CN113918579A

CN113918579A - Big data user portrait processing method and big data server

Info

Publication number: CN113918579A
Application number: CN202111017429.8A
Authority: CN
Inventors: 汪威
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-03-23
Filing date: 2021-03-23
Publication date: 2022-01-11
Also published as: CN112988774A; CN113918580A; CN112988774B

Abstract

The embodiment of the application discloses a big data user portrait processing method and a big data server. Thus, before updating the user image distribution, the user image distribution can be judged based on the designated target big data item, and the user image distribution can be used in time after updating.

Description

Big data user portrait processing method and big data server

The application is a divisional application with the application number of "202110309730. X", the application date of "2021 year 03 month 23 day", and the application name of "a user information updating method and information server based on big data acquisition".

Technical Field

The application relates to the technical field of big data and user analysis, in particular to a big data user portrait processing method and a big data server.

Background

With the rapid development of the IT communication industry including information technologies such as Internet (Internet), Internet of Things (IOT), cloud computing (cloud computing), etc., the rapid growth of data has become a serious challenge and a precious opportunity faced by many industries, so that the modern information society has entered the big data era. In fact, the big data change not only the daily life and work patterns of people, the operation and business patterns of enterprises, but also even the fundamental change of the scientific research pattern.

In a general sense, big data (big data) refers to a collection of data that cannot be perceived, acquired, managed, processed, and serviced within a certain time with conventional machines and software and hardware tools. The network big data refers to big data generated by interaction and fusion of people, machines and objects in a network space and available on the internet. The data is applied to life and production, and people or enterprises can be effectively helped to make relatively accurate judgment on the information so as to take appropriate action. Data analysis is the process by which organizations purposefully collect data, analyze data, and make it informative. That is, it refers to a process of processing data by an analysis method for an individual or an enterprise to solve the decision or marketing problems in life and production.

At present, the application field of big data statistical analysis is wide in range, and the related technology of pushing business by using big data is gradually applied by combining the continuous improvement of various online business service functions. However, the related service push technology still has the problems of poor accuracy and low reliability, and after the inventor researches and analyzes the technical problem, the inventor finds that the deviation and the hysteresis of the user information updating are one of the causes of the problem.

Disclosure of Invention

One of the embodiments of the present application provides a big data user representation processing method, which is applied to a big data user representation processing system, where the system includes a big data user device and a big data server that communicate with each other, and the method includes:

the big data user equipment acquires big data user element information of a target big data item in the first big data interaction information from the acquired first big data interaction information of the first big data interaction state; determining first dynamic big data interaction information corresponding to the big data user element information from the first big data interaction information; at least performing information correction processing on the first dynamic big data interaction information to obtain target big data interaction information in a second big data interaction state, wherein the second big data interaction state is suitable for user information updating of the target big data interaction information; sending the target big data interaction information to the big data server;

and the big data server updates the user information based on the target big data interaction information.

In an optional embodiment, obtaining big data user element information of a target big data transaction in the first big data interaction information from the collected first big data interaction information of the first big data interaction state includes:

performing interactive information conversion on the first big data interactive information to obtain second big data interactive information;

extracting big data user element information of the target big data transaction from the second big data interaction information, and determining the extracted big data user element information as big data user element information of the target big data transaction in the first big data interaction information;

extracting big data user element information of the target big data transaction from the second big data interaction information, and determining the extracted big data user element information as big data user element information of the target big data transaction in the first big data interaction information, wherein the method comprises the following steps:

inputting the second big data interaction information into a first machine learning model which is trained in advance; the first machine learning model realizes the identification and extraction of big data user element information of the target big data item at least through a feature extraction layer for carrying out feature extraction, a feature identification layer for carrying out feature identification, a feature classification layer for carrying out feature classification, a label extraction layer for carrying out user label extraction and an information matching layer for carrying out element information matching;

and determining the result extracted by the first machine learning model as big data user element information of the target big data item in the first big data interaction information.

In an optional embodiment, the at least information correction processing on the first dynamic big data interaction information to obtain target big data interaction information in a second big data interaction state includes:

inputting the first dynamic big data interaction information into a second machine learning model which is trained in advance; the second machine learning model realizes interactive information correction of the input first dynamic big data interactive information at least through a heat identification layer for carrying out service heat identification and an interest classification layer for carrying out user interest classification, and outputs the big data interactive information after interactive information correction;

and taking the big data interaction information output by the second machine learning model as the target big data interaction information.

inputting the first dynamic big data interaction information into a third machine learning model which is trained in advance; the third machine learning model converts the first dynamic big data interaction information into first big data interaction information to be processed through an interaction state conversion layer, and the interaction state conversion layer is used for performing at least one interaction state conversion mode selected from the following modes: interactive state label screening processing and interactive state feature swapping processing are carried out, interactive information correction on the first to-be-processed big data interactive information is realized at least through a heat identification layer for carrying out service heat identification and an interest classification layer for carrying out user interest classification, and the big data interactive information after interactive information correction is output;

and taking the big data interaction information output by the third machine learning model as the target big data interaction information.

