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CN119646054A - Computer intelligent service management system, method, equipment and storage medium - Google Patents

Computer intelligent service management system, method, equipment and storage medium Download PDF

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Publication number
CN119646054A
CN119646054A CN202411723804.4A CN202411723804A CN119646054A CN 119646054 A CN119646054 A CN 119646054A CN 202411723804 A CN202411723804 A CN 202411723804A CN 119646054 A CN119646054 A CN 119646054A
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service
user
data
information
service management
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邓杰仁
张泽亚
王磊
王军波
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Sichuan Jiuzhou ATC Technology Co Ltd
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Sichuan Jiuzhou ATC Technology Co Ltd
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Priority to CN202411723804.4A priority Critical patent/CN119646054A/en
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Abstract

The invention discloses a computer intelligent service management system, a method, equipment and a storage medium, which comprises the steps of acquiring relevant information of a user using computer service, storing the relevant information into a system database, analyzing input data and using behavior data of the user, generating decision support information based on analysis results, completing user portraits by utilizing big data mining, defining user service demand characteristics, providing personalized service recommendation, monitoring personalized service recommendation effects, collecting user evaluation, summarizing and sorting, optimizing a data analysis mode and a service strategy according to evaluation service quality and improvement effects, collecting accurate service effects of the user, repeating operation and optimizing management methods, and realizing accurate service management. According to the invention, the use data of different services are subjected to centralized processing, the use behaviors, the input data and the preference of the user are analyzed, and the final analysis result of the heterogeneous information network relation diagram is constructed through data analysis mining, so that accurate personalized recommendation is provided for the user.

Description

Computer intelligent service management system, method, equipment and storage medium
Technical Field
The invention relates to the technical field of computer service management, in particular to a computer intelligent service management system, method, equipment and storage medium based on network big data.
Background
The computer system is used in the computer hardware and software for data base management and its network system features high accuracy, fast calculation and judgment, high universality, easy use and network connection. With the rapid development of networks, computer networks are increasingly used in various fields of daily life. At present, china has entered into a big data era, and the effective collection and storage of mass data are realized through technical means such as a distributed type, a big data center and a cloud platform, so that in order to meet the analysis requirement of the data, some data mining technologies and data analysis technologies are popularized and applied.
The computer service is frequently used in the big data age, so that the computer platform is enabled to be capable of grasping personal information, use habit and other information of the user, the requirements of the computer user are continuously refined, and finally, the computer service is even on the basis of personal behaviors. When a user retrieves a certain category of computer service, all similar service information is obtained and displayed, and sometimes sentence expression input by the user is not clear enough, and the obtained result also contains a plurality of other irrelevant service categories, so that the user does not know how to select.
In view of this, the present application has been made.
Disclosure of Invention
The invention aims to provide a computer intelligent service management system, a method, equipment and a storage medium based on network big data, which are used for carrying out centralized processing on the use data of different services to form a big data center, analyzing the use behavior, input data and preference of a user on the basis, constructing a heterogeneous information network relation graph through analyzing and mining the known data, synthesizing the two aspects of contents to obtain a final analysis result, automatically optimizing a service strategy based on the analysis result, and providing accurate personalized recommendation for the user.
The invention is realized by the following technical scheme:
In a first aspect, the present invention provides a method for intelligent service management of a computer based on network big data, comprising the following steps:
S1, collecting structured data of computer service, and obtaining relevant information of a user using the computer service, wherein the relevant information of the user comprises user behavior and operation, software use information, network information, system logs, error reports and the like when the user uses the computer service;
S2, analyzing input data and using behavior data of a user by applying a data mining and machine learning algorithm, mining valuable modes and trends, generating decision support information based on analysis results, and automatically optimizing a service strategy to provide personalized recommendation, wherein the input data comprise content input by the user, text input, mouse input, image and multimedia input, command input, option and setting input, identity verification input, behavior input and the like;
S3, converting decision support information into service actions, completing user portraits by utilizing a big data mining technology when the platform provides services, defining user service demand characteristics, carrying out targeted service development, and providing user personalized service recommendation;
S4, monitoring personalized service recommendation effects, collecting user feedback, carrying out induction arrangement on user evaluation data, carrying out overall control according to the existing information, evaluating service quality and improving effects, and continuously optimizing data analysis modes and service strategies;
S5, through the control of the whole flow of the service data, the effect of the accurate service of the platform user is observed, and the optimal management of the accurate service of the user is finally realized according to repeated operation and optimization of the management process.
