CN116416670A - Big data control method and system based on face recognition - Google Patents

Big data control method and system based on face recognition Download PDF

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CN116416670A
CN116416670A CN202310546524.XA CN202310546524A CN116416670A CN 116416670 A CN116416670 A CN 116416670A CN 202310546524 A CN202310546524 A CN 202310546524A CN 116416670 A CN116416670 A CN 116416670A
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刘姣
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Shanxi Ruolin Network Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/30—Arrangements for executing machine instructions, e.g. instruction decode
    • G06F9/30003—Arrangements for executing specific machine instructions
    • G06F9/30076—Arrangements for executing specific machine instructions to perform miscellaneous control operations, e.g. NOP
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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Abstract

The embodiment of the application provides a big data control method and a big data control system based on face recognition, which can determine abnormal execution nodes in application execution examples of a specified face recognition application based on execution cooperative parameters among the application execution examples of the specified face recognition application, further generate function realization data comprising a big data control model based on control model data for configuring big data control parameters, load the function realization data in the abnormal execution nodes of the specified face recognition application, and generate a big data control link program for online. It can be seen that after the abnormal execution node is determined from the specified face recognition application, the function implementation data is loaded in the abnormal execution node to generate the big data control link program, the function implementation data in the abnormal execution node is loaded without affecting the conventional application implementation of the specified face recognition application, and the abnormal handling capability is further improved on the basis of improving the stability of the face recognition application in the face recognition process.

Description

Big data control method and system based on face recognition
Technical Field
The application relates to the technical field of big data, in particular to a big data control method and system based on face recognition.
Background
Face recognition is a biological recognition technology for carrying out identity recognition based on facial feature information of people. A series of related technologies, commonly referred to as image recognition and face recognition, are used to capture images or video streams containing faces with a camera or cameras, and automatically detect and track the faces in the images, thereby performing face recognition on the detected faces. Based on this, a series of online service applications including face recognition, for which the direction of face recognition is generally used for identity authentication and thus big data control such as big data acquisition control, big data reading control, etc. is developed. In the related art, when an abnormal execution node exists in an application execution instance in a face recognition application in the execution process, the corresponding big data control should be optimized to avoid possible data errors. However, in the related art, the interface opening of the face recognition application is suspended in the process of performing the optimization, which results in affecting the conventional application implementation of the face recognition application.
Disclosure of Invention
In view of this, the present application aims to provide a big data control method and system based on face recognition, after determining an abnormal execution node from a specified face recognition application, by loading function implementation data in the abnormal execution node, generating a big data control link program, loading the function implementation data in the abnormal execution node will not affect the conventional application implementation of the specified face recognition application, and further improve the abnormality handling capability on the basis of improving the stability of the face recognition application in the face recognition process.
According to a first aspect of the present application, there is provided a big data control method based on face recognition, applied to a big data control system based on face recognition, the method comprising:
acquiring execution coordination parameters among application execution examples in a specified face recognition application, and determining abnormal execution nodes in the application execution examples of the specified face recognition application based on the execution coordination parameters;
calling control model data for configuring big data control parameters, and generating function realization data comprising a big data control model based on the control model data; the big data control model in the function realization data is used for controlling the abnormal execution data reading aiming at the appointed face recognition application;
and loading the function realization data in the abnormal execution node of the appointed face recognition application, and generating a big data control link program for online.
In a possible implementation manner of the first aspect, the obtaining an execution coordination parameter between application execution instances in a specified face recognition application, and determining an abnormal execution node in the application execution instances of the specified face recognition application based on the execution coordination parameter includes:
Acquiring a priori functional verification sequence, realizing data verification of the appointed face recognition application based on the priori functional verification covered in the priori functional verification sequence, and generating a target priori functional verification event sequence; each functional verification event in the target priori functional verification event sequence is used for connecting two application execution instances in the appointed face recognition application;
acquiring a random functional verification sequence, verifying the appointed face recognition application based on random functional verification data covered in the random functional verification sequence, and generating a random functional verification event sequence; each functionally verified event in the random sequence of functionally verified events is used to link two application execution instances in the specified face recognition application;
determining a first abnormal trigger frequency corresponding to each functional verification event in the target priori functional verification event sequence and a second abnormal trigger frequency corresponding to each functional verification event in the random functional verification event sequence, and determining execution coordination parameters between application execution instances in the appointed face recognition application based on the first abnormal trigger frequency and the second abnormal trigger frequency;
And determining an abnormal execution node of the appointed face recognition application from the random functional verification event sequence based on the execution cooperative parameters.
