An internet of things collaborative control system and method for agricultural production

Abstract

The application provides an Internet of Things cooperative control system and method for agricultural production, and belongs to the technical field of Internet of Things device management, and specifically comprises the following steps: determining the sprinkling irrigation treatment data by using a sprinkling irrigation treatment strategy, combining the Internet of Things device with the change correlation of different sprinkling irrigation treatment processes, determining the identification strategy of the deviation influence risk device in the Internet of Things device, determining the sprinkling irrigation optimization target in the land based on the distribution data of the deviation influence risk device in different lands in the target area and the sprinkling irrigation treatment strategy of the target area, determining the sprinkling irrigation optimization strategy of the sprinkling irrigation optimization target according to the sprinkling irrigation optimization target data and the composition of the deviation influence risk device in different sprinkling irrigation optimization targets, and improving the timeliness and reliability of the sprinkling irrigation treatment.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) device management technology, and in particular relates to an IoT collaborative control system and method for agricultural production. Background Technology

[0002] Traditional sprinkler irrigation systems often employ fixed-zone or uniform irrigation patterns, which cannot adapt to the spatiotemporal heterogeneous changes in farmland soil moisture. This results in over-irrigation in some areas and under-irrigation in others. Furthermore, the fixed operating paths of sprinkler irrigation systems prevent them from dynamically responding to changes in soil moisture.

[0003] To address the aforementioned technical issues, the invention patent application CN202311812336.3, "An Internet of Things Big Data Agricultural Information Monitoring and Management System and its Implementation Method," describes a system for collecting on-site data from greenhouses. Environmental sensors can collect parameters such as temperature, humidity, light intensity, and soil moisture inside and outside the greenhouse in real time, and provide various audible and visual alarm information according to the needs of the crops being grown. This achieves real-time monitoring in the field of agricultural ecological environment monitoring, ensuring high efficiency and quality in farm production. However, the following technical problems remain: When carrying out sprinkler irrigation, the reliability of soil moisture monitoring is crucial. If there is any deviation in the monitoring data, it may lead to deviations in the efficiency and reliability of sprinkler irrigation. Therefore, how to determine the sprinkler irrigation management strategy based on the distribution of IoT monitoring devices and the consistency of monitoring data, so as to ensure the reliability and timeliness of sprinkler irrigation, has become an urgent technical problem to be solved.

[0004] Specifically, this application provides an Internet of Things (IoT) collaborative control system and method for agricultural production. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides an IoT collaborative control method for agricultural production, which includes: S1 uses the distribution data of IoT devices in the target area and the consistency of changes in monitoring data between different IoT devices to determine the sprinkler irrigation treatment strategy for the target area. The sprinkler irrigation treatment strategy is used to determine the sprinkler irrigation treatment data. In addition, the correlation between IoT devices and changes in different sprinkler irrigation processes is combined to determine the identification strategy for the deviation impact risk devices in the IoT devices. S2 determines the sprinkler optimization targets in the plots based on the distribution data of deviation-affected risk equipment in different plots in the target area and the sprinkler irrigation treatment strategy in the target area; S3 determines the sprinkler optimization strategy for the sprinkler optimization target based on the sprinkler optimization target data and the composition of the risk equipment affected by the deviation in different sprinkler optimization targets.

[0006] The beneficial effects of this invention are as follows: The sprinkler irrigation strategy for the target area is determined by using the distribution data of IoT devices in the target area and the consistency of changes in monitoring data among different IoT devices. Based on the distribution data of IoT devices in the target area and the consistency of changes in monitoring data, the reliability of using IoT devices to monitor and analyze soil moisture in the target area is determined. The reliability of monitoring and analysis is then used to determine the sprinkler irrigation strategy for the target area, thus ensuring the timeliness and reliability of sprinkler irrigation even when monitoring risks exist.

[0007] Based on the correlation between sprinkler irrigation data, IoT devices, and different sprinkler irrigation processes, a strategy for identifying risky devices with deviations in IoT devices is determined. For sprinkler irrigation processes where IoT devices fall within the target humidity range, resulting in the need for sprinkler irrigation, the impact of abnormal IoT device monitoring on the overall timeliness of sprinkler irrigation is assessed. Specifically, based on the interval between different sprinkler irrigation processes and the degree of aggregation of IoT devices falling within the target humidity range, the impact of abnormal IoT device monitoring on the overall timeliness of sprinkler irrigation is determined. This allows for the determination of differentiated strategies for identifying risky devices with deviations, ensuring the timeliness of sprinkler irrigation.

[0008] Furthermore, the distribution data of the IoT devices includes the number of IoT devices.

