Intelligent service system for front-end software

Abstract

The invention particularly relates to an intelligent service system for front-end software. The intelligent service system for the front-end software comprises a user interaction module, a data acquisition and preprocessing module, an intelligent analysis module, a service recommendation module, an adaptive adjustment module, a safety protection module, a log management module and a system monitoring module. According to the intelligent service system for the front-end software, through cooperative work of a plurality of functional modules, user data can be efficiently collected and processed, user behaviors and requirements can be deeply analyzed, and personalized service recommendation and self-adaptive system adjustment are achieved; meanwhile, the system has perfect safety protection and log management functions, and the safety and reliability of the system can be guaranteed; by introducing various algorithms and technologies, the intelligence and adaptability of the system are improved, higher-quality front-end software services can be provided for users, and the user experience is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of front-end software development, in particular to a smart service system for front-end software. BACKGROUND

[0002] With the rapid development of Internet technology, the requirements of front-end software in user interface display and interactive experience are becoming higher and higher. The traditional front-end software service system has certain limitations in data processing, user demand response and personalized service.

[0003] For example, when facing a large amount of user data and complex user operations, the response speed of the system is slow, and it cannot provide the required services for users in time and accurately. At the same time, the traditional system lacks in-depth analysis of user behavior and preferences, and it is difficult to realize personalized service recommendation, resulting in poor user experience. Therefore, there is an urgent need for a front-end software smart service system that can efficiently process data, intelligently analyze user needs and provide personalized services.

[0004] Based on the above situation, the present application provides a smart service system for front-end software. SUMMARY

[0005] The present application provides a simple and efficient smart service system for front-end software to overcome the defects of the prior art.

[0006] The present application is realized by the following technical solutions:

[0007] A smart service system for front-end software, comprising a user interaction module, a data collection and preprocessing module, an intelligent analysis module, a service recommendation module, an adaptive adjustment module, a security protection module, a log management module and a system monitoring module;

[0008] The user interaction module is an interface for user interaction with the front-end software, used to receive user input operations and convert user operation information into a system-recognized data format;

[0009] The data collection and preprocessing module includes a data collection submodule, a data cleaning submodule and a data conversion submodule, responsible for implementing data collection using distributed data collection technology, and converting the collected data into a unified format after cleaning;

[0010] The intelligent analysis module includes a user behavior analysis submodule, a user demand prediction submodule and a system performance evaluation submodule, responsible for evaluating the running performance of the front-end software, analyzing user behavior based on user behavior data, and predicting user demand based on user behavior;

[0011] The service recommendation module is responsible for providing personalized service recommendations for users based on user behavior and demand information obtained by the intelligent analysis module.

[0012] The adaptive adjustment module is responsible for adaptively adjusting the interface layout, function modules, and service strategies of the front-end software according to the analysis results of the intelligent analysis module and the recommendation results of the service recommendation module.

[0013] The security protection module includes a data encryption submodule, an access control submodule, and an intrusion detection submodule, and is responsible for ensuring the security of the front-end software system, including user data security, system resource security, and network security.

[0014] The log management module includes a log collection submodule, a log storage submodule, and a log analysis submodule.

[0015] The system monitoring module is responsible for real-time monitoring of the running state of the system, including server status, network status, database status, and the running state of each module.

[0016] The user interaction module is also responsible for displaying the processing results and feedback information of the system to the user through a friendly interface. At the same time, an event-driven programming model is adopted, and when the user performs an operation, the corresponding event is triggered, and the system immediately responds and processes.

[0017] The data collection submodule is responsible for collecting relevant data from the user interaction module, the front-end software running environment, and external data sources, including user behavior data, user attribute data, and front-end software running data.

[0018] The data cleaning submodule is responsible for cleaning the collected data, removing noise data and duplicate data, and adopting a data cleaning strategy based on rules and statistical methods to identify and remove incorrect data by setting the value range and format specification of the data field. Through statistical analysis of the distribution of the data, abnormal data is identified and processed.

