Multi-protocol compatible Internet of Things platform processing method and system

Abstract

The invention discloses a multi-protocol compatible Internet of Things platform processing method and system, and the method comprises the steps: carrying out the embedded coding of the message information and TCP attribute information of a target message object through employing an information analysis and coding technology based on artificial intelligence; then attention multi-dimensional fusion is carried out on the embedded target message information embedding features and the target message TCP attribute embedding features, and meanwhile, processing is carried out on message information and TCP attribute information of the messages to obtain multi-dimensional information interaction fusion features of all reference messages; therefore, the TCP type label of the target message object is intelligently judged according to the quick matching query representation between the multi-dimensional information interaction fusion feature of the target message and the multi-dimensional information interaction fusion feature of each reference message. Therefore, a new or custom extended protocol can be automatically learned and adapted, the dynamic change of a network environment and the evolution of a communication standard can be coped with without manually updating a rule, and the accuracy and the reliability of data processing are further improved.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things platform processing, and more specifically, to a multi-protocol compatible Internet of Things platform processing method and system. Background Art

[0002] With the rapid development of IoT technology, the number of various terminal devices has exploded, and these devices come from different manufacturers and follow different communication protocols. In a complex IoT application scenario, such as smart factories and smart cities, it is often necessary to connect many heterogeneous terminal devices to a unified IoT hub to achieve centralized data collection, processing and collaborative management.

[0003] Patent CN112714153B proposes a processing method and device based on the Internet of Things system that is compatible with multiple TCPs. By defining TCP attribute information covering configuration, link and protocol details, the TCP processing platform can parse data from different devices. The RPC service is started using the NIO framework to ensure reliable information interaction. After receiving the message, a specific JS file is used to parse and convert the business information into a standard format. For downlink instructions, the appropriate JS file is selected for protocol conversion based on the link information to ensure that the instructions are correctly sent to the terminal device.

[0004] In the process of parsing message information in this patent, the TCP processing platform will determine whether the message information matches a predefined TCP protocol based on the start frame headers corresponding to the predetermined multiple TCP protocols. If the message is considered to meet the corresponding specifications, the corresponding JS file will be used for parsing and processing. However, the method that relies on predefined TCP protocol features (such as the start frame header) has poor adaptability to new protocols or custom extended protocols, and the rules need to be updated manually. Moreover, if the start frame header is tampered with or misread, it is easy to cause parsing logic errors, affecting the accuracy of data processing and system stability. In addition, this method lacks flexibility and is difficult to cope with dynamically changing network environments and evolving communication standards, which increases management complexity.

[0005] Therefore, an optimized multi-protocol compatible IoT platform processing solution is desired. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a multi-protocol compatible IoT platform processing method and system, which embeds and encodes the message information and TCP attribute information of the target message object by using information analysis and coding technology based on artificial intelligence, and then performs multi-dimensional fusion of the embedded target message information embedding features and the target message TCP attribute embedding features, and at the same time, the message information and TCP attribute information of the message are also processed as above to obtain the multi-dimensional information interaction fusion features of each reference message, so as to intelligently judge the TCP protocol type label of the target message object according to the fast matching query representation between the multi-dimensional information interaction fusion features of the target message and the multi-dimensional information interaction fusion features of each reference message. In this way, it is possible to automatically learn and adapt to newly emerging or custom extended protocols, and can cope with the dynamic changes of the network environment and the evolution of communication standards without manually updating the rules, thereby improving the accuracy and reliability of data processing.

[0007] According to one aspect of the present application, a multi-protocol compatible IoT platform processing method is provided, which includes: a TCP processing platform, an IoT hub and a terminal device, including: defining TCP attribute information through the TCP processing platform, the TCP attribute information including TCP configuration information, TCP link information and TCP protocol information; based on the NIO framework, starting RPC communication through the TCP processing platform, so that the IoT hub can communicate with the terminal device information through the TCP processing platform; the TCP processing platform receives the message information and TCP attribute information of the target message object sent by the terminal device; the TCP processing platform determines the TCP protocol type of the target message object based on the message information and TCP attribute information of the target message object, including: obtaining the message The message information and TCP attribute information of each message in the database; based on the fast query matching representation of the attention embedding fusion between the message information and TCP attribute information of the target message object and the message information and TCP attribute information of each message, determine the TCP protocol type label of the target message object; the TCP processing platform determines the JS file for parsing the target message object based on the TCP protocol type of the target message object; the TCP processing platform parses the target message object based on the JS file of the target message object to obtain business information; according to the preset conversion standard, the TCP processing platform converts the business information to obtain converted business information; the TCP processing platform sends the converted business information to the Internet of Things hub.

[0008] According to another aspect of the present application, a multi-protocol compatible IoT platform processing system is provided, which includes: a TCP attribute information definition module, used to define TCP attribute information through the TCP processing platform, and the TCP attribute information includes TCP configuration information, TCP link information and TCP protocol information.

[0009] The RPC communication startup module is used to start RPC communication through the TCP processing platform based on the NIO framework, so that the Internet of Things hub can communicate with the terminal device information through the TCP processing platform.

[0010] The target message object information receiving module is used for the TCP processing platform to receive the message information and TCP attribute information of the target message object sent by the terminal device.

[0011] The TCP protocol type determination module is used for the TCP processing platform to determine the TCP protocol type of the target message object based on the message information and TCP attribute information of the target message object.

[0012] The JS file determination module is used for the TCP processing platform to determine the JS file used to parse the target message object based on the TCP protocol type of the target message object.

[0013] The target message object parsing module is used for the TCP processing platform to parse the target message object based on the JS file of the target message object to obtain business information.

[0014] The service information conversion module is used for converting the service information according to a preset conversion standard so as to obtain converted service information.

[0015] The business information sending module is used for the TCP processing platform to send the converted business information to the Internet of Things hub.

