A cloud-computing-based comprehensive service big data processing terminal and method

Abstract

This application provides a cloud computing-based integrated service big data processing terminal and method, applicable to the field of electronic digital data processing. The method includes: collecting information data sent by users to the terminal and storing it in a database; classifying integrated services into local services and cross-domain services based on the user-provided information data; after classifying the information data into integrated services, processing the information data in the database using a clustering algorithm; setting a threshold for cluster center selection on the information data, and sequentially selecting the remaining information data nodes as initial cluster centers with the threshold as the maximum interval before merging them; comparing the processed information data with the initial data stored in the database. Compared with traditional distributed and centralized cloud computing, the edge cloud computing architecture effectively combines the advantages of both traditional network structures, solving problems such as dispersed service resources and difficulties in resource coordination.

Description

Technical Field

[0001] This application relates to the field of electronic digital data processing, and in particular to a cloud computing-based integrated service big data processing terminal and method. Background Technology

[0002] With the development of the internet, demands are constantly changing. The lifecycle of internet-based marketing businesses is becoming shorter, and business development is accelerating. The exponential growth of data volumes necessitates real-time processing of massive amounts of data while ensuring its accuracy and reliability. In response, cloud computing and big data concepts have emerged. Cloud computing refers to a system with extremely high computing power formed through computer networks, capable of storing and aggregating relevant resources and being configured on demand to provide personalized services to users. However, current cloud computing-based data processing faces the following problems:

[0003] (1) In the process of big data processing, it is usually necessary to identify a single time feature or data feature, which cannot obtain more accurate identification results;

[0004] (2) Most existing data processing adopts a fully centralized structure, which is prone to performance bottlenecks under the pressure of big data. Furthermore, the database is difficult to extend, has a single access model, and has a single point of failure problem.

[0005] Reference patent application number CN202011513410.8 - A cloud computing-based big data integrated operation and maintenance platform processing system discloses: including a cloud computing module and a big data processing module; the cloud computing module includes HTCloud cloud operation unit, HTAppdy high-performance application delivery unit, and HTSecurity intelligent security platform; the big data processing module includes xy big data center integrated operation and maintenance management platform and xy multi-source heterogeneous data exchange platform; the xy big data center integrated operation and maintenance management platform includes a unified resource monitoring module, a real-time anomaly alarm module, a precise fault location module, and a visualization and decision analysis module; the xy multi-source heterogeneous data exchange platform adopts a hierarchical structure and is a unidirectional / bidirectional synchronous network topology, supporting branch node diffusion and non-diffusion strategies; the xy multi-source heterogeneous data exchange platform includes an encryption verification module, a data synchronization module, a data synchronization module between mainstream heterogeneous databases, as well as a synchronization monitoring module, a synchronization topology module, and a synchronization scheduling module.

[0006] This prior art optimizes the parallel processing of data transmission and database operations by adopting a client-server synchronous model. However, this model has poor data confidentiality and causes data to be scattered, which is not conducive to processing. Therefore, a cloud computing-based integrated service big data processing terminal and method are proposed. Summary of the Invention

[0007] The purpose of this application is to provide a cloud computing-based integrated service big data processing terminal and method, which aims to solve the problem of not being able to obtain more accurate identification results in the process of big data processing.

[0008] To achieve the above objectives, this application provides the following technical solution:

[0009] This application provides a cloud computing-based integrated service big data processing method, including:

[0010] S1: Collect information data sent by the user to the terminal and save it to the database. Information authentication is required when the user logs into the cloud computing system for the first time.

[0011] S2: Based on the information and data provided by the user, the integrated service is divided into local service and cross-domain service. Local service is the service used by the user and is provided by the edge cloud where the user is located. Cross-domain service is the service provided by the edge cloud to the user under the coordination of the core cloud.

[0012] S3: After performing comprehensive service classification on the information data, the information data is processed in the database using a clustering algorithm to calculate the distance between nodes in the information data set. The formula is as follows: Where d i Let n be the distance between information data nodes, and l be the total number of information data nodes. ij To calculate the distance between information data node i and information data node j, we also need to calculate the average distance between information data nodes. This distance is the arithmetic mean of the distances between all information data nodes, and the formula is: Where n is the total number of data nodes, d i The distance between data nodes of information data node i;

[0013] S4: Select a threshold for cluster centers of information data. The threshold is used as the selection criterion for initial cluster centers. The formula is: Q = 2 × D / K, where D is the average distance between data nodes and K is the set number of initial clusters. The remaining information data nodes are selected sequentially as initial cluster centers with the threshold as the maximum interval, and then the clusters are combined.