[0019] The data conversion submodule is responsible for converting the cleaned data into a format suitable for subsequent analysis and processing using data mapping and data aggregation techniques, converting unstructured data into structured data, and unifying the data format of different data sources to achieve data conversion and integration.

[0020] The user behavior analysis submodule is responsible for analyzing user operation habits, interest preferences, and demand trends based on collected user behavior data. An improved Apriori algorithm is used for user behavior association analysis to mine the association relationships between user operations.

[0021] Let the user behavior data set be D, the transaction T be a subset of D, and the item set I be the set of all theoretical items; the support S(T) represents the frequency of transaction T in the data set D, that is The confidence c(T1→T2) represents the probability of containing item set T2 in the transaction containing item set T1, that is:

[0022] c(T1→T2)=s(T1∪T2) / s(T1)

[0023] By setting the minimum support and minimum confidence thresholds, meaningful association rules are screened out;

[0024] The user demand prediction submodule is responsible for training the user's historical data using machine learning algorithms, establishing a user demand prediction model, and predicting user demand according to the user's current behavior and attribute data;

[0025] Let the input vector be x=(x1,x2,…,x n ), the output vector be y=(y1,y2,…,y n ), and the prediction function of the neural network be f(x)=σ(W x +b), where W x is the weight matrix, b is the bias vector, and σ is the activation function;

[0026] The system performance evaluation submodule is responsible for evaluating the running performance of the front-end software, including page loading speed, response time, and resource occupation; an evaluation method based on an index system is adopted, the weights of various performance indicators are set, and the performance score of the system is calculated by weighted average;

[0027] Let the performance indicator set be p={p1,p2,…,p k}, and the corresponding weight be w={w1,w2,…,w k}, where ∑w i =1, then the system performance score Q=∑(w i *p i ).

[0028] The service recommendation module is responsible for adopting a recommendation algorithm based on collaborative filtering to generate a recommendation list according to the user's historical behavior data and the preferences of similar users;

[0029] Let the user set be U, the item set be I, and the user u's rating matrix for the item i be R; the cosine similarity is used to calculate the similarity sim(u,v) between users u and v, that is:

[0030] sim(u,v)=R u ·R v / (‖R u ‖·‖Rv ‖)

[0031] wherein R u and R v respectively represent the score vectors of users u and v;

[0032] According to the similarity calculation result, the k most similar users to the current user are screened, and then the scores of the current user on the un-scored items are predicted according to the scores of the selected k users on the items, so as to generate a recommendation list.

[0033] When the adaptive adjustment module finds that the frequency of use of a certain function module by the user exceeds a custom threshold, the corresponding function module is automatically adjusted to the homepage window range or the top navigation bar; when the accuracy of service recommendation is lower than a custom threshold, the parameters of the recommendation algorithm are adjusted or the recommendation strategy is switched; an adaptive adjustment mechanism based on feedback control is adopted, the running state of the system and user feedback are continuously monitored, and the parameters and strategies of the system are adjusted in real time.

[0034] The data encryption submodule is responsible for encrypting the sensitive data of the user, adopts a combination of symmetric encryption algorithm and asymmetric encryption algorithm, and ensures the security of the data in the transmission and storage process;

[0035] The access control submodule is responsible for setting user permissions and controlling user access behavior to prevent unauthorized users from accessing system resources; a role-based access control (RBAC) model is adopted, and corresponding permissions are allocated according to the roles of the users;

[0036] The intrusion detection submodule is responsible for real-time monitoring of network traffic and operation logs of the system, discovering and identifying potential intrusion behaviors and security threats; an intrusion detection algorithm based on rules and anomaly detection is adopted to timely issue alarms and take corresponding protection measures.

