Rapid website building method and system based on CMS

Abstract

The invention discloses a CMS-based rapid website construction method and system, and relates to the technical field of website construction, and the method comprises the following components: S1, data acquisition and preprocessing, S2, chaos feature analysis, S3, chaos state prediction, S4, dynamic resource allocation, and S5, website construction process execution. According to the method, the resource use data of each stage of website construction are collected in real time, the chaos theory is used for chaos feature analysis and state prediction, the server resources and bandwidth can be dynamically and adaptively allocated, the intelligent resource allocation mechanism ensures that the website can obtain sufficient and reasonable resource support in different stages, and the resource allocation efficiency is improved. According to the method, resource waste and bottleneck problems are avoided, so that the resource utilization efficiency is remarkably improved, meanwhile, website construction is rapidly completed in combination with the dynamically allocated resources, the website construction speed is further increased, and the overall working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of website construction, and in particular to a CMS-based rapid website construction method and system. Background Art

[0002] With the rapid development of Internet technology, website construction has become an important platform for enterprises, organizations and even individuals to display their image, disseminate information and provide services. Traditional website construction methods often rely on manual design, development and deployment. The process is cumbersome and time-consuming, and it is difficult to meet the rapidly changing market demands and user expectations.

[0003] In traditional website construction technology, resource allocation is a particularly prominent difficulty. Traditional methods often lack the ability to monitor and analyze resource usage in real time during website operation, and cannot accurately predict resource demand in the future. Therefore, conservative strategies are often adopted in resource allocation, and too many resources are reserved to cope with uncertain demand. This not only causes a waste of resources, but also increases operating costs. On the other hand, when the website traffic suddenly increases or resource bottlenecks occur, traditional methods are difficult to respond quickly and adjust resource allocation, resulting in decreased website performance and a poor user experience. In addition, traditional template selection methods often rely on manual judgment and experience, which is inefficient and inaccurate, and cannot meet the needs of rapid website construction.

[0004] In view of the shortcomings of traditional website construction technology in resource allocation and template selection, the present invention proposes a CMS-based rapid website construction method and system, which is particularly important. Summary of the Invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a CMS-based rapid website construction method and system. It can collect resource usage data in real time during the website construction process, and use chaos theory to perform chaos feature analysis and state prediction, thereby realizing dynamic and adaptive allocation of server resources and bandwidth, effectively improving resource utilization efficiency. At the same time, it introduces an intelligent template recommendation method based on multi-attribute decision-making into the website construction process, which greatly shortens the template selection time and improves website construction efficiency.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a method for quickly building a website based on CMS, the specific steps of the method are as follows: S1. Data Collection and Preprocessing: Real-time collection of resource usage data at all stages of website construction, including but not limited to server CPU usage, memory usage, disk I / O read / write speed, network bandwidth usage, website visits, and page load time. Cleaning, denoising, and normalization preprocessing are performed on the collected data to improve data quality and provide an accurate data foundation for subsequent analysis. S2. Chaos Characteristic Analysis: Using the Lyapunov exponent calculation method from chaos theory, we analyze the preprocessed data to determine whether resource usage exhibits chaotic characteristics. Through phase space reconstruction, we map the one-dimensional resource usage time series data into a higher-dimensional space to more comprehensively reveal the data's inherent structure and evolutionary patterns. We then calculate the Lyapunov exponent. If the Lyapunov exponent is greater than zero, it indicates that the resource usage data exhibits chaotic characteristics, meaning that the resource usage status exhibits a high degree of uncertainty and complexity. S3. Chaotic state prediction: When it is determined that resource usage has chaotic characteristics, a chaotic prediction model, such as a neural network-based chaotic prediction model or a chaotic time series prediction model, is used to predict the chaotic state of resource usage. The prediction model is trained using historical resource usage data to learn the chaotic evolution of resource usage. This allows the model to predict resource usage over a period of time in the future and obtain a predicted value for resource demand. S4. Dynamic resource allocation: Based on the predicted resource usage and the preset resource allocation strategy, server resources and bandwidth are dynamically and adaptively allocated. When a significant increase in server CPU usage is predicted within a certain period of time, the server's CPU resource allocation is automatically increased. When an increase in network bandwidth demand is predicted, bandwidth allocation is adjusted in advance to ensure that the website has sufficient and reasonable resource support at different stages, avoiding resource waste and bottleneck problems. S5. Website construction process execution: including template selection, content editing, and function module addition operations. At the same time, combined with dynamically allocated resources, the website construction is quickly completed. During the website construction process, resource usage is continuously monitored and resource allocation is adjusted and optimized according to actual conditions.

