Internet of Things equipment message data analysis method, system and equipment based on large model, and medium

Abstract

The invention discloses an Internet of Things equipment message data analysis method, system, equipment and medium based on a large model, belongs to the technical field of artificial intelligence and Internet of Things, and aims to solve the technical problem of how to improve the accuracy and efficiency of Internet of Things equipment message analysis and better adapt to the complexity and variability of Internet of Things data. According to the technical scheme, the method comprises the following steps: configuring a data analysis rule: inputting the analysis rule of data reported by the Internet of Things equipment into a large model according to a message format reported by the Internet of Things equipment in combination with a specific message analysis requirement; an equipment receiving assembly is arranged between the Internet of Things equipment and the large model and used for summarizing messages reported by the Internet of Things equipment of different protocols and sending the messages to the large model, and the large model analyzes the reported data according to an analysis rule; and the large model outputs analysis data.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and Internet of Things technology, and in particular to a method, system, device and medium for analyzing message data of Internet of Things devices based on a large model. Background Art

[0002] With the rapid development of IoT technology, more and more devices and systems are beginning to connect and exchange data through IoT. The message data generated by these IoT devices are often diverse, complex, and real-time. Traditional data analysis methods require manual code writing, which requires high professional skills. At the same time, inefficient codes are unable to cope with such a large amount of data and diverse data structures.

[0003] Therefore, how to improve the accuracy and efficiency of IoT device message parsing and better adapt to the complexity and variability of IoT data is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The technical task of the present invention is to provide a method, system, device and medium for parsing IoT device message data based on a large model to solve the problem of how to improve the accuracy and efficiency of IoT device message parsing and better adapt to the complexity and variability of IoT data.

[0005] The technical task of the present invention is achieved in the following way: a method for parsing message data of IoT devices based on a large model, the method is as follows:

[0006] Configure data parsing rules: According to the message format reported by IoT devices and the specific message parsing requirements, the parsing rules of the data reported by IoT devices are input into the big model; among them, the big model adopts Hairuo big model, iFlytek big model, Wenxin Yiyan big model, etc.

[0007] The big model accesses IoT device data: a device receiving component is set between the IoT device and the big model to aggregate messages reported by IoT devices of different protocols and send them to the big model. The big model parses and processes the reported data according to the parsing rules.

[0008] The big model outputs parsed data: When the big model completes the parsing of the messages reported by the IoT devices, the output of the big model is sent to the push component. To facilitate the reception and scalability of subsequent programs, the format-converted messages are sent to a message system based on the publish / subscribe model, such as Kafka or RabbitMQ. The big model serves as its only data source and distributes it according to the needs of subscribers. The messages are then pushed to third parties or the data is displayed on the page.

[0009] As a preferred method, when inputting the parsing rules of the data reported by the IoT device into the big model, the prompt word of the big model is configured. The appropriate prompt is conducive to the big model to efficiently parse the messages reported by the device;

[0010] The prompt word Prompt format is as follows:

[0011] Please parse the data reported by the IoT device according to the following parsing rules and output it according to the output rules; the rules are as follows:

[0012] Parsing rules:

[0013] Rule 1: Parse the Base64 string reported by the device;

[0014] Rule 2: Get the field information in the parsed string;

[0015] Output rules:

[0016] Rule 1: Output JSON format: {"field English": "value"};

[0017] The output information is: ".

[0018] Preferably, the device receiving component has the following functions:

[0019] ① Protocol parsing: Written in JAVA language, receiving IoT data of different protocols such as MQTT, COAP, and HTTP using SDKs of different protocols; among them, the MQTT protocol uses the Eclipse Paho library; the COAP protocol uses the Californian library; and the HTTP protocol uses the OkHttp library;

[0020] ②Data processing: convert the parsed data into a unified format encrypted with base64 for easy subsequent processing;

[0021] ③Message forwarding: Use the message queue system (publish / subscribe) to send messages to the large model.

[0022] Preferably, the push component is written in JAVA language and has the following functions:

[0023] ①Use websocket technology to push data to the page;

[0024] ②The Java HTTP client calls the HTTP client interface and pushes data to a third party;

[0025] ③Java integrated publish-subscribe system SDK package, which sends messages to the message system, such as Kafka or MQ;

[0026] ④Java integrated database SDK stores data in the database.

[0027] A large-model-based IoT device message data parsing system comprises an IoT device end, a device receiving component, a large-model component and a push component. The IoT device end sends data to the device receiving component via the MQTT, COAP or HTTP protocol. The device receiving component receives and parses information from the IoT device end, performs preprocessing operations such as excluding empty strings and unifying the received data into base64 strings, and then forwards the preprocessed data to the large-model component. The large-model component further parses the data from the device receiving component and returns the result to the push component. The push component displays the result returned by the large-model component on a page or forwards the data to a third-party service.

