WO2018210039A1 - Data processing method, data processing device, and storage medium - Google Patents

Abstract

Provided are a data processing method, a data processing device, and a storage medium. The method comprises: acquiring a current task state of a target user equipment unit (101); acquiring N warning information items, N being a positive integer (102); and assigning M warning information items to the target user equipment unit according to the current task state, the N warning information items including the M warning information items, M being an integer less than or equal to N (103). The method enables identification of a warning situation and realizes reasonable processing with respect to the warning situation.

Description

Data processing method, data processing device and storage medium

The present application claims the priority of the Chinese Patent Application, which is filed on May 18, 2017, the entire disclosure of which is hereby incorporated by reference. in.

Technical field

The present invention relates to the field of video surveillance technologies, and in particular, to a data processing method, a data processing device, and a storage medium.

Background technique

With the rapid development of economy, society and culture, the influence of domestic and foreign influences is increasing day by day, and more and more foreign populations are flowing to cities. These population increases, while accelerating the process of urbanization, also bring greater challenges to urban management, although Video surveillance provides technical support for urban security. However, at present, the camera has been laid out in the city, and the functions of each camera are relatively independent. Of course, multiple cameras can belong to one system, and the system performs on one area. Monitoring, due to the large number of surveillance cameras, how to implement the reasonable handling of alarms after the alarm is discovered (ie, the target person appears) needs to be resolved.

Summary of the invention

The embodiment of the invention provides a data processing method, a data processing device and a storage medium, which can detect an alarm situation and implement reasonable processing of the alarm.

A first aspect of the embodiments of the present invention provides a data processing method, including:

Obtain the current task status of the target user equipment;

Obtaining N pieces of alarm information, where N is a positive integer;

Assigning M pieces of alarm information to the target user equipment according to the current task status, where the N pieces of alarm information include the M pieces of alarm information, where the M is an integer less than or equal to the N.

With reference to the first aspect of the embodiments of the present invention, in the first possible implementation manner of the first aspect, the acquiring the N pieces of alarm information includes:

Acquiring a video from the X cameras to obtain the X video segments, each of the cameras corresponding to one of the video segments, where X is a positive integer;

Performing behavior analysis on the X video segments to obtain the N pieces of alarm information.

Furthermore, the video can be obtained by multiple cameras, multiple video segments are obtained, and each video segment is subjected to behavior analysis to obtain N pieces of alarm information. Thus, the alarm information can be quickly analyzed from the video segments by behavior analysis.

With reference to the first possible implementation manner of the first aspect of the embodiment, in the second possible implementation manner of the first aspect, the performing, by using the behavior analysis of the X video segments, the N pieces of alarm information, include:

Parsing X video segments to obtain a Y frame video image, the Y being a positive integer;

Performing behavior analysis on each frame of the video image of the Y frame to obtain Z video images conforming to a preset behavior, where Z is a positive integer;

The Z video images are filtered to obtain N video images, and each video image corresponds to one alarm information.

Furthermore, multiple video segments can be parsed first, and each frame image is analyzed for behavior, and a video image conforming to the preset behavior is selected. Since the selected image also has image quality problems, it can be further filtered and filtered. Each frame of the image corresponds to an alarm message, thus reducing the false alarm rate and improving the alarm accuracy.

With reference to the first possible implementation manner of the first aspect of the embodiments of the present disclosure, in a third possible implementation manner of the foregoing aspect, the performing the behavior analysis on the X video segments to obtain the N pieces of alarm information, include:

Performing image segmentation on the X video segments to obtain a P personal face image, where P is a positive integer;

Combining the P face images to obtain a Q face image, wherein the Q is a positive integer less than or equal to the P;

Matching the Q face image with the A face image in the image library to obtain the N face image, the A is a positive integer, the N is smaller than the Q, and the N is smaller than the A;

Generating the N pieces of alarm information according to the N human face image.

Furthermore, image segmentation can be performed on a plurality of videos, the segmented face images are merged, and the merged face images are matched with the face images in the image library, and alarm information is generated according to the matching result. The alarm information can be generated only for the face image in the original library, which can be used to implement alarm processing for the specified target.

With reference to the first aspect of the first embodiment of the present invention, or any one of the first to third aspects of the first aspect, in a fourth possible implementation manner of the first aspect, The target user equipment allocates M pieces of alarm information, including:

Obtaining a location of the target user equipment;

And assigning M pieces of alarm information to the target user equipment according to the location and the current task status.

Further, the location of the user equipment may be obtained, and the alarm information may be allocated according to the location and the corresponding task status. If the user equipment is far away from the alarm information, the user equipment may not be allocated, and the user equipment may be allocated alarm information.

