Networking and optimal configuration method for sensor network of special equipment general assembly workshop

Abstract

The invention discloses a special equipment general assembly workshop sensing network networking and optimal configuration method. A special equipment general assembly workshop sensing network comprises a workshop Internet of Things layer, a data processing interaction layer and an integrated management layer. The workshop Internet of Things layer comprises environment information and a data source bearing the environment information, information in the data source is collected through an intelligent information interaction device in the data processing interaction layer, and the information can be visualized through the intelligent information interaction device after being processed and is used for guiding production work of general assembly workshop personnel or uploading the information to the integrated management layer; therefore, the environment information is communicated with each other, so that the production environment information of the general assembly workshop is controlled. And meanwhile, the sensing network nodes are clustered by adopting a K-means clustering algorithm, and the wireless sensing network clusters are periodically updated along with the moving distance of the sensor, so that the wireless sensing network has a strong life cycle, and the data transmission has reliability and stability.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to a sensor network networking and optimization configuration method for a special equipment assembly workshop. Background Art

[0002] With the advent of the Industrial 4.0 era, the manufacturing industry is moving towards intelligent transformation with the help of key technologies such as the Internet, Internet of Things, and cloud computing. Many companies have carried out a lot of digital transformation and functional upgrades in recent years, but they still cannot meet the urgent requirements for the application of intelligent manufacturing in the assembly process, especially in the specific environment and requirements of the military industry. How to adapt to the sensor network networking and optimized configuration of the intelligent manufacturing model to meet the requirements of real-time perception, collection effectiveness, and data integration of multi-source heterogeneous data of industrial control equipment on the production line of the special equipment assembly workshop has become one of the problems that need to be solved urgently. Summary of the invention

[0003] The purpose of the present invention is to provide a sensor network networking and optimization configuration method for a special equipment assembly workshop. The sensor network of the special equipment assembly workshop includes a workshop Internet of Things layer, a data processing interaction layer and an integrated management layer.

[0004] The workshop Internet of Things layer includes environmental information and a data source that carries the environmental information.

[0005] Information from the data source is collected through the intelligent information interaction device in the data processing interaction layer. After being processed, the information can be visualized through the intelligent information interaction device to guide the production work of the assembly workshop personnel or uploaded to the integrated management layer, so that each environmental information is interconnected, thereby realizing the control of the production environment information of the assembly workshop.

[0006] The steps of the sensor network networking and optimization configuration method for the special equipment assembly workshop include:

[0007] 1) Deploy sensors in the special equipment assembly workshop and treat each sensor as a sensor node and power it on for initialization;

[0008] 2) Each piece of environmental information is assigned an IP address after being collected by the intelligent information interaction device;

[0009] Based on IPv6 technology, the IP address of each message is different;

[0010] 3) Each IP address can communicate directly with the base station;

[0011] When the base station receives the transmission information from the sensor node, it starts the K-means clustering algorithm to cluster the sensor network nodes and transmit the clustering results to the corresponding nodes;

[0012] 4) Based on the sensor movement distance, the wireless sensor network cluster is periodically updated.

[0013] Further, the environment includes workshop personnel, testing equipment, assembly equipment, materials and tooling;

[0014] The data sources include PLC, embedded systems, cameras and industrial sensors.

[0015] Further, the intelligent information interaction device includes a hardware and software platform;

[0016] The hardware includes an information communication technology protocol module, a network sensor information protocol module, a display terminal device, a power module, a sensor module, a processing module, a communication module, a heterogeneous information system integration interface module, and an information positioning and tracking system module;

[0017] The software platform is an Android system platform developed based on the Linux system, including a hardware resource layer, a function layer and an application layer.

[0018] Furthermore, the sensing module includes a sensor and an analog / digital converter; the processing module includes a processor and a storage module; the communication module includes a network module and a signal receiver / transmitter;

[0019] The environmental data in the final assembly workshop is sensed by sensors, and the sensed data is transmitted to the analog / digital converter. The analog signal is converted into a digital signal through the digital / analog converter device, and then the digital signal is transmitted to the processor of the processing module. The processor describes the digital signal information, creates an XML model and generates a document, completes the software packaging, and stores the obtained data results in the storage module.

[0020] Further, in step 3), using the K-means clustering algorithm to cluster the sensor network nodes includes the following steps:

[0021] 3.1) Randomly generate K sensor nodes from the network area as the initial cluster centroids;

[0022] 3.2) Calculate the Euclidean distance between each sensor node and each initial cluster centroid, and assign the sensor node to the initial cluster centroid closest to it to form K clusters;

[0023] 3.3) After the allocation is completed, recalculate the cluster centroid of each cluster;

[0024] 3.4) Repeat steps 3.2) and 3.3) until the cluster centroid is in a balanced state. The centroid in the balanced state is the cluster head of the cluster.

[0025] Furthermore, in order to solve the energy loss existing in the communication transmission level of the wireless sensor network, the sensor with more energy surplus of cluster nodes, closer to the base station and the smallest sum of Euclidean distances with surrounding sensors is selected as the centroid.

[0026] Further, in step 3.1), the wireless sensor network creates a random number t for each node, the value of t is between 0 and 1, and compares t with the revised threshold Compare, if t is less than Then node t is set as the cluster head.

[0027] Further, the revised threshold The calculation formula is as follows:

[0028]

[0029]

[0030] Where: E resi 、E initial are the energy surplus and initial energy storage value of the sensor respectively;

[0031] is the European distance from the sensor to the base station; It is the mark value of the sum of the European distances between the sensor and the surrounding sensing devices;

[0032] T(n) is the design threshold;

[0033] p is the ratio between the number of cluster heads and the total number of nodes in the entire wireless sensor network, r is the number of cycles in the sensor network, n is the total number of nodes in the entire sensor wireless network, and the remainder is solved by the mod function; G is the current round number, is the set of nodes that are not cluster heads in the next round.

