audio data through a predetermined audio recognition algorithm includes:
s701, converting the audio data into text data;
s702, carrying out abnormal semantic recognition on the character text data through a preset abnormal keyword and a preset semantic recognition algorithm to obtain an abnormal semantic recognition result;
s703, a second risk threshold value is obtained according to the abnormal semantic recognition result.
In actual work, the abnormal keywords can be life saving, robbery and the like, the semantic recognition algorithm can be an existing speech recognition model, and the method is not described in detail herein. Based on the obtained first risk threshold and second risk threshold, in an embodiment of the present invention, a risk evaluation value may also be obtained by calculating according to the weight coefficients corresponding to the video data and the audio data, the first risk threshold, and the second risk threshold. Therefore, whether the current early warning is false alarm or not is comprehensively analyzed by utilizing the risk evaluation value, the calculation mode of the risk evaluation value can adopt the existing weighted accumulation mode, for example, the weight coefficient of a video is preset to be 0.6, and the weight coefficient of an audio is preset to be 0.4; at the moment, the risk evaluation value calculated by the first risk threshold value A and the second risk threshold value B is 0.6A +0.4B, when the sum of the first risk threshold value A and the second risk threshold value B is greater than a preset alarm threshold value C, the current early warning is not false alarm, and at the moment, an alarm prompt task can be maintained or triggered in advance; furthermore, the worker may also activate safety protection measures according to the calculation result, such as closing an emergency gate, contacting with a law enforcement agency, and alarming, and the related technical personnel in the field may select settings according to actual needs, and the present invention is not limited herein.
The invention also provides a mesh point safety protection device, which comprises a wearing carrier and a communication module; the communication module is worn at a preset position of the body of a user through the wearing carrier, wherein the communication module comprises a collision detection module, a positioning module and a time delay module; the collision detection module is used for acquiring collision information received by a user; the positioning module is used for acquiring the equipment identifier of the near field communication device according to the collision information and broadcasting the equipment identifier to a preset area; the time delay module is used for constructing an alarm prompt task triggered after a preset period according to the collision information; and terminating the alarm prompt task according to the received control instruction.
Specifically, as shown in fig. 8A to 8B, the nfc device may include a key 151 and/or an audio input unit 152. The audio input unit is a miniature microphone and is used for sending voice to the terminal device. The key is a common number or a number + letter key, and is used for responding to the service request and inputting a response result to the terminal device. The information prompt module comprises a signal display unit 122 and/or a vibrator unit 121, wherein the signal display unit is a signal display, contains a light emitting diode, can display different colors according to different service requests sent by the terminal device, and provides information perceived by a user. The light-emitting diode can display blue, green and red, and the wearing personnel of the near field communication device can quickly judge the type of the on-site emergency according to the display color of the light-emitting diode without talking. The vibrator unit is a vibrating motor, can be automatically vibrated by a signal and provides a user with perception of information arrival. The information output module comprises a screen display unit 132 and/or an audio output unit 131, wherein the screen display unit is a liquid crystal display, and preferably can hold and display 5 × 10 pieces of Chinese character information. The audio output unit is a miniature loudspeaker tube and is used for playing the voice in the service request. The near field communication device wearing person accurately obtains the interactive information of other wearing persons or the control center according to the content displayed by the liquid crystal display screen or the voice played by the miniature loudspeaker.
