Bidirectional protocol conversion method and device between Bluetooth and LoRaWAN

Abstract

The present invention discloses a method and device for bidirectional protocol conversion between Bluetooth and LoRaWAN, which relates to the field of communication technology. The method comprises: processing Bluetooth protocol data through a data segmentation module and dividing it into multiple data packets. The protocol conversion module starts multiple compression operation units, gives priority to processing data packets with strong dependencies, and generates multiple compressed packets. The LoRaWAN module receives and packages them into LoRaWAN protocol packets, and sends them to a remote device through the LoRaWAN network. Data is restored according to the data type and dependency identifier. In the reverse operation, the LoRaWAN module converts the LoRaWAN data sent by the remote device back into Bluetooth protocol data. The technical problems of poor compatibility and difficulty in communication between the existing Bluetooth and LoRaWAN protocols are solved, and the technical effect of realizing seamless communication between the Bluetooth and LoRaWAN protocols and improving protocol compatibility is achieved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method and device for bidirectional protocol conversion between Bluetooth and LoRaWAN. Background Art

[0002] With the rapid development of IoT technology, Bluetooth and LoRaWAN, two common wireless communication protocols, are widely used in various scenarios. Bluetooth is suitable for short-range, high-data-rate device communications, while LoRaWAN, with its long-range and low-power consumption, is suitable for long-distance, low-data-volume communications. However, these two protocols differ significantly in data format, transmission method, and network architecture, leading to interoperability and compatibility issues. Many devices and applications require support for both protocols to leverage the complementary advantages of short-range communication and long-range data transmission. Traditional approaches often rely on additional hardware adaptation or protocol stack conversion, increasing system complexity and cost. Therefore, efficiently implementing protocol conversion and interoperability between Bluetooth and LoRaWAN has become a pressing technical challenge in IoT applications.

[0003] At present, relevant technologies have technical problems such as poor compatibility between Bluetooth and LoRaWAN protocols and difficulty in communication. Summary of the Invention

[0004] This application solves the technical problems of poor compatibility and communication difficulties between existing Bluetooth and LoRaWAN protocols by providing a two-way protocol conversion method and device between Bluetooth and LoRaWAN.

[0005] This application provides a bidirectional protocol conversion method between Bluetooth and LoRaWAN, including:

[0006] The Bluetooth protocol data received by the Bluetooth module is processed by the data fragmentation module to obtain multiple data packets, wherein the multiple data packets have multiple data type identifiers and multiple dependency identifiers. After receiving the multiple data packets sent by the data fragmentation module, the protocol conversion module performs compression tool matching scheduling according to the multiple data type identifiers to obtain M compression operation units. After the protocol conversion module assigns compression priorities to the M compression operation units according to the multiple dependency identifiers, the M compression operation units are started to perform compression processing on the multiple data packets to obtain M groups of compressed packets. The M groups of compressed packets are converted into M groups of LoRaWAN protocol data by the protocol conversion module. After receiving the M groups of compressed packets transmitted by the protocol conversion module, the LoRaWAN module packages the M groups of compressed packets into M groups of LoRaWAN protocol packets and sends them to a remote device via the LoRaWAN network. The remote device receives the M groups of LoRaWAN protocol packets and performs data restoration according to the multiple data type identifiers and the multiple dependency identifiers. In a reverse operation, the protocol conversion module and the LoRaWAN module are reversely applied to convert the return data sent by the remote device into Bluetooth protocol data.

[0007] This application also provides a bidirectional protocol conversion device between Bluetooth and LoRaWAN, including:

[0008] A data packet acquisition module, the data packet acquisition module is used to process the Bluetooth protocol data received by the Bluetooth module through the data fragmentation module to obtain multiple data packets, wherein the multiple data packets have multiple data type identifiers and multiple dependency identifiers; a tool matching scheduling module, the tool matching scheduling module is used for the protocol conversion module to receive the multiple data packets sent by the data fragmentation module, and then perform compression tool matching scheduling according to the multiple data type identifiers to obtain M compression operation units; a compression processing module, the compression processing module is used for the protocol conversion module to assign compression priorities to the M compression operation units according to the multiple dependency identifiers, and then start the M compression operation units to perform compression processing on the multiple data packets to obtain M groups of compressed packets; a protocol data conversion module, the protocol data conversion module The module is used to convert the M groups of compressed packets into M groups of LoRaWAN protocol data through the protocol conversion module; the remote device sending module is used for the LoRaWAN module to receive the M groups of compressed packets transmitted by the protocol conversion module, package the M groups of compressed packets into M groups of LoRaWAN protocol packets, and send them to the remote device through the LoRaWAN network; the data restoration module is used for the remote device to receive the M groups of LoRaWAN protocol packets and restore the data according to the multiple data type identifiers and multiple dependency identifiers; the backhaul conversion module is used for reverse operation, reversely applying the protocol conversion module and the LoRaWAN module to convert the backhaul of the data sent by the remote device into Bluetooth protocol data.

[0009] The bidirectional protocol conversion method and device between Bluetooth and LoRaWAN proposed in this application first processes the Bluetooth protocol data through a data segmentation module, dividing it into multiple data packets, each of which contains a data type and a dependency identifier. The protocol conversion module selects an appropriate compression tool based on the data type identifier, starts multiple compression operation units, prioritizes data packets with strong dependencies, and generates multiple compressed packets. The compressed packets are converted into LoRaWAN protocol data, which the LoRaWAN module receives and packages into LoRaWAN protocol packets, which are sent to a remote device via the LoRaWAN network. After receiving the data, the remote device restores the data based on the data type and dependency identifier. In the reverse operation, the LoRaWAN module converts the LoRaWAN data sent by the remote device back into Bluetooth protocol data. Through the bidirectional protocol conversion method, the technical effect of achieving seamless communication between the Bluetooth and LoRaWAN protocols and improving protocol compatibility is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact sequence. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A flowchart of a bidirectional protocol conversion method between Bluetooth and LoRaWAN provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the structure of a bidirectional protocol conversion device between Bluetooth and LoRaWAN provided in an embodiment of the present application.

