Butt joint method for multi-model grain temperature detection equipment

Abstract

The invention discloses a multi-model grain temperature detection equipment docking method, which comprises the following steps of: pre-integrating an NET interactive execution engine, an HTTP interactive execution engine, a DB interactive execution engine and a JSON configuration file analysis engine to obtain a docking platform; the method further comprises the steps of obtaining grain temperature detection data through a corresponding NET interaction execution engine, an HTTP interaction execution engine or a DB interaction execution engine according to an interaction mode of the grain temperature detection equipment; and according to the grain temperature detection data, a JSON configuration file analysis engine is combined with the point location of the corresponding grain temperature detection equipment to perform data analysis, so that the grain temperature detection equipment is docked through the docking platform. According to the invention, through integration of multiple engines, rapid butt joint of multiple types of grain temperature detection equipment is realized, multiple interaction modes are supported, the development process is simplified, and the maintenance cost is reduced; meanwhile, based on equipment point location information, environment errors are eliminated through configuration data analysis and dynamic correction, and data consistency and analysis accuracy are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of grain condition data acquisition, and particularly relates to a method for docking multi-model grain temperature detection devices. Background Art

[0002] With the development of Internet of Things technology, the demand for grain condition monitoring in the grain storage industry is increasing day by day. To ensure the safe storage of grain, it is necessary to monitor and manage grain condition parameters in real time. Grain temperature detection devices are generally installed in granaries in China, which can detect the atmospheric temperature, the atmospheric temperature in the warehouse, and the temperatures at each grain temperature point.

[0003] However, due to the large differences in interaction methods, data formats, etc. among grain temperature detection devices from different manufacturers, the docking and data integration between these devices have become a major challenge. Especially when using different types of grain condition devices in multiple projects, developers often need to write specific codes for each type of device, which not only increases the development cost but also prolongs the project cycle.

[0004] Currently, there are already some solutions in the market trying to solve the above problems. Some existing systems use JAVA frameworks to implement the data acquisition and processing of grain temperature detection devices. Although this method can provide relatively comprehensive function support, due to the large volume of the JAVA framework itself, the deployment process is complex, and the requirements for hardware resources are high, it limits its application in resource-constrained environments.

[0005] In addition, for specific brands of grain temperature detection devices, special software interfaces are usually developed for data exchange. Although this method can meet specific requirements, it lacks generality. When new devices or devices of different models are added, corresponding interfaces still need to be developed again, increasing the maintenance cost and technical difficulty.

[0006] Generally speaking, in the prior art, whenever a new grain condition device is connected, a large amount of manpower and material resources need to be invested in interface development and debugging, which greatly increases the overall cost of the project. Moreover, most rely on fixed software architectures and are difficult to adapt to changing business requirements and device types, especially when facing the diverse grain condition device market. Therefore, how to achieve the rapid and flexible docking of multi-model grain temperature detection devices has become an urgent problem to be solved. Summary of the Invention

[0007] The present invention provides a method for docking multi-model grain temperature detection devices to solve the problems that it is difficult to adapt to changing business requirements and device types caused by customized software interfaces, and that a large amount of manpower and material resources need to be invested in interface development and debugging whenever a new grain condition device is connected.

[0008] The technical solution adopted by the present invention is as follows:

[0009] A docking method for multi-model grain temperature detection devices, comprising: pre-integrating a NET interaction execution engine, an HTTP interaction execution engine, a DB interaction execution engine, and a JSON configuration file parsing engine to obtain a docking platform; the method further includes,

[0010] According to the interaction mode of the grain temperature detection device, grain temperature detection data is obtained through the corresponding NET interaction execution engine, HTTP interaction execution engine, or DB interaction execution engine;

[0011] According to the grain temperature detection data, through the JSON configuration file parsing engine, combined with the corresponding points of the grain temperature detection device, data parsing is performed to dock the grain temperature detection device through the docking platform.

[0012] The docking method for multi-model grain temperature detection devices in the present invention further includes the following additional technical features:

[0013] According to the interaction mode of the grain temperature detection device, grain temperature detection data is obtained through the corresponding NET interaction execution engine, HTTP interaction execution engine, or DB interaction execution engine, specifically:

[0014] The interaction modes of the grain temperature detection device include TCP / IP interaction, HTTP interaction, and database interaction;

[0015] When the grain temperature detection device supports TCP / IP interaction, the grain temperature detection data is obtained through the NET interaction execution engine;

[0016] When the grain temperature detection device supports HTTP interaction, the grain temperature detection data is obtained through the HTTP interaction execution engine;

[0017] When the grain temperature detection device supports database interaction, the grain temperature detection data is obtained through the DB interaction execution engine.

[0018] According to the grain temperature detection data, through the JSON configuration file parsing engine, combined with the corresponding points of the grain temperature detection device, data parsing is performed, specifically:

[0019] Determine the data temporary storage format of the grain temperature detection data through the acquisition method of the grain temperature detection data;

[0020] Determine the parsing method of the grain temperature detection data according to the corresponding points of the grain temperature detection device;

[0021] Parse the grain temperature detection data through the parsing method and perform data format conversion according to the data temporary storage format.

[0022] The JSON configuration file parsing engine is specifically as follows:

[0023] Pre-set the configuration templates for the acquisition method of the grain temperature detection data, the positions of the grain temperature detection devices, the data temporary storage format, and the data reporting format, so as to perform data parsing through the JSON configuration file parsing engine.

[0024] The JSON configuration file parsing engine further includes:

[0025] When a grain temperature detection device of a new granary is accessed, select the configuration template for data parsing according to the acquisition method of the grain temperature detection data corresponding to the grain temperature detection device of the new granary and the position of the grain temperature detection device; or,

[0026] When a grain temperature detection device of a new granary is accessed, add the acquisition method of the grain temperature detection data corresponding to the grain temperature detection device of the new granary, the position of the grain temperature detection device, the data temporary storage format, and the data reporting format to the configuration template, so as to perform data parsing through the JSON configuration file parsing engine.

[0027] Determine the parsing method of the grain temperature detection data according to the position of the corresponding grain temperature detection device, specifically:

[0028] The position of the grain temperature detection device includes the area where the granary is located and the coordinates of the grain temperature detection device in the granary;

[0029] Perform climate type correction on the grain temperature detection data according to the area where the granary is located, and perform position correction on the grain temperature detection data according to the coordinates of the grain temperature detection device in the granary.

