CN113850836B

CN113850836B - Employee behavior recognition method, device, equipment and medium based on behavior track

Info

Publication number: CN113850836B
Application number: CN202111150070.1A
Authority: CN
Inventors: 谢鹏; 赖众程; 李会璟; 梁俊杰; 李林毅
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Filing date: 2021-09-29
Publication date: 2024-06-28
Anticipated expiration: 2041-09-29

Abstract

The invention relates to the field of artificial intelligence, and discloses an employee behavior identification method based on a behavior track, which comprises the following steps: identifying employee portrait pictures by using a face identification model; if the employee portrait picture is determined to be an internal employee image, generating an internal employee movement track, and detecting the violation condition of the internal employee; if the employee portrait picture is determined to be an external employee image, generating an external employee movement track; if the external staff does not accompany the staff image, obtaining the illegal information of the external staff; if the accompanying staff images exist, the accompanying staff movement track and the external staff movement track are input into a support vector machine model, the distance between the accompanying staff and the external staff is obtained, and the violation condition of the external staff is detected. The invention also relates to a blockchain technique in which violation information can be stored in a blockchain node. The invention also provides a staff behavior recognition device, equipment and medium based on the behavior track. The method and the device can improve the accuracy of employee violation detection.

Description

Employee behavior recognition method, device, equipment and medium based on behavior track

Technical Field

The present invention relates to the field of artificial intelligence, and in particular, to a method and apparatus for identifying employee behavior based on a behavior trace, an electronic device, and a storable medium.

Background

With the development of the mobile internet, face recognition is required for entering and exiting an operation room in most enterprises, but since the enterprise is partially divided into internal staff and external staff, the internal staff and the external staff may have violation conditions due to different behaviors. For example, when an internal employee enters the operation room, no card is punched, and other employees enter the operation room, then the rule is violated, while an external employee does not have access rights and needs to accompany the internal employee, and after the internal employee enters the operation room, the external employee and the internal employee have a distance limit to be too far apart, then the rule is violated. In the prior art, whether staff have illegal conditions in work cannot be accurately identified only through face recognition, and large potential safety hazards are brought to a working area.

Disclosure of Invention

The invention provides an employee behavior recognition method, device, electronic equipment and computer medium based on behavior tracks, and aims to improve accuracy of employee violation detection.

In order to achieve the above object, the present invention provides a method for identifying employee behavior based on behavior trace, including:

acquiring an employee picture to be identified, and identifying the employee picture to be identified by utilizing a pre-constructed face recognition model to obtain an employee image picture;

judging whether the employee portrait picture is matched with a preset internal employee face library or not;

If the staff image picture is matched with the internal staff face library, determining that the staff image picture is an internal staff image, acquiring track coordinates of the staff image picture, generating an internal staff motion track according to the track coordinates, and determining the rule violation condition of the internal staff corresponding to the staff picture to be identified according to the internal staff motion track;

If the matching of the employee portrait picture and the internal employee face library is inconsistent, determining that the employee portrait picture is an external employee picture, acquiring track coordinates of the employee portrait picture, and generating an external employee movement track according to the track coordinates;

judging whether accompanying employee images exist in the employee portrait pictures or not;

if the accompanying employee images are not in the employee portrait images, outputting information of external employee violations;

if the accompanying staff images exist in the staff portrait pictures, acquiring accompanying staff movement tracks, inputting the accompanying staff movement tracks and the external staff movement tracks into a preset support vector machine model, obtaining the distance between the accompanying staff and the external staff, and determining the rule violation condition of the external staff according to the distance between the accompanying staff and the external staff.

Optionally, the generating an internal employee motion trail according to the trail coordinates includes:

acquiring position coordinates of a plurality of cameras for shooting pictures of the staff to be identified, and connecting the position coordinates to obtain path tracks associated with the cameras;

Fusing the track coordinates with the path track to obtain a plurality of fused tracks of the cameras and the track coordinates;

and determining real-time position information of the images of the internal staff according to the fusion track, and generating the motion track of the internal staff according to the real-time position information.