[0009] Furthermore, the consistency of the monitoring data among the IoT devices is determined based on the deviation rate of the monitoring data among the IoT devices at different monitoring times.

[0010] Furthermore, the method for determining the sprinkler irrigation strategy for the target area is as follows: S11 uses the distribution data of IoT devices in the target area to determine the average value of IoT devices per unit area in the target area and uses it as the distribution density. S12 determines the proportion of monitoring times when the deviation rate of the monitoring data between different IoT devices is less than a preset deviation rate threshold based on the consistency of the changes in the monitoring data between different IoT devices, and uses the proportion of monitoring times when the deviation rate is less than the preset deviation rate threshold as the similarity coefficient of the monitoring data between the IoT device and other IoT devices. S13 determines the sprinkler irrigation strategy for the target area based on the distribution density in the target area and the similarity coefficient of monitoring data between different IoT devices.

[0011] Furthermore, the method for determining the sprinkler optimization strategy for the sprinkler optimization objective is as follows: Based on the sprinkler irrigation optimization target data, determine the number of sprinkler irrigation optimization targets; The proportion of the number of risky devices affected by deviations in different sprinkler irrigation optimization objectives is determined based on the proportion of the number of risky devices affected by deviations in all deviations. Based on the number of sprinkler irrigation optimization targets and the proportion of risky equipment affected by deviations in different sprinkler irrigation optimization targets, the sprinkler irrigation optimization strategy for the sprinkler irrigation optimization targets is determined.

[0012] Secondly, the present invention provides an Internet of Things (IoT) collaborative control system for agricultural production, applied to the aforementioned IoT collaborative control method for agricultural production, specifically including: Risk identification module, optimized target identification module, sprinkler optimization module; The risk identification module is responsible for determining the identification strategy for risky devices affected by deviations in the IoT devices. The optimization target identification module is responsible for determining the sprinkler irrigation optimization targets in the plot; The sprinkler optimization module is responsible for determining the sprinkler optimization strategy for the sprinkler optimization target.

[0013] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of an IoT collaborative control method for agricultural production; Figure 2 This is a flowchart illustrating the method for determining the sprinkler irrigation treatment strategy for the target area; Figure 3This is a flowchart illustrating a method for determining a risk identification strategy for devices affected by deviations in Internet of Things (IoT) devices. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0018] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0019] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, an IoT collaborative control method for agricultural production is provided, specifically including: S1 uses the distribution data of IoT devices in the target area and the consistency of changes in monitoring data between different IoT devices to determine the sprinkler irrigation treatment strategy for the target area. The sprinkler irrigation treatment strategy is used to determine the sprinkler irrigation treatment data. In addition, the correlation between IoT devices and changes in different sprinkler irrigation processes is combined to determine the identification strategy for the deviation impact risk devices in the IoT devices. S2 determines the sprinkler optimization targets in the plots based on the distribution data of deviation-affected risk equipment in different plots in the target area and the sprinkler irrigation treatment strategy in the target area; S3 determines the sprinkler optimization strategy for the sprinkler optimization target based on the sprinkler optimization target data and the composition of the risk equipment affected by the deviation in different sprinkler optimization targets.

[0020] Furthermore, the distribution data of the IoT devices includes the number of IoT devices.

[0021] Furthermore, the consistency of the monitoring data among the IoT devices is determined based on the deviation rate of the monitoring data among the IoT devices at different monitoring times.

[0022] Specifically, such as Figure 2 As shown, the method for determining the sprinkler irrigation strategy for the target area is as follows: In this embodiment, the reliability of monitoring and analyzing soil moisture in the target area using IoT devices is determined based on the distribution data of IoT devices in the target area and the consistency of changes in monitoring data. The reliability of monitoring and analysis is then used to determine the sprinkler irrigation treatment strategy for the target area, thereby ensuring the timeliness and reliability of sprinkler irrigation treatment even when monitoring presents risks.

[0023] S11 uses the distribution data of IoT devices in the target area to determine the average value of IoT devices per unit area in the target area and uses it as the distribution density. Distribution density refers to the average number of IoT devices per unit area in a target region, reflecting the spatial density of these devices. It is calculated as: Distribution Density = Total Number of IoT Devices in the Target Region ÷ Total Area of ​​the Target Region, with units per hectare. Higher distribution density indicates a denser distribution of IoT devices and more comprehensive monitoring coverage; lower distribution density indicates a sparser distribution of IoT devices and blind spots in monitoring coverage. This step, through the calculation of distribution density, achieves a quantitative assessment of the spatial distribution characteristics of IoT devices. Its significance lies in providing a basis for determining sprinkler irrigation treatment strategies, ensuring that these strategies can be adjusted according to the density of IoT devices, thereby improving the targeting and effectiveness of the strategies.

