CN118247735B - Industrial art product storage device based on Internet of things and protection system thereof - Google Patents
Industrial art product storage device based on Internet of things and protection system thereof Download PDFInfo
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Abstract
The invention discloses an industrial art product storage device based on the Internet of things and a protection system thereof, relates to the technical field of industrial art product supervision, solves the problem that potential risks cannot be well identified in a systematic way according to acquired data, and reduces the technical problem of overall safety protection of industrial art products.
Description
Technical Field
The invention relates to the technical field of industrial art product supervision, in particular to an industrial art product storage device based on the Internet of things and a protection system thereof.
Background
Industrial art products are products combining industrial art and modern design concepts, and generally integrate traditional technology and modern technology to meet the demands of people on aesthetics and functions, including ceramic artware, sculpture artware, jade articles, metal artware and the like.
When storing the protection to industrial art article, strict safety protection measure is indispensable, and part of current protection system is when using, all is through artificial observation to the control most of time to subjective come to discern the circumstances that has danger or potential danger, can not come the danger that the analysis of system exists according to the data, further can exist and can not in time discover dangerous problem, thereby can not predict the risk, reduced the holistic safety protection to industrial art article.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the industrial art product storage device based on the Internet of things and the protection system thereof, solves the problem that the analysis of the system cannot be performed according to the acquired data, further cannot well identify the potential risk, and reduces the problem of the overall safety protection of industrial art products.
In order to achieve the above purpose, the invention is realized by the following technical scheme: industrial art product protection system based on thing networking includes:
the information acquisition unit is used for transmitting the acquired industrial art information and the monitoring camera shooting information corresponding to the industrial art product storage area to the information comparison and identification unit;
the historical data storage unit is used for transmitting the stored historical data to the information comparison and identification unit, the normal monitoring and analysis unit and the abnormal monitoring and analysis unit;
The information comparison and identification unit is used for classifying the personnel according to the movement track of the personnel to obtain abnormal personnel, analyzing the movement track of the abnormal personnel, determining an in-doubt area by combining the personnel flow in the historical data, secondarily classifying the detention condition of the abnormal personnel in the in-doubt area to obtain normal monitoring personnel and abnormal monitoring personnel, transmitting the normal monitoring personnel to the normal monitoring and analyzing unit, and transmitting the abnormal monitoring personnel to the abnormal monitoring and analyzing unit;
The normal monitoring analysis unit is used for analyzing the obtained normal monitoring personnel, generating an early warning signal or a monitoring signal through comparing and analyzing the twisting frequency of the normal monitoring personnel, transmitting the generated early warning signal to the protection supervision analysis unit and transmitting the monitoring signal to the information output unit;
The abnormal monitoring analysis unit is used for analyzing the acquired abnormal monitoring personnel, performing similarity matching on the abnormal monitoring personnel by combining historical data, classifying to obtain recorded abnormal personnel and non-recorded abnormal personnel, respectively analyzing the recorded abnormal personnel and the non-recorded abnormal personnel to generate early warning monitoring signals or early warning protection signals, transmitting the early warning monitoring signals to the information output unit, and transmitting the early warning protection signals to the protection supervision analysis unit;
The protection supervision analysis unit is used for analyzing the acquired early warning protection signals, judging the change condition of personnel behaviors and generating abnormal behavior change information by analyzing the personnel behaviors of abnormal monitoring personnel, and transmitting the abnormal behavior change information to the information output unit;
and the information output unit is used for displaying the acquired change information and the early warning monitoring signals to corresponding operators.
Preferably, the specific way of classifying the personnel by the information comparison and identification unit is as follows:
Marking different persons, generating movement tracks of the different persons, identifying repeated tracks in the movement tracks, calculating the occupation ratio of the repeated tracks, comparing the calculated occupation ratio with a preset occupation ratio, marking the movement tracks of the persons corresponding to the occupation ratio exceeding the preset occupation ratio, marking the corresponding persons as abnormal persons, and not processing the person with the occupation ratio not exceeding the preset value occupation ratio.