[0013] Explanation of the accompanying symbols: data packet acquisition module 10, tool matching and scheduling module 20, compression processing module 30, protocol data conversion module 40, remote device sending module 50, data restoration module 60, return conversion module 70. DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms “first\second” involved are merely to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0017] The present invention provides a method for bidirectional protocol conversion between Bluetooth and LoRaWAN. Figure 1 As shown, the method includes:

[0018] In step S100, the Bluetooth protocol data received by the Bluetooth module is processed by the data slicing module to obtain multiple data packets, wherein the multiple data packets have multiple data type identifiers and multiple dependency identifiers. Specifically, after the Bluetooth module receives the Bluetooth communication data from the terminal device, the data slicing module first interacts with the LoRaWAN module to obtain the transmission load limit and uses this as a scale to initialize the structure of the Bluetooth protocol data to obtain multiple data subsets. The data subsets have dependencies. Then, a data classification model that stores a set of sample fields of multiple sample data types is called to traverse the data subset records and record the recurrence frequency to determine the subset type. Based on the subset type and dependency relationship, adjacent dependencies of the same type subsets are merged to construct a data structure tree. The data packet is located in the structure tree and the data dependency relationship and data type information are extracted. Finally, the data packet is identified with the information, thereby obtaining multiple data packets with multiple data type identifiers and multiple dependency relationship identifiers, providing an accurate information foundation for subsequent processing and enabling efficient process advancement.

[0019] In one possible implementation, the Bluetooth protocol data received by the Bluetooth module is processed by a data segmentation module to obtain multiple data packets, wherein the multiple data packets have multiple data type identifiers and multiple dependency identifiers, and step S100 further includes step S110, in which the Bluetooth module receives the Bluetooth protocol data from the terminal device. Specifically, the Bluetooth module receives the Bluetooth protocol data from the terminal device. The terminal device can be a variety of smart devices, such as smart phones, smart bracelets, smart home sensors, etc. During operation, the device will generate a large amount of data that needs to be transmitted and send it out in the form of Bluetooth protocol. The Bluetooth module can accurately capture the data signal by virtue of its designed Bluetooth communication interface and protocol stack, and convert it into a format that can be processed by subsequent modules, providing the original data source for the entire data processing process.

[0020] Step S120, performs data structure analysis on the Bluetooth protocol data through the data fragmentation module to obtain a data structure tree. Specifically, after receiving the Bluetooth protocol data, the data fragmentation module performs data structure analysis on the Bluetooth protocol data, and the data fragmentation module needs to analyze the organizational structure of the data. Through data processing technology, the various components in the data and the relationships between them are identified. For example, different data blocks are distinguished based on the specific format tags, field lengths and other features of the data. Then, based on the analysis results, a data structure tree is gradually constructed. The construction process of the data structure tree is similar to building a family tree, which displays the affiliation and connection paths between data in a hierarchical manner. Each node represents a data unit, and the branch represents the relationship between data units, which organizes the seemingly chaotic Bluetooth protocol data into a clear and orderly structural model, laying the foundation for the positioning and information extraction of subsequent data packets.

[0021] Step S130, after locating the multiple data packets in the data structure tree, extracts multiple data dependency relationships and multiple data type information for the multiple data packets from the data structure tree. Specifically, after the data structure tree is constructed, the data slicing module locates the multiple data packets in the data structure tree. After locating the data packets, the data slicing module further analyzes the inherent information of the data packets and extracts multiple data dependency relationships and multiple data type information from the data structure tree. Data dependency relationships reflect the logical connections between data packets. For example, the data of certain data packets is the basis for processing or calculation of other data packets, and there is a sequence or causal relationship between data packets. The data type information clarifies the nature of the data contained in each data packet, such as audio data, video data, sensor measurement data (such as temperature, humidity, pressure data, etc.), or control instruction data. This information is crucial for subsequent data processing and transmission, and will serve as an important identifier for the data packet, helping the system accurately understand and process the characteristics and purpose of each data packet.

[0022] Step S140, using the multiple data dependency relationships and multiple data type information to identify the multiple data packets. Specifically, the data fragmentation module uses the extracted multiple data dependency relationships and multiple data type information to identify the multiple data packets. This allows the protocol conversion module, compression module, and other related modules to quickly and accurately identify the content and characteristics of the data packet in the subsequent processing flow, thereby adopting corresponding processing strategies based on the identification information. For example, the protocol conversion module can select appropriate conversion rules based on the type information of the data packet, and the compression module can determine the order and method of compression based on the data dependency relationship to ensure the efficiency and accuracy of data processing, and ensure the smooth transmission and conversion process between the entire Bluetooth and LoRaWAN.

[0023] In one possible implementation, the data slicing module performs data structure analysis on the Bluetooth protocol data to obtain a data structure tree. Step S120 further includes step S121, interacting with the LoRaWAN module to obtain a transmission load limit. Specifically, the data slicing module interacts with the LoRaWAN module to obtain transmission load limit information. Transmission load limits are typically determined by the LoRaWAN protocol specification or network configuration, such as the maximum transmittable data packet size. This limit information is crucial for subsequent data processing steps, as it serves as a key basis for data partitioning, ensuring that data can be effectively processed and transmitted within the transmission capabilities of the LoRaWAN network.

[0024] Step S122, using 1 / N of the transmission load limit as a data partitioning scale, performs structural initialization on the Bluetooth protocol data to obtain multiple data subsets, wherein the multiple data subsets have multiple subset dependencies. Specifically, after obtaining the transmission load limit, the data slicing module uses 1 / N of the transmission load limit as a data partitioning scale to perform structural initialization operations on the Bluetooth protocol data. A large data set is divided according to a specific size standard to obtain multiple data subsets. The data subsets are not completely unrelated, but have multiple subset dependencies. For example, the data in some subsets are prerequisites for processing the data in other subsets, or there is a logical sequence relationship. The dependency relationship reflects the internal structural and logical nature of the Bluetooth protocol data.

[0025] Step S123, pre-constructing a data classification model, wherein the data classification model stores K sample field sets of K sample data types. Specifically, a data classification model is pre-constructed, which stores K sample field sets of K sample data types. The sample data types cover various data categories that appear in Bluetooth protocol data, such as sensor data, control instruction data, device status data, etc. The sample field set corresponding to each sample data type is a typical feature identifier of this type of data, and the type of data in the data subset can be identified through the feature identifier. For example, for temperature sensor data, its sample field set may include a temperature value field, a sensor number field, etc.

[0026] Step S124: Using the data classification model to traverse the multiple data subsets, record the recurrence frequencies of the K sample field sets, and obtain multiple sets of field recurrence records. Specifically, the constructed data classification model is used to traverse the multiple data subsets obtained previously, and the recurrence frequencies of the K sample field sets are recorded during the traversal process, thereby obtaining multiple sets of field recurrence records. The recurrence records reflect the frequency of occurrence of different sample field sets in the data subsets, that is, the repetitiveness and redundancy information of the data.

[0027] Step S125: The data types of the multiple data subsets are located based on the multiple sets of field recurrence records to obtain multiple subset types. Specifically, based on the multiple sets of field recurrence records, the data types of the multiple data subsets can be located, and the sample data type of each data subset can be determined, thereby obtaining multiple subset types. For example, if the sample field set of temperature sensor data has a high recurrence frequency in a certain data subset, then the type of the data subset can be determined to be the temperature sensor data type.