[0016] Compared with the prior art, the present application provides a multi-protocol compatible IoT platform processing method and system, which embeds and encodes the message information and TCP attribute information of the target message object by using information analysis and coding technology based on artificial intelligence, and then performs multi-dimensional fusion of the embedded target message information embedding features and target message TCP attribute embedding features, and at the same time, the message information and TCP attribute information of the message are also processed as above to obtain the multi-dimensional information interactive fusion features of each reference message, so as to intelligently judge the TCP protocol type label of the target message object according to the fast matching query representation between the multi-dimensional information interactive fusion features of the target message and the multi-dimensional information interactive fusion features of each reference message. In this way, it is possible to automatically learn and adapt to newly emerging or customized extended protocols, and cope with the dynamic changes of the network environment and the evolution of communication standards without manually updating the rules, thereby improving the accuracy and reliability of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 The present invention is a flowchart of a multi-protocol compatible IoT platform processing method according to an embodiment of the present application.

[0019] Figure 2 Schematic diagram of data flow of a multi-protocol compatible IoT platform processing method according to an embodiment of the present application.

[0020] Figure 3 This is a flowchart of sub-step S4 of the wind-solar power generation energy storage management method according to an embodiment of the present application.

[0021] Figure 4 This is a flowchart of sub-step S42 of the wind-solar power generation energy storage management method according to an embodiment of the present application.

[0022] Figure 5 It is a block diagram of a multi-protocol compatible IoT platform processing system according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0024] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0025] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0026] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0027] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0028] In the technical solution of the present application, a multi-protocol compatible IoT platform processing method is proposed. Figure 1 The present invention is a flowchart of a multi-protocol compatible IoT platform processing method according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of a multi-protocol compatible IoT platform processing method according to an embodiment of the present application. Figure 1 and Figure 2As shown, according to the multi-protocol compatible IoT platform processing method of the embodiment of the present application, wherein the IoT platform includes a TCP processing platform, an IoT hub and a terminal device, the method includes the following steps: S1, defining TCP attribute information through the TCP processing platform, wherein the TCP attribute information includes TCP configuration information, TCP link information and TCP protocol information; S2, based on the NIO framework, starting RPC communication through the TCP processing platform, so that the IoT hub can communicate with the terminal device information through the TCP processing platform; S3, the TCP processing platform receives the message information and TCP attribute information of the target message object sent by the terminal device; S4, The TCP processing platform determines the TCP protocol type of the target message object based on the message information and TCP attribute information of the target message object; S5, the TCP processing platform determines the JS file used to parse the target message object based on the TCP protocol type of the target message object; S6, the TCP processing platform parses the target message object based on the JS file of the target message object to obtain business information; S7, according to a preset conversion standard, the TCP processing platform converts the business information to obtain converted business information; S8, the TCP processing platform sends the converted business information to the Internet of Things hub.

[0029] In particular, the S1 defines TCP attribute information through the TCP processing platform, and the TCP attribute information includes TCP configuration information, TCP link information and TCP protocol information. Among them, the TCP configuration information covers port information (used to identify which port the current connection link uses), domain name information (used to identify the domain name / IP when the TCP link is established), message protocol information (used to configure the association between the TCP link configuration and the message protocol), device information (used to maintain device category information, product information, etc.), and operation status information (used to maintain whether the RPC service of the link configuration service is running normally). The TCP link information includes a link unique identifier (used to identify the link channel established by each TCP connection, used to obtain the channel when context data is transmitted), a device serial number (used to identify the link channel of which device the currently established link belongs to), a link status (used to indicate the current link status, such as online, offline, waiting), a link message log (used to maintain the historical data of the link for communication), and a link level (used to identify the functional level and communication priority of the link). TCP protocol information involves the protocol unique identifier (used to identify the unique identifier of the protocol), protocol version (used to indicate the protocol version number, used to distinguish the version iterations of the protocol), protocol context (used to indicate the context format when configuring the protocol for conversion), protocol conversion file configuration (used to configure the path of the conversion JS file corresponding to the protocol, etc.), and protocol encryption and decryption (used to identify the method of protocol encryption and decryption).

[0030] In particular, the S2, based on the NIO framework, starts the RPC communication through the TCP processing platform, so that the IoT hub can communicate with the terminal device information through the TCP processing platform. That is, the RPC communication service is started based on the NIO framework, so that the IoT platform can realize information interaction with the terminal device through the TCP processing platform. The full name of NIO is Non-Blocking IO or New IO, which is non-blocking IO or new version IO. On this basis, the TCP processing platform will create a server startup boot class for implementing the RPC communication service, and create a working thread group, so that each working thread is responsible for processing events on multiple Channels; at the same time, it specifies that a Channel corresponds to only one thread, which is used to process I / O read and write events and business logic, set the Channel type to NIO type, and set the RPC communication service attribute information, including connection configuration information, inbound event type information, outbound operation type information, message decoder configuration information, message encoder configuration information and listening port configuration information.

[0031] In particular, in S3, the TCP processing platform receives the message information and TCP attribute information of the target message object sent by the terminal device. In one example, when the terminal device sends a message through the TCP processing platform, in addition to real-time processing of the currently received message, the TCP processing platform also stores its complete copy in the message database. In this way, a sufficient sample size can be accumulated for training the machine learning model and provide a reference basis when encountering similar types of messages in the future. However, before being officially stored, all incoming messages need to undergo a series of preprocessing operations to ensure the quality and consistency of the data. These preprocessing operations include format verification to ensure that the message complies with basic grammatical specifications and avoid invalid or maliciously constructed data from polluting the database; deduplication filtering to prevent repeated submission of the same message and reduce unnecessary redundant storage; standardized conversion to unify messages of different formats into standard templates for subsequent consistency analysis; and sensitive information desensitization to protect user privacy and commercial secrets, especially data involving personal identity or financial transactions. After completing the above preprocessing, the clean and standardized message and its TCP attribute information will be officially written into the message database. It is worth noting that in order to ensure the real-time responsiveness of the system, this part of the operation should be as lightweight as possible and not affect the normal business process.

[0032] In particular, in S4, the TCP processing platform determines the TCP protocol type of the target message object based on the message information and TCP attribute information of the target message object. Specifically, in a specific example of the present application, Figure 3 As shown, the S4 includes: S41, obtaining the message information and TCP attribute information of each message in the message database; S42, determining the TCP protocol type label of the target message object based on a fast query matching representation of the attention embedding fusion between the message information and TCP attribute information of the target message object and the message information and TCP attribute information of each message.