A second aspect of the embodiments of the present invention provides a data processing apparatus, including:

a first acquiring unit, configured to acquire a current task state of the target user equipment;

a second acquiring unit, configured to acquire N pieces of alarm information, where the N is a positive integer;

And an allocating unit, configured to allocate M pieces of alarm information to the target user equipment according to the current task status, where the N pieces of alarm information include the M pieces of alarm information, where the M is an integer less than or equal to the N.

With reference to the second aspect of the embodiments of the present invention, in a first possible implementation manner of the second aspect, the second acquiring unit includes:

a first acquiring module, configured to acquire video from the X cameras, to obtain the X video segments, each of the cameras corresponding to one of the video segments, where X is a positive integer;

The first analysis module is configured to perform behavior analysis on the X video segments to obtain the N pieces of alarm information.

With reference to the first possible implementation manner of the second aspect of the embodiments of the present invention, in a second possible implementation manner of the second aspect, the first analysis module includes:

a parsing module, configured to parse the X video segments to obtain a Y frame video image, where the Y is a positive integer;

a second analysis module, configured to perform behavior analysis on each frame of the video image of the Y frame to obtain Z video images that conform to a preset behavior, where the Z is a positive integer;

The screening module is configured to filter the Z video images to obtain N video images, and each video image corresponds to one alarm information.

With reference to the first possible implementation manner of the second aspect of the embodiments of the present invention, in a third possible implementation manner of the first aspect, the first analysis module includes:

a segmentation module, configured to perform image segmentation on the X video segments to obtain a P personal face image, where P is a positive integer;

a merging module, configured to merge the P personal face images to obtain a Q personal face image, where the Q is a positive integer less than or equal to the P;

a matching module, configured to match the Q facial image with an A facial image in the image library to obtain the N facial image, wherein the A is a positive integer, and the N is smaller than the Q, the N Less than the A;

And a generating module, configured to generate the N pieces of alarm information according to the N personal face image.

With reference to the second aspect of the embodiment of the present invention or the first to the third possible implementation manner of the second aspect, in the fourth possible implementation manner of the second aspect,

a second acquiring module, configured to acquire a location of the target user equipment;

And an allocating module, configured to allocate M pieces of alarm information to the target user equipment according to the location and the current task status.

A third aspect of the embodiments of the present invention provides a data processing apparatus, wherein the data processing apparatus includes a processor, and the processor is configured to implement the data processing method according to any one of the above, when the computer program is stored in a memory.

A fourth aspect of the embodiments of the present invention provides a computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the method of any of the first aspect or the first aspect .

Embodiments of the present invention have the following beneficial effects:

It can be seen that, by using the embodiment of the present invention, the data processing device can obtain the current task status of the target user equipment, obtain N pieces of alarm information, and N is a positive integer, and allocate M pieces of alarm information to the target user equipment according to the current task status, N pieces. The alarm information includes M alarms, and M is an integer less than or equal to N. Therefore, the current task status of the user equipment can be obtained, such as: pending tasks, processed tasks, and timeout unprocessed. When there are many tasks, Alarm information can be allocated less for it. When there are fewer tasks, more alarm information is assigned to it, and the alarm information can be processed reasonably.

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are some embodiments of the present invention, Those skilled in the art can also obtain other drawings based on these drawings without paying any creative work.

1 is a schematic flow chart of a first embodiment of a data processing method according to an embodiment of the present invention;

2 is a schematic flow chart of a second embodiment of a data processing method according to an embodiment of the present invention;

2a is a schematic diagram showing a demonstration of a current task state of a user equipment according to an embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a first embodiment of a data processing apparatus according to an embodiment of the present invention; FIG.

FIG. 3b is a schematic structural diagram of a second acquiring unit of the data processing apparatus described in FIG. 3a according to an embodiment of the present disclosure;

3c is a schematic structural diagram of a first embodiment of a first analysis module of the second obtaining unit described in FIG. 3a according to an embodiment of the present disclosure;

FIG. 3 is a schematic structural diagram of a second embodiment of a first analysis module of the second obtaining unit depicted in FIG. 3a according to an embodiment of the present disclosure;

3e is a schematic structural diagram of an allocation unit of the data processing apparatus described in FIG. 3a according to an embodiment of the present invention;

FIG. 4 is a schematic structural diagram of a second embodiment of a data processing apparatus according to an embodiment of the present invention.

detailed description

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

The terms "first", "second", "third", and "fourth" and the like in the specification and claims of the present invention are used to distinguish different objects, and are not intended to describe a specific order. . Furthermore, the terms "comprises" and "comprising" and "comprising" are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units not listed, or alternatively Other steps or units inherent to these processes, methods, products or equipment.

