WO2019144762A1 - 基于云服务器的鼠患智能监控系统和方法 - Google Patents

基于云服务器的鼠患智能监控系统和方法 Download PDF

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Publication number
WO2019144762A1
WO2019144762A1 PCT/CN2018/124463 CN2018124463W WO2019144762A1 WO 2019144762 A1 WO2019144762 A1 WO 2019144762A1 CN 2018124463 W CN2018124463 W CN 2018124463W WO 2019144762 A1 WO2019144762 A1 WO 2019144762A1
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Prior art keywords
mouse
cloud server
thermal infrared
video segment
rodent
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PCT/CN2018/124463
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English (en)
French (fr)
Inventor
黄平花
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深圳冠思大数据服务有限公司
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Priority to EP18902510.9A priority Critical patent/EP3745713A4/en
Priority to US16/325,106 priority patent/US10977805B2/en
Publication of WO2019144762A1 publication Critical patent/WO2019144762A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/0022—Radiation pyrometry, e.g. infrared or optical thermometry for sensing the radiation of moving bodies
    • G01J5/0025—Living bodies
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/77—Determining position or orientation of objects or cameras using statistical methods
    • 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
    • G08B21/18—Status alarms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H04N7/188—Capturing isolated or intermittent images triggered by the occurrence of a predetermined event, e.g. an object reaching a predetermined position
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J2005/0077—Imaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30242—Counting objects in image
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
    • G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
    • G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
    • G08B13/19608—Tracking movement of a target, e.g. by detecting an object predefined as a target, using target direction and or velocity to predict its new position
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
    • G08B13/19665—Details related to the storage of video surveillance data
    • G08B13/19669—Event triggers storage or change of storage policy

Definitions

  • the invention relates to the field of intelligent monitoring technology, in particular to a mouse server intelligent monitoring system and method based on a cloud server.
  • Rodent is a vector for the occurrence and prevalence of many diseases. It can spread plague, epidemic hemorrhagic fever, leptospirosis and other diseases, which can cause serious harm to humans. It is very important to prevent rodents. Because mice are fast-growing and highly viable animals in mammals, they are fast-moving, large-scale, widely distributed, frequent migration, and usually appear at night and when there are few people. The activity route is uncertain, usually in three-dimensional mode. Therefore, the current artificial mousetrap is labor intensive and inefficient. Other methods of rodent control, such as physical rodent control, usually set the mousetrap. Because the activity track of the mouse is not clear, the efficiency is low. Chemical rodent control is usually poisoned by drugs, which is easy to occur in food distribution areas such as kitchens.
  • the invention aims at various problems existing in the prior art, such as artificial rattrap, mousetrap or drug poisoning, which is adopted after the occurrence of rodent diseases, and provides a cloud server-based mouse-based intelligent monitoring system, which utilizes camera technology and thermal infrared.
  • the sensing technology effectively monitors the activity track of the mouse, performs rough recognition through the intelligent terminal and finely recognizes the moving mouse with the cloud server, performs data aggregation analysis and further reminds.
  • the invention also relates to a cloud server-based mouse patient intelligence monitoring method.
  • a cloud server-based mouse-based intelligent monitoring system which comprises a thermal infrared camera device, an intelligent terminal and a cloud server connected in sequence,
  • the thermal infrared imaging device monitors the activity track of the mouse by using the thermal infrared sensing technology and the camera technology, and sends the monitored video frame to the intelligent terminal; the intelligent terminal discriminates the moving object through the artificial intelligence algorithm, and records the current video frame and the mark Motion trajectory and transmitting a video segment with a motion trajectory to a cloud server; the cloud server uses an additional artificial intelligence algorithm to identify the moving mouse and save the current video segment and the current video segment for the video segment with the motion trajectory
  • the position and movement trajectory of the mouse are marked, and the data of the mouse position and the trajectory are analyzed and processed to obtain the relevant parameter information of the mouse to realize the intelligent monitoring of the mouse.
  • the smart terminal performs a deep learning by using a moving target detection algorithm to identify a moving object;
  • the cloud server uses an image recognition algorithm and a continuous motion stitching algorithm to identify a moving mouse for a video segment with a motion trajectory.
  • the thermal infrared imaging device includes at least one set of thermal infrared camera groups including a plurality of thermal infrared cameras, the number of the smart terminals corresponding to the number of sets of the thermal infrared camera group, and the smart terminal and the corresponding thermal infrared camera
  • the cloud server is also used to manage the entire monitoring network, and realize data sharing and/or data downloading through the cloud server when multiple intelligent terminals are used.
  • the system further includes an alarm device, the alarm device is connected to the cloud server, and the alarm device is provided with an alarm threshold, and the related information of the mouse active parameter obtained by the cloud server in the data summary analysis process exceeds the corresponding alarm. At the threshold, the alarm device activates an alarm function.
  • system further includes a visual dashboard, the visual dashboard is connected to the cloud server, and the cloud server generates a report after displaying the relevant parameter information of the mouse activity obtained by the data aggregation analysis and processing, and displaying the report through the visual dashboard;
  • the cloud server generates a report after the data related to the active parameter of the mouse obtained by the data aggregation analysis process, and returns the report to the smart terminal and then displays it through the human terminal interface of the smart terminal.
  • the parameter information related to the mouse activity obtained by the cloud server includes the frequency of the mouse infestation, the motion track line, the activity hot zone, the time and/or the quantity.
  • the thermal infrared camera group includes two thermal infrared cameras, the two thermal infrared cameras are respectively disposed opposite to the center of the two short sides of the rectangular monitoring space, and the angle of the thermal infrared camera is horizontal or downward. .
  • a cloud server-based mouse-based intelligent monitoring method is characterized in that the method first uses thermal infrared sensing technology and camera technology to monitor a mouse's activity trajectory and sends the monitored video frame to the intelligent terminal;
  • the artificial intelligence algorithm discriminates the moving object, records the current video frame and the marker motion track, and transmits the video segment with the motion track to the cloud server; then the cloud server uses another artificial intelligence algorithm to identify the video segment with the motion track.
  • the moving mouse is saved and the current video segment is saved and the mouse position and motion trajectory are marked in the current video segment, and then the mouse position and motion trajectory are analyzed and processed to obtain the relevant parameter information of the mouse active to realize the intelligent monitoring of the mouse.
  • the method uses the moving target detection algorithm for deep learning to identify the moving object
  • the cloud server uses the image recognition algorithm and the continuous motion stitching algorithm to identify the moving mouse for the video segment with the motion track.
  • the method updates the rodent statistical data after the cloud server performs the data aggregation analysis and processing of the mouse related parameter information, and starts the alarm function when the rodent statistical data exceeds the preset alarm threshold;
  • the method further generates a report after the data related to the active parameter of the mouse obtained by the cloud server, and generates a report through the visual dashboard, and the obtained parameter information of the active mouse includes the frequency and motion track of the mouse. Line, activity hotspot, time and/or quantity.
