Abnormal behavior analysis method based on image recognition

Abstract

The abnormal behavior analysis method based on image recognition provided by the invention comprises the following steps: acquiring video data of a target area, and performing target detection processing on the video data to judge whether a person exists in a video frame image of the video data; obtaining a distance ri between a first person and a second person in a video frame image of the video data; comparing the distance ri with a preset threshold Td, and acquiring optical flow information of the video frame image when the distance ri is smaller than the preset threshold Td; constructing an optical flow direction histogram based on the optical flow information, and calculating an entropy S according to the optical flow direction histogram; comparing the entropy value S with a preset threshold value Tab, and determining that an abnormal aggregation behavior occurs under the condition that the entropy value S is greater than the preset threshold value Tab; under the condition that the abnormal aggregation behavior occurs, first alarm information is generated; laboratory personnel can be monitored in real time, and abnormal gathering of the personnel in the laboratory can be found in time.

Description

Technical Field

[0001] The present application relates to the technical field of image recognition, and in particular to an abnormal behavior analysis method based on image recognition. Background Art

[0002] Under the current laboratory management system, cameras are widely used to monitor various areas of the laboratory. However, monitoring personnel need to monitor the images of multiple cameras in real time in order to detect potential safety issues in a timely manner. Although this monitoring method has improved the safety of the laboratory to some extent, it is often difficult for monitoring personnel to quickly detect abnormal behavior and cannot rush to the scene to stop it in time, so the monitoring efficiency is relatively low. This traditional monitoring method has certain limitations in dealing with laboratory safety issues. Summary of the invention

[0003] In response to the above problems, the present application provides an abnormal behavior analysis method based on image recognition, which solves the technical problem that traditional monitoring methods in the prior art have certain limitations in dealing with laboratory unsafe issues. It can monitor laboratory personnel in real time and promptly detect abnormal gatherings of personnel in the laboratory.

[0004] The present application embodiment provides an abnormal behavior analysis method based on image recognition, including:

[0005] Acquire video data of a target area, and perform target detection processing on the video data to determine whether there is a person in the video frame image of the video data;

[0006] Obtain the distance r between the first person and the second person in the video frame image of the video data i ;

[0007] The distance r i Compared with the preset threshold value Td, at the distance r i When the value is less than the preset threshold value Td, the optical flow information of the video frame image is obtained;

[0008] Based on the optical flow information, an optical flow direction histogram is constructed, and an entropy value S is calculated according to the optical flow direction histogram;

[0009] The entropy value S is compared with the preset threshold T ab When the entropy value S is greater than the preset threshold T ab In the case of abnormal aggregation behavior, it is determined that abnormal aggregation behavior has occurred;

[0010] In the event of abnormal aggregation behavior, a first alarm message is generated.

[0011] In some embodiments, the distance r between the first person and the second person in the video frame image of the video data is obtained. i,include:

[0012] The corresponding positions of the first person and the second person in the i-th frame of the video data are obtained respectively (x 1i ,y 1i ) and (x 2i ,y 2i ), the distance is calculated by the following formula:

[0013] In some embodiments, constructing an optical flow direction histogram based on the optical flow information, and calculating an entropy value S according to the optical flow direction histogram includes:

[0014] Set the optical flow vector to (u, v), where u and v represent the components of the optical flow direction in the x and y directions;

[0015] For each video frame image, the video frame image is divided into several direction intervals, and the number of optical flow vectors (u, v) in each direction interval is counted to obtain an optical flow direction histogram; where P(i, j) is the number of optical flow vectors (u, v) of the jth pixel in the i-th direction interval, and the value range of i is {1, 2, ..., n}, corresponding to n direction intervals;

[0016] The entropy value S is calculated using the optical flow direction histogram, and the formula is as follows:

[0017]

[0018] Among them, N is the number of direction intervals and M is the number of pixels.

