A big data-based mobile internet operation data intelligent management system

Abstract

The application discloses a kind of big data-based mobile internet operation data intelligent management system, it is related to data management field, including running intelligent management center, wherein is provided with data acquisition module, operation monitoring module, big data analysis module, operation coordination module and intelligent management module;Data acquisition module is used to collect internet base station data, network equipment data, equipment status data and user behavior data;Operation monitoring module is used to set generalized monitoring image, and it is classified and handled, generates monitoring visual image;Big data analysis module obtains corresponding operation coordination data packet according to monitoring visual image;Operation coordination module is used to generate individualized operation image according to user behavior data, and it is compared and analyzed with operation coordination data packet, obtains dynamic resource optimization configuration result;Intelligent management module is used to carry out coordination management according to optimization result;The application improves resource utilization rate in data operation process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, and particularly relates to a mobile internet operation data intelligent management system based on big data. BACKGROUND

[0002] With the rapid development of information technology, mobile internet has penetrated into all aspects of social life, and its application scenarios are increasingly rich, covering social entertainment, e-commerce, online education, mobile office and many other fields, and has become an indispensable part of people's daily life and work. However, with the expansion of the application range of mobile internet and the increase of the use amount, mobile internet needs to meet the functional requirements of strong data real-time, strong data analysis ability and data security; therefore, intelligent management of the operation data of mobile internet is needed to realize the smooth operation of the corresponding mobile internet and to avoid resource waste to the greatest extent, which is a problem to be solved. Therefore, the present application provides a mobile internet operation data intelligent management system based on big data. SUMMARY

[0003] The present application aims at solving the problem of resource waste in the prior art, and provides a mobile internet operation data intelligent management system based on big data.

[0004] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme:

[0005] The present application adopts the following technical scheme:

[0006] The data acquisition module is used for acquiring the corresponding internet base station data, network equipment data, equipment state data and user behavior data in the corresponding management area.

[0007] The operation monitoring module is used for analyzing and processing the obtained internet base station data and network equipment data, obtaining a generalized monitoring image, and performing operation monitoring classification processing on the corresponding equipment state data according to the generalized monitoring image, to generate a corresponding monitoring visualization image.

[0008] The big data analysis module is used for matching analysis according to the operation monitoring classification processing result in the corresponding monitoring visualization image, obtaining a coordination matching index corresponding to different management areas, and generating a corresponding operation coordination data packet.

[0009] The operation coordination module is configured to generate a personalized operation image corresponding to the monitoring area according to the device state data and the user behavior data, compare the obtained personalized operation image with a corresponding operation coordination data packet, and perform dynamic resource optimization configuration processing according to a comparison result.

[0010] The intelligent management module is configured to intelligently allocate and manage the mobile Internet resources in the corresponding monitoring area according to the dynamic resource optimization configuration result.

[0011] The above technical solution further includes the following process of collecting the Internet base station data, the network device data, the device state data, and the user behavior data in the management area:

[0012] The management area range of the operation intelligent management center is obtained, the corresponding base station collection authority is obtained according to the management area range, the Internet base station data of the corresponding base station device in the corresponding management area range is collected according to the base station collection authority, and the Internet base station data is the base station configuration data and the operation state data corresponding to the corresponding base station.

[0013] A base station hardware monitoring terminal is arranged in the corresponding base station device, and the network device data and the device state data connected thereto are obtained through the base station hardware monitoring terminal.

[0014] The corresponding signaling and traffic probes are arranged according to the network device data, and the user behavior data corresponding to the corresponding mobile Internet device is obtained according to the signaling and traffic probes.

[0015] The collected data is marked according to the corresponding time information and the location information.

[0016] Further, the process of obtaining the generalized monitoring image includes:

[0017] The location information corresponding to each base station device is obtained, the corresponding management center point is arranged according to the location information of the corresponding base station device, the location information corresponding to the corresponding network device data is obtained, and the initial device image is generated according to the location information of each data.