  • the cloud server-based mouse-based intelligent monitoring system provides a thermal infrared camera device, an intelligent terminal and a cloud server which are sequentially connected, and the thermal infrared camera device uses thermal infrared sensing technology and camera technology to monitor the activity track of the mouse,
  • the mouse often used the space to use the camera technology to achieve 24-hour surveillance video, monitor the collection and tracking of the mouse's activity trails, use thermal infrared sensing technology to effectively monitor the mouse's nighttime trajectory
  • intelligent terminal can store surveillance video, through artificial intelligence algorithm (For example, the moving target detection algorithm performs deep learning) to discriminate the moving object, record the current video frame and marker motion trajectory, that is, to perform moving target detection and real-time path tracking through the intelligent terminal, real-time monitoring, and roughly identify the mouse; the smart terminal will have motion
  • the video segment of the trajectory is transmitted to the cloud server, that is, the result of roughly recognizing the mouse is sent to the cloud server, and the cloud server uses another artificial intelligence algorithm (for example, using an image recognition algorithm and a continuous motion
  • the moving mouse saves the current video segment and marks the mouse position and motion track in the current video segment to finely identify the moving mouse, and then analyzes and analyzes the mouse position and motion trajectory to obtain the relevant parameter information of the mouse active.
  • Intelligent monitoring The mouse-based intelligent monitoring system of the invention utilizes the camera technology and the thermal infrared sensing technology, adopts the cloud server to manage the entire monitoring network, and reduces the burden of the cloud server according to the two-level data processing algorithm of the smart terminal rough recognition and the cloud server fine recognition.
  • the video segment with motion track needs to be further processed to determine whether the mouse is really moving, and the data is aggregated and analyzed, and reminders can be made to help solve the problem.
  • the system can accurately identify the mouse by intelligent monitoring.
  • the activity track of the mouse which can visually see the movement track of the mouse, enables early detection and control of the effects of the mouse, which completely avoids the prior art that only the artificial mouse can be caught and caught due to the inability to detect the trace of the mouse early.
  • the method of the mouse or the drug poisoning is labor-intensive, inefficient, and has potential safety hazards.
  • the system of the present invention can help the rodent-killing company or the commercial organization to develop a rodent-killing strategy, so as to adopt targeted killing. Rat measures to effectively capture mice and reduce unnecessary losses Intelligent monitoring, reduce labor costs, safety and reliability, improve monitoring efficiency.
  • the thermal infrared imaging device comprises at least one set of thermal infrared camera groups including a plurality of thermal infrared cameras, the number of intelligent terminals corresponding to the number of sets of the thermal infrared camera groups and the heats of the intelligent terminals and the corresponding thermal infrared camera groups
  • the infrared cameras are connected, that is, a set of thermal infrared camera sets and an intelligent terminal can be used, or multiple sets of thermal infrared camera sets and a plurality of corresponding intelligent terminals can be used. In this case, for example, in each monitoring space.
  • a set of thermal infrared camera groups each intelligent terminal is connected to the cloud server to form a multi-level monitoring network, centrally manage the entire monitoring network through the cloud server, realize separate processing and centralized management of data sent by each intelligent terminal, and realize data Sharing and / or data downloads.
  • the use of cloud servers has greatly increased the storage capacity, the number of thermal infrared cameras has also increased greatly, the monitoring space is also wider, and the monitoring efficiency is more reflected.
  • the plurality of thermal infrared cameras in the thermal infrared camera group preferably may include two thermal infrared cameras, and the two thermal infrared cameras may be respectively disposed opposite to each other in the center of the two short sides of the rectangular monitoring space, and the angle of the thermal infrared camera is horizontal or downward. At a certain angle. In this way, for a rectangular monitoring space with a small area, it is possible to use as few thermal infrared cameras as possible to cover the wall, the ground, and the roof to a minimum, and the dead angle is small.
  • the invention also relates to a cloud server-based mouse-based intelligent monitoring method, which corresponds to the above-mentioned cloud server-based mouse-based intelligent monitoring system of the present invention, and can be understood as implementing the above-mentioned cloud server-based mouse-based intelligent monitoring system
  • the method uses camera technology and thermal infrared sensing technology to monitor and track the activity traces of rats, and performs two-level data processing algorithms based on intelligent terminal for rough recognition and cloud server for fine identification.
  • the cloud server data is aggregated, processed and analyzed.
  • the relevant parameter information of the mouse is active, and the report can be further formed to realize the intelligent monitoring of the mouse.
  • FIG. 1 is a schematic structural diagram of a mouse-based intelligent monitoring system based on a cloud server according to the present invention.
  • FIG. 2 is a schematic diagram of a preferred structure of a mouse-based intelligent monitoring system based on a cloud server according to the present invention.
  • 3a and 3b are respectively a top view and a side view of a preferred installation of the thermal infrared imaging device in the rodent intelligent monitoring system of the present invention.
  • FIG. 4 is a schematic diagram of the working principle of the smart terminal.
  • Figure 5 shows the working principle of the cloud server.
  • Figures 6a-6d are visual dashboard representations of the relevant parameters of the mouse activity obtained in the mouse-borne intelligent monitoring system of the present invention.
  • FIG. 7 is a flowchart of a preferred method for monitoring a mouse-based intelligent monitoring method based on a cloud server according to the present invention.
  • the invention relates to a mouse-based intelligent monitoring system based on a cloud server, and the structure thereof is as shown in FIG. 1 , which comprises a thermal infrared imaging device 1 , an intelligent terminal 2 and a cloud server 3 connected in sequence, wherein the thermal infrared imaging device 1 utilizes heat.
  • Infrared sensing technology and camera technology monitor the mouse's activity track and send the monitored video frame to the smart terminal 2; the smart terminal 2 discriminates the moving object through the artificial intelligence algorithm, records the current video frame and the marked motion track, and will have motion
  • the video segment of the trajectory is transmitted to the cloud server 3; the cloud server 3 uses an additional artificial intelligence algorithm to identify the moving mouse for the video segment with the motion trajectory and saves the current video segment and marks the mouse position and motion trajectory in the current video segment. Then, the data of the mouse position and the trajectory are analyzed and processed to obtain the relevant parameter information of the mouse active, so as to realize the intelligent monitoring of the mouse.
  • the thermal infrared camera device 1 uses thermal infrared sensing technology and camera technology to monitor the activity track of the mouse, so the thermal infrared camera device 1 can be understood as a component that integrates the camera and the pyroelectric infrared sensor, and can pass through the main activity range of the mouse.