In a specific embodiment, in step S1, a real-time data stream is processed by using APACHE KAFKA, service data information of different computer services is collected through a plurality of interfaces, the collected information is subjected to standardization processing, and the data is written into a Kafka message queue and the database is updated in real time.
In a specific embodiment, in step S1, data storage is completed in a distributed storage manner, backup of changed data is completed in an incremental backup manner, and data are classified by constructing a classification tree by using a random forest with service items as root nodes.
In a specific embodiment, in step S1, HDFS is selected to perform cluster receiving on the distributed file, and a Name Node is used as a master data Node, a DataNode is used as a slave Node, so as to expand user data throughput.
In a specific embodiment, in step S2, the edge computing concept is utilized to perform preliminary data processing on the service category, and then the information generated in the current service scenario, including user operation information, service usage information, input data and user evaluation, is sent to the system for analysis, so as to generate a data analysis report.
In a specific embodiment, in step S2, input data of a user is analyzed by natural language processing, a correlation degree between the input data and service categories is calculated by supporting a probability distance function, and the top K service types are selected to form a candidate recommendation set according to the correlation degree.
In a specific embodiment, the calculation scheme of the correlation between the input data and the service category is as follows:
Assuming data Y, there are two service categories E 1 and E 2 whose basic probability distribution functions are m 1 and m 2, respectively, and the corresponding support probability functions are S m1 and S m2, respectively, the formulas of the support probability functions for a single service category are as follows:
Where A represents any one of the service features in service category E 1 and the support probability distance for E 1、E2 can be expressed as:
In the formula, |s m1(Y)-Sm2 (Y) | represents the absolute value of the difference value of the support probability function of the data Y between two service categories, the value range of the support probability distance function is [0,1], the larger the value of the support probability distance is, the smaller the similarity degree of the two service categories is, so that the known service categories are divided according to the data, the known service categories are arranged according to the calculated support probability, and the first K service types are screened out to form a candidate recommendation set.
In a specific embodiment, in step S3, a heterogeneous information network relationship diagram is constructed by analyzing and mining known data, and based on the current situation of the user and the service used, the machine learning is utilized to analyze the user using behavior data in combination with the heterogeneous information network relationship diagram, and meanwhile, the candidate recommendation set is taken into comprehensive consideration in the analysis result, so as to provide personalized service recommendation for the user.
In a specific embodiment, in step S3, the construction process of the heterogeneous information network relationship graph is as follows:
generating M nodes corresponding to users, generating 2n nodes for expected and unexpected situations of n services, generating n (n-1) possible preference nodes, wherein each preference data comprises service expected by the users and service not expected by the users, and is respectively represented by p.d and p.u, d represents service content expected by the users, u represents service content not expected by the users, adding connection between the users and the corresponding nodes in a preference layer for each user by scanning a preference data set, and finally scanning the preference nodes, wherein the preference node p represents p.d > p, u, searching corresponding association relation nodes, and inserting a connecting line with each node.
In a specific embodiment, the heterogeneous information network relation graph comprises a user set U, a pair preference set P and an association relation set R;
the user set U comprises nodes of user types and respectively represents each user;
A pair preference set P containing all possible preference information, each preference node being marked in the form of i > j, indicating that the user prefers service i;
The association relation set R is respectively represented by I d and I u and shows the expected and unexpected sets of the user on the service;
E UP = { U, p|f (U, P) =1, U E U, P E P } is the edge set between nodes U, P, E PR = { P, r|s (P, R) =1, P E P, R E R } is the edge set between nodes P, R.
In a specific embodiment, when the relationship data of the current user contains the corresponding preference information in the preference set, the user and the preference node generate a connection relationship, and E UP represents the connection between the user and the preference node;
When the current preference information is supported by the corresponding association relation node, the preference node and the association relation node generate a connection relation, and E PR represents the connection of the preference node and the association relation node.