[0037] The log collection submodule is responsible for real-time collection of various log data during system operation, including user operation logs, system error logs and service call logs;

[0038] The log storage submodule is responsible for storing the collected log data in the log database, adopts distributed log storage technology to ensure the reliability and scalability of the log data;

[0039] The log analysis submodule is responsible for analyzing the log data and extracting valuable information to provide a basis for optimization and improvement of the system; a log analysis technology based on data mining is adopted to perform correlation analysis, trend analysis and anomaly detection on the log data.

[0040] When the system monitoring module detects abnormal conditions of the system, an alarm is immediately issued and relevant personnel are notified for processing; a threshold-based monitoring method is adopted, and the normal range of each monitoring index is set, and when the index exceeds the normal range, the alarm mechanism is triggered.

[0041] The beneficial effects of the present application are that the intelligent service system for front-end software can efficiently collect and process user data, deeply analyze user behavior and needs, and realize personalized service recommendation and adaptive system adjustment through the cooperative work of multiple functional modules.

[0042] At the same time, the system has perfect security protection and log management functions, which can guarantee the security and reliability of the system; by introducing various algorithms and technologies, the intelligence and adaptability of the system are improved, which can provide better front-end software services for users and improve user experience. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0044] Figure 1 The schematic diagram of the intelligent service system for front-end software of the present application.

[0045] Figure 2 The structure schematic diagram of the data collection and preprocessing module of the present application.

[0046] Figure 3 The structure schematic diagram of the intelligent analysis module of the present application.

[0047] Figure 4 The structure schematic diagram of the security protection module of the present application.

[0048] Figure 5 The structure schematic diagram of the log management module of the present application. DETAILED DESCRIPTION

[0049] In order to make the person skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described in detail below. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0050] The intelligent service system for front-end software includes a user interaction module, a data collection and preprocessing module, an intelligent analysis module, a service recommendation module, an adaptive adjustment module, a security protection module, a log management module, and a system monitoring module.

[0051] The user interaction module is an interface for user interaction with the front-end software, used to receive user input operations such as clicking, sliding, and inputting text, and convert user operation information into a system-recognized data format.

[0052] The data collection and preprocessing module includes a data collection submodule, a data cleaning submodule, and a data conversion submodule, responsible for data collection using distributed data collection technology, and converting the collected data into a unified format after cleaning.

[0053] The intelligent analysis module includes a user behavior analysis submodule, a user demand prediction submodule, and a system performance evaluation submodule, responsible for evaluating the running performance of the front-end software, analyzing user behavior based on user behavior data, and predicting user demand based on user behavior.

[0054] The service recommendation module is based on user behavior and demand information obtained by the intelligent analysis module, responsible for providing personalized service recommendations for users.

[0055] The adaptive adjustment module is responsible for self-adaptive adjustment of the interface layout, function modules, and service strategies of the front-end software based on the analysis results of the intelligent analysis module and the recommendation results of the service recommendation module.

[0056] The security protection module includes a data encryption submodule, an access control submodule, and an intrusion detection submodule, responsible for ensuring the security of the front-end software system, including user data security, system resource security, and network security.

[0057] The log management module includes a log collection submodule, a log storage submodule, and a log analysis submodule.

[0058] The system monitoring module is responsible for real-time monitoring of the running state of the system, including server state, network state, database state, and the running state of each module.

[0059] The user interaction module is also responsible for displaying the processing results and feedback information of the system to the user through a friendly interface, including text, pictures, and videos; at the same time, an event-driven programming model is used, when the user performs an operation, the corresponding event is triggered, the system responds immediately and processes.

[0060] The data collection submodule is responsible for collecting relevant data from the user interaction module, the front-end software running environment, and external data sources, including user behavior data, user attribute data, and front-end software running data; during the data collection process, distributed data collection technology is adopted to ensure efficient collection of large-scale data;

[0061] The data cleaning submodule is responsible for cleaning the collected data, removing noise data and duplicate data, and adopting a data cleaning strategy based on rules and statistical methods to identify and remove incorrect data by setting the value range and format specification of the data field; abnormal data is identified and processed by statistical analysis of the distribution of the data;

[0062] The data conversion submodule is responsible for converting the cleaned data into a format suitable for subsequent analysis and processing using data mapping and data aggregation techniques, converting unstructured data into structured data, and unifying the data format of different data sources to achieve data conversion and integration.