[0018] The area monitoring radius R and the area monitoring period T of the corresponding management center point in the initial device image are obtained, the network device area data WY rt of each annular management area connected with the corresponding base station device in the initial device image is obtained according to the area monitoring radius rt . The network device area data WY rt is obtained by calculation, the network device area data WY rt is subjected to limited term summation, and the network device comprehensive data WZ T is obtained. The obtained network device area data WYrt and network equipment comprehensive data WZ T respectively with the internet base station data HJ correlation analysis;

[0019] According to the corresponding correlation data set corresponding regional monitoring indicators and comprehensive monitoring indicators;

[0020] The obtained network equipment data is respectively evaluated and analyzed according to the regional monitoring indicators and the comprehensive monitoring indicators, and the corresponding evaluation deviation data is obtained. The obtained evaluation deviation data is subjected to pixel value mapping processing, and is mapped into the corresponding initial device image according to the pixel value mapping processing result, so as to obtain the corresponding generalized monitoring image.

[0021] Further, the process of generating a monitoring visualization image includes:

[0022] A corresponding classification evaluation system in the corresponding generalized monitoring image is pre-stored, and the classification evaluation system includes classification evaluation indicators corresponding to single device states and classification evaluation indicators corresponding to overall device states in the region;

[0023] According to the classification evaluation system, a single evaluation index set and a regional evaluation index set are respectively set;

[0024] The device state data corresponding to the corresponding network equipment data in the generalized monitoring image is iteratively matched with the single evaluation index set, and the corresponding classification result is obtained. The classification result corresponding to the corresponding device state data in the annular management region is quantitatively counted, and the quantitatively counted result is iteratively matched with the regional evaluation index set, and the classification result of the corresponding annular management region is obtained;

[0025] According to the corresponding classification result, an identification process is performed, and a monitoring visualization image is generated according to the identification result.

[0026] Further, the process of generating a corresponding running coordination data packet includes:

[0027] The classification results corresponding to the regional evaluation index set and the single evaluation index set are sorted according to the corresponding time sequence, the sorted results of the time sequence are filled based on the linear difference formula, the classification results after filling are set with corresponding time sequence data and signal conversion data, and the corresponding periodic characteristic data is obtained according to the corresponding time sequence data and signal conversion data;

[0028] The obtained periodic characteristic data is subjected to statistical analysis, and q characteristic values corresponding to each unit time are obtained. A characteristic standard value set is generated, and the characteristic standard value set is analyzed to obtain a characteristic evaluation indicator corresponding to the corresponding periodic characteristic data;

[0029] The obtained monitoring area is combined and analyzed to generate a corresponding control group. The corresponding periodic characteristic data and characteristic evaluation indexes in the control group are analyzed to obtain the deviation data corresponding to the characteristic evaluation indexes in the corresponding unit time. The corresponding coordination matching data is obtained according to the deviation data. The coordination matching data of each control group corresponding to the corresponding monitoring area is compared and analyzed. The coordination matching indexes of the corresponding control groups are obtained according to the comparison and analysis results. The operation coordination data packet is generated according to the coordination matching indexes.

[0030] Further, the process of generating the individual operation image corresponding to the corresponding monitoring area includes:

[0031] The device state data and user behavior data corresponding to the corresponding network equipment data are obtained. The user behavior data is analyzed and processed to obtain the corresponding user behavior data set. The user behavior data set is analyzed and trained based on a deep learning algorithm to construct a corresponding individual user behavior model.

[0032] The user behavior data is input into the individual user behavior model, and the corresponding deviation data is output. The deviation data corresponding to the corresponding time information and corresponding characteristic evaluation indexes is visually displayed to construct the corresponding individual operation image.

[0033] Further, the process of comparing and analyzing the obtained individual operation image with the corresponding operation coordination data packet includes:

[0034] The corresponding individual operation image in the corresponding monitoring area is regionally complementarily integrated according to the corresponding characteristic evaluation indexes. The operation coordination data is obtained according to the regional complementary integration result.

[0035] The obtained operation coordination data and the corresponding coordination matching index in the operation coordination data packet are weighted to obtain the priority coordination data of the corresponding monitoring area.

[0036] The obtained priority coordination data is sorted and integrated. The monitoring visualization image corresponding to the corresponding monitoring area is obtained according to the sorting and integration result. The resource domination data is set according to the corresponding device state data in the monitoring visualization image and the corresponding internet base station data.