[0014] S5: After processing the information data, compare it with the initial data stored in the database, specify the information data, and then send it to the user terminal.

[0015] Furthermore, the step of collecting information data sent by the user to the terminal and saving it to the database, and requiring information authentication when the user logs into the cloud computing system for the first time, includes:

[0016] The authentication gateway verifies the authentication information entered by the user. If the authentication is successful, the gateway processes the information and grants the user permissions. If the authentication fails, the user is prompted to re-enter the authentication information. If the authentication fails three times in a row, the user is blacklisted.

[0017] Furthermore, the step of dividing the integrated service into local services and cross-domain services based on user-provided information data, where local services are provided by the edge cloud where the user resides, and cross-domain services are provided by the edge cloud to the user under the coordination of the core cloud, includes:

[0018] When the integrated service is divided into local services based on the information data provided by the user, the cloud computing service manager checks whether the service is provided in the local edge cloud. If the service is provided, the cloud computing service manager returns the service address to the terminal. The terminal accesses the corresponding service based on the service address. The cloud computing service manager automatically stores and backs up the information data.

[0019] When a comprehensive service is divided into cross-domain services based on information provided by the user, the cloud service manager queries the core cloud and provides the edge cloud address of the service, and returns the edge cloud address to the terminal. The user can then access the required service through this address.

[0020] Furthermore, step S3 also includes:

[0021] The distance between each data node in the information dataset is calculated, and the calculated distances are sorted in ascending order. The data node with the smallest distance is selected as the first initial cluster center, and the remaining data nodes are selected as initial cluster centers in turn with a threshold as the maximum interval. The clusters of all data nodes to the initial cluster centers are calculated, and they are sequentially assigned to the nearest cluster center. A bottom-up hierarchical clustering algorithm is used to perform the calculation, thereby completing the initialization and merging of the information dataset.

[0022] This application also provides a cloud computing-based integrated service big data processing terminal, including:

[0023] Data collection module: Collects information data sent by users to the terminal and saves it to the database. Information authentication is required when a user logs into the cloud computing system for the first time.

[0024] Classification module: Based on the information and data provided by the user, the integrated services are divided into local services and cross-domain services. Local services are services used by the user and provided by the edge cloud where the user is located, while cross-domain services are services provided by the edge cloud to the user under the coordination of the core cloud.

[0025] Data processing module: After performing comprehensive service classification on the information data, it processes the information data in the database using a clustering algorithm to calculate the distance between nodes in the information data set, using the following formula: Where d i Let n be the distance between information data nodes, and l be the total number of information data nodes. ij To calculate the distance between information data node i and information data node j, we also need to calculate the average distance between information data nodes. This distance is the arithmetic mean of the distances between all information data nodes, and the formula is: Where n is the total number of data nodes, d i The distance between data nodes of information data node i;

[0026] Merging module: The information data is clustered with a threshold value. The threshold value is used as the selection criterion for the initial cluster centers. The formula is: Q = 2 × D / K, where D is the average distance between data nodes and K is the set number of initial clusters. The remaining information data nodes are selected sequentially as the initial cluster centers with the threshold value as the maximum interval, and then the data is merged.

[0027] Sending module: After processing the information data, it compares it with the initial data stored in the database, refines the information data, and then sends it to the user terminal.

[0028] Furthermore, the sending module also includes:

[0029] Object visualization units are used to transform information data into visualized data and objects.

[0030] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described cloud computing-based integrated service big data processing method.

[0031] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described cloud computing-based integrated service big data processing method.

[0032] This application provides a cloud computing-based integrated service big data processing terminal and method, which has the following beneficial effects:

[0033] (1) Allow users to log in to different edge clouds. Depending on whether the user's login location is the same as their registration location, the user's login behavior is divided into local login and remote login. The corresponding authentication methods are local authentication and remote authentication, so as to achieve automatic authentication without human intervention and proactive and timely delivery of authentication results.

[0034] (2) A hybrid clustering algorithm is used to process information data, which overcomes the problem of inaccurate classification caused by initial problems in the partitioning algorithm. Compared with traditional distributed and centralized cloud computing, the edge cloud computing architecture combines the advantages of the two traditional network structures and solves problems such as dispersed service resources and difficulty in resource coordination. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating a cloud computing-based integrated service big data processing method according to an embodiment of this application.

[0036] Figure 2 This is a structural block diagram of a cloud computing-based integrated service big data processing terminal according to an embodiment of this application;

[0037] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of this application.