[0033] Specifically, the S41 obtains the message information and TCP attribute information of each message in the message database. In order to support the effective parsing of different TCP protocols, the system needs to maintain a message database containing historical message records and their related attribute information. This database can be designed as a multi-dimensional structure, in which each record represents a specific message instance and contains all relevant information of the message. For example, the message content refers to the actual transmitted data payload; TCP configuration information includes network layer parameters such as port number, source address, destination address, etc.; TCP link information covers connection status (online / offline), link log, priority, etc.; and TCP protocol information involves application layer details such as protocol version number, context format, encryption and decryption method. In addition, each message may also be associated with additional metadata, such as timestamp, device identifier, geographic location, etc., which is helpful for subsequent analysis and auditing. For large-scale IoT systems, considering performance and scalability, the message database is usually deployed as a distributed architecture, using NoSQL databases (such as MongoDB) or time series databases (such as InfluxDB) to efficiently manage and query massive data.

[0034] Specifically, the S42 determines the TCP protocol type tag of the target message object based on the fast query matching representation of the attention embedding fusion between the message information and TCP attribute information of the target message object and the message information and TCP attribute information of each message. Specifically, in a specific example of the present application, Figure 4 As shown, the S42 includes: S421, respectively embedding and encoding the message information and TCP attribute information of the target message object to obtain a target message information embedding coding vector and a target message TCP attribute embedding coding vector; S422, performing attention mechanism message multi-dimensional feature interaction on the target message information embedding coding vector and the target message TCP attribute embedding coding vector to obtain a target message multi-dimensional information interaction fusion feature vector; S423, respectively embedding and encoding the message information and TCP attribute information in each message and performing attention mechanism message multi-dimensional feature interaction on the message information and TCP attribute information in each message to obtain a set of reference message multi-dimensional information interaction fusion feature vectors; S424, performing fast matching-oriented semantic query encoding on the set of the target message multi-dimensional information interaction fusion feature vector and the reference message multi-dimensional information interaction fusion feature vector to obtain a target message information query response coding vector as the fast query matching representation of the attention embedding fusion; S425, based on the target message information query response coding vector, obtaining the recognition result of the TCP protocol type label representing the target message object.

[0035] More specifically, the S421 embeds and encodes the message information and TCP attribute information of the target message object respectively to obtain the target message information embedding encoding vector and the target message TCP attribute embedding encoding vector. That is, in a specific example of the present application, first, the message information of the target message object is embedded and encoded using the message information embedding matrix to obtain the target message information embedding encoding vector; it should be understood that the message information itself often contains a variety of different types of data content and is usually of high dimension, which also contains key feature information related to this task. Therefore, in order to reduce the dimension of the original data while retaining important information as much as possible, in the technical solution of the present application, the message information of the target message object is embedded and encoded using the message information embedding matrix to map these complex message information to a low-dimensional, dense vector space, so that similar messages are closer in the space, and the target message information embedding encoding vector is obtained, so that it is easier to capture their similarities and differences in structure and field meaning. In particular, the message information embedding matrix is ​​usually trained by deep learning models, which can learn the complex patterns of input data and convert them into more useful representations. Then, the TCP attribute information embedding matrix is ​​used to embed the TCP attribute information of the target message object to obtain the target message TCP attribute embedding coding vector. Similarly, in a multi-protocol compatible IoT platform environment, the TCP attribute information format, expression, etc. of target message objects from different sources may differ. At the same time, the TCP attribute information itself contains many aspects such as TCP configuration information, TCP link information, and TCP protocol information. Directly using the original information for analysis is more complicated and the key features are not obvious. Therefore, in the technical solution of the present application, the TCP attribute information embedding matrix is ​​used to embed the TCP attribute information of the target message object to extract and enhance the core features of these attribute information, so that the uniqueness of different target message objects in TCP attributes can be more clearly displayed, and the target message TCP attribute embedding coding vector is obtained.

[0036] More specifically, in S422, the target message information embedding coding vector and the target message TCP attribute embedding coding vector are subjected to the attention mechanism of message multi-dimensional feature interaction to obtain the target message multi-dimensional information interaction fusion feature vector. In a specific example of the present application, the target message information embedding coding vector and the target message TCP attribute embedding coding vector are subjected to the message multi-dimensional feature interaction fusion device based on the X-linear attention network to obtain the target message multi-dimensional information interaction fusion feature vector. That is, the interaction between the two vectors of these reference messages is fully explored through the message multi-dimensional feature interaction fusion device based on the X-linear attention network, and the two-order feature interaction is captured by the bilinear pooling technology to form a more comprehensive set of reference message multi-dimensional information interaction fusion feature vectors that can better reflect the characteristics of each reference message.

[0037] More specifically, the S423 performs embedding coding and message multi-dimensional feature interaction of the attention mechanism on the message information and TCP attribute information in each message to obtain a set of reference message multi-dimensional information interaction fusion feature vectors. Similarly, in a specific example of the present application, first, the message information and TCP attribute information in each message are embedded and coded using the message information embedding matrix and the TCP attribute information embedding matrix to obtain a set of reference message information embedding coding vectors and a set of reference message TCP attribute embedding coding vectors; then, each set of corresponding reference message information embedding coding vectors and reference message TCP attribute embedding coding vectors in the set of reference message information embedding coding vectors and the set of reference message TCP attribute embedding coding vectors are passed through the message multi-dimensional feature interaction fusion device based on the X-linear attention network to obtain a set of reference message multi-dimensional information interaction fusion feature vectors.

[0038] More specifically, in S424, the set of the target message multi-dimensional information interaction fusion feature vector and the reference message multi-dimensional information interaction fusion feature vector is subjected to fast matching-oriented semantic query encoding to obtain the target message information query response encoding vector as the fast query matching representation of the attention embedding fusion. In particular, although the embedded encoding feature vectors of the target message and the reference message have a certain degree of comprehensiveness and representativeness after the previous processing, the numerical comparison based solely on the vectors may not be able to fully explore the deep semantic associations between them. That is, there is a certain similarity between the target message and the reference message at the semantic level. Therefore, in order to capture the semantic similarities hidden behind the feature vectors, so that the final match is not only based on the surface feature values, so as to improve the accuracy of the match and make the association judgment between the target message and the reference message more accurate, the present application performs a fast matching-oriented semantic query encoding on the set of the target message multi-dimensional information interaction fusion feature vector and the reference message multi-dimensional information interaction fusion feature vector to quickly locate the part with a high correlation with the target message multi-dimensional information interaction fusion feature vector, so as to obtain the target message information query response encoding vector. In this way, through its unique fast positioning mechanism, the search scope can be quickly narrowed down, focusing on the reference message feature vector that is more likely to match, and improving the overall query efficiency.