[0034] Furthermore, the energy consumption value E of the wireless sensor network tx The calculation formula for (l,d) is as follows:

[0035]

[0036] E rx =E elec (4)

[0037] Where: d refers to the sending distance; d0 refers to the distance threshold;

[0038] E elec Refers to the energy consumption required by sensor nodes to analyze and calculate data transmission information;

[0039] ε fsRefers to the energy consumption of wireless sensor networks under the close-range model;

[0040] ε mp Refers to the energy consumption of wireless sensor networks under the long-range model;

[0041] E rx The energy consumption value obtained by transmitting energy to the receiving end via the wireless sensor network;

[0042] Furthermore, the Euclidean distance between each sensor node and each centroid is j represents the cluster number;

[0043] The calculation formula of the identifier J of n sensor nodes to their corresponding cluster centroid is as follows:

[0044]

[0045] Where: is the sensor node, c j is the centroid, i = 1, 2, 3…n, j = 1, 2, 3…k; k is the total number of clusters.

[0046] The technical effect of the present invention is unquestionable, and the beneficial effects of the present invention are as follows:

[0047] 1) The special equipment wireless pilot information perception network architecture proposed in the present invention has a good and consistent information description method and protocol. The massive and heterogeneous assembly workshop information can be uniformly described as an XML format file through an intelligent information interaction device, and all are transmitted through the IPv6 protocol, which facilitates the integration and transmission of production process information between multiple systems in the assembly workshop.

[0048] The interaction efficiency is high. IPv6 enables the networking equipment, logistics, and sensor devices in the special equipment assembly workshop to obtain a unique address. The upper-level management information system of the workshop can directly collect the required information through addressing, eliminating the complex routing information processing links in the traditional communication architecture, and realizing direct point-to-point connection between the underlying environment and the upper-level system.

[0049] High reliability. Compared with traditional sensor networks, the sensor network of the special equipment assembly workshop based on the IPv6 protocol has higher security performance. It can also defend against network malicious behaviors such as data theft and network attacks through the powerful address arrangement capability of IPv6. The clear address prevents information confusion during data transmission and interaction, thereby improving the overall reliability of the transmission network.

[0050] 2) The WSN node network structure is self-organizing and highly flexible. In the self-organizing mode, the topology of the wireless sensor network is always in a changing state. The sensor can find the best way to transmit the sensor information, which improves the sensor energy consumption and efficiency. It also has a wide coverage, large scale, good quality and high reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Model diagram of intelligent information interaction device

[0052] Figure 2 To support the software platform architecture of IPv6-based intelligent information exchange devices

[0053] Figure 3 Network architecture diagram of the information perception system for the assembly process of special equipment assembly workshop

[0054] Figure 4 is the sensor network node graph

[0055] Figure 5 Optimizing the configuration technology structure for workshop wireless sensor networks

[0056] Figure 6 The sensor network node structure diagram of the special equipment assembly workshop based on IPv6

[0057] Figure 7 K-means clustering algorithm flow chart

[0058] Figure 8 This is the regional node deployment diagram under the LEACH protocol

[0059] Fig. 9 A comparison chart of network life cycle

[0060] Fig.10 Comparison chart of total data sent to the network. DETAILED DESCRIPTION

[0061] The present invention is further described below in conjunction with the embodiments, but it should not be understood that the above subject matter of the present invention is limited to the following embodiments. Without departing from the above technical ideas of the present invention, various substitutions and changes are made according to the common technical knowledge and customary means in the art, which should all be included in the protection scope of the present invention.

[0062] Embodiment 1:

[0063] A sensor network networking and optimization configuration method for a special equipment assembly workshop, wherein the sensor network of the special equipment assembly workshop includes a workshop Internet of Things layer, a data processing interaction layer, and an integrated management layer;

[0064] The workshop IoT layer includes environmental information and a data source that carries the environmental information;

[0065] Information from the data source is collected through the intelligent information interaction device in the data processing interaction layer; after being processed, the information can be visualized through the intelligent information interaction device to guide the production work of the assembly workshop personnel or uploaded to the integrated management layer, so that each environmental information is interconnected, thereby realizing the control of the production environment information of the assembly workshop;

[0066] The steps of the sensor network networking and optimization configuration method for the special equipment assembly workshop include:

[0067] 1) Deploy sensors in the special equipment assembly workshop and treat each sensor as a sensor node and power it on for initialization;

[0068] 2) Each piece of environmental information is assigned an IP address after being collected by the intelligent information interaction device;

[0069] Based on IPv6 technology, the IP address of each message is different;

[0070] 3) Each IP address can communicate directly with the base station;

[0071] When the base station receives the transmission information from the sensor node, it starts the K-means clustering algorithm to cluster the sensor network nodes and transmit the clustering results to the corresponding nodes;

[0072] 4) Based on the sensor movement distance, the wireless sensor network cluster is periodically updated.

[0073] Embodiment 2:

[0074] The main structure of this embodiment is the same as that of Embodiment 1. Furthermore, the environment includes workshop personnel, testing equipment, assembly equipment, materials and tooling.

[0075] The data sources include PLC, embedded systems, cameras and industrial sensors.

[0076] Embodiment 3:

[0077] The main structure of this embodiment is the same as any one of Embodiments 1 to 2. Furthermore, the intelligent information interaction device includes a hardware and software platform.