Preferably, the information comparison and identification unit determines the suspicious region according to abnormal personnel in the following manner:
Marking a motion track corresponding to an abnormal person as an abnormal motion track, simultaneously acquiring a repeated track in the abnormal motion track, then determining a suspicious region according to the repeated track, marking products in the suspicious region, and simultaneously positioning the products according to the repeated track;
And then, analyzing the regional environment of the suspicious region by combining the historical data transmitted by the historical data storage unit, comparing the personnel flow amount corresponding to the suspicious region with the personnel flow amount of the residual region, carrying out grading processing on the suspicious region according to the personnel flow amount, generating grading processing information, and transmitting the grading processing information to the information output unit.
Preferably, the specific way of classifying the abnormal personnel in the suspicious region by the information comparison and identification unit is as follows:
The method comprises the steps of marking the regional detention time of an abnormal person in an in-doubt region as T1, marking the artwork detention time as T2, simultaneously acquiring monitoring cameras of the abnormal person in a historical data time period T, and analyzing the behaviors of the abnormal person in the monitoring cameras;
And acquiring the regional detention time length T1 in the abnormal personnel time period T, comparing the regional detention time length T1 with a preset value Ty1, setting the value of the preset value Ty1 by an operator, further analyzing the abnormal personnel when T1 is more than or equal to Ty1, generating a secondary analysis signal, and otherwise, marking the corresponding abnormal personnel as normal monitoring personnel when T1 is less than Ty 1.
Preferably, the specific mode of the information comparison and identification unit for identifying and analyzing the secondary analysis signal is as follows:
And comparing the artwork retention time T2 corresponding to the abnormal person with a preset value Ty2, wherein the specific value of the preset value Ty2 is set by an operator, when T2 is more than or equal to Ty2, marking the corresponding abnormal person as an abnormal monitoring person, and otherwise, when T2 is less than Ty2, marking the corresponding abnormal person as a normal monitoring person.
Preferably, the specific way of the normal monitoring analysis unit for analyzing the normal monitoring personnel is as follows:
recording the suspicious region corresponding to the normal monitoring personnel as a detention region, simultaneously acquiring a monitoring camera corresponding to the normal monitoring personnel, and then acquiring and identifying the behaviors of the normal monitoring personnel in the monitoring camera to obtain behavior action information;
And establishing a transverse X axis by taking the shoulders of the normal monitoring personnel as a reference plane, establishing a Y axis by taking the neck, analyzing the head movements of the normal monitoring personnel at the same time, acquiring the head twisting condition of the normal monitoring personnel within time t1, calculating the twisting frequency of the normal monitoring personnel, comparing the twisting frequency with the normal frequency, generating an early warning signal when the value of the twisting frequency within time t1 is larger than the normal frequency, and otherwise generating a monitoring signal.
Preferably, the specific way of the abnormality monitoring and analyzing unit for analyzing the abnormality monitoring personnel is as follows:
Acquiring abnormal monitoring personnel and historical data, performing similarity matching on the abnormal monitoring personnel and the historical data, judging whether the abnormal monitoring personnel have corresponding historical records, classifying the abnormal monitoring personnel as recording abnormal personnel when the abnormal monitoring personnel have the corresponding historical records, and classifying the abnormal monitoring personnel as recording non-abnormal personnel if the abnormal monitoring personnel have the corresponding historical records;
Aiming at the recorded abnormal personnel, acquiring time intervals of a history record, judging whether the time intervals are regular, acquiring an in-doubt area of the recorded abnormal personnel when the time intervals corresponding to the recorded abnormal personnel are regular, judging whether the in-doubt area is the same area, generating an early warning protection signal when the in-doubt area is the same area, and otherwise, generating an early warning monitoring signal when the in-doubt area is not the same area;
Aiming at the personnel without record abnormality, the motion trail of the personnel without record abnormality is obtained, the motion trail is highlighted, real-time monitoring information is generated, and early warning monitoring signals are further generated.
Preferably, the analysis mode of the protection supervision and analysis unit for the early warning protection signal is as follows:
And acquiring the personnel behaviors of the abnormal monitoring personnel in the time period t2, classifying the personnel behaviors according to whether the personnel behaviors are in contact with the product to obtain normal behaviors and abnormal behaviors, classifying the existing contact as the abnormal behaviors, classifying the abnormal behaviors as the normal behaviors if the abnormal behaviors are not in contact with the product, analyzing the change condition of the times of the abnormal behaviors in the time period t2, and further generating abnormal behavior change information.