Optionally, the acquiring the track coordinates of the employee portrait picture includes:

acquiring indoor position information of a plurality of cameras for shooting pictures of staff to be identified;

Acquiring positioning information of the internal employee image;

and obtaining the track coordinates of the internal employee images according to the position information and the positioning information.

Optionally, the inputting the accompanying employee movement track and the external employee movement track to a preset support vector machine model to obtain a distance between the accompanying employee and the external employee includes:

Mapping the accompanying employee movement track and the external employee movement track into multidimensional coordinates to obtain a movement track coordinate set;

constructing a plurality of hyperplane functions according to the motion trail coordinate set;

determining two parallel hyperplane functions in the hyperplane functions by using a preset geometric interval, and performing formula conversion on the two parallel hyperplane functions to obtain constraint conditions;

Converting the constraint condition into an unconstrained condition by utilizing the Lagrangian number multiplication, and calculating the unconstrained condition to obtain an optimal hyperplane in the two parallel hyperplane functions;

And calculating the motion trail of the accompanying staff and the motion trail of the external staff by using the optimal hyperplane to obtain the distance between the accompanying staff and the external staff.

Optionally, the optimal hyperplane is obtained by the following formula:

f(x)＝(w^tx+b)

Wherein f (x) represents an optimal hyperplane function, w ^t is a motion trail coordinate set, x is a distance between accompanying staff and external staff, and b is a real digital displacement term.

Optionally, the determining whether the employee portrait image matches with a preset internal employee face library includes:

And acquiring employee identifications corresponding to the employee images, matching the employee image pictures and the corresponding employee identifications with a preset internal worker face library to obtain matching values, if the matching values are smaller than a preset threshold, determining that the employee image pictures are inconsistent with the internal worker face library, and if the matching values are greater than or equal to the preset threshold, determining that the employee image pictures are consistent with the internal worker face library.

Optionally, the identifying the employee image to be identified by using the pre-constructed face recognition model to obtain an employee image includes:

extracting features of the employee pictures to be identified by using a convolution pooling layer in the face recognition model to obtain feature images;

upsampling the feature map by using an upsampling layer in the face recognition model to obtain a feature sampling map;

Splicing the characteristic sampling image and the characteristic image by using a full connection layer in the face recognition model to obtain a spliced image;

and outputting the spliced picture by using an activation function in the face recognition model to obtain an employee portrait picture.

In order to solve the above problems, the present invention further provides an employee behavior recognition apparatus based on a behavior trace, the apparatus comprising:

the face recognition module is used for acquiring the pictures of the staff to be recognized, and recognizing the pictures of the staff to be recognized by utilizing a pre-constructed face recognition model to obtain staff figure pictures;

the staff matching module is used for judging whether the staff image picture is matched with a preset internal staff face library or not;

The internal employee violation detection module is used for determining that the employee portrait picture is an internal employee image if the employee portrait picture is matched with the internal employee face library, acquiring track coordinates of the employee portrait picture, generating an internal employee movement track according to the track coordinates, and determining the violation condition of the internal employee corresponding to the employee picture to be identified according to the internal employee movement track;

the external employee track generation module is used for determining the employee figure picture as an external employee image if the employee figure picture is inconsistent with the matching of the internal employee face library, acquiring track coordinates of the employee figure picture and generating an external employee motion track according to the track coordinates;

The accompanying staff judging module is used for judging whether accompanying staff images exist in the staff portrait pictures or not;

And the external employee violation detection module is used for outputting information of external employee violations if the accompanying employee images are not in the employee portrait images, acquiring accompanying employee movement tracks if the accompanying employee images are in the employee portrait images, inputting the accompanying employee movement tracks and the external employee movement tracks into a preset support vector machine model to obtain distances between accompanying employees and external employees, and determining the violation conditions of the external employees according to the distances between the accompanying employees and the external employees.

In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:

a memory storing at least one computer program; and

And the processor executes the computer program stored in the memory to realize the employee behavior identification method based on the behavior track.

In order to solve the above-mentioned problems, the present invention also provides a computer medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned employee behavior identification method based on a behavior trace.

