An internet of things management system

Abstract

The application discloses an Internet of Things management system, and relates to the technical field of Internet of Things, which comprises a region division module, a sensor matrix construction module, and a device management module.The region division module is configured to obtain a planting region division result based on the plant type and the soil block division of the agricultural management plot, and create an agricultural management digital twin model based on the planting region division result.The sensor matrix construction module is configured to arrange a sensor matrix based on the planting region division result and connect each sensor in the sensor matrix to the system.The device management module is configured to detect the running state and human control of each sensor in the sensor matrix, and output device management alarm information if the running state is abnormal or the human control is abnormal.The planting state detection module is configured to obtain a multi-modal planting data pool based on the sensor matrix, filter the multi-modal planting data pool, delete invalid planting data information, and retain valid planting data information.The application has the effect of improving the Internet of Things management efficiency in the agricultural field.

Description

Technical Field

[0001] This application relates to the field of Internet of Things technology, and particularly to an Internet of Things management system. Background Art

[0002] Currently, the Internet of Things (IoT) refers to connecting any object to the network through information sensing devices, radio frequency identification technology, global positioning system, etc., to achieve intelligent perception, identification, and management of things with things and things with people. The core of the Internet of Things lies in real-time collection and exchange of information through various sensors and communication networks, thus forming an interconnected network system. Applying the Internet of Things technology to the agricultural field brings great convenience to agricultural production development. Therefore, the Internet of Things management in the agricultural field is crucial.

[0003] The existing Internet of Things management system applied in the agricultural field mainly takes pictures of crops through cameras, and then analyzes according to the captured images and environmental temperature to determine whether the crops need to be irrigated. However, no scene linkage operation is performed. When it rains after irrigation, it is easy to cause the crops to be flooded and result in property losses. The existing Internet of Things management efficiency in the agricultural field is low and there is room for improvement. Summary of the Invention

[0004] In order to improve the Internet of Things management efficiency in the agricultural field, this application provides an Internet of Things management system.

[0005] An Internet of Things management system provided by this application adopts the following technical solutions:

[0006] An Internet of Things management system includes the following steps:

[0007] A regional division module, configured to obtain the agricultural management area, divide it based on the planted plant types and land blocks to obtain the planting area division result, and create an agricultural management digital twin model based on the planting area division result;

[0008] A sensor matrix construction module, configured to deploy a sensor matrix based on the planting area division result and connect each sensor in the sensor matrix to the system;

[0009] An equipment management module, configured to detect the running status and human operation conditions of each sensor in the sensor matrix in real time, and output equipment management warning information if the running status is abnormal or there are abnormal human operation conditions;

[0010] A planting status detection module, configured to detect the plant status in each area in real time based on each sensor in the sensor matrix to obtain a multi-modal planting data pool, statistically screen various planting data in the multi-modal planting data pool, delete invalid planting data information, and retain and store valid planting data information;

[0011] The irrigation parameter analysis module is configured to determine the irrigation amount for crops in each region based on effective planting data to obtain the regional crop irrigation amount, and to determine the irrigation method for crops in each region based on effective planting data to obtain the regional irrigation method.

[0012] The scene linkage module is configured to link crops within the agricultural management area with the surrounding environment, and obtain and generate irrigation decision results for the agricultural management area based on the rule engine.

[0013] The log management module is configured to build system logs based on the agricultural management digital twin model and the irrigation decision results of the agricultural management area, cache the system logs, and synchronously send the system logs to the background monitoring system.

[0014] Preferably, the agricultural management area is obtained, and the area within the agricultural management area is initially divided based on land block division to obtain a first area division result. The crop type information planted within the agricultural management area is obtained, and the agricultural management area is divided into regions based on the crop type information to obtain a second area division result. The first area division result and the second area division result are combined to form a preliminary area division result.

[0015] Obtain a crop resistance data table, which includes plant resistance of different crop types. Based on the crop type information, obtain the plant resistance of each crop type in the preliminary regional division results. Based on the plant resistance of each crop type, determine the optimal detection area area of ​​each region in the preliminary regional division results.