[0028] As a preferred method, when the analysis rules of the data reported by the IoT device are input into the big model, the prompt word of the big model is configured. The appropriate prompt is conducive to the big model to efficiently analyze the messages reported by the device;

[0029] The prompt word Prompt format is as follows:

[0030] Please parse the data reported by the IoT device according to the following parsing rules and output it according to the output rules; the rules are as follows:

[0031] Parsing rules:

[0032] Rule 1: Parse the Base64 string reported by the device;

[0033] Rule 2: Get the field information in the parsed string;

[0034] Output rules:

[0035] Rule 1: Output JSON format: {"field English": "value"};

[0036] The output information is: ".

[0037] Preferably, the device receiving component has the following functions:

[0038] ① Protocol parsing: Written in JAVA language, receiving IoT data of different protocols such as MQTT, COAP, and HTTP using SDKs of different protocols; among them, the MQTT protocol uses the Eclipse Paho library; the COAP protocol uses the Californian library; and the HTTP protocol uses the OkHttp library;

[0039] ②Data processing: convert the parsed data into a unified format encrypted with base64 for easy subsequent processing;

[0040] ③Message forwarding: Use the message queue system (publish / subscribe) to send messages to the large model.

[0041] Preferably, the push component subscribes to the data published by the large model component through a message system in a publish / subscribe mode, processes the data, and then pushes the data to a page for display or pushes the acquired data to a third-party system; wherein the push method includes calling a third-party HTTP interface and / or sending to a message system provided by a third party and / or storing in a database.

[0042] An electronic device comprising: a memory and at least one processor;

[0043] Wherein, the memory stores computer-executable instructions;

[0044] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the IoT device message data parsing method based on the large model as described above.

[0045] A computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the method for parsing message data of an Internet of Things device based on a large model as described above is implemented.

[0046] The method, system, device and medium for parsing IoT device message data based on a large model of the present invention have the following advantages:

[0047] (i) Based on the experience of IoT data processing in actual scenarios, the present invention performs data parsing on IoT device messages based on a large model, which not only improves the efficiency and accuracy of data processing, but also reduces the requirements for manual coding capabilities, thus providing strong support for the intelligence and automation of IoT applications;

[0048] (ii) The present invention realizes a more intelligent, efficient and convenient way to parse IoT device message data, improves the accuracy and efficiency of IoT device message processing, and also has a high degree of flexibility and scalability;

[0049] (III) The present invention combines the big model to perform IoT device message data analysis, which is a more efficient and flexible choice. The big model can learn the deep-level characteristics and laws of the data by training a large amount of data, so as to perform well in complex data processing tasks. In the field of IoT device message data analysis, the application of the big model can realize the rapid processing and accurate analysis of massive data, thereby greatly improving the efficiency and accuracy of data processing.

[0050] (iv) The present invention utilizes a high-performance large model to parse messages reported by IoT devices and unify the formats, thereby improving the accuracy and efficiency of device message parsing and better adapting to the complexity and variability of IoT data;

[0051] (V) The present invention can improve the accuracy of messages reported by parsing devices, reduce the latency of message processing, efficiently parse massive amounts of data from different IoT protocols, and effectively improve the operating efficiency of the IoT system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention is further described below in conjunction with the accompanying drawings.

[0053] Attached Figure 1 It is a flowchart of a method for parsing message data of IoT devices based on a large model;

[0054] Attached Figure 2 Schematic diagram of connecting the large model to IoT devices;

[0055] Attached Figure 3 Schematic diagram of analytical equipment output for large models. DETAILED DESCRIPTION

[0056] The method, system, device and medium for parsing message data of IoT devices based on a large model of the present invention are described in detail below with reference to the drawings and specific embodiments of the specification.

[0057] Embodiment 1:

[0058] As attached Figure 1 As shown, this embodiment provides a method for parsing IoT device message data based on a large model, and the method is specifically as follows:

[0059] S1. Configure data parsing rules: According to the message format reported by the IoT device and the specific message parsing requirements, the parsing rules of the data reported by the IoT device are input into the big model; among them, the big model adopts the Hairuo big model, iFlytek big model, Wenxin Yiyan big model, etc.

[0060] S2. Large model access to IoT device data: as shown in the attached Figure 2 As shown, a device receiving component is set between the IoT device and the big model to aggregate the messages reported by IoT devices of different protocols and send them to the big model. The big model parses and processes the reported data according to the parsing rules.

[0061] S3. Large model output analysis data: as attached Figure 3As shown in the figure, after the big model parses the message reported by the IoT device, it sends the output of the big model to the push component. To facilitate the reception and scalability of subsequent programs, the format-converted message is sent to a message system based on the publish / subscribe mode, such as Kafka or RabbitMQ. The big model serves as its only data source and distributes it according to the needs of subscribers. The message is then pushed to a third party or the data is displayed on the page.