In a possible implementation manner of the first aspect, the determining, based on the execution coordination parameter, an abnormal execution node of the specified face recognition application from the random sequence of functional verification events includes:
acquiring a functional verification event x from the random functional verification event sequence; the functional verification event x is used for connecting an application execution instance y in the appointed face recognition application with an application execution instance v, x is a positive integer which is not more than the number of the functional verification events in the random functional verification event sequence, and y and v are both positive integers which are not more than the number of the application execution instances in the appointed face recognition application;
outputting the functional verification event x as an abnormal verification event if the execution coordination parameter between the application execution instance y and the application execution instance v is smaller than a set coordination strength and the functional verification event x does not belong to the target priori functional verification event sequence;
combining the abnormal verification events in the random functional verification event sequences to generate R abnormal event sequences, and determining an abnormal execution node of the appointed face recognition application based on application execution examples associated with the abnormal verification events in each abnormal event sequence; r is a positive integer.
In a possible implementation manner of the first aspect, the big data control parameters include big data control definition data, file size definition data, big data control model coverage data, the control model data including first model data for the big data control definition data, second model data for the file size definition data, and third model data for the big data control model coverage data;
the generating the function realization data including the big data control model based on the control model data includes:
generating W big data control models based on the first model data, the second model data, and the third model data in the control model data; w is a positive integer;
and loading a tag for marking the big data control position in each big data control model of the W big data control models to generate function realization data containing the W big data control models.
In a possible implementation manner of the first aspect, the third model data includes first model member data and second model member data, and the data coverage area determined by the first model member data is larger than the data coverage area determined by the second model member data;
The generating W big data control models based on the first model data, the second model data, and the third model data in the control model data includes:
generating a first modality control model based on the first model data, the second model data, and the first model member data in the control model data; the first modality control model characterizes an execution node that maintains execution activity in the reading of exception execution data to execute exception execution;
generating a second modal control model based on the first model data, the second model data and the second model member data in the control model data, and outputting the first modal control model and the second modal control model as the W big data control models; the second modality control model is used for suppressing execution activities of abnormally executing data reading.
In a possible implementation manner of the first aspect, the loading the function implementation data in the abnormal execution node of the specified face recognition application generates a big data control link program for online, including:
determining a core function implementation position corresponding to the function implementation data in the abnormal execution node of the appointed face recognition application, and loading the function implementation data into the core function implementation position based on a function implementation activation function;
And performing function realization activation processing on the appointed face recognition application loaded with the function realization data in the function realization activation function to generate a big data control link program for online.
In a possible implementation manner of the first aspect, the method further includes:
submitting the big data control link program to a big data control system so that a first control component in the big data control system acquires the big data control link program, executing the big data control link program and starting a program file corresponding to the big data control link program.
In a possible implementation manner of the first aspect, the method further includes:
when the second control component is monitored to perform abnormal execution data reading on the big data control link program, acquiring a target functional check sequence provided by the second control component in the abnormal execution data reading;
maintaining target execution activities associated with the abnormal execution node from the target functionality check sequence using the function implementation data in the big data control link program; the target execution activity is used for controlling an error point decision branch in the abnormal execution data reading;
The big data control model in the function realization data comprises a first mode control model;
the maintaining, from the target functionality check sequence, target execution activities associated with the abnormal execution node using the function implementation data in the big data control link program, comprising:
when the implementation node of the execution activity i in the big data control link program in the target functional verification sequence is the abnormal execution node, determining the execution performance effect data of the execution activity i by using the first modal control model in the function implementation data; i is a positive integer;
performing execution activity evaluation on the execution performance effect data of the execution activity i by using the execution activity evaluation model read by the abnormal execution data to generate an execution activity evaluation value of the execution activity i;
when the execution activity evaluation value of the execution activity i meets the set requirement, outputting the execution activity i as the target execution activity;
the big data control model in the function realization data comprises a second mode control model;
the maintaining, from the target functionality check sequence, target execution activities associated with the abnormal execution node using the function implementation data in the big data control link program, comprising:
When the fact that an implementation node of the execution activity j in the target functional verification sequence in the big data control link program is the abnormal execution node is monitored, performing expansion derivatization on the execution activity j in an execution activity expansion derivatization model of abnormal execution data reading based on the second mode control model in the functional implementation data to generate candidate execution activities; j is a positive integer;
outputting the candidate execution activity as the target execution activity when the execution value of the candidate execution activity is monitored to be greater than the execution value of the execution activity j;
wherein the method further comprises:
in the abnormal execution data reading, verifying the big data control link program based on the execution activity in the target functional verification sequence, and generating at least one implementation snapshot data corresponding to the execution activity in the target functional verification sequence by utilizing the function implementation logic covered by the function implementation data in the big data control link program;
obtaining snapshot mining data respectively corresponding to the at least one implementation snapshot data, and analyzing occupied resources of the snapshot mining data corresponding to the execution activities in the target functional verification sequence to generate snapshot mining occupied resources;
When the fact that the resource quantity of the snapshot mining occupied resource is larger than the set resource quantity is monitored, mining expansion indicating data are generated; and the mining extension indicating data characterizes the mining scheduling resources of the abnormal execution data reading extension snapshot mining data.