[0039] Specifically, the specific process of fast matching-oriented semantic query encoding for the set of the target message multidimensional information interaction fusion feature vector and the reference message multidimensional information interaction fusion feature vector includes: first, in combination with the target message multidimensional information interaction fusion feature vector, determine the reference message multidimensional information fusion positioning matching library feature vector from the set of the reference message multidimensional information interaction fusion feature vector. That is, in a specific example of the present application, first, calculate the cross entropy of the target message multidimensional information interaction fusion feature vector relative to each reference message multidimensional information interaction fusion feature vector in the set of the reference message multidimensional information interaction fusion feature vector to obtain a set of target message-reference message fast query positioning factors; that is, by calculating the cross entropy of the target message multidimensional information interaction fusion feature vector relative to each reference message multidimensional information interaction fusion feature vector, a numerical set reflecting the semantic distance or mismatch between the two is obtained. Cross entropy, as an important indicator for measuring the difference between two probability distributions, is used here to quantify the semantic difference between the target message multidimensional information interaction fusion feature vector and each reference message multidimensional information interaction fusion feature vector. A smaller cross entropy value means a more similar semantic expression, which can quickly narrow the search scope to the most likely relevant area. In particular, in the technical solution of the present application, the cross entropy is not just a simple distance measurement tool, but a key indicator for measuring the difference between two probability distributions. When applied to the matching between the target message multidimensional information interaction fusion feature vector and each of the reference message multidimensional information interaction fusion feature vectors, by treating each library feature vector as a possible probability distribution and using the query feature vector to evaluate these distributions, a series of scores reflecting the semantic similarity between the two can be obtained, that is, the target message-reference message fast query positioning factor set. The cross entropy calculation here not only considers direct semantic similarity, but also implicitly captures potential contextual relationships and semantic structures. Further, the reference message multidimensional information interaction fusion feature vector corresponding to the minimum value in the set of the target message-reference message fast query positioning factor is used as the reference message multidimensional information fusion positioning matching library feature vector. It should be understood that the reference message multidimensional information interaction fusion feature vector corresponding to the minimum value is considered to be the candidate that is semantically closest to the target message multidimensional information interaction fusion feature vector. In this way, it is ensured that the preliminarily screened reference message multi-dimensional information interaction fusion feature vector has the highest relevance, providing a good starting point for subsequent more detailed searches. In a specific example, in combination with the target message multi-dimensional information interaction fusion feature vector, the reference message multi-dimensional information fusion positioning matching library feature vector is determined from the set of the reference message multi-dimensional information interaction fusion feature vector using the following formula; wherein the formula is: .

[0040] in, is a set of multi-dimensional information interactive fusion feature vectors of the reference message, and is the first, second, and third in the set of multi-dimensional information interactive fusion feature vectors of the reference message. and The multi-dimensional information interactive fusion feature vector of reference messages, yes The eigenvalues ​​at each position in , is the target message multi-dimensional information interactive fusion feature vector, yes The eigenvalues ​​at each position in , yes and The number of eigenvalues ​​in , represents the logarithmic function value with base 2, and It is the first, second, and third in the set of target message-reference message quick query positioning factors. and Target message-reference message quick query positioning factor, is a set of target message-reference message fast query positioning factors, is the vector at the location of the minimum value, The multi-dimensional information of the reference message is integrated to locate the matching library feature vector.

[0041] Next, based on the reference message multi-dimensional information fusion positioning matching library feature vector, determine the reference message multi-dimensional information fine-grained matching search window, wherein the vector at the center position of the reference message multi-dimensional information fine-grained matching search window is the reference message multi-dimensional information fusion positioning matching library feature vector, and each reference message multi-dimensional information interactive fusion feature vector in the reference message multi-dimensional information fine-grained matching search window is defined as a reference message multi-dimensional information fine-grained query feature vector. That is, by constructing a small-scale search area around the reference message multi-dimensional information fusion positioning matching library feature vector, the system is able to conduct in-depth analysis in a smaller and more targeted data subset. Doing so not only improves search efficiency, but also enhances the model's ability to understand local semantic patterns. In addition, the design of the fine-grained matching search window allows the model to better handle the subtle differences between the target message multi-dimensional information interactive fusion feature vector and each of the reference message multi-dimensional information interactive fusion feature vectors. In a specific example, based on the reference message multi-dimensional information fusion positioning matching library feature vector, the reference message multi-dimensional information fine-grained matching search window is determined by the following formula; wherein the formula is: ;in, is the square of the vector norm, Indicates rounding down. is the size of the search window for fine-grained matching of multi-dimensional information in the reference message, It is the multi-dimensional information fine-grained matching search window of the reference message.

[0042] Subsequently, the target message multidimensional information interactive fusion feature vector and each reference message multidimensional information fine-grained query feature vector in the reference message multidimensional information fine-grained matching search window are subjected to message multidimensional information fine-grained query encoding to obtain the target message information query response encoding vector. That is, in a specific example of the present application, the target message multidimensional information interactive fusion feature vector is linearly transformed to obtain the target message multidimensional information query vector and the target message multidimensional information value vector, and each reference message multidimensional information fine-grained query feature vector in the reference message multidimensional information fine-grained matching search window is used as a key vector, and the target message multidimensional information query vector, the target message multidimensional information value vector and the key vector are subjected to message multidimensional information fine-grained query encoding of a heterogeneous converter to obtain the target message information query response encoding vector. That is, a linear transformation is performed on the target message multidimensional information interactive fusion feature vector to generate a query vector and a value vector. Subsequently, these vectors are input into a fine-grained query encoding module based on a heterogeneous transformer structure in combination with the key vectors in the fine-grained matching search window. In this module, the interaction between the query, key, and value simulates the attention mechanism, captures the complex relationship between features through a multi-head self-attention layer, and finally outputs the target message information query response encoding vector. The heterogeneous transformer structure allows the model to process different types of input data and introduces additional flexibility and expressiveness in the encoding process, thereby producing a richer and more accurate query response representation. In a specific example, the target message multi-dimensional information interactive fusion feature vector and each reference message multi-dimensional information fine-grained query feature vector in the reference message multi-dimensional information fine-grained matching search window are encoded with the following formula to obtain the target message information query response encoding vector; wherein, the formula is: .