[0028] Step S126, according to said multiple subset types and multiple subset dependencies, said multiple data subsets are carried out the adjacent dependency merging of same type subsets, complete the structure of said data structure tree.Particularly, according to definite multiple subset types and multiple subset dependencies, said multiple data subsets are carried out the adjacent dependency merging operation of same type subsets.The data subset with same type and the adjacent dependency is integrated, form a hierarchical, structured whole, thereby complete the structure of said data structure tree.In this data structure tree, each node represents a data subset or the larger data unit after the merging, the connection relationship between the node has embodied the dependency between the data subset, and the nodes of different levels have then reflected the classification of data, such data structure tree provides clear framework and basis for follow-up operations such as locating data packet, extracting data dependency and data type information therein, helps to improve efficiency and accuracy of whole data processing flow.

[0029] In step S200, after receiving the multiple data packets sent by the data slicing module, the protocol conversion module matches and schedules compression tools based on the multiple data type identifiers to obtain M compression operation units. Specifically, after receiving the multiple data packets sent by the data slicing module, the protocol conversion module first reads the multiple data type identifiers of each data packet, analyzes the data features, and then efficiently searches and matches the data type identifiers in a pre-built compression tool library that stores multiple compression tools and their configuration information based on different data types. For data packets identified as different data types such as images, audio, and text, the module selects appropriate compression tools from the corresponding tool set to form compression operation units based on factors such as compression efficiency, compression ratio, and the degree of quality preservation of compressed data. This operation is repeated until compression tools are determined for all data packets, and finally M compression operation units are obtained, so that the data packets can be compressed and processed to achieve optimized data transmission and storage.

[0030] In step S300, the protocol conversion module assigns compression priorities to the M compression operation units according to the multiple dependency identifiers, and then starts the M compression operation units to perform compression processing on the multiple data packets, thereby obtaining M groups of compressed packets. Specifically, after receiving the data packets, the protocol conversion module analyzes the multiple dependency identifiers, comprehensively considers the dependency, importance, and redundancy of the data packets to construct a priority sorting system, formulates a strategy for the M compression operation units, assigns high priority to the units corresponding to the data packets with strong dependencies and high importance, and assigns low priority to the units corresponding to the data packets with high redundancy, and then starts the M compression operation units in order of priority, using appropriate compression algorithms such as dictionary encoding for text and discrete cosine transform for image to compress the data packets. The high-priority units first process the key data packets, and the low-priority units subsequently process other data packets, ultimately obtaining M groups of compressed packets, thereby reducing the data volume while ensuring data integrity and compression effect, so as to facilitate subsequent transmission and storage.

[0031] In one possible implementation, after the protocol conversion module assigns compression priorities to the M compression operation units according to the multiple dependency identifiers, the M compression operation units are started to perform compression processing on the multiple data packets to obtain M groups of compressed packets. Step S300 further includes step S310, where data packets are aggregated according to the multiple data type identifiers to obtain M groups of data packets. Specifically, data packets are aggregated based on multiple data type identifiers. The data type identifier is an important attribute label of the data packet. By identifying and classifying it, data packets with the same or similar data types can be grouped together. For example, if there are several data packets with audio data type identifiers, several data packets with video data type identifiers, etc., the data packets with the same data type identifiers are integrated separately to finally obtain M groups of data packets, providing an ordered set of data packets for subsequent precise processing.

[0032] Step S320, pre-construct a plurality of sample compression units for a plurality of sample data types, wherein the plurality of sample compression units have a plurality of sets of compression execution nodes built in, and the plurality of sample compression units rely on a plurality of compression coding books to perform data compression operations. Specifically, a plurality of sample compression units for a plurality of sample data types are pre-constructed. Each sample compression unit is internally provided with a plurality of sets of compression execution nodes, and the nodes are the core components for actually performing the data compression task, and process the data according to specific compression algorithms and rules. At the same time, the plurality of sample compression units rely on a plurality of compression coding books to perform data compression operations. The compression coding stores information such as compression coding methods, algorithm flows, and parameter settings for different data types. For example, for image data types, the compression coding book may contain detailed steps and parameter specifications of the JPEG encoding algorithm; for text data types, there are specific operation flows based on Huffman coding.

[0033] Step S330 associates and stores the multiple sample data types and the multiple sample compression units, completing the configuration of the compression tool library. Specifically, the multiple sample data types and the corresponding sample compression units are associated and stored, thus constructing the compression tool library. When compression is required for a data packet of a specific data type, the corresponding sample compression unit and its associated resources can be quickly found in the compression tool library, allowing for efficient compression.

[0034] Step S340, compression tool matching and scheduling are performed in the compression tool library according to the M data type identifiers of the M groups of data packets, and M compression operation units are obtained. Specifically, compression tool matching and scheduling are performed in the constructed compression tool library according to the M data type identifiers of each of the M groups of data packets. After the specific data type identifier of a group of data packets is identified, a sample compression unit that matches it is searched in the compression tool library. For example, if a group of data packets is identified as a video data type, a sample compression unit specifically for video data is found in the compression tool library, and it is determined as the compression operation unit for the group of data packets. Through the matching and scheduling process, appropriate compression operation units are determined for each of the M groups of data packets, and finally M compression operation units are obtained. This ensures that each data packet can be processed by the compression tool that best suits its data type, thereby improving the efficiency and quality of compression.

[0035] Step S350: Extract M sets of data dependencies from the M sets of data packets. Specifically, M sets of data dependencies are extracted from the M sets of data packets. These data dependencies reflect the logical interrelationships between data packets. For example, data in some data packets may be the basis for processing or parsing other data packets, and there may be a sequential order or causal relationship between them.

[0036] In step S360, compression priority is assigned based on the M groups of data dependencies, and M compression node assignments are obtained. Specifically, after obtaining the data dependencies, compression priority is assigned based on the dependencies. Data packets at the front end of the data dependency chain that have a significant impact on subsequent data packet processing are assigned a higher compression priority; data packets that are relatively independent or at the end of the dependency chain are assigned a lower priority. Through the assignment, M compression node assignments are obtained, so that the subsequent compression process can be carried out in a reasonable order, ensuring data integrity and improving the overall efficiency of compression.

