Method and system for realizing Internet of Things data alarm processing based on large model

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a method and system for realizing Internet of Things data alarm processing based on a large model, and the method comprises the following steps: data collection and preprocessing: monitoring and receiving data from Internet of Things equipment adopting different communication protocols by designing and operating a message receiving program; analyzing the received data and converting the data into a uniform format to ensure that the data of all equipment follows the same specification and structure before being input into a large model; the method and the system for realizing Internet of Things data alarm processing based on the large model provided by the invention have the beneficial effects that data line analysis and alarm triggering of Internet of Things collected data are realized by utilizing the high-performance large model. According to the method, the accuracy and efficiency of alarm processing can be improved, and the method can better adapt to the complexity and variability of Internet of Things data.

Description

Technical Field

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

[0002] With the rapid development of IoT technology, more and more devices and systems are connected to the Internet, forming a huge IoT network. The amount of data generated by these devices is growing explosively, including various real-time data such as temperature, humidity, pressure, location, power consumption, etc. How to achieve efficient and accurate alarm of IoT data according to user requirements when receiving a large amount of real-time data has become an urgent problem to be solved.

[0003] Traditional data processing methods often rely on fixed rules and thresholds for alarm triggering. This method may be effective when processing simple, regular data, but its accuracy and flexibility are often insufficient when facing complex and changeable IoT data. In addition, with the continuous increase in IoT devices, the amount of data is also growing continuously. Traditional data processing methods often face performance bottlenecks when processing massive data. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for realizing Internet of Things data alarm processing based on a large model, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for implementing IoT data alarm processing based on a large model, the method comprising the following steps:

[0006] Data collection and preprocessing, which involves designing and running a message receiving program to listen to and receive data from IoT devices using different communication protocols;

[0007] Parse the received data and convert it into a unified format to ensure that the data from all devices follows the same specifications and structure before being input into the big model;

[0008] Send the converted data to a message system based on the publish / subscribe model, which serves as a data distribution center and provides real-time data flow for large models;

[0009] The large model performs analysis and prediction based on the real-time data received, and triggers an alarm mechanism when an abnormal situation is detected.

[0010] Preferably, the method further includes inputting the alarm requirements into the large model in the form of prompt words, wherein the prompt words include analysis rules for IoT data and generation format requirements for alarm information, so as to guide the large model to generate accurate alarm responses according to the rules.

[0011] Preferably, the generation of alarm information includes: when the data meets the preset alarm conditions, the large model generates alarm information in a specific format according to the rules; the alarm information includes but is not limited to the abnormality type, occurrence time, and key information of the abnormal data details.

[0012] Preferably, the method further includes sending the generated alarm information to a designated destination, wherein the destination is a user terminal, a monitoring system, a third-party system or a platform, so that the user or a related system can receive and process the alarm information in a timely manner.

[0013] Preferably, further processing of the alarm information is also included, including but not limited to:

[0014] Visually display alarm information through charts, reports or real-time notifications;

[0015] Prioritize alarm information according to its urgency and importance;

[0016] Automatically trigger emergency response mechanisms, such as sending alert emails, SMS notifications, or executing preset emergency actions.

[0017] A system for implementing Internet of Things data alarm processing based on a large model is applied to a method for implementing Internet of Things data alarm processing based on a large model, and the system includes:

[0018] Data collection and preprocessing module, used to monitor and receive data from IoT devices using different communication protocols, parse the data and convert it into a unified format;

[0019] The message system module, as a data distribution center, receives unified format data from the data collection and preprocessing module and distributes it according to the needs of subscribers;

[0020] The large model analysis module receives real-time data from the message system module, performs analysis and prediction based on preset algorithms and rules, and generates alarm information when abnormal conditions are detected;

[0021] The alarm information output module is used to format the alarm information generated by the large model analysis module and display it to the user in an intuitive and easy-to-understand manner or push it to a designated destination.

[0022] Preferably, the data collection and preprocessing module includes a powerful message receiving program, which monitors and receives IoT data from devices with different protocols, and performs corresponding analysis and format conversion according to the communication protocol of the device.

[0023] Preferably, the message system module adopts a publish / subscribe mode, receives messages from multiple data sources, and distributes data efficiently and reliably according to the needs of subscribers, ensuring that the large model can obtain the latest IoT data in real time.

[0024] Preferably, the large model analysis module can perform data analysis in the form of prompts according to the alarm requirements input by the user, and generate alarm information in a specific format based on preset rules and algorithms. The rules include but are not limited to temperature thresholds and humidity thresholds. The format of the alarm information is JSON or other user-specified formats.