References to "an embodiment" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the invention. The appearances of the phrases in various places in the specification are not necessarily referring to the same embodiments, and are not exclusive or alternative embodiments that are mutually exclusive. Those skilled in the art will understand and implicitly understand that the embodiments described herein can be combined with other embodiments.

The data processing apparatus described in the embodiments of the present invention may include a smart phone (such as an Android mobile phone, an iOS mobile phone, a Windows Phone mobile phone, etc.), a tablet computer, a palmtop computer, a notebook computer, a mobile Internet device (MID, Mobile Internet Devices), or a wearable device. The above is only an example, and not an exhaustive, including but not limited to the above device. Of course, the above data processing device may also be a server. The user equipment can be a smart phone (such as an Android phone, an iOS phone, a Windows Phone, etc.), a tablet, a palmtop, a laptop, a mobile Internet device (MID, Mobile Internet Devices) or a wearable device, a walkie-talkie, and the like.

It should be noted that the data processing apparatus in the embodiment of the present invention may be connected to multiple cameras, and each camera may be used to capture video images, and each camera may have a corresponding position mark, or there may be one The number corresponding to it. Typically, cameras can be placed in public places, such as schools, museums, crossroads, pedestrian streets, office buildings, garages, airports, hospitals, subway stations, stations, bus stops, supermarkets, hotels, entertainment venues, and more. After the camera captures the video image, the video image can be saved to the memory of the system where the data processing device is located. A plurality of image libraries can be stored in the memory, and each image library can include different video images of the same person. Of course, each image library can also be used to store a video image of one area or a video image taken by a specified camera.

Further, in the embodiment of the present invention, each frame of the video image captured by the camera corresponds to one attribute information, and the attribute information is at least one of the following: a shooting time of the video image, a position of the video image, and an attribute parameter of the video image ( Format, size, resolution, etc.), the number of the video image, and the character characteristics in the video image. The character feature attributes in the above video image may include, but are not limited to, the number of people in the video image, the position of the person, the angle of the person, and the like.

It should be further noted that the video image captured by each camera is usually a dynamic face image. Therefore, in the embodiment of the present invention, the angle of the face image may be analyzed, and the angle may include, but is not limited to, a horizontal rotation angle and a pitch. Angle or inclination. For example, the definition of dynamic face image data requires that the distance between the two eyes is not less than 30 pixels, and it is recommended to be more than 60 pixels. The horizontal rotation angle does not exceed ±30°, the pitch angle does not exceed ±20°, and the inclination angle does not exceed ±45°. It is recommended that the horizontal rotation angle does not exceed ±15°, the pitch angle does not exceed ±10°, and the inclination angle does not exceed ±15°. For example, it is also possible to screen whether the face image is blocked by other objects. Usually, the ornament should not block the main area of the face, such as dark sunglasses, masks and exaggerated jewelry, and of course, the camera may be covered with dust. , causing the face image to be occluded. The picture format of the video image in the embodiment of the present invention may include, but is not limited to, BMP, JPEG, JPEG2000, PNG, etc., and the size may be between 10-30 KB, and each video image may also correspond to one shooting time and shooting. Information such as a camera number of the video image, a link of a panoramic image corresponding to the face image, and the like (a face image and a global image creation feature correspondence relationship file).

FIG. 1 is a schematic flowchart diagram of a first embodiment of a data processing method according to an embodiment of the present invention. The data processing method described in this embodiment includes the following steps:

101. Obtain a current task status of the target user equipment.

The data processing device may send a task status acquisition request to the target user equipment, and after receiving the task status acquisition request, the target user equipment may send the current task status of the target user equipment to the data processing device. The data processing device can be coupled to a plurality of user devices, the target user device being one of the plurality of user devices.

Optionally, the current task status may include at least one of the following: a task to be processed, a processed task and a timeout unprocessed, a task content being processed, an importance level, and the like. The task to be processed may be the task to be processed next, and may include the content and quantity of the task to be processed. For example, if an alarm condition is found, it needs to be processed.

102. Obtain N pieces of alarm information, where N is a positive integer.

As can be seen from the above, the data processing device can be connected to a plurality of cameras. Therefore, if any target is captured by any of the cameras, an alarm message can be generated, and N pieces of alarm information can be obtained, where N is a positive integer. Of course, a camera may capture 1 target, and may capture 2 targets. Of course, it may not capture any target. Further, several alarm information may be obtained according to actual conditions. Each of the alarm information may include at least one target object, and the target object may be a crime object (for example, a thief appearing in a supermarket), or specify a target person.

