CN111797435A

CN111797435A - Data analysis method based on Internet of things interaction and cloud computing communication and cloud server

Info

Publication number: CN111797435A
Application number: CN202010577590.XA
Authority: CN
Inventors: 石高建
Original assignee: 石高建
Current assignee: JIANGSU YONGDING COMMUNICATIONS Co.,Ltd.
Priority date: 2020-06-22
Filing date: 2020-06-22
Publication date: 2020-10-20
Anticipated expiration: 2040-06-22
Also published as: CN111797435B; CN112380580A; CN112380581A

Abstract

According to the data analysis method and the cloud server based on the Internet of things interaction and cloud computing communication, firstly, the operation record of first Internet of things equipment is extracted, the real-time state data of second Internet of things equipment in a target Internet of things cluster is obtained, cluster state data are generated according to the real-time state data and the operation record, and the cluster state data, the real-time state data and user behavior data are added into a data configuration list. And secondly, indicating third Internet of things equipment to extract equipment interaction data through the data configuration list and a network topology structure corresponding to the target Internet of things cluster. And finally, analyzing the equipment interaction data and cluster resource directory data corresponding to the cluster state data to obtain a first data analysis result and a second data analysis result. Therefore, different working states and different interaction states of the Internet of things equipment can be analyzed, relevance and traceability between different data analysis results are established, and discretization of a plurality of data analysis results is avoided.

Description

Data analysis method based on Internet of things interaction and cloud computing communication and cloud server

Technical Field

The application relates to the technical field of big data and cloud computing processing, in particular to a data analysis method and a cloud server based on Internet of things interaction and cloud computing communication.

Background

With the development of science and technology, the data era has become natural and serves people's life and production in a more intelligent and efficient manner. Data has penetrated into the functional fields of various industries at present and plays a role of an important production factor. Nowadays, huge value behind data can be obtained by mining and analyzing massive data. With the development of 5g communication and edge computing, the cooperation among the internet of things, big data and cloud computing can make the social form develop towards the direction of science and technology and informatization.

In the data era, data mining and data analysis are an important ring for realizing data value, and deep mining and analysis of interactive data among different internet of things devices are particularly important in the internet of things environment with internet of everything interconnected. However, when the related art analyzes the interactive data between the devices of the internet of things, the obtained data analysis result often has discreteness.

Disclosure of Invention

The application provides a data analysis method and a cloud server based on Internet of things interaction and cloud computing communication, so as to solve the problems in the prior art.

In a first aspect, a data analysis method based on internet of things interaction and cloud computing communication is provided, and is applied to a cloud server communicating with multiple internet of things devices, and the method includes:

extracting an operation record of a first Internet of things device from the first Internet of things device, and performing offline data detection on other Internet of things devices included in a target Internet of things cluster; the target Internet of things cluster is the Internet of things cluster where the first Internet of things equipment is located, and the first Internet of things equipment and the other Internet of things equipment in the target Internet of things cluster are communicated with each other;

if at least one second networking device in the target Internet of things cluster passes the offline data detection, and the second networking device is the Internet of things device with the largest device association coefficient in the target Internet of things cluster, acquiring real-time state data of the second networking device in the target Internet of things cluster, generating cluster state data of the target Internet of things cluster according to the real-time state data and the operation record, and adding the cluster state data, the real-time state data and user behavior data of the first Internet of things device corresponding to the operation record into a preset data configuration list; the real-time state data is network resource data generated when the second networking equipment performs data interaction with other networking equipment in the target networking cluster;

loading the data configuration list into a network topological structure corresponding to the target Internet of things cluster, so that at least one third Internet of things device in the target Internet of things cluster extracts device interaction data according to node data of a device node corresponding to the third Internet of things device in the network topological structure, wherein the node data is not updated relative to the data configuration list; receiving device interaction data uploaded by the third Internet of things device;

analyzing the device interaction data and cluster resource directory data corresponding to the cluster state data based on the determined mapping relation between the user behavior data and the network resource data to obtain a first data analysis result of the first internet of things device corresponding to the cluster resource directory data and a second data analysis result of the target internet of things cluster; wherein the first data analysis result and the second data analysis result have at least partially overlapped result information.