In the embodiment of the invention, firstly, the employee image to be identified is identified by utilizing a pre-constructed face identification model, and the employee image is obtained; secondly, generating an internal employee movement track according to track coordinates of the employee portrait pictures, and judging the illegal behaviors of the internal employees according to the internal employee movement track; and further, whether the accompanying staff causes violations or not is recognized, the distance between the accompanying staff and the external staff is calculated by using a support vector machine model, and the violations of the external staff are judged according to the distance range between the accompanying staff and the external staff, so that the problem that whether the violations exist in work or not can be solved, the potential safety hazard problem of a working area is reduced, and the accuracy of detecting the violations of the staff is improved. Therefore, the employee behavior recognition method, the device, the electronic equipment and the storable medium based on the behavior track can improve the accuracy of employee violation detection.

Drawings

FIG. 1 is a flow chart of an employee behavior recognition method based on behavior trace according to an embodiment of the present invention;

FIG. 2 is a schematic block diagram of an employee behavior recognition apparatus based on behavior trace according to an embodiment of the present invention;

Fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a behavior trace-based employee behavior recognition method according to an embodiment of the present invention;

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

The embodiment of the application provides an employee behavior identification method based on a behavior track. The execution subject of the employee behavior recognition method based on the behavior trace includes, but is not limited to, at least one of a server, a terminal and the like capable of being configured to execute the method provided by the embodiment of the application. In other words, the employee behavior recognition method based on the behavior trace may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

Referring to fig. 1, a flow chart of an employee behavior recognition method based on a behavior trace according to an embodiment of the present invention is shown, where in the embodiment of the present invention, the employee behavior recognition method based on a behavior trace includes:

s1, acquiring a staff picture to be identified, and identifying the staff picture to be identified by using a pre-constructed face recognition model to obtain a staff figure picture.

In the embodiment of the invention, the employee picture to be identified is the picture to be identified comprising the employee portrait picture, and the employee picture to be identified can be obtained from the picture comprising the employee portrait shot by the camera. The employee portraits picture is a picture of the identified employee portraits.

In the embodiment of the invention, before the employee picture to be identified is identified by utilizing the pre-constructed face recognition model, the employee picture to be identified can be pre-processed, so that the defects of insufficient gray scale, noise, contrast and the like caused by different acquisition environments (such as illumination brightness and equipment performance) can be avoided, and the problems of uncertain size and position of a portrait in the middle of the whole image caused by different distances and focal distances can be avoided, so that the consistency of the size, position and quality of the portrait in the portrait picture can be improved by pre-processing the image.

In the embodiment of the invention, the pre-constructed face recognition model is utilized to extract the characteristics of the staff picture to be recognized, so as to obtain the portrait characteristics of the staff picture to be recognized, and further, the staff portrait picture of the staff portrait is output. Wherein, the face recognition model includes: a convolution pooling layer, an upsampling layer, a full connection layer and an activation function.

In detail, the identifying the employee image to be identified by using the pre-constructed face recognition model to obtain an employee image comprises:

In the embodiment of the invention, up-sampling refers to sampling the feature map to a specified resolution, for example, a to-be-identified employee image (416, 416,3) is subjected to a series of convolution pooling operations to obtain a feature map (13, 13, 16), and in order to compare the feature map with the corresponding to-be-identified employee image, the feature map needs to be changed to be (416, 416,3) in size, which is called up-sampling.

Further, in order to better understand the feature semantic information of the feature sampling graph, the feature sampling graph and the feature graph may be spliced to obtain a spliced image.

In an alternative embodiment of the present invention, the upsampling may be implemented by a currently known linear interpolation algorithm, the stitching may be implemented by a currently known stitching algorithm, for example, surf (Speeded Up Robust Features) algorithm, the activation function may be a ReLU function, and the stitched image may be activated, so as to obtain an employee portrait image that finally includes an employee portrait.

S2, judging whether the employee figure is matched with a preset internal worker face library or not.

In the embodiment of the invention, the internal staff face library is a face library constructed according to internal staff marks, wherein the internal staff marks comprise: an inside employee ID and an inside employee face.

In detail, the determining whether the employee portrait picture matches with a preset internal employee face database includes:

In the embodiment of the invention, the matching value is a matching value obtained by matching the employee portrait picture and the corresponding employee ID with the internal employee face library.