[0024] Assuming the total area of ​​the target region is several hectares and the total number of IoT devices is several units, the distribution density is several units / hectare. If the distribution density is high, it means that the IoT devices are densely distributed, the monitoring coverage is comprehensive, and a strict sprinkler irrigation strategy can be supported. If the distribution density is low, it means that the IoT devices are sparsely distributed, the monitoring coverage has blind spots, and a more lenient sprinkler irrigation strategy is required to ensure timeliness.

[0025] S12 determines the proportion of monitoring times when the deviation rate of the monitoring data between different IoT devices is less than a preset deviation rate threshold based on the consistency of the changes in the monitoring data between different IoT devices, and uses the proportion of monitoring times when the deviation rate is less than the preset deviation rate threshold as the similarity coefficient of the monitoring data between the IoT device and other IoT devices. The deviation rate of monitoring data refers to the relative difference between monitoring data collected by two IoT devices at the same monitoring time, reflecting the consistency of the monitoring data from the two IoT devices. The deviation rate is calculated as follows: Deviation rate = |Monitoring value of IoT device A at the monitoring time - Monitoring value of IoT device B at the monitoring time| ÷ Average of the monitoring values ​​of IoT device A and IoT device B at the monitoring time. The similarity coefficient of monitoring data refers to the proportion of monitoring times where the deviation rate of monitoring data between two IoT devices is less than a preset deviation rate threshold, reflecting the overall similarity of the monitoring data from the two IoT devices. The similarity coefficient is calculated as follows: Similarity coefficient = Number of monitoring times where the deviation rate of monitoring data is less than the preset deviation rate threshold ÷ Total number of monitoring times. A similarity coefficient closer to 1 indicates greater consistency between the monitoring data of the two IoT devices, while a coefficient closer to 0 indicates greater difference. This step, through the calculation of the similarity coefficient, achieves a quantitative assessment of the consistency of monitoring data between IoT devices. Its significance lies in identifying IoT devices with abnormal monitoring data, providing a basis for subsequent identification of devices at risk of deviation impact, and improving the reliability and accuracy of monitoring data.

[0026] If, in a given number of monitoring times, the deviation rate of the monitoring data of two IoT devices is less than a preset deviation rate threshold, then the monitoring data similarity coefficient is high, indicating that the monitoring data of the two IoT devices are well consistent, and the two IoT devices can be classified into the same device group; if the proportion is small, then the monitoring data similarity coefficient is low, indicating that the monitoring data of the two IoT devices are poorly consistent.

[0027] S13 determines the sprinkler irrigation strategy for the target area based on the distribution density in the target area and the similarity coefficient of monitoring data between different IoT devices.

[0028] "Sprinkler irrigation strategy" refers to a set of rules defining the triggering conditions and execution methods for sprinkler irrigation in a target area, including different types such as lenient sprinkler irrigation strategy, basic sprinkler irrigation strategy, and strict sprinkler irrigation strategy. "Lenient sprinkler irrigation strategy" refers to a strategy that initiates sprinkler irrigation when the proportion of IoT devices in the target area reaching the target humidity range exceeds the first proportion, suitable for scenarios with low monitoring reliability. "Strict sprinkler irrigation strategy" refers to a strategy that initiates sprinkler irrigation when the proportion of IoT devices in the target area reaching the target humidity range exceeds a preset proportion, suitable for scenarios with high monitoring reliability. "Basic sprinkler irrigation strategy" refers to a strategy that initiates sprinkler irrigation when the proportion of IoT devices in the target area reaching the target humidity range exceeds the second proportion, suitable for scenarios with medium monitoring reliability. "Preset distribution density threshold" is a critical value set by the system to determine whether the distribution of IoT devices is dense. "Device group" refers to a set of IoT devices whose monitoring data similarity coefficient is greater than a preset similarity coefficient threshold. "Preset proportion threshold" is a critical value set by the system to determine whether the proportion of IoT devices in a device group meets the reliability requirements. "Preset number of devices" is a critical value set by the system to determine whether the size of the device group is large enough. "Reliable matching device group" refers to a group of IoT devices with a number of devices exceeding a preset number. "Preset device ratio threshold" is a critical value set by the system to determine whether the sum of the proportions of IoT devices in a reliable matching device group meets the reliability requirements.