In a second aspect, a cloud server is provided, the cloud server being in communication with a plurality of internet of things devices, the cloud server being at least configured to:

When the data analysis method based on the Internet of things interaction and cloud computing communication and the cloud server are applied, firstly, the operation records of first Internet of things equipment are extracted, at least one second Internet of things equipment in a target Internet of things cluster obtains real-time state data of the second Internet of things equipment in the target Internet of things cluster through offline data detection, cluster state data are generated according to the real-time state data and the operation records, and the cluster state data, the real-time state data and user behavior data of the first Internet of things equipment corresponding to the operation records are added into a preset data configuration list. And finally, analyzing the device interaction data and cluster resource directory data corresponding to the cluster state data based on the mapping relation between the user behavior data and the network resource data to obtain a first data analysis result and a second data analysis result.

The data random check code and the data signature of each internet of things device in the target internet of things cluster are traceable, wherein the result information is obtained by partially overlapping the first data analysis result and the second data analysis result. Therefore, different working states and different interaction states of the Internet of things equipment can be analyzed, relevance and traceability between different data analysis results are established, and discretization of a plurality of data analysis results is avoided.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

Fig. 1 is a schematic diagram of a data analysis system based on internet of things interaction and cloud computing communication according to an exemplary embodiment of the present application.

Fig. 2 is a flowchart illustrating a data analysis method based on internet of things interaction and cloud computing communication according to an exemplary embodiment of the present application.

Fig. 3 is a block diagram of an embodiment of a data analysis device based on internet of things interaction and cloud computing communication according to an exemplary embodiment of the present application.

Fig. 4 is a hardware structure diagram of a cloud server where the data analysis device based on internet of things interaction and cloud computing communication is located according to the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

The inventor discovers that most of the existing internet of things equipment operates in different working states when the internet of things equipment under different application scenes is analyzed, so that various interaction states can exist when different internet of things equipment interacts. However, in the prior art, only a certain interaction state is subjected to independent data mining and analysis to obtain an analysis result, and the relevance of the internet of things equipment in different working states and the cooperativity between different interaction states of the internet of things equipment are not taken into consideration, which results in no relevance and traceability among a plurality of data analysis results.

In order to solve the above problems, embodiments of the present invention provide a data analysis method and a cloud server based on internet of things interaction and cloud computing communication, which can analyze different working states and different interaction states of an internet of things device, thereby establishing relevance and traceability between different data analysis results, and avoiding discretization of multiple data analysis results.

It should be understood that the solution provided by the embodiment of the present invention can be deployed in different internet of things systems, such as a border computing system, an automatic driving system, a smart city monitoring system, an intelligent medical assistance system, and the like, and is not limited herein. In addition, the embodiment of the invention establishes the relevance and the traceability aiming at the data level, so that the specific application scenes are not illustrated one by one. Technical solutions provided by the embodiments of the present invention can be combined with configuration parameters of specific service scenarios by a person skilled in the art to implement data analysis of different internet of things scenarios.

It can be understood that, in order to achieve the above object, the embodiment of the present invention first illustrates and explains an implementation environment of a data analysis method based on internet of things interaction and cloud computing communication. Referring first to fig. 1, a system architecture diagram of a data analysis system 100 based on internet of things interaction and cloud computing communication is provided. In the data analysis system 100, the cloud server 200 and the internet of things devices 300 communicate with each other, and an interaction network with a variable network topology is formed among the internet of things devices 300.

On the basis of fig. 1, please refer to fig. 2 in combination, a flowchart of a data analysis method based on internet of things interaction and cloud computing communication is provided, and the method may be applied to the cloud server 200 in fig. 1, and specifically may include the contents described in the following steps S210 to S240.

Step S210, extracting an operation record of a first Internet of things device from the first Internet of things device, and performing offline data detection on other Internet of things devices included in a target Internet of things cluster; the target Internet of things cluster is the Internet of things cluster where the first Internet of things equipment is located, and the first Internet of things equipment and the other Internet of things equipment in the target Internet of things cluster are communicated with each other.

In this embodiment, the operation record is a user behavior data record stored when the first internet of things device performs data processing according to an instruction input by a user. The offline data detection is used for verifying whether data tampering behaviors exist in the other Internet of things equipment.

Step S220, if at least one second networking device in the target Internet of things cluster passes the offline data detection and the second networking device is the Internet of things device with the largest device association coefficient in the target Internet of things cluster, acquiring real-time state data of the second networking device in the target Internet of things cluster, generating cluster state data of the target Internet of things cluster according to the real-time state data and the operation records, and adding the cluster state data, the real-time state data and user behavior data of the first Internet of things device corresponding to the operation records into a preset data configuration list.