[0025] Preferably, the alarm information output module includes a built-in parsing mechanism for detailed interpretation of the generated alarm information and presenting it to users in the form of charts, reports, real-time notifications, etc. The module also supports pushing alarm information to third-party destinations, such as other systems, platforms or partners, to meet the needs of different business scenarios.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The method and system for realizing IoT data alarm processing based on a large model proposed in the present invention utilize a high-performance large model to realize data analysis and alarm triggering for IoT data collection. This method can improve the accuracy and efficiency of alarm processing and better adapt to the complexity and variability of IoT data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of the method of the present invention;

[0029] Figure 2 It is a flow chart of the overall analysis of the present invention;

[0030] Figure 3 A flow chart of inputting IoT data into a large model for the present invention;

[0031] Figure 4 Inputting the alarm requirements into the large model flow chart for the present invention;

[0032] Figure 5 The present invention sends the output alarm information to the destination flow chart. DETAILED DESCRIPTION

[0033] In order to make the purpose and technical solution of the present invention clearly and completely described, and the advantages more clearly understood, the embodiments of the present invention are further described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] For example, see Figures 1 to 5 The present invention provides a technical solution: a method for realizing Internet of Things data alarm processing based on a large model, the method comprising the following steps:

[0035] 1. Feed IoT data into the big model

[0036] To implement IoT data alarm processing based on big models, we first need to ensure that the massive data generated by IoT devices can be efficiently and accurately used by big models. This process starts with importing the collected IoT data into the big model. However, the diversity and complexity of devices in the IoT environment make data collection and processing face many challenges.

[0037] There are many IoT devices, and they often use different communication protocols to ensure reliable data transmission. These protocols include but are not limited to MQTT, CoAP, and HTTP. Each protocol has its own specific advantages and applicable scenarios, but it also brings difficulties to data integration.

[0038] In order to overcome this problem, during the development process, we first need to design a powerful message receiving program. The main responsibility of this program is to monitor and receive IoT data from different protocol devices. When the device sends data, the program will quickly capture these messages and parse them accordingly according to the device's communication protocol.

[0039] After parsing is complete, the message receiving program converts the messages sent by the devices into a unified format. This step is critical because it ensures that the data from all devices follows the same specifications and structure before entering the large model. This unified data format not only improves the efficiency of data processing, but also reduces the complexity of subsequent data analysis and alarm processing.

[0040] Next, the format-converted message is sent to a message system based on the publish / subscribe model. Such message systems, such as Kafka or RabbitMQ, are key components in IoT data processing. They can serve as a data hub, receiving messages from various sources and distributing them according to the needs of subscribers.

[0041] In this architecture, the message system becomes the bridge between IoT devices and the big model. As the only message source, it provides a steady stream of real-time data for the big model. The big model can analyze and predict based on this data. Once an abnormal situation is found, the alarm mechanism can be triggered immediately to ensure the stable operation of the IoT system.

[0042] 2. Input the alarm requirements into the big model

[0043] In the step of inputting the alarm requirements into the big model for processing, users can input the alarm requirements into the big model based on specific business needs and the collected IoT data. The information given to the big model is generally called prompt. When using the big model for IoT data alarm processing, designing appropriate prompts is crucial to guide the model to understand and generate accurate alarm responses. Please refer to the following examples:

[0044] "Please analyze the given IoT data according to the following rules and generate warning information in json format. If it does not meet the rules, null will be returned. The rules are as follows:

[0045] Rule 1: If the temperature exceeds 30 degrees, a high temperature and low humidity alarm is generated.

[0046] Rule 2: The JSON format of the output alarm information is as follows: {Defined JSON format}

[0047] Data: {insert data here}

[0048] Warning information: ".

[0049] 3. Send the output alarm information to the destination

[0050] When the big model successfully generates alarm information, we can process these alarm information in a refined manner according to specific business needs. Through the built-in parsing mechanism, the alarm information can be interpreted in detail and presented to users in an intuitive and easy-to-understand way, such as through charts, reports or real-time notifications. In addition, in order to meet the needs of different business scenarios, these alarm information can also be pushed to third-party destinations, such as other systems, platforms or partners.

[0051] Embodiment 2, based on embodiment 1, proposes a system for realizing IoT data alarm processing based on a large model, which is applied to a method for realizing IoT data alarm processing based on a large model. The system includes:

[0052] The data collection and preprocessing module is used to monitor and receive data from IoT devices using different communication protocols, parse the data and convert it into a unified format; it includes a powerful message receiving program that monitors and receives IoT data from devices with different protocols, and performs corresponding parsing and format conversion according to the communication protocol of the device.

[0053] The message system module, as a data distribution center, receives unified format data from the data collection and preprocessing module and distributes it according to the needs of subscribers; it adopts a publish / subscribe model to receive messages from multiple data sources and distributes data efficiently and reliably according to the needs of subscribers, ensuring that the large model can obtain the latest IoT data in real time.