[0016] The preliminary area of ​​each region is obtained by statistically analyzing the area of ​​each region in the preliminary regional division results. The preliminary area of ​​each region is then compared with the area of ​​the optimal detection area of ​​the corresponding region. If the preliminary area of ​​a region is less than or equal to the area of ​​the optimal detection area of ​​that region, then there is no need to divide the region again. If the preliminary area of ​​a region is greater than the area of ​​the optimal detection area of ​​that region, then the region is divided evenly until the area of ​​each region after the division in the preliminary regional division results is less than or equal to the area of ​​the optimal detection area of ​​that region. After comparing all regions in the preliminary regional division results, the planting area division results are obtained.

[0017] A digital twin model for agricultural management is created based on the results of the planting area division, and crop type information is displayed on the digital twin model for agricultural management.

[0018] Preferably, the basic sensor type information is obtained by acquiring the sensor types deployed in each area;

[0019] Based on crop type information, determine the types of sensors that need to be added to each region in the planting area division results, and obtain the sensor information for each region.

[0020] The basic sensor type information and the additional sensor information for each region are combined to form the regional sensor type information;

[0021] Based on the plant resistance of crop types in each region, the sensor deployment density of each region is determined to obtain the regional sensor deployment density information;

[0022] Based on the regional sensor type information and regional sensor deployment density information, the sensors required for monitoring crops in agricultural management areas are constructed to build a sensor matrix.

[0023] The sensor matrix is ​​displayed on the digital twin model of agricultural management;

[0024] Connect each sensor in the sensor matrix to the system.

[0025] Preferably, the sensor matrix is ​​grouped based on the planting area division results to obtain device grouping information;

[0026] Based on the device grouping information, device diagnosis is performed on each sensor group in the sensor matrix to determine whether there are any abnormal operating conditions in each group of sensors. If there are abnormal operating conditions, device operation alarm information is output.

[0027] The location information of the malfunctioning equipment is obtained by locating the sensors that are malfunctioning.

[0028] The system synchronously sends the device operation alarm information and the location information of the abnormal device to the background monitoring system. After receiving the device alarm signal, it repairs the sensor with abnormal operation until it is running normally and then outputs a normal device operation signal.

[0029] Obtain preset user management permission information and obtain real-time information on the operators of each sensor;

[0030] The operator information of each sensor is compared with the user management permission information to determine whether the operator of each sensor has management permission. If there is an operator who does not have management permission, a management permission alarm message is output.

[0031] Information on operators who do not have management authority is marked as abnormal equipment operators, and the equipment operation content of abnormal equipment operators is recorded;

[0032] The management permission alarm information, the abnormal device operator, and the device operation content of the abnormal device operator are simultaneously sent to the background monitoring system.

[0033] The device operation alarm information and the management permission alarm information are combined to form the device management alarm information.

[0034] Preferably, multiple planting-related data are obtained by real-time detection of crop status in each region by each sensor in the sensor matrix, and the multiple planting-related data are summarized to form a multimodal planting data pool;

[0035] Based on the crop growth cycle in each region, invalid content in the planting-related data in the multimodal data pool is screened to obtain invalid planting data information, which is then deleted.

[0036] The effective content of each planting-related data in the multimodal planting data pool is filtered and retained to obtain effective planting data information, which is then stored.

[0037] Preferably, the effective planting data information includes soil moisture, crop root water absorption capacity, ambient temperature value, and ambient moisture evaporation rate;

[0038] Based on the crop type information, crop growth cycle and soil moisture of each region, the theoretical irrigation amount of crops in each region is determined.

[0039] Based on the ambient temperature and the rate of evaporation of ambient moisture, the theoretical irrigation amount for crops in each region is adjusted to obtain the regional crop irrigation amount.

[0040] Based on the water absorption capacity of crop roots, the irrigation methods for crops in each region are determined to obtain the regional irrigation methods.

[0041] Preferably, meteorological information of the agricultural management area is obtained, including rainfall information and rainfall time information;

[0042] Irrigation timing rules are formulated based on rainfall data and rainfall timing information;

[0043] Based on the rule setting engine of irrigation time rules, the regional irrigation amount and regional irrigation method are input into the rule engine to generate irrigation decision results for agricultural management areas;

[0044] The irrigation decision results for the agricultural management area are visualized in the agricultural management digital twin model.