[0029] This step aims to scientifically determine the irrigation strategy for the target area by comprehensively considering the distribution density of IoT devices and the consistency of monitoring data. Distribution density reflects the spatial coverage of monitoring, while monitoring data consistency reflects the reliability of monitoring. Combining the two allows for a comprehensive assessment of the monitoring quality of the target area. Its significance lies in using multi-dimensional evaluation to apply differentiated irrigation triggering conditions for scenarios with different monitoring quality levels. This ensures timely irrigation while avoiding false or missed triggers due to unreliable monitoring data, thus achieving precise and intelligent irrigation treatment.

[0030] For example, if the calculated distribution density of the target area is lower than a preset distribution density threshold, it is initially determined that the monitoring coverage is insufficient, and further analysis of the composition of the device clusters is needed. If there are device clusters with a proportion of IoT devices greater than a preset proportion threshold, it indicates that although the overall coverage is insufficient, there is a reliable monitoring cluster, and a strict sprinkler strategy can be adopted; if there are no large-scale reliable device clusters, a lenient sprinkler strategy should be adopted to ensure timeliness.

[0031] It is understandable that if the distribution density in the target area is less than the preset distribution density threshold, then in order to ensure the timeliness of the sprinkler irrigation treatment for crops, the sprinkler irrigation treatment strategy for the target area is a relaxed sprinkler irrigation strategy. When the proportion of IoT devices in the target area that reach the target humidity range is above the first proportion, sprinkler irrigation treatment is carried out.

[0032] "First percentage" is the threshold for the percentage of IoT devices that trigger sprinkler irrigation under the system's relaxed sprinkler irrigation strategy. This threshold is relatively low to ensure that sprinkler irrigation can still be triggered in a timely manner when monitoring reliability is low. "Target humidity range" refers to the ideal range of soil moisture set by the system. When the soil moisture value monitored by IoT devices falls into this range, it indicates that the soil moisture in that area needs to be adjusted.

[0033] When the distribution density is below the preset distribution density threshold, it indicates that the IoT devices are sparsely distributed, resulting in blind spots in the monitoring coverage and an inability to comprehensively and accurately reflect the soil moisture conditions of the target area. In this case, if strict sprinkler irrigation trigger conditions are applied, abnormal soil moisture in some areas may not be detected and addressed in a timely manner, affecting the normal growth of crops. Therefore, a more lenient sprinkler irrigation strategy is adopted, lowering the trigger threshold to ensure timely response even when monitoring data is incomplete. This prioritizes timely sprinkler irrigation when monitoring conditions are insufficient, preventing crops from suffering from water shortages or overwatering due to monitoring blind spots.

[0034] For example, if the distribution density of the target area is calculated to be a certain number of units per hectare, which is less than the preset distribution density threshold of a certain number of units per hectare, the system will determine that the monitoring coverage is insufficient and directly determine the sprinkler irrigation treatment strategy as a relaxed sprinkler irrigation strategy. When the proportion of IoT devices in the target area that reach the target humidity range is above the first proportion, sprinkler irrigation treatment will be carried out.

[0035] It should also be noted that if the distribution density in the target area is less than a preset distribution density threshold, the following situations also apply: Based on the similarity coefficient of monitoring data between different IoT devices, IoT devices whose monitoring data similarity coefficient is greater than a preset similarity coefficient threshold are grouped into the same device group; The "preset similarity coefficient threshold" is a critical value set by the system to determine whether two IoT devices belong to the same device group. When the similarity coefficient of the monitoring data between two IoT devices is greater than this threshold, it indicates that the monitoring data of the two devices have a high degree of consistency, and they can be considered to be monitoring similar soil moisture environments and belonging to the same device group.

[0036] This step aims to group IoT devices based on the consistency of their monitoring data, grouping devices with high data similarity together. This device grouping helps identify local consistency patterns in the monitoring data; even with a low overall distribution density, reliable groups with densely packed devices and consistent monitoring data may exist in localized areas. Its significance lies in providing more granular analytical units for subsequent strategy determination, avoiding the overlooking of reliable local areas due to insufficient overall distribution.

[0037] For example, assuming there are several IoT devices in the target area, the system calculates the similarity coefficient of the monitoring data between every two IoT devices, and classifies devices with similarity coefficients greater than a preset similarity coefficient threshold into the same device group, ultimately forming several device groups.

[0038] Scenario 1: If there is a group of devices with a proportion of IoT devices greater than a preset proportion threshold, then the reliability of the monitoring data of IoT devices can be determined based on the degree of consistency. Therefore, in order to ensure the timeliness of the sprinkler irrigation treatment for crops, the sprinkler irrigation treatment strategy for the target area is a strict sprinkler irrigation strategy. When the proportion of IoT devices in the target area that reach the target humidity range is above the preset proportion, sprinkler irrigation treatment is carried out.

