A method and system for adapting Internet of Things data transmission protocol based on big data

Abstract

The present invention belongs to the field of data transmission technology and proposes a method and system for adapting an Internet of Things (IoT) data transmission protocol based on big data. The method obtains data transmission protocol adaptation characteristics based on the data acquisition characteristics, data essential characteristic coefficients, and protocol parameters of IoT devices. An IoT data transmission adaptation model is constructed based on the data acquisition protocol adaptation characteristics and the corresponding transmission protocol adaptation method. The model is used to adaptively adapt the transmission protocol to subsequent acquired data and output a real-time transmission protocol adaptation method. The protocol converter device is controlled based on the real-time transmission protocol adaptation method, thereby adapting and managing IoT data transmission. By processing the multi-dimensional characteristics of IoT data and using an improved activation function big data model, the conversion efficiency of the data adaptation transmission protocol is enhanced, the transmission speed and quality of IoT data are improved, and the loss of manual adaptation of the data transmission protocol is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of data transmission, and in particular relates to a method and system for adapting an Internet of Things data transmission protocol based on big data. Background Art

[0002] In industrial intelligent manufacturing, a wide variety of data collection source devices with diverse functional attributes and hardware components require network access. These include CNC machine tools, industrial robots, automated transport vehicles, cleaning equipment, testing equipment, automated production lines, and automated material warehouses. This also includes industrial ancillary equipment such as controllers for multiple sensors and actuators. This diverse industrial production data requires access to the internet for unified storage and analysis to monitor, control, and adjust production processes.

[0003] However, currently, for data from heterogeneous devices across multiple sources, collected data is directly sent to node servers for storage and analysis. This data is then coordinated and managed by a master service, using technologies like data caching and message queues for load balancing. While these approaches are effective for handling massive data collection tasks, they also present a complex process for configuring and deploying servers and servers to support the expansion of access to multiple heterogeneous collection devices and servers.

[0004] Based on the above description, the existing technology has the following problems that need to be solved:

[0005] How to fully consider the application scenarios of protocol adaptation that affects IoT data transmission while keeping the adaptation protocol library unchanged? How to perform specialized processing on the data itself, the protocol itself, and even the vector features that affect data transmission? Utilize the protocol, data, and corresponding acquisition and transmission processes to construct a scenario-based neural network model. Then, use the model to adaptively adapt the transmission protocol to the acquired data and output an immediate transmission protocol adaptation method. Based on this immediate transmission protocol adaptation method, the protocol converter device is controlled, thereby adapting and managing IoT data transmission. To this end, a big data-based IoT data transmission protocol adaptation method and system are proposed. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention proposes a method and system for adapting the Internet of Things data transmission protocol based on big data.

[0007] In a first aspect of the present invention, a method for adapting an IoT data transmission protocol based on big data is provided:

[0008] Upload and obtain data collection characteristics, data essential characteristic coefficients, and protocol parameters of the transmitted IoT data that affect the selection of the IoT data adaptation protocol, and obtain the corresponding IoT data transmission protocol adaptation method;

[0009] Processing the data acquisition characteristics, the data essential characteristic coefficients and the protocol parameters of the transmitted IoT data to obtain data transmission protocol adaptation characteristics;

[0010] Constructing an IoT data transmission adaptation model based on the transmission protocol adaptation characteristics and the corresponding IoT data transmission protocol adaptation method;

[0011] The IoT data transmission adaptation model is used to process the IoT data to be transmitted to obtain an IoT data transmission protocol adaptation method to be transmitted;

[0012] The IoT data transmission is adapted and managed based on the IoT data transmission protocol adaptation method to be transmitted.

[0013] Furthermore, the data acquisition characteristics are obtained by processing the average acquisition transmission time, the data cache time and the temperature difference coefficient between the data source and the data storage location.

[0014] Furthermore, the data essential characteristic coefficient is obtained by processing the characteristics of the data itself, specifically by processing the data dimension, data span size and data type.

[0015] Furthermore, the protocol parameters include adaptation scenario characteristics and adaptation feature parameters of the protocol.