[0062] In step S1 of this embodiment, when the analysis rules of the data reported by the IoT device are input into the big model, the prompt word Prompt of the big model is configured. The appropriate Prompt is conducive to the big model to efficiently analyze the messages reported by the device;

[0063] The prompt word Prompt format is as follows:

[0064] Please parse the data reported by the IoT device according to the following parsing rules and output it according to the output rules; the rules are as follows:

[0065] Parsing rules:

[0066] Rule 1: Parse the Base64 string reported by the device;

[0067] Rule 2: Get the field information in the parsed string;

[0068] Output rules:

[0069] Rule 1: Output JSON format: {"field English": "value"};

[0070] The output information is: ".

[0071] Among them, rule 2 is a custom configuration rule based on the business scenario. For example, if the reported message is a base64-encrypted string of temperature and humidity, and the requirement is to obtain only the temperature string, the parsing rule can be set as follows:

[0072] Rule 1: Parse the Base64 string reported by the device;

[0073] Rule 2: Get the temperature information in the parsed string;

[0074] Output rules:

[0075] Rule 1: Output the following JSON format: {"temp":"temperature value"}.

[0076] The output information is: XX.

[0077] The parsing rules are flexible and customizable according to needs, so that various complex IoT messages uploaded can be parsed.

[0078] The device receiving component in step S2 of this embodiment has the following functions:

[0079] ① Protocol parsing: Written in JAVA language, receiving IoT data of different protocols such as MQTT, COAP, and HTTP using SDKs of different protocols; among them, the MQTT protocol uses the Eclipse Paho library; the COAP protocol uses the Californian library; and the HTTP protocol uses the OkHttp library;

[0080] ②Data processing: convert the parsed data into a unified format encrypted with base64 for easy subsequent processing;

[0081] ③Message forwarding: Use the message queue system (publish / subscribe) to send messages to the large model.

[0082] The push component in step S3 of this embodiment is written in JAVA language and has the following functions:

[0083] ①Use websocket technology to push data to the page;

[0084] ②The Java HTTP client calls the HTTP client interface and pushes data to a third party;

[0085] ③Java integrated publish-subscribe system SDK package, which sends messages to the message system, such as Kafka or MQ;

[0086] ④Java integrated database SDK stores data in the database.

[0087] Embodiment 2:

[0088] The present embodiment provides an IoT device message data parsing system based on a big model, the system comprising an IoT device end, a device receiving component, a big model component and a push component. The IoT device end sends data to the device receiving component via the MQTT, COAP or HTTP protocol. The device receiving component receives and parses information from the IoT device end, and performs preprocessing operations on the received data to exclude empty strings and unify them into base64 strings, and then forwards the preprocessed data to the big model component. The big model component further parses the data from the device receiving component and returns the result to the push component. The push component displays the result returned by the big model component on a page or forwards the data to a third-party service.

[0089] When the parsing rules of the IoT device-side reported data in this embodiment are input into the big model, the prompt word Prompt of the big model is configured. The appropriate Prompt is conducive to the big model to efficiently parse the messages reported by the device;

[0090] The prompt word Prompt format is as follows:

[0091] Please parse the data reported by the IoT device according to the following parsing rules and output it according to the output rules; the rules are as follows:

[0092] Parsing rules:

[0093] Rule 1: Parse the Base64 string reported by the device;

[0094] Rule 2: Get the field information in the parsed string;

[0095] Output rules:

[0096] Rule 1: Output JSON format: {"field English": "value"};

[0097] The output information is: ".

[0098] The device receiving component in this embodiment has the following functions:

[0099] ① Protocol parsing: Written in JAVA language, receiving IoT data of different protocols such as MQTT, COAP, and HTTP using SDKs of different protocols; among them, the MQTT protocol uses the Eclipse Paho library; the COAP protocol uses the Californian library; and the HTTP protocol uses the OkHttp library;

[0100] ②Data processing: convert the parsed data into a unified format encrypted with base64 for easy subsequent processing;

[0101] ③Message forwarding: Use the message queue system (publish / subscribe) to send messages to the large model.

[0102] The push component in this embodiment subscribes to the data published by the large model component through the message system in the publish / subscribe mode, processes the data, and then pushes the data to the page for display or pushes the acquired data to a third-party system; wherein the push method includes calling a third-party HTTP interface and / or sending to a message system provided by a third party and / or storing in a database.

[0103] Embodiment 3:

[0104] This embodiment also provides an electronic device, including: a memory and at least one processor;

[0105] Wherein, the memory stores computer-executable instructions;

[0106] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the large model-based Internet of Things device message data parsing method described in any one of the present inventions.

[0107] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or any conventional processor, etc.

[0108] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory can also include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, at least one disk storage period, a flash memory device, or other volatile solid-state storage devices.