[0037] The operation coordination data is dynamically resource-optimized and configured to the resource domination data corresponding to the corresponding monitoring area according to the corresponding priority coordination data. The dynamic resource optimization and configuration processing result is obtained. The dynamic resource optimization and configuration processing result is obtained according to the deviation data corresponding to the corresponding individual operation image.

[0038] Further, the process of intelligently allocating and managing the mobile internet resources in the corresponding monitoring area includes:

[0039] The dynamic resource optimization and configuration processing results are fed back to the intelligent management module corresponding to the base station equipment. The intelligent management module allocates and manages the operating resource data of the corresponding base station equipment according to the dynamic resource optimization and configuration processing results, so that the corresponding mobile Internet equipment in each monitoring area can operate normally.

[0040] The present invention has the following beneficial effects:

[0041] 1. In this invention, data from Internet base stations, network devices, device status, and user behavior within a corresponding management area are collected. Based on the collection results, the data from Internet base stations, network devices, device status, and user behavior are analyzed and processed to obtain corresponding generalized monitoring images, monitoring visualization images, and personalized operation images. The generalized monitoring images are used to refine the corresponding network devices within the management area to obtain corresponding monitoring visualization images. The monitoring areas are then divided using the monitoring visualization images. Based on the monitoring area division results, corresponding coordination and matching indices are set. The data within the corresponding monitoring areas are then coordinated and processed based on the coordination and matching indices, thereby improving the resource coordination and utilization between various monitoring areas to a certain extent and increasing the utilization rate of corresponding resources within the monitoring areas.

[0042] 2. In this invention, by analyzing and processing user behavior data within the monitoring area, corresponding personalized operation images are obtained. The resource allocation data of the corresponding user is determined from the personalized operation images. In the process of dynamic resource optimization and allocation, resources are allocated according to user usage habits. This ensures that resource utilization is improved to a certain extent, while also taking into account user usage habits to the greatest extent, thereby improving the personalized needs of users. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the structure of a mobile internet operation data intelligent management system based on big data proposed in this invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Example 1

[0046] like Figure 1As shown, the present invention proposes a mobile internet operation data intelligent management system based on big data, which includes an operation intelligent management center. The operation intelligent management center is equipped with a data acquisition module, an operation monitoring module, a big data analysis module, an operation coordination module, and an intelligent management module.

[0047] In this embodiment, the intelligent management center analyzes and processes user behavior data corresponding to mobile internet devices within a specific area. Based on the analysis results, it monitors and manages the data operation process of mobile internet devices within the area. Based on the monitoring results, it coordinates the operation process of the corresponding mobile internet devices to ensure smooth operation of the data operation process of all mobile internet devices within the area. Furthermore, it integrates the coordination process based on the user behavior data of the corresponding mobile internet devices to ensure smooth operation within the scope of user needs, thereby improving the rationality of data allocation and resource utilization. The specific implementation process includes:

[0048] The data acquisition module is used to collect data from Internet base stations, network devices, device status, and user behavior within the corresponding management area.

[0049] The operation monitoring module is used to analyze and process the obtained Internet base station data and network device data, obtain a generalized monitoring image, classify and process the corresponding device status data according to the generalized monitoring image, and generate a corresponding monitoring visualization image.

[0050] The big data analysis module is used to perform matching analysis based on the operation monitoring classification processing results in the corresponding monitoring visualization images, obtain the coordination matching index corresponding to different management areas, and generate corresponding operation coordination data packets;

[0051] The operation coordination module is used to generate personalized operation images corresponding to the monitoring area based on device status data and user behavior data, compare and analyze the obtained personalized operation images with the corresponding operation coordination data packets, and perform dynamic resource optimization and configuration processing based on the comparison and analysis results.

[0052] The intelligent management module is used to intelligently allocate and manage mobile Internet resources within the corresponding monitoring area based on the results of dynamic resource optimization.