According to a second aspect of the present application, there is provided a face recognition-based big data control system, which includes a processor and a readable storage medium storing a program which, when executed by the processor, implements the foregoing face recognition-based big data control method.
According to a third aspect of the present application, there is provided a computer-readable storage medium having stored therein computer-executable instructions for implementing the aforementioned face recognition-based big data control method when it is monitored that the computer-executable instructions are executed.
According to any one of the aspects, in the present application, execution coordination parameters between application execution instances in a specific face recognition application may be acquired, an abnormal execution node may be determined in the application execution instance of the specific face recognition application based on the execution coordination parameters, and further control model data for configuring big data control parameters may be invoked, function implementation data including a big data control model may be generated based on the control model data, where the big data control model in the function implementation data is used to control reading of abnormal execution data for the specific face recognition application, and the function implementation data is loaded in the abnormal execution node of the specific face recognition application, so as to generate a big data control link program for online. It can be seen that after the abnormal execution node is determined from the specified face recognition application, the function implementation data is loaded in the abnormal execution node to generate the big data control link program, the function implementation data in the abnormal execution node is loaded without affecting the conventional application implementation of the specified face recognition application, and the abnormal handling capability is further improved on the basis of improving the stability of the face recognition application in the face recognition process.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting in scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a big data control method based on face recognition according to an embodiment of the present application;
fig. 2 is a schematic component structure diagram of a big data control system based on face recognition, which is provided in an embodiment of the present application and is used for implementing the big data control method based on face recognition.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below according to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are only for the purpose of illustration and description, and are not intended to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. A flowchart, as used in this application, illustrates operations implemented based on some embodiments of the present application. It should be understood that the operations of the flow diagrams may be implemented out of order and that steps without logical context may be performed in reverse order or concurrently. Moreover, one or more other operations may be added to the flow diagrams and one or more operations may be removed from the flow diagrams as directed by those skilled in the art.
In addition, the described embodiments are only some, but not all, of the embodiments of the present application. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
Fig. 1 shows a flow chart of a big data control method based on face recognition according to an embodiment of the present application, and it should be understood that, in other embodiments, the order of part of the steps in the big data control method based on face recognition according to the present embodiment may be interchanged according to actual needs, or part of the steps may be omitted or deleted. The detailed steps of the big data control method based on face recognition are described as follows.
Step S101, acquiring execution coordination parameters between application execution instances in the specified face recognition application, and determining an abnormal execution node in the application execution instances of the specified face recognition application based on the execution coordination parameters.
When executing the specified face recognition application, all or part of application execution instances in the specified face recognition application may be executed, for example, inputting different functional verification data may execute different application execution instances, i.e., different functional verification data may correspond to different implementation snapshot data, where different functional verification data may correspond to different program functions of the program file. And acquiring the execution coordination parameters among the application execution examples covered in the appointed face recognition application by inputting the application execution examples executed by different functional verification data. The execution collaboration parameter may refer to a parameter value for which there is collaboration behavior during execution of any two application execution instances. Illustratively, the path that the functional verification data 1 performs in a given face recognition application is: application execution instance 1— application execution instance 2— application execution instance 3, the path that the functionality verification data 2 is executed in the specified face recognition application is application execution instance 1 — application execution instance 2— application execution instance 4— application execution instance 5, it may be determined that the execution coordination parameter between application execution instance 1 and application execution instance 2 is 2, the execution coordination parameter between application execution instance 2 and application execution instance 3 is 1, the execution coordination parameter between application execution instance 2 and application execution instance 4 is 1, and the execution coordination parameter between application execution instance 4 and application execution instance 5 is 1.