inputting the first dynamic big data interaction information into a fourth machine learning model which is trained in advance, detecting an update frequency statistic of the input first dynamic big data interaction information under a specified information update instruction by an information update frequency detection sub-network of the fourth machine learning model, the element attribute updating frequency of each big data user element in the updating frequency statistic result describes the degree of influence of the element attribute of the corresponding big data user element in the input first dynamic big data interaction information by the specified information updating indication, performing relationship reconstruction processing on the input first dynamic big data interaction information by the information relationship reconstruction sub-network of the fourth machine learning model according to the update frequency statistical result obtained by the information update frequency detection sub-network, and outputting big data interaction information subjected to the relationship reconstruction processing;

using big data interaction information output by a fourth machine learning model as the target big data interaction information;

the information updating frequency detection sub-network detects the updating frequency statistical result of the first dynamic big data interaction information input under each information updating indication, and the method comprises the following steps:

the information updating frequency detection sub-network detects the updating frequency statistical result of the input first dynamic big data interaction information under the indication of the specified information updating at least through an updating frequency detection layer;

the information relationship reconstruction sub-network carries out relationship reconstruction processing on the input first dynamic big data interaction information according to the update frequency statistical result obtained by the information update frequency detection sub-network, and the relationship reconstruction processing comprises the following steps:

the information relation reconstruction sub-network realizes the relation reconstruction processing of the input first dynamic big data interaction information according to the updating frequency statistical result obtained by the information updating frequency detection sub-network at least through a feature extraction layer for performing feature extraction, a feature identification layer for performing feature identification and a relation splitting layer for performing relation splitting.

In an alternative embodiment of the method according to the invention,

before sending the target big data interaction information to the big data server, the method further comprises: acquiring third big data interaction information which is obtained by converting the first big data interaction information and contains the target big data transaction, and determining second dynamic big data interaction information where the target big data transaction is located from the third big data interaction information;

sending the target big data interaction information to the big data server, comprising: and combining the target big data interaction information and the key interaction information of the second dynamic big data interaction information to obtain real-time big data interaction information, and sending the real-time big data interaction information to the big data server, so that the big data server obtains the target big data interaction information from the real-time big data interaction information and updates user information based on the target big data interaction information.

In an alternative embodiment of the method according to the invention,

combining the target big data interaction information with the key interaction information of the second dynamic big data interaction information to obtain real-time big data interaction information, comprising: splitting information fragments of the second dynamic big data interaction information in a designated information fragment splitting mode to obtain an interaction information fragment set; adding first key interaction information of the target big data interaction information into a set position of an interaction information fragment set to obtain the real-time big data interaction information, wherein the first key interaction information comprises attribute category description information of all user element attribute categories in the target big data interaction information;

the big data server acquires the target big data interaction information from the real-time big data interaction information, and the method comprises the following steps: and the big data server extracts first key interaction information from the set position of the real-time big data interaction information and generates the target big data interaction information by using the extracted first key interaction information.

In an alternative embodiment of the method according to the invention,

combining the target big data interaction information with the key interaction information of the second dynamic big data interaction information to obtain real-time big data interaction information, comprising: splitting information fragments of the second dynamic big data interaction information in a designated information fragment splitting mode to obtain an interaction information fragment set; encrypting first key interaction information of the target big data interaction information to obtain encrypted key interaction information, wherein the first key interaction information comprises attribute category description information of all user element attribute categories in the target big data interaction information; adding the encrypted key interaction information into a set position of an interaction information fragment set to obtain the real-time big data interaction information;

the big data server acquires the target big data interaction information from the real-time big data interaction information, and the method comprises the following steps: the big data server extracts encrypted key interaction information from the set position of the real-time big data interaction information, carries out information decryption on the extracted encrypted key interaction information to obtain first key interaction information, and generates the target big data interaction information by using the first key interaction information obtained by the information decryption.

In an optional embodiment, the big data server performs user information update based on the target big data interaction information, including:

the big data server extracts target user portrait distribution from the target big data interaction information, wherein the target user portrait distribution is used for describing user portrait change conditions corresponding to target big data items in the target big data interaction information;

the big data server determines a comparison result between the target user portrait distribution and a preset reference user portrait distribution, and identifies whether a target big data item in the target big data interaction information is a designated target big data item according to the comparison result, wherein the reference user portrait distribution is used for describing the designated target big data item;

when the big data server identifies that the target big data item in the target big data interaction information is a specified target big data item according to the comparison result, updating the reference user portrait distribution based on the target user portrait distribution;

updating the reference user portrait distribution based on the target user portrait distribution, comprising:

acquiring a target portrait relation network of the reference user portrait distribution;

determining a target portrait liveness corresponding to a relationship network updating area of the target portrait relationship network, wherein the relationship network updating area is a portrait distribution area in which the portrait liveness of each user portrait is less than a first global portrait liveness of the target portrait relationship network, the target portrait liveness is the portrait liveness corresponding to the number of target user portraits, and the number of target user portraits is the largest number of user portraits among the number of user portraits corresponding to each portrait liveness in the relationship network updating area;

determining an image liveness range corresponding to the target image relationship network based on a comparison result of the target image liveness and a range limit value of a preset image liveness range;

determining a second global portrait liveness based on the determined range of portrait liveness;

performing portrait liveness adjustment on the relationship network updating area based on the average portrait liveness corresponding to the target user portrait distribution until the global portrait liveness of the target portrait relationship network is the second global portrait liveness, and finishing portrait liveness updating of the target portrait relationship network; wherein the second global portrait liveness is the global portrait liveness of the target portrait relationship network after the portrait liveness is updated for the target portrait relationship network;

updating the association relationship between the user portraits in the relationship network updating area according to the second global portraits liveness corresponding to the target portraits relationship network;

before the step of determining the image liveness range corresponding to the target image relationship network based on the comparison result between the target image liveness and the range limit of the preset image liveness range, the method further includes: judging whether the target portrait liveness meets the preset portrait liveness range determination condition or not;

the step of determining the image liveness range corresponding to the target image relationship network based on the comparison result of the target image liveness and the range limit value of the preset image liveness range includes: and when the target portrait liveness accords with a preset portrait liveness range determination condition, determining a portrait liveness range corresponding to the target portrait relationship network based on a comparison result of the target portrait liveness and a range limit value of the preset portrait liveness range.