[0063] The user behavior analysis submodule is responsible for analyzing user operation habits, interest preferences, and demand trends based on the collected user behavior data; an improved Apriori algorithm is used for user behavior association analysis to mine the association between user operations;

[0064] Let the user behavior data set be D, the transaction T be a subset of D, and the item set I be the set of all theoretical items; the support S(T) represents the frequency of transaction T appearing in the data set D, that is, The confidence c(T1→T2) represents the probability of containing item set T2 in the transaction containing item set T1, that is:

[0065] c(T1→T2)=s(T1∪T2) / s(T1)

[0066] By setting the minimum support and minimum confidence thresholds, meaningful association rules are filtered out;

[0067] The user demand prediction submodule is responsible for training user historical data using machine learning algorithms to establish a user demand prediction model and predict user demand based on the user's current behavior and attribute data;

[0068] Let the input vector be x=(x1,x2,…,x n ), the output vector be y=(y1,y2,…,y n ), and the prediction function of the neural network be f(x)=σ(W x +b), where W x is the weight matrix, b is the bias vector, and σ is the activation function;

[0069] The system performance evaluation submodule is responsible for evaluating the running performance of the front-end software, including page loading speed, response time and resource occupation; an evaluation method based on an index system is adopted, the weights of various performance indicators are set, and the performance score of the system is calculated by weighted average method;

[0070] Let the performance indicator set be p={p1, p2, …, p k}, and the corresponding weight be w={w1, w2, …, w k}, where ∑w i =1, then the system performance score Q=∑(w i *p i ).

[0071] The service recommendation module is responsible for adopting a recommendation algorithm based on collaborative filtering, generating a recommendation list according to the historical behavior data of the user and the preferences of similar users;

[0072] Let the user set be U, the item set be I, and the rating matrix of user u to item i be R; the cosine similarity is used to calculate the similarity sim(u, v) between user u and user v, that is:

[0073] sim(u, v)=R u ·R v / (‖R u ‖·‖R v ‖)

[0074] Where R u and R v represent the rating vectors of user u and user v, respectively;

[0075] According to the similarity calculation result, the k most similar users to the current user are selected, and then the ratings of the selected k users to the items are used to predict the ratings of the current user to the unrated items, thereby generating a recommendation list.

[0076] When the adaptive adjustment module finds that the user's use frequency of a certain function module exceeds a custom threshold, the corresponding function module is automatically adjusted to the homepage window range or the top navigation bar; when the accuracy of service recommendation is lower than a custom threshold, the parameters of the recommendation algorithm are adjusted or the recommendation strategy is switched; an adaptive adjustment mechanism based on feedback control is adopted, and the parameters and strategies of the system are adjusted in real time by continuously monitoring the running state of the system and user feedback.

[0077] The data encryption submodule is responsible for encrypting the sensitive data of the user, and adopts a combination of symmetric encryption algorithm and asymmetric encryption algorithm to ensure the security of the data in the transmission and storage process;

[0078] The access control submodule is responsible for setting user permissions and controlling user access behavior to prevent unauthorized users from accessing system resources; a role-based access control (RBAC) model is adopted to assign corresponding permissions according to user roles;

[0079] The intrusion detection submodule is responsible for real-time monitoring of system network traffic and operation logs, discovering and identifying potential intrusion behavior and security threats; an intrusion detection algorithm based on rule and anomaly detection is adopted to issue timely alarms and take corresponding protective measures.