S3, if the staff image picture is matched with the internal staff face library, determining that the staff image picture is an internal staff image, obtaining track coordinates of the staff image picture, generating an internal staff motion track according to the track coordinates, and determining the violation condition of the internal staff corresponding to the staff picture to be identified according to the internal staff motion track.

In the embodiment of the present invention, if the employee portrait image is matched with the internal employee face library, determining that the employee portrait image is an internal employee image includes: and comparing the matching value with a preset threshold, and if the matching value is greater than or equal to the preset threshold, matching and conforming the employee portrait picture with the internal employee face library, and determining that the employee portrait picture is an internal employee image.

In an embodiment of the present invention, if the matching value is 0.95 and is greater than the preset threshold value 0.9, and the employee identifier is matched and consistent with the facial library of the internal staff, the employee portrait picture may be identified as an internal employee picture.

In the embodiment of the invention, the track coordinates of the internal employee graph are identified by a plurality of cameras arranged in a layout, tracked and positioned, and the track coordinates of the internal employee graph are determined by the position information of the plurality of cameras in a room.

In detail, the acquiring the track coordinates of the employee portrait picture includes:

Acquiring positioning information of the internal employee image;

In an embodiment of the present invention, the positioning information of the internal employee image may be the positioning information of the internal employee image by arranging a plurality of cameras in the indoor location information and the shooting areas of the plurality of cameras.

In the embodiment of the present invention, in order to improve accuracy of the track coordinates, shooting areas of different cameras, coordinates of the shooting areas, and coordinates of markers in the shooting areas may be preset, and the track coordinates of the internal employee images may be determined by combining the coordinates of the markers, the coordinates of the multiple cameras, and positioning information of the internal employee images.

In detail, the generating the internal employee movement track according to the track coordinates includes:

In the embodiment of the invention, the violation condition of the internal staff mainly means that no card is punched when the internal staff enters the operation room, and the internal staff enters the operation room through trailing other staff.

In an optional embodiment of the present invention, by querying whether the preset employee movement track library includes the internal employee movement track, if not, the internal employee enters the operation room for less than six minutes, access control information is obtained, the internal employee ID is matched with the access control information, if the internal employee ID is consistent with the access control information, the internal employee has been checked, no violation exists, and if the internal employee ID is inconsistent with the access control information, the internal employee has not checked, and there is a violation following the other employee entering the operation room.

And S4, if the staff image picture is inconsistent with the matching of the internal staff face library, determining that the staff image picture is an external staff image, acquiring track coordinates of the staff image picture, and generating an external staff movement track according to the track coordinates.

In the embodiment of the present invention, if the employee portrait image is matched with the face library of the external employee, determining that the employee portrait image is an external employee image includes: and comparing the matching value with a preset threshold, and if the matching value is smaller than the preset threshold, determining that the employee portrait picture is an external employee picture if the employee portrait picture is inconsistent with the face library of the internal employee.

In an optional embodiment of the present invention, the method for obtaining the track coordinates of the external employee image and generating the external employee motion track according to the track coordinates is similar to the method for obtaining the track coordinates of the internal employee image and generating the internal employee motion track according to the track coordinates in S3, so that details are not repeated.

S5, judging whether the employee portrait pictures have accompanying employee images or not.

In the embodiment of the invention, the external staff needs to accompany the internal staff when entering and exiting the operation room, and the distance between the external staff and the accompanying staff can not exceed 3 meters when entering the operation room, and the distance exceeds 3 meters, thereby violating regulations.

In an embodiment of the invention, the access control information can be obtained by inquiring the internal employee ID corresponding to the external employee ID through a preset access control work order, and whether the external employee image has an accompanying employee image can be judged by judging whether the access control information comprises the accompanying employee ID and judging whether the image obtained by utilizing a preset gate post camera comprises the accompanying employee image.

S6, if the accompanying employee image is not in the employee portrait image, outputting information of external employee rule violations.

In the embodiment of the invention, if the access control information does not include the accompanying employee ID, the violation information of the external employee is obtained, and if the access control information includes the accompanying employee ID, the feature extraction is performed by using the image acquired by the preset access control camera, the accompanying employee image is not extracted, and the violation information of the external employee can also be obtained.