[0053] Example 2

[0054] Based on Embodiment 1, this embodiment describes in detail the implementation process of the data acquisition module, operation monitoring module, big data analysis module, operation coordination module, and intelligent management module set up in the intelligent management center, wherein:

[0055] The data acquisition module is used to collect data from internet base stations, network devices, device status, and user behavior within the corresponding area, and sends the collected data to the operation monitoring module. The specific implementation process includes:

[0056] Set up management acquisition units and device acquisition units;

[0057] The management collection unit collects internet base station data and network device data within a corresponding area. The management collection unit obtains the management area of ​​the intelligent management center, acquires base station collection permissions within that area, and collects internet base station data from the corresponding base station devices within that management area based on those permissions. The internet base station data includes the base station configuration data and operational status data corresponding to each base station.

[0058] Base station configuration data includes base station identification information, geographical location information, and hardware configuration information, etc.

[0059] Operational status data includes base station equipment operational status data, signal quality data, and connection status data. Base station equipment operational status data also includes the corresponding base station equipment performance data.

[0060] Based on the corresponding Internet base station data within the management area, a base station hardware monitoring terminal is set up. The base station hardware monitoring terminal obtains mobile Internet devices that are interconnected with the corresponding base station through the corresponding network management protocol, integrates the mobile Internet devices interconnected with the corresponding base station, and obtains the corresponding network device data.

[0061] The specific implementation process of collecting relevant device status data and user behavior data through the device acquisition unit includes:

[0062] The network device data obtained by the base station hardware monitoring terminal is analyzed for device status. The network device data is monitored and processed based on the network layer corresponding to the corresponding base station hardware monitoring terminal to obtain the device status data corresponding to the network device data. The device status data is the signal strength, signal quality and connection stability data corresponding to the network device data in the base station within the corresponding management area.

[0063] Obtain the signaling and traffic probes corresponding to the network device data within the base station corresponding to the corresponding management area; monitor the user behavior data corresponding to the network device data based on the signaling and traffic probes corresponding to the network device data; and obtain the user behavior data corresponding to the network device data, wherein the user behavior data includes the signaling data and traffic data of the corresponding network device.

[0064] The data collected by the management acquisition unit, network equipment data, equipment status data, and user behavior data are respectively marked with time and location information, and then sent to the operation monitoring module for monitoring and processing based on the time and location information marking results.

[0065] The operation monitoring module is used to analyze and process the obtained Internet base station data and network device data to obtain a generalized monitoring image. Based on the generalized monitoring image, the corresponding device status data is classified and processed for operation monitoring, and corresponding monitoring visualization images are generated. The specific implementation process includes:

[0066] Set up a generalized monitoring unit and a classification processing unit;

[0067] The system analyzes and processes internet base station data, network device data, and device status data obtained from various base station devices within the corresponding management area using a generalized monitoring unit. It then quantifies the operational process of the corresponding base station devices and generates corresponding generalized monitoring images based on the quantification results. The specific implementation process includes:

[0068] Internet base station data is labeled HJ, and network device data corresponding to the relevant time and location information is labeled WS. st Where s represents the location information corresponding to the network device data, and t represents the time information corresponding to the network device data;

[0069] Internet base station data (HJ) and network device data (WS) st Perform correlation analysis based on network device data WS st The corresponding location information within the area maps the corresponding network device data to the corresponding location within the management area. Based on the mapping result, an initial device image within the corresponding management area is obtained. Based on the initial device image, the network device data and Internet base station data are analyzed and processed. The specific implementation process includes:

[0070] Obtain the distribution of corresponding network device data within the initial device image. Based on the location information of the Internet base station data, set the corresponding area monitoring radius R and area monitoring period T within the initial device image. Based on the area monitoring radius, obtain the network device area data WY corresponding to each circular management area within the initial device image. rt ,in:

[0071] α is the weighting factor for the initial device image corresponding to the monitoring radius of the corresponding area, applied to the network device area data WY. rt Perform a finite summation to obtain the comprehensive network device data WZ corresponding to the initial device image. T ,in: m represents the number of data points in the corresponding network device area;

[0072] WY, the obtained network device area data rt And network equipment comprehensive data WZ T Correlation analysis was performed between the network equipment data and the Internet base station data HJ. The obtained data was preprocessed to obtain a standard dataset. This standard dataset was then mapped to a two-dimensional coordinate system. Correlation curves were obtained for network device regional data and network device comprehensive data with respect to the Internet base station data. Correlation analysis was then performed on these curves to obtain the corresponding correlation data P(X, Y), where X and Y represent the data types of the corresponding variables, and X = (x1, x2, ..., x...). n Y = (y1, y2, ..., y) n The coordinates of the associated curves in the two-dimensional spatial coordinate system are respectively.