Optionally, in the foregoing step 102, acquiring N pieces of alarm information may include the following steps:

21) acquiring video from X cameras, and obtaining the X video segments, each of the cameras corresponding to one of the video segments, where X is a positive integer;

22) Perform behavior analysis on the X video segments to obtain the N pieces of alarm information.

The data processing device can acquire X video segments through X cameras connected thereto, each camera can correspond to one video segment, and can analyze X video segments, and can be analyzed in the following two manners. Get N alarms.

Optionally, in the foregoing step 22, performing behavior analysis on the X video segments to obtain the N pieces of alarm information may include the following steps:

221) parsing the X video segments to obtain a Y frame video image, where Y is a positive integer;

222) performing behavior analysis on each frame of the video image in the Y frame to obtain Z video images that conform to a preset behavior, where Z is a positive integer;

223) screening the Z video images to obtain N video images, each video image corresponding to one alarm information.

The preset behavior may be set by the user equipment, for example, destroying an item, stealing an item, and the like. The data processing device can parse the X video segments, thereby obtaining an image of one frame and one frame, that is, a Y frame video image, and performing behavior analysis on each frame video image in the Y frame video image, thereby obtaining Z A video image conforming to the preset behavior, Z is a positive integer. Further, Z video images can be filtered, and Z video images can be filtered as follows, for example, image quality evaluation of Z video images is obtained. Z image quality evaluation values are selected, and the video images corresponding to the image quality evaluation values whose image quality evaluation values are larger than the preset image quality threshold are selected, and N video images are obtained, and each video image corresponds to one alarm information, and N pieces of alarm information are obtained, wherein The preset image quality threshold can be set by the user equipment or the system defaults.

Further, the data processing apparatus may perform image quality evaluation on the Z video images by using at least one image quality evaluation index to obtain an image quality evaluation value, wherein the image quality evaluation index may include Not limited to: average gray, mean square error, entropy, edge retention, signal to noise ratio, and so on. It can be defined that the larger the image quality evaluation value obtained, the better the image quality.

It should be noted that since the image quality is evaluated by using a single evaluation index, there are certain limitations. Therefore, multiple image quality evaluation indicators can be used to evaluate the image quality. Of course, when evaluating the image quality, it is not an image. The more quality evaluation indicators, the better, because the more image quality evaluation indicators, the higher the computational complexity of the image quality evaluation process, and the better the image quality evaluation effect. Therefore, in the case of high image quality evaluation requirements Image quality can be evaluated by using 2 to 10 image quality evaluation indicators. Specifically, the number of image quality evaluation indicators and which indicator are selected are determined according to specific implementation conditions. Of course, it is also necessary to select image quality evaluation indicators in combination with specific scenes, and the image quality indicators in the dark environment and the image quality evaluation in the bright environment may be different.

Optionally, in the case that the image quality evaluation accuracy is not high, an image quality evaluation index may be used for evaluation. For example, the image quality evaluation value is processed by entropy processing, and the entropy is larger, indicating that the image quality is higher. Well, conversely, the smaller the entropy, the worse the image quality.

Optionally, in the case that the image quality evaluation accuracy is high, the image to be evaluated may be evaluated by using multiple image quality evaluation indicators, and the image quality evaluation may be performed when the image quality evaluation index is used for image quality evaluation. The weight of each image quality evaluation index in the plurality of image quality evaluation indicators may obtain a plurality of image quality evaluation values, and the final image quality evaluation value may be obtained according to the plurality of image quality evaluation values and corresponding weights, for example, three The image quality evaluation indicators are: A index, B index and C index. The weight of A is a1, the weight of B is a2, and the weight of C is a3. When A, B and C are used to evaluate the image quality of an image. The image quality evaluation value corresponding to A is b1, the image quality evaluation value corresponding to B is b2, and the image quality evaluation value corresponding to C is b3. Then, the final image quality evaluation value=a1b1+a2b2+a3b3. In general, the larger the image quality evaluation value, the better the image quality.

Optionally, in the foregoing step 223, screening the Z video images may also be implemented as follows:

Determining the angle of the face image included in the video image i, determining whether the angle is within a preset angle range, and if so, retaining the video image i, the video image i is any one of the Z video images.

Optionally, in the foregoing step 22, performing behavior analysis on the X video segments to obtain the N pieces of alarm information may include the following steps:

224) performing image segmentation on the X video segments to obtain a P personal face image, where P is a positive integer;

225) combining the P personal face images to obtain a Q personal face image, wherein the Q is a positive integer less than or equal to the P;

226) matching the Q facial image with the A facial image in the image library to obtain the N facial image, wherein A is a positive integer, the N is smaller than the Q, and the N is smaller than A;

227) Generate the N pieces of alarm information according to the N personal face image.