[0016] Furthermore, the adaptation scenario characteristics obtain adaptation values according to the adaptation scenario, specifically according to the bandwidth and delay conditions in the network environment. The adaptation scenario characteristics are one- and two-dimensional matrix vectors, and the adaptation characteristic parameters are obtained by processing the protocol adaptation distance, power consumption and data volume.

[0017] Furthermore, the data acquisition features, the data essential feature coefficients and the protocol parameters of the transmitted IoT data are processed to obtain the data transmission protocol adaptation features by using a vector dimension-raising method. The vector dimension-raising method is to process the data acquisition features , the data essential characteristic coefficient And the protocol parameters Matrix representation.

[0018] Furthermore, the IoT data transmission adaptation model utilizes a neural network model with an improved activation function based on adaptation scenario features and adaptation characteristic parameters.

[0019] Furthermore, the IoT data transmission adaptation model utilizes an improved activation function based on adaptation scenario features and adaptation characteristic parameters:

[0020] ;

[0021] Where S(X) is the activation function value, is the rank of the matrix that adapts to the scene features, To adapt the characteristic parameters, The adaptation features for the transmission protocol are calculated by adding the weights of the neurons and the bias.

[0022] A big data-based IoT data transmission protocol adaptation system is also provided, which includes an IoT device data feature acquisition module, an IoT data feature module, a protocol adaptation feature module, an IoT data transmission adaptation terminal module, a transmission protocol adaptation feature processing module, and an IoT data transmission adaptation model construction module, and is characterized by:

[0023] The IoT device data feature acquisition module is used to acquire the first data acquisition feature in data acquisition and also to acquire the second data acquisition feature in data acquisition;

[0024] The IoT data feature module is used to obtain the first data essential feature coefficient and the second data essential feature coefficient;

[0025] The protocol adaptation feature module uploads the acquired first protocol parameters;

[0026] The transmission protocol adaptation feature processing module performs parameter feature processing on the first data acquisition feature, the first data essential feature coefficient, and the first protocol parameter to obtain a first data transmission protocol adaptation feature, and further performs parameter feature processing on the second data acquisition feature, the second data essential feature coefficient, and the first protocol parameter to obtain a second data transmission protocol adaptation feature;

[0027] The IoT data transmission adaptation model construction module is configured to construct an IoT data transmission adaptation model according to the first data transmission protocol adaptation feature and the first IoT data transmission protocol adaptation method;

[0028] The Internet of Things data transmission adaptation terminal module: obtains the uploaded first Internet of Things data transmission protocol adaptation method, and is also connected to the transmission protocol adaptation feature processing module and the Internet of Things data transmission adaptation model construction module, obtains the second data transmission protocol adaptation feature and processes to obtain the second Internet of Things data transmission protocol adaptation method, and controls the protocol converter device to perform adaptive selection of the Internet of Things data transmission protocol.

[0029] Furthermore, the IoT data transmission adaptation model utilizes a neural network model with an improved activation function based on adaptation scenario features and adaptation characteristic parameters;

[0030] The improved activation function is;

[0031] ;

[0032] Where S(X) is the activation function value, is the rank of the matrix that adapts to the scene features, To adapt the characteristic parameters, The adaptation features for the transmission protocol are calculated by adding the weights of the neurons and the bias.

[0033] The present invention fully considers that under the condition that the adaptation protocol library remains unchanged, the protocol adaptation affecting the data transmission of the Internet of Things is carried out to construct feature processing based on the fusion properties of the data itself, the protocol itself, and even the vector features affecting the data transmission, and uses the protocol and data and the corresponding acquisition and transmission process to construct the activation function of the neural network model in a scenario-based manner, thereby using the model to perform adaptive transmission protocol adaptation for the collected data and output an immediate transmission protocol adaptation method, and controls the protocol converter device according to the immediate transmission protocol adaptation method, thereby performing adaptation management on the data transmission of the Internet of Things. By processing the multi-dimensional features of the Internet of Things data and enhancing the conversion efficiency of the data adaptation transmission protocol through the improved activation function big data model, the transmission speed and quality of the Internet of Things data are improved, and the artificial adaptation loss of the data transmission protocol is reduced.