One of the embodiments of the present application provides a big data server, which includes a processing engine, a network module, and a memory; the processing engine and the memory are communicated through the network module, and the processing engine reads a computer program from the memory and runs the computer program to execute the step of updating the user information based on the target big data interaction information.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and like reference numerals refer to like structures throughout these embodiments.

FIG. 1 is a block diagram of an exemplary big data user representation processing system, shown in accordance with some embodiments of the present invention.

FIG. 2 is a flow diagram illustrating an exemplary big data user representation processing method and/or process according to some embodiments of the invention.

FIG. 3 is another flow diagram illustrating an exemplary big data user representation processing method and/or process according to some embodiments of the invention.

FIG. 4 is a diagram illustrating the hardware and software components of an exemplary big data server, according to some embodiments of the invention.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

In order to realize accurate and real-time update of the knowledge graph of the user portrait and ensure the accuracy and the reliability of the follow-up business push by using the knowledge graph, the inventor provides a big data user portrait processing method and a big data server in a targeted manner.

To facilitate understanding of the overall scheme, and initially to describe the application environment of the scheme, referring to FIG. 1, a communication architecture diagram of a big data user representation processing system 100 is shown, which may include a big data user device 110 and a big data server 120 in communication with each other. The big data user device 110 includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, and the like, and the big data server 120 includes, but is not limited to, a cloud server, a relational database, and the like. It is understood that there may be a plurality of big data user devices 110 and a plurality of big data servers 120, and further, the communication connection relationship between the big data user devices 110 and the big data servers 120 may be one-to-one, one-to-many, many-to-one, or many-to-many, and for convenience of description, this embodiment is described with a one-to-one communication connection relationship, that is, the communication connection relationship shown in fig. 1. In an actual implementation process, the user information updating system 100 may be applied to an online service scenario or a cloud service scenario in the fields of e-commerce, online office, remote education, cloud game service, smart city, smart factory, and smart medical, and the like, and is not limited herein.

The user information updating method provided by the scheme can be understood as an updating method of a Knowledge Graph aiming at user portrait, wherein the Knowledge Graph (Knowledge Graph) is named as a Knowledge domain visualization or Knowledge domain mapping map in a book intelligence field, and is a series of different graphs for displaying Knowledge development progress and structure relation, Knowledge resources and carriers thereof are described by using a visualization technology, and Knowledge and mutual relation among the Knowledge resources, the carriers, the Knowledge resources, the Knowledge construction, the Knowledge drawing and the Knowledge display are mined, analyzed, constructed, drawn and displayed. The knowledge graph is a modern theory which achieves the aim of multi-discipline fusion by combining theories and methods of applying subjects such as mathematics, graphics, information visualization technology, information science and the like with methods such as metrology introduction analysis, co-occurrence analysis and the like and utilizing the visualized graph to vividly display the core structure, development history, frontier field and overall knowledge framework of the subjects, and can provide practical and valuable references for subject research. The knowledge graph is mostly used in the decision process of online service pushing, and the problem of low efficiency of the related service pushing technology is also caused by the fact that the knowledge graph of the user portrait is not updated in place, so that the embodiment improves the knowledge graph of the user portrait on both the big data user equipment 110 side and the big data server 120 side, and therefore accurate and real-time updating of the knowledge graph of the user portrait is achieved, and accuracy and reliability of follow-up service pushing by using the knowledge graph are guaranteed.

Referring to FIG. 2 in conjunction with FIG. 1, a flow chart of an exemplary big data user representation processing method and/or process is shown, according to some embodiments of the present invention, and the big data user representation processing method may be applied to the system shown in FIG. 1, and further may include the following steps S100-S200.

S100: the big data user equipment acquires big data user element information of a target big data item in first big data interaction information from the acquired first big data interaction information of a first big data interaction state; determining first dynamic big data interaction information corresponding to the big data user element information from the first big data interaction information; at least performing information correction processing on the first dynamic big data interaction information to obtain target big data interaction information in a second big data interaction state, wherein the second big data interaction state is suitable for user information updating of the target big data interaction information; and sending the target big data interaction information to the big data server.

For example, when the big data user equipment communicates with the big data server, different interaction states may exist, such as a one-way interaction state or a two-way interaction state, the one-way interaction state refers to an interaction state in which only one party between the big data user equipment and the big data server performs a business action, and the two-way interaction state refers to an interaction state in which both the big data user equipment and the big data server perform a business action. Further, the big data interaction information may be an interaction record between the big data user equipment and the big data server, taking an electronic commerce service as an example, the big data interaction information may be page operation information and page access information of the big data user equipment, taking an online office service as an example, the big data interaction information may be an office software usage record of the big data user equipment, and the like. More recently, the target big data item may be a big data item with a higher business interaction heat or search index determined in advance according to a large number of samples, such as a certain shopping business in an e-commerce business, a certain office function in an online office business, and the like. The element information of the big data user can be understood as feature information aiming at a service user layer, and the element information user records the characteristics and hobbies of different users and can be understood as fuzzy portrait information to a certain extent. On the basis of the above, the first dynamic big data interaction information may be understood as big data interaction information that changes with time, that is, big data interaction information that is continuously updated and changed when the big data user equipment interacts with the big data server. In this embodiment, the second big data interaction state is suitable for the target big data interaction information to perform user information update, and accordingly, the second big data interaction state may be understood as an interaction state matched with the running state of the information service period, and the second big data interaction state is opposite to the first big data interaction state, for example, if the first big data interaction state is a unidirectional interaction state, the second big data interaction state may be a bidirectional interaction state, and if the first big data interaction state is a bidirectional interaction state, the second big data interaction state may be a unidirectional interaction state. After the target big data interaction information is determined, the big data user equipment sends the target big data interaction information to the big data server so that the big data server updates user information based on the target big data interaction information. In the related embodiment, the target big data interaction information may be interaction information corrected by user interest, and the determination of the target big data interaction information is performed on the big data user equipment side, so that the target big data interaction information can be ensured to be matched with the real-time service condition of the big data user equipment as much as possible. Of course, in an actual implementation process, the above S100 may be executed in a related device corresponding to the big data user device, and may also be executed in the big data user device. For example, when the big data user equipment is a device with a weak processing capability, such as a mobile phone, the above S100 may be executed in an associated device corresponding to the big data user equipment. For another example, when the big data user device is a device with a relatively high processing capability, such as a large computer, the above S100 may be executed in the big data user device.

