Node optimization method and system based on Internet of Things

Abstract

The invention discloses a node optimization method based on the Internet of Things, and the method comprises the following steps: 1, registering a device node, collecting the data of the device node, and transmitting the data to a data processing module; 2, receiving the data acquired by the data acquisition module, and performing clustering processing on the data according to the membership degree of a data object to a data feature; 3, identifying the data subjected to clustering processing, recording a data feature type, and sending a storage node registration instruction to a data storage module; 4, clustering the data subjected to clustering processing, and registering storage nodes; and 5, constructing a data storage structure in the data storage module, and storing the clustered data in a database. The method has the advantages of being capable of quickly and efficiently storing data and reasonably planning storage resources.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, specifically to a node optimization method and system based on IoT. Background Technology

[0002] With the rapid development of "Internet+" technology, the Internet of Things (IoT) technology, centered on "Internet+", has developed rapidly on the basis of the Internet. IoT integrates with the Internet through various wired and wireless networks, collecting and transmitting information about objects accurately and in real time. This rapid development generates massive amounts of data, posing a significant challenge to data storage nodes in terms of how to store this data quickly and efficiently. Existing storage technologies suffer from inefficiency and inability to rationally plan storage resources when storing large amounts of data. Therefore, it is essential to design an IoT-based node optimization method and system that can quickly and efficiently store data and rationally plan storage resources. Summary of the Invention

[0003] The purpose of this invention is to provide a node optimization method and system based on the Internet of Things (IoT) to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: a node optimization method based on the Internet of Things, the method comprising the following steps:

[0005] Step 1: Register device nodes, collect data from device nodes, and transmit the data to the data processing module;

[0006] Step 2: Receive the data collected by the data acquisition module and cluster the data according to the membership degree of the data objects to the data features;

[0007] Step 3: Identify the clustered data, record the data feature types, and send a storage node registration instruction to the data storage module;

[0008] Step 4: Divide the clustered data into clusters and register the storage nodes;

[0009] Step 5: Build the data storage structure in the data storage module and store the clustered data in the database.

[0010] According to the above technical solution, the steps of registering device nodes, collecting device node data, and transmitting the data to the data processing module include:

[0011] The device node registration module registers its own node for each device for data transmission, and the cluster head node manages and transmits data to all device nodes in its cluster.

[0012] The data collection module collects data transmitted by the cluster head node and transmits it through the data transmission module; the data transmission module acquires the data transmitted by the cluster head node and transmits it to the data processing module through a wired or wireless network.

[0013] According to the above technical solution, the step of receiving data collected by the data acquisition module and clustering the data according to the membership degree of the data objects to the data features includes:

[0014] The data receiving module receives data collected by the data collection module via a wireless network and transmits the data to the feature clustering module. The feature clustering module performs clustering processing based on the membership degree of different features of the data, dividing the data into multiple clusters.

[0015] For example, the specific method of clustering by the feature clustering module is as follows: acquire the data received by the data receiving module, calculate the distance L between each data object and each cluster center, determine the membership degree of each data object to each cluster center through the distance L, sort the membership degrees in descending order, select the part with the membership degree greater than the threshold set by the system and classify it into a cluster, obtain the cluster to which each data object belongs according to this clustering algorithm, and cluster the data to facilitate the feature identification of data objects. The storage structure construction module can complete the construction of the data storage structure according to the identified feature type.

[0016] According to the above technical solution, the steps of identifying the clustered data, recording the data feature types, and sending a storage node registration instruction to the data storage module include:

[0017] The data feature analysis module acquires clustered data, scans the data objects, identifies their clustering characteristics, and statistically integrates the data feature set K = {K1, K2, K3, ..., K}. n}, respectively with each data feature K n The corresponding data objects generate a data storage model;

[0018] The node registration instruction sending module obtains the data clustering feature set K = {K1, K2, K3, ..., K}. n For each cluster feature, a storage node registration instruction is generated for the data object. The storage node registration instruction is then bound to its data storage model and stored in the node registration instruction sending module.