[0034] More embodiments and improved effects of the present invention will be further introduced in conjunction with the drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a diagram of a model training and usage of an IoT data transmission protocol adaptation method based on big data of the present invention;

[0036] Figure 2 This is a schematic diagram of an IoT data transmission protocol adaptation system based on big data of the present invention;

[0037] Figure 3 This is a schematic diagram of the MQTT message transmission protocol in the present invention;

[0038] Figure 4 This is a schematic diagram of the improved activation function in the present invention. DETAILED DESCRIPTION

[0039] The invention is further described below with reference to the accompanying drawings and specific implementation methods.

[0040] In the first aspect of the present invention, the present invention proposes a method and system for adapting the Internet of Things data transmission protocol based on big data.

[0041] In a first aspect of the present invention, a method for adapting an IoT data transmission protocol based on big data is provided:

[0042] Upload and obtain data collection characteristics, data essential characteristic coefficients, and protocol parameters of the transmitted IoT data that affect the selection of the IoT data adaptation protocol, and obtain the corresponding IoT data transmission protocol adaptation method;

[0043] Processing the data acquisition characteristics, the data essential characteristic coefficients and the protocol parameters of the transmitted IoT data to obtain data transmission protocol adaptation characteristics;

[0044] Constructing an IoT data transmission adaptation model based on the transmission protocol adaptation characteristics and the corresponding IoT data transmission protocol adaptation method;

[0045] The IoT data transmission adaptation model is used to process the IoT data to be transmitted to obtain an IoT data transmission protocol adaptation method to be transmitted;

[0046] The IoT data transmission is adapted and managed based on the IoT data transmission protocol adaptation method to be transmitted.

[0047] There are many compatible transmission protocols, such as lightweight protocols (MQTT / CoAP).

[0048] MQTT: Based on a publish / subscribe model, it supports QoS levels (0-2) and is suitable for low-bandwidth, unstable network environments (such as remote monitoring and sensor data transmission). Its retained message and will message mechanisms can improve the efficiency of device status management.

[0049] CoAP: Based on the RESTful architecture, it is designed for resource-constrained devices. It supports UDP transmission and observer mode, making it suitable for smart home and industrial control scenarios. The block transfer mechanism optimizes the transmission of large data segments.

[0050] Comparison: MQTT focuses more on reliability and message management, while CoAP emphasizes low power consumption and simple interaction.

[0051] Real-time and high-performance protocols (DDS / HTTP2)

[0052] DDS: It is designed for real-time systems and supports low-latency, high-throughput communications. It is suitable for fields with high real-time requirements, such as autonomous driving and aerospace.

[0053] HTTP / 2: Improves transmission efficiency through multiplexing and header compression technologies, and is suitable for scenarios requiring high-frequency interactions, such as intelligent transportation systems.

[0054] Low-power wide area network protocol (LoRaWAN / NB-IoT)

[0055] LoRaWAN: Supports long-distance communication and ADR (Adaptive Data Rate Regulation), suitable for smart cities and agricultural monitoring.

[0056] NB-IoT: Based on cellular networks, it offers wide coverage and low cost, making it suitable for large-scale deployment scenarios such as smart meter reading and asset tracking.

[0057] In the present invention, data transmission speed is divided into two required categories according to actual needs, and the IoT data adaptation method is further matched based on the model.

[0058] Furthermore, the data acquisition characteristics are obtained by processing the average acquisition transmission time, data cache time, and the temperature difference coefficient between the data source and the data storage location, and the calculation formula is:

[0059] ;

[0060] Where, For data collection characteristics, in this application, after many experiments, it was found that the longer the average collection and transmission time of IoT data is, the stronger the model value corresponding to the adaptation method obtained by training the model is, and the longer the data cache time is, the more the data transmission rate will be affected accordingly, thus showing an inverse proportion. is the average collection and transmission time of IoT data, The data cache time, is the temperature at the data source, The temperature of the data storage location is calculated and processed to obtain the temperature difference coefficient.