[0007] Furthermore, in the data collection process, multi-source heterogeneous data fusion technology is adopted: for data from different data sources such as server monitoring systems, network traffic monitoring tools, and user behavior analysis systems, data format conversion and unified encoding are first performed so that these data with different formats have the same data structure, which is convenient for subsequent processing. Then, a data fusion method based on evidence theory is adopted to calculate the basic probability distribution of different data sources for each data feature, so as to measure the degree of support of each data source for different value sets of data features. Then, through specific fusion rules, the data from different data sources are fused and processed, and finally the fused data is obtained. This method effectively improves the accuracy and completeness of the data, and provides a more reliable data foundation for subsequent resource analysis and allocation.

[0008] Furthermore, in the cleaning, denoising and normalization preprocessing of the collected data, data denoising adopts a joint denoising method based on local mean decomposition and sparse representation: first, local mean decomposition is performed on the original data to decompose the original data into a series of product function components and a residual component. For each obtained product function component, its sparse representation model is constructed, and a sparse coefficient vector is obtained by solving a specific optimization problem. The sparse coefficient vector represents the sparse representation of the product function component under an over-complete dictionary. Subsequently, the sparse coefficient vector is thresholded to remove the coefficients related to noise, thereby obtaining a denoised sparse coefficient vector. Finally, the denoised sparse coefficient vector is used to reconstruct the denoised product function component, and all the denoised product function components are added to the residual component to obtain the denoised data, which effectively removes the noise interference in the data and significantly improves the data quality.

[0009] Furthermore, the chaos theory is used to analyze the pre-processed data to determine whether the resource usage presents chaotic characteristics. Specifically, the improved phase space reconstruction algorithm is used to analyze the collected resource usage time series data. , according to the formula Reconstruction, where , is the embedding dimension, is the delay time, embedding dimension By calculating the false neighbor rate OK, the formula is:

[0010] in is a step function, Represents the reconstruction vector and Euclidean distance, delay time Through the autocorrelation function , select First drop to initial value of Corresponding to Value, after the above reconstruction, calculate the maximum Lyapunov exponent ,like , it indicates that the resource usage data has chaotic characteristics, and the maximum Lyapunov exponent The improved small data method is used for calculation. The specific formula is: ,in is the time interval, is the number of data, and are the initial moment and The distance between adjacent tracks at a given moment.

[0011] Furthermore, after determining that resource usage has chaotic characteristics, the chaotic state of resource usage is predicted, specifically using a chaotic prediction model based on adaptive weight optimization: assuming that the resource usage time series is , obtained by phase space reconstruction dimensional vector ,in , build a prediction model:

[0012] in is a nonlinear mapping function, is the weight vector, the weight vector Through the improved particle swarm optimization algorithm, the particle position vector is adaptively adjusted. Represents a set of weight values, velocity vector Control the update direction and step size of the weight, fitness function By continuously iteratively updating the position and velocity of the particles, the fitness function is minimized to determine the optimal weight vector During the prediction process, the inertia weight in the particle swarm optimization algorithm is dynamically adjusted according to the changing trend of historical data. , the formula is:

[0013] in and are the maximum and minimum values ​​of the inertia weight, is the current iteration number, is the maximum number of iterations to improve the accuracy of prediction.

[0014] Furthermore, after the chaotic state is predicted, the uncertainty of the prediction result is evaluated: a prediction interval is constructed using an interval prediction method. , by calculating the standard deviation of the prediction error , combined with the confidence level , using the formula , Determine the lower and upper bounds of the prediction interval, where is the point forecast value, It is the quantile of the standard normal distribution. The uncertainty of the prediction result is evaluated according to the width of the prediction interval and the coverage probability. When the uncertainty is high, the parameters of the prediction model are automatically adjusted or multiple prediction models are used for combined prediction to improve the reliability of the prediction result.

[0015] Furthermore, the server resources and bandwidth are dynamically and adaptively allocated based on the predicted value of resource usage and the preset resource allocation strategy. The preset resource allocation strategy adopts a dynamic allocation strategy based on fuzzy logic: the difference between the predicted value of resource usage and the actual resource usage is used as the Sum difference change rate As the input variable of the fuzzy logic system, the allocation adjustment of server resources and bandwidth As an output variable, 、 and Divide into multiple fuzzy sets, establish a fuzzy rule base, and use fuzzy reasoning and defuzzification operations to calculate the current and Calculate the resource allocation adjustment , to achieve dynamic and adaptive allocation of server resources and bandwidth to meet the changes in resource requirements during website construction.

[0016] Furthermore, during the website construction process, the resource usage is continuously monitored and resource allocation is adjusted and optimized according to the actual situation, specifically by building a resource allocation feedback adjustment mechanism: setting a threshold range for resource usage, including an upper threshold and lower threshold When the actual resource usage exceeds the upper threshold, the resource recovery mechanism is triggered to reallocate excess resources to other places where they are needed. When the actual resource usage is lower than the lower threshold, the resource replenishment mechanism is triggered to appropriately increase resource allocation based on the resource usage forecast. At the same time, the feedback coefficient is introduced. , dynamically adjust the intensity of resource allocation according to the frequency and magnitude of changes in resource usage. The formula is ,in is the initial adjustment calculated according to the resource allocation strategy, Adjust the amount for the final resource allocation to achieve precise optimization of resource allocation.

[0017] Furthermore, the website building process executed by the website building process includes template selection, content editing, and function module addition operations. At the same time, combined with dynamically allocated resources, the website construction is quickly completed. In the template selection stage, an intelligent template recommendation method based on multi-attribute decision-making is adopted: a template evaluation index system is established, including template style matching, functional completeness, responsive design level, loading speed and other indicators. For each indicator, the weight is determined by expert scoring and historical user feedback data. , ,in is the number of indicators, for candidate templates , , according to the scores of each indicator , using the weighted summation formula Calculate the comprehensive score of the templates, sort them from high to low according to the comprehensive score, and recommend suitable templates to users to improve the efficiency and accuracy of template selection and speed up website building.

[0018] On the other hand, a CMS-based rapid website building system is characterized in that the system includes a data acquisition module, a data processing module, a resource allocation module and a website building execution module: The data acquisition module is used to collect various resource usage data and website-related data in real time during the website construction process, and transmit the collected data to the data processing module. This module can realize comprehensive data collection by interacting with the server monitoring system and network traffic monitoring tools, and adopts multi-source heterogeneous data fusion technology to integrate data from different data sources; The data processing module performs preprocessing operations on the collected data, including data cleaning, denoising, and normalization, and uses chaos theory-related methods to analyze the chaotic characteristics and predict the chaotic state of the preprocessed data, providing data support and prediction results for resource allocation. This module integrates an improved phase space reconstruction algorithm, a maximum Lyapunov exponent calculation algorithm, a chaos prediction model based on adaptive weight optimization, and a data denoising algorithm; The resource allocation module dynamically and adaptively allocates server resources and bandwidth based on the prediction results of the data processing module and a preset dynamic resource allocation strategy based on fuzzy logic. This module can interact with the server management system and network bandwidth management equipment to achieve automatic adjustment and allocation of resources, and has a resource allocation feedback adjustment mechanism; The website construction execution module is responsible for executing the regular website construction process, using dynamically allocated resources to complete website construction, monitoring resource usage during the construction process, and providing timely feedback to the data acquisition module and resource allocation module for resource optimization and adjustment. During the website construction process, an intelligent template recommendation method based on multi-attribute decision-making is used to select templates.

[0019] Compared with the existing technology, this CMS-based rapid website building method and system has the following beneficial effects: 1. This invention collects resource usage data at each stage of website construction in real time and uses chaos theory to perform chaos feature analysis and state prediction. This method can dynamically and adaptively allocate server resources and bandwidth. This intelligent resource allocation mechanism ensures that the website can obtain sufficient and reasonable resource support at different stages, avoiding resource waste and bottleneck problems, thereby significantly improving resource utilization efficiency. At the same time, combined with dynamically allocated resources, the website construction can be quickly completed, further accelerating the website construction speed and improving overall work efficiency.

[0020] 2. In the website building process, especially in the template selection stage, this system adopts an intelligent template recommendation method based on multi-attribute decision-making. By establishing a template evaluation index system, it comprehensively considers multiple indicators such as template style matching, functional integrity, responsive design level, and loading speed, and uses a weighted summation formula to calculate the comprehensive score of the template. This method can recommend the most suitable template to users. This intelligent template selection method not only improves the accuracy of template selection, but also greatly shortens the selection time and improves website building efficiency.

[0021] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0023] Figure 1 This is a process diagram of a rapid website building method based on CMS; Figure 2 This is a process operation diagram of a CMS-based rapid website building system. DETAILED DESCRIPTION

[0024] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0025] Example 1

[0026] When building the official website of a certain technology company, the system used the data acquisition module to monitor the server's operating status in real time, and simultaneously obtained basic indicators such as CPU usage and memory usage. At the same time, it connected to the network traffic monitoring tool to collect bandwidth fluctuation data. Since these data came from different platforms such as the server monitoring system and network traffic tools, there were differences in format and encoding. Therefore, multi-source heterogeneous data fusion technology was first used to convert the format and unify the encoding. Then, a data fusion method based on evidence theory was used to eliminate data conflicts and ensure data consistency. Subsequently, a joint denoising method based on local mean decomposition and sparse representation was used to filter out abnormal fluctuations and noise in the original data. Finally, normalization was performed to make data of different dimensions comparable, laying the foundation for subsequent analysis.

[0027] The data processing module uses the Lyapunov exponent calculation method in chaos theory to analyze the complex system characteristics of the pre-processed data. First, an improved phase space reconstruction algorithm is used to use time series data for the collected resources. , according to the formula Reconstruction, where , is the embedding dimension, is the delay time, embedding dimension By calculating the false neighbor rate OK, the formula is:

[0028] in is a step function, Represents the reconstruction vector and Euclidean distance, delay time Through the autocorrelation function , select First drop to initial value of Corresponding to Value, after the above reconstruction, calculate the maximum Lyapunov exponent ,like , it indicates that the resource usage data has chaotic characteristics, and the maximum Lyapunov exponent The improved small data method is used for calculation. The specific formula is: ,in is the time interval, is the number of data, and are the initial moment and The distance between adjacent tracks at a certain moment can be used to determine whether resource utilization exhibits the high uncertainty and complexity unique to chaotic systems.

[0029] When it is determined that resource usage has chaotic characteristics, the system starts the chaotic prediction model based on adaptive weight optimization, assuming that the resource usage time series is , obtained by phase space reconstruction dimensional vector ,in , build a prediction model:

[0030] in is a nonlinear mapping function, is the weight vector. During the prediction process, the inertia weight in the particle swarm optimization algorithm is dynamically adjusted according to the changing trend of historical data. , the formula is:

[0031] in and are the maximum and minimum values ​​of the inertia weight, is the current iteration number, The model is trained based on historical resource usage data (such as CPU, memory, and bandwidth usage records for the past week) to simulate the evolution of chaotic systems and learn the dynamic changes in resource usage. After training, the model can predict resource usage in the next 24 hours. For example, it can predict that the CPU usage may increase at 3 pm due to an increase in official website visits, or that memory usage may peak during the nightly data backup period.

[0032] The resource allocation module implements resource scheduling based on the prediction results and a preset dynamic allocation strategy based on fuzzy logic. For example, if the model predicts that the official website will host a product launch live broadcast at 10:00 am the next day and the CPU utilization rate may soar from the current 30% to 70%, the system will automatically trigger the resource allocation mechanism to increase CPU core resources for the server in advance. If it is predicted that the network bandwidth demand during the user browsing peak period will increase from 50Mbps to 100Mbps, it will interact with the network bandwidth management device in advance to adjust the bandwidth allocation strategy to ensure smooth network operation during the live broadcast.