In a specific embodiment, in step S3, association analysis is performed on a service scenario where a user is currently located and a history service scenario, and the specific steps are as follows:
Initializing thresholds alpha and beta;
information retrieval is realized by utilizing database query sentences, a history record of related operation of a user is obtained, and a data set is constructed;
Acquiring a history service record F (H) = { F 1H,F2H,…,FNH }, corresponding to the user;
analyzing service information F= { F 1,F2,…FN } used by the current user;
Calculating the similarity sim_bh=sim of the current service and the historical service (F N,FNH);
And comparing the sim_bh with the threshold value alpha, and if sim_bh is larger than alpha, performing service pushing through a recommendation mode, namely information pushing operation approved by a user in the computer or sending the recommended service to the user for selection through the default information pushing operation of the computer.
In a specific embodiment, in step S3, a user figure is constructed for a new user using the big data mining technique, and the content information in the existing user figure is updated, and the user figure is sent to step S2 to perform analysis mining again.
In a specific embodiment, in step S4, a machine learning algorithm is used to evaluate the quality of service and the improvement effect, and the data analysis mode and the service policy are optimized according to the evaluation result.
In a second aspect, the present invention provides a computer intelligent service management system for executing the computer intelligent service management method based on network big data, which comprises a data preparation module, a data analysis module, a service push module and a system configuration module;
the data preparation module is used for acquiring related information of a user using computer service and storing the related information into the system database;
the data analysis module is used for analyzing input data and using behavior data of a user;
the service pushing module is used for providing user personalized service recommendation based on data analysis for the user;
The system configuration module is used for managing user identity information and system background setting.
In a third aspect, the present invention provides a computer intelligent service management apparatus based on network big data, characterized in that the computer intelligent service management apparatus comprises a memory, a processor, and a program stored on the memory for implementing the computer intelligent service management method,
The memory is used for storing a program for realizing the intelligent service management method of the computer;
the processor is used for executing a program for realizing the computer intelligent service management method so as to realize the steps of the computer intelligent service management method.
In a fourth aspect, the present invention provides a readable storage medium having stored thereon a program for implementing a computer intelligent service management method, the program for implementing a computer intelligent service management method being executed by a processor to implement the steps of the computer intelligent service management method.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. The embodiment of the invention provides a computer intelligent service management system, a method, equipment and a storage medium based on network big data, which are used for carrying out centralized processing on the use data of different services to form a big data center, analyzing the use behaviors, input data and preference of a user on the basis, constructing a heterogeneous information network relation graph through analyzing and mining the known data, obtaining a final analysis result by integrating two aspects of contents, automatically optimizing a service strategy based on the analysis result, and providing accurate personalized recommendation for the user;
2. According to the computer intelligent service management system, the method, the equipment and the storage medium based on the network big data, which are provided by the embodiment of the invention, in the service process, the user portrait is built for each user so as to obtain a more accurate analysis result, and the service content can be accurately recommended to the client, so that the computer intelligent service management system has very important significance for the management of the computer service;
3. The embodiment of the invention provides a computer intelligent service management system, a method, equipment and a storage medium based on network big data, which are used for collecting the change of the demands and the use behaviors of users and a platform browsing history track, and obtaining the behavior rules and the favorites of the users through data analysis so as to provide accurate services for the users;
4. the intelligent service management system, the intelligent service management method, the intelligent service management equipment and the intelligent service management storage medium based on the network big data can manage accurate service of a computer platform user, can improve service experience for the platform user, and are important ways for improving service quality of the computer platform and improving competitiveness and main stream product control.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for managing intelligent service of a computer based on network big data according to an embodiment of the present invention;
fig. 2 is a diagram of a computer intelligent service management system based on network big data constructed according to an embodiment of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that these specific details need not be employed to practice the present invention. In other instances, well-known structures have not been described in detail in order to avoid obscuring the present invention.