[0030] The climate type correction and position correction are specifically:

[0031] Set a climate type correction coefficient according to the climate type corresponding to the area where the granary is located, where the climate type at least includes any one of the northern cold region, the southern humid and hot region, and the northwestern arid region;

[0032] Determine the relative position of the grain temperature detection device and the grain pile according to the coordinates of the grain temperature detection device in the granary, and set a relative position correction coefficient,

[0033] When the grain temperature detection device is located in the air to measure the air temperature, set the relative position correction coefficient as the first relative position correction coefficient,

[0034] When the grain temperature detection device is located in the grain pile to measure the grain temperature, set different correction coefficients for the relative position correction coefficient according to the distance between the grain temperature detection device and the outer surface of the grain pile.

[0035] The position correction further includes:

[0036] According to the relative distance between the grain temperature detection device and the ventilation opening, and in combination with the ventilation rate of the ventilation opening, different ventilation correction coefficients are set for correction.

[0037] The docking platform uses Koa2 as the lightweight backend Web framework, Vue3 as the frontend framework, SQLite3 as the embedded database, and PM2 as the process manager;

[0038] The docking platform is built based on the Node.js technology stack.

[0039] The present invention also provides a processing device, including:

[0040] A memory for storing a computer program;

[0041] A processor for implementing the steps of the multi-model grain temperature detection device docking method when executing the computer program.

[0042] Due to the adoption of the above technical solutions, the beneficial effects obtained by the present invention are:

[0043] 1. In the present invention, a NET interaction execution engine, an HTTP interaction execution engine, a DB interaction execution engine, and a JSON configuration file parsing engine are pre-integrated to obtain a docking platform. By pre-integrating multiple interaction engines (such as NET, HTTP, DB) and a JSON configuration file parsing engine, developers do not need to rewrite code for each device, but can quickly achieve device docking through a configuration file. This greatly simplifies the development process, reduces the development cost, and shortens the project cycle. It solves the problem that traditional solutions require writing specific code for each device, resulting in a long development cycle and high development cost.

[0044] Moreover, according to the interaction mode of the grain temperature detection device, grain temperature detection data is obtained through the corresponding NET interaction execution engine, HTTP interaction execution engine, or DB interaction execution engine. The present invention supports multiple interaction modes (such as TCP / IP, HTTP, database), and can flexibly handle different types of grain situation devices. Regardless of which interaction mode, data can be obtained through the corresponding interaction engine, greatly improving the flexibility and adaptability of the system, and reducing the maintenance cost and technical difficulty. It solves the problem that customized software interfaces lack generality, and when new devices or devices of different models are added, corresponding interfaces still need to be redeveloped, increasing the maintenance cost and technical difficulty.

[0045] 2. In the present invention, according to the grain temperature detection data, through the JSON configuration file parsing engine, combined with the positions of the corresponding grain temperature detection devices, data parsing is performed to dock the grain temperature detection devices through the docking platform. Due to different environmental conditions (such as ventilation, humidity, temperature gradient, etc.) at different positions of the grain temperature detection devices, there are significant differences in the temperature readings at different positions at the same moment. When directly using the original temperature data for analysis, the influence of these environmental factors is ignored, resulting in inaccurate temperature measurement evaluation. By correcting in combination with the positions of the grain temperature detection devices, the actual temperature situation can be more accurately reflected, and errors caused by environmental differences can be avoided. The corrected temperature data is more consistent, which is helpful for subsequent data analysis and decision support, such as grain storage status assessment, pest warning, etc.

[0046] In addition, different types of grain temperature detection devices may output different data formats, such as binary, ASCII, JSON, etc., and the field names and orders may also be different. These differences increase the complexity of data integration and parsing, which may lead to data processing errors or delays. Through the JSON configuration file parsing engine, automatic parsing and format conversion of data from different types of devices are realized, reducing manual intervention and improving data processing efficiency. At the same time, the consistency and readability of the data are improved, which is convenient for subsequent data analysis and sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0048] Figure 1 It is a schematic flow chart of the method for docking multi-model grain temperature detection devices under an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to more clearly illustrate the overall concept of the present invention, the following will be described in detail by way of examples in combination with the drawings of the specification.

[0050] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0051] As Figure 1 shown, a method for docking multi-model grain temperature detection devices includes:

[0052] S000: Pre-integrate the NET interaction execution engine, HTTP interaction execution engine, DB interaction execution engine, and JSON configuration file parsing engine to obtain a docking platform.

[0053] The main purpose of this step is to build a flexible and efficient docking platform that can support the access of various types of grain temperature detection devices. By pre-integrating multiple interaction engines and configuration file parsing engines, this platform can significantly reduce development costs, simplify the development process, and improve the flexibility and adaptability of the system.

[0054] The NET interaction execution engine is a module for handling network communication, usually used for data transmission tasks based on the TCP / IP protocol.

[0055] The HTTP interaction execution engine is a module for handling HTTP requests and responses, often used for data exchange with remote servers or APIs.

[0056] The DB interaction execution engine is a module for interacting with databases, supporting multiple database types such as SQL Server, MySQL, etc.

[0057] The JSON configuration file parsing engine is a tool for parsing JSON format configuration files, allowing data processing logic to be defined through configuration files, thereby reducing hard coding work.

[0058] The present invention supports multiple interaction methods (such as TCP / IP, HTTP, database), and can flexibly handle different types of grain condition devices. Regardless of which interaction method is used, data can be obtained through the corresponding interaction engine, greatly enhancing the flexibility and adaptability of the system. By pre-integrating multiple interaction engines and configuration file parsing engines, developers do not need to rewrite code for each device, but can quickly achieve device docking through configuration files. This greatly simplifies the development process, reduces development costs, and shortens the project cycle.

[0059] The JSON configuration file parsing engine combines the specific location information of the device for data parsing, ensuring data consistency and integrity. In addition, this method also improves the concurrent processing ability of the system, effectively addressing the performance bottleneck problem in large-scale application scenarios and enhancing data processing efficiency.

[0060] Taking a large grain storage enterprise as an example, this enterprise uses grain temperature detection devices from different manufacturers in multiple storage areas, including traditional grain temperature detection devices based on the TCP / IP protocol and new intelligent grain temperature detection devices based on the HTTP protocol. In the past, whenever new devices were connected, a large amount of manpower and material resources had to be invested in interface development and debugging, resulting in an extended project cycle and increased costs.