[0019] According to the above technical solution, the step of clustering the clustered data and registering storage nodes includes:

[0020] The clustering module divides the clustered data into multiple clusters (C) based on the data source device. i，jEach cluster consists of device nodes with identical equipment and data clustering characteristics. The cluster head F of each cluster is... c As a node for data transmission in each cluster, the clustering result data is transmitted to the data feature rational analysis module. The data feature type analysis module adjusts the data storage model according to the clustering result. The node registration instruction sending module sends the adjusted storage node registration instruction to the storage node registration module.

[0021] Calculate the cluster head F of the data clustering c The specific method is as follows: according to the formula

[0022]

[0023] Where a, b∈[1, M] j ], X a The x-coordinate represents the device's coordinates. b The x-coordinate, Y, represents the coordinates of the data transmission device. a The ordinate (Y) represents the device coordinate system. b M represents the vertical axis of the data transmission device. j This represents the number of device nodes in the j-th cluster within the n-th cluster, when F min When it reaches its minimum value, the coordinate is X. a and Y a The device serves as the cluster head F c .

[0024] According to the above technical solution, the step of constructing a data storage structure in the data storage module and storing the clustered data in the database includes:

[0025] The storage structure construction module obtains the data storage model of the storage nodes, constructs the data storage structure based on the data storage model, and divides the database into a×b equal and independent data modules. Each data module is used to store a cluster K. n The storage box is further divided into c×d small data blocks of the same size. Each small data block is used to store data transmitted from a cluster head node.

[0026] The cluster storage module acquires the data transmitted by the cluster head node, packages the data, and sends it to the storage nodes. The storage nodes then locate the cluster head F according to their data storage structure model. c The small data block containing the cluster head F c The packaged data is stored in small data blocks.

[0027] According to the above technical solution, the system includes:

[0028] The data acquisition module is used to register device nodes, collect device data, and locate device positions.

[0029] The data processing module is used to cluster data and identify cluster feature types;

[0030] The data storage module is used for constructing the database data storage structure and storing data in clusters.

[0031] According to the above technical solution, the data acquisition module includes:

[0032] The device node registration module is used to register data acquisition nodes for data acquisition devices;

[0033] The data collection module is used to collect data;

[0034] The data transmission module is used to transmit the data collected by the data collection module.

[0035] According to the above technical solution, the data processing module includes:

[0036] The data receiving module is used to receive data collected by the data collection module;

[0037] The feature clustering module is used to cluster sensor nodes;

[0038] The data feature type analysis module is used to identify the clustering characteristics of sensor nodes;

[0039] The node registration instruction sending module is used to send storage node registration instructions carrying data characteristic types to the storage module.

[0040] According to the above technical solution, the data storage module includes:

[0041] The storage node registration module is used to register storage nodes;

[0042] The storage structure construction module is used to construct the storage structure of storage nodes based on data characteristic types;

[0043] The clustering module is used to cluster data;

[0044] The clustered storage module is used to store data in a way that minimizes the average distance.

[0045] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention, by setting up a feature clustering module, a clustering module, and a storage structure construction module, performs feature clustering processing on the data to classify the data. The clustering module clusters the data based on the device nodes from which the data originates, and the storage structure construction module divides the database into storage objects after clustering based on the clustering results. Based on the clustering results, the above data blocks are further divided into smaller data blocks, and the cluster head node data is stored in the corresponding cluster head small data block. This data partitioning and storage greatly improves the storage efficiency of storage nodes, and dividing the database into small data blocks greatly improves the utilization rate of data storage resources and improves storage efficiency. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0047] Figure 1 This is a flowchart of the steps of a node optimization method based on the Internet of Things provided in Embodiment 1 of the present invention;

[0048] Figure 2 This is a schematic diagram of the module composition of a node optimization system based on the Internet of Things provided in Embodiment 2 of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Example 1: Figure 1 This is a flowchart of a node optimization method and system based on the Internet of Things (IoT) provided in Embodiment 1 of the present invention. This embodiment can be applied to IoT data storage scenarios. The method can be executed by the node optimization method and system based on the IoT provided in this embodiment, such as... Figure 1 As shown, the method specifically includes the following steps:

[0051] Step 1: Register device nodes, collect device data, and transmit the data to the data processing module;

[0052] In this embodiment of the invention, during the data acquisition process, the device node registration module registers its own node for each device for data transmission, and the cluster head node manages and transmits data to all device nodes in its cluster. Users can manage devices within the cluster through the cluster head device node, which facilitates the management of device nodes and reduces the workload of users' device management.