In this embodiment, the device association coefficient is used to represent the number of the internet of things devices in the target internet of things cluster, which communicate with the second internet of things device, and the larger the device association coefficient is, the larger the number of the internet of things devices in the target internet of things cluster, which communicate with the second internet of things device is. The real-time status data is network resource data generated when the second internet-of-things device performs data interaction with other internet-of-things devices in the target internet-of-things cluster.

Step S230, loading the data configuration list into a network topology structure corresponding to the target internet of things cluster, so that at least one third internet of things device in the target internet of things cluster performs device interaction data extraction according to node data of a device node corresponding to the third internet of things device in the network topology structure, the node data being not updated with respect to the data configuration list; and receiving the equipment interaction data uploaded by the third Internet of things equipment.

In this embodiment, a network topology structure corresponding to the target internet of things cluster includes a plurality of device nodes, and each device node corresponds to one internet of things device. The node data comprises sensing data corresponding to the equipment node, and the equipment interaction data is recorded data when the third internet of things equipment is switched between different working states.

Step S240, analyzing the device interaction data and cluster resource directory data corresponding to the cluster state data based on the determined mapping relationship between the user behavior data and the network resource data to obtain a first data analysis result of the first Internet of things device corresponding to the cluster resource directory data and a second data analysis result of the target Internet of things cluster; wherein the first data analysis result and the second data analysis result have at least partially overlapped result information.

In this embodiment, the cluster resource directory data includes data resource allocation records of the target internet of things cluster in different interaction states and change records of data interface information of each internet of things device. The result information of the first data analysis result and the second data analysis result which are partially overlapped comprises a data random check code and a data signature of each Internet of things device in the target Internet of things cluster, and the data random check code and the data signature are traceable.

It can be understood that, through the steps S210 to S240, the operation record of the first internet of things device is extracted, the real-time state data of the second internet of things device in the target internet of things cluster is obtained through offline data detection by at least one second internet of things device in the target internet of things cluster, the cluster state data is generated according to the real-time state data and the operation record, and the cluster state data, the real-time state data, and the user behavior data of the first internet of things device corresponding to the operation record are added to the preset data configuration list. And finally, analyzing the device interaction data and cluster resource directory data corresponding to the cluster state data based on the mapping relation between the user behavior data and the network resource data to obtain a first data analysis result and a second data analysis result.

When the method is implemented, the inventor finds that the problems of low detection accuracy and overlong detection time may exist when offline data detection is performed on other internet of things equipment in the target internet of things cluster. The inventor further analyzes and researches the above problems, and the offline data detection of other internet of things devices is performed based on the same detection thread, which does not take into account the differences of device types and device network states among different internet of things devices. In order to solve the above technical problem, the offline data detection on other internet of things devices included in the target internet of things cluster described in step S210 may specifically include the contents described in steps S211 to S215 below.

Step S211, obtaining device type data of each other Internet of things device in the target Internet of things cluster and a device network state corresponding to each other Internet of things device in the real-time network state of the target Internet of things cluster; and converting the equipment type data and the equipment network state into at least two groups of parameter sets to be detected according to the historical detection records corresponding to each other Internet of things equipment.

Step S212, extracting the detection logic information of each parameter set to be detected and the target type data corresponding to the parameter set to be detected, where the target type data is a part of registration data that does not change with the change of the operating state of other internet of things devices in the device type data.

Step S213, determining a parameter defect trajectory when each parameter set to be detected is imported into the target detection thread corresponding to the device type data, based on the detection logic information of each parameter set to be detected and the target type data, where the parameter defect trajectory includes a change trajectory of the parameter set to be detected.

Step S214, aiming at the change track corresponding to each parameter set to be detected, if the change track is convergent in a preset time period, importing the parameter characteristic array corresponding to the parameter set to be detected into a running list of a corresponding target detection thread in the equipment type data; after the at least two groups of parameter sets to be detected are imported based on the target detection thread, the at least two groups of parameter sets to be detected are split, and state difference information and type difference information between every two other pieces of internet-of-things equipment are obtained.

Step S215, performing mirror image processing on target detection threads corresponding to every two other Internet of things devices according to the state difference information and the type difference information to obtain mirror image detection threads corresponding to the target detection threads, performing detection parameter adjustment on the target detection threads and the mirror image detection threads based on the state difference information and the type difference information, and driving the target detection threads and the mirror image detection threads to operate through a first operation list corresponding to the target detection threads and a second operation list corresponding to the mirror image detection threads so as to perform offline data detection on every other Internet of things device.