Reference throughout this specification to "one embodiment," "an embodiment," "one example," or "an example" means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment," "in an example," or "in an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and that the illustrations are not necessarily drawn to scale. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In the description of the present invention, the terms "front", "rear", "left", "right", "upper", "lower", "vertical", "horizontal", "high", "low", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, merely to facilitate description of the present invention and simplify description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the scope of the present invention.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a method for managing intelligent services of a computer based on network big data, including the following steps:
S1, collecting structured data of computer service, and obtaining relevant information of a user using the computer service, wherein the relevant information of the user comprises user behavior and operation, software use information, network information, system logs, error reports and the like when the user uses the computer service;
S2, analyzing input data and using behavior data of a user by applying a data mining and machine learning algorithm, mining valuable modes and trends, generating decision support information based on analysis results, and automatically optimizing a service strategy to provide personalized recommendation, wherein the input data comprise content input by the user, text input, mouse input, image and multimedia input, command input, option and setting input, identity verification input, behavior input and the like;
S3, converting decision support information into service actions, completing user portraits by utilizing a big data mining technology when the platform provides services, defining user service demand characteristics, carrying out targeted service development, and providing user personalized service recommendation;
S4, monitoring personalized service recommendation effects, collecting user feedback, carrying out induction arrangement on user evaluation data, carrying out overall control according to the existing information, evaluating service quality and improving effects, and continuously optimizing data analysis modes and service strategies;
S5, through the control of the whole flow of the service data, the effect of the accurate service of the platform user is observed, and the optimal management of the accurate service of the user is finally realized according to repeated operation and optimization of the management process.
In a specific embodiment, in step S1, a real-time data stream is processed by using APACHE KAFKA, service data information of different computer services is collected through a plurality of interfaces, the collected information is subjected to standardization processing, and the data is written into a Kafka message queue and the database is updated in real time.
In a specific embodiment, in step S1, data storage is completed in a distributed storage manner, backup of changed data is completed in an incremental backup manner, and data are classified by constructing a classification tree by using a random forest with service items as root nodes.
In a specific embodiment, in step S1, HDFS is selected to perform cluster receiving on the distributed file, and a Name Node is used as a master data Node, a DataNode is used as a slave Node, so as to expand user data throughput.
In a specific embodiment, in step S2, the edge computing concept is utilized to perform preliminary data processing on the service category, and then the information generated in the current service scenario, including user operation information, service usage information, input data and user evaluation, is sent to the system for analysis, so as to generate a data analysis report.
In a specific embodiment, in step S2, input data of a user is analyzed by natural language processing, a correlation degree between the input data and service categories is calculated by supporting a probability distance function, and the top K service types are selected to form a candidate recommendation set according to the correlation degree.
In a specific embodiment, the calculation scheme of the correlation between the input data and the service category is as follows:
Assuming data Y, there are two service categories E 1 and E 2 whose basic probability distribution functions are m 1 and m 2, respectively, and the corresponding support probability functions are S m1 and S m2, respectively, the formulas of the support probability functions for a single service category are as follows:
Where A represents any one of the service features in service category E 1 and the support probability distance for E 1、E2 can be expressed as:
In the formula, |s m1(Y)-Sm2 (Y) | represents the absolute value of the difference value of the support probability function of the data Y between two service categories, the value range of the support probability distance function is [0,1], the larger the value of the support probability distance is, the smaller the similarity degree of the two service categories is, so that the known service categories are divided according to the data, the known service categories are arranged according to the calculated support probability, and the first K service types are screened out to form a candidate recommendation set.
In a specific embodiment, in step S3, a heterogeneous information network relationship diagram is constructed by analyzing and mining known data, and based on the current situation of the user and the service used, the machine learning is utilized to analyze the user using behavior data in combination with the heterogeneous information network relationship diagram, and meanwhile, the candidate recommendation set is taken into comprehensive consideration in the analysis result, so as to provide personalized service recommendation for the user.
In a specific embodiment, in step S3, the construction process of the heterogeneous information network relationship graph is as follows:
generating M nodes corresponding to users, generating 2n nodes for expected and unexpected situations of n services, generating n (n-1) possible preference nodes, wherein each preference data comprises service expected by the users and service not expected by the users, and is respectively represented by p.d and p.u, d represents service content expected by the users, u represents service content not expected by the users, adding connection between the users and the corresponding nodes in a preference layer for each user by scanning a preference data set, and finally scanning the preference nodes, wherein the preference node p represents p.d > p, u, searching corresponding association relation nodes, and inserting a connecting line with each node.