[0037] Step S370, using the M compression node allocations as constraints, starts the M compression operation units to perform cluster compression on the M groups of data packets, and obtains the M groups of compressed packages. Specifically, using the M compression node allocations as constraints, starts the M compression operation units to perform cluster compression on the M groups of data packets. Each compression operation unit uses the built-in compression execution node and the dependent compression code book to carry out compression work according to the compression node priority assigned to it and the corresponding data packet characteristics. For example, a compression operation unit with a high priority first performs efficient compression processing on its corresponding key data packet, using a more sophisticated but potentially slightly longer compression algorithm to ensure the accuracy and integrity of the data; while a compression operation unit with a lower priority uses a relatively fast but slightly lower compression ratio algorithm to compress non-key data packets. After each compression operation unit completes the compression task in accordance with the established order and rules, M groups of compressed packages are finally obtained. These compressed packages will provide a more optimized data format for subsequent data transmission or storage, reducing the amount of data while retaining the core value and logical relationship of the data.

[0038] In one possible implementation, after compression priority is assigned based on the M groups of data dependencies, M compression node assignments are obtained, and step S360 further includes step S361, constructing a data association topology for the first group of data packets based on the first group of data dependencies. Specifically, for the first group of data packets, a data association topology is constructed based on their data dependencies. Each data packet is regarded as a topological node. If a data packet has data dependencies with other data packets, a connection relationship is established between their corresponding nodes. For example, if the data processing of data packet B requires the result of data packet A, then a directed edge will be constructed between the nodes representing A and B, indicating the data flow dependency from A to B. The complex dependencies between the first group of data packets are clearly presented in the form of a topological graph, providing an intuitive structural model for subsequent analysis and processing.

[0039] Step S362, extracting and obtaining multiple dangling data points from the data association topology. Specifically, in the constructed data association topology, carefully search and extract multiple dangling data points. Dangling data points refer to data packets corresponding to nodes that only have in-degree connections or no connection relationships in the topology structure. Data packets are relatively independent in the data dependency chain and do not directly affect the processing order of other data packets. For example, some data packets that only contain their own device status information and do not participate in other data calculations or processing processes may become dangling data points. Extracting dangling data points helps to further optimize the topology structure so as to more accurately determine the compression priority of data packets.

[0040] Step S363, after removing the multiple dangling data points from the data association topology, the topological nodes in the data association topology are graded based on the out-degree connection relationship to obtain multi-layer out-degree connection points. Specifically, after removing the multiple extracted dangling data points from the data association topology, the remaining topological nodes are graded based on the out-degree connection relationship to obtain multi-layer out-degree connection points. The out-degree connection relationship reflects the number of connections from a node to other nodes. The larger the out-degree, the more influence the data of the node has on subsequent data packets. By counting and analyzing the out-degree connection relationship, the topological nodes are divided into different levels. For example, a node with an out-degree of 0 may be divided into the lowest level, while a node with a higher out-degree is divided into a higher level, forming a multi-layer out-degree connection point structure. The grading method can intuitively reflect the importance and influence level of the data packet in the entire data dependency system.

[0041] Step S364, configure multiple compression priority levels for the multi-layer out-degree connection points, and configure compression end levels for the multiple dangling data points. Specifically, according to the grading results of the topological nodes, multiple compression priority levels are configured for the multi-layer out-degree connection points, and compression end levels are configured for the multiple dangling data points extracted previously. For multi-layer out-degree connection points at a higher level, that is, the data packets corresponding to the nodes that provide important data for more subsequent data packets, a higher compression priority level is assigned, because the accurate transmission and rapid processing of data packets are crucial to the integrity and efficiency of the entire data processing process. Due to their relative independence, dangling data points have less impact on the overall data dependency, so they are configured as the compression end level, which means that they can be compressed after the compression processing of other important data packets is completed. The configuration method can improve the overall compression efficiency under the premise of ensuring data integrity.

[0042] Step S365, configure the compression execution node with the compression terminal level and multiple compression priority levels as constraints to obtain the first compression node allocation. Specifically, configure the compression execution node with the configured compression terminal level and multiple compression priority levels as constraints to obtain the first compression node allocation. According to different compression priority levels, corresponding compression execution node resources are allocated to data packets at each level. For example, for data packets at high compression priority levels, compression execution nodes with better performance and faster processing speed are allocated to ensure that they can complete compression quickly and accurately; and for data packets corresponding to dangling data points at the compression terminal level, compression execution nodes that are relatively simple and occupy less resources are allocated. Through the configuration method, the node allocation scheme for the first group of data packets during the compression process is determined, that is, the first compression node allocation.

[0043] Step S366, and so on, compression priority allocation is performed according to the M groups of data dependencies, and the M compression node allocations are obtained. Specifically, the M groups of data packets are processed in sequence according to the same method and steps. According to the respective data dependencies of each group of data packets, a data association topology is constructed, dangling data points are extracted, and after removing the dangling data points, topology node classification is performed, compression priority levels and compression terminal levels are configured, and finally, compression execution node configuration is performed with the levels as constraints, thereby obtaining compression node allocations corresponding to each group of data packets, and finally obtaining M compression node allocations. The compression node allocation scheme will provide precise execution guidance for subsequent cluster compression operations, ensuring that the dependencies between data packets can be fully considered during the compression process, and that compression resources can be efficiently utilized to achieve optimized data compression processing.

[0044] In one possible implementation, the M compression nodes are allocated as a constraint, the M compression operation units are started to perform cluster compression of the M groups of data packets, and the M groups of compressed packets are obtained. Step S370 further includes step S371, which uses the first compression node allocation as a constraint to start the first group of compression execution nodes of the first compression operation unit. Specifically, according to the first compression node allocation scheme, the first group of compression execution nodes in the first compression operation unit is started. The first compression node allocation clarifies the compression execution resources and priority corresponding to each data packet, which directly affects the compression operation. For example, if a data packet is allocated to a high-performance compression execution node and has a high priority, then the node will be activated and started first to ensure that important data can be processed in a timely manner.

[0045] In step S372, the first group of compression execution nodes uses the data volume of the first group of data packets as a computing power allocation constraint, and the compression end level and multiple compression priority levels as activation constraints to perform step-by-step data compression on the first group of data packets, obtaining a first group of compressed packets. Specifically, during compression, the first group of compression execution nodes uses the data volume of the first group of data packets as a computing power allocation constraint and allocates computing resources appropriately based on the packet size. Larger data packets are allocated more computing resources to speed up compression, while smaller data packets are allocated relatively fewer resources to avoid resource waste. Furthermore, compression is performed according to a pre-set priority order, using the compression end level and multiple compression priority levels as activation constraints. High-priority data packets are compressed first, using a more sophisticated compression algorithm that may consume more resources to ensure data accuracy and integrity. Low-priority data packets are compressed using a relatively simpler and faster algorithm after the high-priority data packets are processed. For example, when processing video data, keyframe data packets, as high-priority data, are compressed using a high-quality algorithm to ensure image quality, while non-keyframe data packets are compressed using a higher-ratio algorithm to reduce data size. Under the aforementioned constraints, the first set of compression execution nodes compresses the first set of data packets step by step. Starting with the highest-priority packet, each packet is compressed in turn. During the compression process, additional processing is performed based on inter-packet dependencies, such as waiting or preloading packets that depend on other packets to ensure that the compressed data can be correctly restored during decompression. This step-by-step compression process ultimately yields the first set of compressed packets.