It can be understood that, through the steps S211 to S215, differences between device types and device network states of different internet of things devices can be taken into consideration, so that detection parameters and detection standards can be adjusted for a detection thread, and thus, when offline data detection is performed on other internet of things devices in a target internet of things cluster, detection accuracy can be ensured and detection duration can be reduced.

In practical applications, in order to ensure accuracy and integrity of data added to the data configuration list, in step S220, cluster state data of the target internet of things cluster is generated according to the real-time state data and the operation record, and adding the cluster state data, the real-time state data, and the user behavior data of the first internet of things device corresponding to the operation record to a preset data configuration list may specifically include the contents described in steps S221 to S224 below.

Step S221, extracting a state variable from the real-time state data, generating a first feature cluster between variable features of the state variable and recording features of the operation records, and determining a frequency resource distribution table of the target Internet of things cluster according to the first feature cluster; and in the process of extracting the state variable, acquiring the operation data parameter of the operation record and determining the parameter change degree of the state variable relative to the operation data parameter of the operation record.

Step S222, acquiring a second feature cluster between the variable feature of the state variable and the record feature of the operation record based on the parameter variation degree of the state variable relative to the operation data parameter of the operation record, and judging whether the second feature cluster is matched with the frequency resource distribution table of the target Internet of things cluster; if so, generating the cluster state data according to the frequency resource distribution table; and if not, modifying the frequency resource distribution table of the target Internet of things cluster based on the parameter change degree, and generating the cluster state data by utilizing the modified frequency resource distribution table.

Step S223, determining a first data structure model of the cluster state data, a second data structure model of the real-time state data, and a third data structure model of the user behavior data; generating a data structure transformation matrix for performing data format conversion on the cluster state data, the real-time state data and the user behavior data according to the first data structure model, the second data structure model and the third data structure model before adding the cluster state data, the real-time state data and the user behavior data to the data configuration list.

Step S224, converting the cluster state data, the real-time state data and the user behavior data into data to be imported in a data format matched with the data configuration list by adopting the data structure transformation matrix; and adding the data to be imported into the data configuration list through a list interface of the data configuration list.

In specific implementation, based on the content described in the above steps S221 to S224, the cluster state data can be accurately determined, and different data formats of the cluster state data, the real-time state data, and the user behavior data are considered, so that the cluster state data, the real-time state data, and the user behavior data are added to the data configuration list, and the consistency of the data formats is converted, thereby ensuring the accuracy and integrity of the data added to the data configuration list.

In a specific implementation process, in order to accurately determine device interaction data, the data configuration list is loaded into a network topology structure corresponding to the target internet of things cluster in step S230, so that at least one third internet of things device in the target internet of things cluster extracts device interaction data according to node data, which is not updated with respect to the data configuration list, of a device node corresponding to the third internet of things device in the network topology structure, and specifically, the method may include the following contents described in steps S231 to S234.

Step S231, determining list structure information corresponding to list linear parameters of a data configuration list and a list sequence of the list linear parameters; wherein the list sequence represents a resource configuration result of a list linearity parameter of the data configuration list, and the list sequence at least includes: current ordering information and historical ordering information representing list linearity parameters of the data configuration list.

Step S232, obtaining list configuration information corresponding to the list structure information, where the list configuration information includes pre-extracted data configuration information, and the data configuration information represents a resource configuration result of a list linear parameter that is located in a setting information field in the list configuration information and corresponds to the list structure information.

Step S233, encapsulating the target data in the data configuration list according to the list structure information, the list sequence, and the list configuration information to obtain a data packet; and loading the data packet into a logical link data table corresponding to the network topology structure and activating at least one third internet of things device in the target internet of things cluster through the logical link data table.

Step S234, enabling the activated third Internet of things device to determine node data, which is not updated relative to the data configuration list, of a device node corresponding to the third Internet of things device in the network topology structure according to the data packet, enabling the third Internet of things device to sort each data set of the node data based on current sorting information and historical sorting information included in the data packet to obtain a sorting sequence, and extracting a set number of data sets positioned in the middle of the sorting sequence as device interaction data; and the set number is determined according to the number of the Internet of things devices in the target Internet of things cluster.

It can be understood that when the contents described in steps S231 to S234 are executed, the device interaction data can be accurately determined, so as to provide a complete and reliable data base for subsequent data analysis.