Further, the application execution instances in the specified face recognition application may be analyzed based on the execution coordination parameters between the application execution instances, and an abnormal execution node may be determined from the application execution instances covered by the specified face recognition application, where the abnormal execution node may refer to a node on which the failure functionality check data is executed in the specified face recognition application.
The embodiment can acquire a priori functional verification sequence, execute a specified face recognition application based on priori functional verification realization data covered in the priori functional verification sequence, and generate a target priori functional verification event sequence, wherein each of the functionally verified events in the target priori functional verification event sequence is used for connecting two application execution instances in the specified face recognition application, namely, each of the functionally verified events in the target priori functional verification event sequence can be used for representing a calling relationship of the two application execution instances under the prior verification functional verification realization data; in addition, a random functional check sequence can be obtained, a specified face recognition application is executed based on random functional check data contained in the random functional check sequence, a random functional check event sequence is generated, each functional check event in the random functional check event sequence is used for connecting two application execution instances in the specified face recognition application, namely, each functional check event in the random functional check event sequence can represent the calling relationship of the two application execution instances under the random functional check data. The prior functional verification sequence can contain at least one prior functional verification realization data, the random functional verification sequence can comprise at least one random functional verification data, and the random functional verification data can be the prior functional verification realization data or invalid functional verification data; the target prior functional verification event sequence comprises links between application execution instances executed by each prior functional verification realization data in the prior functional verification sequence in the appointed face recognition application, and different prior functional verification realization data may comprise the same functional verification event in an implementation node in the appointed face recognition application, so that at least one identical functional verification event can occur in the target prior functional verification event sequence; similarly, at least one already functional verification event may occur in the random sequence of functional verification events. Of course, the same already functional verification event may be included in the target a priori sequence of functional verification events and the random sequence of functional verification events, i.e. there may be a portion of the already functional verification events that are repeated in the target a priori sequence of functional verification events and the random sequence of functional verification events.
The embodiment can determine the first abnormal triggering frequency corresponding to each functional verification event in the target priori functional verification event sequence, and acquire the second abnormal triggering frequency corresponding to each functional verification event in the random functional verification event sequence; based on the first abnormal trigger frequency and the second abnormal trigger frequency, execution coordination parameters between application execution instances in the appointed face recognition application are determined, and further abnormal execution nodes of the appointed face recognition application can be determined from the random functional verification event sequence based on the execution coordination parameters. In addition, for any one of the random sequence of functionally verified events (e.g., functionally verified event x), the functionally verified event x may be used to join the application execution instance y and the application execution instance v in the specified face recognition application, that is, to represent a call relationship between the application execution instance y and the application execution instance v, x is a positive integer no greater than the number of functionally verified events in the random sequence of functionally verified events, and y and v are both positive integers no greater than the number of application execution instances in the specified face recognition application; if the execution coordination parameter between the application execution instance y and the application execution instance v is smaller than the set coordination strength, and the functional verification event x does not belong to the target priori functional verification event sequence, the functional verification event x can be output as an abnormal verification event; by combining the abnormal verification events in the random functional verification event sequences, R abnormal event sequences can be obtained, and based on application execution examples associated with the abnormal verification events in each abnormal event sequence, an abnormal execution node of a specific face recognition application is determined, wherein R is a positive integer, and if R can take values of 1,2 and … …; because the functional verification events in the target priori functional verification event sequence are all links executed by the priori functional verification implementation data, the functional verification events in the target priori functional verification event sequence cannot be abnormal verification events, and the abnormal verification events belong to random functional verification event sequences.
In a possible implementation manner, after determining that the execution cooperative parameter is obtained, an anomaly check event may be determined from a random functional check event sequence, and all anomaly check events may be combined to obtain R anomaly event sequences, where the anomaly check event in each anomaly event sequence may form at least one anomaly execution node.
Step S102, calling control model data for configuring big data control parameters, and generating function realization data comprising a big data control model based on the control model data; the big data control model in the function implementation data is used for controlling the abnormal execution data reading aiming at the appointed face recognition application.