[0043] in, and They are respectively the target message multi-dimensional information query embedding matrix and the target message multi-dimensional information query bias vector, is the target message multi-dimensional information query vector, and are respectively the target message multi-dimensional information value embedding matrix and the target message multi-dimensional information value bias vector, is the multi-dimensional information value vector of the target message, is the first The multi-dimensional information of the reference message is fine-grained to query the feature vector, for The transposed vector of yes Length, is matrix multiplication, is the activation function, is the target message information query response encoding vector.

[0044] More specifically, the S425 obtains the identification result of the TCP protocol type label representing the target message object based on the target message information query response coding vector. In the technical solution of the present application, the target message information query response coding vector is passed through a TCP protocol type identifier based on a classifier to obtain the identification result, and the identification result is used to represent the TCP protocol type label of the target message object. That is, the target message information query response coding vector obtained by quickly matching the query response using the set of the target message multi-dimensional information interactive fusion feature vector and the reference message multi-dimensional information interactive fusion feature vector is classified and processed, so as to utilize the powerful discrimination ability of the classifier, based on the input feature vector, based on the learned pattern features of different categories (here, different TCP protocol types), the target message information query response coding vector is classified and discriminated, thereby mining the corresponding protocol type information behind it, and playing a key role in judging from features to specific categories, so as to intelligently judge the TCP protocol type label of the target message object, and provide a key basis for the subsequent correct parsing of the message and extracting business information according to the corresponding protocol specification.

[0045] Preferably, in one example, passing the target message information query response encoding vector through a classifier-based TCP protocol type identifier to obtain an identification result includes: respectively calculating the first Eigenvalue With Eigenvalue and Eigenvalue The absolute value of the difference between the two is used to obtain the forward dispersion value of the target message information query response code And the target message information query response encoding backward dispersion value , here, if ,but ,like , is the length of the target message information query response encoding vector, .

[0046] In response to the target message information query response encoding forward dispersion value The target message information query response code backward discrete value The absolute value of the difference between them is less than or equal to the distance difference hyperparameter , calculate the The eigenvalues ​​are similar to the The weighted sum between the eigenvalues ​​is optimized Eigenvalue , and represents a weighted hyperparameter.

[0047] In response to the target message information query response encoding forward dispersion value The target message information query response code backward discrete value The absolute value of the difference between them is greater than the distance difference hyperparameter , perform the following steps: calculate the target message information query response encoding forward discrete value corresponding to all eigenvalues ​​of the target message information query response encoding vector The target message information query response code backward discrete value The square root of the sum of the squares of the target message information query response encoding heterogeneous distribution meta-domain coupling value : .

[0048] The target message information query response is encoded forward discrete value The target message information query response code backward discrete value The absolute value of the difference multiplied by the corresponding eigenvalue of the target message information query response encoding vector Then divided by the target message information query response encoding heterogeneous distribution domain coupling value To obtain the target message information query response code adaptive modulation value .

[0049] Each eigenvalue of the target message information query response encoding vector The corresponding target message information query response code adaptive modulation value After multiplication, calculate the product and the The weighted reduction between the eigenvalues ​​is the optimized Eigenvalue , and represents a weighted hyperparameter.

[0050] On the basis of keeping the first eigenvalue of the target message information query response sequence encoding vector unchanged, the optimized The characteristic value is used to obtain the optimized target message information query response encoding vector.

[0051] The optimized target message information query response encoding vector is passed through a classifier-based TCP protocol type identifier to obtain an identification result.

[0052] Here, when the target message multi-dimensional information interaction fusion feature vector represents the linear attention fusion embedding coding features of the message information and TCP attribute information of the target message object in a single sample domain, and the set of reference message multi-dimensional information interaction fusion feature vectors represents the linear attention fusion embedding coding features of the message information and TCP attribute information of each message in the message database in the full sample domain, when performing dynamic semantic query encoding based on a fast positioning mechanism, the complexity of the network positioning mechanism of the full sample domain for the single sample domain will lead to insufficient long-distance dynamic semantic query fusion representation of the target message information query response encoding vector, thereby reducing the expression effect of the target message information query response encoding vector and affecting the accuracy of the recognition result obtained by the classifier-based TCP protocol type identifier.

[0053] Therefore, for the long-distance feature query response of a given feature set of the target message information query response coding vector, its attribute cluster, under the dynamic weight sorting mode through the classifier, has a problem that the long-distance correlation between cross-modal features is weakened because the spatial neighborhood discreteness exceeds the critical threshold of the multimodal representation integrity. By adopting a topological mapping method based on the heterogeneous feature projection of the target message information query response coding vector, the feature parameter global association network architecture of the target message information query response coding vector is mapped to the implicit basis function space coupled with the distributed meta-domain, and by constructing the parameterized adaptive modulation of the target message information query response coding vector, the cross-dimensional association retrieval model between the feature parameters is reconstructed to effectively capture the reconstruction path of the remote temporal dependency. Therefore, by enhancing the efficiency of multi-dimensional semantic fusion, the long-distance joint encoding of the feature interaction relationship of the target message information query response coding vector is realized, which significantly improves the representation accuracy and retrieval robustness of the long-distance context association, and improves the accuracy of the recognition result obtained by the classifier-based TCP protocol type identifier.