[0033] The website construction execution module starts the website construction process based on dynamically allocated resources. In the template selection stage, an intelligent template recommendation method based on multi-attribute decision-making is used to screen candidate templates from the company's official website template library. This method establishes an evaluation system that includes indicators such as template style matching, functional completeness, responsive design level, and loading speed. Each indicator is weighted based on expert experience and historical user feedback. After calculating the comprehensive score, the top-ranked templates are recommended. Finally, a simple style template that fits the image of a technology company is selected. Subsequently, content editing is carried out, and text such as the company's development history and core technology introduction are entered. Product images are uploaded, and functional modules such as online message boards and contact information are added. During the website construction process, the system continuously monitors resource usage. When the actual CPU usage rate exceeds the preset upper threshold, the resource recovery mechanism is automatically triggered to allocate idle memory resources to the CPU demand side. If the bandwidth usage is lower than the lower threshold, bandwidth resources are replenished in advance based on the prediction results. The allocation intensity is dynamically adjusted through the feedback coefficient to ensure maximum resource utilization efficiency.

[0034] Example 2

[0035] When building a clothing e-commerce platform, the data acquisition module is connected to the server monitoring system in real time to collect server performance data such as disk I / O operation frequency and number of database connections. At the same time, user behavior data such as user visits, product browsing tracks, and number of purchases are obtained from the user behavior analysis system. Since the data sources include multi-source heterogeneous platforms such as server logs and user tracking systems, the format is first standardized through multi-source heterogeneous data fusion technology to unify different types of data into a structured format. Then, a data fusion method based on evidence theory is used to integrate multi-source information and eliminate data contradictions. Subsequently, a joint denoising method based on local mean decomposition and sparse representation is used to clean noise data such as sudden jumps in user visits and abnormal fluctuations in disk I / O. Finally, normalization is performed to put data of different magnitudes (such as the number of database connections and bandwidth traffic) in the same metric space.

[0036] The data processing module uses the Lyapunov exponent calculation method to analyze the chaotic characteristics of pre-processed resource usage data (such as changes in server load during promotional activities). Through an improved phase space reconstruction algorithm, the one-dimensional database connection number time series is converted into a high-dimensional space vector. During the reconstruction process, the optimal embedding dimension is determined by calculating the false neighbor rate to ensure that the high-dimensional space can fully present the data evolution trajectory. At the same time, the delay time is determined based on the autocorrelation function to avoid distortion of timing information. After completing the phase space reconstruction, the maximum Lyapunov exponent is calculated to determine whether the resource usage during the operation of the e-commerce platform has the unpredictable characteristics of a chaotic system. For example, during a major promotion, the sudden increase in user visits caused by the sharp fluctuations in server load.

[0037] If chaotic resource usage is determined, the system uses a chaotic prediction model based on adaptive weight optimization. This model is trained using resource usage data from historical promotional events, allowing it to learn the chaotic evolution of resource usage in promotional scenarios. After training, the model can predict resource usage for upcoming promotional events. For example, it can predict that within one hour of the start of an event, the number of database connections may surge from the usual 500 to 2,000, and bandwidth demand may soar from 200 Mbps to 500 Mbps.

[0038] The resource allocation module implements resource scheduling based on the prediction results and a dynamic allocation strategy based on fuzzy logic. For example, when the model predicts that user ordering behavior during the "618" midnight rush period will lead to a sharp increase in the number of database connections, the system automatically increases computing resources and memory allocation for the database server. If it is predicted that bandwidth congestion may occur during the peak period of image loading, it will interact with the network equipment in advance and dynamically adjust the bandwidth allocation strategy to prioritize the image transmission bandwidth of the product details page.

[0039] The website execution module initiates e-commerce platform construction based on dynamically allocated resources. During the template selection phase, an intelligent template recommendation method based on multi-attribute decision-making is used to screen candidate templates from the e-commerce template library. This method establishes an evaluation system that includes indicators such as template style matching, functional completeness, responsive design level, and loading speed. Each indicator is weighted based on scores from industry experts and user satisfaction surveys. After calculating the overall score, a template suitable for apparel e-commerce is recommended. Ultimately, a visual template with features such as product category navigation and carousel promotional images is selected. Content editing then begins, entering clothing category classification, product details (size, material, etc.), and brand stories. Functional modules such as shopping carts, online payment, and order inquiries are added. During the website construction process, the system continuously monitors resource usage and sets upper thresholds for disk I / O and database connections. When actual usage exceeds the threshold, a resource recovery mechanism is triggered, allocating resources from idle servers to high-load modules. If usage falls below the lower threshold, resources are replenished in advance based on forecast results. Dynamic allocation is adjusted through a feedback coefficient to ensure stable platform operation during the promotion period.