[0061] After introducing the docking platform of the present invention, enterprises can quickly complete the access of new devices through simple configuration files. For example, for a grain temperature detection device S1 newly installed on the first-layer shelf of Warehouse B, the R & D personnel first set its data acquisition method as an HTTP request in the JSON configuration file, specifying the corresponding URL and request parameters. The platform automatically calls the HTTP interaction execution engine to obtain data, and parses and corrects the data through the JSON configuration file parsing engine, and finally stores the standardized data in the database and reports it to the superior platform.

[0062] S100: According to the interaction method of the grain temperature detection device, obtain the grain temperature detection data through the corresponding NET interaction execution engine, HTTP interaction execution engine, or DB interaction execution engine.

[0063] The main purpose of this step is to obtain temperature data from different types of grain temperature detection devices. Since each device may adopt different communication protocols (such as TCP / IP, HTTP, or database interface), this step supports these different communication methods by integrating multiple interaction engines, so as to achieve flexible docking of grain temperature detection devices.

[0064] It should be noted that the grain temperature detection data refers to the temperature information collected by the grain temperature detection device and transmitted to the system. These data can be used for subsequent analysis and decision support. Because grain temperature detection devices are generally installed in domestic granaries to detect the atmospheric temperature, the in-warehouse atmospheric temperature, and the temperatures at each grain temperature point, the grain condition is judged using the grain temperature monitoring data. Of course, other grain condition data such as humidity can also be used, and only the JSON configuration file parsing engine in the present invention needs to be modified to adapt to the parsing of other grain condition data, and this is not restricted.

[0065] The grain temperature detection device is an important tool for monitoring the temperature changes in the grain storage environment and is crucial for ensuring the safe storage of grain. These devices help managers timely discover potential problems (such as local overheating, pests, etc.) by monitoring the temperatures at different positions inside the granary in real time, so as to take corresponding measures to ensure the grain quality. The grain temperature detection devices are mainly divided into the following types: wired sensors, wireless sensors, handheld thermometers, etc. Different grain temperature detection devices may use different data interaction methods, mainly including the following: TCP / IP protocol, HTTP protocol, database interface.

[0066] In this step, determine the device interaction method according to the grain temperature detection device. Determine the specific communication protocol used by the grain temperature detection device (such as TCP / IP, HTTP, or database interface).

[0067] Select the corresponding interaction engine (NET interaction execution engine, HTTP interaction execution engine, or DB interaction execution engine) for data collection according to the interaction method of the device.

[0068] Use the selected interaction engine to obtain the original temperature data from the device and transfer it to the system for further processing.

[0069] By integrating multiple interaction engines, the system of the present invention can flexibly respond to different types of grain condition devices. Regardless of the interaction method (TCP / IP, HTTP, database), data can be obtained through the corresponding interaction engine, greatly improving the flexibility and adaptability of the system. Therefore, developers do not need to rewrite specific code for each device, but can quickly implement device docking through a configuration file. This greatly simplifies the development process, reduces the development cost, and shortens the project cycle.

[0070] In addition, by using a dedicated interaction engine for data collection, the stability and reliability of data transmission can be ensured, and data loss or errors caused by differences in device types can be reduced. After data collection, the data can be immediately parsed and processed, reducing the time consumption of intermediate links and improving the overall data processing efficiency.

[0071] S200: According to the grain temperature detection data, through the JSON configuration file parsing engine, combined with the positions of the corresponding grain temperature detection devices, perform data parsing to dock the grain temperature detection devices through the docking platform.

[0072] The main purpose of this step is to parse and correct the data obtained from different types of grain temperature detection devices to ensure the consistency and accuracy of the data. By combining the specific position information of the grain temperature detection devices and using the JSON configuration file parsing engine for data parsing, the system can better process and standardize the data from multiple devices, thereby achieving efficient data docking.

[0073] Among them, the positions of the grain temperature detection devices help to accurately understand and correct the data collected by the grain temperature detection devices.

[0074] In this step, determine the specific positions of each grain temperature detection device in the granary (such as three-dimensional coordinates, the number of the warehouse where it is located, etc.), and record the relevant environmental characteristics (such as whether it is close to the ventilation opening, whether it is on the surface or inside the grain pile, etc.).

[0075] Load the JSON configuration file. Query the pre-set JSON configuration file according to the grain temperature detection device ID to obtain detailed information such as the data acquisition method, temporary storage format, parsing method call, data reporting format, etc. of the device.

[0076] Execute data parsing and calibration. Use the JSON configuration file parsing engine to convert the original data according to the preset parsing method to generate a standardized data format. Combine the specific location information and environmental characteristics of the device, and apply the corresponding calibration algorithm to adjust the temperature readings to ensure the authenticity and consistency of the data.

[0077] Finally, store and report the data. Store the parsed standardized data in the local database and report it to the superior platform for further analysis and display as needed.

[0078] By combining the locations of the grain temperature detection devices for calibration, the present invention can more accurately reflect the actual temperature situation and avoid errors caused by environmental differences. For example, the grain temperature detection devices located on the surface of the grain pile may be greatly affected by the external air temperature and need to be adjusted using specific correction factors.

[0079] In addition, by using the JSON configuration file parsing engine, the automatic parsing and format conversion of data from different types of devices are realized, reducing manual intervention and improving data processing efficiency. The data with a unified format conversion has higher readability and consistency, facilitating subsequent data analysis and sharing.

[0080] Through the configuration file-driven method, the need to write specific codes for each device is reduced, simplifying the development process and lowering the development cost. R & D personnel only need to configure the corresponding template files, and on-site implementers can then perform device docking according to the templates to quickly realize the data docking and networking of various existing grain condition devices. Moreover, with a variety of data formats and parsing methods, the system can flexibly handle various types of grain temperature detection devices, enhancing the adaptability and scalability of the system.

[0081] As a preferred implementation manner of the present invention, according to the interaction method of the grain temperature detection device, the grain temperature detection data is obtained through the corresponding NET interaction execution engine, HTTP interaction execution engine, or DB interaction execution engine, specifically as follows:

[0082] The interaction methods of the grain temperature detection device include TCP / IP interaction, HTTP interaction, and database interaction;

[0083] When the grain temperature detection device supports TCP / IP interaction, the grain temperature detection data is obtained through the NET interaction execution engine;

[0084] When the grain temperature detection device supports HTTP interaction, the grain temperature detection data is obtained through the HTTP interaction execution engine;

[0085] When the grain temperature detection device supports database interaction, the grain temperature detection data is obtained through the DB interaction execution engine.