In practical operation, the near field communication device comprises a wearable carrier 120 and an information communication device 110, wherein the wearable carrier is used for fixing the information communication device. In this embodiment, wearable carrier 120 is a wrist strap, and mainly used wears on user's wrist, and this wrist strap comprises the silica gel material, contains the sheetmetal, and the curl can be at will, only need gently one clap just can tightly overlap on hand, and convenience of customers uses, need not the timing watchband, can adapt to the crowd of not equidimension wrist and wear. Fig. 8B is a schematic three-position view of the near field communication device wrist band from a flat state to a rolled state, wherein the wrist band can be automatically rolled around the wrist when the near field communication device is placed on the wrist. In other embodiments, the wearable carrier can also be a neck ring, which is worn on the chest and is convenient for the user to carry; in this embodiment, the connection relationship between the wearable carrier and the information communication device may be implemented by clamping, adhering, or fixing with screws, which is not limited in the present invention. Referring to fig. 8A, the information prompting module includes a signal display unit 122 and/or a vibrator unit 121, where the signal display unit is a signal display, and includes a light emitting diode, and can display different colors according to different service requests sent by the terminal device, so as to provide users with perception of arrival of information. The light-emitting diode can display blue, green and red, and the wearing personnel of the near field communication device can quickly judge the type of the on-site emergency according to the display color of the light-emitting diode without talking. The vibrator unit is a vibrating motor, can be automatically vibrated by a signal and provides a user with perception of information arrival. The information output module comprises a screen display unit 132 and/or an audio output unit 131, wherein the screen display unit is a liquid crystal display screen, and preferably can contain and display 5 × 10 Chinese character information; the audio output unit is a miniature loudspeaker tube and is used for playing the voice in the service request. The near field communication device wearing person accurately obtains the interactive information of other wearing persons or the control center according to the content displayed by the liquid crystal display screen or the voice played by the miniature loudspeaker.
The circuit structure of the near field communication device provided by the present invention can be referred to fig. 9, in which the control module is connected to the power supply module, the wireless communication module, the vibrator unit, the signal display unit, the power switch, the positioning tag, the key, the audio input unit, the audio output unit, the screen display unit, the delayer, and the collision sensor.
The time delay device can ensure the misinformation condition caused by accidental operation and collision, and provides the error correction time for a user and a safety monitoring system.
The positioning tag can send out positioning data signals so that positioning base stations in different directions capture the signals, the three-dimensional space coordinates of the tag can be accurately positioned, and the position of the positioning tag can be accurately judged. This location label technique is totally different with traditional RFID, bluetooth, WIFI data location mode, and above several kinds of traditional modes can only fix a position in certain regional scope, also can't give accurate position on three-dimensional space, and WIFI can only reach about 2 meters at the precision of indoor location for example, can't accomplish accurate location. The invention adopts UWB (ultra wide band) pulse signals, and the ultra wide band positioning technology is a brand new technology which is greatly different from the traditional communication positioning technology. The method uses anchor nodes and bridge nodes with known directions which are arranged in advance to communicate with newly joined blind nodes, uses a triangle positioning or perhaps 'fingerprint' positioning method to confirm the directions, uses a plurality of sensors to select TDOA and AOA positioning algorithms to analyze the label directions, and has strong multipath resolution and high precision, and the positioning precision can reach centimeter level. The three-dimensional space coordinate acquisition system can accurately acquire three-dimensional space coordinates, assist a bank safety system to accurately adjust a camera to acquire monitoring images and audio data, greatly improve monitoring accuracy and avoid frequent false alarm.
The positioning tag, the collision sensor, the delayer and other three electrical components are installed in the communicator, the collision sensor is connected with the delayer and is powered by the power supply module, when the collision sensor is collided, knocked or extruded, the delayer can be started, a buzzer of the security system cannot be started immediately to give an alarm, so that the situation of false alarm is avoided, the power switch indicator lamp is lightened to remind a wearer at the moment, the communicator is in an early warning state, the delayer is started, the buzzer is about to be started to give an alarm, and if the alarm is in a false operation, a user can manually turn off the power switch to finish the early warning state, so that the false alarm is avoided; meanwhile, the collision sensor sends a signal to a network of a bank through the wireless communication module so as to inform a financial institution security system of the bank, the security system positions the communication device through the positioning tag and takes a picture, the system automatically discriminates the on-site situation by using an image recognition technology, and the person and behavior analysis is carried out on the on-site situation so as to prevent the false alarm situation caused by the equipment false alarm. If the analysis condition is abnormal, the system informs other personnel of the bank or gives an alarm.