The industrial art product storage device based on the Internet of things comprises a storage box body, a data acquisition module, a camera, a data processing center and a signal display lamp;
the storage box body is used for storing industrial art articles;
The data acquisition module is used for acquiring information corresponding to the industrial art product storage area, including regional personnel information, and transmitting the regional personnel information to the data processing center;
the camera is used for acquiring the regional monitoring video corresponding to the industrial art product storage region and transmitting the monitoring video to the data processing center;
The data processing center is used for comprehensively analyzing the acquired regional personnel information and regional monitoring video, generating corresponding analysis signals and transmitting the analysis signals to the signal display lamp;
and the signal display lamp is used for displaying the acquired analysis signals by adopting signal lamps with different colors.
The invention provides an industrial art product storage device and a protection system thereof based on the Internet of things. Compared with the prior art, the method has the following beneficial effects:
According to the method, the personnel motion trail in the storage area is analyzed, the personnel are classified according to the motion trail, meanwhile, the suspicious area is determined by combining the corresponding motion trail of the abnormal personnel, the products in the suspicious area are positioned, the detention condition of the abnormal personnel in the suspicious area is further analyzed, the abnormal personnel are secondarily classified, the abnormal personnel after the secondary classification are monitored and analyzed, the historical data are combined in the analysis process to comprehensively process, the corresponding analysis signals are generated, and the comprehensive data are analyzed, so that the accuracy of the overall analysis is improved, the error in the analysis process is reduced, and the potential danger is further reduced.
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FIG. 1 is a schematic block diagram of a system of the present invention;
fig. 2 is a schematic diagram of a storage device according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present application provides an industrial art product protection system based on internet of things, comprising: the system comprises an information acquisition unit, an information comparison and identification unit, a historical data storage unit, a normal monitoring and analysis unit, an abnormal monitoring and analysis unit, a protection supervision and analysis unit and an information output unit, wherein the functional units are in one-way electric connection.
As an embodiment of the invention
The information acquisition unit is used for acquiring basic information corresponding to the industrial art product storage area, and transmitting the acquired basic information to the information comparison and identification unit, wherein the basic information comprises industrial art information corresponding to different areas and video information acquired by monitoring camera shooting.
The information comparison and identification unit is used for analyzing and identifying different people according to the acquired basic information, and the specific identification mode is as follows:
And marking different persons, generating motion tracks of the different persons, classifying the motion tracks according to the motion tracks of the different persons to obtain all the motion tracks, identifying repeated tracks in the motion tracks, calculating the occupation ratio of the repeated tracks, comparing the calculated occupation ratio with a preset occupation ratio, marking the motion tracks of the persons corresponding to the occupation ratio exceeding the preset occupation ratio, marking the corresponding persons as abnormal persons, and not processing the occupation ratio which does not exceed the preset value occupation ratio. Specifically, the collected motion trajectories are classified through data analysis software, all trajectory data are comprehensively examined, repeated or abnormally similar trajectories are identified through an algorithm, the duty ratio of the repeated trajectories in all trajectories is calculated, and the universality and the importance of the repeated trajectories are evaluated in a quantized mode.