[0061] Furthermore, the data essential characteristic coefficient is obtained by processing the characteristics of the data itself, specifically by processing the data dimension, data span size and data type, and its calculation formula is:

[0062] ;

[0063] Since the essential characteristics of data in addition to knowing the data source can intuitively display the data situation, and the dimension, span size and data type of the data have a significant impact on the adaptation of the IoT data transmission protocol, the experiment in this application found that the higher the data dimension and the larger the data span, the more difficult it is for data transmission, and the corresponding value of the corresponding transmission protocol adaptation method is significantly enhanced. In the formula, is the essential characteristic coefficient of the data, is the data dimension, for The total span size of each dimension of the data, For the set data type, corresponding label values are set according to the needs of technical personnel in this field based on the source of the collected data, such as agricultural data is set to 1, industrial data is set to 2, and service industry data is set to 3.

[0064] Furthermore, the protocol parameters include adaptation scenario characteristics and adaptation feature parameters of the protocol.

[0065] Furthermore, the adaptation scenario feature obtains the adaptation value according to the adaptation scenario, specifically according to the bandwidth and delay conditions in the network environment. The adaptation scenario feature is a one-two-dimensional matrix vector, specifically expressed as a matrix , if the matrix is , which means broadband and low latency. The adaptation characteristic parameters are calculated based on the protocol adaptation distance, power consumption and data volume:

[0066]

[0067] Affected by the protocol itself, the adaptability to IoT data transmission will be different. Therefore, the model considers the protocol itself to correct the efficiency of the model output adaptation protocol. In the formula, is the adaptation characteristic parameter, d is the protocol adaptation distance. Since the transmission protocol adaptation distance has ultra-long distance transmission, in order to reduce the span of the training model input data and improve the model robustness, the distance value is scaled by the logarithmic function. Different protocols have different effects on power consumption. In order to reflect the power consumption impact between different protocols, the average power consumption of P protocols is calculated. , is the maximum power consumption among P protocols, The amount of data transmitted.

[0068] Furthermore, the data acquisition features, the data essential feature coefficients and the protocol parameters of the transmitted IoT data are processed to obtain the data transmission protocol adaptation features by using a vector dimension-raising method. The vector dimension-raising method is to process the data acquisition features , the data essential characteristic coefficient And the protocol parameters The matrix representation is , making the processing of subsequent models more convenient and facilitating the processing of neural network models.

[0069] Furthermore, the IoT data transmission adaptation model utilizes a neural network model with an improved activation function based on adaptation scenario features and adaptation characteristic parameters.

[0070] Furthermore, the IoT data transmission adaptation model utilizes an improved activation function based on adaptation scenario features and adaptation characteristic parameters:

[0071] ;

[0072] Where S(X) is the activation function value, is the rank of the matrix that adapts to the scene features, which plays a role in correcting the activation function value. In order to adapt the characteristic parameters, the adaptation protocol output by the model has a corresponding impact on the adaptation protocol itself, and the existing models rarely take into account the situation of the IoT data adaptation protocol manually calibrated and selected in the database. Therefore, by correcting the calculation of the activation function in the model based on the selection library of the adaptation protocol itself, it is possible to determine the content transmitted to the next neuron of the neural network that is more in line with the required IoT data transmission protocol. The adaptation features for the transmission protocol are calculated by adding the weights of the neurons and the bias.

[0073] It also provides an IoT data transmission protocol adaptation system based on big data, including an IoT device data feature acquisition module, an IoT data feature module, a protocol adaptation feature module, an IoT data transmission adaptation terminal module, a transmission protocol adaptation feature processing module, and an IoT data transmission adaptation model construction module:

[0074] The IoT device data feature acquisition module is used to acquire the first data acquisition feature in data acquisition and also to acquire the second data acquisition feature in data acquisition;

[0075] The IoT data feature module is used to obtain the first data essential feature coefficient and the second data essential feature coefficient;

[0076] The protocol adaptation feature module uploads the acquired first protocol parameters;

[0077] The transmission protocol adaptation feature processing module performs parameter feature processing on the first data acquisition feature, the first data essential feature coefficient, and the first protocol parameter to obtain a first data transmission protocol adaptation feature, and further performs parameter feature processing on the second data acquisition feature, the second data essential feature coefficient, and the first protocol parameter to obtain a second data transmission protocol adaptation feature;

[0078] The IoT data transmission adaptation model construction module is configured to construct an IoT data transmission adaptation model according to the first data transmission protocol adaptation feature and the first IoT data transmission protocol adaptation method;

[0079] The Internet of Things data transmission adaptation terminal module: obtains the uploaded first Internet of Things data transmission protocol adaptation method, and is also connected to the transmission protocol adaptation feature processing module and the Internet of Things data transmission adaptation model construction module, obtains the second data transmission protocol adaptation feature and processes to obtain the second Internet of Things data transmission protocol adaptation method, and controls the protocol converter device to perform adaptive selection of the Internet of Things data transmission protocol.