[0086] The core objective of this embodiment is to efficiently and flexibly obtain temperature data from different types of grain temperature detection devices. Since grain temperature detection devices may adopt different communication protocols such as TCP / IP interaction, HTTP interaction, or database interaction, this embodiment realizes compatibility support for multiple models of devices by integrating dedicated interaction engines (NET, HTTP, DB), thus solving the problems of complex development and high docking costs caused by protocol differences in traditional solutions.

[0087] This embodiment determines the interaction method (TCP / IP, HTTP, or database) supported by the grain temperature detection device through device configuration files or dynamic detection.

[0088] It should be noted that in this embodiment, a database is pre-configured to record information such as the model, communication protocol, and interface address of each device for automatic matching. If the device information is unknown, the system can actively try different protocols (such as sending an HTTP request or a TCP handshake packet) to identify the supported interaction method.

[0089] Secondly, select the corresponding interaction engine. Select the engine according to the interaction method. When the grain temperature detection device supports TCP / IP interaction, obtain the grain temperature detection data through the NET interaction execution engine for processing binary data streams.

[0090] When the grain temperature detection device supports HTTP interaction, obtain the grain temperature detection data through the HTTP interaction execution engine for processing RESTful APIs or web services.

[0091] When the grain temperature detection device supports database interaction, obtain the grain temperature detection data through the DB interaction execution engine for directly querying database tables or stored procedures.

[0092] It should be noted that each engine is independently encapsulated and supports hot plugging to avoid the impact of protocol changes on other parts of the system. If the device supports multiple protocols (such as supporting both TCP / IP and HTTP at the same time), the user-configured main protocol is preferred.

[0093] Finally, data collection and protocol adaptation. Execute data collection through the selected engine.

[0094] Establish a TCP connection through the NET engine, send predefined instructions (such as binary commands for reading temperature data), and receive and parse the binary response.

[0095] Send HTTP GET / POST requests to the API endpoints of the device through the HTTP engine to obtain data in JSON / XML format.

[0096] For the DB engine, extract data from the database through SQL statements.

[0097] Specifically, a provincial grain depot manages multiple granaries and has a variety of equipment types. Equipment A is an XKCON-MT-W-01 wireless probe rod (supporting the TCP / IP protocol and transmitting data through the LORA gateway). Equipment B is a third-party intelligent temperature and humidity grain temperature detection device (supporting the HTTP API). Equipment C is an old system database (storing historical temperature records).

[0098] Configure device information. Equipment A is configured with the TCP / IP protocol. Equipment B is configured with the HTTP protocol. Equipment C is configured with the database protocol.

[0099] Data acquisition process. Equipment A (TCP / IP) sends an instruction request for temperature data through the NET engine. Receive the response, parse it, and verify the CRC code.

[0100] Equipment B (HTTP) sends a request through the HTTP engine. Receive the JSON response and map it to an internal system object.

[0101] Equipment C (database) extracts historical data through the DB engine.

[0102] This embodiment realizes the compatibility support for multi-protocol grain temperature detection devices by integrating the NET interaction execution engine, the HTTP interaction execution engine, and the DB interaction execution engine, significantly reducing the development cost and deployment complexity.

[0103] For example, a grain depot simultaneously uses an XKCON-MT-W-01 wireless probe rod (TCP / IP protocol based on LORA) and a third-party temperature and humidity grain temperature detection device (supporting the HTTP API). The system does not need to develop dedicated interfaces for each type of device. By simply configuring the NET engine to connect to the XKCON device and the HTTP engine to connect to the third-party device, it can achieve "managing multiple brands of devices with one system".

[0104] In addition, the system supports dynamic expansion. For example, when adding an intelligent ventilator that supports the HTTP protocol, only parameters such as the API address and key need to be configured, and the docking and synchronization of temperature data can be completed within 2 hours without modifying the code. For the MySQL database (storing historical grain temperature data) already deployed in old granaries, the system directly reads the data through the DB interaction engine, avoiding hardware transformation or repeated wiring, and further simplifying the deployment process. This modular design and protocol adaptation ability enable the system to flexibly respond to the diverse requirements of device types, communication protocols, and data sources, significantly improving the digital management efficiency of the grain storage industry.

[0105] As a preferred embodiment of the present invention, according to the grain temperature detection data, through the JSON configuration file parsing engine, combined with the positions of the corresponding grain temperature detection devices, data parsing is performed, specifically as follows:

[0106] Determine the data temporary storage format of the grain temperature detection data through the acquisition method of the grain temperature detection data;

[0107] Determine the parsing method of the grain temperature detection data according to the positions of the corresponding grain temperature detection devices;

[0108] Parse the grain temperature detection data through the parsing method and perform data format conversion according to the data temporary storage format.

[0109] The core objective of this embodiment is to uniformly parse multi-source and multi-format grain temperature detection data into standardized data, and perform calibration according to the specific position information of the devices to ensure the accuracy and consistency of the data. Through the JSON configuration file parsing engine, the system can dynamically adapt to the data formats and parsing logics of different devices, and solve the problems of complex parsing and difficult maintenance caused by inconsistent data formats in traditional solutions.

[0110] First, determine the data temporary storage format. Determine the temporary storage format according to the data acquisition method (such as TCP / IP, HTTP, database interaction).

[0111] Pre-define the data temporary storage formats corresponding to different protocols (such as TCP / IP data is temporarily stored as a binary stream, HTTP data is temporarily stored as JSON / XML, and database data is temporarily stored as a relational table structure). Specify the temporary storage format of each device through the JSON configuration file.

[0112] Secondly, select and calibrate the parsing method. Determine the parsing method according to the position information of the device (such as three-dimensional coordinates, environmental characteristics). Define the mapping relationship between the position and the parsing logic in the JSON configuration file. Apply the preset calibration formula in combination with the position environmental characteristics (such as whether it is close to the ventilation opening, the depth of the grain pile).

[0113] Finally, data parsing and format conversion. Extract key fields (such as temperature value, timestamp) through the parsing method, and perform data format conversion according to the temporary storage format. Map the fields returned by the device to the internal fields of the system. Uniformly convert the parsed data into a standardized format (such as a JSON object). Mark abnormal data (such as temperature values outside the reasonable range) as invalid and record the log.

[0114] Specifically, a provincial-level grain depot manages multiple granaries and has a variety of equipment types. Equipment A: XKCON-MT-W-01 wireless probe (returns binary data and is installed on the surface of the grain pile). Equipment B: Third-party HTTP grain temperature detection equipment (returns JSON data and is installed in the middle of the grain pile). Equipment C: Old system database (stores historical temperature data).