In practical applications, in order to ensure relevance and traceability between different data analysis results, the analysis of the device interaction data and the cluster resource directory data corresponding to the cluster state data based on the determined mapping relationship between the user behavior data and the network resource data, which is described in step S240, is performed to obtain a first data analysis result of the first internet of things device corresponding to the cluster resource directory data and a second data analysis result of the target internet of things cluster, which may specifically include the contents described in steps S241 to S244.

Step S241, establishing a mapping relationship between the user behavior data and the network resource data according to the user behavior data and the dimension list information of the network resource data.

Step S242, determining data direction information between the device interaction data and the cluster resource directory data based on the mapping relationship; wherein the data pointing information is used to indicate data analysis logic of the device interaction data and the cluster resource directory data.

In step S243, a plurality of first pointing information having a first matching rate with the device interaction data exceeding a first set value and a plurality of second pointing information having a second matching rate with the cluster resource directory data exceeding a second set value are determined from the data pointing information.

Step S244, performing feature clustering on the first direction information and the second direction information to obtain a plurality of clustering categories; and analyzing the equipment interaction data and the cluster resource directory data according to the pointing information characteristics corresponding to each cluster category to obtain a first data analysis result of the first internet of things equipment corresponding to the cluster resource directory data and a second data analysis result of the target internet of things cluster.

When the contents described in the above steps S241 to S244 are applied, different pieces of orientation information can be determined based on the mapping relationship, so that the orientation information is clustered and then data analysis is performed according to the orientation information features of different cluster categories, so that the relevance and traceability between different data analysis results can be ensured.

In an alternative embodiment, in order to accurately determine the mapping relationship between the user behavior data and the network resource data, step S241 may specifically include the following steps S2411 to S2416.

Step S2411, acquiring a dimension label list of the user behavior data; and the dimension label list of the user behavior data corresponds to a plurality of signature certificates.

Step S2412, converting each dimension description value in the dimension label list of the user behavior data into a label field respectively, and determining a label field set corresponding to the user behavior data; the label fields can be signed on the Internet of things devices corresponding to different user data, the signature authority level of the label field converted by each dimension description value is the same as that of the dimension description value, and each label field is provided with a random check code used for uniquely determining the label field.

Step S2413, extracting authentication fields corresponding to each authentication request message in a dimension authentication list corresponding to the network resource data, and integrating the authentication fields based on field distribution tracks of the tag field set to generate an authentication field set corresponding to the network resource data; wherein the set of tag fields and the set of authentication fields each comprise a plurality of fields having different field weights.

Step S2414, extracting a first character string of the user behavior data in one of the tag fields in the tag field set, and determining the authentication field with the minimum field weight in the authentication field set as a target field.

Step S2415, mapping the first character string to the target field according to a first similarity between the dimension label list of the user behavior data and the dimension authentication list corresponding to the network resource data, and obtaining a second character string in the target field; and establishing a mapping relation between the user behavior data and the network resource data based on a second similarity between the first character string and the second character string.

In this embodiment, by executing the contents described in the above steps S2411 to S2415, the mapping relationship between the user behavior data and the network resource data can be accurately determined.

In another alternative embodiment, the analyzing the device interaction data and the cluster resource directory data according to the direction information feature corresponding to each cluster category described in step S244 to obtain the first data analysis result of the first internet of things device and the second data analysis result of the target internet of things cluster corresponding to the cluster resource directory data may specifically include the contents described in steps S2441 to S2444 below.

Step S2441, listing the directional information characteristics of each cluster category and establishing a cluster characteristic grid; the clustering feature grid comprises a plurality of sub-grids, each sub-grid corresponds to one clustering mark, each clustering mark corresponds to at least one pointing information feature, and the clustering marks of all sub-networks of the clustering feature grid have weight grades from high to low.

Step S2442, adding the data to be distributed corresponding to each sub-grid in the device interaction data to the corresponding data segment of the cluster resource directory data according to the sequence from high to low of the weight grade, and establishing a data association matrix of the data to be distributed and the cluster resource directory data; the data association matrix is used for describing data association between the data to be distributed and the cluster resource directory data.

Step S2443, converting the cluster resource directory data added with the data to be distributed into a data node format according to the data association matrix, and determining a data node path corresponding to the data node format.

Step S2444, extracting a plurality of result information of the cluster resource directory data with respect to the data node path as a second data analysis result, and extracting result information of the device interaction data with respect to each data node in the data node path as a first data analysis result.