In a related embodiment, in order to ensure the accuracy of the obtained big data user element information, the step "obtaining big data user element information of the target big data transaction in the first big data interaction information from the collected first big data interaction information of the first big data interaction state" may be implemented by: performing interactive information conversion on the first big data interactive information to obtain second big data interactive information; extracting big data user element information of the target big data transaction from the second big data interaction information, and determining the extracted big data user element information as the big data user element information of the target big data transaction in the first big data interaction information. The method can ensure the uniformity of the information format of the big data interactive information as much as possible through interactive information conversion, thus reducing the problems of information distortion and the like as much as possible when the big data user element information of the target big data item is extracted, and ensuring the accuracy of the obtained big data user element information. Further, in an actual implementation process, in order to improve the efficiency of determining big data user element information, the above-mentioned step "extracting big data user element information of the target big data transaction from the second big data interaction information, and determining the extracted big data user element information as big data user element information of the target big data transaction in the first big data interaction information" may be implemented by using a related machine learning model. Machine Learning (Machine Learning) is a multi-field cross subject, and relates to a plurality of subjects such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Therefore, the corresponding data information processing is realized by using a machine learning model, such as a Neural Network (NN), and the accuracy and efficiency of the processing can be improved. To achieve this, the step of extracting big data user element information of the target big data transaction from the second big data interaction information, and determining the extracted big data user element information as the big data user element information of the target big data transaction in the first big data interaction information may be implemented by: inputting the second big data interaction information into a first machine learning model which is trained in advance; the first machine learning model realizes the identification and extraction of big data user element information of the target big data item at least through a feature extraction layer for carrying out feature extraction, a feature identification layer for carrying out feature identification, a feature classification layer for carrying out feature classification, a label extraction layer for carrying out user label extraction and an information matching layer for carrying out element information matching; and determining the result extracted by the first machine learning model as big data user element information of the target big data item in the first big data interaction information.

For example, the first machine learning model may be a neural network model, and the first machine learning model may include a plurality of network layers, such as the above-mentioned feature extraction layer for performing feature extraction, feature recognition layer for performing feature recognition, feature classification layer for performing feature classification, tag extraction layer for performing user tag extraction, and information matching layer for performing element information matching, and these network layers may implement learning and optimization of corresponding functions through pre-training, so as to be better applied to the determination process of large data user element information. For example, when the first machine learning model is trained, the sample set can be divided into a training set and a testing set according to a certain proportion, the first machine learning model is trained through the training set, the first machine learning model is tested through the testing set, and the testing rate is ensured to meet the set conditions by adjusting related model parameters, so that the training of the first machine learning model is completed. The trained first machine learning model comprises different functional layers, and input and output transmission can be performed among the functional layers, so that errors generated when large data user element information of a target large data item is identified and extracted can be reduced, and the efficiency of determining the large data user element information is improved.

In an actual implementation process, further embodiments of the step of performing at least information correction processing on the first dynamic big data interaction information to obtain target big data interaction information of the second big data interaction state may include multiple types, and at least some embodiments will be described below.

Regarding the step "the first dynamic big data interactive information is at least subjected to information correction processing to obtain the target big data interactive information of the second big data interactive state", the implementation mode a: inputting the first dynamic big data interaction information into a second machine learning model which is trained in advance; the second machine learning model realizes interactive information correction of the input first dynamic big data interactive information at least through a heat identification layer for carrying out service heat identification and an interest classification layer for carrying out user interest classification, and outputs the big data interactive information after interactive information correction; and taking the big data interaction information output by the second machine learning model as the target big data interaction information. In embodiment a, the training mode of the second machine learning model is similar to the training mode of the first machine learning model, and details thereof are not repeated herein. Due to the fact that the service heat and the user interests are considered when the interactive information is corrected, the obtained target big data interactive information can be matched with different service heat and different user interests, and accuracy and reliability of subsequent user information updating are further guaranteed. It can be understood that the big data interaction information after the interaction information correction output by the second machine learning model is the target big data interaction information of the second big data interaction state.

Embodiment B regarding the step "performing at least information correction processing on the first dynamic big data interaction information to obtain target big data interaction information of a second big data interaction state": inputting the first dynamic big data interaction information into a third machine learning model which is trained in advance; the third machine learning model converts the first dynamic big data interaction information into first big data interaction information to be processed through an interaction state conversion layer, and the interaction state conversion layer is used for performing at least one interaction state conversion mode selected from the following modes: interactive state label screening processing and interactive state feature swapping processing are carried out, interactive information correction on the first to-be-processed big data interactive information is realized at least through a heat identification layer for carrying out service heat identification and an interest classification layer for carrying out user interest classification, and the big data interactive information after interactive information correction is output; and taking the big data interaction information output by the third machine learning model as the target big data interaction information. In embodiment B, the training mode of the third machine learning model is similar to the training mode of the first machine learning model, and is not repeated here. Because the interactive state conversion is considered when the interactive information correction is carried out, the complete conversion of the big data interactive information from the big data user equipment side to the big data server side can be realized. In this embodiment, the interactive state label screening process is used for grouping different interactive states, and the interactive state feature swapping process is used for adjusting the interactive state according to the filling difference between the big data user equipment side and the big data server side, so that after the interactive state conversion is completed, the interactive information correction of the first to-be-processed big data interactive information is realized through the heat identification layer for performing service heat identification and the interest classification layer for performing user interest classification, and the big data interactive information after the interactive information correction is output, so that the high adaptability of the target big data interactive information and the operating state of the big data server can be ensured.