[0039] It is understandable that the preset percentage is greater than the first percentage.

[0040] "The proportion of IoT devices" refers to the ratio of the number of IoT devices in the device group to the total number of IoT devices in the target area. "Preset proportion" is the threshold for the proportion of IoT devices that trigger sprinkler processing under the strict sprinkler strategy set by the system. If this threshold is greater than the first proportion, it means that the strict sprinkler strategy requires higher triggering conditions.

[0041] When the proportion of IoT devices in a device group exceeds a preset threshold, it indicates the existence of a large device group with consistent monitoring data in the target area. The monitoring data from this group is highly reliable and can serve as the primary basis for sprinkler irrigation decisions. In this case, a stricter sprinkler irrigation strategy is adopted, requiring more stringent triggering conditions. This ensures timely irrigation while avoiding false triggers due to a few abnormal data points. The significance lies in achieving precise sprinkler irrigation control using a reliable device group, thereby improving water resource utilization efficiency.

[0042] Assuming the target area is divided into several device groups, and the proportion of IoT devices in device group A is calculated to be greater than a preset proportion threshold, the system determines that the monitoring data of this group is reliable and determines the sprinkler irrigation strategy as a strict sprinkler irrigation strategy. When the proportion of IoT devices in the target area that reach the target humidity range is above the preset proportion, sprinkler irrigation will be carried out.

[0043] Scenario 2: If there is no device group with a proportion of IoT devices greater than a preset proportion threshold, the number of IoT devices in different device groups is obtained. When there is no device group with a number of IoT devices greater than the preset number, it is impossible to determine whether the monitoring data of IoT devices is reliable based on the degree of consistency. Therefore, in order to ensure the timeliness of the irrigation treatment of crops, the irrigation treatment strategy for the target area is a relaxed irrigation strategy. When the proportion of IoT devices in the target area that reach the target humidity range is greater than the first proportion, irrigation treatment is carried out.

[0044] "Preset number of devices" is a threshold set by the system to determine whether the size of a device group is large enough. When the number of IoT devices in a device group reaches or exceeds this threshold, it indicates that the group has a sufficient sample size, and the statistical characteristics of its monitoring data are of reference value.

[0045] When there are no device groups with a proportion exceeding a preset threshold for IoT devices, and also no device groups with a number of IoT devices exceeding a preset number, it indicates that the target area lacks both large-scale reliable device groups and sufficiently large small-to-medium-sized reliable device groups. Therefore, consistency analysis cannot determine which devices' monitoring data are reliable. In this case, overall monitoring reliability is low, requiring a lenient irrigation strategy to ensure timely response. Its significance lies in prioritizing crop growth needs with a conservative strategy when monitoring reliability cannot be determined.

[0046] If the target area is divided into several device groups, and the proportion of IoT devices in all device groups is no greater than a preset proportion threshold, and the number of IoT devices in all device groups is no greater than a preset number of devices, then the system determines that the monitoring reliability cannot be determined and determines the sprinkler irrigation treatment strategy as a relaxed sprinkler irrigation strategy.

[0047] Scenario 3: If there is a device group with more than a preset number of IoT devices, the device group with more than a preset number of IoT devices is considered a reliable matching device group. It is determined whether the sum of the proportions of IoT devices in the reliable matching device group is greater than a preset device proportion threshold. If so, the reliability of the monitoring data of IoT devices can be determined based on the degree of consistency. Therefore, in order to ensure the timeliness of the sprinkler irrigation treatment for crops, the sprinkler irrigation treatment strategy for the target area is a strict sprinkler irrigation strategy. When the proportion of IoT devices in the target area that reach the target humidity range is greater than a preset proportion, sprinkler irrigation treatment is carried out. If not, the sprinkler irrigation treatment strategy for the target area is a basic sprinkler irrigation strategy. When the proportion of IoT devices in the target area that reach the target humidity range is greater than a second proportion, sprinkler irrigation treatment is carried out.

[0048] It should be noted that the second percentage is less than the preset percentage, and the second percentage is greater than the first percentage.

[0049] "Reliable Matching Device Group" refers to a group of IoT devices with a number exceeding a preset number. While each individual group may be small, the sheer number of devices within the group makes the statistical characteristics of its monitoring data valuable for reference. "Sum of IoT Device Proportions in Reliable Matching Device Groups" refers to the sum of the proportions of IoT devices in all reliable matching device groups, used to assess the overall coverage of reliable devices. "Preset Device Proportion Threshold" is a system-set threshold for determining whether the overall coverage of reliable devices meets reliability requirements. "Second Proportion" is a system-set threshold for the proportion of IoT devices triggering sprinkler irrigation under the basic sprinkler irrigation strategy. If this threshold is less than the preset proportion but greater than the first proportion, it indicates that the triggering conditions of the basic sprinkler irrigation strategy are between strict and lenient.