It can be understood that through the content described in the above steps S241 to S244, different operating states and different interaction states of the internet of things device can be analyzed based on the data node path, so that relevance and traceability between different data analysis results are established, and discretization of multiple data analysis results is avoided.

Based on the same inventive concept, please refer to fig. 3 in combination, a functional module block diagram of a data analysis apparatus 400 based on internet of things interaction and cloud computing communication is provided, where the data analysis apparatus 400 includes the following functional modules.

The record obtaining module 410 is configured to extract an operation record of a first internet of things device from the first internet of things device, and perform offline data detection on other internet of things devices included in a target internet of things cluster; the target Internet of things cluster is the Internet of things cluster where the first Internet of things equipment is located, and the first Internet of things equipment and the other Internet of things equipment in the target Internet of things cluster are communicated with each other.

A data adding module 420, configured to, if at least one second internet-of-things device in the target internet-of-things cluster passes the offline data detection, and the second internet-of-things device is an internet-of-things device with a largest device association coefficient in the target internet-of-things cluster, obtain real-time state data of the second internet-of-things device in the target internet-of-things cluster, generate cluster state data of the target internet-of-things cluster according to the real-time state data and the operation record, and add the cluster state data, the real-time state data, and user behavior data of the first internet-of-things device corresponding to the operation record into a preset data configuration list.

A data extraction module 430, configured to load the data configuration list into a network topology structure corresponding to the target internet of things cluster, so that at least one third internet of things device in the target internet of things cluster performs device interaction data extraction according to node data, in the network topology structure, of a device node corresponding to the third internet of things device, that is not updated with respect to the data configuration list; and receiving the equipment interaction data uploaded by the third Internet of things equipment.

A data analysis module 440, configured to analyze the device interaction data and cluster resource directory data corresponding to the cluster state data based on the determined mapping relationship between the user behavior data and the network resource data, so as to obtain a first data analysis result of the first internet of things device corresponding to the cluster resource directory data and a second data analysis result of the target internet of things cluster; wherein the first data analysis result and the second data analysis result have at least partially overlapped result information.

Optionally, the record obtaining module 410 is configured to:

obtaining device type data of each other internet of things device in the target internet of things cluster and a device network state corresponding to each other internet of things device in the real-time network state of the target internet of things cluster; converting the equipment type data and the equipment network state into at least two groups of parameter sets to be detected according to historical detection records corresponding to each other Internet of things equipment;

extracting detection logic information of each parameter set to be detected and target type data corresponding to the parameter set to be detected, wherein the target type data is part of registration data which does not change along with the change of the working state of other Internet of things equipment in the equipment type data;

determining a parameter defect track when each parameter set to be detected is imported into a target detection thread corresponding to the equipment type data based on the detection logic information of each parameter set to be detected and the target type data, wherein the parameter defect track comprises a change track of the parameter set to be detected;

for a change track corresponding to each parameter set to be detected, if the change track is convergent in a preset time period, importing a parameter feature array corresponding to the parameter set to be detected into a running list of a corresponding target detection thread in the equipment type data; after the at least two groups of parameter sets to be detected are imported based on the target detection thread, splitting the at least two groups of parameter sets to be detected and obtaining state difference information and type difference information between every two other pieces of internet-of-things equipment;

performing mirror image processing on target detection threads corresponding to every two other internet of things devices according to the state difference information and the type difference information to obtain mirror image detection threads corresponding to the target detection threads, performing detection parameter adjustment on the target detection threads and the mirror image detection threads based on the state difference information and the type difference information, and driving the target detection threads and the mirror image detection threads to operate through a first operation list corresponding to the target detection threads and a second operation list corresponding to the mirror image detection threads so as to perform offline data detection on every other internet of things device.

Optionally, the data adding module 420 is configured to:

extracting a state variable from the real-time state data, generating a first feature cluster between variable features of the state variable and recording features of the operation records, and determining a frequency resource distribution table of the target Internet of things cluster according to the first feature cluster; in the process of extracting the state variable, acquiring the operation data parameter of the operation record and determining the parameter change degree of the state variable relative to the operation data parameter of the operation record;

acquiring a second feature cluster between the variable feature of the state variable and the record feature of the operation record based on the parameter variation degree of the state variable relative to the operation data parameter of the operation record, and judging whether the second feature cluster is matched with the frequency resource distribution table of the target Internet of things cluster or not; if so, generating the cluster state data according to the frequency resource distribution table; if not, modifying the frequency resource distribution table of the target Internet of things cluster based on the parameter change degree, and generating the cluster state data by utilizing the modified frequency resource distribution table;