Embodiment C regarding the step "performing at least information correction processing on the first dynamic big data interaction information to obtain target big data interaction information of a second big data interaction state": inputting the first dynamic big data interaction information into a fourth machine learning model which is trained in advance, detecting an update frequency statistic of the input first dynamic big data interaction information under a specified information update instruction by an information update frequency detection sub-network of the fourth machine learning model, the element attribute updating frequency of each big data user element in the updating frequency statistic result describes the degree of influence of the element attribute of the corresponding big data user element in the input first dynamic big data interaction information by the specified information updating indication, performing relationship reconstruction processing on the input first dynamic big data interaction information by the information relationship reconstruction sub-network of the fourth machine learning model according to the update frequency statistical result obtained by the information update frequency detection sub-network, and outputting big data interaction information subjected to the relationship reconstruction processing; and taking the big data interaction information output by the fourth machine learning model as the target big data interaction information. In embodiment C, the training mode of the fourth machine learning model is similar to the training mode of the first machine learning model, and is not repeated here. Since the analysis of the element attribute update frequency is introduced in the embodiment C, the information correction processing of the first dynamic big data interaction information can be realized based on the entity, relationship, and attribute level of the knowledge graph itself. For example, the information update indication may be input by the user through the big data user device, or may be sent by the big data server after the adaptive learning, which is not limited herein. The big data user element may be understood as an attribute in the knowledge-graph, and accordingly, the element attribute update frequency may be understood as an update rate of the big data user element, for example, the element attribute update frequency r1 characterizes an update rate of the big data user element as r1, the element attribute update frequency r2 characterizes an update rate of the big data user element as r2, the element attribute update frequency r3 characterizes an update rate of the big data user element as r3, the element attribute update frequency r4 characterizes an update rate of the big data user element as r4, and the element attribute update frequency r5 characterizes an update rate of the big data user element as r5, which is not limited herein. The relationship reconstruction processing can be understood as reconnecting and adjusting the relationship between the entities to the knowledge graph corresponding to the first dynamic big data interactive information, so that the information of the first dynamic big data interactive information is corrected to obtain the target big data interactive information. By the design, the target big data interaction information can be ensured to be closer to the application scene of the knowledge graph, so that the efficiency of updating the user information of the follow-up big data server based on the target big data interaction information is improved. In embodiment C, the step "statistical result of update frequency of the first dynamic big data interaction information detected and input by the information update frequency detection sub-network under each information update instruction" may include: the information updating frequency detection sub-network detects the updating frequency statistical result of the input first dynamic big data interaction information under the indication of the specified information updating at least through an updating frequency detection layer. In embodiment C, the step "the information relationship reconstruction sub-network performs the relationship reconstruction processing on the input first dynamic big data interaction information according to the update frequency statistical result obtained by the information update frequency detection sub-network" may include the following steps: the information relation reconstruction sub-network realizes the relation reconstruction processing of the input first dynamic big data interaction information according to the updating frequency statistical result obtained by the information updating frequency detection sub-network at least through a feature extraction layer for performing feature extraction, a feature identification layer for performing feature identification and a relation splitting layer for performing relation splitting. It can be understood that by performing the relationship splitting, the influence of the previous relationship network on the subsequent relationship reconstruction processing can be reduced, thereby ensuring the accuracy of information correction.

It is to be understood that the above-mentioned embodiment a, embodiment B and embodiment C may be used alternatively according to actual situations, and are not limited herein.

In a related embodiment, the updating of the user information by the big data server based on the target big data interaction information may mainly be updating a knowledge graph corresponding to a user image of the big data user equipment. In actual implementation, in order to ensure comprehensiveness of knowledge graph update, other dynamic big data interaction information that may correspond to a target big data item needs to be considered, and to achieve this purpose, before the step "sending the target big data interaction information to the big data server", the method may further include: the step of obtaining third big data interaction information which is obtained by converting the first big data interaction information and contains the target big data transaction, and determining second dynamic big data interaction information where the target big data transaction is located from the third big data interaction information, on the basis, sending the target big data interaction information to the big data server, may include the following contents: and combining the target big data interaction information and the key interaction information of the second dynamic big data interaction information to obtain real-time big data interaction information, and sending the real-time big data interaction information to the big data server, so that the big data server obtains the target big data interaction information from the real-time big data interaction information and updates user information based on the target big data interaction information. In this embodiment, the key interaction information may be interaction information with a higher user behavior index (user interaction popularity), or may be understood as more popular interaction information. The real-time big data interaction information is obtained by combining the key interaction information of the target big data interaction information and the second dynamic big data interaction information, so that the real-time big data service information received by the big data server can be ensured to have time sequence consistency with an actual service scene, and the timeliness of subsequent user information updating can be ensured. In a related embodiment, the step of combining the target big data interaction information and the key interaction information of the second dynamic big data interaction information to obtain the real-time big data interaction information may be implemented by: splitting information fragments of the second dynamic big data interaction information in a designated information fragment splitting mode to obtain an interaction information fragment set; adding first key interaction information of the target big data interaction information to a set position of an interaction information fragment set to obtain the real-time big data interaction information, wherein the first key interaction information comprises attribute category description information of all user element attribute categories in the target big data interaction information. In the implementation, mutual influence between the interactive information can be reduced by splitting the second dynamic big data interactive information. For example, the set of interaction information fragments may be { m1, m2, m3, m4, m5,. and.mi }, the first key interaction information of the target big data interaction information may be mk, the set position may be determined according to the relevance between every two adjacent information fragments in the set of interaction information fragments, for example, a position between two information fragments with the smallest relevance may be selected as the set position, for example, the relevance between m3 and m4 is smallest, mk may be added between m3 and m4, so as to obtain { m1, m2, m3, mk, m4, m5,. and.mi }, and further obtain real-time big data interaction information m12345.. i corresponding to { m1, m2, m3, mk, m4, m5,. and.mi }. The user element attribute type is used for distinguishing user element attributes, and the attribute type description information may be determined in a numerical manner, which is not limited herein. In another embodiment, to ensure the security of information transmission, the step "combining the target big data interaction information with the key interaction information of the second dynamic big data interaction information to obtain real-time big data interaction information" may include: splitting information fragments of the second dynamic big data interaction information in a designated information fragment splitting mode to obtain an interaction information fragment set; encrypting first key interaction information of the target big data interaction information to obtain encrypted key interaction information, wherein the first key interaction information comprises attribute category description information of all user element attribute categories in the target big data interaction information; and adding the encrypted key interaction information into a set position of an interaction information fragment set to obtain the real-time big data interaction information. Further, the encryption method may use an existing encryption technology, and is not limited herein.