[0045] Preferably, a system log for the agricultural management area is constructed based on the digital twin model of agricultural management and the irrigation decision results of the agricultural management area;

[0046] Cache system logs from different time versions;

[0047] The system logs are acquired and synchronously sent to the background monitoring system via the wireless communication module.

[0048] In summary, this application includes at least one of the following beneficial technical effects:

[0049] 1. By dividing agricultural management areas into regions and deploying sensors in each region to construct a sensor matrix, the sensor matrix is ​​connected to the system to monitor the operating status of each sensor and human operation in real time. If the operating status is abnormal or there is abnormal human operation, the device management alarm information is output. Based on the real-time detection of plant status in each region by each sensor in the sensor matrix, a multimodal planting data pool is obtained. Then, valid planting data information is filtered and retained. Based on the valid planting data information, the crop irrigation amount and regional irrigation method are determined. Scene linkage is carried out and irrigation decision results for agricultural management areas are generated based on the rule engine, which improves the irrigation efficiency of agricultural management areas and thus improves the IoT management efficiency in the agricultural field.

[0050] 2. By leveraging the sensor access systems of each sensor in the sensor matrix, it supports multi-protocol (MQTT, HTTP, CoAP, UDP, TCP, WebSocket) custom codec plugin access, cloud platform integration access, ModBus / TCP, OPC UA channel access, video access based on GB / T 28181 national standard protocol, and self-developed edge computing gateway access, meeting the requirements of various access scenarios and shortening the R&D cycle of IoT device access. Attached Figure Description

[0051] Figure 1 This is a schematic diagram illustrating the modules of the Internet of Things (IoT) management system, which is the main feature of this embodiment.

[0052] Attached reference numerals: 1. Area division module; 2. Sensor matrix construction module; 3. Equipment management module; 4. Planting status detection module; 5. Irrigation parameter analysis module; 6. Scene linkage module; 7. Log management module. Detailed Implementation

[0053] The present application will be further described in detail below with reference to the accompanying drawings.

[0054] This application discloses an Internet of Things (IoT) management system.

[0055] Reference Figure 1 An Internet of Things (IoT) management system, comprising:

[0056] The region division module is configured to acquire agricultural management areas and divide them based on the type of plant and land blocks to obtain the planting region division results, and create an agricultural management digital twin model based on the planting region division results;

[0057] The sensor matrix construction module is configured to deploy a sensor matrix based on the planting area division results and connect each sensor in the sensor matrix to the system.

[0058] The device management module is configured to monitor the operating status of each sensor in the sensor matrix and the status of human operation in real time. If the operating status is abnormal or there is abnormal human operation, the device management alarm information will be output.

[0059] The planting status detection module is configured to detect the plant status in each region in real time based on each sensor in the sensor matrix to obtain a multimodal planting data pool, and to perform statistical filtering on each planting data in the multimodal planting data pool, deleting invalid planting data information and retaining and storing valid planting data information.

[0060] The irrigation parameter analysis module is configured to determine the irrigation amount for crops in each region based on effective planting data to obtain the regional crop irrigation amount, and to determine the irrigation method for crops in each region based on effective planting data to obtain the regional irrigation method.

[0061] The scene linkage module is configured to link crops within the agricultural management area with the surrounding environment, and obtain and generate irrigation decision results for the agricultural management area based on the rule engine.

[0062] The log management module is configured to build system logs based on the agricultural management digital twin model and the irrigation decision results of agricultural management areas, cache the system logs, and synchronously send the system logs to the background monitoring system.

[0063] The specific execution method of the region division module is as follows:

[0064] The agricultural management area is obtained, and the area is initially divided into two regions based on land block division to obtain the first region division result. The crop type information planted within the agricultural management area is obtained, and the agricultural management area is further divided into two regions based on the crop type information to obtain the second region division result. The first region division result and the second region division result are combined to form the preliminary region division result.

[0065] Obtain the crop resistance data table, which includes plant resistance of different crop types. Based on the crop type information, obtain the plant resistance of each crop type in the preliminary regional division results. Based on the plant resistance of each crop type, determine the optimal detection area area of ​​each region in the preliminary regional division results.