Then, a motion track corresponding to an abnormal person is obtained, the motion track is marked as an abnormal motion track, a repeated track in the abnormal motion track is obtained, a suspicious region is determined according to the repeated track, products in the suspicious region are marked, the marks of the products are marked according to the distance from an outlet, other judging modes can be used for marking, and meanwhile, the products are positioned according to the repeated track;
Then, the regional environment of the suspicious region is analyzed by combining the historical data transmitted by the historical data storage unit, the regional environment is represented as the personnel flow amount and the camera number corresponding to the suspicious region, the personnel flow amount corresponding to the suspicious region is compared with the personnel flow amount of the rest region, grading processing is carried out on the suspicious region according to the personnel flow amount, grading processing information is generated at the same time, and the grading processing information is transmitted to the information output unit;
Acquiring the detention condition of an abnormal person in a suspicious region, wherein the detention condition of the abnormal person represents the regional detention time of the abnormal person and the detention time of the artwork, the regional detention time is marked as T1, the detention time of the artwork is marked as T2, the monitoring camera of the abnormal person in the historical data time period T is acquired, the behavior of the abnormal person in the monitoring camera is analyzed, and the specific analysis mode is as follows: acquiring the regional retention time length T1 in the abnormal personnel time period T, comparing the regional retention time length T1 with a preset value Ty1, setting the value of the preset value Ty1 by an operator, further analyzing the abnormal personnel when T1 is more than or equal to Ty1, generating a secondary analysis signal, and otherwise, marking the corresponding abnormal personnel as normal monitoring personnel when T1 is less than Ty 1;
And comparing the artwork retention time length T2 corresponding to the abnormal personnel with a preset value Ty2 aiming at the generated secondary analysis signal, setting a specific value of the preset value Ty2 by an operator, marking the corresponding abnormal personnel as abnormal monitoring personnel when T2 is more than or equal to Ty2, otherwise marking the corresponding abnormal personnel as normal monitoring personnel when T2 is less than Ty2, further transmitting the normal monitoring personnel to a normal monitoring analysis unit, and transmitting the abnormal monitoring personnel to the abnormal monitoring analysis unit.
Specifically, for example, operators of the security team set a preset retention period Ty2, for example, 30 minutes, based on experience or past data analysis. If the time is considered long enough, if the time exceeds the time which can indicate abnormal behaviors such as potential theft or damage risks, when the system detects that the retention time period T2 of a tourist before a specific exhibit exceeds a preset value Ty2 (namely T2 is more than or equal to Ty 2), the tourist is automatically marked as an abnormal monitor, if the retention time period of the tourist is less than the preset value (namely T2< Ty 2), the tourist is marked as a normal monitor, and the information of the tourist marked as the normal monitor is transmitted to a normal monitoring analysis unit, and the staff can conduct routine examination to confirm that no abnormal behaviors are missed.
The normal monitoring analysis unit is used for analyzing the acquired normal monitoring personnel, and the specific analysis mode is as follows:
acquiring an in-doubt area corresponding to a normal monitoring person, classifying the in-doubt area into a detention area, acquiring a monitoring camera corresponding to the normal monitoring person, acquiring and identifying the behavior of the normal monitoring person in the monitoring camera to obtain behavior action information, and analyzing the obtained behavior action information;
The method comprises the steps of establishing a transverse X axis by taking the shoulders of a normal monitor as a reference plane, establishing a Y axis by taking the neck, analyzing the head movements of the normal monitor at the same time, obtaining the head twisting situation of the normal monitor within time t1, further calculating the twisting frequency of the normal monitor, comparing the twisting frequency, indicating that the normal monitor has abnormal behaviors when the value of the twisting frequency within time t1 is larger than the normal frequency, generating an early warning signal, otherwise, indicating that the normal monitor does not have abnormal behaviors when the value of the twisting frequency within time t1 is smaller than the normal frequency, and generating a monitoring signal.
By combining with actual analysis, a three-dimensional space positioning technology is used, a transverse X axis is established by taking the shoulder of a normal monitor as a reference point, a longitudinal Y axis is established by taking the neck position to capture the head motion data of the monitor, the system records and analyzes the motion track of the head of the normal monitor in real time within a specific time interval t1 (for example, one minute), the system comprises multi-directional motions such as rotation, inclination and the like, the head twisting frequency within the time t1 is calculated according to the track data of the head motion, namely, the frequency of head motion is divided by the time length, and the calculated twisting frequency is compared with a preset normal frequency threshold. This normal frequency threshold is an average value or range derived from a large number of past monitored data and represents the average level of activity of the person's head movements under normal conditions.
And transmitting the generated early warning signal to the protection supervision and analysis unit, and transmitting the generated monitoring signal to the information output unit.