[0054] The large model analysis module receives real-time data from the message system module, performs analysis and prediction based on preset algorithms and rules, and generates alarm information when an abnormal situation is detected; according to the alarm requirements input by the user, data analysis is performed in the form of prompts, and alarm information in a specific format is generated based on preset rules and algorithms. The rules include but are not limited to temperature thresholds and humidity thresholds. The format of the alarm information is JSON or other user-specified formats.

[0055] The alarm information output module is used to format the alarm information generated by the large model analysis module, and display it to the user in an intuitive and easy-to-understand manner or push it to the specified destination; it includes a built-in parsing mechanism for detailed interpretation of the generated alarm information and displaying it to the user in the form of charts, reports, real-time notifications, etc. The module also supports pushing alarm information to third-party destinations, such as other systems, platforms or partners, to meet the needs of different business scenarios.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for implementing IoT data alarm processing based on a large model, characterized in that: The method comprises the following steps: Data collection and preprocessing, which involves designing and running a message receiving program to listen to and receive data from IoT devices using different communication protocols; Parse the received data and convert it into a unified format to ensure that the data from all devices follows the same specifications and structure before being input into the big model; Send the converted data to a message system based on the publish / subscribe model, which serves as a data distribution center and provides real-time data flow for large models; The large model performs analysis and prediction based on the real-time data received, and triggers an alarm mechanism when an abnormal situation is detected.

2. According to claim 1, a method for realizing Internet of Things data alarm processing based on a large model is characterized in that: It also includes inputting the alarm requirements into the big model in the form of prompt words, where the prompt words contain the analysis rules for IoT data and the generation format requirements of the alarm information, so as to guide the big model to generate accurate alarm responses according to the rules.

3. The method for implementing IoT data alarm processing based on a large model according to claim 2 is characterized in that: The generation of alarm information includes: when the data meets the preset alarm conditions, the big model generates alarm information in a specific format according to the rules; the alarm information includes but is not limited to the abnormality type, occurrence time, and key information of the abnormal data details.

4. The method for implementing IoT data alarm processing based on a large model according to claim 1 is characterized in that: It also includes sending the generated alarm information to a designated destination, where the destination is a user terminal, a monitoring system, a third-party system or a platform, so that the user or the related system can receive and process the alarm information in a timely manner.

5. The method for implementing IoT data alarm processing based on a large model according to claim 1 is characterized in that: It also includes further processing of the alarm information, including but not limited to: Visually display alarm information through charts, reports or real-time notifications; Prioritize alarm information according to its urgency and importance; Automatically trigger emergency response mechanisms, such as sending alert emails, SMS notifications, or executing preset emergency actions.

6. A system for implementing IoT data alarm processing based on a large model, applied to the method for implementing IoT data alarm processing based on a large model as described in any one of claims 1 to 5, characterized in that: The system comprises: Data collection and preprocessing module, used to monitor and receive data from IoT devices using different communication protocols, parse the data and convert it into a unified format; The message system module, as a data distribution center, receives unified format data from the data collection and preprocessing module and distributes it according to the needs of subscribers; The large model analysis module receives real-time data from the message system module, performs analysis and prediction based on preset algorithms and rules, and generates alarm information when abnormal conditions are detected; The alarm information output module is used to format the alarm information generated by the large model analysis module and display it to the user in an intuitive and easy-to-understand manner or push it to a designated destination.

7. The system for implementing IoT data alarm processing based on a large model according to claim 6 is characterized in that: The data collection and preprocessing module includes a powerful message receiving program that monitors and receives IoT data from devices with different protocols, and performs corresponding parsing and format conversion according to the communication protocol of the device.

8. The system for implementing IoT data alarm processing based on a large model according to claim 6 is characterized in that: The message system module adopts a publish / subscribe model to receive messages from multiple data sources and distribute data efficiently and reliably according to the needs of subscribers, ensuring that large models can obtain the latest IoT data in real time.

9. The system for implementing IoT data alarm processing based on a large model according to claim 6 is characterized in that: The large model analysis module performs data analysis in the form of prompts according to the alarm requirements input by the user, and generates alarm information in a specific format based on preset rules and algorithms. The rules include but are not limited to temperature thresholds and humidity thresholds. The format of the alarm information is JSON or other user-specified formats.

10. The system for implementing IoT data alarm processing based on a large model according to claim 6, characterized in that: The alarm information output module includes a built-in parsing mechanism for detailed interpretation of the generated alarm information and presenting it to users in the form of charts, reports, real-time notifications, etc. The module also supports pushing alarm information to third-party destinations, such as other systems, platforms or partners, to meet the needs of different business scenarios.

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