In a specific embodiment, the heterogeneous information network relation graph comprises a user set U, a pair preference set P and an association relation set R;
the user set U comprises nodes of user types and respectively represents each user;
A pair preference set P containing all possible preference information, each preference node being marked in the form of i > j, indicating that the user prefers service i;
The association relation set R is respectively represented by I d and I u and shows the expected and unexpected sets of the user on the service;
E UP = { U, p|f (U, P) =1, U E U, P E P } is the edge set between nodes U, P, E PR = { pr|s (P, R) =1, P E P, R E R } is the edge set between nodes P, R.
In a specific embodiment, when the relationship data of the current user contains the corresponding preference information in the preference set, the user and the preference node generate a connection relationship, and E UP represents the connection between the user and the preference node;
When the current preference information is supported by the corresponding association relation node, the preference node and the association relation node generate a connection relation, and E PR represents the connection of the preference node and the association relation node.
In a specific embodiment, in step S3, association analysis is performed on a service scenario where a user is currently located and a history service scenario, and the specific steps are as follows:
Initializing thresholds alpha and beta;
information retrieval is realized by utilizing database query sentences, a history record of related operation of a user is obtained, and a data set is constructed;
Acquiring a history service record F (H) = { F 1H,F2H,…,FNH }, corresponding to the user;
analyzing service information F= { F 1,F2,…FN } used by the current user;
Calculating the similarity sim_bh=sim of the current service and the historical service (F N,FNH);
And comparing the sim_bh with the threshold value alpha, and if sim_bh is larger than alpha, performing service pushing through a recommendation mode, namely information pushing operation approved by a user in the computer or sending the recommended service to the user for selection through the default information pushing operation of the computer.
In a specific embodiment, in step S3, a user figure is constructed for a new user using the big data mining technique, and the content information in the existing user figure is updated, and the user figure is sent to step S2 to perform analysis mining again.
In a specific embodiment, in step S4, a machine learning algorithm is used to evaluate the quality of service and the improvement effect, and the data analysis mode and the service policy are optimized according to the evaluation result.
Example 2
As shown in fig. 2, an embodiment of the present invention provides a computer intelligent service management system for executing the computer intelligent service management method based on network big data according to embodiment 1, including a data preparation module, a data analysis module, a service push module, and a system configuration module;
the data preparation module is used for acquiring related information of a user using computer service and storing the related information into the system database;
the data analysis module is used for analyzing input data and using behavior data of a user;
the service pushing module is used for providing user personalized service recommendation based on data analysis for the user;
The system configuration module is used for managing user identity information and system background setting.
Example 3
The embodiment of the invention provides a computer intelligent service management device based on network big data, which is characterized by comprising a memory, a processor and a program stored on the memory and used for realizing the computer intelligent service management method,
The memory is used for storing a program for realizing the intelligent service management method of the computer;
the processor is used for executing a program for realizing the computer intelligent service management method so as to realize the steps of the computer intelligent service management method.
Example 4
The embodiment of the invention provides a readable storage medium, wherein a program for realizing a computer intelligent service management method is stored on the readable storage medium, and the program for realizing the computer intelligent service management method is executed by a processor to realize the steps of the computer intelligent service management method.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (17)