[0066] Specifically, if the plant resistance of crops in a certain region is low in the preliminary regional division results, then the precision of the detection of crops in that region should be higher, and the area of ​​the optimal detection region should be smaller.

[0067] The preliminary area of ​​each region is obtained by statistically analyzing the area of ​​each region in the preliminary regional division results. The preliminary area of ​​each region is then compared with the area of ​​the optimal detection area of ​​the corresponding region. If the preliminary area of ​​a region is less than or equal to the area of ​​the optimal detection area of ​​that region, then there is no need to further divide the region. If the preliminary area of ​​a region is greater than the area of ​​the optimal detection area of ​​that region, then the region is divided evenly until the area of ​​each region after the preliminary regional division results is less than or equal to the area of ​​the optimal detection area of ​​that region. After comparing all regions in the preliminary regional division results, the planting area division results are obtained.

[0068] A digital twin model for agricultural management is created based on the results of planting area division, and crop type information is displayed on the digital twin model for agricultural management.

[0069] The sensor matrix construction module is executed as follows:

[0070] The basic sensor type information is obtained by acquiring the sensor types deployed in each area. Specifically, the basic sensor type information includes environmental sensors, soil sensors, cameras, and remote sensing sensors.

[0071] Based on crop type information, the types of sensors required for each planting area are determined from the resulting planting area divisions, thus providing the sensor information for each area. For example, if the crop type is greenhouse flowers, such as orchids, roses, and anthuriums, and the planting goal is to control flowering time, improve bud rate, and enhance flower quality (i.e., color and flower diameter), then differential temperature sensors, soil substrate water potential sensors, and photoperiod sensors are required. The differential temperature sensor calculates the diurnal temperature range. Many flowers require specific diurnal temperature ranges for flower bud differentiation (DIF technology). By precisely controlling the "accumulated temperature" effect of the temperature difference, flowering time can be accurately predicted and controlled to meet the timely supply needs of markets such as Spring Festival and Valentine's Day. The soil substrate water potential sensor directly measures the ease with which crop roots absorb water from the soil, physiologically guiding irrigation, especially suitable for precious flowers sensitive to waterlogging (such as Phalaenopsis orchids). The photoperiod sensor monitors the actual duration of light exposure to control supplemental lighting or shading curtains, meeting the flowering needs of short-day or long-day flowers.

[0072] The basic sensor type information and the information on additional sensors in each area are combined to form the regional sensor type information.

[0073] The sensor deployment density information for each region is obtained by determining the sensor deployment density based on the plant resistance of crop types in each region. The lower the plant resistance, the higher the sensor deployment density in that region.

[0074] Based on regional sensor type information and regional sensor deployment density information, a sensor matrix is ​​constructed to build up the sensors required for monitoring crops in agricultural management areas.

[0075] The sensor matrix is ​​displayed on a digital twin model for agricultural management.

[0076] Each sensor in the sensor matrix is ​​connected to the system. It should be noted that, in this embodiment, the connection of each sensor in the sensor matrix to the system supports multi-protocol (MQTT, HTTP, CoAP, UDP, TCP, WebSocket) custom codec plugin access, cloud platform integration access, ModBus / TCP, OPC UA channel access, video access based on GB / T 28181 national standard protocol, and self-developed edge computing gateway access, meeting the requirements of various access scenarios and shortening the development cycle of IoT device access.

[0077] The specific execution method of the equipment management module is as follows:

[0078] Based on the results of the planting area division, the sensor matrix is ​​grouped to obtain the device grouping information.

[0079] Based on the device grouping information, device diagnosis is performed on each sensor group in the sensor matrix to determine whether there are any abnormal operating conditions in each group of sensors. If there are abnormal operating conditions, device operation alarm information is output.

[0080] The location information of the abnormal equipment is obtained by locating the sensors that are malfunctioning.

[0081] The system simultaneously sends equipment operation alarm information and abnormal equipment location information to the background monitoring system. After receiving the equipment alarm signal, it repairs the sensors with abnormal operation until they are running normally, and then outputs a normal equipment operation signal.