[0053] The data collection module collects data transmitted by the cluster head node and transmits it through the data transmission module. The data transmission module obtains the data transmitted by the cluster head node and transmits it to the data processing module through a wired or wireless network. During the data transmission process, there may be interference factors that affect the stability of the data transmission. Choosing a wired connection can ensure the integrity of the data during the data transmission process.

[0054] Step 2: Receive the data collected by the data acquisition module and cluster the data according to the membership degree of the data objects to the data features;

[0055] In this embodiment of the invention, the data receiving module is communicatively connected to the data collection module. The data receiving module receives the data collected by the data collection module through a wireless network and transmits the data to the feature clustering module. The feature clustering module performs clustering processing based on the membership degree of different features of the data, dividing the data into multiple clusters.

[0056] For example, the feature clustering module acquires the data received by the data receiving module, calculates the distance L between each data object and each cluster center, determines the membership degree of each data object to each cluster center based on the distance L, sorts the membership degrees in descending order, and selects the parts with membership degrees greater than the threshold set by the system to be classified into a cluster. According to this clustering algorithm, the cluster to which each data object belongs is obtained. Clustering the data facilitates the feature identification of the data objects. The storage structure construction module can quickly complete the construction of the data storage structure according to the identified feature types, and can store the data into the database quickly and efficiently.

[0057] Step 3: Identify the clustered data, record the data feature types, and send a storage node registration instruction to the data storage module;

[0058] In this embodiment of the invention, the data feature analysis module identifies the clustering features of data objects, counts the number of clustering features, and generates a data storage model for each data object based on the clustering features. The storage structure construction module can quickly and accurately build data storage based on the data storage model.

[0059] The node registration instruction sending module stores the data storage model. The node registration instruction sending module sends node registration instructions to the data storage module to establish storage nodes for data objects. It stores data objects in a targeted manner. Each cluster feature has its own storage node. Data storage can be performed in a multi-threaded manner, reducing the time of data storage process and speeding up data storage efficiency.

[0060] For example, the data feature analysis module acquires clustered data, scans the data objects, identifies the clustering features of the data objects, and statistically integrates the data feature set K = {K1, K2, K3, ... K}. n}, respectively with each data feature K n The corresponding data objects generate a data storage model;

[0061] The node registration instruction sending module obtains the data clustering feature set K = {K1, K2, K3, ..., K}. n For each cluster feature, a storage node registration instruction is generated for the data object. The storage node registration instruction is then bound to its data storage model and stored in the node registration instruction sending module.

[0062] Step 4: Divide the clustered data into clusters and register the storage nodes;

[0063] In this embodiment of the invention, the clustering module divides the clustered data into multiple clusters C according to the data source device. i，j Each cluster consists of device nodes with identical equipment and data clustering characteristics. The cluster head F of each cluster is... c As a node for data transmission in each cluster, the clustering result data is transmitted to the data feature rational analysis module. The data feature type analysis module adjusts the data storage model according to the clustering result. The node registration instruction sending module sends the adjusted storage node registration instruction to the storage node registration module, which classifies the data objects layer by layer and splits a large amount of data into small data blocks for storage, greatly improving the data storage efficiency of the storage node.

[0064] The storage node registration module receives the storage node registration instruction, extracts the data storage model stored therein, and registers storage nodes for each feature type of data object according to the storage node registration instruction. Multiple storage nodes store data simultaneously, forming a multi-threaded data storage mode, which can store data quickly, greatly improve the efficiency of data processing by storage nodes, and speed up the data storage speed of storage nodes.

[0065] For example, the specific method for calculating the cluster head of data clusters is as follows: according to the formula...