S200: and the big data server updates the user information based on the target big data interaction information.

In a related embodiment, an information fragment splitting mode is adopted to split the second dynamic big data interaction information to obtain an interaction information fragment set; adding first key interaction information of the target big data interaction information to a set position of an interaction information fragment set to obtain the real-time big data interaction information, wherein the first key interaction information comprises attribute category description information of all user element attribute categories in the target big data interaction information, and the big data server obtains the target big data interaction information from the real-time big data interaction information, and further comprises the following contents: and the big data server extracts first key interaction information from the set position of the real-time big data interaction information and generates the target big data interaction information by using the extracted first key interaction information. By the design, the first key interaction information is added to the set position, and the first key interaction information can be prevented from being lost in the information transmission process. Therefore, the integrity of the target big data interaction information obtained subsequently is ensured.

In a related embodiment, an information fragment splitting mode is adopted to split the second dynamic big data interaction information to obtain an interaction information fragment set; encrypting first key interaction information of the target big data interaction information to obtain encrypted key interaction information, wherein the first key interaction information comprises attribute category description information of all user element attribute categories in the target big data interaction information; on the basis of the embodiment that the encrypted key interaction information is added to the set position of the interaction information fragment set to obtain the real-time big data interaction information', the big data server obtains the target big data interaction information from the real-time big data interaction information, and the method comprises the following steps: the big data server extracts encrypted key interaction information from the set position of the real-time big data interaction information, carries out information decryption on the extracted encrypted key interaction information to obtain first key interaction information, and generates the target big data interaction information by using the first key interaction information obtained by the information decryption. By the design, the information transmission safety can be ensured on the premise of ensuring the integrity of the information through information encryption and decryption, so that the privacy of a user is protected from being revealed.

On the basis of the content described in S200, the step "the big data server performs user information update based on the target big data interaction information" may be implemented in the following manner: the big data server extracts target user portrait distribution from the target big data interaction information, wherein the target user portrait distribution is used for describing user portrait change conditions corresponding to target big data items in the target big data interaction information; the big data server determines a comparison result between the target user portrait distribution and a preset reference user portrait distribution, and identifies whether a target big data item in the target big data interaction information is a designated target big data item according to the comparison result, wherein the reference user portrait distribution is used for describing the designated target big data item; and when the big data server identifies that the target big data item in the target big data interaction information is a specified target big data item according to the comparison result, updating the reference user portrait distribution based on the target user portrait distribution. In a related embodiment, the user portrait distribution may be understood as a knowledge graph corresponding to a user portrait, and the comparison result between the target user portrait distribution and the preset reference user portrait distribution may be difference information between the target user portrait distribution and the preset reference user portrait distribution, where the difference information may be related to a relationship connection between entities and an attribute node distribution of the entities, and is not limited herein. The designated target big data item can be understood as a big data item which is predetermined by the big data server and corresponds to the need of carrying out the portrait distribution updating, namely the big data item which needs to be carried out the business push is considered subsequently. By adopting the design, before the user portrait distribution is updated, the user portrait distribution can be used in time after the user portrait distribution is updated by judging based on the designated target big data item. In a related embodiment, the step of "updating the reference user representation distribution based on the target user representation distribution" may be accomplished as described in steps S310-S360 of FIG. 3.

S310: and acquiring a target portrait relation network of the reference user portrait distribution.

For example, an object representation relationship network may be understood as a topology of a knowledge graph that references user representation distribution.

S320: and determining the target portrait liveness corresponding to the relational network updating area of the target portrait relational network.

For example, the relationship network updating area is an image distribution area where the image liveness of each user image is less than the first global image liveness of the target image relationship network, the target image liveness is the image liveness corresponding to the number of target user images, and the number of target user images is the largest number of user images in the number of user images corresponding to each image liveness in the relationship network updating area. In this embodiment, the portrait liveness may be used to represent the degree of hotness of the user portrait, and the higher the portrait liveness is, the hotter the corresponding user portrait is.