[0078] The hardware includes an information communication technology protocol module, a network sensor information protocol module, a display terminal device, a power module, a sensor module, a processing module, a communication module, a heterogeneous information system integration interface module, and an information positioning and tracking system module.

[0079] The software platform is an Android system platform developed based on the Linux system, including a hardware resource layer, a function layer and an application layer.

[0080] Embodiment 4:

[0081] The main structure of this embodiment is the same as that of Embodiment 3. Furthermore, the sensing module includes a sensor and an analog / digital converter; the processing module includes a processor and a storage module; and the communication module includes a network module and a signal transceiver / transmitter.

[0082] The environmental data in the final assembly workshop is sensed by sensors, and the sensed data is transmitted to the analog / digital converter. The analog signal is converted into a digital signal through the digital / analog converter device, and then the digital signal is transmitted to the processor of the processing module. The processor describes the digital signal information, creates an XML model and generates a document, completes the software packaging, and stores the obtained data results in the storage module.

[0083] Embodiment 5:

[0084] The main structure of this embodiment is the same as any one of Embodiments 1 to 4. Further, in step 3), using the K-means clustering algorithm to cluster the sensor network nodes includes the following steps:

[0085] 3.1) Randomly generate K sensor nodes from the network area as the initial cluster centroids;

[0086] 3.2) Calculate the Euclidean distance between each sensor node and each initial cluster centroid, and assign the sensor node to the initial cluster centroid closest to it to form K clusters;

[0087] 3.3) After the allocation is completed, recalculate the cluster centroid of each cluster;

[0088] 3.4) Repeat steps 3.2) and 3.3) until the cluster centroid is in a balanced state. The centroid in the balanced state is the cluster head of the cluster.

[0089] Embodiment 6:

[0090] The main structure of this embodiment is the same as that of Embodiment 5. Furthermore, in order to solve the energy loss existing in the communication transmission layer of the wireless sensor network, a sensor with more energy surplus of cluster nodes, closer to the base station, and the smallest sum of Euclidean distances to surrounding sensors is selected as the centroid.

[0091] Embodiment 7:

[0092] The main structure of this embodiment is the same as any one of Embodiments 5 to 6. Further, in step 3.1), the wireless sensor network creates a random number t for each node, the value of t is between 0 and 1, and compares t with the revised threshold Compare, if t is less than Then node t is set as the cluster head and serves as the initial cluster centroid.

[0093] Embodiment 8:

[0094] The main structure of this embodiment is the same as that of embodiment 7. Furthermore, the threshold value is revised. The calculation formula is as follows:

[0095]

[0096]

[0097] Where: E resi 、E initial are the energy surplus and initial energy storage value of the sensor respectively;

[0098] is the European distance from the sensor to the base station; It is the mark value of the sum of the European distances between the sensor and the surrounding sensing devices;

[0099] T(n) is the design threshold;

[0100] p is the ratio between the number of cluster heads and the total number of nodes in the entire wireless sensor network, r is the number of cycles in the sensor network, n is the total number of nodes in the entire sensor wireless network, and the remainder is solved by the mod function; G is the current round number, is the set of nodes that are not cluster heads in the next round.

[0101] Embodiment 9:

[0102] The main structure of this embodiment is the same as any one of Embodiments 5 to 8. Furthermore, the energy consumption value E of the wireless sensor network tx The calculation formula is as follows:

[0103]

[0104] E rx =E elec (4)

[0105] Where: d refers to the sending distance; d0 refers to the distance threshold;

[0106] E elec Refers to the energy consumption required by sensor nodes to analyze and calculate data transmission information;

[0107] ε fs Refers to the energy consumption of wireless sensor networks under the close-range model;

[0108] ε mp Refers to the energy consumption of wireless sensor networks under the long-range model;

[0109] E rxIt is the energy consumption value obtained when the energy is transmitted to the receiving end (i.e., the intelligent information interaction device) via the wireless sensor network.

[0110] Embodiment 10:

[0111] The main structure of this embodiment is the same as any one of Embodiments 5 to 9. Furthermore, the Euclidean distance between each sensor node and each centroid is j represents the cluster number.

[0112] The calculation formula of the identifier J of n sensor nodes to their corresponding cluster centroid is as follows:

[0113]

[0114] Where: is the sensor node, c j is the centroid, i = 1, 2, 3…n, j = 1, 2, 3…k; k is the total number of clusters.

[0115] Embodiment 11:

[0116] The main structure of this embodiment is the same as any one of Embodiments 1 to 10. Furthermore, the invention relates to a sensor network networking and optimization configuration method for a special equipment assembly workshop. The method proposes and designs a WSN node network structure for a special equipment assembly workshop based on IPv6. Based on the principle of energy balance, the configuration optimization method of the LEACH routing protocol is improved by using WSN, which improves the communication quality and meets the requirements of the special equipment assembly workshop for an efficient, low-cost, and low-energy wireless sensor network. The technical method of the present invention is:

[0117] The present invention first proposes an intelligent information interaction device supporting IPv6, designs a WSN node network structure based on IPv6 for special equipment assembly workshops, studies the sensor network networking and optimization configuration method applicable to special equipment assembly workshops, and finally proposes a configuration optimization method for improving the LEACH routing protocol of WSN for special equipment assembly workshops based on energy balance. The implementation steps of the method include:

[0118] 1. Support for IPv6-based intelligent information interaction devices

[0119] Different from the information interaction devices of traditional industrial systems, the intelligent information interaction device supporting IPv6 proposed in this paper includes two parts: hardware and software design.