[0019] In some embodiments, including:

[0020] Obtaining a width-height change rate K and a center-of-mass difference distribution value β of a detection frame of a person in a video frame image of the video data;

[0021] Compare the aspect ratio change rate K with a preset threshold R1, and compare the centroid difference distribution value β with a preset threshold R2;

[0022] If the aspect ratio change rate K is greater than the preset threshold R1, and the center of mass difference distribution value β is greater than the preset threshold R2, it is determined that a fall has occurred;

[0023] In the event of a fall, a second warning message is generated.

[0024] In some embodiments, including:

[0025] When there is a person in the video frame image of the video data, the aspect ratio change rate K of the detection frame between the current video frame image and the next video frame image is calculated in sequence. The calculation formula of the aspect ratio change rate K is as follows:

[0026]

[0027] Where P ti , P ti+1 Indicates the aspect ratio of the bounding rectangle of the moving target at time ti and time ti+1, L ti , H ti With L ti+1 , H ti+1 Represents the length and width of the moving target detection frame at time ti and time ti+1;

[0028] For each video frame image in the video, determine the detection frame of each person;

[0029] For continuous video frame images, the change of the center of mass coordinate o of the same person in adjacent frames is calculated using the formula:

[0030]

[0031] Among them, Δo is the centroid difference, Δx and Δ are the changes in the centroid position in the x and y directions in adjacent frames, and the centroid coordinates (x1, y1), (x2, y2) are the coordinates of the upper left corner and lower right corner of the detection box respectively;

[0032] The centroid difference values ​​Δo of all video frame images are counted to obtain the distribution of the centroid difference values ​​Δo;

[0033] According to the distribution of the centroid difference Δo, the centroid difference distribution value β is calculated:

[0034] β=σΔo;

[0035] In the formula, σ represents the standard deviation.

[0036] In some embodiments, including:

[0037] In a preset time period, counting the number of people in the video data;

[0038] comparing said quantity to a preset threshold;

[0039] When the number is less than the preset threshold, third warning information is generated.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] Acquire video data of a target area, and perform target detection processing on the video data to determine whether there is a person in the video frame image of the video data; obtain the distance r between the first person and the second person in the video frame image of the video data i ; Set the distance ri Compared with the preset threshold value Td, at the distance r i When the optical flow information of the video frame image is less than the preset threshold value Td, the optical flow information of the video frame image is obtained; based on the optical flow information, an optical flow direction histogram is constructed, and the entropy value S is calculated according to the optical flow direction histogram; the entropy value S is compared with the preset threshold value T ab When the entropy value S is greater than the preset threshold T ab In the case of abnormal gathering behavior, it is determined that abnormal gathering behavior has occurred; in the case of abnormal gathering behavior, the first alarm information is generated; the laboratory personnel can be monitored in real time and abnormal gathering of laboratory personnel can be discovered in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The embodiments of the present invention are further described below with reference to the accompanying drawings:

[0043] Figure 1 A schematic diagram of the implementation process of the abnormal behavior analysis method based on image recognition provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0046] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0047] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0049] Based on the problems existing in the related art, the embodiment of the present application provides an abnormal behavior analysis method based on image recognition, and the execution subject of the analysis method can be an electronic device. The electronic device can be a laptop, a tablet computer, a desktop computer, a set-top box, a mobile device (for example, a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, a portable gaming device) and other various types of terminals, and can also be implemented as a server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content De l ivery Network, CDN) and basic cloud computing services such as big data and artificial intelligence platforms.

[0050] In some embodiments, the functions implemented by the analysis method provided in the embodiments of the present application can be implemented by a processor of an electronic device calling a program code, wherein the program code can be stored in a computer storage medium.

[0051] The present application embodiment provides an abnormal behavior analysis method based on image recognition. Figure 1 A schematic diagram of the implementation process of the abnormal behavior analysis method based on image recognition provided in the embodiment of the present application is shown in FIG. Figure 1 As shown, including:

[0052] Step S1: acquiring video data of a target area, and performing target detection processing on the video data to determine whether there is a person in the video frame image of the video data;

[0053] In an embodiment of the present application, video data of the target area is acquired through a camera, and the acquired video data is transmitted to a background system. The background system is configured with a target detection algorithm, and target detection processing is performed on the video data through the target detection algorithm. Among them, the target detection algorithm can use YOLO, SSD, Faster R-CNN, etc. to identify and locate people in video frame images of the video data.