[0080] The log collection submodule is responsible for real-time collection of various log data during system operation, including user operation logs, system error logs and service call logs;

[0081] The log storage submodule is responsible for storing collected log data in a log database, and adopts distributed log storage technology to ensure the reliability and scalability of log data;

[0082] The log analysis submodule is responsible for analyzing log data and extracting valuable information to provide a basis for system optimization and improvement; a log analysis technology based on data mining is adopted to perform correlation analysis, trend analysis and anomaly detection on log data.

[0083] When the system monitoring module detects abnormal conditions in the system, it immediately issues an alarm and notifies relevant personnel for handling; a threshold-based monitoring method is adopted to set the normal range of each monitoring indicator, and when the indicator exceeds the normal range, the alarm mechanism is triggered.

[0084] The application of the intelligent service system for front-end software in front-end software can realize efficient interaction between users and front-end software, intelligent processing of data, personalized service recommendation, adaptive adjustment of the system and secure and reliable operation guarantee.

[0085] The above-described embodiments are only one of the specific implementations of the present application, and any usual changes and substitutions made by those skilled in the art within the scope of the technical solutions of the present application should be included in the protection scope of the present application.

Claims

1. A smart service system for front-end software, characterized by: The user interaction module, the data collection and preprocessing module, the intelligent analysis module, the service recommendation module, the adaptive adjustment module, the security protection module, the log management module and the system monitoring module are comprised. The user interaction module is an interface for user interaction with the front-end software, used to receive user input operations and convert user operation information into a data format recognized by the system. The data collection and preprocessing module includes a data collection submodule, a data cleaning submodule and a data conversion submodule, responsible for data collection using distributed data collection technology, and converting the collected data into a unified format after cleaning. The intelligent analysis module includes a user behavior analysis submodule, a user demand prediction submodule and a system performance evaluation submodule, responsible for evaluating the running performance of the front-end software, analyzing user behavior based on user behavior data, and predicting user demand based on user behavior. The service recommendation module is based on user behavior and demand information obtained by the intelligent analysis module, responsible for providing personalized service recommendations for users. The adaptive adjustment module is responsible for adaptive adjustment of the interface layout, function modules and service strategies of the front-end software based on the analysis results of the intelligent analysis module and the recommendation results of the service recommendation module. The security protection module includes a data encryption submodule, an access control submodule and an intrusion detection submodule, responsible for ensuring the security of the front-end software system, including user data security, system resource security and network security. The log management module includes a log collection submodule, a log storage submodule and a log analysis submodule. The system monitoring module is responsible for real-time monitoring of the running state of the system, including server state, network state, database state and the running state of each module.

2. The intelligent service system for front-end software according to claim 1, wherein: The user interaction module is also responsible for displaying the system's processing results and feedback information to the user through the interface; at the same time, an event-driven programming model is used, when the user operates, the corresponding event is triggered, the system responds immediately and processes.

3. The intelligent service system for front-end software according to claim 1, wherein: The data collection submodule is responsible for collecting relevant data from the user interaction module, the front-end software running environment and external data sources, including user behavior data, user attribute data and front-end software running data. The data cleaning submodule is responsible for cleaning the collected data, removing noise data and duplicate data, using a data cleaning strategy based on rules and statistical methods, identifying and removing incorrect data by setting the value range and format specification of the data field; through statistical analysis of the distribution of the data, abnormal data is identified and processed. The data conversion submodule is responsible for converting the cleaned data into a format suitable for subsequent analysis and processing using data mapping and data aggregation techniques, converting unstructured data into structured data, and unifying the data format of different data sources to achieve data conversion and integration.