[0040] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any brief modifications, changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A rapid website building method based on CMS, characterized in that: The specific steps of this method are: S1. Data collection and preprocessing: Real-time collection of resource usage data at each stage of website construction, and preprocessing of the collected data by cleaning, denoising, and normalization. S2. Chaos Characteristic Analysis: Using the Lyapunov exponent calculation method from chaos theory, we analyze the preprocessed data to determine whether resource usage exhibits chaotic characteristics. Through phase space reconstruction, we map the one-dimensional resource usage time series data into a higher-dimensional space to more comprehensively reveal the data's inherent structure and evolutionary patterns. We then calculate the Lyapunov exponent. If the Lyapunov exponent is greater than zero, it indicates that the resource usage data exhibits chaotic characteristics, meaning that the resource usage status exhibits a high degree of uncertainty and complexity. S3. Chaos state prediction: When it is determined that resource usage has chaotic characteristics, a chaos prediction model is used. The model is trained using historical resource usage data to learn the chaotic evolution of resource usage and predict resource usage in the future. S4. Dynamic resource allocation: Based on resource usage predictions and pre-set resource allocation policies, server resources and bandwidth are dynamically and adaptively allocated. If a significant increase in server CPU usage is predicted within a certain period of time, the server's CPU resource allocation is automatically increased. If an increase in network bandwidth demand is predicted, bandwidth allocation is adjusted in advance. S5. Website construction process execution: including template selection, content editing, and function module addition operations. At the same time, combined with dynamically allocated resources, the website construction is quickly completed. During the website construction process, resource usage is continuously monitored and resource allocation is adjusted and optimized according to actual conditions.

2. A CMS-based rapid website building method according to claim 1, characterized in that: During the data collection process, multi-source heterogeneous data fusion technology is adopted: for data from different data sources such as server monitoring systems, network traffic monitoring tools, and user behavior analysis systems, data format conversion and unified encoding are first performed so that these data with different formats have the same data structure. Then, a data fusion method based on evidence theory is adopted to calculate the basic probability distribution of different data sources for each data feature. Then, the data from different data sources are fused through specific fusion rules to finally obtain the fused data.

3. A CMS-based rapid website building method according to claim 1, characterized in that: In the cleaning, denoising and normalization preprocessing of the collected data, data denoising adopts a joint denoising method based on local mean decomposition and sparse representation: first, local mean decomposition is performed on the original data to decompose the original data into a series of product function components and a residual component. For each obtained product function component, a sparse representation model is constructed, and a sparse coefficient vector is obtained by solving a specific optimization problem. The sparse coefficient vector represents the sparse representation of the product function component under an overcomplete dictionary. Subsequently, the sparse coefficient vector is thresholded to remove the coefficients related to noise, thereby obtaining a denoised sparse coefficient vector. Finally, the denoised sparse coefficient vector is used to reconstruct the denoised product function component, and all the denoised product function components are added to the residual component to obtain the denoised data.

4. A CMS-based rapid website building method according to claim 1, characterized in that: The method of using chaos theory to analyze the pre-processed data and determine whether resource usage presents chaotic characteristics is as follows: using an improved phase space reconstruction algorithm to analyze the collected resource usage time series data. , according to the formula Reconstruction, where , is the embedding dimension, is the delay time, embedding dimension By calculating the false neighbor rate OK, the formula is: in is a step function, Represents the reconstruction vector and Euclidean distance, delay time Through the autocorrelation function , select First drop to initial value of Corresponding to Value, after the above reconstruction, calculate the maximum Lyapunov exponent ,like , it indicates that the resource usage data has chaotic characteristics, and the maximum Lyapunov exponent The improved small data method is used for calculation. The specific formula is: ,in is the time interval, is the number of data, and are the initial moment and The distance between adjacent tracks at a given moment.

5. A CMS-based rapid website building method according to claim 4, characterized in that: After determining that resource usage has chaotic characteristics, the chaotic state of resource usage is predicted, specifically using a chaotic prediction model based on adaptive weight optimization: assuming that the resource usage time series is , obtained by phase space reconstruction dimensional vector ,in , build a prediction model: in is a nonlinear mapping function, is the weight vector. During the prediction process, the inertia weight in the particle swarm optimization algorithm is dynamically adjusted according to the changing trend of historical data. , the formula is: in and are the maximum and minimum values ​​of the inertia weight, is the current iteration number, is the maximum number of iterations.