determining a first data structure model of the cluster state data, a second data structure model of the real-time state data, and a third data structure model of the user behavior data; generating a data structure transformation matrix for performing data format conversion on the cluster state data, the real-time state data and the user behavior data according to the first data structure model, the second data structure model and the third data structure model before adding the cluster state data, the real-time state data and the user behavior data to the data configuration list;

converting the cluster state data, the real-time state data and the user behavior data into data to be imported in a data format matched with the data configuration list by adopting the data structure transformation matrix; and adding the data to be imported into the data configuration list through a list interface of the data configuration list.

Optionally, the data extracting module 430 is configured to:

determining list structure information corresponding to list linear parameters of a data configuration list and a list sequence of the list linear parameters; wherein the list sequence represents a resource configuration result of a list linearity parameter of the data configuration list, and the list sequence at least includes: current ordering information and historical ordering information representing list linearity parameters of the data configuration list;

acquiring list configuration information corresponding to the list structure information, wherein the list configuration information comprises pre-extracted data configuration information, and the data configuration information represents a resource configuration result of a list linear parameter which is positioned in a setting information field in the list configuration information and corresponds to the list structure information;

according to the list structure information, the list sequence and the list configuration information, packaging target data in the data configuration list to obtain a data packet; loading the data packet into a logical link data table corresponding to the network topology structure and activating at least one third internet of things device in the target internet of things cluster through the logical link data table;

enabling the activated third internet-of-things device to determine node data, which is not updated relative to the data configuration list, of a device node corresponding to the third internet-of-things device in the network topological structure according to the data packet, enabling the third internet-of-things device to sort each data set of the node data based on current sorting information and historical sorting information included in the data packet to obtain a sorting sequence, and extracting a set number of data sets positioned in the middle of the sorting sequence to serve as device interaction data; and the set number is determined according to the number of the Internet of things devices in the target Internet of things cluster.

Optionally, the data analysis module 440 is configured to:

establishing a mapping relation between the user behavior data and the network resource data according to the user behavior data and dimension list information of the network resource data;

determining data direction information between the device interaction data and the cluster resource directory data based on the mapping relationship; wherein the data pointing information is used to indicate data analysis logic of the device interaction data and the cluster resource directory data;

determining a plurality of first pointing information with a first matching rate between the data pointing information and the device interaction data exceeding a first set value and a plurality of second pointing information with a second matching rate between the data pointing information and the cluster resource directory data exceeding a second set value;

performing feature clustering on the first pointing information and the second pointing information to obtain a plurality of clustering categories; and analyzing the equipment interaction data and the cluster resource directory data according to the pointing information characteristics corresponding to each cluster category to obtain a first data analysis result of the first internet of things equipment corresponding to the cluster resource directory data and a second data analysis result of the target internet of things cluster.

Optionally, the data analysis module 440 is further configured to:

acquiring a dimension label list of the user behavior data; the dimension label list of the user behavior data corresponds to a plurality of signature certificates;

converting each dimension description value in a dimension label list of the user behavior data into a label field respectively, and determining a label field set corresponding to the user behavior data; the label fields can be signed on the Internet of things equipment corresponding to different user data, the signature authority level of the label field converted by each dimension description value is the same as that of the dimension description value, and each label field is provided with a random check code for uniquely determining the label field;

extracting an authentication field corresponding to each authentication request message in a dimension authentication list corresponding to the network resource data and integrating the authentication fields based on a field distribution track of the tag field set to generate an authentication field set corresponding to the network resource data; wherein the set of tag fields and the set of authentication fields each comprise a plurality of fields having different field weights;

extracting a first character string of the user behavior data in one of the tag fields in the tag field set, and determining the authentication field with the minimum field weight in the authentication field set as a target field;

mapping the first character string to the target field according to a first similarity between a dimension label list of the user behavior data and a dimension authentication list corresponding to the network resource data, and obtaining a second character string in the target field; and establishing a mapping relation between the user behavior data and the network resource data based on a second similarity between the first character string and the second character string.