[0082] Obtain preset user management permission information and obtain real-time information on the operators of each sensor.

[0083] The operator information for each sensor is compared with the user management permission information to determine whether the operator of each sensor has management permission. If there is an operator without management permission, a management permission alarm message is output.

[0084] Information on operators who do not have management authority is marked as abnormal equipment operators, and the equipment operation content of abnormal equipment operators is recorded.

[0085] The system will simultaneously send management permission alarm information, abnormal device operators, and the device operation content of abnormal device operators to the background monitoring system.

[0086] The equipment management alarm information is formed by combining the first device alarm information and the management authority alarm information.

[0087] The specific execution method of the planting status detection module is as follows:

[0088] Based on the real-time detection of crop status in each region by each sensor in the sensor matrix, multiple planting-related data are obtained, and the multiple planting-related data are summarized to form a multimodal planting data pool.

[0089] Based on the crop growth cycle in each region, invalid content in the planting-related data of the multimodal data pool is filtered to obtain invalid planting data information, which is then deleted. An example is given below: The purpose of the remote sensing sensor in this embodiment is to generate multispectral images of farmland in agricultural management areas. Based on these images, the leaf and stem growth of crops in the agricultural management areas is analyzed. When crops are in the germination stage, the seeds begin to absorb water, swell, and break through the seed coat to germinate. Therefore, the planting-related data obtained by the remote sensing sensor during the germination stage is considered invalid planting data information.

[0090] The effective planting data information is obtained by filtering and retaining the valid content from various planting-related data in the multimodal planting data pool, and then storing the effective planting data information. It should be noted that this application embodiment supports multiple data storage methods, including time-series databases (supporting data storage middleware such as Elasticsearch, ClickHouse, InfluxDB, TDengine, and Cassandra), allowing dynamic selection of which middleware to store the effective planting data information in. Data storage also includes relational databases (supporting PostgreSQL, MySQL, and the domestically developed DM database), and in-memory databases.

[0091] The specific execution method of the irrigation parameter analysis module is as follows:

[0092] Effective planting data includes soil moisture, crop root water absorption capacity, ambient temperature, and ambient water evaporation rate.

[0093] Based on crop type information, crop growth cycle, and soil moisture in each region, the theoretical irrigation amount for crops in each region is determined. For example, for the same crop type and the same soil moisture, the theoretical irrigation amount required when the crop is in the seedling stage is lower than that required when the crop is in the growth stage. If two regions have the same crop type and the same growth cycle, the higher the soil moisture, the lower the theoretical irrigation amount required.

[0094] Based on the ambient temperature and the rate of evaporation of ambient moisture, the theoretical irrigation amount for crops in each region is adjusted to obtain the regional crop irrigation amount.

[0095] The process involves obtaining the suitable environmental temperature range for crops in each region based on their growth cycle, comparing the environmental temperature values ​​of each region with the suitable environmental temperature range for crops in the corresponding region to obtain the regional environmental temperature comparison results, and determining the impact of environmental temperature on the irrigation amount of crops in each region to obtain the first irrigation adjustment value.

[0096] Based on the rate of environmental water evaporation, the impact of environmental water evaporation on the irrigation amount for crops in each region is assessed to obtain a second irrigation adjustment value. The higher the rate of environmental water evaporation, the larger the second irrigation adjustment value.

[0097] The regional crop irrigation amount is obtained by adjusting the theoretical irrigation amount of crops in each region based on the first irrigation adjustment value and the second irrigation adjustment value.

[0098] Based on the water absorption capacity of crop roots, the irrigation methods for each region are determined. Specifically, if the crop root system has a strong water absorption capacity, the regional irrigation method is surface irrigation or subsurface irrigation; if the crop root system has a weak water absorption capacity, the regional irrigation method is sprinkler irrigation or drip irrigation.

[0099] The specific execution method of the scene linkage module is as follows:

[0100] Obtain meteorological information for agricultural management areas, including rainfall amount and timing.

[0101] Irrigation timing rules are formulated based on rainfall data and rainfall timing information.

[0102] Based on the rule engine set by irrigation time rules, the regional irrigation amount and regional irrigation method are input into the rule engine to generate irrigation decision results for agricultural management areas.

