CN114598840A - Intelligent badge action track monitoring system and method - Google Patents
Intelligent badge action track monitoring system and method Download PDFInfo
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Abstract
本发明公开了智能徽章行动轨迹监控系统及方法，包括自动录像模块、实时通讯模块、周边识别模块、轨迹监控模块和轨迹预测模块，所述自动录像模块的输出端连接实时通讯模块的输入端，实时通讯模块的输出端连接周边识别模块的输入端，周边识别模块的输出端连接轨迹监控模块的输入端，轨迹监控模块的输出端连接轨迹预测模块的输入端；该发明可实时监控徽章佩带者的行动轨迹，在佩带者走丢或失踪的情况下，他人易于寻找和搜救，保障了佩带者的人身安全，实用性强，且利用人工智能算法构建并优化出行动轨迹预测模型，即便徽章失去了信号，也能根据当前行动轨迹预测徽章佩带者的未来行动轨迹，工作可靠，使用范围广。
The invention discloses an intelligent badge action track monitoring system and method, comprising an automatic video recording module, a real-time communication module, a peripheral identification module, a trajectory monitoring module and a trajectory prediction module, wherein the output end of the automatic video recording module is connected to the input end of the real-time communication module, The output end of the real-time communication module is connected to the input end of the peripheral identification module, the output end of the peripheral identification module is connected to the input end of the trajectory monitoring module, and the output end of the trajectory monitoring module is connected to the input end of the trajectory prediction module; the invention can monitor the badge wearer in real time If the wearer is lost or missing, it is easy for others to find and rescue, which ensures the personal safety of the wearer. It has strong practicability, and uses artificial intelligence algorithms to build and optimize an action trajectory prediction model, even if the badge is lost. It can also predict the future movement trajectory of the badge wearer according to the current movement trajectory, which is reliable and widely used.
Description
Technical Field
The invention relates to the technical field of action track monitoring, in particular to an intelligent badge action track monitoring system and method.
Background
Badge is a mark worn on body to indicate the meaning of identity, occupation, honor, etc. The smart badge, as the name implies, refers to a badge with a microchip embedded therein. At present, the common intelligent badges on the market mainly have the functions of lighting, positioning, communication, video recording and the like.
However, most of the existing intelligent badges cannot monitor the action track of a badge wearer, are difficult to find and search for and rescue by others under the condition that the wearer is lost or lost, do not have the function of image recognition, cannot recognize scene objects around the badge wearer, increase the difficulty of finding and searching and rescuing by others, meet the use requirements of different wearers, and in addition, when the badge loses signals, other people cannot predict the future action track of the badge wearer according to the current action track, so that the working reliability is poor, and the use range is limited.
Disclosure of Invention
The invention aims to provide an intelligent badge action track monitoring system and an intelligent badge action track monitoring method, which aim to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: intelligent badge action track monitoring system, including automatic video recording module, real-time communication module, peripheral identification module, track monitoring module and track prediction module, the input of real-time communication module is connected to the output of automatic video recording module, and peripheral identification module's input is connected to real-time communication module's output, and the input of track monitoring module is connected to peripheral identification module's output, and the input of track prediction module is connected to track monitoring module's output.
Preferably, the automatic video recording module comprises an image acquisition module, a satellite positioning module, a time marking module, a video recording module, a video coding module and a local storage module, wherein the output end of the image acquisition module is connected with the input end of the satellite positioning module, the output end of the satellite positioning module is connected with the input end of the time marking module, the output end of the time marking module is connected with the input end of the video recording module, the output end of the video recording module is connected with the input end of the video coding module, the output end of the video coding module is connected with the input end of the local storage module, and the output end of the local storage module is connected with the input end of the real-time communication module.
Preferably, the real-time communication module comprises 5G communication module, 4G communication module, satellite communication module, high in the clouds storage module, and local storage module's output is all connected to 5G communication module, 4G communication module and satellite communication module's input, and high in the clouds storage module's input is all connected to 5G communication module, 4G communication module and satellite communication module's output, and peripheral identification module's input is connected to high in the clouds storage module's output.
Preferably, the peripheral identification module is by user login module, the video calls the module, the video decoding module, image processing module, scene identification module, map reference module and position correction module constitute, user login module's input is connected the output of high in the clouds storage module, user login module's output is connected the input of video calling the module, video decoding module's input is connected to video calling module's output, image processing module's input is connected to video decoding module's output, image processing module's input is connected to image processing module's output, scene identification module's input is connected to scene identification module's output, map reference module's output is connected the input of position correction module, the input of orbit monitoring module is connected to position correction module's output.