[0038] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0039] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0040] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0041] Reference Appendix Figure 1 This is a flowchart illustrating the cloud computing-based integrated service big data processing method proposed in this application.

[0042] The cloud computing-based integrated service big data processing method provided in this application includes the following steps:

[0043] S1: Collect information data sent by the user to the terminal and save it to the database. When the user logs into the cloud computing system for the first time, information authentication is required. The user enters authentication information, and the authentication gateway authenticates the authentication information. If the authentication is successful, the information data is processed and the user is granted permissions. If the authentication fails, the user is prompted to re-enter the authentication information. If the authentication fails three times in a row, the user is blacklisted.

[0044] In this step, the main function of the terminal is to complete input and output, including submitting user information data and displaying processed data and service results. Therefore, any device that can access the cloud computing service manager through the network and complete input and output functions can be a terminal, such as tablets and smartphones. In order to provide users with convenient and fast services, users are allowed to log in to different edge clouds. According to whether the user's login location is the same as its registration location, the user's login behavior is divided into local login and remote login, and the corresponding authentication methods are local authentication and remote authentication, realizing automatic authentication without human intervention and proactive and timely delivery of authentication results.

[0045] S2: Based on the information provided by the user, the integrated service is divided into local services and cross-domain services. Local services are those provided by the edge cloud where the user resides, while cross-domain services are those provided by the edge cloud under the coordination of the core cloud. When the integrated service is divided into local services based on the information provided by the user, the cloud service manager checks whether the service is provided in the local edge cloud. If the service is provided, the cloud service manager returns the service address to the terminal, and the terminal accesses the corresponding service based on the service address. The cloud service manager automatically stores and backs up the information data. When the integrated service is divided into cross-domain services based on the information provided by the user, the cloud service manager queries the core cloud and provides the edge cloud address of the service, and returns the edge cloud address to the terminal. The user accesses the required service through this address.

[0046] In this step, when a user generates data while using the service, the cloud computing service manager automatically stores and backs up the data. The data management method is as follows: the user's virtual machine generates a storage request M1 (user identifier, information data size). After receiving the request, the storage allocation and reclamation system requests storage space M2 (storage size) from the unused storage system. The unused storage system returns storage space to the storage allocation and reclamation system M3 (storage address). After receiving the storage space, the storage allocation and reclamation system sends information M4 (user identifier, data size, storage address) and records the sent information. In cross-domain services, assuming a user logs into edge cloud A and submits information data, edge cloud A receives the request information and searches for the corresponding resource in resource management. If it is not found, it sends a request to the core cloud computing service management system to search for the information. Assuming the core cloud computing service management system finds that edge cloud B provides the corresponding service, the request information is routed to edge cloud B, which then searches for the service address and sends it to edge cloud A. Compared with traditional distributed and centralized cloud computing, the edge cloud computing architecture combines the advantages of both traditional network structures and solves problems such as dispersed service resources and difficulties in resource coordination.

[0047] S3: After performing comprehensive service classification on the information data, the information data is processed in the database using a clustering algorithm to calculate the distance between nodes in the information data set. The formula is as follows: Where d i Let n be the distance between information data nodes, and l be the total number of information data nodes. ij To calculate the distance between information data node i and information data node j, we also need to calculate the average distance between information data nodes. This distance is the arithmetic mean of the distances between all information data nodes, and the formula is: Where n is the total number of data nodes, d i Let i be the distance between data nodes in the information data set; calculate the distance between each data node in the information data set, and sort the calculated distances in ascending order. Select the data node with the smallest distance as the first initial cluster center, and select the remaining data nodes as initial cluster centers in turn with a threshold as the maximum interval; calculate the clusters of all data nodes to the initial cluster centers, and divide them into the nearest cluster centers in turn. Use a bottom-up hierarchical clustering algorithm to calculate, thereby completing the initialization and merging of the information data set.

[0048] In this step, taking advantage of the clear classification characteristic of the k-means algorithm, the k-means algorithm is first used once to initialize the information data to be clustered, and the resulting dataset has a clear initial classification. On this basis, a bottom-up hierarchical clustering algorithm is used to continue the calculation. The algorithm ends when the threshold is reached. This hybrid algorithm can overcome the problem of inaccurate classification caused by the initial problem in the partitioning algorithm, and can also greatly reduce the complexity and iteration depth of the hierarchical algorithm.