1.一种基于网络大数据的计算机智能服务管理方法，其特征在于，包括如下步骤：1. A computer intelligent service management method based on network big data, characterized in that it includes the following steps: S1，收集计算机服务的结构化数据，获取用户使用计算机服务的相关信息，并将其存储至系统数据库；S1, collects structured data of computer services, obtains relevant information about users’ use of computer services, and stores it in the system database; S2，对用户的输入数据以及使用行为数据进行分析，并基于分析结果生成决策支持信息；S2, analyzing the user's input data and usage behavior data, and generating decision support information based on the analysis results; S3，将决策支持信息转化为服务行动，利用大数据挖掘技术完成用户画像，明确用户服务需求特征，提供用户个性化服务推荐；S3, converts decision support information into service actions, uses big data mining technology to complete user portraits, clarifies user service demand characteristics, and provides users with personalized service recommendations; S4，监控个性化服务推荐效果，收集用户评价数据并进行归纳整理，依据评估服务质量和改进效果，优化数据分析方式和服务策略；S4, monitor the effectiveness of personalized service recommendations, collect and summarize user evaluation data, and optimize data analysis methods and service strategies based on the evaluation of service quality and improvement effects; S5，收集用户精准服务效果，重复操作与优化管理方法，实现对用户精准服务的管理。S5, collects the precise service effects of users, repeats operations and optimizes management methods to achieve precise service management for users. 2.根据权利要求1所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S1中，使用Apache Kafka处理实时数据流，通过多个接口对不同的计算机服务的业务数据信息进行采集，对采集到的信息进行标准化处理，将数据写入Kafka消息队列中并即时更新数据库。2. According to a computer intelligent service management method based on network big data as described in claim 1, it is characterized in that in step S1, Apache Kafka is used to process real-time data streams, business data information of different computer services is collected through multiple interfaces, the collected information is standardized, the data is written into the Kafka message queue and the database is updated in real time. 3.根据权利要求1所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S1中，采用分布式存储方式完成数据存储，采用增量备份方式完成变更数据的备份，使用随机森林将服务项目作为根节点构建分类树对数据进行分类。3. According to the computer intelligent service management method based on network big data described in claim 1, it is characterized in that in step S1, a distributed storage method is used to complete data storage, an incremental backup method is used to complete the backup of changed data, and a random forest is used to construct a classification tree with the service project as the root node to classify the data. 4.根据权利要求3所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S1中，选择HDFS对分布式文件进行集群接收，以Name Node为主数据节点，DataNode为从属节点，扩充用户数据吞吐量。4. According to a computer intelligent service management method based on network big data as described in claim 3, it is characterized in that in step S1, HDFS is selected to perform cluster reception of distributed files, with Name Node as the main data node and DataNode as the slave node to expand user data throughput. 5.根据权利要求3所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S2中，利用边缘计算思想在服务类目上进行初步的数据处理，然后将当前服务场景下产生的信息：用户操作信息、服务使用信息、输入数据以及用户评价，发送到系统进行分析，生成数据分析报表。5. According to a computer intelligent service management method based on network big data as described in claim 3, it is characterized in that in step S2, the edge computing concept is used to perform preliminary data processing on the service category, and then the information generated in the current service scenario: user operation information, service usage information, input data and user evaluation, is sent to the system for analysis to generate a data analysis report. 6.根据权利要求1所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S2中，采用自然语言处理分析用户的输入数据，通过支持概率距离函数计算输入数据与服务类目之间的相关度，根据相关度大小进行排序，筛选出前K个服务类型构成候选推荐集合。6. According to the computer intelligent service management method based on network big data described in claim 1, it is characterized in that in step S2, natural language processing is used to analyze the user's input data, the correlation between the input data and the service category is calculated by supporting the probability distance function, and the data is sorted according to the correlation, and the top K service types are screened out to form a candidate recommendation set. 7.根据权利要求6所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，输入数据与服务类目之间的相关度的计算方案如下：7. According to claim 6, a computer intelligent service management method based on network big data is characterized in that the calculation scheme of the correlation between the input data and the service category is as follows: 假设数据Y，存在两个服务类目E1和E2，它们的基本概率分配函数分别为m1和m2，与之对应的支持概率函数分别为Sm1和Sm2，单个服务类目的支持概率函数的公式如下：Assume data Y, there are two service categories E1 and E2 , their basic probability distribution functions are m1 and m2 , and their corresponding support probability functions are Sm1 and Sm2 . The formula of the support probability function of a single service category is as follows: 式中，A表示服务类目E1中的任意一个服务特征；E1、E2的支持概率距离可以表示成：Where A represents any service feature in service category E1 ; the support probability distance between E1 and E2 can be expressed as: 式中，|Sm1(Y)-Sm2(Y)|表示数据Y的支持概率函数在两个服务类目之间的差值的绝对值，支持概率距离函数的取值范围为[0，1]，支持概率距离的值越大，两个服务类目相似程度越小，从而将已知的服务类目根据数据进行划分，并且根据计算所得支持概率对已知的服务类目进行排列，筛选出前K个服务类型构成候选推荐集合。