[0054] In particular, in S5, the TCP processing platform determines the JS file for parsing the target message object based on the TCP protocol type of the target message object. That is, the TCP protocol type of the target message selects the most suitable parsing script (JS file). Since different TCP protocols usually have unique message formats and field definitions, and the JS file contains parsing logic for a specific protocol, it can help the TCP processing platform to accurately decode and understand the message content. The TCP processing platform maintains a protocol configuration management system internally, which records various TCP protocols and their corresponding JS file paths. Whenever a new TCP protocol type is determined, the system automatically finds and loads the corresponding JS file. In order to support flexible expansion, the TCP processing platform adopts a dynamic loading mechanism. This means that even if a new protocol that has never been seen before is encountered during operation, as long as the correct JS file is provided, the system can immediately apply these parsing logics without restarting the service or recompiling the code. All JS files are designed to be hot-pluggable and can be added, modified or removed at any time without affecting existing services. In addition, considering that the protocol may evolve over time, the TCP processing platform also implements a version control system. Each JS file carries a version number to ensure that even different versions of the same protocol can be correctly parsed. Version control systems can also help administrators track change history, facilitating troubleshooting and performance optimization.

[0055] In particular, in S6, the TCP processing platform parses the target message object based on the JS file of the target message object to obtain business information. In one example, first, the TCP processing platform creates an independent working environment for the message to ensure that the parsing process will not be affected by other tasks. Then, the computing resources, such as CPU time and memory space, are dynamically adjusted according to the characteristics of the message to improve the parsing efficiency. Subsequently, the TCP processing platform loads the selected JS file from the specified location and prepares all necessary parsing tools and configuration parameters. Once the environment is set up, the TCP processing platform begins to parse the target message line by line according to the rules defined in the JS file. The first step of parsing is to perform grammatical analysis on the message to identify the various fields and their meanings. For example, when processing an HTTP request, the TCP processing platform first parses the status line, and then reads the header field and the message body that may exist in turn. For custom or non-standard protocols, the JS file provides a more flexible parsing path, allowing data segments to be divided by specific flags, delimiters, etc. In this way, the TCP processing platform can accurately understand the structure and content of the message. Next is the data conversion and standardization stage. The parsed raw data usually needs to be converted before it can be used by the subsequent application layer. The JS file defines specific data conversion rules. In addition, the TCP processing platform will standardize the data to ensure that all relevant information is correctly interpreted and mapped to the preset data model. For example, all sensor readings are uniformly converted to floating-point format so that different types of sensor data can be compared and analyzed on the same platform. During the parsing process, the TCP processing platform will also strictly implement error detection and exception handling mechanisms. If an unexpected situation is encountered, such as illegal characters or incomplete messages, the TCP processing platform will immediately trigger an error report and take appropriate measures, such as discarding invalid data or logging. For problems that may cause system crashes or instability, the JS file also designs security protection strategies, such as limiting the maximum parsing depth or setting timeout protection to prevent maliciously constructed data from causing damage. Finally, the TCP processing platform extracts valuable business information from the target message, which constitutes the core data source of the IoT application. In order to facilitate subsequent processing, the TCP processing platform will perform preliminary processing on the extracted business information, such as unit conversion or numerical normalization, to ensure that its form meets the expected requirements.

[0056] In particular, in S7, according to the preset conversion standard, the TCP processing platform converts the business information to obtain the converted business information. That is, when the TCP processing platform completes the parsing of the target message object and extracts the preliminary business information, it will enter the conversion processing stage. Prior to this, the TCP processing platform will perform a series of preparatory work to ensure that the conversion process proceeds smoothly. These preparatory work includes creating an independent working environment for each business information to be converted to avoid mutual interference between different tasks; dynamically adjusting computing resources (such as CPU, memory, etc.) according to the complexity and quantity of business information to improve conversion efficiency; and loading the conversion rule configuration file for this type of business information from a predefined location, and preparing all function libraries and configuration parameters required to execute the conversion logic. Once the environment is set up, the TCP processing platform begins to convert the business information according to the preset conversion standard. This process involves operations at multiple levels, the first of which is data standardization. The parsed original business information often has different formats and units, so it needs to be standardized first. For example, a timestamp may be an integer expressed in Unix time format, while a temperature value may be a floating point number. The conversion rules define specific standardization operations, such as converting all timestamps to ISO 8601 format or converting temperature values ​​to degrees Celsius. In addition, for numerical data, normalization may be required to facilitate subsequent comparison and analysis. The next stage is the unit conversion stage. If the business information contains physical quantity measurement results (such as length, weight, pressure, etc.), the TCP processing platform will convert them to the target unit according to the preset unit conversion rules. For example, the distance value in the sensor reading may be in meters, but the target application requires it to be displayed in feet. At this time, the TCP processing platform will automatically perform the corresponding unit conversion to ensure the consistency and accuracy of the data. This step ensures that data from different sources can be correctly understood and used on the same platform, and promotes cross-device and cross-system collaboration. In addition, in order to make the business information easier for the target application to understand, the TCP processing platform may adjust the display format of the data. This includes but is not limited to changing the format of the date and time, adjusting the case of the string, adding or removing specific characters, etc. For example, some applications may require the date format to be "YYYY-MM-DD", while the format of the parsed raw data is "MM / DD / YYYY". In this case, the TCP processing platform will convert the format according to the preset rules. In this way, the TCP processing platform can ensure the readability and ease of use of the data, and improve the interaction experience between the end user and the system. During the entire conversion process, the TCP processing platform will also strictly implement the error detection and exception handling mechanism to ensure that the system can maintain stable operation even in the event of an abnormal situation.