[0120] The hardware part includes information communication technology protocol module, network sensor information protocol module, display terminal equipment, heterogeneous information system integration interface module, information fusion and processing module, information positioning and tracking system, power module, mobile system, etc. It is an embedded process information perception device with the characteristics of stable operation, low power consumption and high autonomy. It can realize real-time collection, automatic encoding and high-quality data transmission of assembly process information of special equipment assembly workshop.

[0121] The software platform is an Android system platform developed based on the Linux system, which has better autonomy and controllability. It includes hardware resource layer, function layer and application layer. The hardware resource layer includes integrated interface and Linux system kernel, which can support receiving audio, video and other information.

[0122] 2. Sensor network node structure of special equipment assembly workshop based on IPv6

[0123] (1) Information-aware network architecture

[0124] 1) Workshop IoT layer

[0125] The workshop IoT layer consists of production environment elements and data sources that carry environmental information. The production environment includes workshop personnel, testing equipment, assembly equipment, materials, tooling, etc. The data sources include PLC, embedded systems, cameras, and a large number of industrial sensors.

[0126] 2) Data processing interaction layer

[0127] The intelligent information interaction device is mainly responsible for processing the data of the data interaction layer to obtain real-time information on core elements such as the production and assembly progress of the final assembly workshop, the operating status of the production line and unit, the supply chain logistics, and the operator. The collected data is multi-source heterogeneous and can be divided into structured data, unstructured data, and semi-structured data. Therefore, in order to facilitate the transmission and integration of subsequent information, the multi-source heterogeneous data will be uniformly described, encapsulated, and stored through the XML construction module in the intelligent information interaction device at the data processing interaction layer, and the real-time data will be transmitted upward to the management system of the integrated management layer through the data adapter, and downward to the workshop Internet of Things layer.

[0128] 3) Integration management layer

[0129] The perception information of the final assembly workshop will be applied at the integrated management level, and the XML data information encapsulated by the data processing interaction layer will be integrated between the management information systems of the integrated management level to achieve interconnection between the equipment, personnel, tasks, and systems in the final assembly workshop, thereby realizing applications such as material management and control of the final assembly workshop, intelligent management of final assembly resources, intelligent control of the assembly process, and intelligent follow-up of the final assembly plan.

[0130] Compared with the traditional sensor network architecture of the general assembly workshop, the special equipment wireless pilot information perception network architecture proposed in this paper has better advantages and characteristics:

[0131] ①Consistent information description method and protocol

[0132] Massive and heterogeneous assembly workshop information can be uniformly described as XML format files through intelligent information interaction devices, and all transmitted through the IPv6 protocol, which facilitates the integration and transmission of production process information between multiple systems in the assembly workshop.

[0133] ②High interaction efficiency

[0134] IPv6 enables the networking equipment, logistics, and sensor devices in the special equipment assembly workshop to obtain a unique address. The workshop's upper-level management information system can directly collect the required information through addressing, eliminating the complex routing information processing links in the traditional communication architecture and achieving direct point-to-point connection between the underlying environment and the upper-level system.

[0135] ③High reliability

[0136] Compared with traditional sensor networks, the sensor network of special equipment assembly workshop based on IPv6 protocol has higher security performance. It can also defend against network malicious behaviors such as data theft and network attacks through the powerful address arrangement capability of IPv6. The clear address prevents information confusion during data transmission and interaction, thereby improving the overall reliability of the transmission network.

[0137] (2) Data transmission interaction process

[0138] 1) Collection of environmental data

[0139] The resource data, equipment data, logistics data, personnel information, etc. of the final assembly workshop are collected through the sensor module in the intelligent information interaction device. Each information element is assigned an IP address. Since the IPv6 technology is collected to realize perception networking, the IP address of each information element is different. The different IP addresses bring great convenience to information tracking.

[0140] 2) Processing and description of collected data

[0141] The collected data will appear in the intelligent interactive device and be converted into digital signals through analog / digital conversion and then sent to the processing module in the IPv6 border gateway for processing. The processing module will describe the collected multi-dimensional heterogeneous information, create XML models for structured, unstructured, and semi-structured data in the production environment, and then generate documents, which will be transmitted to the upper-level system through the wireless transceiver module.

[0142] 3) Integration and application of perception information

[0143] On the one hand, the processed data can be directly visualized to the workshop staff through intelligent interactive devices to guide the production work of the general assembly workshop personnel; on the other hand, it will be used by the workshop's management information system, such as ERP, MES, etc., to achieve control over the five elements of the general assembly workshop: people, machines, materials, methods, and environment, and realize the interconnection and interoperability of the special equipment general assembly workshop from the bottom-level links to the upper-level systems.

[0144] (3)WSN node network structure

[0145] 1) Definition of WSN

[0146] Wireless Sensor Network (WSN) is a multi-hop, self-organizing network formed by small hardware and sensor devices through communication transmission protocols. The sensor devices involved in the network have the characteristics of low energy consumption and small size, and are equivalent to a microcomputer. In addition to the perception ability, they also have information assembly and storage, wireless communication and other functions. Wireless sensor networks have the advantages of strong flexibility, easy deployment, wide coverage, dynamic data monitoring, and low cost. They can be composed of terminal / sensor nodes on equipment, materials, assembly lines, and personnel, routing nodes composed of networks and routes, demodulator nodes responsible for IP address allocation, and gateway nodes as workshop wireless sensor centers.