Illustratively, the present embodiment may invoke control model data for configuring big data control parameters, where the big data control parameters may be at least one, for example, the big data control parameters may include big data control limit data, file size limit data, big data control model overlay data, and the like, and the control model data may include configuration information of the above-described big data control parameters; the function implementation data including the big data control model is generated based on the control model data, and core logic of the function implementation data may refer to a set of big data control models, and big data control models in the function implementation data may be used to control abnormal execution data reading for a specified face recognition application (program file).
Alternatively, when the big data control parameter includes big data control definition data, file size definition data, big data control model overlay data, the control model data may include first model data for the big data control definition data, second model data for the file size definition data, and third model data for the big data control model overlay data; the computer device may generate W big data control models based on the first model data, the second model data, and the third model data in the control model data; w is a positive integer; and loading a tag for marking the big data control position in each big data control model of the W big data control models to generate function realization data containing the W big data control models.
For example, when the third model data includes first model member data and second model member data, and the data coverage area determined by the first model member data is larger than the data coverage area determined by the second model member data, the present embodiment may generate, based on the first model data, the second model data, and the first model member data in the control model data, a first modality control model that characterizes an execution activity maintained in an abnormal execution data reading to execute the abnormal execution node, or may understand that the first modality control model is used to misguide an abnormal execution data reader to maintain more execution activities to execute the abnormal execution node, where the abnormal execution data reader may refer to a tool for performing the abnormal execution data reading; and generating a second mode control model based on the first model data, the second model data and the second model member data in the control model data, and outputting the first mode control model and the second mode control model as W big data control models, wherein the second mode control model is used for inhibiting the abnormal execution of the execution activity of reading the data.
Step S103, loading function realization data in the abnormal execution node of the appointed face recognition application, and generating a big data control link program for online.
By way of example, the present embodiment may load function implementation data in an abnormal execution node of a specified face recognition application, and generate a link program for abnormal reading protection in the big data control process, that is, a big data control link program. The big data control link program can control the abnormal execution data reading behavior on the basis of ensuring the normal execution of the appointed face recognition application.
The embodiment can determine the core function implementation position corresponding to the function implementation data in the abnormal execution node of the appointed face recognition application, and load the function implementation data into the core function implementation position based on the function implementation activation function; and performing function realization activation processing on the appointed face recognition application loaded with the function realization data in the function realization activation function to generate a big data control link program for online.
Further, in this embodiment, the big data control linking program may be submitted to the big data control system, so that the first control component in the big data control system obtains the big data control linking program, executes the big data control linking program, enables the program file corresponding to the big data control linking program, and may normally use the program file corresponding to the big data control linking program through the first control component.
In a possible embodiment, the above method may comprise the steps of:
step S201, when it is monitored that the second control component performs abnormal execution data reading on the big data control link program, a target functional check sequence provided by the second control component in the abnormal execution data reading is obtained.
For example, when the second control component is about to attack the program file, the second control component may perform abnormal execution data reading on the big data control link program by using the execution activity in the target functional check sequence, and may acquire the target functional check sequence provided by the second control component in the abnormal execution data reading, where the target functional check sequence may include the execution activity used when the second control component performs abnormal execution data reading on the big data control link program.
After the second control component obtains the target functional check sequence, the second control component can start a target process for executing abnormal execution data reading, send the target functional check sequence to the functional check system, and open a big data control link program and the like in the functional check system
Step S202, maintaining target execution activities associated with abnormal execution nodes from a target functional check sequence through function implementation data in a big data control link program; the target execution activity is used to control the error point decision branch in the abnormal execution data read.
Illustratively, since the function implementation data is injected into the big data control link program, the present embodiment may maintain the target execution activity associated with the abnormal execution node from the target functional check sequence by the function implementation data in the big data control link program, and the target execution activity may be used to perform abnormal execution data reading for the big data control link program.
The big data control model in the function implementation data may include a first modality control model, when an implementation node of an execution activity i in a target functional verification sequence in a big data control link program is an abnormal execution node, determining execution performance effect data of the execution activity i through the first modality control model in the function implementation data, where i may be a positive integer, for example, i may take a value of 1,2, … …, and the execution activity i may be any one of the target functional verification sequences; performing execution activity evaluation on the execution performance effect data of the execution activity i through an execution activity evaluation model read by the abnormal execution data to generate an execution activity evaluation value of the execution activity i; and outputting the execution activity i as a target execution activity when the execution activity evaluation value of the execution activity i meets the set requirement.