[0066]

[0067] Where a, b∈[1, M] j ], X a The x-coordinate represents the device's coordinates. b The x-coordinate, Y, represents the coordinates of the data transmission device. a The ordinate (Y) represents the device coordinate system. b M represents the vertical axis of the data transmission device. j This represents the number of device nodes in the j-th cluster within the n-th cluster, when F min When it reaches its minimum value, the coordinate is X. a and Y a The device serves as the cluster head F c By calculating the cluster head and using it as a data transmission node, the device can be effectively managed and the data transmission speed can be accelerated.

[0068] Step 5: Build the data storage structure in the data storage module and store the clustered data in the database.

[0069] In this embodiment of the invention, the storage structure construction module obtains the data storage model of the storage node, constructs the data storage structure according to the data storage model, and divides the database into a×b data modules of the same size and independent of each other. Each data module is used to store a cluster K. n The storage box is further divided into c×d small data blocks of the same size. Each small data block is used to store data transmitted from a cluster head node. This can greatly utilize the storage resources of the storage nodes, improve the database resource utilization of the storage nodes, and each cluster storage node is independent of each other. If one of them fails, the other cluster storage nodes can still store data, ensuring that the data storage nodes can effectively store data.

[0070] For example, the cluster storage module obtains the data transmitted by the cluster head node, packages the data, and sends it to the storage node. The storage node then locates the cluster head F according to its data storage structure model. c The small data block containing the cluster head F c Packed data is stored in small data blocks, which enables the data to be stored in an orderly manner and improves the efficiency of data storage nodes.

[0071] Example 2: Example 2 of the present invention provides a node optimization method and system based on the Internet of Things. Figure 2 This is a schematic diagram of the module composition of a node optimization method and system based on the Internet of Things provided in Embodiment 2 of the present invention, as shown below. Figure 2 As shown, the system includes:

[0072] The data acquisition module is used to register device nodes, collect device data, and locate device positions.

[0073] The data processing module is used to cluster data and identify cluster feature types;

[0074] The data storage module is used for constructing the database data storage structure and storing data in clusters.

[0075] In some embodiments of the present invention, the data acquisition module includes:

[0076] The device node registration module is used to register data acquisition nodes for data acquisition devices;

[0077] The data collection module is used to collect data;

[0078] The data transmission module is used to transmit the data collected by the data collection module.

[0079] In some embodiments of the present invention, the data processing module includes:

[0080] The data receiving module is used to receive data collected by the data collection module;

[0081] The feature clustering module is used to cluster sensor nodes;

[0082] The data feature type analysis module is used to identify the clustering characteristics of sensor nodes;

[0083] The node registration instruction sending module is used to send storage node registration instructions carrying data characteristic types to the storage module.

[0084] In some embodiments of the present invention, the data storage module includes:

[0085] The storage node registration module is used to register storage nodes;

[0086] The storage structure construction module is used to construct the storage structure of storage nodes based on data characteristic types;

[0087] The clustering module is used to cluster data;

[0088] The clustered storage module is used to store data in a way that minimizes the average distance.

[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0090] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A node optimization method based on the Internet of Things, characterized in that: The method includes the following steps: Step 1: Register device nodes, collect data from device nodes, and transmit the data to the data processing module; Step 2: Receive the data collected by the data acquisition module and cluster the data according to the membership degree of the data objects to the data features; Step 3: Identify the clustered data, record the data feature types, and send a storage node registration instruction to the data storage module; Step 4: Divide the clustered data into clusters and register the storage nodes; Step 5: Build the data storage structure in the data storage module and store the clustered data in the database.

2. The node optimization method and system based on the Internet of Things according to claim 1, characterized in that: The steps of registering device nodes, collecting device node data, and transmitting the data to the data processing module include: The device node registration module registers its own node for each device for data transmission, and the cluster head node manages and transmits data to all device nodes in its cluster. The data collection module collects data transmitted by the cluster head node and transmits it through the data transmission module; the data transmission module acquires the data transmitted by the cluster head node and transmits it to the data processing module through a wired or wireless network.