[0147] 2)WSN node design

[0148] The variability of the assembly workshop environment, the complexity of the assembly process, and the need for real-time data monitoring require that the sensor equipment in WSN not only has sensing and communication capabilities, but also requires certain computing and data storage capabilities. The large number of sensors deployed also makes it necessary to consider factors such as saving sensor energy consumption and ensuring the coordination of sensor data transmission. This requires WSN to have the following characteristics:

[0149] (1) Self-organizing network, strong flexibility

[0150] There are a large number of mobile materials and equipment in the assembly workshop, and most of the assembly parts are always in a mobile state. If the same wireless sensor network cluster structure is always used, the physical distance between sensors in the cluster will become farther and farther, which will increase communication energy consumption unnecessarily and seriously reduce communication quality. Self-organizing network refers to the networking mode in which the sensor selects the cluster head closest to itself to automatically generate a new wireless sensor network cluster. In the self-organizing network mode, the topology of the wireless sensor network is always in a changing state, and the sensor will find the best way to transmit sensor information, so that the sensor energy consumption and efficiency are improved.

[0151] (2) Wide coverage and large scale

[0152] The assembly process in the special equipment assembly workshop is a manufacturing process involving a large amount of data interaction and transmission in a relatively large space. Each link requires data transmission speed, which makes the physical coverage area and network volume of WSN very large. In order to make the data transmission process smooth, WSN cannot rely too much on a single node. At the same time, WSN must be scalable and can expand the network scale as the capacity of the assembly workshop expands.

[0153] (3) Good quality and high reliability

[0154] The working environment of the final assembly workshop is often filled with production metabolites such as dust, oil mist, and iron filings. These production metabolites will damage and corrode the sensor devices distributed in various parts of the final assembly workshop. If the real-time and accuracy of the perceived information is to be ensured, the sensor devices deployed in the final assembly workshop must be able to work in harsh environments.

[0155] 3. WSN Optimization Configuration Scheme for Assembly Workshop Based on Improved LEACH

[0156] The perception of multi-source heterogeneous data information in the special equipment assembly workshop depends on the support of the WSN sensor communication network. However, the complex and changeable production environment, various production tasks and equipment resources make the data in the special equipment assembly workshop dynamic and complex. The bus network layout of the general production workshop cannot meet the current production and assembly work requirements for network quality in terms of communication quality. It is an urgent problem to improve new network communication technologies using theoretical knowledge such as operations research and optimization algorithms to configure a special equipment assembly workshop WSN with higher network communication quality, less cost and stronger robustness.

[0157] The routing protocol algorithm configures and optimizes the transmission path according to the transmission energy consumption and energy consumption value of the wireless sensor network nodes, so that the energy consumption and transmission time consumption between nodes in the WSN are optimized, and the robustness of the wireless sensor network in the special equipment assembly workshop is increased. The special equipment assembly workshop has many assembly processes and a large amount of communication. There is an urgent need for efficient, low-cost, and low-energy wireless sensor networks, and the above needs must be achieved through routing protocols, thereby forming a large-scale self-sensing self-organizing network system. Therefore, this paper proposes a configuration optimization method for the improved LEACH routing protocol of the special equipment assembly workshop WSN based on energy balance to optimize the energy consumption, cost, and communication quality of the wireless sensor network in the special equipment assembly workshop.

[0158] (1) Model design

[0159] The general LEACH protocol is carried out in rounds, and each round consists of two parts, namely the cluster creation stage and the balanced operation stage. In the creation stage of the wireless sensor network cluster, all nodes in the WSN must belong to a sensor network cluster. Each sensor network cluster has a cluster head, which is randomly generated. In order to balance the tasks assigned to each node in the wireless sensor network, it is necessary to design a random generation method for the cluster; the random generation process is: the wireless sensor network creates a random number t for each node, the value of t is between 0 and 1, and a threshold T(n) is designed. If t is less than T(n), the node t is set as the cluster head. The calculation formula of T(n) is as follows:

[0160]

[0161] Where p is the ratio between the number of cluster heads and the total number of nodes in the entire wireless sensor network, the number of cycles in the sensor network is r, the total number of nodes in the entire sensor wireless network is defined as n, and the remainder is solved by the mod function; G is the current round number; is the set of nodes that are not cluster heads in the next round.

[0162] There are two cases for calculating the energy consumption of wireless sensor networks, namely short-distance calculation and long-distance calculation. When the transmission distance is short (d≤d0), the communication used by the wireless sensor network is the free space channel model; when the transmission distance is long (d>d0), the communication used by the wireless sensor network is the multipath attenuation model. Both modes use the first-order radio energy consumption calculation formula to quantify the energy consumption value, and the calculation formula is as follows:

[0163]

[0164] The energy consumption value obtained by transmitting energy to the receiving end through the wireless sensor network is:

[0165] E rx (l) = lE rs-elec (l) = lE elec

[0166] oeLh elec Refers to the energy consumption required by the sensor node for data transmission information analysis and calculation, ε fs and ε mp They refer to the energy consumption of wireless sensor networks under short-range and long-range models respectively. The above planning and configuration methods for sensors still have shortcomings, which are specifically reflected in the following aspects:

[0167] ① The random method of selecting cluster heads is too random and has major performance defects;

[0168] ② The transmission mode of wireless sensor network adopts multi-hop mode, which will make the wireless sensor network consume more energy;

[0169] ③Special equipment assembly workshops often require technical confidentiality, so there is a need for data transmission encryption.

[0170] Based on the above analysis, this paper adopts an improved LEACH wireless sensor network protocol to improve the decision-making cluster head method. The theoretical assumptions include the following points:

[0171] ①The coverage of the wireless sensor network includes all nodes;

[0172] ②Sensors can determine the location through positioning identification and quantify the energy consumption of processing and transmission;

[0173] ③Sensors are assigned addresses through IPv6, which are unique and can communicate directly with the base station;

[0174] ④ There is only one cluster head in the sensor cluster, and the cluster head can be switched along with the displacement of the sensor device through self-organizing networking.