[0115] Configure parsing rules. For Equipment A, define in the JSON configuration and specify "JSON". For Equipment B, configure the extraction fields and perform mapping. For Equipment C, configure and define the SQL query statement.

[0116] Data parsing and calibration. For Equipment A, receive binary data (original temperature 26.5°C). According to the rules, combined with the ambient temperature of 22°C, calculate the actual temperature to be 25.3°C. Convert it to standardized JSON. For Equipment B, parse the HTTP response and map the fields. No calibration is required (internal grain temperature detection equipment), and directly output the standardized data. For Equipment C, query historical data through the DB engine and uniformly convert it to the JSON format.

[0117] This embodiment realizes the standardized processing and intelligent calibration of multi-source grain temperature detection data through the JSON configuration file parsing engine, significantly improving the system compatibility and data accuracy: First, for the original data of different devices, according to the requirements of the data temporary storage format, uniformly convert it to the standardized JSON format through the parsing engine, supporting the unified call and analysis of multi-type data by the early warning system.

[0118] Second, based on the device location information (such as three-dimensional coordinates, environmental characteristics), dynamically apply the preset calibration rules to effectively eliminate environmental interference; in addition, define the parsing logic through the configuration file, and it is possible to adapt to new devices or adjust the rules without modifying the code, significantly simplifying the development and maintenance costs; finally, directly read the historical data through the DB interaction engine, parse it according to the same standardized rules and manage it uniformly with the real-time data, realizing data traceability and trend analysis, comprehensively improving the data management efficiency and reliability.

[0119] As an embodiment under this embodiment, the JSON configuration file parsing engine is specifically:[[]]

[0120] Pre-set the configuration templates for the acquisition method of the grain temperature detection data, the positions of the grain temperature detection devices, the data temporary storage format, and the data reporting format, so as to perform data parsing through the JSON configuration file parsing engine.

[0121] In this embodiment, the JSON configuration file parsing engine uniformly manages the data parsing logic of grain temperature detection devices through a preset configuration template, achieving flexible adaptation to multiple types of devices and multiple data formats. By standardizing parameters such as data acquisition methods, point location information, temporary storage formats, and reporting formats into a JSON configuration template, the parsing rules can be dynamically adjusted without code modification, significantly reducing development costs and enhancing system scalability.

[0122] First, the creation and management of the configuration template. Design a standardized JSON configuration template, including the following core fields:

[0123] Device identifier, which uniquely identifies the grain temperature detection device;

[0124] Data acquisition method, specifying the device interaction protocol (such as TCP / IP, HTTP, database);

[0125] Point location information, recording the three-dimensional coordinates of the grain temperature detection device and environmental characteristics (such as "grain pile surface", "near the ventilation opening");

[0126] Data temporary storage format, defining the format for data temporary storage (such as JSON, binary, relational table);

[0127] Data reporting format, specifying the final output format (such as standardized JSON, CSV);

[0128] Parsing rules, defining logic such as calibration algorithms and field mapping.

[0129] In this embodiment, preset templates (such as "TCP grain temperature detection device template", "HTTP grain temperature detection device template") are provided to support user-defined extensions.

[0130] Loading and parsing of the configuration file. When starting up or a device is connected, dynamically load the JSON configuration file corresponding to the device. Parse the configuration file and extract key parameters.

[0131] This embodiment supports dynamic loading of new configuration files during runtime without restarting the system. And perform legality checks on the configuration items (such as whether the protocol type is supported, whether the fields are complete).

[0132] Data parsing and format conversion. According to the configuration file, select a calibration algorithm in combination with the point location information. Temporarily store the original data (such as binary → JSON). Convert the temporarily stored data into the final format (such as standardized JSON reported to the cloud).

[0133] In this embodiment, parsing, calibration, and format conversion are split into independent modules, supporting dynamic combination according to the configuration. If a certain link fails, retain the original data and record the log to avoid data loss.

[0134] The standardized and dynamic adaptation mechanism implemented by the JSON configuration file parsing engine in this embodiment significantly improves the compatibility and scalability of the grain temperature detection system: First, in terms of standardization and compatibility improvement, the data acquisition method, parsing rules, and format are uniformly defined through a preset configuration template, supporting seamless access of multi-protocol devices; Second, dynamic adaptation and rapid expansion are achieved through configuration-driven, and the parsing logic can be extended without modifying the code; Third, the intelligent correction based on points dynamically eliminates environmental interference by associating the point information of the grain temperature detection device with the correction rules in the configuration file; Finally, the core of simplifying development and maintenance costs lies in replacing hard coding with configuration files, without migrating the database or writing additional code. This mechanism significantly reduces the system development complexity and ensures the flexibility and reliability of data processing.

[0135] Specifically, the JSON configuration file parsing engine further includes:

[0136] When a grain temperature detection device of a new granary is accessed, select the configuration template for data parsing according to the data acquisition method and the point position of the grain temperature detection device corresponding to the new granary; or,

[0137] When a grain temperature detection device of a new granary is accessed, add the data acquisition method, the point position of the grain temperature detection device, the data temporary storage format, and the data reporting format corresponding to the new granary to the configuration template for data parsing through the JSON configuration file parsing engine.

[0138] The core objective of this embodiment is to quickly realize the access of the grain temperature detection device of the new granary through the flexible selection or extension of the configuration template, ensuring that the system can adapt to devices with different protocols, points, and data formats without code modification, significantly reducing the deployment cost and improving the scalability. It is specifically divided into two modes:

[0139] Select an existing configuration template, which is applicable to the situation where the new device is of the same type or protocol as the existing device;

[0140] Expand the configuration template, which is applicable to the situation where the new device adopts a new protocol or has special point requirements.

[0141] Mode 1: Select an existing configuration template.

[0142] The device for the new granary is of the same type or protocol as the existing device (such as the XKCON-MT-W-01 probe rod of another granary).

[0143] Record the key parameters of the new device, including the acquisition method (such as TCP / IP, HTTP), point information (such as "Warehouse B - Second Floor - Inside"), and data temporary storage / reporting format (such as JSON). The parameters can be determined through the device manual or on-site testing.

[0144] Match the existing template and search for matching items in the configuration template library. For example, if the device is the XKCON-MT-W-01 probe rod, select the preset template.

[0145] It should be noted that when matching the existing template, the template is automatically recommended according to fields such as protocol and point location, or the administrator selects the template through the interface.