In an embodiment of the present invention, by judging whether the accompanying employee ID exists in the access control information, further, the face recognition is performed on the accompanying employee to avoid the situation that the internal employee uses the own ID and the accompanying employee ID to perform access control and card punching when the external employee enters the operation room, but only the external employee actually enters the operation room.

S7, if the accompanying employee images exist in the employee portrait images, acquiring accompanying employee movement tracks, inputting the accompanying employee movement tracks and the external employee movement tracks into a preset support vector machine model, obtaining distances between accompanying employees and external employees, and determining violation conditions of the external employees according to the distances between the accompanying employees and the external employees.

In the embodiment of the present invention, if the employee portrait picture has the accompanying employee image, the method for acquiring the accompanying employee motion trail is similar to the method for acquiring the trail coordinate of the employee portrait picture and generating the internal employee motion trail according to the trail coordinate in S3, so that the description is omitted.

In the embodiment of the invention, the working principle of the support vector machine is that a plurality of hyperplane functions are constructed by mapping the accompanying employee motion trail and the external employee motion trail to multidimensional coordinates, and the optimal hyperplane in the hyperplane functions is selected, so that the distance between the accompanying employee and the external employee is obtained according to the optimal hyperplane.

In detail, the inputting the accompanying employee movement track and the external employee movement track into a preset support vector machine model to obtain the distance between the accompanying employee and the external employee includes:

In the embodiment of the invention, the maximum distance between the two parallel hyperplane functions is the maximized interval, and constraint conditions can be obtained according to the maximized interval; the constraint condition is that the optimal value of the objective function is found in a limited space; the optimal hyperplane is a plane for dividing an external person motion trail coordinate subset and an accompanying person motion trail coordinate subset, wherein the motion trail coordinate set comprises: an external person motion trail coordinate subset and a accompanying person motion trail coordinate subset. Further, the optimal hyperplane is obtained by the following formula:

f(x)＝(w^tx+b)

In an optional embodiment of the present invention, the accompanying employee movement track and the external employee movement track may each sample 12 sets of movement track sets at intervals of 5 seconds, and the optimal hyperplane is used to calculate 12 sets of movement track sets, so as to obtain distances between multiple sets of accompanying employees and external employees.

In the embodiment of the invention, after the external employee violation information and the internal employee violation information are obtained, the violation employee is warned, so that the occurrence of a violation event is prevented, and the safety of the employee is further protected.

In the embodiment of the invention, firstly, the employee image to be identified is identified by utilizing a pre-constructed face identification model, and the employee image is obtained; secondly, generating an internal employee movement track according to track coordinates of the employee portrait pictures, and judging the illegal behaviors of the internal employees according to the internal employee movement track; and further, whether the accompanying staff causes violations or not is recognized, the distance between the accompanying staff and the external staff is calculated by using a support vector machine model, and the violations of the external staff are judged according to the distance range between the accompanying staff and the external staff, so that the problem that whether the violations exist in work or not can be solved, the potential safety hazard problem of a working area is reduced, and the accuracy of detecting the violations of the staff is improved. Therefore, the employee behavior recognition method based on the behavior track can improve the accuracy of employee violation detection.

As shown in fig. 2, a functional block diagram of the employee behavior recognition apparatus according to the present invention based on the behavior trace is shown.

The employee behavior recognition apparatus 100 based on the behavior trace according to the present invention may be installed in an electronic device. Depending on the implemented functions, the employee behavior recognition apparatus based on the behavior trace may include a face recognition module 101, an employee matching module 102, an internal employee violation detection module 103, an external employee trace generation module 104, a accompanying employee judgment module 105, and an external employee violation detection module 106, where the modules may also be referred to as units, refer to a series of computer program segments capable of being executed by a processor of an electronic device and performing a fixed function, and stored in a memory of the electronic device.

In the present embodiment, the functions concerning the respective modules/units are as follows:

the face recognition module 101 is configured to obtain a picture of an employee to be recognized, and recognize the picture of the employee to be recognized by using a pre-constructed face recognition model to obtain a picture of a human image of the employee.