[0050] When there are device groups with more IoT devices than a preset number, it indicates that while the proportion of a single device group is insufficient, there are multiple small to medium-sized reliable device groups. By calculating the sum of the proportions of IoT devices in these groups, the overall coverage of reliable devices can be assessed. If the sum of the proportions is greater than a preset device proportion threshold, the overall coverage of reliable devices is sufficiently high, and a strict sprinkler strategy can be adopted. If the sum of the proportions is not greater than the preset device proportion threshold, the overall coverage of reliable devices is moderate, and a basic sprinkler strategy is adopted as a compromise. Its significance lies in allowing for refined grading based on the coverage of reliable devices, enabling adaptive adjustments to the sprinkler strategy.

[0051] For example, suppose the target area is divided into several device groups, where the number of IoT devices in device groups B, C, and D is greater than the preset number of devices, and these groups are identified as reliable matching device groups. The system calculates the sum of the proportions of IoT devices in these three groups. If the sum is greater than a preset device proportion threshold, the sprinkler irrigation strategy is determined to be a strict sprinkler irrigation strategy; if the sum is not greater than the preset device proportion threshold, the sprinkler irrigation strategy is determined to be a basic sprinkler irrigation strategy.

[0052] It should be noted that the second percentage is less than the preset percentage, and the second percentage is greater than the first percentage.

[0053] This explanation clarifies the relative size of the trigger thresholds for the three sprinkler irrigation strategies: the first has the smallest proportion, the second has a medium proportion, and the preset proportion has the largest proportion. This reflects that the triggering difficulty of the lenient sprinkler irrigation strategy, the basic sprinkler irrigation strategy, and the strict sprinkler irrigation strategy increases sequentially.

[0054] Three different trigger thresholds are set, corresponding to three different levels of monitoring reliability. The lower the monitoring reliability, the lower the trigger threshold to ensure timely response; the higher the monitoring reliability, the higher the trigger threshold to achieve precise control. The significance lies in establishing a correspondence between monitoring reliability and sprinkler irrigation triggering conditions, thereby achieving a more scientific and refined strategy.

[0055] In one possible specific embodiment, the distribution density is determined: An agricultural demonstration zone is implementing a smart sprinkler irrigation collaborative control application. The target area covers a total area of ​​50 hectares and includes 100 IoT devices, primarily soil moisture sensors. The specific process of the system for collaborative sprinkler irrigation control in this target area is as follows: S11: The system calculates the distribution density based on the deployment location data of IoT devices and the area data of the target area. The total number of IoT devices in the target area is 100, and the area is 50 hectares. The distribution density is 100 ÷ 50 = 2 devices / hectare. The system compares the distribution density of 2 devices / hectare with the preset distribution density threshold of 2.5 devices / hectare. Since the distribution density is less than the preset threshold, device group identification and analysis are required.

[0056] S12: The system calculates the monitoring data similarity coefficient based on the monitoring data of all IoT devices in the target area within a set time period. The set time period is the past 7 days, with a total of 168 monitoring moments. The monitoring data includes soil moisture, ambient temperature, and light intensity. The system calculates the deviation rate of the monitoring data for each device moment by moment. The number of monitoring moments with a deviation rate less than the preset deviation rate threshold of 15% is 145. The monitoring data similarity coefficient = 145 ÷ 168 ≈ 86.3%. The system compares the monitoring data similarity coefficient of 86.3% with the preset similarity coefficient threshold of 80%. The monitoring data similarity coefficient is greater than the preset similarity coefficient threshold.

[0057] S13: The system determines the sprinkler irrigation treatment strategy based on distribution density and monitoring data similarity coefficient. Since the distribution density is less than a preset distribution density threshold, the system groups IoT devices with a monitoring data similarity coefficient greater than 80% of the preset similarity coefficient threshold into the same device group. Three device groups were identified in the target area: Device Group A contains 35 devices, Device Group B contains 40 devices, and Device Group C contains 25 devices. The system calculates the proportion of IoT devices in each device group in the target area: IoT device proportion in Device Group A = 35 ÷ 100 = 35%, IoT device proportion in Device Group B = 40 ÷ 100 = 40%, and IoT device proportion in Device Group C = 25 ÷ 100 = 25%.