S330: and determining the image liveness range corresponding to the target image relationship network based on the comparison result of the target image liveness and the range limit value of the preset image liveness range. For example, the image activity range may be set according to actual conditions, such as [ h1, h2 ]. In a related embodiment, before the step "determining the image liveness range corresponding to the target image relationship network based on the comparison result between the target image liveness and the range limit of the preset image liveness range", the method may further include: determining whether the target portrait liveness meets a preset portrait liveness range determination condition, and based on this, determining the portrait liveness range corresponding to the target portrait relationship network based on the comparison result between the target portrait liveness and the range limit value of the preset portrait liveness range may include: and when the target portrait liveness accords with a preset portrait liveness range determination condition, determining a portrait liveness range corresponding to the target portrait relationship network based on a comparison result of the target portrait liveness and a range limit value of the preset portrait liveness range. By the aid of the design, the phenomenon that subsequent knowledge graph updating is abnormal due to the fact that the determined image liveness range is too large or too small can be avoided.

In a related embodiment, the step of determining whether the target image liveness meets a preset image liveness range determination condition may include the following steps: determining a first image activity corresponding to a relational network updating area of at least one group of image relational networks, wherein the at least one group of image relational networks are previous group of image relational networks of the target image relational network, or previous continuous N groups of image relational networks of the target image relational network, the first image activity corresponding to the relational network updating area of any group of image relational networks is the image activity corresponding to the number of first user images, and the number of the first user images is the maximum user image number in the number of user images corresponding to each image activity in the relational network updating area of the group of image relational networks; judging a comparison result of the target image liveness and a range limit value of a preset image liveness range to obtain a first judgment result; judging the comparison result of the range limit value of the liveness of each first image and the range limit value of the range of the liveness of the preset image to obtain a second judgment result; detecting whether the first judgment result is consistent with each obtained second judgment result; if yes, judging that the target image liveness accords with a preset image liveness range determination condition. Further, the determining process of the first image activity corresponding to the relationship network updating area of any group of image relationship networks comprises the following steps: determining a statistical result of a user representation having a representation liveness for each representation of a plurality of representation liveness of a region updated for a relationship network of a set of representation relationship networks; sequencing a plurality of determined statistical results according to the size sequence of the image liveness of each image updating area of the relationship network updating area of the target image relationship network; after sorting, aiming at each statistical result, fusing a plurality of statistical results corresponding to the liveness of the continuous images containing the statistical result, and determining the fused result as the number of the user images of the liveness of the images corresponding to the statistical result; and determining the image liveness corresponding to the determined maximum user image quantity as the first image liveness corresponding to the relationship network updating area of the image relationship network. On the basis of the above, the step "determining the comparison result between the target image liveness and the range limit of the preset image liveness range to obtain the first determination result" may include the following steps: when the target portrait liveness is smaller than a first range limit value of a preset portrait liveness range, determining that the target portrait liveness is smaller than the first range limit value as a first judgment result; when the target image liveness is larger than or equal to a first range limit value of a preset image liveness range and is smaller than or equal to a second range limit value of the preset image liveness range, determining the target image liveness is larger than or equal to the first range limit value and is smaller than or equal to the second range limit value as a first judgment result; and when the target image liveness is larger than a second range limit value of a preset image liveness range, determining that the target image liveness is larger than the second range limit value as a first judgment result. The step of determining the comparison result between the liveness of each first portrait and the range limit of the range of the liveness of the preset portrait to obtain the second determination result may include the following steps: for each image liveness in each first image liveness, when the first image liveness is smaller than a first range limit value of a preset image liveness range, determining that the first image liveness is smaller than the first range limit value as a second determination result; when the first image liveness is larger than or equal to a first range limit value of a preset image liveness range and is smaller than or equal to a second range limit value of the preset image liveness range, determining the first image liveness is larger than or equal to the first range limit value and is smaller than or equal to the second range limit value as a second determination result; and when the first image liveness is larger than a second range limit value of the preset image liveness range, determining that the first image liveness is larger than the second range limit value as a second determination result. For example, the first range limit may be h1 and the second range limit may be h 2.

S340: based on the determined range of portrait liveness, a second global portrait liveness is determined.

In a related embodiment, the first and second global portrait liveness may be understood as a global level of portrait liveness of the target portrait relationship network. Generally, the value of the image liveness of the second global image is within the range of the image liveness. For example, the second global portrait activity may be h3, and h1< h3< h 2.

S350: and performing portrait liveness adjustment on the relation network updating area based on the average portrait liveness corresponding to the target user portrait distribution until the global portrait liveness of the target portrait relation network is the second global portrait liveness, and finishing portrait liveness updating of the target portrait relation network.

For example, the second global image liveness is the global image liveness of the target image relationship network after image liveness update of the target image relationship network. It can be understood that when the portrait liveness is adjusted, the portrait liveness of the relation network updating area can be adjusted in an iterative adjustment mode by adjusting the portrait liveness of part of the user portrait.

S360: and updating the association relationship among the user portraits in the relationship network updating area according to the second global portraits liveness corresponding to the target portraits relationship network.

It can be understood that after the portrayal liveness is adjusted, the association relationship between the user portrayal in the relationship network updating area is updated, so that the accurate and real-time updating of the knowledge spectrogram can be realized, the latest knowledge spectrogram can be used for determining the interest tendency of the user when a subsequent service pushing decision is made, and the accuracy of service pushing is further ensured. It should be understood that updating the association relationship between the user representations in the relationship network update area may be understood as readjusting the association relationship and association coefficient between different user representations, and may be implemented by other embodiments, which are not limited herein.