In the formula, | Sm1 (Y) -Sm2 (Y)| represents the absolute value of the difference between the support probability function of data Y between the two service categories. The value range of the support probability distance function is [0, 1]. The larger the value of the support probability distance, the smaller the similarity between the two service categories. In this way, the known service categories are divided according to the data, and the known service categories are arranged according to the calculated support probability, and the top K service types are screened out to form the candidate recommendation set. 8.根据权利要求7所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S3中，通过对已知数据的分析挖掘构建异构信息网络关系图，基于用户当前所处情景以及所使用的服务，利用机器学习结合异构信息网络关系图对用户使用行为数据进行分析，同时将候选推荐集合纳入分析结果进行综合考虑，为用户提供个性化的服务推荐。8. According to claim 7, a computer intelligent service management method based on network big data is characterized in that in step S3, a heterogeneous information network relationship diagram is constructed by analyzing and mining known data, and based on the user's current situation and the services used, machine learning is combined with the heterogeneous information network relationship diagram to analyze the user's usage behavior data, and at the same time, the candidate recommendation set is included in the analysis results for comprehensive consideration to provide users with personalized service recommendations. 9.根据权利要求8所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S3中，异构信息网络关系图的构架过程如下：9. According to the computer intelligent service management method based on network big data of claim 8, it is characterized in that in step S3, the construction process of the heterogeneous information network relationship diagram is as follows: 生成与用户对应的M节点，并为n个服务的期望和不期望情况生成2n个节点，然后生成n(n-1)个可能出现的偏好节点，每个偏好数据包含用户期待的服务和用户不期待的服务，分别由p.d和p.u表示，其中，d代表用户期待的服务内容，u代表用户不期待的服务内容，通过扫描偏好数据集，为每个用户添加用户与偏好层中相应节点之间的连接，最后扫描偏好节点，其中偏好节点p表示p.d>p，u，查找相应的关联关系节点，并与每个节点之间插入一条连线。Generate M nodes corresponding to the user, and generate 2n nodes for the expected and unexpected situations of n services, and then generate n(n-1) possible preference nodes. Each preference data contains the services expected by the user and the services not expected by the user, represented by p.d and p.u respectively, where d represents the service content expected by the user and u represents the service content not expected by the user. By scanning the preference data set, add a connection between the user and the corresponding node in the preference layer for each user. Finally, scan the preference nodes, where the preference node p represents p.d>p,u, find the corresponding association relationship node, and insert a connection between each node. 10.根据权利要求9所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，所述异构信息网络关系图包括用户集U、成对偏好集P和关联关系集R；10. A computer intelligent service management method based on network big data according to claim 9, characterized in that the heterogeneous information network relationship diagram includes a user set U, a pairwise preference set P and an association relationship set R; 用户集U：包含用户类型的节点，分别表示每个用户；User set U: contains user type nodes, representing each user; 成对偏好集P：包含所有可能的偏好信息，每个偏好节点以i>j的形式标记，表明用户更加偏好服务i；Pairwise preference set P: contains all possible preference information, and each preference node is marked as i>j, indicating that the user prefers service i more; 关联关系集R：分别由Id和Iu表示，展示用户对服务的期望和不期望的集合；The association relationship set R is represented by I d and I u , which shows the set of users' expectations and dislikes of services; EUP＝{u，p|f(u，p)＝1，u∈U，p∈P}是节点U、P之间的边集，EPR＝{p，r|s(p，r)＝1，p∈P，r∈R}是节点P、R之间的边集。E UP ={u, p|f(u, p)=1, u∈U, p∈P} is the edge set between nodes U and P, and E PR ={p, r|s(p, r)=1, p∈P, r∈R} is the edge set between nodes P and R. 11.根据权利要求10所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，当前用户的关系数据包含偏好集中相应的偏好信息时，用户与偏好节点产生连线关系，RUP表示用户与偏好节点的连线；11. A computer intelligent service management method based on network big data according to claim 10, characterized in that when the relationship data of the current user contains the corresponding preference information in the preference set, the user and the preference node have a connection relationship, and R UP represents the connection between the user and the preference node; 当前的偏好信息由对应的关联关系节点予以支持时，偏好节点与关联关系节点产生连线关系，EPR表示偏好节点与关联关系节点的连线。