[0175] (2) Cluster Generation

[0176] The method of summarizing and aggregating chaotic discrete objects by similarity analysis of their attributes to form larger units is called clustering. There are many ways to achieve clustering. Generally, the clustering method used to generate clustering algorithms to solve the problem of classification and induction is determined according to the existing data material form and the expected purpose. However, most clustering algorithms require finding the clustering core called the centroid in advance, and then quantifying the similarity between elements through the Euclidean interval between other elements and the centroid. The paper intends to use K-means clustering to generate suitable clusters for WSN. The design of K-means requires full consideration of the structure of WSN, the network distribution of wireless sensor network nodes, and the quantitative standard of fitting similar values. This method will first randomly generate multiple K clusters, then quantify the core of the K cluster, and distribute the nodes around the core of the K cluster to form initial clusters. This process will repeat multiple cycles. When the cluster is in a relatively balanced state, the cycle will stop, and the distance between the centroids of the clusters will be set to the current evaluation value. This method can minimize the Euclidean distance between the node and the centroid, which needs to be implemented by calculating the mean square error function. The quantization formula is as follows:

[0177]

[0178] Refers to the selected Cluster elements to centroid c j The Euclidean distance of n elements is , and J refers to the identifier of the centroid of the corresponding cluster. The clustering process is as follows:

[0179] ① Point K to the centroid in the clustering space.

[0180] ② Assign cluster elements to the nearest centroid to participate in the formation of clusters;

[0181] ③After the allocation is completed, calculate the centroid position again;

[0182] ④ Repeat steps ② and ③ until the center of mass is in a state of equilibrium.

[0183] When receiving the transmission information from the node, the base station starts the clustering algorithm. After deciding the cluster head and calculating all the Euclidean distances, the base station transmits the calculated data to the corresponding node. Therefore, each sensor device in the wireless sensor network can know who is its centroid and determine whether to become the centroid through the information sent by the base station.

[0184] (3) Cluster Generation

[0185] Wireless sensor networks suffer from energy loss at the communication transmission level. To solve this problem, the paper has processed the energy surplus of cluster nodes, the Euclidean distance from the sensor to the base station, and the sum of the distances between sensors to optimize the selection strategy of the sensor centroid. Therefore, the sensor with more energy surplus, closer to the base station, and the smallest sum of the Euclidean distance to the surrounding sensors is more likely to become the centroid. Quantifying it can be obtained as follows:

[0186]

[0187] Where E resi With E initial They are the energy surplus and initial energy storage value of the sensor respectively. and Each is a label value of the sum of the Euclidean distance from the sensor to the base station and the Euclidean distance between the sensor and surrounding sensing devices. and Can be superimposed into the transmission cost of the sensor to quantify the spacing This number refers to the extreme value of the transmission overhead quantization distance when the sensor node in the sensor cluster becomes the centroid, as shown below:

[0188]

[0189] Therefore, the sensor with the largest internal energy surplus and the smallest transmission overhead has the highest probability of becoming the cluster head, which can improve the transmission energy consumption of the entire wireless sensor network and increase the revision threshold. The probability value is greater than the calculated random number t. The Zigbee protocol is used by the base station in the wireless sensor network to transmit data such as the centroid and Euclidean distance of the sensor cluster. The sensors in the wireless sensor network find the cluster head based on the centroid. Whenever a sensor in the wireless sensor network obtains its own centroid, the Zigbee protocol will transmit the audit information to the cluster head (the centroid of the cluster head of the wireless sensor network). The sensor's capacity surplus will be calculated by the cluster head that manages the sensor to create a scheduling plan, and the node in the equilibrium state will send the time slot configuration information used by the node, thereby completing the creation of the cluster.

[0190] Embodiment 12:

[0191] The main structure of this embodiment is the same as any one of Embodiments 1 to 11, and further,

[0192] Figure 1 It is a model diagram of intelligent information interaction device, such as Figure 1 As shown in the figure: the hardware of the process information perception model includes a sensing module, a processing module and a communication module. The sensing module is composed of a sensor and an analog / digital converter. The dynamic information of the special equipment assembly workshop is captured by the sensor in the sensing module. The sensor transmits the data captured in the workshop to the analog / digital converter, and the analog signal is converted into a digital signal that is easier to transmit and process through the digital / analog converter. The processing module is composed of a processor for processing digital information and a storage module for storing processing results. The main task of the processor module is to describe the digital signal and complete the software packaging. The obtained data results are stored in the storage device module. The communication device module is composed of a network module and a signal transceiver / transmitter. The communication module mainly transmits the collected and processed information to the Internet, so that the upper management system can use the collected real-time information to make decisions. Data from different network ports, serial ports, and parallel ports are connected to the intelligent information interaction device model through heterogeneous integrated interfaces. The system module can identify the location information of the intelligent information interaction device, so that the upper management system can track the location of the intelligent information interaction device. The information processed by the intelligent interactive device can be transmitted to the Internet through the communication module on the one hand, and can be visualized through the display terminal on the other hand, so as to show the real-time information of the assembly workshop to the production personnel, so that the production personnel can timely and accurately grasp the real-time changes of production resources, equipment, and logistics.