[0057] In particular, in S8, the TCP processing platform sends the converted business information to the IoT hub. In one example, first, in order to ensure that the data can maintain integrity and accuracy during transmission, the TCP processing platform will appropriately encapsulate the converted business information. This includes but is not limited to adding necessary header information (such as message ID, timestamp, source address, destination address, etc.), setting checksums to verify data integrity, and possible compression operations to reduce transmission load. In addition, the TCP processing platform will also select a suitable message format (such as JSON, XML, Protobuf, etc.) to encapsulate the data according to the needs of the target application to ensure that it is easy to be parsed and used by the receiving end. Then, the TCP processing platform will send the data to the IoT hub through a pre-established secure connection. In this process, the TCP processing platform uses a variety of technologies and mechanisms to ensure the quality and reliability of data transmission. First, considering that the security of data transmission is crucial, the TCP processing platform will use the SSL / TLS protocol to encrypt the communication link. This encryption method can prevent data from being eavesdropped or tampered with during transmission, ensuring the confidentiality and integrity of the data. In addition, the TCP processing platform also supports a two-way authentication mechanism, which means that it not only verifies the identity of the IoT hub, but also requires the IoT hub to confirm the identity of the TCP processing platform, thereby establishing a more secure and reliable communication environment. In particular, in order to ensure the reliability of data transmission, the TCP processing platform implements a retransmission mechanism and a confirmation response mechanism. Whenever a data packet is successfully sent to the IoT hub, the latter returns an acknowledgment message (ACK). If the TCP processing platform does not receive the acknowledgment message within the specified time, it will automatically resend the data packet until a successful confirmation is received. This method effectively avoids data loss caused by network fluctuations or temporary failures, and improves the robustness and stability of the system. After completing the data transmission, the TCP processing platform waits for the final confirmation message from the IoT hub. This indicates that the data has successfully reached the destination and has been processed correctly.

[0058] In summary, the multi-protocol compatible IoT platform processing method according to the embodiment of the present application is explained, which embeds and encodes the message information and TCP attribute information of the target message object by utilizing information analysis and coding technology based on artificial intelligence, and then performs multi-dimensional fusion of the embedded target message information embedding features and the target message TCP attribute embedding features, and at the same time, the message information and TCP attribute information of the message are also processed as above to obtain the multi-dimensional information interaction fusion features of each reference message, so as to intelligently judge the TCP protocol type label of the target message object according to the fast matching query representation between the multi-dimensional information interaction fusion features of the target message and the multi-dimensional information interaction fusion features of each reference message. In this way, it is possible to automatically learn and adapt to newly emerging or customized extended protocols, and cope with the dynamic changes of the network environment and the evolution of communication standards without manually updating the rules, thereby improving the accuracy and reliability of data processing.

[0059] Furthermore, a multi-protocol compatible IoT platform processing system is also provided.

[0060] Figure 5 FIG. 1 is a block diagram of a multi-protocol compatible IoT platform processing system according to an embodiment of the present application. Figure 5 As shown, according to the multi-protocol compatible IoT platform processing system 300 of the embodiment of the present application, it includes: a TCP attribute information definition module 310, which is used to define TCP attribute information through the TCP processing platform, and the TCP attribute information includes TCP configuration information, TCP link information and TCP protocol information; an RPC communication startup module 320, which is used to start RPC communication through the TCP processing platform based on the NIO framework, so that the IoT hub can communicate with the terminal device information through the TCP processing platform; a target message object information receiving module 330, which is used for the TCP processing platform to receive the message information and TCP attribute information of the target message object sent by the terminal device; a TCP protocol type determination module 340, which is used for the TCP processing platform to determine the type of message based on the NIO framework. The message information and TCP attribute information of the target message object are used to determine the TCP protocol type of the target message object; a JS file determination module 350 is used for the TCP processing platform to determine the JS file used to parse the target message object based on the TCP protocol type of the target message object; a target message object parsing module 360 ​​is used for the TCP processing platform to parse the target message object based on the JS file of the target message object to obtain business information; a business information conversion module 370 is used for the TCP processing platform to convert the business information according to a preset conversion standard to obtain converted business information; a business information sending module 380 is used for the TCP processing platform to send the converted business information to the Internet of Things hub.

[0061] As described above, the multi-protocol compatible IoT platform processing system 300 according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with a multi-protocol compatible IoT platform processing algorithm. In a possible implementation, the multi-protocol compatible IoT platform processing system 300 according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the multi-protocol compatible IoT platform processing system 300 can be a software module in the operating system of the wireless terminal, or can be an application developed for the wireless terminal; of course, the multi-protocol compatible IoT platform processing system 300 can also be one of the many hardware modules of the wireless terminal.

[0062] Alternatively, in another example, the multi-protocol compatible IoT platform processing system 300 and the wireless terminal may also be separate devices, and the multi-protocol compatible IoT platform processing system 300 may be connected to the wireless terminal via a wired and / or wireless network, and transmit interactive information in accordance with an agreed data format.

[0063] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A multi-protocol compatible IoT platform processing method, wherein: The IoT platform includes a TCP processing platform, an IoT hub and a terminal device, and is characterized in that: defining TCP attribute information through the TCP processing platform, the TCP attribute information including TCP configuration information, TCP link information and TCP protocol information; based on the NIO framework, starting RPC communication through the TCP processing platform, so that the IoT hub can communicate with the terminal device information through the TCP processing platform; the TCP processing platform receives the message information and TCP attribute information of the target message object sent by the terminal device; the TCP processing platform determines the TCP protocol type of the target message object based on the message information and TCP attribute information of the target message object, including: obtaining the message information of each message in the message database; information and TCP attribute information; determine the TCP protocol type label of the target message object based on a fast query matching representation of the attention embedding fusion between the message information and TCP attribute information of the target message object and the message information and TCP attribute information of each message; the TCP processing platform determines the JS file used to parse the target message object based on the TCP protocol type of the target message object; the TCP processing platform parses the target message object based on the JS file of the target message object to obtain business information; according to a preset conversion standard, the TCP processing platform converts the business information to obtain converted business information; the TCP processing platform sends the converted business information to the Internet of Things hub.

2. The multi-protocol compatible IoT platform processing method according to claim 1, characterized in that: Based on the fast query matching representation of the attention embedding fusion between the message information and TCP attribute information of the target message object and the message information and TCP attribute information of each message, the TCP protocol type label of the target message object is determined, including: respectively embedding and encoding the message information and TCP attribute information of the target message object to obtain a target message information embedding coding vector and a target message TCP attribute embedding coding vector; performing message multi-dimensional feature interaction of the attention mechanism on the target message information embedding coding vector and the target message TCP attribute embedding coding vector to obtain a target message multi-dimensional information interaction fusion feature vector. feature vector; respectively embedding and encoding the message information and TCP attribute information in each message and interacting with the message multi-dimensional features of the attention mechanism to obtain a set of reference message multi-dimensional information interaction fusion feature vectors; performing fast matching-oriented semantic query encoding on the set of the target message multi-dimensional information interaction fusion feature vector and the reference message multi-dimensional information interaction fusion feature vector to obtain a target message information query response encoding vector as a fast query matching representation of the attention embedding fusion; based on the target message information query response encoding vector, obtaining the recognition result of the TCP protocol type label representing the target message object.