  • the camera realizes 24-hour surveillance video, which can monitor the time, number of times, key activity areas and trend changes of rats, monitor the collection and tracking of mouse activity trails, and effectively monitor the nighttime trajectory of rats by pyroelectric infrared sensor.
  • the imaging apparatus 1 transmits the monitored video frame to the smart terminal 2.
  • the working principle of the intelligent terminal 2 is as shown in FIG. 4, which collects and receives the video frame of the thermal infrared imaging device 1, and then performs motion detection.
  • the artificial intelligence algorithm used preferably includes a moving target detection algorithm and a deep learning algorithm, specifically through motion.
  • the target detection algorithm combines the deep learning algorithm to perform deep learning to identify a moving object (or a moving object), which may be a mouse or other moving object, and records the current video frame and the marker motion track when the moving object is detected, and continues Acquiring and receiving the video frame of the thermal infrared imaging device 1, continuing to perform the subsequent steps of motion detection, etc., finally obtaining a video segment with a motion track; and returning to the work of collecting the video frame of the receiving thermal infrared imaging device 1 when the moving object is not detected.
  • the intelligent terminal 2 is used to store surveillance video, real-time monitoring, and roughly identify the mouse.
  • the smart terminal 2 can specifically adopt a PC, a notebook computer, a mobile phone or a PAD.
  • the smart terminal 2 can adopt other artificial intelligence algorithms or artificial intelligence technologies capable of real-time moving target detection and real-time path tracking, and finally transmit the video segment with the motion track to the cloud server 3.
  • the working principle of the cloud server 3 is as shown in FIG. 5, which receives a video segment with a motion trajectory, or selects a video segment with a motion region and an active trajectory, and then performs mouse detection, and another artificial intelligence algorithm is adopted.
  • the image recognition algorithm and the continuous motion stitching algorithm are included, and the interference of dust and other items may be preliminarily excluded, and the image recognition algorithm performs volume filtering, color filtering and other characteristics of the mouse to identify the mouse, and the size of the mouse is usually 5 cm- 20cm, the color of the mouse is usually gray, black and white.
  • the image recognition algorithm can exclude other moving objects other than mice according to the characteristics of the mouse, identify the mouse, and combine the continuous motion stitching algorithm to recognize the movement pattern of the mouse, combining the two algorithms. Identify the moving mouse, save the current video segment when the mouse is detected, delete the video segment when no mouse is detected, and return to continue selecting the next video segment with the motion area and active track to continue the mouse detection, after detecting the mouse And after saving the video segment, mark the current video segment Record the mouse position and motion trajectory, and then analyze and process the mouse position and motion trajectory to obtain the relevant parameter information of the mouse active, and update the mouse statistic data to further alarm and continue to return to the next motion zone.
  • the video segment of the activity track continues to be tested by the mouse to realize intelligent monitoring of the mouse.
  • the cloud server 3 can also adopt other artificial intelligence algorithms or artificial intelligence technologies that can realize the mouse that accurately recognizes the movement. Cloud server 3 is used to manage the entire monitoring network, to finely identify mice, to summarize, process, analyze, and further form reports.
  • the thermal infrared imaging device 1 may include at least one set of thermal infrared camera groups including a plurality of thermal infrared cameras 11 , the number of intelligent terminals corresponding to the number of sets of thermal infrared camera groups and the intelligent terminal and the corresponding thermal infrared camera group Each of the thermal infrared cameras 11 is connected.
  • FIG. 2 is a schematic diagram showing a preferred structure of a cloud server-based mouse-based intelligent monitoring system according to the present invention.
  • the thermal infrared camera device 1 includes three sets of thermal infrared camera groups, each set of thermal infrared camera sets includes two thermal infrared cameras 11, and the smart terminals 2 are also correspondingly arranged three, and each intelligent terminal 2 is associated with The respective thermal infrared cameras 11 in the corresponding thermal infrared camera group are connected, and the three smart terminals 2 are connected to the cloud server 3.
  • the mouse-based intelligent monitoring system shown in FIG. 2 further includes a visual dashboard 4 connected to the cloud server 3, and the cloud server 3 generates a report after displaying the relevant parameters of the mouse activity obtained by the data aggregation analysis and processing, and displays the data through the visual dashboard 4.
  • the visual dashboard 4 can be a mobile phone, a computer or a tablet.
  • the cloud server 3 instead of setting the visual dashboard 4, the cloud server 3 generates a report after the data related to the mouse activity obtained by the data aggregation analysis processing, and returns the report to the smart terminal 2 and then displays it through the human-machine interface of the smart terminal 2.
  • the cloud server-based mouse-based intelligent monitoring system of the present invention may further comprise an alarm device, the alarm device is connected to the cloud server, and the alarm device is provided with an alarm threshold, and the cloud server obtains relevant parameters of the mouse activity obtained by the data summary analysis and processing. When the corresponding alarm threshold is exceeded, the alarm device activates the alarm function to prevent the rodent from occurring before the occurrence of the rodent, reducing unnecessary losses.
  • a plurality of sets of thermal infrared camera sets and a plurality of corresponding intelligent terminals 2 are used. At this time, for example, a set of thermal infrared camera sets are set in each monitoring space, and each intelligent terminal 2 is connected to the cloud server 3 to form a multi-level monitoring.
  • the network centrally manages the entire monitoring network through the cloud server 3, and realizes separate processing and centralized management of data sent by each intelligent terminal 2, and implements data sharing and/or data downloading.
  • the monitoring space can be specifically a room, such as a kitchen where rats often appear.
  • the following describes the preferred installation and deployment scheme of the thermal infrared camera device. Use as few thermal infrared cameras as possible to cover walls, floors, and roofs with minimal dead angles.
  • the long side of the rectangular monitoring space is L
  • the space length is usually not more than 7 meters, that is, L ⁇ 7m
  • the short side is S
  • the space height is H
  • the thermal infrared camera set in the thermal infrared imaging device 1 comprises two thermal infrared cameras 11, preferably two thermal infrared cameras 11 are preferably arranged in the center of the two short sides S of the rectangular monitoring space, respectively, for relative shooting, thermal infrared cameras
  • the angle of 11 can be horizontal or slightly downward.
  • the horizontal angle C of the thermal infrared camera 11 is 80°-90°, and a monitoring space is formed between the opposite two thermal infrared cameras 11.
  • Fig. 3b it can be seen that the heights of the two thermal infrared cameras 11 are set to h, preferably h is about 2 meters, and the vertical angle C of the thermal infrared camera 11 is 50°-60°, and the opposite two thermal infrared rays A monitoring space is formed between the cameras 11, which has only a very small dead angle at the upper end of the line of intersection of the vertical angles of illumination of the two thermal infrared cameras 11 of Fig. 3b.