6. A CMS-based rapid website building method according to claim 5, characterized in that: After the chaotic state prediction, the uncertainty of the prediction result is evaluated: the prediction interval is constructed by using the interval prediction method. , by calculating the standard deviation of the prediction error , combined with the confidence level , using the formula , Determine the lower and upper bounds of the prediction interval, where is the point forecast value, It is the quantile of the standard normal distribution. The uncertainty of the prediction result is evaluated according to the width of the prediction interval and the coverage probability. When the uncertainty is high, the parameters of the prediction model are automatically adjusted or multiple prediction models are used for combined prediction.

7. A CMS-based rapid website building method according to claim 1, characterized in that: The server resources and bandwidth are dynamically and adaptively allocated according to the predicted value of resource usage and the preset resource allocation strategy. The preset resource allocation strategy adopts a dynamic allocation strategy based on fuzzy logic: the difference between the predicted value of resource usage and the actual resource usage is used as the Sum difference change rate As the input variable of the fuzzy logic system, the allocation adjustment of server resources and bandwidth As an output variable, 、 and Divide into multiple fuzzy sets, establish a fuzzy rule base, and use fuzzy reasoning and defuzzification operations to calculate the current and Calculate the resource allocation adjustment .

8. A CMS-based rapid website building method according to claim 1, characterized in that: During the website construction process, the resource usage is continuously monitored and resource allocation is adjusted and optimized according to the actual situation. Specifically, a resource allocation feedback adjustment mechanism is adopted: a threshold range for resource usage is set, including an upper threshold. and lower threshold When the actual resource usage exceeds the upper threshold, the resource recovery mechanism is triggered to reallocate the excess resources to other places where they are needed. When the actual resource usage is lower than the lower threshold, the resource replenishment mechanism is triggered. According to the resource usage forecast, at the same time, the feedback coefficient is introduced. , dynamically adjust the intensity of resource allocation according to the frequency and magnitude of changes in resource usage. The formula is ,in is the initial adjustment calculated according to the resource allocation strategy, Adjust the amount for the final resource allocation.

9. A CMS-based rapid website building method according to claim 1, characterized in that: The website building process executed by the website building process includes template selection, content editing, and function module addition operations. At the same time, combined with dynamically allocated resources, the website construction is quickly completed. In the template selection stage, an intelligent template recommendation method based on multi-attribute decision-making is adopted: a template evaluation index system is established, including template style matching, functional completeness, responsive design level, loading speed and other indicators. For each indicator, the weight is determined by expert scoring and historical user feedback data , ,in is the number of indicators, for candidate templates , , according to the scores of each indicator , using the weighted summation formula Calculate the comprehensive score of the templates and sort them from high to low based on the comprehensive score.

10. A CMS-based rapid website building system, applicable to the CMS-based rapid website building method according to any one of claims 1 to 9, characterized in that: The system includes data acquisition module, data processing module, resource allocation module and website execution module: The data acquisition module is used to collect various resource usage data and website-related data in real time during the website construction process, and transmit the collected data to the data processing module. This module can interact with the server monitoring system and network traffic monitoring tools, and adopt multi-source heterogeneous data fusion technology to integrate data from different data sources; The data processing module performs preprocessing operations on the collected data, including data cleaning, denoising, and normalization, and uses chaos theory-related methods to analyze the chaotic characteristics and predict the chaotic state of the preprocessed data, providing data support and prediction results for resource allocation. This module integrates an improved phase space reconstruction algorithm, a maximum Lyapunov exponent calculation algorithm, a chaos prediction model based on adaptive weight optimization, and a data denoising algorithm; The resource allocation module dynamically and adaptively allocates server resources and bandwidth according to the prediction results of the data processing module and the preset dynamic resource allocation strategy based on fuzzy logic. This module can interact with the server management system and network bandwidth management equipment; The website construction execution module is responsible for executing the regular website construction process, using dynamically allocated resources to complete website construction, monitoring resource usage during the construction process, and providing timely feedback to the data acquisition module and resource allocation module. During the website construction process, an intelligent template recommendation method based on multi-attribute decision-making is used to select templates.

Images