3. The node optimization method and system based on the Internet of Things according to claim 2, characterized in that: The step of receiving data collected by the data acquisition module and clustering the data according to the membership degree of the data objects to the data features includes: The data receiving module receives data collected by the data collection module via a wireless network and transmits the data to the feature clustering module. The feature clustering module performs clustering processing based on the membership degree of different features of the data, dividing the data into multiple clusters. For example, the specific method of clustering by the feature clustering module is as follows: acquire the data received by the data receiving module, calculate the distance L between each data object and each cluster center, determine the membership degree of each data object to each cluster center through the distance L, sort the membership degrees in descending order, select the part with the membership degree greater than the threshold set by the system and classify it into a cluster, obtain the cluster to which each data object belongs according to this clustering algorithm, and cluster the data to facilitate the feature identification of data objects. The storage structure construction module can complete the construction of the data storage structure according to the identified feature type.

4. The node optimization method and system based on the Internet of Things according to claim 3, characterized in that: The steps of identifying the clustered data, recording the data feature types, and sending a storage node registration instruction to the data storage module include: The data feature analysis module acquires clustered data, scans the data objects, identifies their clustering characteristics, and statistically integrates the data feature set K = {K1, K2, K3, ..., K}. n }, respectively with each data feature K n The corresponding data objects generate a data storage model; The node registration instruction sending module obtains the data clustering feature set K = {K1, K2, K3, ..., K}. n For each cluster feature, a storage node registration instruction is generated for the data object. The storage node registration instruction is then bound to its data storage model and stored in the node registration instruction sending module.

5. The node optimization method and system based on the Internet of Things according to claim 4, characterized in that: The steps of clustering the clustered data and registering storage nodes include: The clustering module divides the clustered data into multiple clusters (C) based on the data source device. i，j Each cluster consists of device nodes with identical equipment and data clustering characteristics. The cluster head F of each cluster is... c As a node for data transmission in each cluster, the clustering result data is transmitted to the data feature rational analysis module. The data feature type analysis module adjusts the data storage model according to the clustering result. The node registration instruction sending module sends the adjusted storage node registration instruction to the storage node registration module. The specific method for calculating the cluster heads of data clusters is as follows: according to the formula... in, X a The x-coordinate represents the device's coordinates. b The x-coordinate, Y, represents the coordinates of the data transmission device. a The ordinate (Y) represents the device coordinate system. b M represents the vertical axis of the data transmission device. j This represents the number of device nodes in the j-th cluster within the n-th cluster, when F min When it reaches its minimum value, the coordinate is X. a and Y a The device serves as the cluster head F c .

6. The node optimization method and system based on the Internet of Things according to claim 5, characterized in that: The steps of constructing a data storage structure in the data storage module and storing clustered data in the database include: The storage structure construction module obtains the data storage model of the storage nodes, constructs the data storage structure based on the data storage model, and divides the database into a×b equal and independent data modules. Each data module is used to store a cluster K. n The storage box is further divided into c×d small data blocks of the same size. Each small data block is used to store data transmitted from a cluster head node. The cluster storage module acquires the data transmitted by the cluster head node, packages the data, and sends it to the storage nodes. The storage nodes then locate the cluster head F according to their data storage structure model. c The small data block containing the cluster head F c The packaged data is stored in small data blocks.

7. A node optimization system based on the Internet of Things, characterized in that: The system includes: The data acquisition module is used to register device nodes, collect device data, and locate device positions. The data processing module is used to cluster data and identify cluster feature types; The data storage module is used for constructing the database data storage structure and storing data in clusters.

8. The node optimization method and system based on the Internet of Things according to claim 7, characterized in that: The data acquisition module includes: The device node registration module is used to register data acquisition nodes for data acquisition devices; The data collection module is used to collect data; The data transmission module is used to transmit the data collected by the data collection module.

9. The node optimization method and system based on the Internet of Things according to claim 8, characterized in that: The data processing module includes: The data receiving module is used to receive data collected by the data collection module; The feature clustering module is used to cluster sensor nodes; The data feature type analysis module is used to identify the clustering characteristics of sensor nodes; The node registration instruction sending module is used to send storage node registration instructions carrying data characteristic types to the storage module.

10. The node optimization method and system based on the Internet of Things according to claim 9, characterized in that: The data storage module includes: The storage node registration module is used to register storage nodes; The storage structure construction module is used to construct the storage structure of storage nodes based on data characteristic types; The clustering module is used to cluster data; The clustered storage module is used to store data in a way that minimizes the average distance.

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