[0073] Where, x i and y i Each corresponds to a separate two-dimensional curve;

[0074] Based on the relevant correlation data, set corresponding regional monitoring indicators and comprehensive monitoring indicators;

[0075] The obtained network device data is evaluated and analyzed according to regional monitoring indicators and comprehensive monitoring indicators to obtain corresponding evaluation deviation data. The obtained evaluation deviation data is then processed by pixel value mapping. Based on the pixel value mapping processing results, the data is mapped to the corresponding initial device image to obtain the corresponding generalized monitoring image. The generalized monitoring image is then sent to the classification processing unit.

[0076] The classification processing unit is used to perform classification processing on the generalized monitoring image to obtain a monitoring visualization image. Its specific implementation process includes:

[0077] Priority sorting is performed based on the pixel values ​​corresponding to the mapping results within the generalized monitoring image. Evaluation deviation data corresponding to the corresponding pixel values ​​is obtained. Priority data corresponding to the generalized monitoring image and the corresponding monitoring area is set based on the evaluation deviation data. Operation monitoring classification processing is performed on the corresponding equipment status data and user behavior data in sequence according to the priority data.

[0078] Among them, the generalized monitoring image pre-stores the classification and evaluation system corresponding to the equipment status data. Different classification and evaluation systems include classification and evaluation indicators corresponding to a single device and classification and evaluation indicators corresponding to multiple devices in the area.

[0079] Based on the classification and evaluation system, separate sets of single evaluation indicators and regional evaluation indicators are set up.

[0080] The corresponding device status data within the generalized monitoring image is traversed and matched with the single evaluation index set corresponding to the classification evaluation index to obtain the classification results of the corresponding device status data and user behavior data.

[0081] The corresponding classification results are quantitatively statistically analyzed, and the obtained quantitative statistical results are matched with the regional evaluation index set corresponding to the corresponding classification evaluation index to obtain the classification results of the equipment status data and user behavior data in the corresponding monitoring area.

[0082] The classification results corresponding to the regional evaluation index set and the single evaluation index set are marked with corresponding horizontal and vertical table labels, where the horizontal axis represents the monitoring results within the corresponding region and the vertical axis represents the monitoring results of the corresponding single type of data.

[0083] The results of the cross-sectional and horizontal charts are labeled and the corresponding classification results are processed to generate a monitoring visualization image based on the labeling results.

[0084] The big data analysis module is used to perform matching analysis based on the operation monitoring classification processing results within the corresponding monitoring visualization images, obtain the coordination matching index corresponding to different management areas, and generate corresponding operation coordination data packages. Its specific implementation process includes:

[0085] Obtain the corresponding cross-sectional and horizontal chart markings within the monitoring visualization image, analyze and process the cross-sectional and horizontal chart markings, and obtain the corresponding operational monitoring cycle for each monitoring area, where:

[0086] The results of the vertical and horizontal tables corresponding to the classification results of the regional evaluation index set and the single evaluation index set are sorted according to the corresponding time order;

[0087] Based on the sorting results in chronological order, the corresponding horizontal and vertical processing results within the marked results of the horizontal and vertical tables are used to obtain the corresponding operation monitoring cycles in sequence.

[0088] Missing values ​​were handled for the data corresponding to the horizontal and vertical processing results respectively, and imputation was performed based on the linear interpolation formula;

[0089] For the horizontal and vertical processing results after the filling process is completed, set the corresponding time series data and signal transformation data. Label the time series data as t, and label the corresponding signal transformation data as f(t). Obtain the corresponding periodic feature data W. f (a, b), where:

[0090] Where a is the corresponding scale parameter and b is the corresponding translation parameter. The signal conversion data is controlled and converted by the scale parameter and translation parameter. The distribution and changes of the signal conversion data under different scale parameters and translation parameters are obtained according to the control conversion results. The periodic feature data corresponding to the corresponding horizontal and vertical processing results are obtained according to the corresponding changes.