The big data control model in the function implementation data may include a second modality control model; when an implementation node of the execution activity j in the target functional verification sequence in the big data control link program is an abnormal execution node, performing expansion derivatization on the execution activity j in an execution activity expansion derivatization model for reading abnormal execution data based on a second mode control model in function implementation data, and generating candidate execution activities; wherein j can be a positive integer, for example, j can take values of 1,2, … …, and the execution activity j can be any execution activity in the target functional check sequence; when the execution value of the candidate execution activity is greater than the execution value of the execution activity j, the candidate execution activity is output as the target execution activity.
In the abnormal execution data reading, executing a big data control link program based on the execution activity in the target functional verification sequence, and generating at least one realization snapshot data corresponding to the execution activity in the target functional verification sequence through a function realization logic covered by the function realization data in the big data control link program; obtaining at least one snapshot mining data respectively corresponding to the snapshot data, and analyzing occupied resources of the snapshot mining data corresponding to the execution activities in the target functional check sequence to generate snapshot mining occupied resources; when the amount of resources occupied by snapshot mining is larger than the set amount of resources, mining extension indicating data is generated, wherein the mining extension indicating data can characterize abnormal execution data to read mining scheduling resources of the extended snapshot mining data.
Fig. 2 schematically illustrates a face recognition based big data control system 100 that may be used to implement various embodiments described herein.
For one embodiment, fig. 2 illustrates a face recognition based big data control system 100, the face recognition based big data control system 100 having one or more processors 102, a control module (chipset) 104 coupled to at least one of the processor(s) 102, a memory 106 coupled to the control module 104, a non-volatile memory (NVM)/storage 108 coupled to the control module 104, one or more input/output devices 110 coupled to the control module 104, and a network interface 112 coupled to the control module 106.
The processor 102 may include one or more single-core or multi-core processors, and the processor 102 may include any combination of general-purpose or special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In some embodiments, the big data control system 100 based on face recognition can be used as a server device such as a gateway in the embodiments of the present application.
In some embodiments, face recognition based big data control system 100 may include one or more computer readable media (e.g., memory 106 or NVM/storage 108) having instructions 114 and one or more processors 102 coupled with the one or more computer readable media and configured to execute instructions 114 to implement modules to perform actions described in this disclosure.
For one embodiment, the control module 104 may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) 102 and/or any suitable device or component in communication with the control module 104.
The control module 104 may include a memory controller module to provide an interface to the memory 106. The memory controller modules may be hardware modules, software modules, and/or firmware modules.
The memory 106 may be used to load and store data and/or instructions 114 for the face recognition based big data control system 100, for example. For one embodiment, memory 106 may comprise any suitable volatile memory, such as, for example, a suitable DRAM. In some embodiments, memory 106 may comprise double data rate type four synchronous dynamic random access memory (DDR 4 SDRAM).
For one embodiment, control module 104 may include one or more input/output controllers to provide interfaces to NVM/storage 108 and input/output device(s) 110.
For example, NVM/storage 108 may be used to store data and/or instructions 114. NVM/storage 108 may include any suitable nonvolatile memory (e.g., flash memory) and/or may include any suitable nonvolatile storage(s) (e.g., one or more Hard Disk Drives (HDDs), one or more Compact Disc (CD) drives, and/or one or more Digital Versatile Disc (DVD) drives).
NVM/storage 108 may include storage resources that are physically part of the device on which face recognition based big data control system 100 is installed, or which may be accessible by the device, but may not be necessary as part of the device. For example, NVM/storage 108 may be accessed via input/output device(s) 110 according to a network.
Input/output device(s) 110 may provide an interface for face recognition based big data control system 100 to communicate with any other suitable device, input/output device 110 may include a communication component, pinyin component, sensor component, and the like. The network interface 112 may provide an interface for the face recognition based big data control system 100 to communicate in accordance with one or more networks, the face recognition based big data control system 100 may communicate wirelessly with one or more components of a wireless network in accordance with any of one or more wireless network standards and/or protocols, such as accessing a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, etc., or a combination thereof.
For one embodiment, at least one of the processor(s) 102 may be packaged together with logic of one or more controllers (e.g., memory controller modules) of the control module 104. For one embodiment, at least one of the processor(s) 102 may be packaged together with logic of one or more controllers of the control module 104 to form a system in a package (SiD). For one embodiment, at least one of the processor(s) 102 may be integrated on the same mold as logic of one or more controllers of the control module 104. For one embodiment, at least one of the processor(s) 102 may be integrated on the same die with logic of one or more controllers of the control module 104 to form a system on chip (SoC).