In detail, the face recognition module 101 recognizes the employee image to be recognized by using a pre-constructed face recognition model by performing the following operations, to obtain an employee image, including:

In the embodiment of the invention, up-sampling refers to sampling the feature map to a specified resolution, for example, a to-be-identified employee image (416, 416,3) is subjected to a series of convolution pooling operations to obtain a feature map (13, 13, 16), and in order to compare the feature map with the corresponding to-be-identified employee image, the feature map needs to be changed to be (416, 416,3) in size, which is called up-sampling. Further, in order to better understand the feature semantic information of the feature sampling graph, the feature sampling graph and the feature graph may be spliced to obtain a spliced image.

The employee matching module 102 is configured to determine whether the employee portrait image is matched with a preset internal employee face library.

In detail, the employee matching module 102 determines whether the employee portrait image matches a preset internal employee face library by performing the following operations, including:

The internal employee violation detection module 103 is configured to determine that the employee portrait picture is an internal employee image if the employee portrait picture matches with the internal employee face library, obtain track coordinates of the employee portrait picture, generate an internal employee motion track according to the track coordinates, and determine a violation condition of an internal employee corresponding to the employee picture to be identified according to the internal employee motion track.

In detail, the internal employee violation detection module 103 obtains the track coordinates of the employee portrait picture by performing the following operations, including:

Acquiring positioning information of the internal employee image;

In detail, the internal employee violation detection module 103 generates an internal employee movement track according to the track coordinates by performing the following operations, including:

The external employee trajectory generation module 104 is configured to determine that the employee portrait picture is an external employee image if the employee portrait picture is inconsistent with the matching of the internal employee face library, obtain the trajectory coordinates of the employee portrait picture, and generate an external employee motion trajectory according to the trajectory coordinates.

In an optional embodiment of the present invention, the method for obtaining the track coordinates of the external employee image and generating the external employee motion track according to the track coordinates is similar to the method for obtaining the track coordinates of the internal employee image and generating the internal employee motion track according to the track coordinates, so that the description thereof is omitted.

The accompanying employee judging module 105 is configured to judge whether there is an accompanying employee image in the employee portrait image.

The external employee violation detection module 106 is configured to output information of external employee violation if the accompanying employee image is not included in the employee image, obtain an accompanying employee movement track if the accompanying employee image is included in the employee image, and input the accompanying employee movement track and the external employee movement track to a preset support vector machine model to obtain a distance between an accompanying employee and the external employee, and determine a violation condition of the external employee according to the distance between the accompanying employee and the external employee.

In the embodiment of the present invention, if the employee portrait picture has the accompanying employee image, the method for acquiring the accompanying employee motion trail is similar to the method for acquiring the trail coordinate of the employee portrait picture and generating the internal employee motion trail according to the trail coordinate, so that the description is omitted.

In detail, the external employee violation detection module 106 inputs the accompanying employee movement track and the external employee movement track to a preset support vector machine model to obtain a distance between the accompanying employee and the external employee, which includes:

f(x)＝(w^tx+b)

In the embodiment of the invention, firstly, the employee image to be identified is identified by utilizing a pre-constructed face identification model, and the employee image is obtained; secondly, generating an internal employee movement track according to track coordinates of the employee portrait pictures, and judging the illegal behaviors of the internal employees according to the internal employee movement track; and further, whether the accompanying staff causes violations or not is recognized, the distance between the accompanying staff and the external staff is calculated by using a support vector machine model, and the violations of the external staff are judged according to the distance range between the accompanying staff and the external staff, so that the problem that whether the violations exist in work or not can be solved, the potential safety hazard problem of a working area is reduced, and the accuracy of detecting the violations of the staff is improved. Therefore, the employee behavior recognition device based on the behavior track can improve the accuracy of employee violation detection.

Fig. 3 is a schematic structural diagram of an electronic device for implementing the employee behavior recognition method based on a behavior trace according to the present invention.

The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as an employee behavior recognition program based on a behavior trace.

The memory 11 includes at least one type of medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a local magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of employee behavior recognition programs based on behavior trajectories, but also for temporarily storing data that has been output or is to be output.