[0054] Step S2: Obtain the distance r between the first person and the second person in the video frame image of the video data i ;

[0055] In some embodiments, the distance r between the first person and the second person in the video frame image of the video data is obtained. i ,include:

[0056] The corresponding positions of the first person and the second person in the i-th frame of the video data are obtained respectively (x1i ,y 1i ) and (x 2i ,y 2i ), the distance is calculated by the following formula:

[0057] In the embodiment of the present application, when there are persons in the video frame image of the video data, the position information of each person in the video frame image can be obtained, so as to calculate the distance r between two persons according to the position information. i .

[0058] Step S3: Set the distance r i Compared with the preset threshold value Td, at the distance r i When the value is less than the preset threshold value Td, the optical flow information of the video frame image is obtained;

[0059] In the embodiment of the present application, the distance information r i Compared with the preset threshold Td, at the distance r i If the value is greater than or equal to the preset threshold value Td, it means that there is no abnormal aggregation behavior. i When it is less than the preset threshold Td, it indicates that there may be abnormal aggregation behavior, and therefore, the optical flow information of all video frame images of the video data is obtained.

[0060] Step S4: constructing an optical flow direction histogram based on the optical flow information, and calculating an entropy value S according to the optical flow direction histogram;

[0061] In some embodiments, constructing an optical flow direction histogram based on the optical flow information, and calculating an entropy value S according to the optical flow direction histogram includes:

[0062] Set the optical flow vector to (u, v), where u and v represent the components of the optical flow direction in the x and y directions;

[0063] For each video frame image, the video frame image is divided into several direction intervals, and the number of optical flow vectors (u, v) in each direction interval is counted to obtain an optical flow direction histogram; where P(i, j) is the number of optical flow vectors (u, v) of the jth pixel in the i-th direction interval, and the value range of i is {1, 2, ..., n}, corresponding to n direction intervals;

[0064] The entropy value S is calculated using the optical flow direction histogram, and the formula is as follows:

[0065]

[0066] Among them, N is the number of direction intervals and M is the number of pixels.

[0067] Step S5: Compare the entropy value S with the preset threshold T ab When the entropy value S is greater than the preset threshold T ab In the case of abnormal aggregation behavior, it is determined that abnormal aggregation behavior has occurred;

[0068] In the embodiment of the present application, the optical flow direction of the personnel behavior under video surveillance is generally uniform and stable, and the entropy value S used to describe it is generally small; when abnormal aggregation behavior occurs, the optical flow direction distribution is disordered, and the entropy value S is generally large. Therefore, the entropy value S is compared with the preset threshold value T ab When the entropy value S is greater than the preset threshold T ab In this case, it is determined that abnormal aggregation behavior has occurred.

[0069] Step S6: When abnormal aggregation behavior occurs, generate a first alarm message.

[0070] In summary, the video data of the target area is obtained, and the target detection processing is performed on the video data to determine whether there is a person in the video frame image of the video data; the distance r between the first person and the second person in the video frame image of the video data is obtained. i ; Set the distance r i Compared with the preset threshold value Td, at the distance r i When the optical flow information of the video frame image is less than the preset threshold value Td, the optical flow information of the video frame image is obtained; based on the optical flow information, an optical flow direction histogram is constructed, and the entropy value S is calculated according to the optical flow direction histogram; the entropy value S is compared with the preset threshold value T ab When the entropy value S is greater than the preset threshold T ab In the case of abnormal gathering behavior, it is determined that abnormal gathering behavior has occurred; in the case of abnormal gathering behavior, the first alarm information is generated; the laboratory personnel can be monitored in real time to detect abnormal gathering of laboratory personnel in time

[0071] In some embodiments, including:

[0072] Obtaining a width-height change rate K and a center-of-mass difference distribution value β of a detection frame of a person in a video frame image of the video data;

[0073] Compare the aspect ratio change rate K with a preset threshold R1, and compare the centroid difference distribution value β with a preset threshold R2;

[0074] If the aspect ratio change rate K is greater than the preset threshold R1, and the center of mass difference distribution value β is greater than the preset threshold R2, it is determined that a fall has occurred;

[0075] In the event of a fall, a second warning message is generated.