[0109] Embodiment 4:

[0110] This embodiment also provides a computer-readable storage medium, which stores a plurality of instructions, which are loaded by a processor to enable the processor to execute the large model-based IoT device message data parsing method in any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code that implements the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0111] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0112] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0113] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0114] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for parsing message data of IoT devices based on a large model, characterized in that: The method is as follows: Configure data parsing rules: According to the message format reported by IoT devices and in combination with specific message parsing requirements, the parsing rules for the data reported by IoT devices are input into the big model; The big model accesses IoT device data: a device receiving component is set between the IoT device and the big model to aggregate messages reported by IoT devices of different protocols and send them to the big model. The big model parses and processes the reported data according to the parsing rules. The big model outputs parsed data: When the big model completes the parsing of the message reported by the IoT device, the big model output result is sent to the push component, and the format-converted message is sent to the message system based on the publish / subscribe model, and distributed according to the needs of the subscriber, and then the message is pushed to a third party or the data is displayed on the page.

2. The method for parsing IoT device message data based on a large model according to claim 1, characterized in that: When inputting the parsing rules of the data reported by IoT devices into the big model, configure the prompt word of the big model. The appropriate prompt is conducive to the big model to efficiently parse the messages reported by the device; The prompt word Prompt format is as follows: Please parse the data reported by the IoT device according to the following parsing rules and output it according to the output rules; the rules are as follows: Parsing rules: Rule 1: Parse the Base64 string reported by the device; Rule 2: Get the field information in the parsed string; Output rules: Rule 1: Output JSON format: {"field English": "value"}; The output information is: ".

3. The method for parsing IoT device message data based on a large model according to claim 1 or 2, characterized in that: The device receiving component has the following functions: ① Protocol parsing: Written in JAVA language, receiving IoT data of different protocols such as MQTT, COAP, and HTTP using SDKs of different protocols; among them, the MQTT protocol uses the Eclipse Paho library; the COAP protocol uses the Californian library; and the HTTP protocol uses the OkHttp library; ②Data processing: convert the parsed data into a unified format encrypted by base64; ③Message forwarding: Use the message queue system to send messages to the large model.

4. The method for parsing IoT device message data based on a large model according to claim 3 is characterized in that: The push component is written in JAVA and has the following functions: ①Use websocket technology to push data to the page; ②The Java HTTP client calls the HTTP client interface and pushes data to a third party; ③Java integrated publish-subscribe system SDK package to send messages to the message system; ④Java integrated database SDK stores data in the database.

5. A large-model-based IoT device message data parsing system, characterized in that: The system includes an IoT device end, a device receiving component, a large model component and a push component. The IoT device end sends data to the device receiving component through the MQTT, COAP or HTTP protocol. The device receiving component receives and parses information from the IoT device end, and performs preprocessing operations on the received data to exclude empty strings and unify them into base64 strings, and then forwards the preprocessed data to the large model component. The large model component further parses the data from the device receiving component and returns the result to the push component. The push component displays the result returned by the large model component on the page or forwards the data to a third-party service.

6. The IoT device message data parsing system based on a large model according to claim 5 is characterized in that: When the parsing rules of the data reported by the IoT device are input into the big model, configure the prompt word of the big model. The appropriate prompt is conducive to the big model to efficiently parse the messages reported by the device; The prompt word Prompt format is as follows: Please parse the data reported by the IoT device according to the following parsing rules and output it according to the output rules; the rules are as follows: Parsing rules: Rule 1: Parse the Base64 string reported by the device; Rule 2: Get the field information in the parsed string; Output rules: Rule 1: Output JSON format: {"field English": "value"}; The output information is: ".

7. The IoT device message data parsing system based on a large model according to claim 5 is characterized in that: The device receiving component has the following functions: ① Protocol parsing: Written in JAVA language, receiving IoT data of different protocols such as MQTT, COAP, and HTTP using SDKs of different protocols; among them, the MQTT protocol uses the Eclipse Paho library; the COAP protocol uses the Californian library; and the HTTP protocol uses the OkHttp library; ②Data processing: convert the parsed data into a unified format encrypted by base64; ③Message forwarding: Use the message queue system to send messages to the large model.

8. The IoT device message data parsing system based on a large model according to any one of claims 5 to 7, characterized in that: The push component subscribes to the data published by the large model component through the message system of the publish / subscribe mode, processes the data, and then pushes the data to the page for display or pushes the acquired data to a third-party system; the push method includes calling a third-party HTTP interface and / or sending to a message system provided by a third party and / or storing it in a database.

9. An electronic device, characterized in that: include: memory and at least one processor; Wherein, the memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the large model-based IoT device message data parsing method as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the processor executes the computer-executable instructions, the method for parsing IoT device message data based on a large model as described in any one of claims 1 to 4 is implemented.

Images