[0146] Configuration adaptation and deployment. Replace the default values in the template with the point location information of the new device (such as Warehouse B - Second Floor - Inside). Load the configuration through the JSON configuration file parsing engine and start data collection and parsing.

[0147] When performing configuration adaptation and deployment, it is necessary to ensure that the new point location is compatible with the template rules (such as whether the calibration algorithm is applicable).

[0148] Mode 2: Expand the configuration template.

[0149] It is used for new devices that adopt a brand-new protocol (such as Modbus RTU) or have special point location requirements (such as needing to combine nitrogen concentration calibration).

[0150] Parameter definition and template creation. Create a new template according to the technical parameters of the new device.

[0151] During the parameter definition and template creation process, split the calibration rules into independent functions to support combined use. At the same time, expand based on the existing template (such as inheriting the general parameters of the existing template).

[0152] Verification and deployment. Test the parsing logic of the new template through simulated data. Upload the template to the system and trigger the JSON configuration engine to load and verify the parameter legality.

[0153] When performing verification and deployment, test the new template in an isolated environment to avoid affecting the existing devices. If the configuration fails, it can be quickly rolled back to the previous version.

[0154] In this embodiment, first, a standardized template library is provided, which can quickly reuse the configurations of similar devices, avoid repeated development, and shorten the new device access time from several days to the minute level; second, through modular rules and template inheritance, it supports adaptation to complex scenarios. For example, when a new Modbus grain temperature detection device needs to monitor both temperature and nitrogen concentration at the same time, the rules can be combined to achieve multi-parameter dynamic calibration. This mechanism comprehensively reduces the development and maintenance costs, and at the same time ensures the flexibility, accuracy, and system stability of data processing.

[0155] As another embodiment under this implementation manner, according to the point location of the corresponding grain temperature detection device, determine the parsing method of the grain temperature detection data, specifically:

[0156] The points of the grain temperature detection device include the area where the granary is located and the coordinates of the grain temperature detection device within the granary;

[0157] Perform climate type correction on the grain temperature detection data according to the area where the granary is located, and perform position correction on the grain temperature detection data according to the coordinates of the grain temperature detection device within the granary.

[0158] The core objective of this embodiment is to dynamically correct the original data through the geographical information (area) and spatial coordinates of the points of the grain temperature detection device, eliminate the interference of the climate environment and the internal position of the granary on temperature measurement, and thus improve the accuracy and reliability of the data. It is specifically divided into two parts:

[0159] Climate type correction, compensating the temperature data according to the climate characteristics (such as temperate zone, tropical zone, plateau, etc.) of the area where the granary is located, and eliminating the influence of regional environmental differences;

[0160] Position correction, selecting a correction rule according to the three-dimensional coordinates of the grain temperature detection device within the granary (such as the surface of the grain pile, inside, near the ventilation opening), and eliminating the interference of the local environment on temperature measurement.

[0161] First, climate type correction. Load a preset climate correction coefficient library according to the area where the granary belongs (such as "northern cold region", "southern humid and hot region").

[0162] Apply the climate correction formula, specifically:

[0163] T 气候修正 = T 原始 × climate coefficient + climate offset value

[0164] Position correction. Select a preset rule according to the device coordinates (such as x = 10, y = 20, z = 5) or position type (such as "near the ventilation opening", "surface of the grain pile").

[0165] Apply the position correction formula, specifically:

[0166] T 位置修正 = T 气候修正 × position coefficient + position offset value

[0167] In this embodiment, the error is reduced to within ±0.5°C through double correction of climate and position. Different grain temperature detection devices at different positions within the same granary can apply different rules (such as the surface and inside).

[0168] The climate type correction and position correction are specifically as follows:

[0169] Set the climate type correction coefficient according to the climate type corresponding to the area where the granary is located, where the climate type at least includes any one of the northern cold region, southern humid and hot region, and northwestern arid region;

[0170] Determine the relative position between the grain temperature detection device and the grain pile according to the coordinates of the grain temperature detection device in the grain bin, and set a relative position correction coefficient.

[0171] When the grain temperature detection device is located in the air to measure the air temperature, set the relative position correction coefficient to the first relative position correction coefficient.

[0172] When the grain temperature detection device is located in the grain pile to measure the grain temperature, set different correction coefficients for the relative position correction coefficient according to the distance between the grain temperature detection device and the outer surface of the grain pile.

[0173] In this embodiment, the parameter settings of climate and position correction are refined, and precise correction is achieved through the climate type correction coefficient and the relative position correction coefficient.

[0174] Among them, for the setting of the climate type correction coefficient, define the coefficient according to the climate type (northern cold region, southern humid and hot region, northwest arid region).

[0175] For example, for the northern cold region, the climate coefficient is 1.02, and the offset value (°C) is +2°C.

[0176] For the southern humid and hot region, the climate coefficient is 0.98, and the offset value (°C) is -1°C.

[0177] For the northwest arid region, the climate coefficient is 1.05, and the offset value (°C) is +0.5°C.

[0178] In addition, dynamically update the coefficient through the configuration file (such as adjusting the coefficient of the northern cold region in summer to 1.00).

[0179] Among them, for the setting of the relative position correction coefficient, distinguish the measurement scenarios. For air temperature measurement, set the first relative position correction coefficient (such as 0.95).

[0180] For grain temperature measurement, set the coefficient in segments according to the distance (d) from the grain pile surface. Specifically:

[0181]

[0182] In this embodiment, distinguish air and grain temperature measurement to avoid misjudgment (such as the air temperature at the ventilation opening is high but the actual temperature of the grain pile is normal). The correction coefficient of the grain temperature detection device inside the grain pile changes dynamically with the depth, reflecting the heat conduction characteristics of the grain pile.

[0183] The position correction further includes:

[0184] Set different ventilation correction coefficients for correction according to the relative distance between the grain temperature detection device and the ventilation opening, in combination with the ventilation rate of the ventilation opening.

[0185] In this embodiment, the correction coefficient is dynamically adjusted by combining the position of the ventilation opening and the ventilation rate to eliminate the interference of ventilation on temperature measurement.

[0186] For the calculation of the ventilation correction coefficient, coefficients are defined based on the relative distance (r) between the temperature measurement device and the ventilation opening and the ventilation rate (v):

[0187]

[0188] Specifically, the basic coefficient is 0.9, the threshold distance is 5 meters, and the maximum rate is 10 m / s.

[0189] When the temperature measurement device is 3 meters away from the ventilation opening and the ventilation rate is 6 m / s, the ventilation coefficient = 0.9×(1 - 0.6)×(1 + 0.6) = 0.9×0.4×1.6 = 0.576.