[0046] Step S373, and so on, with the M compression node allocation as a constraint, start the M compression operation units to perform cluster compression on the M groups of data packets, and obtain the M groups of compressed packets. Specifically, in the same way, with the M compression node allocation as a constraint, start the M compression operation units to perform cluster compression on the M groups of data packets. Each compression operation unit follows its corresponding compression node allocation scheme, and compresses the corresponding data packet group under the computing power allocation and activation constraints. The various compression operation units cooperate with each other, work in parallel or serially, and jointly complete the compression task of all data packets, and finally obtain M groups of compressed packets. This cluster compression method makes full use of compression resources, improves compression efficiency, and ensures data integrity and validity through reasonable priority setting, so that the compressed data packets can better meet the needs of subsequent data transmission, storage or processing.

[0047] Step S400: The protocol conversion module converts the M groups of compressed packets into M groups of LoRaWAN protocol data. Specifically, after receiving the M groups of compressed packets, the protocol conversion module first initializes and verifies the data accuracy and format, reversely decompresses the packets based on the corresponding compression algorithm to extract the original data, and then performs various data format conversions according to LoRaWAN protocol requirements, including conversion of data structure, type, and encoding, as well as padding, segmentation, or reassembly operations according to specifications. After a series of conversions, M groups of LoRaWAN protocol data are finally generated, and the data are verified and organized during generation to ensure that they can be received by the LoRaWAN module and accurately and completely transmitted in the LoRaWAN network.

[0048] In step S500, after receiving the M groups of compressed packets transmitted by the protocol conversion module, the LoRaWAN module packages the M groups of compressed packets into M groups of LoRaWAN protocol packets and sends them to the remote device via the LoRaWAN network. Specifically, after receiving the M groups of compressed packets transmitted by the protocol conversion module, the LoRaWAN module first performs a preliminary check on their integrity and accuracy. Then, according to the LoRaWAN protocol specification, it adds header information (such as sequence number, source and destination address, length, etc.) and tail information (such as checksum data) to the M groups of compressed packets to package them into M groups of LoRaWAN protocol packets. It then connects and interacts with the LoRaWAN network, negotiates parameters such as transmission rate, power, and frequency band based on network load and signal quality, completes a handshake operation, sends protocol packets to the remote device via the LoRaWAN network, monitors the transmission status in real time, and adjusts the transmission strategy based on the feedback to ensure the complete delivery of the data.

[0049] In step S600, the remote device receives the M groups of LoRaWAN protocol packets and performs data restoration based on the multiple data type identifiers and multiple dependency identifiers. Specifically, the remote device first activates the receiving function to receive the M groups of LoRaWAN protocol packets from the LoRaWAN network and performs integrity checks, ensuring that there are no transmission errors in the data packets through technical means such as checksums or CRCs. The remote device then extracts multiple data type identifiers and multiple dependency identifiers from the protocol packets. Multiple sample decompression units are then constructed based on the compression code books of multiple sample data types and stored in association to complete the construction of a decompression tool library. A decompression tool is then matched in the decompression tool library based on the data type identifier, and the protocol packet is structurally restored according to the data dependency. Based on the matched decompression tool and the restored data structure, the protocol packet is decompressed using a corresponding decompression algorithm and the decompressed data is converted and processed based on the data type identifier. Finally, data restoration is completed, allowing the remote device to obtain information consistent with the sender for subsequent operations.

[0050] In one possible implementation, the remote device receives the M groups of LoRaWAN protocol packets and performs data restoration based on the multiple data type identifiers and the multiple dependency identifiers. Step S600 further includes step S610, constructing multiple sample decompression units based on the multiple compression code books corresponding to the multiple sample data types. Specifically, the multiple sample decompression units are constructed based on the multiple compression code books corresponding to the multiple sample data types. Each sample data type, such as audio data, video data, and text data, has its own specific compression encoding method recorded in the corresponding compression code book. Based on the information in these compression code books, a sample decompression unit capable of decompressing the specific data type is developed. For audio data types, the sample decompression unit may include decompression algorithms and related processing logic for audio compression formats such as MP3 and WAV. For video data types, the sample decompression unit includes decompression functional modules for video compression formats such as H.264 and H.265. The sample decompression unit is the core component of the subsequent decompression operation and is carefully designed to meet the decompression requirements of different data types to ensure accurate restoration of the original data.

[0051] Step S620, the multiple sample data types and the multiple sample decompression units are stored in an associated manner to complete the construction of the decompression tool library. Specifically, after the multiple sample decompression units are constructed, the multiple sample data types and the corresponding multiple sample decompression units are stored in an associated manner to complete the construction of the decompression tool library. The associated storage method allows when data is received, the corresponding decompression unit can be quickly found according to the type of data. For example, in the decompression tool library, a clear mapping relationship is established between the audio data type and the audio sample decompression unit, and the video data type and the video sample decompression unit correspond to each other. When a certain type of data needs to be decompressed, the decompression tool library can quickly locate and call the appropriate decompression unit, thereby improving the efficiency and accuracy of the decompression operation and providing powerful tool support for the entire data restoration process.

[0052] Step S630, extracting and obtaining the M groups of data dependencies and M data type identifiers from the M groups of LoRaWAN protocol packets. Specifically, extracting M groups of data dependencies and M data type identifiers from the M groups of LoRaWAN protocol packets. Data dependencies reflect the logical correlation between data packets. For example, the data processing of some data packets depends on the data results of other data packets. This relationship information is crucial for the subsequent correct restoration of the data structure. The data type identifier clarifies the category to which the data in each data packet belongs. As mentioned above, it may be one of the types such as audio, video, and text. By accurately extracting this information, a key basis is provided for the next step of structural restoration and decompression operations, allowing subsequent operations to be carried out in a targeted manner.