[0076] In some embodiments, including:

[0077] When there is a person in the video frame image of the video data, the aspect ratio change rate K of the detection frame between the current video frame image and the next video frame image is calculated in sequence. The calculation formula of the aspect ratio change rate K is as follows:

[0078]

[0079] Where P ti , P ti+1 Indicates the aspect ratio of the bounding rectangle of the moving target at time ti and time ti+1, L ti , H ti With L ti+1 , H ti+1 Represents the length and width of the moving target detection frame at time ti and time ti+1;

[0080] For each video frame image in the video, determine the detection frame of each person;

[0081] For continuous video frame images, the change of the center of mass coordinate o of the same person in adjacent frames is calculated using the formula:

[0082]

[0083] Among them, Δo is the centroid difference, Δx and Δ are the changes in the centroid position in the x and y directions in adjacent frames, and the centroid coordinates (x1, y1), (x2, y2) are the coordinates of the upper left corner and lower right corner of the detection box respectively;

[0084] The centroid difference values ​​Δo of all video frame images are counted to obtain the distribution of the centroid difference values ​​Δo;

[0085] According to the distribution of the centroid difference Δo, the centroid difference distribution value β is calculated:

[0086] β=σΔo;

[0087] In the formula, σ represents the standard deviation.

[0088] In an embodiment of the present application, the aspect ratio of the detection frame of a person will change with the change of the person's behavior. If a person falls, the aspect ratio P of the detection frame will change from less than 1 to greater than 1. Therefore, the detection result can be judged by comparing the aspect ratio change rate K. When a person is in a normal state, the center of mass difference distribution value β often changes little because the center of gravity does not change much. When a person falls, the center of mass difference distribution value β increases significantly due to the sudden decrease in the center of gravity. Therefore, the detection result can be judged by the center of mass difference distribution value β. The present application combines the aspect ratio change rate K and the center of mass difference distribution value β to determine whether a person falls, so as to improve the accuracy of determining whether a person falls. When the aspect ratio change rate K is greater than the preset threshold R1, and the center of mass difference distribution value β is greater than the preset threshold R2, it is determined that a fall has occurred.

[0089] In some embodiments, including:

[0090] In a preset time period, counting the number of people in the video data;

[0091] comparing said quantity to a preset threshold;

[0092] When the number is less than the preset threshold, third warning information is generated.

[0093] In an embodiment of the present application, the number of people in the video data is counted in a preset time period, the number is compared with a preset threshold, and a third alarm signal is generated when the number is less than the preset threshold. This facilitates monitoring of the number of people in the target area and timely discovers the absence of people.

[0094] It should be noted that, in the embodiment of the present application, if the above-mentioned analysis method is implemented in the form of a software function module, and when it is sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) perform all or part of the method described in each embodiment of the present application. And the aforementioned storage medium includes: various media that can store program codes such as U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), disk or optical disk. In this way, the embodiment of the present application is not limited to any specific hardware and software combination.

[0095] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps in the analysis method provided in the above embodiment are implemented.

[0096] An embodiment of the present application provides an electronic device; Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the electronic device 400 includes: a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and a memory 405. The communication bus 402 is configured to realize the connection and communication between these components. The user interface 403 may include a display screen, and the external communication interface 404 may include a standard wired interface and a wireless interface. The processor 401 is configured to execute the program of the analysis method stored in the memory to implement the steps in the analysis method provided in the above embodiment.

[0097] It should be noted here that the description of the above storage medium and electronic device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0098] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0099] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0100] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0101] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0102] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0103] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read On ly Memory), disks or optical disks, etc. Various media that can store program codes.