In various embodiments, the face recognition based big data control system 100 may be, but is not limited to: a server, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet, a netbook, etc.), among other terminal devices. In various embodiments, the face recognition based big data control system 100 may have more or fewer components and/or different architectures. For example, in some embodiments, server 100 includes one or more cameras, keyboards, liquid Crystal Display (LCD) screens (including touch screen displays), non-volatile memory ports, multiple antennas, graphics chips, application Specific Integrated Circuits (ASICs), and speakers.
The embodiment of the application provides electronic equipment, which comprises: one or more processors; and one or more machine readable media having instructions stored thereon, which when executed by the one or more processors, cause the electronic device to perform the data processing method as described in one or more of the present application.
The foregoing has outlined rather broadly the more detailed description of the present application, wherein specific examples have been provided to illustrate the principles and embodiments of the present application, the description of the examples being provided solely to assist in the understanding of the method of the present application and the core concepts thereof; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.

Claims (10)

1. The big data control method based on face recognition is characterized by comprising the following steps:
acquiring execution coordination parameters among application execution examples in a specified face recognition application, and determining abnormal execution nodes in the application execution examples of the specified face recognition application based on the execution coordination parameters;
calling control model data for configuring big data control parameters, and generating function realization data comprising a big data control model based on the control model data; the big data control model in the function realization data is used for controlling the abnormal execution data reading aiming at the appointed face recognition application;
and loading the function realization data in the abnormal execution node of the appointed face recognition application, and generating a big data control link program for online.
2. The big data control method based on face recognition according to claim 1, wherein the acquiring the execution coordination parameter between application execution instances in a specified face recognition application, and determining an abnormal execution node in the application execution instance of the specified face recognition application based on the execution coordination parameter, comprises:
Acquiring a priori functional verification sequence, realizing data verification of the appointed face recognition application based on the priori functional verification covered in the priori functional verification sequence, and generating a target priori functional verification event sequence; each functional verification event in the target priori functional verification event sequence is used for connecting two application execution instances in the appointed face recognition application;
acquiring a random functional verification sequence, verifying the appointed face recognition application based on random functional verification data covered in the random functional verification sequence, and generating a random functional verification event sequence; each functionally verified event in the random sequence of functionally verified events is used to link two application execution instances in the specified face recognition application;
determining a first abnormal trigger frequency corresponding to each functional verification event in the target priori functional verification event sequence and a second abnormal trigger frequency corresponding to each functional verification event in the random functional verification event sequence, and determining execution coordination parameters between application execution instances in the appointed face recognition application based on the first abnormal trigger frequency and the second abnormal trigger frequency;
And determining an abnormal execution node of the appointed face recognition application from the random functional verification event sequence based on the execution cooperative parameters.
3. The big data control method based on face recognition according to claim 2, wherein the determining the abnormal execution node of the specified face recognition application from the random sequence of functional verification events based on the execution cooperative parameters includes:
acquiring a functional verification event x from the random functional verification event sequence; the functional verification event x is used for connecting an application execution instance y in the appointed face recognition application with an application execution instance v, x is a positive integer which is not more than the number of the functional verification events in the random functional verification event sequence, and y and v are both positive integers which are not more than the number of the application execution instances in the appointed face recognition application;
outputting the functional verification event x as an abnormal verification event if the execution coordination parameter between the application execution instance y and the application execution instance v is smaller than a set coordination strength and the functional verification event x does not belong to the target priori functional verification event sequence;
Combining the abnormal verification events in the random functional verification event sequences to generate R abnormal event sequences, and determining an abnormal execution node of the appointed face recognition application based on application execution examples associated with the abnormal verification events in each abnormal event sequence; r is a positive integer.
4. The face recognition-based big data control method according to claim 1, wherein the big data control parameters include big data control definition data, file size definition data, big data control model coverage data, the control model data including first model data for the big data control definition data, second model data for the file size definition data, and third model data for the big data control model coverage data;
the generating the function realization data including the big data control model based on the control model data includes:
generating W big data control models based on the first model data, the second model data, and the third model data in the control model data; w is a positive integer;
and loading a tag for marking the big data control position in each big data control model of the W big data control models to generate function realization data containing the W big data control models.