4. The intelligent service system for front-end software according to claim 1, wherein: The user behavior analysis submodule is responsible for analyzing user operation habits, interest preferences and demand trends based on collected user behavior data; improved Apriori algorithm is used for user behavior association analysis to mine the association between user operations. Let the user behavior dataset be D, a transaction T be a subset of D, and an item set I be the set of all theoretical items. The support S(T) represents the frequency of the transaction T in the dataset D, i.e. The confidence c(T1→T2) represents the probability of containing item set T2 in the transaction containing item set T1, i.e.: c(T1→T2) = s(T1∪T2) / s(T1) By setting the minimum support and minimum confidence threshold, the meaningful association rules are filtered out; The user demand prediction submodule is responsible for training the user's historical data using machine learning algorithms, establishing a user demand prediction model, and predicting user demand according to the user's current behavior and attribute data; Let the input vector be x = (x1, x2, …, x n ), the output vector be y = (y1, y2, …, y n ), and the prediction function of the neural network be f(x) = σ(W x +b), where W x is a weight matrix, b is a bias vector, and σ is an activation function. The system performance evaluation submodule is responsible for evaluating the running performance of the front-end software, including page loading speed, response time and resource occupation; an evaluation method based on index system is adopted, the weights of various performance indicators are set, and the performance score of the system is calculated by weighted average method; Let the performance indicator set be p = {p1, p2, …, p k}, and the corresponding weights be w = {w1, w2, …, w k}, where ∑w i = 1, then the system performance score Q = ∑(w i * p i ).

5. The intelligent service system for front-end software according to claim 1, wherein: The service recommendation module is responsible for adopting a recommendation algorithm based on collaborative filtering to generate a recommendation list according to the user's historical behavior data and the preferences of similar users; Let the user set be U, the item set be I, and the user u's rating matrix for item i be R; the cosine similarity is used to calculate the similarity sim(u,v) between user u and user v, that is: sim(u, v) = R u • R v (‖R u ‖ • ‖R v ‖) where R u and R v denote the rating vectors of users u and v, respectively; According to the similarity calculation result, the k most similar users to the current user are selected, and then the ratings of the current user for the unrated items are predicted according to the ratings of the selected k users for the items, thereby generating a recommendation list.

6. The intelligent service system for front-end software according to claim 1, wherein: When the adaptive adjustment module finds that the user's usage frequency of a certain function module exceeds the custom threshold, it automatically adjusts the corresponding function module to the homepage window range or the top navigation bar; when the accuracy of service recommendation is lower than the custom threshold, the parameters of the recommendation algorithm are adjusted or the recommendation strategy is switched; an adaptive adjustment mechanism based on feedback control is adopted to monitor the running state of the system and user feedback in real time, and adjust the parameters and strategies of the system in real time.

7. The intelligent service system for front-end software according to claim 1, wherein: The data encryption submodule is responsible for encrypting the user's sensitive data, and adopts a combination of symmetric encryption algorithm and asymmetric encryption algorithm to ensure the security of data transmission and storage; The access control submodule is responsible for setting user permissions and controlling user access behavior to prevent unauthorized users from accessing system resources; the Role-Based Access Control (RBAC) model is adopted to assign appropriate permissions to users according to their roles; The intrusion detection submodule is responsible for real-time monitoring of system network traffic and operation logs, discovering and identifying potential intrusion behavior and security threats; an intrusion detection algorithm based on rule and anomaly detection is adopted to issue alarms and take appropriate protective measures in a timely manner.

8. The intelligent service system for front-end software according to claim 1, wherein: The log collection submodule is responsible for real-time collection of various log data during system operation, including user operation logs, system error logs and service call logs; The log storage submodule is responsible for storing the collected log data in the log database, and adopts distributed log storage technology to ensure the reliability and scalability of log data; The log analysis submodule is responsible for analyzing log data and extracting valuable information to provide basis for system optimization and improvement; a log analysis technology based on data mining is adopted to perform correlation analysis, trend analysis and anomaly detection on log data.

9. The intelligent service system for front-end software according to claim 1, wherein: When the system monitoring module detects abnormal conditions of the system, it immediately issues an alarm and informs relevant personnel to handle; a threshold-based monitoring method is adopted, and the normal range of each monitoring index is set, and when the index exceeds the normal range, the alarm mechanism is triggered.

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