[0103] Specifically, the total irrigation volume of the agricultural management area is obtained by summing the regional irrigation volumes of each area, and the rainfall absorption volume of the agricultural management area is determined based on rainfall information.

[0104] When the rainfall absorption is greater than or equal to the total irrigation volume, and the rainfall time information is within the preset safe time, there is no need to irrigate the agricultural management area, and an irrigation-not-needed instruction is output.

[0105] When the rainfall absorption is less than the comprehensive irrigation amount, and the rainfall time information is within the preset safe time, the difference between the rainfall absorption and the comprehensive irrigation amount is calculated to obtain the comprehensive characteristic irrigation amount of the agricultural management area. Based on the regional irrigation amount of each area of ​​the agricultural management area, the comprehensive characteristic irrigation amount of the agricultural management area is segmented to obtain the regional characteristic irrigation amount of each area of ​​the agricultural management area, and the first irrigation instruction is output.

[0106] If the rainfall time information is outside the preset safe time, a second irrigation instruction will be generated based on the regional irrigation volume.

[0107] The irrigation decision result for the agricultural management area is formed by the combination of no irrigation instruction, the first irrigation instruction, and the second irrigation instruction.

[0108] The irrigation decision results for agricultural management areas are visualized in the agricultural management digital twin model.

[0109] The specific execution method of the log management module is as follows:

[0110] Based on the digital twin model of agricultural management and the irrigation decision results of agricultural management areas, a system log of agricultural management areas is constructed.

[0111] System logs from different time periods are cached. Furthermore, based on user management permission information, personnel with administrative privileges can access system logs from different time periods at any time.

[0112] The system logs are acquired and synchronously sent to the background monitoring system via the wireless communication module.