[0049] S4: Select a threshold for cluster centers of the information data. The threshold is used as the selection criterion for initial cluster centers. The formula is: Q = 2 × D / K, where D is the average distance between data nodes and K is the set number of initial clusters. The remaining information data nodes are selected sequentially as initial cluster centers with the threshold as the maximum interval, and then the clusters are combined.

[0050] In this step, the initial cluster centers are selected based on the distance between the information data nodes, from largest to smallest. The selected initial cluster centers will be the farthest data nodes among all the information data nodes, so the probability that these nodes belong to the same cluster will be the smallest. This will greatly improve the accuracy of the initial cluster center selection and the efficiency of the algorithm.

[0051] S5: After processing the information data, compare it with the initial data stored in the database, specify the information data, and then send it to the user terminal.

[0052] Reference Appendix Figure 2 The present invention also provides a cloud computing-based integrated service big data processing terminal, comprising:

[0053] Data collection module: Collects information data sent by users to the terminal and saves it to the database. Information authentication is required when a user logs into the cloud computing system for the first time.

[0054] Classification module: Based on the information and data provided by the user, the integrated services are divided into local services and cross-domain services. Local services are services used by the user and provided by the edge cloud where the user is located, while cross-domain services are services provided by the edge cloud to the user under the coordination of the core cloud.

[0055] Data processing module: After performing comprehensive service classification on the information data, it processes the information data in the database using a clustering algorithm to calculate the distance between nodes in the information data set, using the following formula: Where d i Let n be the distance between information data nodes, and l be the total number of information data nodes. ij To calculate the distance between information data node i and information data node j, we also need to calculate the average distance between information data nodes. This distance is the arithmetic mean of the distances between all information data nodes, and the formula is: Where n is the total number of data nodes, d i The distance between data nodes of information data node i;

[0056] Merging module: The information data is clustered with a threshold value. The threshold value is used as the selection criterion for the initial cluster centers. The formula is: Q = 2 × D / K, where D is the average distance between data nodes and K is the set number of initial clusters. The remaining information data nodes are selected sequentially as the initial cluster centers with the threshold value as the maximum interval, and then the data is merged.

[0057] Sending module: After processing the information data, it compares it with the initial data stored in the database, refines the information data, and then sends it to the user terminal.

[0058] Reference Figure 3 This application also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 3As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores information data and other data. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a cloud computing-based integrated service big data processing method.

[0059] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0060] One embodiment of this application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of a cloud computing-based integrated service big data processing method, specifically:

[0061] S1: Collect information data sent by the user to the terminal and save it to the database. Information authentication is required when the user logs into the cloud computing system for the first time.

[0062] S2: Based on the information and data provided by the user, the integrated service is divided into local service and cross-domain service. Local service is the service used by the user and is provided by the edge cloud where the user is located. Cross-domain service is the service provided by the edge cloud to the user under the coordination of the core cloud.

[0063] S3: After performing comprehensive service classification on the information data, the information data is processed in the database using a clustering algorithm to calculate the distance between nodes in the information data set. The formula is as follows: Where d i Let n be the distance between information data nodes, and l be the total number of information data nodes. ij To calculate the distance between information data node i and information data node j, we also need to calculate the average distance between information data nodes. This distance is the arithmetic mean of the distances between all information data nodes, and the formula is: Where n is the total number of data nodes, d i The distance between data nodes of information data node i;

[0064] S4: Select a threshold for cluster centers of information data. The threshold is used as the selection criterion for initial cluster centers. The formula is: Q = 2 × D / K, where D is the average distance between data nodes and K is the set number of initial clusters. The remaining information data nodes are selected sequentially as initial cluster centers with the threshold as the maximum interval, and then the clusters are combined.

[0065] S5: After processing the information data, compare it with the initial data stored in the database, specify the information data, and then send it to the user terminal.

[0066] In summary, this application collects information data sent by users to the terminal, divides the integrated service into local services and cross-domain services based on the information data, processes the data using a hybrid clustering algorithm after classification, greatly improves the accuracy of the initial cluster center selection and the efficiency of the algorithm, and finally compares the processed information data with the initial data stored in the database, concretizes the information data and then sends it to the user terminal.