[0053] Step S640: After structurally restoring the M groups of LoRaWAN protocol packets based on the M groups of data dependencies, the decompression tool library is used to call decompression operation tools and perform data decompression and restoration in multiple rounds, using the M data type identifiers as constraints. Specifically, the M groups of LoRaWAN protocol packets are structurally restored based on the M groups of data dependencies. Because there are dependencies between data packets, the data packets need to be reassembled into the original data structure according to these relationships. For example, if the data of data packet A is a prerequisite for processing data packet B, then when restoring the data structure, data packet A will be processed first, and then its results will be associated and integrated with data packet B, gradually constructing a complete data structure framework. After completing the structural restoration, the decompression tool library is used to call decompression operation tools and perform data decompression and restoration in multiple rounds, using the M data type identifiers as constraints. In each round, the corresponding sample decompression unit is called to perform decompression operations on data packets with different data type identifiers. For example, when the data type of a data packet is identified as video data, the video sample decompression unit is called from the decompression tool library to decompress it, and the compressed data is restored to the original video data format. Through multiple rounds of such operations, the decompression and restoration of M groups of LoRaWAN protocol packets are finally completed, so that the data can be presented in an original form that can be directly used by the application, laying the foundation for subsequent data processing, analysis or storage operations.

[0054] Step S700, in the reverse operation, reversely apply the protocol conversion module and the LoRaWAN module to convert the return data sent by the remote device into Bluetooth protocol data. Specifically, in the reverse operation process, the remote device first sends data, which enters the LoRaWAN network through the communication link and is received and checked and sorted by the LoRaWAN module. After confirmation, the data is pre-processed to adapt to the protocol conversion module, remove the LoRaWAN protocol specific identifier, restore the main content and transmit it. After receiving the data, the protocol conversion module parses the data, adjusts the structure and performs encoding conversion according to the Bluetooth and LoRaWAN protocol specifications and mapping relationships, such as converting the length representation, re-encoding and adding the Bluetooth protocol header and check bit to construct a complete Bluetooth protocol data format, and then transmits it to the Bluetooth module. The Bluetooth module receives and processes it according to the Bluetooth protocol communication mechanism, and then transmits it to the terminal device, thereby realizing data backhaul and conversion into Bluetooth protocol data, achieving two-way communication and solving protocol compatibility and interoperability problems.

[0055] The embodiment of the present application processes Bluetooth protocol data through a data segmentation module, dividing it into multiple data packets, each of which contains a data type and a dependency identifier. The protocol conversion module selects a suitable compression tool based on the data type identifier, starts multiple compression operation units, prioritizes data packets with strong dependencies, and generates multiple compressed packets. The compressed packet is converted into LoRaWAN protocol data, which the LoRaWAN module receives and packages into a LoRaWAN protocol packet, and sends to a remote device via the LoRaWAN network. After receiving it, the remote device restores the data based on the data type and dependency identifier. In the reverse operation, the LoRaWAN module converts the LoRaWAN data sent by the remote device back into Bluetooth protocol data. Through the two-way protocol conversion method, the technical effect of achieving seamless communication between the Bluetooth and LoRaWAN protocols and improving protocol compatibility is achieved.

[0056] In the above, refer to Figure 1 The bidirectional protocol conversion method between Bluetooth and LoRaWAN according to an embodiment of the present invention is described in detail. Figure 2 A bidirectional protocol conversion device between Bluetooth and LoRaWAN according to an embodiment of the present invention is described.

[0057] The bidirectional protocol conversion device between Bluetooth and LoRaWAN, according to an embodiment of the present invention, addresses the technical issues of poor compatibility and communication difficulties between existing Bluetooth and LoRaWAN protocols. By implementing a bidirectional protocol conversion method, seamless communication between Bluetooth and LoRaWAN protocols is achieved, improving protocol compatibility. The bidirectional protocol conversion device between Bluetooth and LoRaWAN includes a data packet acquisition module 10, a tool matching and scheduling module 20, a compression processing module 30, a protocol data conversion module 40, a remote device transmission module 50, a data restoration module 60, and a return conversion module 70.

[0058] The data packet acquisition module 10 is used to process the Bluetooth protocol data received by the Bluetooth module through the data segmentation module to obtain multiple data packets, wherein the multiple data packets have multiple data type identifiers and multiple dependency identifiers.

[0059] The tool matching and scheduling module 20 is used for performing compression tool matching and scheduling according to the multiple data type identifiers after the protocol conversion module receives the multiple data packets sent by the data segmentation module, so as to obtain M compression operation units.

[0060] The compression processing module 30 is used for the protocol conversion module to allocate compression priorities to the M compression operation units according to the multiple dependency identifiers, and then start the M compression operation units to perform compression processing on the multiple data packets to obtain M groups of compressed packets.

[0061] The protocol data conversion module 40 is used to convert the M groups of compressed packets into M groups of LoRaWAN protocol data through the protocol conversion module.

[0062] The remote device sending module 50 is used for the LoRaWAN module to receive the M groups of compressed packets transmitted by the protocol conversion module, package the M groups of compressed packets into M groups of LoRaWAN protocol packets, and send them to the remote device through the LoRaWAN network.

[0063] The data restoration module 60 is used for the remote device to receive the M groups of LoRaWAN protocol packets and restore the data according to the multiple data type identifiers and the multiple dependency identifiers.

[0064] The backhaul conversion module 70 is used to reversely apply the protocol conversion module and the LoRaWAN module in the reverse operation to convert the backhaul data sent by the remote device into Bluetooth protocol data.

[0065] The specific configuration of the data packet acquisition module 10 will be described in detail below. As described above, the Bluetooth protocol data received by the Bluetooth module is processed by the data fragmentation module to obtain multiple data packets, wherein the multiple data packets have multiple data type identifiers and multiple dependency identifiers. The data packet acquisition module 10 further includes: a Bluetooth protocol data receiving unit, which is used for the Bluetooth module to receive Bluetooth protocol data from the terminal device; a data structure analysis unit, which is used to perform data structure analysis on the Bluetooth protocol data through the data fragmentation module to obtain a data structure tree; a data packet locating unit, which is used to extract multiple data dependency relationships and multiple data type information of the multiple data packets from the data structure tree after locating the multiple data packets; and a data packet identification unit, which is used to identify the multiple data packets using the multiple data dependency relationships and multiple data type information.

[0066] Among them, the data structure analysis of the Bluetooth protocol data is performed through the data fragmentation module to obtain a data structure tree, and the data structure analysis unit further includes: a transmission load limit acquisition subunit, the transmission load limit acquisition subunit is used to interact with the LoRaWAN module to obtain the transmission load limit; a data partitioning scale subunit, the data partitioning scale subunit is used to use 1 / N of the transmission load limit as the data partitioning scale, perform structural initialization on the Bluetooth protocol data, and obtain multiple data subsets, wherein the multiple data subsets have multiple subset dependencies; a data classification model pre-construction subunit, the data classification model pre-construction subunit is used to pre-construct a data classification model, wherein the data The class model stores K sample field sets of K types of sample data; a recurrence frequency record subunit, which is used to use the data classification model to traverse the multiple data subsets to perform recurrence frequency records of the K sample field sets, and obtain multiple groups of field recurrence records; a subset type acquisition subunit, which is used to locate the data types of the multiple data subsets according to the multiple groups of field recurrence records, and obtain multiple subset types; an adjacent dependency merging subunit, which is used to perform adjacent dependency merging of subsets of the same type on the multiple data subsets according to the multiple subset types and multiple subset dependencies, and complete the construction of the data structure tree.