The abnormality monitoring and analyzing unit is used for analyzing the acquired abnormality monitoring personnel, and the specific analysis mode is as follows:
Acquiring an abnormal monitoring person, carrying out similarity matching on the abnormal monitoring person and the historical data by combining the acquired historical data, judging whether the abnormal monitoring person has a corresponding historical record, wherein the similarity matching means judging whether the abnormal monitoring person has a corresponding monitoring data record in the past time period, classifying the abnormal monitoring person as a record abnormal person when the abnormal monitoring person has the corresponding historical record, and classifying the abnormal monitoring person as a record-free abnormal person when the abnormal monitoring person does not have the corresponding historical record;
Aiming at recording abnormal personnel, acquiring time intervals of a history record, judging whether the time intervals are regular, acquiring an in-doubt area of the recording abnormal personnel when the time intervals corresponding to the recording abnormal personnel are regular, judging whether the in-doubt area is the same area, generating an early warning protection signal when the in-doubt area is the same area, otherwise, generating an early warning monitoring signal when the in-doubt area is not the same area, transmitting the early warning protection signal to a protection supervision analysis unit, and transmitting the early warning monitoring signal to an information output unit;
in combination with the actual analysis, the system first collects and analyzes the historical occurrence times of the recorded abnormal persons to identify whether any regular time intervals exist, such as the same time period every two days or specific minutes every hour, if the occurrence of the recorded abnormal persons is found to have certain time regularity, the system further acquires the position information of the in-doubt areas where the abnormal persons occur, further analyzes to show that the positions of the occurrence are in the same area, and based on the regularity and the area consistency, the system generates and sends an early warning protection signal to the safety center.
When the time interval corresponding to the abnormal personnel is recorded and no regularity exists, an early warning and monitoring signal is directly generated and transmitted to the information output unit.
Aiming at the person without record abnormality, the motion trail of the person without record abnormality is obtained, highlight marking is carried out on the motion trail, real-time monitoring information is generated, early warning monitoring signals are further generated, and then the generated early warning monitoring signals are transmitted to an information output unit. Specifically, once a person with abnormal monitoring is classified as a "no-record abnormal person", the system immediately acquires a real-time motion trail of the person in the place, and in order to facilitate identification and analysis of a security team, the system highlights the motion trail of the no-record abnormal person on a monitoring interface, and meanwhile, the system generates real-time monitoring information including behavior characteristics and the motion trail of the person.
The protection supervision analysis unit is used for analyzing the acquired early warning protection signals, and the specific analysis mode is as follows:
The method comprises the steps of obtaining personnel behaviors of abnormal monitoring personnel in a time period t2, classifying the personnel behaviors to obtain normal behaviors and abnormal behaviors, carrying out modeling analysis on specific classification standards according to big data, judging whether the behaviors of the current personnel are in contact with products, classifying the behaviors as abnormal behaviors if the behaviors are in contact with the products, otherwise classifying the behaviors as normal behaviors, specifically extracting key features from the data, wherein the features can represent whether an individual is in contact with the products or not, establishing a classification model according to the extracted features by using a machine learning algorithm or a statistical method, analyzing the change condition of the times of the abnormal behaviors in the time period t2, further generating abnormal behavior change information, and transmitting the abnormal behavior change information to an information output unit.
Specifically, the abnormal behavior change condition may be classified as an increase or decrease in the number of times, and corresponding change information may be generated for such change condition.
And the information output unit is used for displaying the acquired change information and the early warning monitoring signal to an operator.
As embodiment II of the present invention
Referring to fig. 2, the industrial art product storage device based on the internet of things comprises a storage box body, a data acquisition module, a camera, a data processing center and a signal display lamp.
The storage box body is used for storing industrial art articles.
The data acquisition module is used for acquiring information corresponding to the industrial art product storage area, including regional personnel information, and transmitting the regional personnel information to the data processing center;
the camera is used for acquiring the regional monitoring video corresponding to the industrial art product storage region and transmitting the monitoring video to the data processing center;
The data processing center is used for comprehensively analyzing the acquired regional personnel information and regional monitoring video, generating corresponding analysis signals and transmitting the analysis signals to the signal display lamp;
and the signal display lamp is used for displaying the acquired analysis signals by adopting signal lamps with different colors.
As embodiment three of the present invention, the emphasis is placed on the implementation of the first and second embodiments in combination.