The invention also provides a near field communication system, which further comprises a positioning base station, an audio acquisition device, a video acquisition device and a processing device; the positioning base station is used for receiving the equipment identification of the near-field communication device broadcasted by the positioning module through the ultra-wideband pulse signal according to the collision information, and calculating and obtaining position information by using a TDOA and AOA positioning algorithm; the video acquisition device and the audio acquisition device are used for acquiring video data and audio data of corresponding areas according to the position information; the processing device is used for analyzing and obtaining a first risk threshold and a second risk threshold through a preset video identification algorithm and a preset audio identification algorithm according to the video data and the audio data respectively; calculating to obtain a risk evaluation value according to the first risk threshold and the second risk threshold; and generating a control instruction according to the risk evaluation value, and providing the control instruction to the delay module. The processing device comprises a weighting module, and the weighting module is used for calculating and obtaining a risk evaluation value according to the weight coefficient corresponding to the video data and the audio data, the first risk threshold and the second risk threshold.
Referring to fig. 10, in practical operation, the nfc system provided in the present invention may include an nfc apparatus 100, a positioning base station 200, a high-definition monitoring camera 300, a bank security system server 400, a remote security monitoring agent device 500, and a security gate 600. The near field communication device 100 is in wireless communication with the positioning base station 200 through a wireless communication module, the positioning base station 200 is connected with the bank security system server 400, the bank security system server 400 is in data connection with the high-definition monitoring camera 300 and the remote security monitoring seat device 500, and the remote security monitoring seat device 500 controls the security gate 600 through a data line. The positioning base station 200 is an important component of the security system, and is responsible for data communication with the near field communication device 100, and performs "dual channel" data interaction with the wireless communication module and the positioning tag on the near field communication device 100. The safety protection system adopts an indoor positioning technology of an ultra-wideband technology, and can accurately judge the spatial position of the near-field communication device by matching with the positioning label on the near-field communication device 100.
Referring to fig. 8 to 12, when the near field communication system provided by the present invention is used, there are two processes, one is that the user manually turns on the alarm power switch on the near field communication device, the bank security system starts to accurately locate the position of the communication device and collect field data, and automatically determines and analyzes the field condition, thereby effectively avoiding false alarm and improving the early warning accuracy. The other way is that when the collision sensor is collided, knocked or extruded, the communication device starts the time delay device, and the flash lamp or the buzzing horn cannot be started immediately, so that the situation of false alarm caused by accidental collision is avoided, the bank safety system starts to accurately position the communication device and collects field data, the field situation is automatically judged and analyzed, false alarm is effectively avoided, and the early warning accuracy is improved.
For the first active type, please refer to fig. 11, the process is as follows:
step S1101: the user manually opens the alarm power switch on the near field communication device, the power switch opens the flash light and the buzzing horn through the control module, the control module sends a starting signal to the outside through the wireless communication module, and the starting signal comprises positioning label data.
Step S1102: and N (N > -3) positioning base stations deployed at the periphery of the room acquire data sent by the wireless communication module and the positioning tag on the near field communication device.
Step S1103: the positioning calculation module analyzes and calculates the data acquired by different positioning base stations, calculates the current position of the label according to the time difference of information reaching N base stations, and acquires X, Y, Z three-dimensional coordinates of the shield equipment.
Step S1104: the camera control module adjusts the direction and the focal length of the camera according to the three-dimensional coordinates of the communication device equipment obtained by calculation of the positioning calculation module, so that the image information of the position where the near-field communication device is located and the surrounding environment can be clearly and accurately captured, and the main control module controls the camera to collect the field video image and the voice data.
Step S1105: the image analysis module captures a face image from the image, performs face recognition according to the similarity between the pre-stored face image and the captured face image, acquires depth three-dimensional information of a target person from the image based on a depth vision algorithm, and performs abnormal behavior analysis according to the depth three-dimensional information of the target person.
Step S1106: the voice analysis module analyzes the voice of the security inspection area collected by the microphone, performs abnormal semantic analysis according to the voice, and sends an abnormal semantic analysis result to the main control module.