[0113] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. An Internet of Things (IoT) management system, characterized in that, include: The region division module is configured to acquire agricultural management areas and divide them based on the type of plant and land blocks to obtain the planting region division results, and create an agricultural management digital twin model based on the planting region division results; The specific execution methods of the region division module include: The agricultural management area is obtained, and the area within the agricultural management area is initially divided based on land block division to obtain the first area division result. The crop type information planted within the agricultural management area is obtained, and the agricultural management area is divided into regions based on the crop type information to obtain the second area division result. The first area division result and the second area division result are combined to form the preliminary area division result. Obtain a crop resistance data table, which includes plant resistance of different crop types. Based on the crop type information, obtain the plant resistance of each crop type in the preliminary regional division results. Based on the plant resistance of each crop type, determine the optimal detection area area of ​​each region in the preliminary regional division results. The preliminary area of ​​each region is obtained by statistically analyzing the area of ​​each region in the preliminary regional division results. The preliminary area of ​​each region is then compared with the area of ​​the optimal detection area of ​​the corresponding region. If the preliminary area of ​​a region is less than or equal to the area of ​​the optimal detection area of ​​that region, then there is no need to divide the region again. If the preliminary area of ​​a region is greater than the area of ​​the optimal detection area of ​​that region, then the region is divided evenly until the area of ​​each region after the division in the preliminary regional division results is less than or equal to the area of ​​the optimal detection area of ​​that region. After comparing all regions in the preliminary regional division results, the planting area division results are obtained. A digital twin model for agricultural management is created based on the results of planting area division, and crop type information is displayed on the digital twin model for agricultural management. The sensor matrix construction module is configured to deploy a sensor matrix based on the planting area division results and connect each sensor in the sensor matrix to the system. The specific execution methods of the sensor matrix construction module include: Obtain the basic sensor type information by acquiring the sensor types deployed in each area; Based on crop type information, determine the types of sensors that need to be added to each region in the planting area division results, and obtain the sensor information for each region. The basic sensor type information and the additional sensor information for each region are combined to form the regional sensor type information; Based on the plant resistance of crop types in each region, the sensor deployment density of each region is determined to obtain the regional sensor deployment density information; Based on the regional sensor type information and regional sensor deployment density information, the sensors required for monitoring crops in agricultural management areas are constructed to build a sensor matrix. The sensor matrix is ​​displayed on the digital twin model of agricultural management; Connect each sensor in the sensor matrix to the system; The device management module is configured to monitor the operating status of each sensor in the sensor matrix and the status of human operation in real time. If the operating status is abnormal or there is abnormal human operation, the device management alarm information will be output. The specific execution methods of the equipment management module include: Based on the results of planting area division, the sensor matrix is ​​grouped to obtain device grouping information; Based on the device grouping information, device diagnosis is performed on each sensor group in the sensor matrix to determine whether there are any abnormal operating conditions in each group of sensors. If there are abnormal operating conditions, device operation alarm information is output. The location information of the malfunctioning equipment is obtained by locating the sensors that are malfunctioning. The device operation alarm information and the location information of the abnormal device are sent to the background monitoring system simultaneously. After receiving the device alarm signal, the sensor with abnormal operation is repaired until it is running normally, and then the device operation normal signal is output. Obtain preset user management permission information and obtain real-time information on the operators of each sensor; The operator information of each sensor is compared with the user management permission information to determine whether the operator of each sensor has management permission. If there is an operator who does not have management permission, a management permission alarm message is output. Information on operators who do not have management authority is marked as abnormal equipment operators, and the equipment operation content of abnormal equipment operators is recorded; The management permission alarm information, the abnormal device operator, and the device operation content of the abnormal device operator are simultaneously sent to the background monitoring system. The device operation alarm information and the management authority alarm information are combined to form device management alarm information; The planting status detection module is configured to detect the plant status in each region in real time based on each sensor in the sensor matrix to obtain a multimodal planting data pool, and to perform statistical filtering on each planting data in the multimodal planting data pool, deleting invalid planting data information and retaining and storing valid planting data information. The specific execution methods of the planting status detection module include: Based on the real-time detection of crop status in each region by each sensor in the sensor matrix, multiple planting-related data are obtained, and the multiple planting-related data are summarized to form a multimodal planting data pool; Based on the crop growth cycle in each region, invalid content in the planting-related data in the multimodal data pool is screened to obtain invalid planting data information, which is then deleted. The effective content of each planting-related data in the multimodal planting data pool is filtered and retained to obtain effective planting data information, and the effective planting data information is stored. The irrigation parameter analysis module is configured to determine the irrigation amount for crops in each region based on effective planting data to obtain the regional crop irrigation amount, and to determine the irrigation method for crops in each region based on effective planting data to obtain the regional irrigation method. The scene linkage module is configured to link crops within the agricultural management area with the surrounding environment, and obtain and generate irrigation decision results for the agricultural management area based on the rule engine. The log management module is configured to build system logs based on the agricultural management digital twin model and the irrigation decision results of the agricultural management area, cache the system logs, and synchronously send the system logs to the background monitoring system.

2. The Internet of Things management system according to claim 1, characterized in that, The specific execution methods of the irrigation parameter analysis module include: The effective planting data includes soil moisture, crop root water absorption capacity, ambient temperature, and ambient moisture evaporation rate. Based on the crop type information, crop growth cycle and soil moisture of each region, the theoretical irrigation amount of crops in each region is determined. Based on the ambient temperature and the rate of evaporation of ambient moisture, the theoretical irrigation amount for crops in each region is adjusted to obtain the regional crop irrigation amount. Based on the water absorption capacity of crop roots, the irrigation methods for crops in each region are determined to obtain the regional irrigation methods.

3. The Internet of Things management system according to claim 2, characterized in that, The specific execution methods of the scene linkage module include: Obtain meteorological information for agricultural management areas, including rainfall amount and rainfall time information; Irrigation timing rules are formulated based on rainfall data and rainfall timing information; Based on the rule setting engine of irrigation time rules, the regional irrigation amount and regional irrigation method are input into the rule engine to generate irrigation decision results for agricultural management areas; The irrigation decision results for the agricultural management area are visualized in the agricultural management digital twin model.

4. The Internet of Things management system according to claim 3, characterized in that, The specific execution methods of the log management module include: Based on the digital twin model of agricultural management and the irrigation decision results of agricultural management areas, a system log of agricultural management areas is constructed. Cache system logs from different time versions; The system logs are acquired and synchronously sent to the background monitoring system via the wireless communication module.

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