[0190] In this embodiment, the ventilation correction coefficient is superimposed with the climate and position coefficients:

[0191] T 最终 = T 原始 ×(climate coefficient × position coefficient × ventilation coefficient) + comprehensive offset value

[0192] In a specific embodiment, a grain depot manages three granaries, and the equipment points and the environment are complex: Granary A is located in the cold northern region, the grain temperature detection device S1 is located on the surface of the grain pile (0 meters from the surface), and S2 is 3 meters away from the ventilation opening.

[0193] Granary B is located in the humid and hot southern region, and the grain temperature detection device S3 is buried 1.5 meters deep inside the grain pile.

[0194] Granary C is located in the arid northwestern region, and the grain temperature detection device S4 is located at the top of the grain pile (near the ventilation opening).

[0195] First, perform climate correction:

[0196] For Granary A in winter in the cold northern region, the climate coefficient is 1.02, and the offset value is +2°C.

[0197] For Granary B in summer in the humid and hot southern region, the climate coefficient is 0.98, and the offset value is -1°C.

[0198] For Granary C in the arid northwestern region, the climate coefficient is 1.05, and the offset value is +0.5°C.

[0199] Secondly, perform position and ventilation correction:

[0200] For S1 (on the surface of the grain pile), the position coefficient is 1.00 (0 meters from the surface), and there is no ventilation interference.

[0201] Final temperature: Original 25°C × 1.02 × 1.00 + 2 = 27.5°C.

[0202] S002 (near the vent), ventilation coefficient 0.576 (distance 3 m, ventilation rate 6 m / s).

[0203] Final temperature: 28°C × (1.02 × 0.95 × 0.576) + 2 = 16.0°C + 2 = 18.0°C.

[0204] S003 (1.5 m inside the grain pile), position coefficient 0.95 (distance 1.5 m from the surface), no ventilation interference.

[0205] Final temperature: 26°C × 0.98 × 0.95 - 1 = 23.5°C.

[0206] S004 (top of the arid northwest region), ventilation coefficient 0.9 (distance 1 m from the vent, low-speed ventilation).

[0207] Final temperature: 30°C × (1.05 × 1.00 × 0.9) + 0.5 = 28.35°C + 0.5 = 28.85°C.

[0208] The error between the corrected data of S1 in the cold northern region and the manual measurement is < 0.3°C. The corrected temperature of S2 is 18.0°C, which is consistent with the actual temperature of the grain pile, avoiding false alarms. The correction coefficient at a depth of 1.5 m of S3 is 0.95, reflecting the heat conduction characteristics inside the grain pile.

[0209] Generally speaking, the present invention significantly improves the accuracy and reliability of grain temperature data through a multi-dimensional dynamic correction mechanism. First, based on the climate characteristics of the region where the granary is located, the preset climate type correction coefficient is used to dynamically compensate for regional environmental deviations, eliminating the influence of climate factors such as temperature difference and humidity on temperature measurement; second, according to the three-dimensional coordinates or relative positions of the grain temperature detection equipment in the granary, combined with the measurement scenario and the distance from the grain pile surface, the relative position correction coefficient is dynamically adjusted to accurately adapt to the complex structure inside the grain pile; third, for the grain temperature detection equipment near the vent, by real-time monitoring the relative distance between the equipment and the vent and the ventilation rate, the ventilation correction coefficient is dynamically superimposed to eliminate the interference of air flow on temperature measurement; finally, all correction rules are uniformly defined through a JSON configuration file, supporting rapid expansion (such as adding climate parameters in the arid northwest region) and flexible adjustment, and can adapt to new equipment or scenarios without code modification, significantly reducing the maintenance cost. This mechanism takes into account multi-dimensional environmental factors such as climate, position, and ventilation, providing high-precision and highly adaptable data support for the intelligent monitoring of grain storage.

[0210] As a preferred embodiment of the present invention, the docking platform uses Koa2 as the lightweight backend Web framework, Vue3 as the frontend framework, SQLite3 as the embedded database, and PM2 as the process manager;

[0211] The docking platform is built based on the Node.js technology stack.

[0212] The core objective of this embodiment is to build a lightweight, efficient, and easy-to-maintain grain temperature detection docking platform to achieve multi-device data collection, real-time monitoring, and intelligent analysis. By selecting technical components such as Koa2, Vue3, SQLite3, and PM2, the platform achieves breakthroughs in the following aspects:

[0213] Backend efficiency: The asynchronous mechanism and lightweight design of Koa2 support high-concurrency access and processing of grain temperature data;

[0214] Frontend interactivity: The reactive framework and component-based development of Vue3 enhance the user operation experience;

[0215] Data reliability: The ACID characteristics of SQLite3 ensure data integrity in the embedded scenario;

[0216] System stability: The process management of PM2 ensures high service availability and reduces operation and maintenance costs.

[0217] Koa2 is a lightweight Web framework based on Node.js. It uses async / await to simplify the asynchronous process, and the onion model middleware mechanism supports flexible expansion. For example, device data parsing and error handling are implemented through middleware.

[0218] Vue3 is a frontend framework. It implements a dynamic UI based on reactive data binding and the Composition API and supports component-based development. For example, real-time temperature charts are dynamically rendered for the sensor list through v-for.

[0219] SQLite3 is an embedded relational database. It does not require an independent server, supports ACID transactions, and is suitable for lightweight scenarios. For example, when storing grain temperature data, it ensures the integrity of concurrent writes from multiple devices.

[0220] PM2 is a process manager that provides load balancing, automatic restart, and log management to enhance service stability. For example, the Koa2 service is extended to multiple CPU cores through the cluster mode.

[0221] Among them, the selection and construction of the backend framework (Koa2) mainly aim to build a high-performance and scalable backend service to support the parsing and real-time processing of multi-protocol device data.

[0222] Frame initialization and middleware configuration, create a project structure based on Koa2, and introduce core middleware.

[0223] For example, the Modbus device data parsing middleware of a certain granary realizes the conversion from binary to JSON, and the response time is shortened to 50ms.

[0224] API route design, realize route grouping, divide routes by functional modules, and improve code maintainability. Automatically parse device data in JSON format.

[0225] Secondly, select and develop the front-end framework (Vue3), the main purpose is to build a highly interactive visual interface, supporting real-time data monitoring and device management.

[0226] Project initialization and component-based development, use Vue CLI to create a Vue3 project and divide components. Data visualization and interaction, integrate ECharts or Vue3-Chartjs to implement temperature trend charts and device status dashboards.