3. The multi-protocol compatible IoT platform processing method according to claim 2, characterized in that: The message information and TCP attribute information of the target message object are respectively embedded and encoded to obtain a target message information embedded coding vector and a target message TCP attribute embedded coding vector, including: using a message information embedding matrix to embed the message information of the target message object to obtain the target message information embedded coding vector; using a TCP attribute information embedding matrix to embed the TCP attribute information of the target message object to obtain the target message TCP attribute embedded coding vector.

4. The multi-protocol compatible IoT platform processing method according to claim 3, characterized in that: The target message information embedding coding vector and the target message TCP attribute embedding coding vector are subjected to message multi-dimensional feature interaction of an attention mechanism to obtain a target message multi-dimensional information interaction fusion feature vector, including: the target message information embedding coding vector and the target message TCP attribute embedding coding vector are subjected to a message multi-dimensional feature interaction fusion device based on an X-linear attention network to obtain the target message multi-dimensional information interaction fusion feature vector.

5. The multi-protocol compatible IoT platform processing method according to claim 4, characterized in that: The message information and TCP attribute information in each message are respectively embedded and coded and the message multi-dimensional feature interaction of the attention mechanism is performed to obtain a set of reference message multi-dimensional information interaction fusion feature vectors, including: using the message information embedding matrix and the TCP attribute information embedding matrix to respectively embed the message information and TCP attribute information in each message to obtain a set of reference message information embedding coding vectors and a set of reference message TCP attribute embedding coding vectors; each group of corresponding reference message information embedding coding vectors and reference message TCP attribute embedding coding vectors in the set of reference message information embedding coding vectors and the set of reference message TCP attribute embedding coding vectors is passed through the message multi-dimensional feature interaction fusion device based on the X-linear attention network to obtain the set of reference message multi-dimensional information interaction fusion feature vectors.

6. The multi-protocol compatible IoT platform processing method according to claim 5, characterized in that: The method comprises: performing fast matching-oriented semantic query encoding on the set of the target message multi-dimensional information interactive fusion feature vector and the reference message multi-dimensional information interactive fusion feature vector to obtain the target message information query response encoding vector, including: combining the target message multi-dimensional information interactive fusion feature vector, determining the reference message multi-dimensional information fusion positioning matching library feature vector from the set of the reference message multi-dimensional information interactive fusion feature vector; determining the reference message multi-dimensional information fine-grained matching search window based on the reference message multi-dimensional information fusion positioning matching library feature vector, wherein the reference message multi-dimensional information The vector at the center position of the information fine-grained matching search window is the reference message multi-dimensional information fusion positioning matching library feature vector, and each reference message multi-dimensional information interactive fusion feature vector in the reference message multi-dimensional information fine-grained matching search window is defined as a reference message multi-dimensional information fine-grained query feature vector; the target message multi-dimensional information interactive fusion feature vector and each reference message multi-dimensional information fine-grained query feature vector in the reference message multi-dimensional information fine-grained matching search window are subjected to message multi-dimensional information fine-grained query encoding to obtain the target message information query response encoding vector.

7. The multi-protocol compatible IoT platform processing method according to claim 6, characterized in that: In combination with the target message multi-dimensional information interaction fusion feature vector, a reference message multi-dimensional information fusion positioning matching library feature vector is determined from the set of reference message multi-dimensional information interaction fusion feature vectors, including: calculating the cross entropy of the target message multi-dimensional information interaction fusion feature vector relative to each reference message multi-dimensional information interaction fusion feature vector in the set of reference message multi-dimensional information interaction fusion feature vectors to obtain a set of target message-reference message quick query positioning factors; and using the reference message multi-dimensional information interaction fusion feature vector corresponding to the minimum value in the set of target message-reference message quick query positioning factors as the reference message multi-dimensional information fusion positioning matching library feature vector.

8. The multi-protocol compatible IoT platform processing method according to claim 7, characterized in that: The target message multidimensional information interactive fusion feature vector and each reference message multidimensional information fine-grained query feature vector in the reference message multidimensional information fine-grained matching search window are subjected to message multidimensional information fine-grained query encoding to obtain the target message information query response encoding vector, including: linearly transforming the target message multidimensional information interactive fusion feature vector to obtain a target message multidimensional information query vector and a target message multidimensional information value vector, and using each reference message multidimensional information fine-grained query feature vector in the reference message multidimensional information fine-grained matching search window as a key vector, and performing message multidimensional information fine-grained query encoding of a heterogeneous converter on the target message multidimensional information query vector, the target message multidimensional information value vector and the key vector to obtain the target message information query response encoding vector.

9. The multi-protocol compatible IoT platform processing method according to claim 8, characterized in that: Based on the target message information query response coding vector, an identification result of the TCP protocol type label representing the target message object is obtained, including: passing the target message information query response coding vector through a classifier-based TCP protocol type identifier to obtain the identification result, and the identification result is used to represent the TCP protocol type label of the target message object.

10. A multi-protocol compatible IoT platform processing system, comprising: A TCP attribute information definition module, used to define TCP attribute information through the TCP processing platform, wherein the TCP attribute information includes TCP configuration information, TCP link information and TCP protocol information; An RPC communication startup module is used to start RPC communication through the TCP processing platform based on the NIO framework, so that the IoT hub can communicate with the terminal device information through the TCP processing platform; A target message object information receiving module, used for the TCP processing platform to receive message information and TCP attribute information of the target message object sent by the terminal device; A TCP protocol type determination module, used for the TCP processing platform to determine the TCP protocol type of the target message object based on the message information and TCP attribute information of the target message object; A JS file determination module, used for the TCP processing platform to determine a JS file for parsing the target message object based on the TCP protocol type of the target message object; A target message object parsing module, used for the TCP processing platform to parse the target message object based on the JS file of the target message object to obtain service information; A service information conversion module, configured to convert the service information according to a preset conversion standard so as to obtain converted service information; The business information sending module is used for the TCP processing platform to send the converted business information to the Internet of Things hub.

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