[0058] Scenario 1: The system determines whether there exists a device group where the proportion of IoT devices exceeds a preset threshold of 30%. Device group A has an IoT device proportion of 35%, which exceeds the preset threshold of 30%, satisfying the judgment condition of Scenario 1. Since there is a device group where the proportion of IoT devices exceeds the preset threshold, the reliability of the IoT device monitoring data can be determined based on the degree of consistency. Therefore, to ensure the timeliness of crop irrigation, the system determines the irrigation strategy for the target area to be a strict irrigation strategy, meaning that irrigation will be initiated when the proportion of IoT devices in the target area reaching the target humidity range exceeds a preset proportion of 20%. The preset proportion of 20% is greater than the first proportion of 10%.

[0059] Scenario 2: If no device group has an IoT device ratio greater than the preset threshold of 30%, the system obtains the number of IoT devices in different device groups. When no device group has more than the preset number of 20 IoT devices, the reliability of the IoT device monitoring data cannot be determined based on the consistency. Therefore, to ensure the timeliness of crop irrigation, the system determines the irrigation strategy for the target area to be a relaxed irrigation strategy. That is, irrigation is performed when the proportion of IoT devices in the target area reaching the target humidity range is above 10%. In this embodiment, the proportion of IoT devices in device group A is 35%, which is greater than the preset threshold of 30%, thus satisfying the condition of Scenario 1, and there is no need to proceed to the judgment process of Scenario 2.

[0060] Scenario 3: If there is no device group with an IoT device ratio greater than the preset threshold of 30%, but there is a device group with more than 20 IoT devices, the system will consider the device group with more than 20 IoT devices as a reliable matching device group. The system will then determine if the sum of the IoT device ratios in the reliable matching device group is greater than the preset threshold of 60%. If so, the reliability of the IoT device monitoring data can be determined based on the degree of consistency. Therefore, to ensure the timeliness of crop irrigation, the system determines the irrigation strategy for the target area as a strict irrigation strategy, meaning that irrigation will be performed when the percentage of IoT devices in the target area reaching the target humidity range is greater than the preset threshold of 20%. If not, the system determines the irrigation strategy for the target area as a basic irrigation strategy, meaning that irrigation will be performed when the percentage of IoT devices in the target area reaching the target humidity range is greater than the second threshold of 15%. In this embodiment, the IoT device ratio of device group A is 35%, which is greater than the preset threshold of 30%, thus satisfying the condition of Scenario 1, and there is no need to proceed to the judgment process of Scenario 3.

[0061] This embodiment achieves adaptive determination of sprinkler irrigation treatment strategy through three case judgments in step S13. Its core value is reflected in three aspects: First, by quantitatively comparing the proportion of equipment groups, the distribution characteristics of the dominant equipment group are accurately identified; second, by making secondary judgments on the number and proportion of equipment, the strategy selection under different scales and consistency levels is effectively distinguished; and third, by using a hierarchical and progressive judgment logic, the triggering conditions of the sprinkler irrigation treatment strategy are ensured to match the equipment distribution characteristics and data reliability, thereby improving water resource utilization efficiency while ensuring the timeliness of crop sprinkler irrigation.

[0062] Specifically, such as Figure 3 As shown, the method for determining the identification strategy of risky devices affected by deviations in the IoT devices is as follows: In this embodiment, based on the situation where IoT devices fall within the target humidity range, resulting in the need for sprinkler irrigation, the impact of abnormal monitoring of IoT devices on the overall timeliness of sprinkler irrigation is assessed. Specifically, based on the interval between different sprinkler irrigation processes and the degree of aggregation of IoT devices falling within the target humidity range during the sprinkler irrigation process, the impact of abnormal monitoring of IoT devices on the overall timeliness of sprinkler irrigation is determined. This allows for the determination of differentiated strategies for identifying devices at risk of deviation, ensuring the timeliness of sprinkler irrigation.

[0063] S21 uses the sprinkler irrigation treatment data to determine the interval between adjacent sprinkler irrigation processes in the target area; "Sprinkler irrigation treatment data" refers to the data recorded during the sprinkler irrigation treatment process, including the start time, end time, irrigation volume, and information on the IoT devices involved in triggering the process. "Interval duration" refers to the time interval between two adjacent sprinkler irrigation treatment processes in the target area, reflecting the frequency of sprinkler irrigation treatment and the periodic characteristics of soil moisture changes.

[0064] This step aims to quantify the temporal distribution characteristics of the sprinkler irrigation process, providing a temporal dimension for subsequent analysis to identify equipment at risk of deviation. Longer intervals indicate a longer sprinkler irrigation cycle, thus increasing the need for reliable monitoring by IoT devices. Its significance lies in identifying the regularity of the sprinkler irrigation process through time analysis, providing fundamental data for assessing the impact of IoT devices on the timeliness of sprinkler irrigation.