  • the number of thermal infrared cameras 11 needs to be increased in irregular rooms and oversized rooms.
  • the smart terminal 2 can know the number of times the mouse enters the monitoring field, the main active time, the active area, according to the recording time.
  • the increase can be seen in the daily/weekly/monthly activity change of the mouse in the kitchen and the change of the movement trajectory and the motion detection roughly identifies the video that may have the mouse appearing, and transmits it to the cloud server 3 for further analysis, through the cloud server 3 Data collection and analysis, fine identification of the mouse, and then provide warnings to help develop solutions.
  • Figures 6a-6d are visual dashboard representations of the relevant parameters of the mouse activity obtained in the mouse-borne intelligent monitoring system of the present invention.
  • the cloud server data aggregation processing analysis realizes the fine identification of the mouse, and the obtained parameter information of the active mouse can include the frequency of the mouse infestation, the trajectory line, the time and the number, etc., that is, finally grasp the haunting situation of the mouse (the time of the mouse haunting) , frequency, trajectory, key activity areas, trend changes, quantity, etc.) and the characteristics of the mouse, and can be displayed using a visual dashboard.
  • Visual dashboards show the index, hotspots, paths, and videos in detail.
  • the activity hot zone of the mouse on a certain date can be changed from the warm color to the cool color according to the frequency of the mouse infested.
  • the warmer color of the hot zone where the mouse is less frequently is the deeper.
  • the colder color of the hot zone where the mouse is less frequent is the lighter.
  • the hot zone with the highest frequency of rat haunt is displayed in red
  • the hot zone with the lowest frequency of rat haunt is displayed in blue.
  • Figure 6b shows the mouse at that date.
  • Figure 5c shows the activity index of rats in a week.
  • the data can be updated at a fixed time every day. For example, the data can be updated every day at 12 noon.
  • Figure 6c shows the activity index as updated on February 1.
  • Figure 6c shows the activity index of mice in the past 7 days. Only the latest updated 24-hour data is displayed in detail.
  • the bar icon on the timeline is the time period when the mouse is infested. Further, click on the bar chart of the mouse, and a pop-up dialog box may be displayed to display the specific time period, as shown in Figure 6d for the specific time period of the mouse. Yue curve.
  • an alarm is issued by the alarm device to help formulate a specific mousetrap solution.
  • the mouse can be set to the conditions of the infested, the alarm is automatically exceeded beyond the set conditions, and the mouse is caught in time to reduce unnecessary losses. Effectively reduce and prevent the occurrence of rodents. It saves the manpower and material resources of the mouse, and the efficiency of catching rats is higher.
  • the system can also be used for monitoring different scenarios, such as conducting customer satisfaction surveys, monitoring service quality, etc., and intervening less on the objects to be observed and evaluated, and the results are more authentic.
  • the invention also relates to a cloud server-based mouse-based intelligent monitoring method, which corresponds to the above-mentioned cloud server-based mouse-based intelligent monitoring system of the present invention, and can be understood as implementing the above-mentioned cloud server-based mouse-based intelligent monitoring system
  • the method as shown in the preferred flow chart of FIG. 7, firstly uses thermal infrared sensing technology and camera technology to monitor the mouse's activity trajectory and send the monitored video frame to the intelligent terminal; and then the intelligent terminal passes the artificial intelligence algorithm such as moving target detection.
  • the algorithm performs deep learning to identify the moving object, records the current video frame and the marker motion track, and transmits the video segment with the motion track to the cloud server to realize the rough recognition of the mouse; then the cloud server utilizes the video segment with the motion track
  • Another artificial intelligence algorithm identifies moving mice, such as using image recognition algorithms and continuous motion stitching algorithms to identify moving mice and save the current video segment and mark the mouse position and motion trajectory in the current video segment to achieve fine recognition of the mouse.
  • Mouse position and motion trajectory data It may also generate summary reports after analyzing the data obtained in mice treated active parameter information related to the cloud server and display by visualizing the dashboard; mice treated active parameters obtained information so as to realize intelligent monitoring rodent.
  • the relevant parameter information of the obtained mouse activity includes the frequency of the mouse infestation, the trajectory line, the time and/or the number, and the like. Further preferably, after the cloud server performs the data aggregation analysis and processing, the mouse vitality related parameter information is updated, and the alarm data is updated, and the alarm function is started when the mouse statistical data exceeds the preset alarm threshold.
  • the working principle diagrams of the smart terminal and the cloud server used in the mouse-based intelligent monitoring method based on the cloud server of the present invention can be respectively referred to FIG. 4 and FIG. 5.
  • the mouse monitoring method uses the camera technology and the thermal infrared sensing technology to monitor and track the activity trace of the mouse, and roughly recognizes through the intelligent terminal: the moving object is determined by the moving target detection algorithm and the deep learning algorithm, and the moving object may be a mouse or other Target; collaborative cloud server fine identification: for discriminating moving objects, eliminating the interference of dust and other items, using image recognition algorithm for volume filtering, color filtering combined with other characteristics of mice to identify mice, combined with continuous motion stitching algorithm to identify mice The motion pattern, the two algorithms can finally clearly identify the moving mouse.
  • the cloud server data is summarized, processed and analyzed, and the relevant parameter information of the mouse is obtained, and the report can be further formed to realize the intelligent monitoring of the mouse. It can also issue an alarm through the results of data analysis and processing, that is, to realize early warning before the occurrence of rodents, to remind the staff to take corresponding measures, effectively reduce and prevent the occurrence of rodents, avoid property damage caused by rodents, and save money.
  • the manpower and material resources of the mouse have improved the efficiency of the mousetrapping and are conducive to widespread application.