[0104] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0105] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The abnormal behavior analysis method based on image recognition is characterized by: include: Acquire video data of a target area, and perform target detection processing on the video data to determine whether there is a person in the video frame image of the video data; Obtain the distance r between the first person and the second person in the video frame image of the video data i ; The distance r i Compared with the preset threshold Td, at the distance r i When the value is less than the preset threshold value Td, the optical flow information of the video frame image is obtained; Based on the optical flow information, an optical flow direction histogram is constructed, and an entropy value S is calculated according to the optical flow direction histogram; The entropy value S is compared with the preset threshold T ab When the entropy value S is greater than the preset threshold T ab In the case of abnormal aggregation behavior, it is determined that abnormal aggregation behavior has occurred; In the event of abnormal aggregation behavior, a first alarm message is generated.

2. The abnormal behavior analysis method based on image recognition according to claim 1 is characterized in that: The distance r between the first person and the second person in the video frame image of the video data is obtained. i ,include: The corresponding positions of the first person and the second person in the i-th frame of the video data are obtained respectively (x 1i ,y 1i ) and (x 2i ,y 2i ), the distance is calculated by the following formula:

3. The abnormal behavior analysis method based on image recognition according to claim 2 is characterized in that: The step of constructing an optical flow direction histogram based on the optical flow information and calculating an entropy value S according to the optical flow direction histogram includes: Set the optical flow vector to (u, v), where u and v represent the components of the optical flow direction in the x and y directions; For each video frame image, the video frame image is divided into several direction intervals, and the number of optical flow vectors (u, v) in each direction interval is counted to obtain an optical flow direction histogram; wherein, let P(i, j) be the number of optical flow vectors (u, v) of the jth pixel in the i-th direction interval, and the value range of i is {1, 2, ..., n}, corresponding to n direction intervals; The entropy value S is calculated using the optical flow direction histogram. The formula is as follows: Among them, N is the number of direction intervals and M is the number of pixels.

4. The abnormal behavior analysis method based on image recognition according to claim 1 is characterized in that: include: Obtaining a width-height change rate K and a center-of-mass difference distribution value β of a detection frame of a person in a video frame image of the video data; Compare the aspect ratio change rate K with a preset threshold R1, and compare the centroid difference distribution value β with a preset threshold R2; If the aspect ratio change rate K is greater than the preset threshold R1, and the center of mass difference distribution value β is greater than the preset threshold R2, it is determined that a fall has occurred; In the event of a fall, a second warning message is generated.

5. The abnormal behavior analysis method based on image recognition according to claim 4 is characterized in that: include: When there is a person in the video frame image of the video data, the aspect ratio change rate K of the detection frame between the current video frame image and the next video frame image is calculated in sequence. The calculation formula of the aspect ratio change rate K is as follows: Where P ti , P ti+1 Indicates the aspect ratio of the bounding rectangle of the moving target at time ti and time ti+1, L ti , H ti With L ti+1 , H ti+1 Represents the length and width of the moving target detection frame at time ti and time ti+1; For each video frame image in the video, determine the detection frame of each person; For continuous video frame images, the change of the center of mass coordinate o of the same person in adjacent frames is calculated using the formula: Among them, Δo is the centroid difference, Δx and Δ are the changes in the centroid position in the x and y directions in adjacent frames, and the centroid coordinates (x1, y1), (x2, y2) are the coordinates of the upper left corner and lower right corner of the detection box respectively; The centroid difference values ​​Δo of all video frame images are counted to obtain the distribution of the centroid difference values ​​Δo; According to the distribution of the centroid difference Δo, the centroid difference distribution value β is calculated: β=σΔo; In the formula, σ represents the standard deviation.

6. The abnormal behavior analysis method based on image recognition according to claim 1, characterized in that: include: In a preset time period, counting the number of people in the video data; comparing said quantity to a preset threshold; When the number is less than the preset threshold, third warning information is generated.

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