In summary, when the above technical solution is implemented, the big data user equipment can obtain big data user element information of a target big data transaction in the first big data interaction information from the collected first big data interaction information in the first big data interaction state, determine first dynamic big data interaction information corresponding to the big data user element information from the first big data interaction information, perform at least information correction processing on the first dynamic big data interaction information to obtain target big data interaction information in the second big data interaction state, and then send the target big data interaction information to the big data server, so that the big data server can perform user information update based on the target big data interaction information, and since the second big data interaction state is suitable for user information update of the target big data interaction information, the first dynamic big data interaction information is determined on the big data user equipment side, the method can ensure that the target big data interaction information is matched with the actual service scene of the user as much as possible. In addition, the first dynamic big data interaction information is at least subjected to information correction processing, so that the target big data interaction information can be adapted to the running state of the big data server, the big data server can be ensured to completely and accurately utilize the target big data interaction information to update the user information, the knowledge graph of the user portrait can be accurately updated in real time, and the accuracy and the reliability of follow-up service pushing by utilizing the knowledge graph are ensured.

Further, referring to fig. 4 in combination, the big data server 120 may include a processing engine 121, a network module 122 and a memory 123, and the processing engine 121 and the memory 123 communicate through the network module 122.

Processing engine 121 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 121 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, the Processing engine 121 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network module 122 may facilitate the exchange of information and/or data. In some embodiments, the network module 122 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 122 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 122 may include at least one network access point. For example, the network module 122 may include a wired or wireless network access point, such as a base station and/or a network access point.

The Memory 123 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 123 is configured to store a program, and the processing engine 121 executes the program after receiving the execution instruction.

It is to be understood that the configuration shown in fig. 4 is merely illustrative and that the big data server 120 may also include more or fewer components than shown in fig. 4, or have a different configuration than shown in fig. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.

In some optional aspects, there is also provided a big data user representation processing method, applied to a big data server in communication with a big data user device, the method comprising:

updating user information based on target big data interaction information sent by big data user equipment; the target big data interaction information is determined by the big data user equipment through the following modes: acquiring big data user element information of a target big data item in first big data interaction information from the acquired first big data interaction information of the first big data interaction state; determining first dynamic big data interaction information corresponding to the big data user element information from the first big data interaction information; and at least performing information correction processing on the first dynamic big data interaction information to obtain target big data interaction information in a second big data interaction state, wherein the second big data interaction state is suitable for user information updating of the target big data interaction information.

In some optional aspects, there is also provided a big data user representation processing method, for use with a big data user device in communication with a big data server, the method comprising:

acquiring big data user element information of a target big data item in first big data interaction information from the acquired first big data interaction information of the first big data interaction state;

determining first dynamic big data interaction information corresponding to the big data user element information from the first big data interaction information;

at least performing information correction processing on the first dynamic big data interaction information to obtain target big data interaction information in a second big data interaction state, wherein the second big data interaction state is suitable for user information updating of the target big data interaction information;

and sending the target big data interaction information to the big data server so that the big data server updates the user information based on the target big data interaction information.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A big data user representation processing method, for use in a big data user representation processing system, the system comprising a big data user device and a big data server in communication with each other, the method comprising:

the big data server extracts target user portrait distribution from target big data interaction information, the target user portrait distribution is used for describing user portrait change conditions corresponding to target big data items in the target big data interaction information, and the target big data interaction information is sent to the big data server by the big data user equipment;

and when the big data server identifies that the target big data item in the target big data interaction information is a specified target big data item according to the comparison result, updating the reference user portrait distribution based on the target user portrait distribution.

2. The method of claim 1, wherein updating the reference user representation distribution based on the target user representation distribution comprises:

and updating the association relationship among the user portraits in the relationship network updating area according to the second global portraits liveness corresponding to the target portraits relationship network.

3. The method of claim 2, wherein prior to said step of determining a range of image liveness corresponding to said target image relationship network based on a comparison of said target image liveness to a range limit of a preset image liveness range, said method further comprises:

and judging whether the target portrait liveness meets the preset portrait liveness range determination condition.

4. The method of claim 3, wherein determining the range of image liveness corresponding to the target image relationship network based on the comparison of the target image liveness to the range limit of the preset image liveness range comprises:

and when the target portrait liveness accords with a preset portrait liveness range determination condition, determining a portrait liveness range corresponding to the target portrait relationship network based on a comparison result of the target portrait liveness and a range limit value of the preset portrait liveness range.

5. The method of claim 1, further comprising:

the big data user equipment acquires big data user element information of a target big data item in the first big data interaction information from the acquired first big data interaction information of the first big data interaction state; correspondingly: the big data user element information is characteristic information aiming at a service user layer;

the big data user equipment determines first dynamic big data interaction information corresponding to the big data user element information from the first big data interaction information; correspondingly: the first dynamic big data interaction information is big data interaction information which changes along with the change of time;

the big data user equipment at least performs information correction processing on the first dynamic big data interaction information to obtain target big data interaction information in a second big data interaction state, and the second big data interaction state is suitable for user information updating of the target big data interaction information;

and the big data user equipment sends the target big data interaction information to the big data server.

6. The method of claim 5, wherein obtaining big data user element information of a target big data transaction in the first big data interaction information from the collected first big data interaction information of the first big data interaction state comprises:

7. The method according to claim 5, wherein the at least information correction processing of the first dynamic big data interactive information to obtain the target big data interactive information of the second big data interactive state comprises:

8. The method according to claim 5, wherein the at least information correction processing of the first dynamic big data interactive information to obtain the target big data interactive information of the second big data interactive state comprises:

9. The method according to claim 5, wherein the at least information correction processing of the first dynamic big data interactive information to obtain the target big data interactive information of the second big data interactive state comprises:

10. A big data server is characterized by comprising a processing engine, a network module and a memory; the processing engine and the memory are communicated through the network module, and the processing engine reads a computer program from the memory and runs the computer program to execute the step of updating the user information based on the target big data interaction information according to any one of claims 1 to 9.