[0080] Furthermore, the IoT data transmission adaptation model utilizes a neural network model with an improved activation function based on adaptation scenario features and adaptation characteristic parameters;

[0081] The improved activation function is:

[0082] ;

[0083] Where S(X) is the activation function value, The rank of the matrix that adapts to the scene characteristics plays a role in correcting the activation function value. In order to adapt the characteristic parameters, the adaptation protocol output by the model has a corresponding impact on the adaptation protocol itself, and the existing models rarely take into account the situation of the IoT data adaptation protocol manually calibrated and selected in the database. Therefore, by correcting the calculation of the activation function in the model based on the selection library of the adaptation protocol itself, it is possible to determine the content transmitted to the next neuron of the neural network that is more in line with the required IoT data transmission protocol. The adaptation features for the transmission protocol are calculated by adding the weights of the neurons and the bias.

[0084] The present invention fully considers that under the condition that the adaptation protocol library remains unchanged, the protocol adaptation affecting the data transmission of the Internet of Things is carried out to construct feature processing based on the fusion properties of the data itself, the protocol itself, and even the vector features affecting the data transmission, and uses the protocol and data and the corresponding acquisition and transmission process to construct the activation function of the neural network model in a scenario-based manner, thereby using the model to perform adaptive transmission protocol adaptation for the collected data and output an immediate transmission protocol adaptation method, and controls the protocol converter device according to the immediate transmission protocol adaptation method, thereby performing adaptation management on the data transmission of the Internet of Things. By processing the multi-dimensional features of the Internet of Things data and enhancing the conversion efficiency of the data adaptation transmission protocol through the improved activation function big data model, the transmission speed and quality of the Internet of Things data are improved, and the artificial adaptation loss of the data transmission protocol is reduced.

[0085] Of course, it can be understood that each embodiment of the present invention can achieve one of the effects alone, and a combination of multiple embodiments of the present invention can achieve all of the above effects, but it is not required that every embodiment of the present invention achieve all of the above advantages and effects, because each embodiment of the present invention can constitute a separate technical solution and make one or more contributions to the existing technology.

[0086] For any module structure not specifically defined in this invention, the prior art shall prevail. The prior art mentioned in the aforementioned background and specific embodiments of this invention may be considered as part of this invention and used to understand the meaning of certain technical features or parameters. The scope of protection of this invention shall be based on the actual content of the claims.

Claims

1. A method for adapting an Internet of Things data transmission protocol based on big data, characterized in that: The method comprises: Upload and obtain data collection characteristics, data essential characteristic coefficients, and protocol parameters of the transmitted IoT data that affect the selection of the IoT data adaptation protocol, and obtain the corresponding IoT data transmission protocol adaptation method; The data acquisition characteristics are obtained by processing the average acquisition transmission time, data cache time and the temperature difference coefficient between the data source and the data storage location. The calculation formula is: Where D c is the data collection feature, T t is the average collection and transmission time of IoT data, T c is the data cache time, R s is the temperature at the data source, R l is the temperature of the data storage location; The data essential characteristic coefficient is obtained by processing the characteristics of the data itself, specifically by processing the data dimension, data span size and data type. The calculation formula is: Where D e is the data essential characteristic coefficient, [D] is the data dimension, is the sum of the spans of each dimension of the [D]-dimensional data, D t The data type to be set; The protocol parameters include the adaptation scenario characteristics and adaptation feature parameters of the protocol; The adaptation scenario feature obtains the adaptation value according to the adaptation scenario, specifically according to the bandwidth and delay conditions in the network environment. The adaptation scenario feature is a one-two-dimensional matrix vector. The adaptation characteristic parameters are obtained by processing the protocol adaptation distance, power consumption and data volume: Where A f is the adaptation characteristic parameter, d is the protocol adaptation distance, |K P | is the average power consumption of P protocols, P max is the maximum power consumption among P protocols, D v The amount of data transmitted; Processing the data acquisition characteristics, the data essential characteristic coefficients and the protocol parameters of the transmitted IoT data to obtain data transmission protocol adaptation characteristics; Constructing an IoT data transmission adaptation model based on the transmission protocol adaptation characteristics and the corresponding IoT data transmission protocol adaptation method; The IoT data transmission adaptation model is used to process the IoT data to be transmitted to obtain an IoT data transmission protocol adaptation method to be transmitted; The IoT data transmission is adapted and managed based on the IoT data transmission protocol adaptation method to be transmitted.