When the current preference information is supported by the corresponding association node, a connection relationship is generated between the preference node and the association node, and E PR represents the connection between the preference node and the association node. 12.根据权利要求8所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S3中，对用户当前所处服务场景与历史服务场景进行关联分析，具体步骤如下：12. According to claim 8, a computer intelligent service management method based on network big data is characterized in that in step S3, the user's current service scene and historical service scene are associated with each other, and the specific steps are as follows: 初始化阈值α和β；Initialize thresholds α and β; 利用数据库查询语句实现信息检索，获取用户相关操作的历史记录，并构建数据集；Use database query statements to retrieve information, obtain historical records of user-related operations, and build data sets; 获取对应用户的历史服务记录F(H)＝{F1H，F2H，…，FNH}；Obtain the historical service record of the corresponding user F(H)={ F1H , F2H , ..., FNH }; 分析当前用户使用的服务信息F＝{F1，F2，…FN}；Analyze the service information F = {F 1 , F 2 , ... F N } used by the current user; 计算当前服务与历史服务相似度sim_bh＝sim(FN，FNH)；Calculate the similarity between the current service and the historical service sim_bh=sim(F N , F NH ); 比较Sim_bh与阈值α的大小，如果sim_bh>α，通过推荐方式进行服务推送。Compare Sim_bh with the threshold α. If sim_bh>α, push the service through the recommendation method. 13.根据权利要求1所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S3中，利用大数据挖掘技术为新用户构建用户画像，并更新已有用户画像中的内容信息，将用户画像发送给步骤S2中重新进行分析挖掘。13. According to the computer intelligent service management method based on network big data as described in claim 1, it is characterized in that in step S3, big data mining technology is used to build a user portrait for the new user, and the content information in the existing user portrait is updated, and the user portrait is sent to step S2 for re-analysis and mining. 14.根据权利要求1所述的一种基于网络大数据的计算机智能服务管理方法，其特征在于，步骤S4中，使用机器学习算法评估服务质量和改进效果，根据评估结果优化数据分析方式和服务策略。14. According to a computer intelligent service management method based on network big data as described in claim 1, it is characterized in that in step S4, a machine learning algorithm is used to evaluate service quality and improvement effect, and the data analysis method and service strategy are optimized according to the evaluation results. 15.用于执行权利要求1～14中任一所述的基于网络大数据的计算机智能服务管理方法的计算机智能服务管理系统，其特征在于，包括数据准备模块、数据分析模块、服务推送模块和系统配置模块；15. A computer intelligent service management system for executing the computer intelligent service management method based on network big data as described in any one of claims 1 to 14, characterized in that it comprises a data preparation module, a data analysis module, a service push module and a system configuration module; 所述数据准备模块用于获取用户使用计算机服务的相关信息，并将其存储至系统数据库；The data preparation module is used to obtain relevant information about the user's use of computer services and store it in the system database; 所述数据分析模块用于对用户的输入数据以及使用行为数据进行分析；The data analysis module is used to analyze the user's input data and usage behavior data; 所述服务推送模块用于向用户提供基于数据分析的用户个性化服务推荐；The service push module is used to provide users with personalized service recommendations based on data analysis; 所述系统配置模块用于管理用户身份信息以及系统后台设置。The system configuration module is used to manage user identity information and system background settings. 16.一种基于网络大数据的计算机智能服务管理设备，其特征在于，所述计算机智能服务管理设备包括：存储器、处理器以及存储在存储器上的用于实现所述计算机智能服务管理方法的程序，16. A computer intelligent service management device based on network big data, characterized in that the computer intelligent service management device comprises: a memory, a processor, and a program stored in the memory for implementing the computer intelligent service management method, 所述存储器用于存储实现计算机智能服务管理方法的程序；The memory is used to store a program for implementing a computer intelligent service management method; 所述处理器用于执行实现所述计算机智能服务管理方法的程序，以实现如权利要求1～14任一所述计算机智能服务管理方法的步骤。The processor is used to execute a program for implementing the computer intelligent service management method to implement the steps of the computer intelligent service management method according to any one of claims 1 to 14. 17.一种可读存储介质，其特征在于，所述可读存储介质上存储有实现计算机智能服务管理方法的程序，所述实现计算机智能服务管理方法的程序被处理器执行以实现如权利要求1～14任一所述计算机智能服务管理方法的步骤。17. A readable storage medium, characterized in that a program for implementing a computer intelligent service management method is stored on the readable storage medium, and the program for implementing the computer intelligent service management method is executed by a processor to implement the steps of the computer intelligent service management method as claimed in any one of claims 1 to 14.
CN202411723804.4A 2024-11-28 2024-11-28 Computer intelligent service management system, method, equipment and storage medium Pending CN119646054A (en)

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