Some of the data in the above formulas are numerical calculated by removing their dimensionality, and the contents not described in detail in the present specification are all well known in the prior art.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (9)
1. Industrial art product protection system based on thing networking, its characterized in that includes:
the information acquisition unit is used for transmitting the acquired industrial art information and the monitoring camera shooting information corresponding to the industrial art product storage area to the information comparison and identification unit;
the historical data storage unit is used for transmitting the stored historical data to the information comparison and identification unit, the normal monitoring and analysis unit and the abnormal monitoring and analysis unit;
The information comparison and identification unit is used for classifying the personnel according to the movement track of the personnel to obtain abnormal personnel, analyzing the movement track of the abnormal personnel, determining an in-doubt area by combining the personnel flow in the historical data, secondarily classifying the detention condition of the abnormal personnel in the in-doubt area to obtain normal monitoring personnel and abnormal monitoring personnel, transmitting the normal monitoring personnel to the normal monitoring and analyzing unit, and transmitting the abnormal monitoring personnel to the abnormal monitoring and analyzing unit;
The normal monitoring analysis unit is used for analyzing the obtained normal monitoring personnel, generating an early warning signal or a monitoring signal through comparing and analyzing the twisting frequency of the normal monitoring personnel, transmitting the generated early warning signal to the protection supervision analysis unit and transmitting the monitoring signal to the information output unit;
The abnormal monitoring analysis unit is used for analyzing the acquired abnormal monitoring personnel, performing similarity matching on the abnormal monitoring personnel by combining historical data, classifying to obtain recorded abnormal personnel and non-recorded abnormal personnel, respectively analyzing the recorded abnormal personnel and the non-recorded abnormal personnel to generate early warning monitoring signals or early warning protection signals, transmitting the early warning monitoring signals to the information output unit, and transmitting the early warning protection signals to the protection supervision analysis unit;
The protection supervision analysis unit is used for analyzing the acquired early warning protection signals, judging the change condition of personnel behaviors and generating abnormal behavior change information by analyzing the personnel behaviors of abnormal monitoring personnel, and transmitting the abnormal behavior change information to the information output unit;
and the information output unit is used for displaying the acquired change information and the early warning monitoring signals to corresponding operators.
2. The industrial art product protection system based on the internet of things according to claim 1, wherein the specific way for the information comparison and identification unit to classify the personnel is as follows:
Marking different persons, generating motion tracks of the different persons, identifying repeated tracks in the motion tracks, calculating the occupation ratio of the repeated tracks, comparing the calculated occupation ratio with a preset occupation ratio, marking the motion tracks of the persons corresponding to the occupation ratio exceeding the preset occupation ratio, marking the corresponding persons as abnormal persons, and otherwise, not processing.
3. The industrial art product protection system based on the internet of things according to claim 1, wherein the information comparison and identification unit determines the suspicious region according to the abnormal person in such a manner that:
Marking a motion track corresponding to an abnormal person as an abnormal motion track, simultaneously acquiring a repeated track in the abnormal motion track, then determining a suspicious region according to the repeated track, marking products in the suspicious region, and simultaneously positioning the products according to the repeated track;
And then, analyzing the regional environment of the suspicious region by combining the historical data transmitted by the historical data storage unit, comparing the personnel flow amount corresponding to the suspicious region with the personnel flow amount of the residual region, carrying out grading processing on the suspicious region according to the personnel flow amount, generating grading processing information, and transmitting the grading processing information to the information output unit.
4. The industrial art product protection system based on the internet of things according to claim 1, wherein the specific way for the information comparison and identification unit to classify abnormal people in the suspicious region is as follows:
The method comprises the steps of marking the regional detention time of an abnormal person in an in-doubt region as T1, marking the artwork detention time as T2, simultaneously acquiring monitoring cameras of the abnormal person in a historical data time period T, and analyzing the behaviors of the abnormal person in the monitoring cameras;
And acquiring the regional detention time length T1 in the abnormal personnel time period T, comparing the regional detention time length T1 with a preset value Ty1, setting the value of the preset value Ty1 by an operator, further analyzing the abnormal personnel when T1 is more than or equal to Ty1, generating a secondary analysis signal, and otherwise, marking the corresponding abnormal personnel as normal monitoring personnel when T1 is less than Ty 1.