Wherein, X video segments may include multiple frames of images. Of course, not every frame image includes a face image. Of course, some frames may also include multiple face images, and thus, each frame may be The image is segmented to obtain a P face image, and P is a positive integer. Of course, different image frames may also include a face image of the same person. Further, the P face image may be further combined to obtain a Q face image, and Q is a positive integer less than or equal to P. Further, the Q face image can be matched with the A face image in the image library to obtain an N face image, where N is smaller than Q and N is smaller than A. The A personal face image in the image library may be a face image of an alarm target that is pre-recorded into the system, that is, an alarm target appears, and an alarm information may be generated. Further, N pieces of alarm information are generated according to the N personal face image, and each face image can correspond to one piece of alarm information.

Further, in the above step 226, matching the Q personal face image with the A personal face image in the image library may include the following steps:

A1, performing feature extraction on each face image in the Q facial image to obtain the Q first feature sets;

A2, performing feature extraction on each face image in the A personal face image to obtain the A second feature set;

A3. Match the Q first feature sets with the A second feature sets to obtain Q*A matching values.

A4. Select, from the Q*A matching values, a feature value that is greater than a preset matching threshold, to obtain the N matching values.

A5. Obtain a face image corresponding to the N matching values from the Q face image to obtain the N face image.

In the step A1, the Q face image can be extracted, and the Q first feature sets can be obtained. Of course, the feature extraction method can include, but is not limited to, a Harris corner detection algorithm and a scale invariant feature (Scale Invariant Feature Transform). , SIFT) extraction algorithm, using classifier for feature extraction, classifiers may include but are not limited to: Support Vector Machine (SVM), convolutional neural network, cascading neural network, genetic algorithm and so on. In this way, the feature extraction of the A personal face image can also be implemented, taking the face image i as an example, wherein the face image i is any one of the A personal face images, and a second feature set can be obtained. The Q first feature sets and the A second feature sets may be matched by using Structural Similarity Index Measurement (SSIM) to obtain Q*A matching values. The above preset matching threshold may be set by the system default or by the user equipment. Therefore, a matching value whose matching value is greater than a preset matching threshold value may be selected from the Q*A matching values to obtain N eigenvalues, and further, a face image corresponding to the N matching values may be obtained from the Q facial image. Get N face images.

103. Allocate M pieces of alarm information to the target user equipment according to the current task status, where the N pieces of alarm information include the M pieces of alarm information, where the M is an integer less than or equal to the N.

The data processing device may allocate M pieces of alarm information to the target user equipment according to the current task status. There may be multiple user equipments in the current environment. Therefore, the N pieces of alarm information may not be allocated to one user equipment. The target user equipment allocates M pieces of alarm information, where M is an integer less than or equal to N. For example, the alarm information may be allocated less frequently according to the needs of the current task to be processed. Of course, the alarm information may be allocated according to the location of the user. If the location of the alarm information is close to the target user equipment, the alarm information may be prioritized.

Optionally, in the foregoing step 103, allocating the M pieces of alarm information to the target user equipment according to the current task status may include the following steps:

31) acquiring a location of the target user equipment;

32) Allocating M pieces of alarm information to the target user equipment according to the location and the current task status.

The data processing device can obtain the location of the target user equipment by using a camera or a positioning technology, and then allocates M pieces of alarm information to the user equipment according to the location and the current task status. Of course, the user equipment may have fewer tasks to be processed.

It can be seen that, by using the embodiment of the present invention, the data processing device can obtain the current task status of the target user equipment, obtain N pieces of alarm information, and N is a positive integer, and allocate M pieces of alarm information to the target user equipment according to the current task status, N pieces. The alarm information includes M alarms, and M is an integer less than or equal to N. Therefore, the current task status of the user equipment can be obtained, such as: pending tasks, processed tasks, and timeout unprocessed. When there are many tasks, Alarm information can be allocated less for it. When there are fewer tasks, more alarm information is assigned to it, and the alarm information can be processed reasonably.

With reference to FIG. 2, it is a schematic flowchart of a second embodiment of a data processing method according to an embodiment of the present invention. The data processing method described in this embodiment includes the following steps:

201. The video processing device acquires a current task status of the target user equipment.

202. The video processing device acquires N pieces of alarm information, where N is a positive integer.

203. The video processing device allocates M pieces of alarm information to the target user equipment according to the current task status, where the N pieces of alarm information include the M pieces of alarm information, where the M is less than or equal to the N Integer.

Optionally, the specific description of the foregoing step 201 - step 203 can be referred to step 101 - step 103 of the data processing method described in FIG. 1 .