Step S1107: the main control module judges whether the alarm is false alarm or not according to the analysis results of the image analysis module and the voice analysis module, and if the alarm is false alarm, the main control module sends a closing instruction to a power switch of the communication device to close the alarm; and if the situation is suspicious, the remote security monitoring agent is notified to carry out follow-up confirmation processing.
Step S1108: the remote security monitoring seat watches the field condition through the monitoring camera or carries out voice communication with the user, and if the remote security monitoring seat is abnormal, a series of safety measures such as alarming or controlling to close the safety door can be taken.
For the second passive mode, please refer to fig. 12, the process is as follows:
step S1201: when the collision sensor is collided, knocked or extruded, the time delay device can be started, the flash lamp or the buzzing horn can not be started immediately, so that the situation of false alarm is avoided, the power switch indicator lamp is turned on to remind a user, the communication device is in an early warning state, the time delay device is started, and the buzzing horn is started to give an alarm sound; meanwhile, the collision sensor sends a starting signal to the outside through the wireless communication module, and the starting signal comprises positioning label data. Furthermore, if the operation is wrong, a user can manually turn off the power switch at any time in the process to finish the early warning state, so that the false alarm is avoided, but a starting signal is still required to be sent to the outside by the communicator, and the starting signal comprises positioning label data.
Step S1202: and N (N > -3) positioning base stations deployed at the periphery of the room acquire data sent by the signal communicator and the positioning tag on the safety protection device.
Step S1203: the positioning calculation module analyzes and calculates the data acquired by different positioning base stations, calculates the current position of the label according to the time difference of information reaching N base stations, and acquires X, Y, Z three-dimensional coordinates of the communication device equipment.
Step S1204: the camera control module adjusts the direction and the focal length of the camera according to the three-dimensional coordinates of the communication device equipment obtained by calculation of the positioning calculation module, so that the image information of the position where the near-field communication device is located and the surrounding environment can be clearly and accurately captured, and the main control module controls the camera to collect the field video image and the voice data.
Step S1205: the image analysis module captures a face image from the image, performs face recognition according to the similarity between the pre-stored face image and the captured face image, acquires depth three-dimensional information of a target person from the image based on a depth vision algorithm, and performs abnormal behavior analysis according to the depth three-dimensional information of the target person.
Step S1206: the voice analysis module analyzes the voice of the security inspection area collected by the microphone, performs abnormal semantic analysis according to the voice, and sends an abnormal semantic analysis result to the main control module.
Step S1207: the main control module judges whether the alarm is false alarm or not according to the analysis results of the image analysis module and the voice analysis module, and if the alarm is false alarm, the main control module sends a closing instruction to a power switch of the communication device to close the alarm; and if the situation is suspicious, the remote security monitoring agent is notified to carry out follow-up confirmation processing.
Step S1208: the remote security monitoring seat watches the field condition through the monitoring camera or carries out voice communication with the user, and if the remote security monitoring seat is abnormal, a series of safety measures such as alarming or controlling to close the safety door can be taken.
The invention has the beneficial technical effects that: in emergency, the early warning mode can be automatically started without extra operation in the fighting process of an operator, so that the situation that the operator cannot call for help in time due to over-tension in emergency is avoided. The time delay device can ensure the misinformation condition caused by accidental operation and collision, and provides the time for users and the safety monitoring system to correct errors. The three-dimensional space coordinate can be accurately obtained, the safety system can accurately adjust the camera according to the coordinate to obtain effective and clear monitoring images and audio data, abnormal conditions are automatically identified through image identification and voice identification, the alarm system is quickly closed before the time delay device is finished, the monitoring accuracy rate is greatly improved, and the condition of false alarm is avoided frequently.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the computer program.
The present invention also provides a computer-readable storage medium storing a computer program for executing the above method.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (11)

1. A near field communication method based on the Internet of things is characterized by comprising the following steps:
detecting collision information received by a user through wearing equipment worn at a preset position of the body of the user;
acquiring position information of the wearable device according to the collision information, and constructing an alarm prompt task triggered after a preset period according to the collision information;
acquiring video data and audio data of a corresponding area according to the position information, and analyzing the video data and the audio data respectively through a preset video identification algorithm and a preset audio identification algorithm to obtain a first risk threshold and a second risk threshold;
and calculating to obtain a risk evaluation value according to the first risk threshold and the second risk threshold, and terminating or maintaining the alarm prompt task according to the risk evaluation value.