Preferably, the trajectory monitoring module comprises a map loading module, a point location anchoring module, a time sequence marking module, a point location connecting module, a trajectory generating module and a real-time monitoring module, wherein the input end of the map loading module is connected with the output end of the point location correcting module, the output end of the map loading module is connected with the input end of the point location anchoring module, the output end of the point location anchoring module is connected with the input end of the time sequence marking module, the output end of the time sequence marking module is connected with the input end of the point location connecting module, the output end of the point location connecting module is connected with the input end of the trajectory generating module, the output end of the trajectory generating module is connected with the input end of the real-time monitoring module, and the output end of the real-time monitoring module is connected with the input end of the trajectory predicting module.
Preferably, the trajectory prediction module is composed of a data screening module, a model building module, a model optimization module and a real-time prediction module, wherein the input end of the data screening module is connected with the output end of the real-time monitoring module, the output end of the data screening module is connected with the input end of the model building module, the output end of the model building module is connected with the input end of the model optimization module, and the output end of the model optimization module is connected with the input end of the real-time prediction module.
The intelligent badge action track monitoring method comprises the following steps: step one, recording a video; step two, communication is carried out; step three, identifying; step four, monitoring; step five, predicting;
in the first step, a light and shadow image in the front area of the badge is acquired through the image acquisition module, the shooting place of the light and shadow image is determined through the satellite positioning module, a time stamp is marked on the light and shadow image through the time marking module, an image in the front area of the badge is recorded for a period of time through the video recording module, and the image is encoded through the video encoding module and then stored in the local storage module;
in the second step, the coded video is uploaded to a cloud end through a 5G communication module, a 4G communication module or a satellite communication module according to the signal intensity and is stored in a cloud end storage module;
in the third step, a monitoring system at the cloud is logged in through a user login module, a coded video is called through a video calling module, after the coded video is decoded through a video decoding module and subjected to gray level processing through an image processing module, various scene objects in the video are identified through a scene identification module, street view data in a satellite map are compared through a map reference module, and the point position of a badge on an electronic map is corrected through a point position correction module;
in the fourth step, a satellite map is loaded through a map loading module, the position of each action point on the satellite map is determined through a point location anchoring module, the time sequence of each action point is marked through a time sequence marking module, each action point is connected into a line through a point location connecting module according to the time sequence, the action track of the badge is continuously generated on the satellite map after being processed by a track generating module, and then the action track of the badge is monitored in real time through a real-time monitoring module;
and in the fifth step, historical data with strong correlation with the current action are screened out from the historical action tracks of the badge through the data screening module, a track prediction initial model is built through the model building module by utilizing a deep learning algorithm, the track prediction initial model is trained through the model optimizing module by utilizing the existing data of the current action, the track prediction initial model is optimized, and the action track behind the badge is predicted through the real-time predicting module.
Compared with the prior art, the invention has the beneficial effects that: the intelligent badge action track monitoring system and the method monitor the action track of a badge wearer in real time, and other people can easily find and search and rescue under the condition that the wearer is lost or lost, thereby ensuring the personal safety of the wearer; the badge positioning device has an image recognition function, can recognize scene objects around a badge wearer, is more accurate in positioning, reduces the difficulty of searching and rescuing by other people, and meets the use requirements of different wearers; the travel movement track prediction model is constructed and optimized by using an artificial intelligence algorithm, when the badge loses signals, the future movement track of a badge wearer can be predicted according to the current movement track, the operation is reliable, and the application range is wide.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a system flow diagram of the present invention;
FIG. 3 is a flow chart of the method of the present invention;
in the figure: 1. an automatic video recording module; 11. an image acquisition module; 12. a satellite positioning module; 13. a time stamp module; 14. a video recording module; 15. a video encoding module; 16. a local storage module; 2. a real-time communication module; 21. 5G communication module; 22. a 4G communication module; 23. a satellite communication module; 24. a cloud storage module; 3. a periphery identification module; 31. a user login module; 32. a video calling module; 33. a video decoding module; 34. an image processing module; 35. a scene recognition module; 36. a map reference module; 37. a point location correction module; 4. a trajectory monitoring module; 41. a map loading module; 42. a point location anchoring module; 43. a timing sequence marking module; 44. a point location connecting module; 45. a trajectory generation module; 46. a real-time monitoring module; 5. a trajectory prediction module; 51. a data screening module; 52. a model building module; 53. a model optimization module; 54. and a real-time prediction module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, an embodiment of the present invention is shown: an intelligent badge action track monitoring system comprises an automatic video recording module 1, a real-time communication module 2, a periphery recognition module 3, a track monitoring module 4 and a track prediction module 5, wherein the output end of the automatic video recording module 1 is connected with the input end of the real-time communication module 2, the output end of the real-time communication module 2 is connected with the input end of the periphery recognition module 3, the output end of the periphery recognition module 3 is connected with the input end of the track monitoring module 4, the output end of the track monitoring module 4 is connected with the input end of the track prediction module 5, the automatic video recording module 1 is composed of an image acquisition module 11, a satellite positioning module 12, a time marking module 13, a video recording module 14, a video coding module 15 and a local storage module 16, the output end of the image acquisition module 11 is connected with the input end of the satellite positioning module 12, and the output end of the satellite positioning module 12 is connected with the input end of the time marking module 13, the output end of the time marking module 13 is connected with the input end of the video recording module 14, the output end of the video recording module 14 is connected with the input end of the video coding module 15, the output end of the video coding module 15 is connected with the input end of the local storage module 16, the output end of the local storage module 16 is connected with the input end of the real-time communication module 2, the real-time communication module 2 is composed of a 5G communication module 21, a 4G communication module 22, a satellite communication module 23 and a cloud storage module 24, the input ends of the 5G communication module 21, the 4G communication module 22 and the satellite communication module 23 are connected with the output end of the local storage module 16, the output ends of the 5G communication module 21, the 4G communication module 22 and the satellite communication module 23 are connected with the input end of the cloud storage module 24, the output end of the cloud storage module 24 is connected with the input end of the peripheral identification module 3, and the peripheral identification module 3 is composed of a user login module 31, The input end of the user login module 31 is connected with the output end of the cloud storage module 24, the output end of the user login module 31 is connected with the input end of the video calling module 32, the output end of the video calling module 32 is connected with the input end of the video decoding module 33, the output end of the video decoding module 33 is connected with the input end of the image processing module 34, the output end of the image processing module 34 is connected with the input end of the scene recognition module 35, the output end of the scene recognition module 35 is connected with the input end of the map reference module 36, the output end of the map reference module 36 is connected with the input end of the point correction module 37, the output end of the point correction module 37 is connected with the input end of the track monitoring module 4, and the video recognition function is achieved, scene objects around a badge wearer can be identified, positioning is more accurate, difficulty in searching and rescuing by other people is reduced, and using requirements of different wearers are met; the track monitoring module 4 is composed of a map loading module 41, a point location anchoring module 42, a time sequence marking module 43, a point location connecting module 44, a track generating module 45 and a real-time monitoring module 46, wherein the input end of the map loading module 41 is connected with the output end of the point location correcting module 37, the output end of the map loading module 41 is connected with the input end of the point location anchoring module 42, the output end of the point location anchoring module 42 is connected with the input end of the time sequence marking module 43, the output end of the time sequence marking module 43 is connected with the input end of the point location connecting module 44, the output end of the point location connecting module 44 is connected with the input end of the track generating module 45, the output end of the track generating module 45 is connected with the input end of the real-time monitoring module 46, the output end of the real-time monitoring module 46 is connected with the input end of the track predicting module 5, the action track of a badge wearer is monitored in real time, and under the condition that the wearer is lost or lost, other people are easy to find, search and rescue, and personal safety of wearers is guaranteed; the trajectory prediction module 5 consists of a data screening module 51, a model construction module 52, a model optimization module 53 and a real-time prediction module 54, wherein the input end of the data screening module 51 is connected with the output end of the real-time monitoring module 46, the output end of the data screening module 51 is connected with the input end of the model construction module 52, the output end of the model construction module 52 is connected with the input end of the model optimization module 53, the output end of the model optimization module 53 is connected with the input end of the real-time prediction module 54, when a badge loses signals, the future action trajectory of a badge wearer can be predicted according to the current action trajectory, the operation is reliable, and the application range is wide.
Referring to fig. 3, an embodiment of the present invention: the intelligent badge action track monitoring method comprises the following steps: step one, recording a video; step two, communication is carried out; step three, identifying; step four, monitoring; step five, predicting;
in the first step, a light and shadow image of an area in front of the badge is acquired through the image acquisition module 11, a shooting place of the light and shadow image is determined through the satellite positioning module 12, a time stamp is marked on the light and shadow image through the time marking module 13, an image of the area in front of the badge is recorded for a period of time through the video recording module 14, and the image is encoded through the video encoding module 15 and then stored in the local storage module 16;
in the second step, the coded video is uploaded to a cloud terminal through the 5G communication module 21, the 4G communication module 22 or the satellite communication module 23 according to the signal intensity and is stored in the cloud terminal storage module 24;
in the third step, the user logs in the monitoring system in the cloud through the user login module 31, calls the coded video through the video call module 32, decodes the coded video through the video decoding module 33, performs gray processing on the coded video through the image processing module 34, recognizes various scene objects in the coded video through the scene recognition module 35, compares street view data in the satellite map through the map reference module 36, and corrects the point position of the badge on the electronic map through the point position correction module 37;
in the fourth step, a satellite map is loaded through the map loading module 41, the position of each action point on the satellite map is determined through the point location anchoring module 42, the time sequence of each action point is marked through the time sequence marking module 43, each action point is connected into a line through the point location connecting module 44 according to the time sequence, the action track of the badge is continuously generated on the satellite map after being processed by the track generating module 45, and then the action track of the badge is monitored in real time through the real-time monitoring module 46;
in the fifth step, historical data with strong correlation with the current action is screened out from historical action tracks of the badge through the data screening module 51, a track prediction initial model is built through the model building module 52 by using a deep learning algorithm, the track prediction initial model is trained through the model optimizing module 53 by using existing data of the current action, the track prediction initial model is optimized, and the action track behind the badge is predicted through the real-time predicting module 54.
The working principle is as follows: when the badge is used, a light and shadow image in an area in front of a badge is collected through the image collection module 11, a shooting place of the light and shadow image is determined through the satellite positioning module 12, a time stamp is marked on the light and shadow image through the time marking module 13, an image in the area in front of the badge is recorded for a period of time through the video recording module 14, and the image is coded through the video coding module 15 and then stored in the local storage module 16; then, the coded video is uploaded to the cloud through the 5G communication module 21, the 4G communication module 22 or the satellite communication module 23 according to the signal intensity and is stored in the cloud storage module 24; then logging in a monitoring system at the cloud end through a user login module 31, calling a coded video through a video calling module 32, decoding through a video decoding module 33, performing gray processing through an image processing module 34, identifying various scene objects in the video through a scene identification module 35, comparing street view data in a satellite map through a map reference module 36, and further correcting the point positions of badges on the electronic map through a point position correction module 37; finally, a satellite map is loaded through a map loading module 41, the position of each action point on the satellite map is determined through a point anchoring module 42, the time sequence of each action point is marked through a time sequence marking module 43, each action point is connected into a line through a point connecting module 44 according to the time sequence, the action track of the badge is continuously generated on the satellite map after being processed through a track generating module 45, and then the action track of the badge is monitored in real time through a real-time monitoring module 46; meanwhile, historical data with strong correlation with the current action can be screened from historical action tracks of the badge through the data screening module 51, a track prediction initial model is built through the model building module 52 by using a deep learning algorithm, the track prediction initial model is trained through the model optimizing module 53 by using existing data of the current action, the travel action track prediction model is optimized, the action track behind the badge is predicted through the real-time prediction module 54, when the badge loses signals, the future action track of a badge wearer can be predicted according to the current action track, the working is reliable, the use range is wide, the difficulty of searching and rescuing by other people is reduced, and the personal safety of the wearer is guaranteed.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (7)
1. Intelligence badge action track monitored control system, including automatic video recording module (1), real-time communication module (2), peripheral identification module (3), track monitoring module (4) and track prediction module (5), its characterized in that: the output end of the automatic video recording module (1) is connected with the input end of the real-time communication module (2), the output end of the real-time communication module (2) is connected with the input end of the peripheral identification module (3), the output end of the peripheral identification module (3) is connected with the input end of the track monitoring module (4), and the output end of the track monitoring module (4) is connected with the input end of the track prediction module (5).
2. The intelligent badge action track monitoring system of claim 1, wherein: the automatic video recording module (1) is composed of an image acquisition module (11), a satellite positioning module (12), a time marking module (13), a video recording module (14), a video coding module (15) and a local storage module (16), the output end of the image acquisition module (11) is connected with the input end of the satellite positioning module (12), the output end of the satellite positioning module (12) is connected with the input end of the time marking module (13), the output end of the time marking module (13) is connected with the input end of the video recording module (14), the output end of the video recording module (14) is connected with the input end of the video coding module (15), the output end of the video coding module (15) is connected with the input end of the local storage module (16), and the output end of the local storage module (16) is connected with the input end of the real-time communication module (2).
3. The intelligent badge action track monitoring system of claim 2, characterized in that: real-time communication module (2) comprises 5G communication module (21), 4G communication module (22), satellite communication module (23), high in the clouds storage module (24), 5G communication module (21), the output of local storage module (16) is all connected to the input of 4G communication module (22) and satellite communication module (23), 5G communication module (21), the input of high in the clouds storage module (24) is all connected to the output of 4G communication module (22) and satellite communication module (23), the input of peripheral identification module (3) is connected to the output of high in the clouds storage module (24).
4. The intelligent badge action track monitoring system of claim 3, characterized in that: the periphery identification module (3) consists of a user login module (31), a video calling module (32), a video decoding module (33), an image processing module (34), a scene identification module (35), a map reference module (36) and a point correction module (37), wherein the input end of the user login module (31) is connected with the output end of the cloud storage module (24), the output end of the user login module (31) is connected with the input end of the video calling module (32), the output end of the video calling module (32) is connected with the input end of the video decoding module (33), the output end of the video decoding module (33) is connected with the input end of the image processing module (34), the output end of the image processing module (34) is connected with the input end of the scene identification module (35), the output end of the scene identification module (35) is connected with the input end of the map reference module (36), the output end of the map reference module (36) is connected with the input end of the point location correction module (37), and the output end of the point location correction module (37) is connected with the input end of the track monitoring module (4).
5. The intelligent badge action track monitoring system of claim 4, characterized in that: the track monitoring module (4) consists of a map loading module (41), a point location anchoring module (42), a time sequence marking module (43), a point location connecting line module (44), a track generating module (45) and a real-time monitoring module (46), the input end of the map loading module (41) is connected with the output end of the point location correction module (37), the output end of the map loading module (41) is connected with the input end of the point location anchoring module (42), the output end of the point location anchoring module (42) is connected with the input end of the time sequence marking module (43), the output end of the time sequence marking module (43) is connected with the input end of the point location connecting module (44), the output end of the point location connecting module (44) is connected with the input end of the track generation module (45), the output end of the track generation module (45) is connected with the input end of the real-time monitoring module (46), and the output end of the real-time monitoring module (46) is connected with the input end of the track prediction module (5).
6. The intelligent badge action track monitoring system of claim 5, wherein: the trajectory prediction module (5) is composed of a data screening module (51), a model building module (52), a model optimization module (53) and a real-time prediction module (54), wherein the input end of the data screening module (51) is connected with the output end of the real-time monitoring module (46), the output end of the data screening module (51) is connected with the input end of the model building module (52), the output end of the model building module (52) is connected with the input end of the model optimization module (53), and the output end of the model optimization module (53) is connected with the input end of the real-time prediction module (54).
7. The intelligent badge action track monitoring method comprises the following steps: recording a video; step two, communication is carried out; step three, identifying; step four, monitoring; step five, predicting; the method is characterized in that:
in the first step, a light and shadow image in the area in front of the badge is acquired through an image acquisition module (11), the shooting place of the light and shadow image is determined through a satellite positioning module (12), a time stamp is marked on the light and shadow image through a time marking module (13), an image in the area in front of the badge is recorded for a period of time through a video recording module (14), and the image is stored in a local storage module (16) after being encoded through a video encoding module (15);
in the second step, the coded video is uploaded to a cloud end through a 5G communication module (21), a 4G communication module (22) or a satellite communication module (23) according to the signal intensity and is stored in a cloud end storage module (24);
in the third step, a monitoring system at the cloud is logged in through a user login module (31), a coded video is called through a video calling module (32), after the coded video is decoded through a video decoding module (33) and subjected to gray level processing through an image processing module (34), various scene objects in the video are identified through a scene identification module (35), street view data in a satellite map are compared through a map reference module (36), and the point position of a badge on an electronic map is corrected through a point position correction module (37);
in the fourth step, a satellite map is loaded through a map loading module (41), the position of each action point on the satellite map is determined through a point location anchoring module (42), the time sequence of each action point is marked through a time sequence marking module (43), each action point is connected into a line through a point location connecting module (44) according to the time sequence, an action track of a badge is continuously generated on the satellite map after being processed through a track generating module (45), and then the action track of the badge is monitored in real time through a real-time monitoring module (46);
in the fifth step, historical data with strong correlation with the current action is screened out from historical action tracks of the badge through a data screening module (51), a track prediction initial model is built through a model building module (52) by utilizing a deep learning algorithm, the track prediction initial model is trained through a model optimizing module (53) by utilizing existing data of the current action, the track prediction model is optimized, and the action track behind the badge is predicted through a real-time predicting module (54).
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