[0227] For example, the Vue3 interface of a certain grain depot displays grain temperature data in real time through WebSocket, and users can drag the timeline to view historical trends.

[0228] Secondly, data storage and transaction management (SQLite3), the main purpose is to realize the lightweight deployment of an embedded database, ensuring the persistence and consistency of grain temperature data.

[0229] Database design and connection, use the sqlite3 module to create a database and define the table structure. Data persistence and query, write the parsed data into SQLite3 through Koa2 middleware.

[0230] For example, 2000 grain temperature detection data of a certain granary are written into the database per second, and the embedded architecture of SQLite3 ensures low latency (<10ms).

[0231] Secondly, system deployment and process management (PM2), the main purpose is to ensure the high availability and stability of the platform in the production environment.

[0232] Process monitoring and load balancing, use PM2 to start and manage the Koa2 service. Logging and alerting, configure PM2 log rolling and alerting.

[0233] This embodiment constructs a highly available and easily extensible grain temperature detection docking platform through the Koa2+Vue3+SQLite3+PM2 technology stack. This technical solution has achieved innovative breakthroughs in the field of grain temperature monitoring in terms of lightweight, high concurrency, and low latency.

[0234] The present invention also provides a processing device, including:

[0235] A memory for storing a computer program;

[0236] A processor for implementing the steps of the multi-model grain temperature detection device docking method when executing the computer program.

[0237] Therefore, this processing device can achieve any effect of the multi-model grain temperature detection device docking method, which will not be elaborated here.

[0238] In the present invention, those not described can be implemented by adopting or referring to existing technologies.

[0239] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

[0240] The above are only embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, various changes and modifications can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A method for docking multiple types of grain temperature detection equipment, characterized in that: include: Pre-integrate NET interactive execution engine, HTTP interactive execution engine, DB interactive execution engine and JSON configuration file parsing engine to obtain a docking platform; The method further comprises, According to the interaction mode of the grain temperature detection device, the grain temperature detection data is obtained through the corresponding NET interaction execution engine, HTTP interaction execution engine or DB interaction execution engine; According to the grain temperature detection data, data analysis is performed through the JSON configuration file parsing engine in combination with the corresponding points of the grain temperature detection equipment to dock the grain temperature detection equipment through the docking platform.

2. The method for docking multiple types of grain temperature detection equipment according to claim 1, characterized in that: According to the interaction mode of the grain temperature detection device, the grain temperature detection data is obtained through the corresponding NET interaction execution engine, HTTP interaction execution engine or DB interaction execution engine, specifically: The interaction modes of the grain temperature detection device include TCP / IP interaction, HTTP interaction and database interaction; When the grain temperature detection device supports TCP / IP interaction, the grain temperature detection data is obtained through the NET interactive execution engine; When the grain temperature detection device supports HTTP interaction, the grain temperature detection data is obtained through the HTTP interaction execution engine; When the grain temperature detection device supports database interaction, the grain temperature detection data is obtained through the DB interaction execution engine.

3. The method for docking multiple types of grain temperature detection equipment according to claim 1, characterized in that: According to the grain temperature detection data, the JSON configuration file parsing engine is used to perform data parsing in combination with the corresponding points of the grain temperature detection equipment, specifically: Determine the data temporary storage format of the grain temperature detection data through the method of acquiring the grain temperature detection data; Determine the analysis method of the grain temperature detection data according to the corresponding point position of the grain temperature detection equipment; The grain temperature detection data is parsed by the parsing method, and the data format is converted according to the data temporary storage format.

4. The method for docking multiple types of grain temperature detection equipment according to claim 3, characterized in that: The JSON configuration file parsing engine is specifically: The configuration templates of the method for obtaining the grain temperature detection data, the locations of the grain temperature detection equipment, the data temporary storage format, and the data reporting format are pre-set to perform data parsing through the JSON configuration file parsing engine.

5. The method for docking multiple types of grain temperature detection equipment according to claim 4, characterized in that: The JSON configuration file parsing engine also includes: When the grain temperature detection device of the new granary is connected, the configuration template is selected for data analysis according to the acquisition method of the grain temperature detection data corresponding to the grain temperature detection device of the new granary and the location of the grain temperature detection device; or, When the grain temperature detection equipment of the new granary is connected, the method of obtaining the grain temperature detection data corresponding to the grain temperature detection equipment of the new granary, the location of the grain temperature detection equipment, the data temporary storage format, and the data reporting format are added to the configuration template to perform data parsing through the JSON configuration file parsing engine.

6. The method for docking multiple types of grain temperature detection equipment according to claim 3, characterized in that: According to the corresponding point of the grain temperature detection equipment, the analysis method of the grain temperature detection data is determined, specifically: The location of the grain temperature detection device includes the area where the granary is located, and the coordinates of the grain temperature detection device in the granary; The grain temperature detection data is corrected for the climate type according to the area where the granary is located, and the grain temperature detection data is corrected for the position according to the coordinates of the grain temperature detection equipment in the granary.

7. The method for docking multiple types of grain temperature detection equipment according to claim 6, characterized in that: The climate type correction and position correction are specifically: According to the climate type corresponding to the region where the granary is located, a climate type correction coefficient is set, wherein the climate type includes at least one of the northern cold region, the southern hot and humid region, and the northwest arid region; According to the coordinates of the grain temperature detection device in the grain bin, the relative position of the grain temperature detection device and the grain pile is determined, and a relative position correction coefficient is set. When the grain temperature detection device is located in the air to measure the air temperature, the relative position correction coefficient is set to the first relative position correction coefficient. When the grain temperature detection device is located in a grain pile for measuring grain temperature, different correction coefficients are set for the relative position correction coefficients according to the distance between the grain temperature detection device and the outer surface of the grain pile.

8. The method for docking multiple types of grain temperature detection equipment according to claim 7, characterized in that: The position correction further includes: According to the relative distance between the grain temperature detection equipment and the ventilation opening, and in combination with the ventilation rate of the ventilation opening, different ventilation correction coefficients are set for correction.

9. The method for docking multiple types of grain temperature detection equipment according to claim 1, characterized in that: The docking platform uses Koa2 as the backend lightweight Web framework, Vue3 as the frontend framework, SQLite3 as the embedded database, and PM2 as the process manager; The docking platform is built based on the Node.js technology stack.

10. A processing device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the method for docking multiple types of grain temperature detection equipment as described in any one of claims 1 to 9 when executing the computer program.

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