2. The near field communication method based on the internet of things of claim 1, wherein the collecting the position information of the wearable device according to the collision information comprises:
broadcasting the equipment identification of the current wearable equipment to a positioning base station in a preset area through an ultra-wideband pulse signal according to the collision information; and calculating and obtaining the position information by the positioning base station according to the TDOA and AOA positioning algorithm.
3. The near field communication method based on the internet of things of claim 1, wherein terminating or maintaining the alarm prompt task according to the risk assessment value further comprises:
and when the alarm prompt task is maintained according to the risk evaluation value, storing the video data and the audio data corresponding to the risk evaluation value in a partition mode according to the number and the acquisition time of acquisition equipment.
4. The near field communication method based on the internet of things of claim 1, wherein the obtaining a first risk threshold value through a predetermined video recognition algorithm analysis according to the video data comprises:
capturing face data in the video data through a face recognition algorithm according to the video data;
comparing the face data with preset face data to obtain a face similarity value, and obtaining target identity information according to the comparison result of the face similarity value and a preset similarity threshold value;
analyzing and obtaining depth three-dimensional information of a target person in the video data through a depth vision algorithm, and analyzing the video data through preset behavior type definition and the depth three-dimensional information to obtain behavior data of the target person;
and calculating to obtain a first risk threshold value through a preset risk rule according to the target identity information and the behavior data.
5. The near field communication method based on the internet of things of claim 1, wherein the obtaining of the second risk threshold through analysis of a predetermined audio recognition algorithm according to the audio data comprises:
converting the audio data into text data;
performing abnormal semantic recognition on the character text data through a preset abnormal keyword and a preset semantic recognition algorithm to obtain an abnormal semantic recognition result;
and obtaining a second risk threshold according to the abnormal semantic recognition result.
6. The near field communication method based on the internet of things of claim 1, wherein the calculating the risk evaluation value according to the first risk threshold and the second risk threshold comprises:
and calculating to obtain a risk evaluation value according to the weight coefficient corresponding to the video data and the audio data, the first risk threshold and the second risk threshold.
7. A near field communication device based on the Internet of things is characterized by comprising a wearable carrier and a communication module;
the communication module is worn at a preset position of the body of a user through the wearing carrier, wherein the communication module comprises a collision detection module, a positioning module and a time delay module;
the collision detection module is used for acquiring collision information received by a user; the positioning module is used for acquiring the equipment identifier of the near field communication device according to the collision information and broadcasting the equipment identifier to a preset area; the time delay module is used for constructing an alarm prompt task triggered after a preset period according to the collision information; and terminating the alarm prompt task according to the received control instruction.
8. A near field communication system comprising the internet of things based near field communication device of claim 7, wherein the system further comprises a positioning base station, an audio acquisition device, a video acquisition device and a processing device;
the positioning base station is used for receiving the equipment identification of the near-field communication device broadcasted by the positioning module through the ultra-wideband pulse signal according to the collision information, and calculating and obtaining position information by using a TDOA and AOA positioning algorithm;
the video acquisition device and the audio acquisition device are used for acquiring video data and audio data of corresponding areas according to the position information;
the processing device is used for analyzing and obtaining a first risk threshold and a second risk threshold through a preset video identification algorithm and a preset audio identification algorithm according to the video data and the audio data respectively; calculating to obtain a risk evaluation value according to the first risk threshold and the second risk threshold; and generating a control instruction according to the risk evaluation value, and providing the control instruction to the delay module.
9. The nfc system of claim 8, wherein the processing device comprises a weighting module, and the weighting module is configured to calculate a risk evaluation value according to the weight coefficients corresponding to the video data and the audio data, the first risk threshold, and the second risk threshold.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 6 when executing the computer program.
11. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 6 by a computer.
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