5. The face recognition-based big data control method of claim 4, wherein the third model data includes first model member data and second model member data, the first model member data determining a data coverage area greater than the second model member data determining a data coverage area;
the generating W big data control models based on the first model data, the second model data, and the third model data in the control model data includes:
generating a first modality control model based on the first model data, the second model data, and the first model member data in the control model data; the first modality control model characterizes an execution node that maintains execution activity in the reading of exception execution data to execute exception execution;
generating a second modal control model based on the first model data, the second model data and the second model member data in the control model data, and outputting the first modal control model and the second modal control model as the W big data control models; the second modality control model is used for suppressing execution activities of abnormally executing data reading.
6. The big data control method based on face recognition according to claim 1, wherein loading the function implementation data in the abnormal execution node of the specified face recognition application, generating a big data control link program for online, comprises:
determining a core function implementation position corresponding to the function implementation data in the abnormal execution node of the appointed face recognition application, and loading the function implementation data into the core function implementation position based on a function implementation activation function;
and performing function realization activation processing on the appointed face recognition application loaded with the function realization data in the function realization activation function to generate a big data control link program for online.
7. The face recognition-based big data control method according to claim 1, further comprising:
submitting the big data control link program to a big data control system so that a first control component in the big data control system acquires the big data control link program, executing the big data control link program and starting a program file corresponding to the big data control link program.
8. The face recognition-based big data control method according to claim 1, further comprising:
when the second control component is monitored to perform abnormal execution data reading on the big data control link program, acquiring a target functional check sequence provided by the second control component in the abnormal execution data reading;
maintaining target execution activities associated with the abnormal execution node from the target functionality check sequence using the function implementation data in the big data control link program; the target execution activity is used for controlling an error point decision branch in the abnormal execution data reading;
the big data control model in the function realization data comprises a first mode control model;
the maintaining, from the target functionality check sequence, target execution activities associated with the abnormal execution node using the function implementation data in the big data control link program, comprising:
when the implementation node of the execution activity i in the big data control link program in the target functional verification sequence is the abnormal execution node, determining the execution performance effect data of the execution activity i by using the first modal control model in the function implementation data; i is a positive integer;
Performing execution activity evaluation on the execution performance effect data of the execution activity i by using the execution activity evaluation model read by the abnormal execution data to generate an execution activity evaluation value of the execution activity i;
when the execution activity evaluation value of the execution activity i meets the set requirement, outputting the execution activity i as the target execution activity;
the big data control model in the function realization data comprises a second mode control model;
the maintaining, from the target functionality check sequence, target execution activities associated with the abnormal execution node using the function implementation data in the big data control link program, comprising:
when the fact that an implementation node of the execution activity j in the target functional verification sequence in the big data control link program is the abnormal execution node is monitored, performing expansion derivatization on the execution activity j in an execution activity expansion derivatization model of abnormal execution data reading based on the second mode control model in the functional implementation data to generate candidate execution activities; j is a positive integer;
outputting the candidate execution activity as the target execution activity when the execution value of the candidate execution activity is monitored to be greater than the execution value of the execution activity j;
Wherein the method further comprises:
in the abnormal execution data reading, verifying the big data control link program based on the execution activity in the target functional verification sequence, and generating at least one implementation snapshot data corresponding to the execution activity in the target functional verification sequence by utilizing the function implementation logic covered by the function implementation data in the big data control link program;
obtaining snapshot mining data respectively corresponding to the at least one implementation snapshot data, and analyzing occupied resources of the snapshot mining data corresponding to the execution activities in the target functional verification sequence to generate snapshot mining occupied resources;
when the fact that the resource quantity of the snapshot mining occupied resource is larger than the set resource quantity is monitored, mining expansion indicating data are generated; and the mining extension indicating data characterizes the mining scheduling resources of the abnormal execution data reading extension snapshot mining data.
9. A face recognition-based big data control system, characterized in that the face recognition-based big data control system comprises a processor and a readable storage medium, the readable storage medium storing a program which, when executed by the processor, implements the face recognition-based big data control method according to any of the preceding claims 1-8.
10. A readable storage medium, characterized in that the readable storage medium stores a program which, when executed by a processor, implements the face recognition-based big data control method of any of the preceding claims 1-8.
CN202310546524.XA 2023-05-16 2023-05-16 Big data control method and system based on face recognition Withdrawn CN116416670A (en)

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