2. The method for adapting an IoT data transmission protocol based on big data according to claim 1, wherein: The data acquisition feature, the data essential feature coefficient and the protocol parameter of the transmitted IoT data are processed to obtain the data transmission protocol adaptation feature by using a vector dimension raising method. The vector dimension raising method is to obtain the data acquisition feature D a , the data essential characteristic coefficient D o And the protocol parameter P c Matrix representation.

3. The method for adapting an Internet of Things data transmission protocol based on big data according to claim 1 or 2, characterized in that: The IoT data transmission adaptation model utilizes a neural network model with an improved activation function based on adaptation scenario features and adaptation characteristic parameters.

4. The method for adapting an Internet of Things data transmission protocol based on big data according to claim 3, wherein: The IoT data transmission adaptation model uses an improved activation function based on adaptation scenario features and adaptation characteristic parameters: In the formula, S(X) is the activation function value, r(A) is the rank of the matrix that adapts to the scene features, and A f is the adaptation characteristic parameter, X is the transmission protocol adaptation characteristic calculated by adding the weight of the neuron and the bias.

5. A big data-based IoT data transmission protocol adaptation system, configured to implement the big data-based IoT data transmission protocol adaptation method according to any one of claims 1 to 4, the system comprising an IoT device data feature acquisition module, an IoT data feature module, a protocol adaptation feature module, an IoT data transmission adaptation terminal module, a transmission protocol adaptation feature processing module, and an IoT data transmission adaptation model construction module, characterized in that: The IoT device data feature acquisition module is used to acquire the first data acquisition feature in data acquisition and also to acquire the second data acquisition feature in data acquisition; The IoT data feature module is used to obtain the first data essential feature coefficient and the second data essential feature coefficient; The protocol adaptation feature module uploads the acquired first protocol parameters; The transmission protocol adaptation feature processing module performs parameter feature processing on the first data acquisition feature, the first data essential feature coefficient, and the first protocol parameter to obtain a first data transmission protocol adaptation feature, and further performs parameter feature processing on the second data acquisition feature, the second data essential feature coefficient, and the first protocol parameter to obtain a second data transmission protocol adaptation feature; The IoT data transmission adaptation model construction module is configured to construct an IoT data transmission adaptation model according to the first data transmission protocol adaptation feature and the first IoT data transmission protocol adaptation method; The Internet of Things data transmission adaptation terminal module: obtains the uploaded first Internet of Things data transmission protocol adaptation method, and is also connected to the transmission protocol adaptation feature processing module and the Internet of Things data transmission adaptation model construction module, obtains the second data transmission protocol adaptation feature and processes to obtain the second Internet of Things data transmission protocol adaptation method, and controls the protocol converter device to perform adaptive selection of the Internet of Things data transmission protocol.

6. The IoT data transmission protocol adaptation system based on big data according to claim 5, characterized in that: The IoT data transmission adaptation model utilizes a neural network model with an improved activation function based on adaptation scenario features and adaptation characteristic parameters; The improved activation function is: In the formula, S(X) is the activation function value, r(A) is the rank of the matrix that adapts to the scene features, and A f is the adaptation characteristic parameter, X is the transmission protocol adaptation characteristic calculated by adding the weight of the neuron and the bias.

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