204. The target user equipment receives the M pieces of alarm information.

205. The target user equipment acquires a current location.

206. The target user equipment determines a distance between the current location and the M locations corresponding to the M pieces of alarm information, to obtain the M distance values.

207. The target user equipment displays the M pieces of alarm information on the target user equipment according to the M distance values.

Optionally, the user can click “Alarm Statistics” in the task bar on the target user device to display all the alarm data statistics of the day, including: total number of alarms, false positives, normal shopping, theft stop, no finds, timeout Unprocessed, pending; click on each column of information, you can display the list of related alarm information, of course, all the columns in the alarm statistics can only be operated on the "pending" items, and others can only be viewed. The target user equipment clicks the alarm information to enter the corresponding page directly, and the person who receives the information can process the information, including (false positives, normal shopping, stop, undiscovered), and can also be assigned to the store. Any one of the people can track the event. If the user equipment is mobile, the video of the camera next to the current location can be provided. Thus, the user equipment can search for the target appearing in the alarm information according to the video.

It can be seen that, by using the embodiment of the present invention, the data processing device can obtain the current task status of the target user equipment, obtain N pieces of alarm information, and N is a positive integer, and allocate M pieces of alarm information to the target user equipment according to the current task status, N pieces. The alarm information includes M alarms. M is an integer less than or equal to N. The target user equipment receives M alarms, obtains the current location, and determines the distance between the current location and the M locations corresponding to the M alarms. The distance values are based on the M distance values, and M pieces of alarm information are displayed on the target user equipment. Therefore, the current task status of the user equipment can be obtained, such as: a task to be processed, a processed task, and a timeout unprocessed, etc., when there are many tasks, less alarm information can be allocated for it, and when there are fewer tasks, more The alarm information is allocated to the user equipment. In addition, the user equipment can display the M alarm information according to the position of the M alarm information and the distance between the M alarm information, so that the user can preferentially select the alarm information to be processed nearby, thereby implementing the The alarm information is processed reasonably.

Consistent to the above, the following is an apparatus for implementing the above data processing method, as follows:

FIG. 3 is a schematic structural diagram of a first embodiment of a data processing apparatus according to an embodiment of the present invention. The data processing apparatus described in this embodiment includes: a first obtaining unit 301, a second obtaining unit 302, and an allocating unit 303, as follows:

The first obtaining unit 301 is configured to acquire a current task status of the target user equipment.

The second obtaining unit 302 is configured to acquire N pieces of alarm information, where the N is a positive integer;

The allocating unit 303 is configured to allocate M pieces of alarm information to the target user equipment according to the current task status, where the N pieces of alarm information include the M pieces of alarm information, where the M is an integer less than or equal to the N .

Optionally, as shown in FIG. 3b, FIG. 3b is a specific refinement structure of the second obtaining unit 302 of the video processing apparatus described in FIG. 3a, where the second obtaining unit 302 may include: a first obtaining module 3021 and a first analysis. Module 3022 is as follows:

a first obtaining module 3021, configured to acquire video from the X cameras, to obtain the X video segments, each of the cameras corresponding to one of the video segments, where X is a positive integer;

The first analysis module 3022 is configured to perform behavior analysis on the X video segments to obtain the N pieces of alarm information.

Optionally, as shown in FIG. 3c, FIG. 3c is a specific refinement structure of the first analysis module 3022 of the video processing device described in FIG. 3b, where the first analysis module 3022 may include: a parsing module 401, and a second analysis. The module 402 and the screening module 403 are as follows:

The parsing module 401 is configured to parse the X video segments to obtain a Y frame video image, where the Y is a positive integer;

The second analysis module 402 is configured to perform behavior analysis on each frame of the video image of the Y frame to obtain Z video images that meet preset behaviors, where the Z is a positive integer;

The screening module 403 is configured to filter the Z video images to obtain N video images, and each video image corresponds to one alarm information.

Optionally, as shown in FIG. 3d, FIG. 3d is a specific refinement structure of the first analysis module 3022 of the video processing device described in FIG. 3b, where the first analysis module 3022 may include a segmentation module 404 and a merge module 405. The matching module 406 and the generating module 407 are as follows:

a segmentation module 404, configured to perform image segmentation on the X video segments to obtain a P personal face image, where P is a positive integer;

The merging module 405 is configured to combine the P personal face images to obtain a Q personal face image, where the Q is a positive integer less than or equal to the P;

The matching module 406 is configured to match the Q personal face image with the A personal face image in the image library to obtain the N personal face image, where A is a positive integer, and the N is smaller than the Q, N is less than the A;

The generating module 407 is configured to generate the N pieces of alarm information according to the N personal face image.