[0091] Based on the obtained periodic characteristic data, set the characteristic standard value corresponding to the corresponding unit time. Perform statistical analysis on the characteristic values ​​within the corresponding period of the periodic characteristic data to obtain the corresponding characteristic standard value. Obtain p periodic characteristic data, obtain q characteristic values ​​corresponding to each unit time, generate a characteristic standard value set, analyze and process the characteristic standard value set, and obtain the corresponding standard deviation data S, where:

[0092] in, Based on the corresponding standard deviation data, obtain the characteristic evaluation indicators corresponding to the periodic characteristic data;

[0093] Based on the periodic characteristic data and characteristic evaluation indicators of each monitoring area and individual mobile Internet device, the corresponding coordination and matching index is obtained through analysis and processing. The specific implementation process includes:

[0094] The obtained monitoring areas are combined and analyzed to obtain the corresponding control group. The corresponding periodic characteristic data and characteristic evaluation indicators in the control group are compared and analyzed to obtain the deviation data corresponding to the characteristic evaluation indicators within the corresponding unit time. The deviation data within the unit time is integrated and processed to obtain the weight reference coefficient within the corresponding unit time. Based on the weight reference coefficient and deviation data within the unit time, the coordination and matching data of the corresponding control group are obtained.

[0095] The coordination matching data of each control group corresponding to the corresponding monitoring area are compared and analyzed. The data are sorted according to the comparison and analysis results. The coordination matching index of the corresponding control group is obtained according to the sorting results. The corresponding operation coordination data package is generated according to each coordination matching index and sent to the operation coordination module.

[0096] The operation coordination module is used to generate personalized operation images corresponding to the monitoring area based on equipment status data and user behavior data. It then compares and analyzes the obtained personalized operation images with the corresponding operation coordination data packets, and performs dynamic resource optimization and configuration based on the comparison and analysis results. The specific implementation process includes:

[0097] Set up personalized analysis units and coordinated analysis units;

[0098] The personalized analysis unit is used to acquire device status data and user behavior data corresponding to the network device data, analyze and process the user behavior data to acquire the corresponding user behavior dataset, analyze and train the user behavior dataset based on deep learning algorithms, and construct the corresponding personalized user behavior model.

[0099] User behavior data is input into a personalized user behavior model, and corresponding deviation data is output. Based on the corresponding time information and the deviation data corresponding to the corresponding feature evaluation index, a corresponding personalized operation image is constructed. The personalized operation image includes the deviation data corresponding to each user in the corresponding monitoring area. Idle deviation data and load deviation data are visualized through the corresponding RGB values, so as to facilitate the coordination and analysis unit to perform regional complementary integration of personalized operation images in the corresponding monitoring area.

[0100] The coordination and analysis unit is used to perform regional complementary integration of the personalized operation images corresponding to the respective monitoring areas based on corresponding feature evaluation indicators. Its specific implementation process includes:

[0101] Based on the personalized operation images corresponding to the respective monitoring areas, the data is integrated according to the corresponding RGB values ​​to obtain comprehensive idle data and comprehensive load data for the area.

[0102] Based on the obtained regional comprehensive idle data and regional comprehensive load data, mapping difference analysis is performed according to the corresponding RGB values ​​in the corresponding monitoring areas. Based on the deviation data corresponding to the RGB values ​​of the personalized operation images of the corresponding users, regional complementary integration is performed. The regional complementary integration results are analyzed and processed to obtain the inter-regional complementary integration results. Based on the regional complementary integration results, the corresponding operation coordination data is obtained.

[0103] The obtained operational coordination data is weighted with the corresponding coordination matching index in the operational coordination data package to obtain the priority coordination data for the corresponding monitoring area.

[0104] The obtained priority coordination data is sorted and integrated. Based on the sorting and integration results, the corresponding monitoring visualization images of the monitoring areas are obtained. Based on the corresponding equipment status data and corresponding Internet base station data in the monitoring visualization images, resource allocation data is set.