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Abstract

本发明涉及一种基于云服务器的鼠患智能监控系统和方法，该系统包括依次连接的热红外摄像设备、智能终端和云服务器，热红外摄像设备利用热红外传感技术和摄像技术监测老鼠的活动轨迹并将监测的视频帧发送至智能终端；智能终端判别出移动对象，记录当前视频帧和标记运动轨迹，并将带有运动轨迹的视频段传输至云服务器；云服务器识别出移动的老鼠并保存当前视频段以及在当前视频段中标记老鼠位置和运动轨迹，再数据汇总分析处理得到老鼠活跃的相关参数信息实现鼠患智能监控。该系统通过智能终端进行粗略识别并协同云服务器精细识别出移动的老鼠，实现鼠患智能监控，及早发现并控制鼠患，减少不必要的损失，降低了人工成本，提高了监控效率。

Description

基于云服务器的鼠患智能监控系统和方法 技术领域
本发明涉及智能监控技术领域，特别是一种基于云服务器的鼠患智能监控系统和方法。
背景技术
鼠患是很多疾病发生和流行的传播媒介，能传播鼠疫、流行性出血热、钩端螺旋体病等多种疾病，对人类可以造成严重危害，防制鼠患非常重要。由于老鼠是哺乳动物中繁殖很快、生存能力很强的动物，其行动迅速、数量多，分布广，迁徙频繁，并且通常在夜间以及人少的时候出没，活动路线不定，通常呈三维模式，因此，现在的人工抓鼠，耗费人力，效率低。其他灭鼠方法如物理学灭鼠，通常是设置捕鼠器，由于老鼠的活动轨迹不明确，因此，效率低；化学灭鼠，通常是用药物毒杀，在厨房等食物分布的地区容易发生安全隐患，因此不建议采用。综上可知，现有技术均是在鼠患发生以后采用的，往往是第二天发现被破坏的现场后才知道前一夜有老鼠出现过，很少能够提早发现，更无法看到老鼠的活动轨迹，导致无法进一步采取行而有效的措施防制，这就给客户造成了不必要的损失。
发明内容
本发明针对现有技术在鼠患发生后采用的人工抓鼠、捕鼠器或药物毒杀等方式存在的种种问题，提供一种基于云服务器的鼠患智能监控系统，利用摄像技术和热红外传感技术有效监测老鼠的活动轨迹，通过智能终端进行粗略识别并协同云服务器精细识别出移动的老鼠，进行数据汇总分析并可进一步做出提醒。本发明还涉及一种基于云服务器的鼠患智能监控方法。
本发明的技术方案如下：
一种基于云服务器的鼠患智能监控系统，其特征在于，包括依次连接的热红外摄像设备、智能终端和云服务器，
所述热红外摄像设备利用热红外传感技术和摄像技术监测老鼠的活动轨迹并将监测的视频帧发送至智能终端；所述智能终端通过人工智能算法判别出移动对象，记录当前视频帧和标记运动轨迹，并将带有运动轨迹的视频段传输至云服务器；所述云服务器针对带有运动轨迹的视频段利用另外的人工智能算法识别出移动的老鼠并保存当前视频段以及在当前视频段中标记老鼠位置和运动轨迹，再对老鼠位置和运动轨迹进行数据汇总分析处理得到老鼠活跃的相关参数信息进而实现鼠患智能监控。
进一步地，所述智能终端采用运动目标检测算法进行深度学习以判别出移动对象；所述云服务器针对带有运动轨迹的视频段采用图像识别算法和持续运动拼接算法识别出移动的老鼠。
进一步地，所述热红外摄像设备包括至少一组含若干热红外摄像头的热红外摄像头组，所述智能终端的个数与热红外摄像头组的组数相对应且智能终端与相应的热红外摄像头组中的各热红外摄像头均相连；所述云服务器还用于管理整个监控网络，并在多个智能终端时通过云服务器实现数据共享和/或数据下载。
进一步地，所述系统还包括报警装置，所述报警装置与云服务器相连且所述报警装置设置有报警阈值，所述云服务器在数据汇总分析处理得到的老鼠活跃的相关参数信息超出相应的报警阈值时，所述报警装置启动报警功能。
进一步地，所述系统还包括可视化仪表盘，所述可视化仪表盘与云服务器相连，所述云服务器在数据汇总分析处理得到的老鼠活跃的相关参数信息后生成报表并通过可视化仪表盘展示；
或，所述云服务器在数据汇总分析处理得到的老鼠活跃的相关参数信息后生成报表并传回智能终端再通过智能终端的人机界面展示。
进一步地，所述云服务器得到的老鼠活跃的相关参数信息包括老鼠出没的频次、运动轨迹线路、活动热区、时间和/或数量。
进一步地，所述热红外摄像头组包括两个热红外摄像头，所述两个热红外摄像头分别相对布置在矩形监控空间的两短边的中央，且热红外摄像头的角度水平或向下呈一定角度。
一种基于云服务器的鼠患智能监控方法，其特征在于，所述方法先利用热红外传感技术和摄像技术监测老鼠的活动轨迹并将监测的视频帧发送至智能终端；再由智能终端通过人工智能算法判别出移动对象，记录当前视频帧和标记运动轨迹，并将带有运动轨迹的视频段传输至云服务器；然后由云服务器针对带有运动轨迹的视频段利用另外的人工智能算法识别出移动的老鼠并保存当前视频段以及在当前视频段中标记老鼠位置和运动轨迹，再对老鼠位置和运动轨迹进行数据汇总分析处理得到老鼠活跃的相关参数信息进而实现鼠患智能监控。
进一步地，所述方法由智能终端采用运动目标检测算法进行深度学习以判别出移动对象；并由云服务器针对带有运动轨迹的视频段采用图像识别算法和持续运动拼接算法识别出移动的老鼠。
进一步地，所述方法在云服务器进行数据汇总分析处理得到的老鼠活跃的相关参数信息后更新鼠患统计数据，并在鼠患统计数据超出预设的报警阈值时启动报警功能；
和/或，所述方法在云服务器进行数据汇总分析处理得到的老鼠活跃的相关参数信息后还 生成报表并通过可视化仪表盘展示，得到的老鼠活跃的相关参数信息包括老鼠出没的频次、运动轨迹线路、活动热区、时间和/或数量。
本发明的技术效果如下：
本发明提供的基于云服务器的鼠患智能监控系统，设置依次连接的热红外摄像设备、智能终端和云服务器，热红外摄像设备利用热红外传感技术和摄像技术监测老鼠的活动轨迹，可在老鼠常出没的空间利用摄像技术实现二十四小时监控录像，监测采集和跟踪老鼠的活动踪迹，利用热红外传感技术有效监测老鼠的夜间活动轨迹；智能终端可存储监控录像，通过人工智能算法(比如运动目标检测算法进行深度学习)判别出移动对象，记录当前视频帧和标记运动轨迹，即通过智能终端进行运动目标检测和实时路径追踪，实时监测，粗略识别老鼠；智能终端将带有运动轨迹的视频段传输至云服务器，也就是将粗略识别老鼠的结果发送至云服务器，云服务器利用接收智能终端结果后对其采用另一人工智能算法(比如采用图像识别算法和持续运动拼接算法)识别出移动的老鼠并保存当前视频段以及在当前视频段中标记老鼠位置和运动轨迹进而精细识别移动的老鼠，再对老鼠位置和运动轨迹进行数据汇总分析处理得到老鼠活跃的相关参数信息，实现鼠患智能监控。本发明的鼠患智能监控系统利用摄像技术、热红外传感技术，采用云服务器管理整个监控网络，按照智能终端粗略识别和云服务器精细识别的两级数据处理算法，减轻云服务器的负担，只需进一步处理带有运动轨迹的视频段以判断是否真的老鼠移动，进行数据汇总分析，可做出提醒，并帮助解决问题，该系统通过实现鼠患智能监控，通过人工智能技术最终能够精确识别老鼠的活动轨迹，即能够直观看到老鼠的活动轨迹，使得能够及早发现并且控制鼠患的影响，这就完全避免了现有技术由于无法及早发现老鼠的踪迹导致只能采用人工抓鼠、捕鼠器或药物毒杀等方式存在的耗费人力、效率低以及存在安全隐患等问题，本发明所述系统能够为灭鼠公司或者商业组织制定灭鼠策略带来帮助，以便采取有针对性的灭鼠措施来有效捕捉老鼠，减少不必要的损失，监控智能化，降低了人工成本，安全可靠，提高了监控效率。
优选设置热红外摄像设备包括至少一组含若干热红外摄像头的热红外摄像头组，智能终端的个数与热红外摄像头组的组数相对应且智能终端与相应的热红外摄像头组中的各热红外摄像头均相连，也就是说，可以采用一组热红外摄像头组和一个智能终端，也可以采用多组热红外摄像头组和多个相应的智能终端，此时，比如在每个监控空间内设置一组热红外摄像头组，各智能终端均连接至云服务器，形成多层级的监控网络，通过云服务器集中管理整个监控网络，实现各智能终端发来的数据的分别处理和集中管理，并实现数据共享和/或数据下载。云服务器的使用，使得存储容量极大提高，热红外摄像头的数量也极大的增加，监控的空间也更广泛，并且更能体现出监控效率。
热红外摄像头组中的若干热红外摄像头优选可以包括两个热红外摄像头，可将两个热红外摄像头分别相对布置在矩形监控空间的两短边的中央，且热红外摄像头的角度水平或向下呈一定角度。这样，针对面积不大的矩形监测空间，可以采用尽量少的热红外摄像头最大程度的涵盖墙、地面、屋顶，死角少。
本发明还涉及一种基于云服务器的鼠患智能监控方法，该方法与本发明上述的基于云服务器的鼠患智能监控系统相对应，可理解为是实现上述基于云服务器的鼠患智能监控系统的方法，利用摄像技术和热红外传感技术监控跟踪老鼠的活动踪迹，按照智能终端进行粗略识别和云服务器进行精细识别的两级数据处理算法，最终由云服务器数据汇总、处理、分析，得到老鼠活跃的相关参数信息，并可进一步形成报表，实现鼠患智能监控。还可通过数据分析处理的结果发出警报，提醒工作人员采取相应地针对性并行而有效的灭鼠措施，及时发现，防止鼠患规模进一步扩大，有效的减少和预防鼠患的发生，减少了不必要的财产损失，还节省了抓鼠的人力物力，提高了抓鼠效率。
附图说明
图1为本发明基于云服务器的鼠患智能监控系统的结构示意图。
图2为本发明基于云服务器的鼠患智能监控系统的优选结构示意图。
图3a和图3b分别为本发明鼠患智能监控系统中的热红外摄像设备优选安装的俯视图和侧视图。
图4为智能终端的工作原理图。
图5为云服务器的工作原理图。
图6a—6d均为本发明鼠患智能监控系统中得到的老鼠活跃的相关参数信息的可视化仪表盘展示图。
图7为本发明基于云服务器的鼠患智能监控方法的优选流程图。
图中各标号列示如下：
1—热红外摄像设备；11—热红外摄像头；2—智能终端；3—云服务器；4—可视化仪表盘。
具体实施方式
下面结合附图对本发明进行说明。
本发明涉及一种基于云服务器的鼠患智能监控系统，其结构如图1所示，包括依次连接的热红外摄像设备1、智能终端2和云服务器3，其中，热红外摄像设备1利用热红外传感技 术和摄像技术监测老鼠的活动轨迹并将监测的视频帧发送至智能终端2；智能终端2通过人工智能算法判别出移动对象，记录当前视频帧和标记运动轨迹，并将带有运动轨迹的视频段传输至云服务器3；云服务器3针对带有运动轨迹的视频段利用另外的人工智能算法识别出移动的老鼠并保存当前视频段以及在当前视频段中标记老鼠位置和运动轨迹，再对老鼠位置和运动轨迹进行数据汇总分析处理得到老鼠活跃的相关参数信息进而实现鼠患智能监控。
热红外摄像设备1利用热红外传感技术和摄像技术监测老鼠的活动轨迹，故热红外摄像设备1可理解为是集成了摄像头和热释电红外传感器的部件，可在老鼠的主要活动范围通过摄像实现二十四小时监控录像，可监控老鼠活跃的时间、次数、重点活动区域以及趋势变化，监测采集和跟踪老鼠的活动踪迹，利用热释电红外传感器有效监测老鼠的夜间活动轨迹，热红外摄像设备1将监测的视频帧发送至智能终端2。
智能终端2的工作原理如图4所示，其采集接收热红外摄像设备1的视频帧，然后进行运动检测，其采用的人工智能算法优选包括运动目标检测算法和深度学习算法，具体是通过运动目标检测算法结合深度学习算法进行深度学习判别出移动对象(或者说是运动对象)，该移动对象可能是老鼠或其他运动对象，在检测到运动对象时记录当前视频帧和标记运动轨迹，并继续采集接收热红外摄像设备1的视频帧，继续执行运动检测等后续步骤，最终得到带有运动轨迹的视频段；在未检测到运动对象时同样返回采集接收热红外摄像设备1的视频帧的工作。运用智能终端2存储监控录像，实时监测，粗略识别老鼠。智能终端2具体可采用PC机、笔记本电脑、移动手机或PAD。智能终端2可以采用能够进行实时运动目标检测和实时路径追踪功能的其它人工智能算法或者说是人工智能技术，最终将带有运动轨迹的视频段传输至云服务器3。
云服务器3的工作原理如图5所示，其接收带有运动轨迹的视频段，或者说是选取带有运动区域和活动轨迹的视频段，然后进行老鼠检测，其采用的另一人工智能算法优选包括图像识别算法和持续运动拼接算法，具体可预先排除灰尘及其他物品反光的干扰，通过图像识别算法进行体积过滤、颜色过滤并结合老鼠的其它特征识别出老鼠，老鼠的大小通常为5cm-20cm，老鼠的颜色通常为灰色、黑色和白色，图像识别算法能够按照老鼠的特征排除老鼠以外的其它移动对象，识别出老鼠，并结合持续运动拼接算法识别老鼠的运动模式，结合两种算法从而识别移动的老鼠，在检测到老鼠时保存当前视频段，在未检测到老鼠时删除该视频段并返回继续选取下一个带有运动区域和活动轨迹的视频段继续进行老鼠检测，在检测到老鼠并保存视频段后，在该当前视频段中标记老鼠位置和运动轨迹，再对老鼠位置和运动轨迹进行数据汇总分析处理得到老鼠活跃的相关参数信息，并可更新鼠患统计数据还可以进一步报警，并继续返回到选取下一个带有运动区域和活动轨迹的视频段继续进行老鼠检测， 实现鼠患智能监控。当然，云服务器3也可以采用能够实现精确识别移动的老鼠的其它人工智能算法或者说是人工智能技术。采用云服务器3，用于管理整个监控网络，进行精细识别老鼠，数据汇总、处理、分析、并可进一步形成报表。
优选地，热红外摄像设备1可包括至少一组含若干热红外摄像头11的热红外摄像头组，智能终端的个数与热红外摄像头组的组数相对应且智能终端与相应的热红外摄像头组中的各热红外摄像头11均相连。如图2所示的本发明基于云服务器的鼠患智能监控系统的优选结构示意图。该优选实施例中，热红外摄像设备1包括的热红外摄像头组有三组，每组的热红外摄像头组包括两个热红外摄像头11，智能终端2也相应设置三个，各智能终端2均与相应的热红外摄像头组中的各热红外摄像头11相连，三个智能终端2均与云服务器3相连。图2所示的鼠患智能监控系统还包括与云服务器3相连的可视化仪表盘4，云服务器3在数据汇总分析处理得到的老鼠活跃的相关参数信息后生成报表并通过可视化仪表盘4展示，可视化仪表盘4可采用手机、电脑或平板。当然，也可以不设置可视化仪表盘4，而是云服务器3在数据汇总分析处理得到的老鼠活跃的相关参数信息后生成报表并传回智能终端2再通过智能终端2的人机界面展示。进一步优选地，本发明基于云服务器的鼠患智能监控系统还可包括报警装置，报警装置与云服务器相连且报警装置设置有报警阈值，云服务器在数据汇总分析处理得到的老鼠活跃的相关参数信息超出相应的报警阈值时，报警装置启动报警功能，在鼠患发生前进行预防，减少不必要的损失。采用多组热红外摄像头组和多个相应的智能终端2，此时，比如在每个监控空间内设置一组热红外摄像头组，各智能终端2均连接至云服务器3，形成多层级的监控网络，通过云服务器3集中管理整个监控网络，实现各智能终端2发来的数据的分别处理和集中管理，并实现数据共享和/或数据下载。
监控空间可以具体是一个房间，比如老鼠经常出没的厨房，以下介绍热红外摄像设备的优选安装部署方案。采用尽量少的热红外摄像头最大程度的涵盖墙、地面、屋顶，死角少。如图3a和图3b所示的矩形监控空间的俯视图和侧视图，矩形监控空间的长边为L，空间长度通常不超过7米，即L≤7m，短边为S，空间高度为H，在热红外摄像设备1中的热红外摄像头组包括两个热红外摄像头11，优选地，两个热红外摄像头11分别优选布置在矩形监控空间的两短边S的中央，相对拍摄，热红外摄像头11的角度可水平或略向下呈一定角度。此时，在图3a中，热红外摄像头11照射的水平角度C为80°-90°，相对的两个热红外摄像头11之间形成监控空间。在图3b中，能够看到两个热红外摄像头11设置的高度为h，优选设置h约为2米，热红外摄像头11照射的垂直角度C为50°-60°，相对的两个热红外摄像头11之间形成监控空间，该设置仅在图3b的两个热红外摄像头11的照射的垂直角度的交叉线的上端存在非常少的死角。在不规则房间和超大房间需要增加热红外摄像头11的数量。
由此，我们通过部署热红外摄像头11进行老鼠的活跃度检测，通过摄录的视频，由智能终端2可以了解老鼠进入监控领域的次数，主要活跃的时间，重点活跃的区域，根据摄录时间的增加，可以知道老鼠在厨房的日/周/月的活动趋势变化以及运动轨迹变化并且运动检测粗略识别对可能有老鼠出现的视频进行截取，传输至云服务器3作进一步分析，通过云服务器3进行数据收集分析，精细识别出老鼠，进而提出警告，帮助制定解决办法。
图6a—6d均为本发明鼠患智能监控系统中得到的老鼠活跃的相关参数信息的可视化仪表盘展示图。云服务器数据汇总处理分析后实现对精细识别老鼠，得到的老鼠活跃的相关参数信息可以包括老鼠出没的频次、运动轨迹线路、时间以及数量等等，即最终掌握老鼠的出没情况(老鼠出没的时间、频率、运动轨迹、重点活动区域、趋势变化、数量等)以及标出老鼠的特征，并可以利用可视化仪表盘展示。可视化仪表盘可详细展示指数、热区、路径和视频。如图6a展示的在某一日期下老鼠的活动热区，即老鼠出没热点图，可以按照老鼠出没频次的多少由暖色系向冷色系渐变，老鼠出没频次越高的热区的暖色系越深，老鼠出没频次越低的热区的冷色系越浅，比如将老鼠出没频次最高的热区显示为红色，将老鼠出没频次最低的热区显示为蓝色；图6b展示了在该日期下老鼠的出没路径；图6c展示了在一周内的老鼠的活跃指数，可在每天的固定时间点更新数据，如可在每天中午12点更新数据，图6c显示的如2月1日更新的活跃指数，是以1月31日中午12点到2月1日中午12点，历时24小时的数据计算得出，图6c显示了近7天的老鼠的活跃指数，只详细显示最近更新的24小时数据，时间轴上的条形图标是老鼠出没的时间段，进一步地，点击老鼠出没的条形图，还可弹出对话框显示具体时间段，如图6d显示的具体时间段的老鼠的活跃曲线。
通过云服务器的数据分析后由报警装置发出警报，为制定具体的捕鼠方案提供帮助。运用报警功能，可设定的老鼠出没的条件，超出设定条件自动报警，及时进行捕鼠，减少不必要的损失。有效的减少和预防鼠患的发生。节省了抓鼠的人力物力，抓鼠效率更高。同时本系统也可用于不同场景的监测，如进行客户满意度调查、服务质量监测等方面，对所要观察和考核的对象干预较少，结果更真实可信。