Optionally, the matching module 406 may include the following modules: a feature extraction module (not shown), a feature matching module (not shown), a feature value selection module (not shown), and a face acquisition. Module (not shown), as follows:

a feature extraction module, configured to perform feature extraction on each face image in the Q personal face image to obtain the Q first feature sets;

The feature extraction module is further configured to perform feature extraction on each face image in the A personal face image to obtain the A second feature set;

a feature matching module, configured to match the Q first feature sets with the A second feature sets to obtain Q*A matching values;

An eigenvalue selection module, configured to select, from the Q*A matching values, a feature value that is greater than a preset matching threshold, to obtain the N matching values;

a face acquisition module, configured to acquire a face image corresponding to the N matching values from the Q facial image to obtain the N personal face image.

Optionally, as shown in FIG. 3e, FIG. 3e is a specific refinement structure of the allocating unit 303 of the video processing apparatus described in FIG. 3a, where the allocating unit 303 may include: a second obtaining module 3031 and an allocating module 3032, as follows:

The second obtaining module 3031 is configured to acquire a location of the target user equipment.

The allocating module 3032 is configured to allocate M pieces of alarm information to the target user equipment according to the location and the current task status.

It can be seen that, by using the embodiment of the present invention, the data processing device can obtain the current task status of the target user equipment, obtain N pieces of alarm information, and N is a positive integer, and allocate M pieces of alarm information to the target user equipment according to the current task status, N pieces. The alarm information includes M alarms, and M is an integer less than or equal to N. Therefore, the current task status of the user equipment can be obtained, such as: pending tasks, processed tasks, and timeout unprocessed. When there are many tasks, Alarm information can be allocated less for it. When there are fewer tasks, more alarm information is assigned to it, and the alarm information can be processed reasonably.

With reference to FIG. 4, it is a schematic structural diagram of a second embodiment of a data processing apparatus according to an embodiment of the present invention. The data processing apparatus described in this embodiment includes: at least one input device 1000; at least one output device 2000; at least one processor 3000, such as a CPU; and a memory 4000, the input device 1000, the output device 2000, and the processor 3000. And the memory 4000 is connected through the bus 5000.

The input device 1000 may be a touch panel, a physical button, or a mouse.

The output device 2000 described above may specifically be a display screen.

The above memory 4000 may be a high speed RAM memory or a non-volatile memory such as a magnetic disk memory. The above memory 4000 is used to store a set of program codes, and the input device 1000, the output device 2000, and the processor 3000 are used to call the program code stored in the memory 4000, and perform the following operations:

The processor 3000 is configured to:

Obtain the current task status of the target user equipment;

Obtaining N pieces of alarm information, where N is a positive integer;

Assigning M pieces of alarm information to the target user equipment according to the current task status, where the N pieces of alarm information include the M pieces of alarm information, where the M is an integer less than or equal to the N.

Optionally, the processor 3000 obtains N pieces of alarm information, including:

Acquiring a video from the X cameras to obtain the X video segments, each of the cameras corresponding to one of the video segments, where X is a positive integer;

Performing behavior analysis on the X video segments to obtain the N pieces of alarm information.

Optionally, the processor 3000 performs behavior analysis on the X video segments to obtain the N pieces of alarm information, including:

Parsing X video segments to obtain a Y frame video image, the Y being a positive integer;

Performing behavior analysis on each frame of the video image of the Y frame to obtain Z video images conforming to a preset behavior, where Z is a positive integer;

The Z video images are filtered to obtain N video images, and each video image corresponds to one alarm information.

Optionally, the processor 3000 performs behavior analysis on the X video segments to obtain the N pieces of alarm information, including:

Performing image segmentation on the X video segments to obtain a P personal face image, where P is a positive integer;

Combining the P face images to obtain a Q face image, wherein the Q is a positive integer less than or equal to the P;

Matching the Q face image with the A face image in the image library to obtain the N face image, the A is a positive integer, the N is smaller than the Q, and the N is smaller than the A;

Generating the N pieces of alarm information according to the N human face image.

Optionally, the processor 3000 is configured to allocate M pieces of alarm information to the target user equipment according to the current task status, including:

Obtaining a location of the target user equipment;

And assigning M pieces of alarm information to the target user equipment according to the location and the current task status.

The embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium can store a program, and the program includes some or all of the steps of any one of the data processing methods described in the foregoing method embodiments.

Although the present invention has been described herein in connection with the embodiments of the present invention, it will be understood by those skilled in the <RTIgt; Other variations of the disclosed embodiments are achieved. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill several of the functions recited in the claims. Certain measures are recited in mutually different dependent claims, but this does not mean that the measures are not combined to produce a good effect.