[0067] The specific configuration of the compression processing module 30 will be described in detail below. As described above, after the protocol conversion module assigns compression priorities to the M compression operation units according to the multiple dependency identifiers, the M compression operation units are started to perform compression processing on the multiple data packets to obtain M groups of compressed packets. The compression processing module 30 further includes: a data packet aggregation unit, the data packet aggregation unit is used to aggregate data packets according to the multiple data type identifiers to obtain M groups of data packets; a compression unit pre-construction unit, the compression unit pre-construction unit is used to pre-construct multiple sample compression units of multiple sample data types, wherein the multiple sample compression units have multiple groups of compression execution nodes built in, and the multiple sample compression units rely on multiple compression encoding books to perform data compression operations; a tool library configuration unit, the tool library configuration unit is used to associate and store the multiple A sample data type and multiple sample compression units complete the configuration of the compression tool library; a compression operation unit acquisition unit, the compression operation unit acquisition unit is used to perform compression tool matching scheduling in the compression tool library according to the M data type identifiers of the M groups of data packets, and obtain M compression operation units; a dependency extraction unit, the dependency extraction unit is used to extract and obtain M groups of data dependencies from the M groups of data packets; a compression priority allocation unit, the compression priority allocation unit is used to perform compression priority allocation according to the M groups of data dependencies, and obtain M compression node allocations; a cluster compression unit, the cluster compression unit is used to start the M compression operation units to perform cluster compression of the M groups of data packets with the M compression node allocations as constraints, and obtain the M groups of compressed packets.

[0068] Wherein, after performing compression priority assignment according to the M groups of data dependencies and obtaining M compression node assignments, the compression priority assignment unit further includes: a data association topology construction subunit, the data association topology construction subunit is used to construct a data association topology of the first group of data packets according to the first group of data dependencies; a dangling data point extraction subunit, the dangling data point extraction subunit is used to extract and obtain multiple dangling data points from the data association topology; a topology node classification subunit, the topology node classification subunit is used to perform the data association topology based on the out-degree connection relationship after removing the multiple dangling data points from the data association topology. The topological nodes in the data are graded to obtain multi-layer out-degree connection points; a compression priority level configuration subunit is used to configure multiple compression priority levels for the multi-layer out-degree connection points and configure a compression terminal level for the multiple dangling data points; a node configuration execution subunit is used to perform compression execution node configuration based on the compression terminal level and multiple compression priority levels as constraints to obtain a first compression node allocation; a compression priority allocation subunit is used to perform compression priority allocation based on the M groups of data dependencies and so on to obtain the M compression node allocations.

[0069] Among them, with the M compression node allocation as a constraint, the M compression operation units are started to perform cluster compression of the M groups of data packets to obtain the M groups of compressed packets. The cluster compression unit further includes: a node allocation constraint sub-unit, the node allocation constraint sub-unit is used to start the first group of compression execution nodes of the first compression operation unit with the first compression node allocation as a constraint; a computing power allocation constraint sub-unit, the computing power allocation constraint sub-unit is used for the first group of compression execution nodes to use the data volume of the first group of data packets as the computing power allocation constraint, and the compression end level and multiple compression priority levels as activation constraints to perform step-by-step data compression of the first group of data packets to obtain the first group of compressed packets; a cluster compression sub-unit, the cluster compression sub-unit is used to, by analogy, start the M compression operation units to perform cluster compression of the M groups of data packets with the M compression node allocation as a constraint to obtain the M groups of compressed packets.

[0070] The specific configuration of the data restoration module 60 will be described in detail below. As described above, the remote device receives the M groups of LoRaWAN protocol packets and performs data restoration based on the multiple data type identifiers and the multiple dependency identifiers. The data restoration module 60 further includes: a sample decompression unit acquisition unit, the sample decompression unit acquisition unit being configured to construct and obtain multiple sample decompression units based on the multiple compression code books of the multiple sample data types; a decompression tool library construction unit, the decompression tool library construction unit being configured to associate and store the multiple sample data types and the multiple sample decompression units to complete the construction of the decompression tool library; a protocol packet extraction unit, the protocol packet extraction unit being configured to extract the M groups of data dependencies and the M data type identifiers from the M groups of LoRaWAN protocol packets; and a structure restoration unit, the structure restoration unit being configured to perform structure restoration on the M groups of LoRaWAN protocol packets based on the M groups of data dependencies, and then, using the M data type identifiers as constraints, perform multiple rounds of decompression operation tool calls and data decompression restoration in the decompression tool library.

[0071] The bidirectional protocol conversion device between Bluetooth and LoRaWAN provided in an embodiment of the present invention can execute the bidirectional protocol conversion method between Bluetooth and LoRaWAN provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0072] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0073] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A bidirectional protocol conversion method between Bluetooth and LoRaWAN, characterized in that: The method comprises: Processing the Bluetooth protocol data received by the Bluetooth module through the data segmentation module to obtain a plurality of data packets, wherein the plurality of data packets have a plurality of data type identifiers and a plurality of dependency identifiers; After receiving the multiple data packets sent by the data slicing module, the protocol conversion module performs compression tool matching scheduling according to the multiple data type identifiers to obtain M compression operation units; After allocating compression priorities to the M compression operation units according to the multiple dependency identifiers, the protocol conversion module starts the M compression operation units to perform compression processing on the multiple data packets to obtain M groups of compressed packets; Converting the M groups of compressed packets into M groups of LoRaWAN protocol data by the protocol conversion module; After receiving the M groups of compressed packets transmitted by the protocol conversion module, the LoRaWAN module packages the M groups of compressed packets into M groups of LoRaWAN protocol packets and sends them to the remote device through the LoRaWAN network; The remote device receives the M groups of LoRaWAN protocol packets and performs data restoration according to the multiple data type identifiers and the multiple dependency identifiers; In the reverse operation, the protocol conversion module and the LoRaWAN module are reversely applied to convert the return data sent by the remote device into Bluetooth protocol data.