The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules (e.g., employee behavior recognition programs based on behavior traces, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device and process the data.

The communication bus 12 may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.

Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.

For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.

Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.

Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.

It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.

The employee behavior recognition program based on behavior traces stored by the memory 11 in the electronic device is a combination of a plurality of computer programs, which when run in the processor 10, may implement:

In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.

Further, the electronic device integrated modules/units may be stored in a computer readable medium if implemented in the form of software functional units and sold or used as stand alone products. The computer readable medium may be non-volatile or volatile. The computer readable medium may include: any entity or device capable of carrying the computer program code to be described, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).

Embodiments of the present invention may also provide a computer medium storing a computer program which, when executed by a processor of an electronic device, may implement:

Further, the computer usable medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.

In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.

Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for identifying employee behavior based on a behavior trace, the method comprising:

If the accompanying staff images exist in the staff portrait pictures, acquiring accompanying staff motion tracks, mapping the accompanying staff motion tracks and the external staff motion tracks to multidimensional coordinates to obtain a motion track coordinate set, constructing a plurality of hyperplane functions according to the motion track coordinate set, determining two parallel hyperplane functions in the hyperplane functions by using preset geometric intervals, performing formula conversion on the two parallel hyperplane functions to obtain constraint conditions, converting the constraint conditions into unconstrained conditions by using Lagrange number multiplication, calculating the unconstrained conditions to obtain optimal hyperplanes in the two parallel hyperplane functions, calculating the accompanying staff motion tracks and the external staff motion tracks by using the optimal hyperplanes to obtain distances between accompanying staff and external staff, and determining rule violations of the external staff according to the distances between the accompanying staff and the external staff, wherein the optimal hyperplane is obtained by using the following formula:

f(x)＝(wtx+b)

wherein f (x) represents an optimal hyperplane function, wt is a motion trail coordinate set, x is the distance between the accompanying staff and the external staff, and b is a real digital displacement term.

2. An employee behavior recognition method based on a behavior trace as defined in claim 1, wherein said generating an internal employee motion trace from said trace coordinates comprises:

3. An employee behavior recognition method based on a behavior trace as defined in claim 1, wherein said obtaining trace coordinates of said employee portrait picture includes:

Acquiring positioning information of the internal employee image;

4. A method for identifying employee behavior based on a behavior trace as defined in claim 1, wherein said determining whether the employee portrait images match a predetermined internal employee face library comprises:

5. A method of identifying employee behavior based on a behavior trace as defined in any one of claims 1 to 4 wherein the face recognition model comprises: the convolution pooling layer, the up-sampling layer, the full connection layer and the activation function, the employee image to be identified is identified by utilizing a pre-constructed face identification model, and an employee image is obtained, and the method comprises the following steps:

6. An employee behavior recognition apparatus based on a behavior trace, the apparatus comprising:

The system comprises an external employee violation detection module, a constraint condition generation module and a constraint condition generation module, wherein the external employee violation detection module is used for outputting information of an external employee violation if no accompanying employee image exists in the employee portrait image, acquiring accompanying employee motion tracks if the accompanying employee image exists in the employee portrait image, mapping the accompanying employee motion tracks and the external employee motion tracks into multidimensional coordinates to obtain a motion track coordinate set, constructing a plurality of hyperplane functions according to the motion track coordinate set, determining two parallel hyperplane functions in the hyperplane functions by using a preset geometric interval, performing formula conversion on the two parallel hyperplane functions to obtain the constraint condition, performing operation on the unconstrained condition to obtain the optimal hyperplane in the two parallel hyperplane functions by using the optimal hyperplane to calculate the accompanying employee motion tracks and the external employee motion tracks, and obtaining the distance between the accompanying employee and the external employee, and determining the external rule according to the distance between the accompanying employee and the external employee, wherein the optimal hyperplane is obtained by using the following formula:

f(x)＝(wtx+b)

7. An electronic device, the electronic device comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform a behavior trace-based employee behavior identification method as claimed in any one of claims 1 to 5.

8. A computer medium storing a computer program, wherein the computer program when executed by a processor implements a method of identifying employee behavior based on a behavior trace according to any one of claims 1 to 5.