Optionally, the data analysis module 440 is further configured to:

listing the directional information characteristics of each clustering category and establishing a clustering characteristic grid; the clustering characteristic grid comprises a plurality of sub-grids, each sub-grid corresponds to a clustering identification, each clustering identification corresponds to at least one directional information characteristic, and the clustering identifications of all sub-networks of the clustering characteristic grid have weight grades from high to low;

adding the data to be distributed corresponding to each sub grid in the device interaction data to the corresponding data segment of the cluster resource directory data according to the high-to-low sequence of the weight grades, and establishing a data association matrix of the data to be distributed and the cluster resource directory data; the data association matrix is used for describing data association between the data to be distributed and the cluster resource directory data;

converting the cluster resource directory data added with the data to be distributed into a data node format according to the data incidence matrix, and determining a data node path corresponding to the data node format;

extracting a plurality of result information of the cluster resource directory data relative to the data node paths as second data analysis results, and extracting result information of the device interaction data relative to each data node in the data node paths as first data analysis results.

Based on the same inventive concept, the data analysis system based on the internet of things interaction and cloud computing communication comprises a cloud server and a plurality of internet of things devices, wherein the cloud server and the internet of things devices are communicated with each other, and an interaction network with a variable network topology structure is formed among the plurality of internet of things devices;

the cloud server is configured to:

extracting an operation record of a first Internet of things device from the first Internet of things device, and performing offline data detection on other Internet of things devices included in a target Internet of things cluster; the target Internet of things cluster is the Internet of things cluster where the first Internet of things equipment is located, and the first Internet of things equipment and the other Internet of things equipment in the target Internet of things cluster are communicated with each other.

If at least one second networking device in the target Internet of things cluster passes the offline data detection, and the second networking device is the Internet of things device with the largest device association coefficient in the target Internet of things cluster, acquiring real-time state data of the second networking device in the target Internet of things cluster, generating cluster state data of the target Internet of things cluster according to the real-time state data and the operation records, and adding the cluster state data, the real-time state data and user behavior data of the first Internet of things device corresponding to the operation records into a preset data configuration list.

Loading the data configuration list into a network topology structure corresponding to the target Internet of things cluster;

at least one third internet of things device in the target internet of things cluster, configured to:

extracting device interaction data according to node data of a device node corresponding to the third internet-of-things device in the network topology structure, wherein the node data is not updated relative to the data configuration list;

the cloud server is configured to:

receiving device interaction data uploaded by the third Internet of things device;

Optionally, the cloud server is configured to:

On the basis, please refer to fig. 4 in combination, which provides a cloud server 200, including: a processor 210, and a memory 220 and a network interface 230 connected to the processor 210, wherein the network interface 230 is connected to a non-volatile memory 240 in the cloud server 200. The processor 210 retrieves a computer program from the non-volatile memory 240 via the network interface 230 and runs the computer program via the memory 220 to perform the above-described method.

Optionally, a readable storage medium applied to a computer is further provided, and the readable storage medium is burned with a computer program, and the computer program implements the method when running in the memory 220 of the cloud server 200.

For further description and explanation of the above devices, systems, and cloud servers, please refer to the explanation of the method shown in fig. 2, which is not described herein again.

Claims

1. A data analysis method based on Internet of things interaction and cloud computing communication is applied to a cloud server which is communicated with a plurality of Internet of things devices, and the method comprises the following steps:

2. The method of claim 1, wherein analyzing the device interaction data and cluster resource directory data corresponding to the cluster state data based on the determined mapping relationship between the user behavior data and the network resource data to obtain a first data analysis result of the first internet of things device corresponding to the cluster resource directory data and a second data analysis result of the target internet of things cluster comprises:

3. The method of claim 1, wherein loading the data configuration list into a network topology corresponding to the target internet of things cluster, so that at least one third internet of things device in the target internet of things cluster performs device interaction data extraction according to node data of a device node corresponding to the third internet of things device in the network topology, the node data being not updated with respect to the data configuration list, the method comprising:

4. The method of claim 1, wherein performing offline data detection on other internet of things devices included in the target internet of things cluster comprises:

5. The method of claim 1, wherein generating cluster state data of the target internet of things cluster according to the real-time state data and the operation record, and adding the cluster state data, the real-time state data, and user behavior data of the first internet of things device corresponding to the operation record to a preset data configuration list comprises:

6. A cloud server in communication with a plurality of internet of things devices, the cloud server at least to:

7. The cloud server of claim 6, wherein the cloud server is specifically configured to:

8. The cloud server of claim 6, wherein the cloud server is specifically configured to:

9. The cloud server of claim 6, wherein the cloud server is specifically configured to:

10. The cloud server of claim 6, wherein the cloud server is specifically configured to: