CN116993265A - Intelligent warehouse safety management system based on Internet of things - Google Patents
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Abstract
The invention discloses an intelligent warehouse safety management system based on the Internet of things, which relates to the technical field of intelligent management systems, and adopts a target detection algorithm, a differential-based YOLO detection algorithm can accurately detect target objects in a warehouse environment in real time and provide position information thereof, and in combination with a Camshift hybrid tracking algorithm, continuous tracking of the targets can be realized in subsequent video frames, the tracking effect and stability of the targets are improved, the results of target detection and tracking and the human body posture information are combined, the human skeleton sequences are extracted and the human body actions are identified, so that the system can more accurately identify unsafe actions, the effect and timeliness of warehouse safety management are improved, the intelligent warehouse safety management system based on the Internet of things can monitor and identify the targets and actions in the warehouse environment in real time, and the real-time early warning and management are carried out through the transmission data of the Internet of things, the efficiency of warehouse safety management is improved, and the risk of manual intervention and erroneous judgment is reduced.
Description
Technical Field
The invention relates to the technical field of intelligent management systems, in particular to an intelligent warehouse safety management system based on the Internet of things.
Background
Warehouse safety management refers to a series of measures and specifications for safety management and protection of warehouses and warehouse facilities. The safety and integrity of articles in a warehouse are ensured, adverse events such as theft, damage, disasters and the like are prevented, and a safe working environment is provided.
Warehouse safety management includes the following aspects of ensuring that safety facilities inside and outside a warehouse are perfect, such as a safety access control system, a closed-circuit television monitoring system, a fireproof facility, a safety alarm system and the like, reasonable planning of the warehouse layout, ensuring orderly storage and rapid taking and placing of goods, setting up clear marks and indication marks, facilitating staff and visitors to recognize and follow safety regulations, performing warehouse access control, taking measures to limit personnel in the warehouse to enter and exit, such as through the access control system, identity verification, visitor registration and the like, ensuring that only authorized personnel can enter the warehouse and inventory management, establishing an effective inventory management system, including checking, recording, sorting, marking and the like, performing safety protection and monitoring on special or valuable goods, staff training and safety consciousness, performing safety training on the warehouse staff, enabling the staff to know and master basic requirements of warehouse safety management, and how to cope with emergency and disaster accidents, performing safety inspection and patrol on the warehouse regularly, ensuring normal operation of the safety facilities and discovering potential safety hazards, fire extinguishers and management, preventing and other potential hazards, preventing and fire disaster accidents, reasonably setting up, performing security measures such as security measures, anti-theft and security monitoring, taking RFID (radio frequency) and security, and security monitoring measures, and security monitoring, and anti-theft and security measures are adopted at the same time.
However, the conventional system generally relies on manual inspection or periodic inspection to monitor the warehouse environment and the safety condition, and this method cannot provide real-time monitoring and early warning capability, which results in delayed perception of potential risks and increased risk of occurrence of accidents, and meanwhile lacks technical support for target detection and tracking, and cannot identify and alarm abnormal situations, such as theft, entry of unauthorized personnel, etc., in real time. This increases the security risk and delays the response to the potential threat, so there is a need for an intelligent warehouse security management system based on internet of things that monitors feedback in real time to solve such problems.
Disclosure of Invention
The invention aims to solve the problems that the prior art cannot provide real-time monitoring and early warning capability, lacks technical support for target detection and tracking, and cannot identify and alarm abnormal conditions in real time.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the invention provides an intelligent warehouse safety management system based on the Internet of things, which is characterized by comprising:
the sensor system is used for monitoring the warehouse environment in the warehouse area and the peripheral area and capturing video data in real time;
a central management system for receiving, storing and analyzing sensor data;
and the visualization and alarm module is used for performing visualization display on the identification result and triggering a corresponding alarm mechanism according to unsafe behavior, wherein the alarm mechanism comprises an acoustic alarm and a short message notification.
The invention is further arranged to: the sensor system comprises a video monitoring system and a sensor network,
the video monitoring system is provided with a camera and video monitoring equipment to cover a key area of the warehouse area;
the sensor network comprises a temperature sensor, a humidity sensor, a smoke sensor and a door magnetic sensor, and is used for monitoring environmental parameters and storage equipment states in real time, and the sensor is connected to the central management system through the Internet of things;
the invention is further arranged to: the central management system comprises a video stream processing module, a differential-based YOLO detection algorithm module, a Camshift hybrid tracking algorithm module, an OpenPose algorithm module, an unsafe behavior identification module and a data transmission and storage module,
the video stream processing module is used for processing the video stream captured by the camera in real time;
the YOLO detection algorithm module based on the difference realizes real-time detection and positioning of the target based on a YOLO network structure and a difference technology;
the Camshift hybrid tracking algorithm module processes the video frame according to the position information of the target to realize continuous tracking of the target;
the invention is further arranged to: the OpenPose algorithm module extracts human body posture information in the video frame and identifies human body actions;
the unsafe behavior recognition module is used for recognizing unsafe behaviors by combining the result of the target detection and tracking algorithm and the human body action information extracted by the OpenPose algorithm;
the data transmission and storage module is used for transmitting the identification result and other environmental parameters through the Internet of things and storing the data in the cloud server;
the invention is further arranged to: the specific algorithm of target detection and tracking is as follows,
differential-based YOLO detection algorithm, input: a sequence of video frames;
and (3) outputting: target detection result (target position and category)
a. Performing differential operation on the video frame sequence to obtain a differential image between the front frame and the rear frame;
b. performing target detection on the differential image by utilizing a YOLO network structure to obtain a target position and a category;
c. using the detected target position and category information to initialize a target tracking window;
the Camshift hybrid tracking algorithm, input: video frame sequence, initial target position
And (3) outputting: updated target position per frame
a. Performing target tracking on the initial target position by applying a Camshift algorithm;
b. for the subsequent video frames, updating the tracking window according to the target position of the current frame;
c. repeating steps a and b until the video sequence ends;
the invention is further arranged to: the unsafe behavior identification specific algorithm is as follows,
extracting a human skeleton sequence by using an OpenPose algorithm, and inputting: video frame sequence
And (3) outputting: human skeleton sequence
a. Applying an OpenPose algorithm to the video frame sequence, and extracting joint point information of a human body in each frame;
b. constructing a human skeleton sequence according to the joint point information;
unsafe behavior identification, input: human skeleton sequence
And (3) outputting: unsafe behavior recognition results
a. Preprocessing the human skeleton sequence, including normalization and filtering;
b. according to specific unsafe behavior types, corresponding characteristics are designed, the specific unsafe behavior is determined according to actual storage high-risk actions, and the corresponding characteristics comprise joint angles and movement speeds;
c. training a classifier using the training dataset to classify unsafe behavior;
d. applying a trained classifier to the extracted features to identify unsafe behaviors;
the invention is further arranged to: and when the sensor or the video monitoring system detects an abnormal event, the abnormal event comprises abnormal behavior and sensor super threshold, the central management system sends an alarm to inform related personnel in a sound alarm and short message mode, and the alarm event is recorded and stored in the central management system.
Compared with the known public technology, the technical scheme provided by the invention has the following beneficial effects:
according to the invention, a target detection algorithm based on traditional image processing is used, a differential-based YOLO detection algorithm can accurately detect target objects in a storage environment in real time and provide position information thereof, and in combination with a Camshift hybrid tracking algorithm, continuous tracking of targets can be realized in subsequent video frames, the tracking effect and stability of the targets are improved, meanwhile, based on the target detection and tracking algorithm and an OpenPose algorithm, the results of target detection and tracking and the human body posture information are combined, the human skeleton sequence is extracted and human body actions are identified, so that the system can accurately identify unsafe behaviors, such as personnel fall and improper operation, and the like, thereby improving the effect and timeliness of storage safety management, and the intelligent storage safety management system based on the Internet of things can monitor and identify targets and behaviors in the storage environment in real time, and perform real-time early warning and management through Internet of things transmission data, improve the efficiency of storage safety management, reduce the risks of manual intervention and error judgment, and solve the problems that the prior art cannot provide real-time monitoring and early warning capability and technical support of target detection and tracking, and cannot identify abnormal conditions in real time.
Drawings
FIG. 1 is a flow chart of an intelligent warehouse safety management system based on the Internet of things;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. 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 invention provides a technical solution: an intelligent warehouse safety management system based on the internet of things is characterized in that the system comprises:
1. the sensor system is used for monitoring the warehouse environment in the warehouse area and the peripheral area and capturing video data in real time.
The sensor system comprises a video monitoring system and a sensor network, wherein the video monitoring system is provided with a camera and video monitoring equipment to cover a key area of a storage area, the video monitoring system can be connected to a central management system through the Internet of things and provides real-time video streaming and video playback functions, meanwhile, an image recognition and behavior analysis algorithm can be used for detecting abnormal events, the sensor network comprises a temperature sensor, a humidity sensor, a smoke sensor and a door magnetic sensor and is used for monitoring environmental parameters and storage equipment states in real time, and the sensor is connected to the central management system through the Internet of things.
2. A central management system for receiving, storing and analyzing sensor data.
The central management system comprises a video stream processing module, a differential-based YOLO detection algorithm module, a Camshift hybrid tracking algorithm module, an OpenPose algorithm module, an unsafe behavior recognition module and a data transmission and storage module, wherein the video stream processing module is used for processing a video stream captured by a camera in real time, the differential-based YOLO detection algorithm module is used for realizing real-time detection and positioning of a target based on a YOLO network structure and a differential technology, the Camshift hybrid tracking algorithm module is used for processing a subsequent video frame according to position information of the target to realize continuous tracking of the target, the OpenPose algorithm module is used for extracting human body posture information in the video frame and identifying human body actions, the unsafe behavior recognition module is used for recognizing unsafe behaviors by combining the result of the target detection and tracking algorithm and the human body action information extracted by the OpenPose algorithm, and the data transmission and storage module is used for transmitting recognition results and other environment parameters through an Internet of things, and the data is stored in a cloud server.
Differential-based YOLO detection algorithm and Camshift hybrid tracking algorithm:
a. the algorithm for the detection of YOLO,
input: video frame sequence
And (3) outputting: detection resultRepresenting detected object bounding boxes in each frame
The steps are as follows: the YOLO network is initialized and pre-training weights are loaded.
For each frameTarget detection using YOLO network, resulting in a bounding box set +.>. And screening out a final target boundary box set Bt according to the confidence and non-maximum suppression.
Returning to a set of target bounding boxes
b. The Camshift hybrid tracking algorithm,
input: initial target bounding boxVideo frame sequence->
And (3) outputting: target bounding box sequenceRepresenting tracked target bounding boxes in each frame
The steps are as follows: the Camshift algorithm is initialized, and the initial target bounding box is used as a tracking window.
For each frameA histogram of the tracking window is calculated.
The histogram is updated and equalized using the Camshift algorithm.
And calculating the new tracking window position and size according to the updated histogram.
Returning to the sequence of target bounding boxes,combining a target detection and tracking algorithm and an OpenPose algorithm to perform unsafe behavior identification:
a. the openwise algorithm is used to determine the parameters,
input: video frame sequence
And (3) outputting: human joint coordinate sequenceRepresenting the coordinates of the joints of the human body extracted from each frame
The steps are as follows: and initializing an OpenPose model and loading pre-training weights.
For each frameExtracting joint coordinates of a human body by using an OpenPose model to obtain a joint coordinate set +.>. Return to human joint coordinate sequence->
b. The unsafe behavior recognition algorithm is used to identify,
input: target bounding box sequenceHuman joint coordinate sequence->And (3) outputting: unsafe behavior recognition result sequence->Representing recognized unsafe behavior in each frame
The steps are as follows: and according to the target boundary frame sequence and the human joint coordinate sequence, the target boundary frame and the joint coordinates are corresponding.
For each frameJudging and classifying unsafe behavior according to joint coordinates to obtain unsafe behavior labels +.>. Return unsafe behavior recognition result sequence +.>
3. And the visualization and alarm module is used for performing visualization display on the identification result and triggering a corresponding alarm mechanism according to unsafe behavior, wherein the alarm mechanism comprises an acoustic alarm and a short message notification.
And the visualization and alarm module is used for sending an alarm to related personnel in a mode of sound alarm and short message by the central management system when the sensor or the video monitoring system detects an abnormal event, and meanwhile, the alarm event can be recorded and stored in the central management system.
The intelligent warehouse safety management system based on the internet of things can monitor and identify targets and behaviors in the warehouse environment in real time, perform early warning and management through transmission data of the internet of things, improve efficiency of warehouse safety management, and reduce risks of manual intervention and error judgment.
Although the present invention has been described with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the present invention.
Claims (7)
1. An intelligent warehouse safety management system based on the internet of things is characterized in that the system comprises:
the sensor system is used for monitoring the warehouse environment in the warehouse area and the peripheral area and capturing video data in real time;
a central management system for receiving, storing and analyzing sensor data;
and the visualization and alarm module is used for performing visualization display on the identification result and triggering a corresponding alarm mechanism according to unsafe behavior, wherein the alarm mechanism comprises an acoustic alarm and a short message notification.
2. The intelligent warehouse security management system based on the Internet of things as set forth in claim 1, wherein the sensor system comprises a video monitoring system and a sensor network,
the video monitoring system is provided with a camera and video monitoring equipment to cover a key area of the warehouse area;
the sensor network comprises a temperature sensor, a humidity sensor, a smoke sensor and a door magnetic sensor, and is used for monitoring environmental parameters and storage equipment states in real time, and the sensor is connected to the central management system through the Internet of things.
3. The intelligent warehouse security management system based on the Internet of things as set forth in claim 1, wherein the central management system comprises a video stream processing module, a differential-based YOLO detection algorithm module, a cam shift hybrid tracking algorithm module, an openPose algorithm module, an unsafe behavior identification module, a data transmission and storage module,
the video stream processing module is used for processing the video stream captured by the camera in real time;
the YOLO detection algorithm module based on the difference realizes real-time detection and positioning of the target based on a YOLO network structure and a difference technology;
the Camshift hybrid tracking algorithm module processes video frames according to the position information of the targets, and achieves continuous tracking of the targets.
4. The intelligent warehouse security management system based on the Internet of things as set forth in claim 1, wherein,
the OpenPose algorithm module extracts human body posture information in the video frame and identifies human body actions;
the unsafe behavior recognition module is used for recognizing unsafe behaviors by combining the result of the target detection and tracking algorithm and the human body action information extracted by the OpenPose algorithm;
and the data transmission and storage module is used for transmitting the identification result and other environmental parameters through the Internet of things and storing the data in the cloud server.
5. The intelligent warehouse security management system based on the Internet of things as set forth in claim 4, wherein the target detection and tracking specific algorithm is as follows,
differential-based YOLO detection algorithm, input: a sequence of video frames;
and (3) outputting: target detection result (target position and category)
a. Performing differential operation on the video frame sequence to obtain a differential image between the front frame and the rear frame;
b. performing target detection on the differential image by utilizing a YOLO network structure to obtain a target position and a category;
c. using the detected target position and category information to initialize a target tracking window;
the Camshift hybrid tracking algorithm, input: video frame sequence, initial target position
And (3) outputting: updated target position per frame
a. Performing target tracking on the initial target position by applying a Camshift algorithm;
b. for the subsequent video frames, updating the tracking window according to the target position of the current frame;
c. repeating steps a and b until the video sequence ends.
6. The intelligent warehouse security management system based on the Internet of things as set forth in claim 4, wherein the unsafe behavior identification specific algorithm is as follows,
extracting a human skeleton sequence by using an OpenPose algorithm, and inputting: video frame sequence
And (3) outputting: human skeleton sequence
a. Applying an OpenPose algorithm to the video frame sequence, and extracting joint point information of a human body in each frame;
b. constructing a human skeleton sequence according to the joint point information;
unsafe behavior identification, input: human skeleton sequence
And (3) outputting: unsafe behavior recognition results
a. Preprocessing the human skeleton sequence, including normalization and filtering;
b. according to specific unsafe behavior types, corresponding characteristics are designed, the specific unsafe behavior is determined according to actual storage high-risk actions, and the corresponding characteristics comprise joint angles and movement speeds;
c. training a classifier using the training dataset to classify unsafe behavior;
d. and applying a trained classifier to the extracted features to identify unsafe behaviors.
7. The intelligent warehouse safety management system based on the internet of things according to claim 1, wherein the visualization and alarm module is characterized in that when the sensor or the video monitoring system detects an abnormal event, the abnormal event comprises abnormal behavior and a sensor super threshold value, the central management system sends an alarm to inform related personnel in a sound alarm and short message mode, and the alarm event is recorded and stored in the central management system.
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| CN117351405B (en) * | 2023-12-06 | 2024-02-13 | 江西珉轩智能科技有限公司 | Crowd behavior analysis system and method |
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