[0105] The operation coordination data is dynamically optimized and configured based on the corresponding priority coordination data and the resource allocation data corresponding to the corresponding monitoring area to obtain the corresponding dynamic resource optimization and configuration processing results. The dynamic resource optimization and configuration processing results are obtained by matching the deviation data corresponding to the corresponding personalized operation image.

[0106] The intelligent management module is used to intelligently allocate and manage mobile internet resources within the corresponding monitoring area based on the corresponding dynamic resource optimization configuration results. Its specific implementation process includes:

[0107] The dynamic resource optimization and configuration processing results are fed back to the intelligent management module corresponding to the base station equipment. The intelligent management module allocates and manages the operating resource data of the corresponding base station equipment according to the dynamic resource optimization and configuration processing results, so that the corresponding mobile Internet equipment in each monitoring area can operate normally.

[0108] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A mobile internet operation data intelligent management system based on big data, comprising an operation intelligent management center, characterized in that, The intelligent operation management center includes a data acquisition module, an operation monitoring module, a big data analysis module, an operation coordination module, and an intelligent management module. The data acquisition module is used to collect data from the corresponding Internet base stations, network devices, device status, and user behavior within the management area. The operation monitoring module is used to analyze and process the obtained Internet base station data and network equipment data, obtain generalized monitoring images, classify and process the corresponding equipment status data according to the generalized monitoring images, and generate corresponding monitoring visualization images. The big data analysis module is used to perform matching analysis based on the operation monitoring classification processing results in the corresponding monitoring visualization images, obtain the coordination matching index corresponding to different management areas, and generate corresponding operation coordination data packages; The operation coordination module is used to generate personalized operation images corresponding to the monitoring area based on equipment status data and user behavior data, compare and analyze the obtained personalized operation images with the corresponding operation coordination data packages, and perform dynamic resource optimization and configuration based on the comparison and analysis results. The intelligent management module is used to intelligently allocate and manage mobile Internet resources within the corresponding monitoring area based on the results of dynamic resource optimization. The process of acquiring generalized surveillance images includes: Obtain the location information corresponding to each base station device, set the corresponding management center point according to the location information of the corresponding base station device, obtain the location information corresponding to the data of the corresponding network device, and generate an initial device image according to the location information of each data. Obtain the corresponding management center point within the initial device image, set the area monitoring radius R and area monitoring period T, and based on the area monitoring radius, obtain the network device area data WY connecting each annular management area within the initial device image to the corresponding base station equipment. rt For network device area data WY rt Perform a finite summation to obtain the comprehensive network device data WZ. T The obtained network device area data WY rt And network equipment comprehensive data WZ T Correlation analysis was performed between the data and the Internet base station data HJ. Based on the relevant correlation data, set corresponding regional monitoring indicators and comprehensive monitoring indicators; The obtained network device data is evaluated and analyzed according to regional monitoring indicators and comprehensive monitoring indicators to obtain corresponding evaluation deviation data. The obtained evaluation deviation data is then processed by pixel value mapping, and the pixel value mapping results are mapped to the corresponding initial device image to obtain the corresponding generalized monitoring image.

2. The intelligent management system for mobile internet operation data based on big data according to claim 1, characterized in that, The process of collecting data from internet base stations, network devices, device status, and user behavior within the managed area includes: Obtain the management area of ​​the intelligent management center, obtain the corresponding base station data collection permission based on the management area, and collect the Internet base station data of the corresponding base station equipment within the corresponding management area based on the base station data collection permission. The Internet base station data is the base station configuration data and operation status data corresponding to the corresponding base station. A base station hardware monitoring terminal is set up in the corresponding base station equipment, and the corresponding network equipment data and equipment status data are obtained through the base station hardware monitoring terminal. Based on network device data, set up corresponding signaling and traffic probes, and obtain user behavior data corresponding to the mobile Internet devices based on the signaling and traffic probes; The collected data is labeled according to the corresponding time and location information.