[0067] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media provided in this application and in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0068] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0069] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0070] Although embodiments of this application 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 this application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive big data processing method based on cloud computing, characterized in that, include: S1: Collect information data sent by the user to the terminal and save it to the database. Information authentication is required when the user logs into the cloud computing system for the first time. S2: Based on the information and data provided by the user, the integrated service is divided into local service and cross-domain service. Local service is the service used by the user and is provided by the edge cloud where the user is located. Cross-domain service is the service provided by the edge cloud to the user under the coordination of the core cloud. S3: After performing comprehensive service classification on the information data, the information data is processed in the database using a clustering algorithm to calculate the distance between nodes in the information data set. The formula is as follows: Where d i Let n be the distance between information data nodes, and l be the total number of information data nodes. ij To calculate the distance between information data node i and information data node j, we also need to calculate the average distance between information data nodes. This distance is the arithmetic mean of the distances between all information data nodes, and the formula is: Where n is the total number of data nodes, d i The distance between data nodes of information data node i; S4: Select a threshold for cluster centers of information data. The threshold is used as the selection criterion for initial cluster centers. The formula is: Q = 2 × D / K, where D is the average distance between data nodes and K is the set number of initial clusters. The remaining information data nodes are selected sequentially as initial cluster centers with the threshold as the maximum interval, and then the clusters are combined. S5: After processing the information data, compare it with the initial data stored in the database, specify the information data, and then send it to the user terminal.

2. The cloud computing-based integrated service big data processing method according to claim 1, characterized in that, The step of collecting information data sent by the user to the terminal and saving it to the database, and requiring information authentication when the user logs into the cloud computing system for the first time, includes: The authentication gateway verifies the authentication information entered by the user. If the authentication is successful, the gateway processes the information and grants the user permissions. If the authentication fails, the user is prompted to re-enter the authentication information. If the authentication fails three times in a row, the user is blacklisted.

3. The cloud computing-based integrated service big data processing method according to claim 1, characterized in that, The step of dividing the integrated service into local services and cross-domain services based on user-provided information data, where local services are provided by the edge cloud where the user resides, and cross-domain services are provided by the edge cloud under the coordination of the core cloud, includes: When the integrated service is divided into local services based on the information data provided by the user, the cloud computing service manager checks whether the service is provided in the local edge cloud. If the service is provided, the cloud computing service manager returns the service address to the terminal. The terminal accesses the corresponding service based on the service address. The cloud computing service manager automatically stores and backs up the information data. When a comprehensive service is divided into cross-domain services based on information provided by the user, the cloud service manager queries the core cloud and provides the edge cloud address of the service, and returns the edge cloud address to the terminal. The user can then access the required service through this address.

4. The cloud computing-based integrated service big data processing method according to claim 1, characterized in that, Step S3 further includes: The distance between each data node in the information dataset is calculated, and the calculated distances are sorted in ascending order. The data node with the smallest distance is selected as the first initial cluster center, and the remaining data nodes are selected as initial cluster centers in turn with a threshold as the maximum interval. The clusters of all data nodes to the initial cluster centers are calculated, and they are sequentially assigned to the nearest cluster center. A bottom-up hierarchical clustering algorithm is used to perform the calculation, thereby completing the initialization and merging of the information dataset.

5. A cloud computing-based integrated service big data processing terminal, characterized in that, include: Data collection module: Collects information data sent by users to the terminal and saves it to the database. Information authentication is required when a user logs into the cloud computing system for the first time. Classification module: Based on the information and data provided by the user, the integrated services are divided into local services and cross-domain services. Local services are services used by the user and provided by the edge cloud where the user is located, while cross-domain services are services provided by the edge cloud to the user under the coordination of the core cloud. Data processing module: After performing comprehensive service classification on the information data, it processes the information data in the database using a clustering algorithm to calculate the distance between nodes in the information data set, using the following formula: Where d i Let n be the distance between information data nodes, and l be the total number of information data nodes. ij To calculate the distance between information data node i and information data node j, we also need to calculate the average distance between information data nodes. This distance is the arithmetic mean of the distances between all information data nodes, and the formula is: Where n is the total number of data nodes, d i The distance between data nodes of information data node i; Merging module: The information data is clustered with a threshold value. The threshold value is used as the selection criterion for the initial cluster centers. The formula is: Q = 2 × D / K, where D is the average distance between data nodes and K is the set number of initial clusters. The remaining information data nodes are selected sequentially as the initial cluster centers with the threshold value as the maximum interval, and then the data is merged. Sending module: After processing the information data, it compares it with the initial data stored in the database, refines the information data, and then sends it to the user terminal.

6. The cloud computing-based integrated service big data processing terminal according to claim 5, characterized in that, The sending module further includes: Object visualization units are used to transform information data into visualized data and objects.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the cloud computing-based integrated service big data processing method as described in any one of claims 1 to 4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the cloud computing-based integrated service big data processing method as described in any one of claims 1 to 4.

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