2. The method for bidirectional protocol conversion between Bluetooth and LoRaWAN according to claim 1, wherein: After the protocol conversion module assigns compression priorities to the M compression operation units according to the multiple dependency identifiers, the M compression operation units are started to perform compression processing on the multiple data packets to obtain M groups of compressed packets. The method further includes: Aggregating data packets according to the multiple data type identifiers to obtain M groups of data packets; Pre-building a plurality of sample compression units of a plurality of sample data types, wherein the plurality of sample compression units have a plurality of sets of compression execution nodes built in, and the plurality of sample compression units rely on a plurality of compression codebooks to perform data compression operations; Associatively storing the multiple sample data types and the multiple sample compression units to complete the configuration of the compression tool library; Perform compression tool matching scheduling in the compression tool library according to the M data type identifiers of the M groups of data packets to obtain M compression operation units; Extracting and obtaining M groups of data dependencies from the M groups of data packets; Performing compression priority assignment according to the M groups of data dependencies to obtain M compression node assignments; With the M compression node allocations as a constraint, the M compression operation units are started to perform cluster compression on the M groups of data packets to obtain the M groups of compressed packets.

3. The method for bidirectional protocol conversion between Bluetooth and LoRaWAN according to claim 2, wherein: After allocating compression priorities according to the M groups of data dependencies to obtain M compression node allocations, the method further includes: constructing a data association topology for the first group of data packets according to the first group of data dependency relationships; Extracting and obtaining a plurality of dangling data points from the data association topology; After removing the plurality of dangling data points from the data association topology, performing grading of topological nodes in the data association topology based on out-degree connection relationships to obtain multiple layers of out-degree connection points; configuring a plurality of compression priority levels for the multi-layer out-degree connection points, and configuring a compression end level for the plurality of dangling data points; Performing compression execution node configuration based on the compression terminal level and the plurality of compression priority levels as constraints to obtain a first compression node allocation; Similarly, compression priority allocation is performed according to the M groups of data dependencies to obtain the M compression node allocations.

4. The method for bidirectional protocol conversion between Bluetooth and LoRaWAN according to claim 3, wherein: Using the M compression node allocations as a constraint, starting the M compression operation units to perform cluster compression on the M groups of data packets to obtain the M groups of compressed packets, the method further comprising: Starting a first group of compression execution nodes of a first compression operation unit with the first compression node allocation as a constraint; The first group of compression execution nodes uses the data volume of the first group of data packets as a computing power allocation constraint and the compression terminal level and the plurality of compression priority levels as activation constraints to perform step-by-step data compression on the first group of data packets to obtain a first group of compressed packets; Similarly, with the M compression node allocations as constraints, the M compression operation units are started to perform cluster compression on the M groups of data packets to obtain the M groups of compressed packets.

5. The method for bidirectional protocol conversion between Bluetooth and LoRaWAN according to claim 1, wherein: Processing the Bluetooth protocol data received by the Bluetooth module through the data segmentation module to obtain multiple data packets, the method further includes: The Bluetooth module receives Bluetooth protocol data from the terminal device; Performing data structure analysis on the Bluetooth protocol data through a data slicing module to obtain a data structure tree; After locating the plurality of data packets in the data structure tree, extracting and obtaining a plurality of data dependency relationships and a plurality of data type information of the plurality of data packets from the data structure tree; The plurality of data packets are identified using the plurality of data dependency relationships and the plurality of data type information.

6. The method for bidirectional protocol conversion between Bluetooth and LoRaWAN according to claim 5, wherein: Performing data structure analysis on the Bluetooth protocol data by a data segmentation module to obtain a data structure tree, the method further comprising: Interact with LoRaWAN modules to obtain transmission load limits; Using 1 / N of the transmission load limit as a data partitioning scale, structurally initializing the Bluetooth protocol data to obtain a plurality of data subsets, wherein the plurality of data subsets have a plurality of subset dependencies; Pre-building a data classification model, wherein the data classification model stores K sample field sets of K sample data types; Using the data classification model to traverse the multiple data subsets to record the recurrence frequency of the K sample field sets, and obtain multiple groups of field recurrence records; Locating the data types of the multiple data subsets according to the multiple groups of field recurrence records to obtain multiple subset types; According to the multiple subset types and the multiple subset dependency relationships, adjacent dependencies of the same type of subsets are merged for the multiple data subsets to complete the construction of the data structure tree.

7. The method for bidirectional protocol conversion between Bluetooth and LoRaWAN according to claim 2, wherein: The remote device receives the M groups of LoRaWAN protocol packets and performs data restoration according to the multiple data type identifiers and the multiple dependency identifiers. The method further includes: Constructing and obtaining a plurality of sample decompression units according to the plurality of compression codebooks of the plurality of sample data types; Associatively storing the multiple sample data types and the multiple sample decompression units, and completing the construction of the decompression tool library; Extracting and obtaining the M groups of data dependencies and M data type identifiers from the M groups of LoRaWAN protocol packets; After structural restoration of the M groups of LoRaWAN protocol packets according to the M groups of data dependencies, the decompression operation tool is called and the data decompression and restoration is performed in multiple rounds in the decompression tool library using the M data type identifiers as constraints.

8. A bidirectional protocol conversion device between Bluetooth and LoRaWAN, characterized in that: The device is used to implement the bidirectional protocol conversion method between Bluetooth and LoRaWAN according to any one of claims 1 to 7, and the device includes: A data packet acquisition module, configured to process the Bluetooth protocol data received by the Bluetooth module through the data slicing module to obtain a plurality of data packets, wherein the plurality of data packets have a plurality of data type identifiers and a plurality of dependency identifiers; a tool matching and scheduling module, wherein the tool matching and scheduling module is used to perform compression tool matching and scheduling according to the multiple data type identifiers after the protocol conversion module receives the multiple data packets sent by the data slicing module, and obtain M compression operation units; a compression processing module, configured to, after the protocol conversion module assigns compression priorities to the M compression operation units according to the multiple dependency identifiers, activate the M compression operation units to perform compression processing on the multiple data packets, thereby obtaining M groups of compressed packets; A protocol data conversion module, configured to convert the M groups of compressed packets into M groups of LoRaWAN protocol data through the protocol conversion module; A remote device sending module, wherein the remote device sending module is used for the LoRaWAN module to receive the M groups of compressed packets transmitted by the protocol conversion module, package the M groups of compressed packets into M groups of LoRaWAN protocol packets, and send them to the remote device through the LoRaWAN network; A data restoration module, configured for the remote device to receive the M groups of LoRaWAN protocol packets and perform data restoration according to the multiple data type identifiers and the multiple dependency identifiers; A backhaul conversion module is used to reversely apply the protocol conversion module and the LoRaWAN module in a reverse operation to convert the backhaul of the data sent by the remote device into Bluetooth protocol data.

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