3. The intelligent management system for mobile internet operation data based on big data according to claim 1, characterized in that, The process of generating monitoring visualization images includes: A corresponding classification and evaluation system is pre-set within the generalized monitoring image. The classification and evaluation system includes classification and evaluation indicators corresponding to the status of a single device and classification and evaluation indicators corresponding to the overall status of devices within the region. Based on the classification and evaluation system, separate sets of single evaluation indicators and regional evaluation indicators are set up. The device status data corresponding to the network device data within the generalized monitoring image is matched with a single evaluation index set to obtain the corresponding classification results. The classification results corresponding to the device status data within the circular management area are quantified and statistically analyzed. The quantified and statistical results are matched with the regional evaluation index set to obtain the classification results for the corresponding circular management area. The images are labeled according to the corresponding classification results, and monitoring visualization images are generated based on the labeling results.

4. The intelligent management system for mobile internet operation data based on big data according to claim 3, characterized in that, The process of generating the corresponding runtime coordination data package includes: The classification results corresponding to the regional evaluation index set and the single evaluation index set are sorted according to the corresponding time order. The sorted results according to the time order are filled based on the linear difference formula. The classification results after the filling process are set with corresponding time series data and signal conversion data. The corresponding periodic feature data are obtained based on the corresponding time series data and signal conversion data. Statistical analysis is performed on the obtained periodic characteristic data to obtain q characteristic values ​​corresponding to each unit of time, generate a set of characteristic standard values, analyze and process the set of characteristic standard values, and obtain the characteristic evaluation index corresponding to the periodic characteristic data. The obtained monitoring areas are combined and analyzed to generate corresponding control groups. The corresponding periodic characteristic data and characteristic evaluation indicators in each control group are compared and analyzed to obtain the deviation data corresponding to the characteristic evaluation indicators within the corresponding unit time. Based on the corresponding deviation data, the corresponding coordination and matching data are obtained. The coordination and matching data of each control group corresponding to the corresponding monitoring area are compared and analyzed. The results of the comparison and analysis are sorted to obtain the coordination and matching index of the corresponding control group. Based on the corresponding coordination and matching index, the running coordination data package is generated.

5. The intelligent management system for mobile internet operation data based on big data according to claim 4, characterized in that, The process of generating personalized operational images corresponding to the monitoring area includes: Obtain the device status data and user behavior data corresponding to the network device data, analyze and process the user behavior data to obtain the corresponding user behavior dataset, analyze and train the user behavior dataset based on deep learning algorithms, and build the corresponding personalized user behavior model. User behavior data is input into a personalized user behavior model, and corresponding deviation data is output. Based on the deviation data corresponding to the relevant time information and the corresponding feature evaluation indicators, a corresponding personalized operation image is constructed for visualization.

6. The intelligent management system for mobile internet operation data based on big data according to claim 5, characterized in that, The process of comparing and analyzing the obtained personalized runtime images with the corresponding runtime coordination data packages includes: Personalized operational images within the corresponding monitoring area are integrated based on relevant feature evaluation indicators, and corresponding operational coordination data are obtained based on the results of the regional integration. The obtained operational coordination data is weighted with the corresponding coordination matching index in the operational coordination data package to obtain the priority coordination data for the corresponding monitoring area. The obtained priority coordination data is sorted and integrated. Based on the sorting and integration results, the corresponding monitoring visualization images of the monitoring areas are obtained. Based on the corresponding equipment status data and corresponding Internet base station data in the monitoring visualization images, resource allocation data is set. The operation coordination data is dynamically optimized and configured based on the corresponding priority coordination data and the resource allocation data corresponding to the corresponding monitoring area to obtain the corresponding dynamic resource optimization and configuration processing results. The dynamic resource optimization and configuration processing results are obtained by matching the deviation data corresponding to the corresponding personalized operation image.

7. The intelligent management system for mobile internet operation data based on big data according to claim 6, characterized in that, The process of intelligently allocating and managing mobile internet resources within the corresponding monitoring area includes: The dynamic resource optimization and configuration processing results are fed back to the intelligent management module corresponding to the base station equipment. The intelligent management module allocates and manages the operating resource data of the corresponding base station equipment according to the dynamic resource optimization and configuration processing results, so that the corresponding mobile Internet equipment in each monitoring area can operate normally.

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