[0193] Figure 2It is a software platform architecture diagram that supports IPv6-based intelligent information interaction devices. The functional layer consists of a programming development environment and an application toolbox. The intelligent interactive device uses Java language to develop functional applications, which not only include packaged application packages such as information description, service quality detection, and component management, but also provide coding interfaces to third-party users through the Android system, allowing users to customize software functions. Through the functional layer, the relevant services of the application layer of the intelligent information interaction device can be realized, including desktop applications, browser applications, storage applications, exchange applications, communication applications, etc.

[0194] Figure 3 It is the network architecture diagram of the information perception system of the assembly process of the special equipment assembly workshop, which includes: workshop Internet of Things layer (workshop sensor network and environment), data processing interaction layer (terminal interaction) and integrated management layer (workshop management).

[0195] Figure 4 It is a sensor network node diagram. WSN consists of terminal / sensor nodes on equipment, materials, assembly lines, and personnel, routing nodes composed of networks and routes, demodulator nodes responsible for IP address allocation, and gateway nodes as the wireless sensor center of the workshop.

[0196] Figure 5 This is the technical structure diagram of the workshop wireless sensor network optimization configuration. This paper uses gateway access to connect the WSN network to IPv6. First, the WSN is designed according to the type and function of the sensor and combined with the environmental characteristics of the special equipment assembly workshop. Then, from the perspective of increasing efficiency and reducing costs, the WSN is optimized by improving the LEACH protocol and energy balance theory.

[0197] Figure 6 This is a sensor network node structure diagram of a special equipment assembly workshop based on IPv6. The WSN node network structure designed in this paper includes:

[0198] (1) Communication protocol structure: Nowadays, WSN can usually use IEEE802.154 and other protocols in the bottom layer and control part, but there is still no unified standard protocol for the Internet communication part. Popular protocols such as Zigbee and Z-wave can be used. It is precisely because there is no unified protocol standard for the Internet communication part that the interconnection between WSNs of various enterprises is blocked. In view of the above reasons, this paper proposes an IPv6-based WSN Internet protocol for special equipment assembly workshops.

[0199] (2) Network communication protocol: This part mainly includes physical layer, data link layer, network layer, transport layer and application layer.

[0200] (3) Network management platform: This part is used for the management and control of sensors, including structural control: control of the overall structure of the sensor network in the special equipment assembly workshop; energy consumption control: maintaining the energy consumption of WSN nodes within a controllable range; privacy protection: ensuring the security of sensor data communication; portability management: control of the networking logic components of mobile sensor bearer nodes.

[0201] (4) Application support platform: Provides real-time data materials for the management information system of the special equipment assembly workshop, and supports the smooth implementation of functions such as dynamic monitoring of the workshop and real-time decision-making in the workshop.

[0202] Figure 7 This is the flow chart of the K-means clustering algorithm. First, when the transmission information of the node is received, the base station starts the clustering algorithm. After deciding the cluster head and calculating all the Euclidean distances, it groups the nodes based on the minimum distance. Then it waits for the cluster members to stabilize and ends. Otherwise, it recalculates.

[0203] Embodiment 13:

[0204] The main structure of this embodiment is the same as any one of Embodiments 1 to 12. Further, in order to better illustrate the advantages of the technical solution of the present invention, this embodiment discloses the following experiments:

[0205] 1. Experimental setup

[0206] A simulation test environment with MATLAB as the test platform was built. The simulation parameters of the wireless networking equipment in the special equipment assembly workshop based on the improved LEACH protocol were set as follows: the number of sensor nodes was set to 150 and randomly distributed in the network area; the coordinates of the corresponding base station were (100, 100); the size of the network area was 200×200m 2 ; The initial energy of the node is E0 = 0.16 J; The probability of cluster head selection of the LEACH protocol is P = 0.5; The energy loss value of the free space model is ε fs =6J / (bit·m2), calculate the energy loss value ε of the multipath fading model mp =0.0018 J / (bit·m4); the energy consumed by a wireless network to receive and send 1 bit of data E eles =10nJ / bit; the energy consumed by wireless network to fuse 1bit of data E DA =1nJ / bit, the specific simulation parameters are shown in Table 1 below:

[0207] Table 1 Simulation parameters of improved wireless network protocol

[0208]

[0209] 2. Experimental results

[0210] Based on the analysis and discussion in the previous section, this paper combines the relevant algorithms and processes used, and uses MATLAB software for simulation testing.

[0211] 1) Node deployment in the network

[0212] In 200×200m 2 In the network area, 150 nodes are randomly deployed, represented by red, and green represents the energy loss nodes of the free space model. The position of the base station is set to (100, 100). Figure 8 Its random distribution diagram.

[0213] 2) Analysis of simulation results

[0214] ①. Network life cycle comparison: Both are simulated under the same network conditions and environment using MATLAB software. It can be seen that the LEACH protocol and the improved LEACH protocol WSN are simulated in the network life cycle. The simulation results are as follows: Fig. 9 As shown in the figure, it can be concluded from the simulation data that the improved LEACH protocol method in this paper can significantly strengthen the life cycle of the network, while the original LEACH protocol significantly weakens the life cycle of the network. The use of the improved protocol significantly prolongs the number of abort rounds of the starting and last network nodes.

[0215] ②Comparison of the amount of data sent by the network: Under the same network environment conditions, MATLAB software was used to simulate and compare the amount of data sent by the WSN of the LEACH protocol and the improved LEACH protocol. Fig.10 As shown. It can be seen that the improved LEACH protocol significantly increases the amount of data received by the network compared to the original protocol, with an increase of 13.4%. This shows that the sensor network optimization configuration method for the special equipment assembly workshop provided in the paper can effectively improve the reliability and stability of the sensor network in the assembly workshop, reduce the network data transmission packet loss rate, and thus provide a reliable network environment support for the collection and transmission of multi-source heterogeneous data in the workshop.