Those skilled in the art will appreciate that embodiments of the present invention can be provided as a method, a data processing apparatus (device), or a computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code. The computer program is stored/distributed in a suitable medium, provided with other hardware or as part of the hardware, or in other distributed forms, such as over the Internet or other wired or wireless telecommunication systems.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of a method, a data processing apparatus (a device), and a computer program product of an embodiment of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. Means for implementing the functions specified in one or more of the flow or in a block or blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

While the invention has been described with respect to the specific embodiments and embodiments thereof, various modifications and combinations may be made without departing from the spirit and scope of the invention. Accordingly, the specification and drawings are to be construed as the It is apparent that those skilled in the art can make various modifications and variations to the invention without departing from the spirit and scope of the invention. Thus, it is intended that the present invention cover the modifications and modifications of the invention

Claims (11)

1. A data processing method, comprising:

   Obtain the current task status of the target user equipment;

   Obtaining N pieces of alarm information, where N is a positive integer;

   Assigning M pieces of alarm information to the target user equipment according to the current task status, where the N pieces of alarm information include the M pieces of alarm information, where the M is an integer less than or equal to the N.
2. The method of claim 1, wherein the obtaining the N pieces of alarm information comprises:

   Acquiring a video from the X cameras to obtain the X video segments, each of the cameras corresponding to one of the video segments, where X is a positive integer;

   Performing behavior analysis on the X video segments to obtain the N pieces of alarm information.
3. The method according to claim 2, wherein the performing behavior analysis on the X video segments to obtain the N pieces of alarm information comprises:

   Parsing X video segments to obtain a Y frame video image, the Y being a positive integer;

   Performing behavior analysis on each frame of the video image of the Y frame to obtain Z video images conforming to a preset behavior, where Z is a positive integer;

   The Z video images are filtered to obtain N video images, and each video image corresponds to one alarm information.
4. The method according to claim 2, wherein the performing behavior analysis on the X video segments to obtain the N pieces of alarm information comprises:

   Performing image segmentation on the X video segments to obtain a P personal face image, where P is a positive integer;

   Combining the P face images to obtain a Q face image, wherein the Q is a positive integer less than or equal to the P;

   Matching the Q face image with the A face image in the image library to obtain the N face image, the A is a positive integer, the N is smaller than the Q, and the N is smaller than the A;

   Generating the N pieces of alarm information according to the N human face image.
5. The method according to any one of claims 1 to 4, wherein the allocating M pieces of alarm information to the target user equipment according to the current task status comprises:

   Obtaining a location of the target user equipment;

   And assigning M pieces of alarm information to the target user equipment according to the location and the current task status.
6. A data processing device, comprising:

   a first acquiring unit, configured to acquire a current task state of the target user equipment;

   a second acquiring unit, configured to acquire N pieces of alarm information, where the N is a positive integer;

   And an allocating unit, configured to allocate M pieces of alarm information to the target user equipment according to the current task status, where the N pieces of alarm information include the M pieces of alarm information, where the M is an integer less than or equal to the N.
7. The apparatus according to claim 6, wherein the second obtaining unit comprises:

   a first acquiring module, configured to acquire video from the X cameras, to obtain the X video segments, each of the cameras corresponding to one of the video segments, where X is a positive integer;

   The first analysis module is configured to perform behavior analysis on the X video segments to obtain the N pieces of alarm information.
8. The apparatus according to claim 7, wherein the first analysis module comprises:

   a parsing module, configured to parse the X video segments to obtain a Y frame video image, where the Y is a positive integer;

   a second analysis module, configured to perform behavior analysis on each frame of the video image of the Y frame to obtain Z video images that conform to a preset behavior, where the Z is a positive integer;

   The screening module is configured to filter the Z video images to obtain N video images, and each video image corresponds to one alarm information.
9. The apparatus according to claim 7, wherein the first analysis module comprises:

   a segmentation module, configured to perform image segmentation on the X video segments to obtain a P personal face image, where P is a positive integer;

   a merging module, configured to merge the P personal face images to obtain a Q personal face image, where the Q is a positive integer less than or equal to the P;

   a matching module, configured to match the Q facial image with an A facial image in the image library to obtain the N facial image, wherein the A is a positive integer, and the N is smaller than the Q, the N Less than the A;

   And a generating module, configured to generate the N pieces of alarm information according to the N personal face image.
10. A data processing apparatus, comprising: a processor, wherein the processor is configured to implement the data processing method according to any one of claims 1 to 5 when the computer program of the memory in the memory is executed.
11. A computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the data processing method according to any one of claims 1 to 5.

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