5. The industrial art product protection system based on the internet of things according to claim 4, wherein the specific manner of the information comparison and identification unit for identifying and analyzing the secondary analysis signal is as follows:
And comparing the artwork retention time T2 corresponding to the abnormal person with a preset value Ty2, wherein the specific value of the preset value Ty2 is set by an operator, when T2 is more than or equal to Ty2, marking the corresponding abnormal person as an abnormal monitoring person, and otherwise, when T2 is less than Ty2, marking the corresponding abnormal person as a normal monitoring person.
6. The industrial art product protection system based on the internet of things according to claim 1, wherein the specific way for the normal monitoring analysis unit to analyze the normal monitoring personnel is as follows:
recording the suspicious region corresponding to the normal monitoring personnel as a detention region, simultaneously acquiring a monitoring camera corresponding to the normal monitoring personnel, and then acquiring and identifying the behaviors of the normal monitoring personnel in the monitoring camera to obtain behavior action information;
And establishing a transverse X axis by taking the shoulders of the normal monitoring personnel as a reference plane, establishing a Y axis by taking the neck, analyzing the head movements of the normal monitoring personnel at the same time, acquiring the head twisting condition of the normal monitoring personnel within time t1, calculating the twisting frequency of the normal monitoring personnel, comparing the twisting frequency with the normal frequency, generating an early warning signal when the value of the twisting frequency within time t1 is larger than the normal frequency, and otherwise generating a monitoring signal.
7. The industrial art product protection system based on the internet of things according to claim 1, wherein the specific way for the abnormality monitoring analysis unit to analyze the abnormality monitoring personnel is as follows:
Acquiring abnormal monitoring personnel and historical data, performing similarity matching on the abnormal monitoring personnel and the historical data, judging whether the abnormal monitoring personnel have corresponding historical records, classifying the abnormal monitoring personnel as recording abnormal personnel when the abnormal monitoring personnel have the corresponding historical records, and classifying the abnormal monitoring personnel as recording non-abnormal personnel if the abnormal monitoring personnel have the corresponding historical records;
Aiming at the recorded abnormal personnel, acquiring time intervals of a history record, judging whether the time intervals are regular, acquiring an in-doubt area of the recorded abnormal personnel when the time intervals corresponding to the recorded abnormal personnel are regular, judging whether the in-doubt area is the same area, generating an early warning protection signal when the in-doubt area is the same area, and otherwise, generating an early warning monitoring signal when the in-doubt area is not the same area;
Aiming at the personnel without record abnormality, the motion trail of the personnel without record abnormality is obtained, the motion trail is highlighted, real-time monitoring information is generated, and early warning monitoring signals are further generated.
8. The industrial art product protection system based on the internet of things according to claim 1, wherein the analysis mode of the protection supervision and analysis unit for the early warning protection signal is as follows:
And acquiring the personnel behaviors of the abnormal monitoring personnel in the time period t2, classifying the personnel behaviors according to whether the personnel behaviors are in contact with the product to obtain normal behaviors and abnormal behaviors, classifying the existing contact as the abnormal behaviors, classifying the abnormal behaviors as the normal behaviors if the abnormal behaviors are not in contact with the product, analyzing the change condition of the times of the abnormal behaviors in the time period t2, and further generating abnormal behavior change information.
9. The industrial art product storage device based on the Internet of things is characterized by comprising a storage box body, a data acquisition module, a camera, a data processing center and a signal display lamp;
the storage box body is used for storing industrial art articles;
The data acquisition module is used for acquiring information corresponding to the industrial art product storage area, including regional personnel information, and transmitting the regional personnel information to the data processing center;
the camera is used for acquiring the regional monitoring video corresponding to the industrial art product storage region and transmitting the monitoring video to the data processing center;
The data processing center is used for comprehensively analyzing the acquired regional personnel information and regional monitoring video, generating corresponding analysis signals and transmitting the analysis signals to the signal display lamp;
and the signal display lamp is used for displaying the acquired analysis signals by adopting signal lamps with different colors.
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| CN112132045A (en) * | 2020-09-24 | 2020-12-25 | 天津锋物科技有限公司 | Community personnel abnormal behavior monitoring scheme based on computer vision |
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