Claims

1. A sensor network networking and optimization configuration method for a special equipment assembly workshop, characterized in that: The sensor network of the special equipment assembly workshop includes the workshop Internet of Things layer, the data processing interaction layer and the integrated management layer; The workshop IoT layer includes environmental information and a data source that carries the environmental information; Collect information from the data source through the intelligent information interaction device in the data processing interaction layer; After being processed, the information can be visualized through intelligent information interaction devices to guide the production work of the assembly workshop personnel or uploaded to the integrated management layer, so that various environmental information can be interconnected, thereby realizing the control of the production environment information of the assembly workshop; The steps of the sensor network networking and optimization configuration method for the special equipment assembly workshop include: 1) Deploy sensors in the special equipment assembly workshop and treat each sensor as a sensor node and power it on for initialization; 2) Each piece of environmental information is assigned an IP address after being collected by the intelligent information interaction device; Based on IPv6 technology, the IP address of each message is different; 3) Each IP address can communicate directly with the base station; When the base station receives the transmission information from the sensor node, it starts the K-means clustering algorithm to cluster the sensor network nodes and transmit the clustering results to the corresponding nodes; 4) Based on the sensor movement distance, the wireless sensor network cluster is periodically updated.

2. The method for sensor network networking and optimization configuration of a special equipment assembly workshop according to claim 1, characterized in that: The environment includes workshop personnel, testing equipment, assembly equipment, materials and tooling; The data sources include PLC, embedded systems, cameras and industrial sensors.

3. The method for sensor network networking and optimization configuration of a special equipment assembly workshop according to claim 1, characterized in that: The intelligent information interaction device includes a hardware part and a software platform; The hardware includes an information communication technology protocol module, a network sensor information protocol module, a display terminal device, a power module, a sensor module, a processing module, a communication module, a heterogeneous information system integration interface module, and an information positioning and tracking system module; The software is an Android system platform developed based on the Linux system, including a hardware resource layer, a function layer and an application layer.

4. The method for sensor network networking and optimal configuration of a special equipment assembly workshop according to claim 3, characterized in that: The sensing module includes a sensor and an analog / digital converter; the processing module includes a processor and a storage module; and the communication module includes a network module and a signal transceiver / transmitter. The environmental data in the final assembly workshop is sensed by sensors, and the sensed data is transmitted to the analog / digital converter. The analog signal is converted into a digital signal through the digital / analog converter device, and then the digital signal is transmitted to the processor of the processing module. The processor describes the digital signal information, creates an XML model and generates a document, completes the software packaging, and stores the obtained data results in the storage module.

5. The sensor network networking and optimization configuration method for a special equipment assembly workshop according to claim 1, characterized in that: In step 3), the K-means clustering algorithm is used to cluster the sensor network nodes, including the following steps: 3.1) Randomly generate K sensor nodes from the network area as the initial cluster centroids; 3.2) Calculate the Euclidean distance between each sensor node and the initial cluster centroid, and assign the sensor node to the initial cluster centroid closest to it to form K clusters; 3.3) After the allocation is completed, recalculate the cluster centroid of each cluster; 3.4) Repeat steps 3.2) and 3.3) until the cluster centroid is in a balanced state. The centroid in the balanced state is the cluster head of the cluster.

6. The method for sensor network networking and optimal configuration of a special equipment assembly workshop according to claim 5, characterized in that: In order to solve the energy loss in the communication transmission layer of wireless sensor networks, the sensor with more energy surplus, closer to the base station and the smallest sum of Euclidean distances to surrounding sensors is selected as the centroid.

7. A sensor network networking and optimization configuration method for a special equipment assembly workshop according to claim 5 or 6, characterized in that: In step 3.1), the wireless sensor network creates a random number t for each node, the value of t is between 0 and 1, and compares t with the revised threshold Compare, if t is less than Then node t is set as the cluster head.

8. The method for sensor network networking and optimal configuration of a special equipment assembly workshop according to claim 7, characterized in that: Revision threshold The calculation formula is as follows: Where: E resi 、E initial are the energy surplus and initial energy storage value of the sensor respectively; is the European distance from the sensor to the base station; It is the mark value of the sum of the European distances between the sensor and the surrounding sensing devices; T(n) is the design threshold; p is the ratio between the number of cluster heads and the total number of nodes in the entire wireless sensor network, r is the number of cycles in the sensor network, n is the total number of nodes in the entire sensor wireless network, and the remainder is solved by the mod function; G is the current round number, is the set of nodes that are not cluster heads in the next round.

9. The method for sensor network networking and optimal configuration of a special equipment assembly workshop according to claim 6, characterized in that: Energy consumption value E of wireless sensor network tx The calculation formula for (l,d) is as follows: E rx =E elec (4) Where: d refers to the transmission distance; d0 refers to the distance threshold; E elec Refers to the energy consumption required by sensor nodes to analyze and calculate data transmission information; ε fs Refers to the energy consumption of wireless sensor networks under the close-range model; ε mp Refers to the energy consumption of wireless sensor networks under the long-range model; E rx The energy consumption value obtained by transmitting energy to the receiving end via the wireless sensor network; 10. The method for sensor network networking and optimal configuration of a special equipment assembly workshop according to claim 5, characterized in that: The Euclidean distance between each sensor node and each centroid is j represents the cluster number; The calculation formula of the identifier J of n sensor nodes to their corresponding cluster centroid is as follows: Where: is the sensor node, c j is the centroid, i = 1, 2, 3…n, j = 1, 2, 3…k; k is the total number of clusters.

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