本发明还涉及一种基于云服务器的鼠患智能监控方法，该方法与本发明上述的基于云服务器的鼠患智能监控系统相对应，可理解为是实现上述基于云服务器的鼠患智能监控系统的方法，如图7所示优选流程图，先利用热红外传感技术和摄像技术监测老鼠的活动轨迹并将监测的视频帧发送至智能终端；再由智能终端通过人工智能算法如运动目标检测算法进行深度学习判别出移动对象，记录当前视频帧和标记运动轨迹，并将带有运动轨迹的视频段传输至云服务器，实现粗略识别老鼠；然后由云服务器针对带有运动轨迹的视频段利用另外的人工智能算法识别出移动的老鼠，如采用图像识别算法和持续运动拼接算法识别出移动的老鼠 并保存当前视频段以及在当前视频段中标记老鼠位置和运动轨迹，实现精细识别老鼠，对老鼠位置和运动轨迹进行数据汇总分析处理得到老鼠活跃的相关参数信息进而实现鼠患智能监控；在云服务器进行数据汇总分析处理得到的老鼠活跃的相关参数信息后还可生成报表并通过可视化仪表盘展示。得到的老鼠活跃的相关参数信息包括老鼠出没的频次、运动轨迹线路、时间和/或数量等等。进一步优选地，在云服务器进行数据汇总分析处理得到的老鼠活跃的相关参数信息后更新鼠患统计数据，并在鼠患统计数据超出预设的报警阈值时启动报警功能。
本发明基于云服务器的鼠患智能监控方法中采用的智能终端和云服务器的工作原理图可分别参考图4和图5。该鼠患智能监控方法利用摄像技术和热红外传感技术监控跟踪老鼠的活动踪迹，通过智能终端粗略识别：通过运动目标检测算法协同深度学习算法判别出移动对象，该移动对象可能是老鼠或其它目标；协同云服务器精细识别：针对判别出的移动对象，排除灰尘及其他物品反光的干扰，利用图像识别算法进行体积过滤，颜色过滤结合老鼠的其它特征识别出老鼠，结合持续运动拼接算法识别老鼠的运动模式，两种算法最终能够明确识别出移动的老鼠。按照智能终端进行粗略识别和云服务器进行精细识别的两级数据处理算法，最终由云服务器数据汇总、处理、分析，得到老鼠活跃的相关参数信息，并可进一步形成报表，实现鼠患智能监控。还可通过数据分析处理的结果发出警报，即在鼠患发生前实现预警，提醒工作人员采取相应地措施，有效的减少和预防鼠患的发生，避免了鼠患导致的财产损失，还节省了抓鼠的人力物力，提高了抓鼠效率，有利于广泛推广应用。
应当指出，以上所述具体实施方式可以使本领域的技术人员更全面地理解本发明创造，但不以任何方式限制本发明创造。因此，尽管本说明书参照附图和实施例对本发明创造已进行了详细的说明，但是，本领域技术人员应当理解，仍然可以对本发明创造进行修改或者等同替换，总之，一切不脱离本发明创造的精神和范围的技术方案及其改进，其均应涵盖在本发明创造专利的保护范围当中。

Claims (10)

  1. 一种基于云服务器的鼠患智能监控系统，其特征在于，包括依次连接的热红外摄像设备、智能终端和云服务器，
    所述热红外摄像设备利用热红外传感技术和摄像技术监测老鼠的活动轨迹并将监测的视频帧发送至智能终端；所述智能终端通过人工智能算法判别出移动对象，记录当前视频帧和标记运动轨迹，并将带有运动轨迹的视频段传输至云服务器；所述云服务器针对带有运动轨迹的视频段利用另外的人工智能算法识别出移动的老鼠并保存当前视频段以及在当前视频段中标记老鼠位置和运动轨迹，再对老鼠位置和运动轨迹进行数据汇总分析处理得到老鼠活跃的相关参数信息进而实现鼠患智能监控。
  2. 根据权利要求1所述的鼠患智能监控系统，其特征在于，所述智能终端采用运动目标检测算法进行深度学习以判别出移动对象；所述云服务器针对带有运动轨迹的视频段采用图像识别算法和持续运动拼接算法识别出移动的老鼠。
  3. 根据权利要求2所述的鼠患智能监控系统，其特征在于，所述热红外摄像设备包括至少一组含若干热红外摄像头的热红外摄像头组，所述智能终端的个数与热红外摄像头组的组数相对应且智能终端与相应的热红外摄像头组中的各热红外摄像头均相连；所述云服务器还用于管理整个监控网络，并在多个智能终端时通过云服务器实现数据共享和/或数据下载。
  4. 根据权利要求1或2或3所述的鼠患智能监控系统，其特征在于，还包括报警装置，所述报警装置与云服务器相连且所述报警装置设置有报警阈值，所述云服务器在数据汇总分析处理得到的老鼠活跃的相关参数信息超出相应的报警阈值时，所述报警装置启动报警功能。
  5. 根据权利要求4所述的鼠患智能监控系统，其特征在于，还包括可视化仪表盘，所述可视化仪表盘与云服务器相连，所述云服务器在数据汇总分析处理得到的老鼠活跃的相关参数信息后生成报表并通过可视化仪表盘展示；
    或，所述云服务器在数据汇总分析处理得到的老鼠活跃的相关参数信息后生成报表并传回智能终端再通过智能终端的人机界面展示。
  6. 根据权利要求1或2或3所述的鼠患智能监控系统，其特征在于，所述云服务器得到的老鼠活跃的相关参数信息包括老鼠出没的频次、运动轨迹线路、活动热区、时间和/或数量。
  7. 根据权利要求2所述的鼠患智能监控系统，其特征在于，所述热红外摄像头组包括两个热红外摄像头，所述两个热红外摄像头分别相对布置在矩形监控空间的两短边的中央，且热红外摄像头的角度水平或向下呈一定角度。
  8. 一种基于云服务器的鼠患智能监控方法，其特征在于，所述方法先利用热红外传感技术和摄像技术监测老鼠的活动轨迹并将监测的视频帧发送至智能终端；再由智能终端通过人工智能算法判别出移动对象，记录当前视频帧和标记运动轨迹，并将带有运动轨迹的视频段 传输至云服务器；然后由云服务器针对带有运动轨迹的视频段利用另外的人工智能算法识别出移动的老鼠并保存当前视频段以及在当前视频段中标记老鼠位置和运动轨迹，再对老鼠位置和运动轨迹进行数据汇总分析处理得到老鼠活跃的相关参数信息进而实现鼠患智能监控。
  9. 根据权利要求8所述的鼠患智能监控方法，其特征在于，所述方法由智能终端采用运动目标检测算法进行深度学习以判别出移动对象；并由云服务器针对带有运动轨迹的视频段采用图像识别算法和持续运动拼接算法识别出移动的老鼠。
  10. 根据权利要求8或9所述的鼠患智能监控方法，其特征在于，在云服务器进行数据汇总分析处理得到的老鼠活跃的相关参数信息后更新鼠患统计数据，并在鼠患统计数据超出预设的报警阈值时启动报警功能；
    和/或，在云服务器进行数据汇总分析处理得到的老鼠活跃的相关参数信息后还生成报表并通过可视化仪表盘展示，得到的老鼠活跃的相关参数信息包括老鼠出没的频次、运动轨迹线路、活动热区、时间和/或数量。
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