[0065] Assuming the target area has undergone several sprinkler irrigation treatments in the historical record, the system calculates the time interval between each sprinkler irrigation treatment and the previous sprinkler irrigation treatment, obtaining several interval duration data.

[0066] The above steps include the following: Based on the average interval between adjacent irrigation processes in the target area, the average interval is determined, and it is determined whether the average interval is greater than a preset interval threshold. If so, the IoT device with the associated influence process is regarded as a device with deviation influence risk. If not, proceed to step S22.

[0067] "Average Interval Duration" refers to the average interval duration between all adjacent sprinkler irrigation processes in the target area, reflecting the average frequency of sprinkler irrigation. "Preset Interval Duration Threshold" is a system-set critical value used to determine whether the sprinkler irrigation frequency is too low. "Associated Influence Process" refers to the sprinkler irrigation process triggered by an IoT device falling within the target humidity range; that is, the device's monitoring data triggered the sprinkler irrigation. "devices with Deviation Risk" refers to IoT devices that pose a monitoring anomaly risk and affect the timeliness of sprinkler irrigation.

[0068] When the average interval duration exceeds the preset interval duration threshold, it indicates that the sprinkler irrigation frequency is low and the time interval between each sprinkler irrigation is long. In this case, if the monitoring data of some devices are biased, it may lead to a further extension of the overall sprinkler irrigation cycle. Therefore, the identification strategy can be simplified by directly identifying devices with correlated influencing processes as devices at risk of bias, thus ensuring the reliability of device monitoring and processing.

[0069] If the average interval time in the target area is greater than the preset interval time threshold, the system determines that the sprinkler treatment frequency is low and directly identifies IoT devices with related influence processes as devices with deviation risk.

[0070] S22 determines the irrigation process caused by the IoT device falling into the target humidity range based on the changes in the IoT device and different irrigation processes, and takes the irrigation process caused by the IoT device falling into the target humidity range as the associated influence process. "Change Correlation" refers to the correlation between changes in monitoring data from IoT devices and the sprinkler irrigation process, i.e., whether the monitoring data of a certain device is one of the reasons that triggers a certain sprinkler irrigation process. "Correlation Influence Process" refers to the sprinkler irrigation process caused by the IoT device falling within the target humidity range, i.e., the monitoring data of the device directly participates in the determination of sprinkler irrigation trigger.

[0071] This step aims to identify the level of participation of each IoT device in the historical sprinkler irrigation process, determining which devices' monitoring data frequently trigger sprinkler irrigation actions. The more associated processes a device has, the greater its impact on sprinkler irrigation; conversely, the fewer associated processes, the smaller its impact. Its significance lies in quantifying the contribution of IoT devices to sprinkler irrigation, providing a basis for identifying devices at risk of deviations.

[0072] Suppose that IoT device A has repeatedly fallen within the target humidity range and triggered sprinkler irrigation processes in its historical records. These sprinkler irrigation processes constitute the associated impact processes of device A. The system counts the number of associated impact processes of device A as the basis for subsequent analysis.

[0073] S23 determines the identification strategy for devices at risk of deviation in the IoT devices based on the interval between adjacent irrigation processes in the target area and the IoT device data of the related processes.

[0074] "The strategy for identifying devices at risk of deviation" refers to a set of rules used to determine which devices may have monitoring anomalies and affect the timeliness of sprinkler irrigation based on the associated impact processes of IoT devices and the frequency of sprinkler irrigation treatment.

[0075] This step aims to scientifically identify devices that may exhibit monitoring anomalies and affect the timeliness of sprinkler irrigation, based on the level of participation of IoT devices in sprinkler irrigation and the frequency characteristics of sprinkler irrigation. Different devices have varying degrees of impact on sprinkler irrigation, requiring differentiated identification standards. Its significance lies in achieving accurate identification of devices at risk of deviations, providing a basis for determining subsequent sprinkler irrigation optimization goals.

[0076] For example, assuming that there are several IoT devices in the target area that have associated impact processes, the system determines which devices belong to the deviation impact risk devices based on the number and proportion of these associated impact processes and the frequency of sprinkler treatment.

[0077] The above steps include the following: S231. IoT devices with associated influence processes are selected as screening influence devices. It is determined whether the proportion of IoT devices with the screening influence devices in the target area is less than a preset screening proportion threshold. If so, IoT devices with associated influence processes are selected as deviation influence risk devices. If not, proceed to step S232. "Screening affected devices" refers to IoT devices that have a related impact process, meaning that the monitoring data of these devices has triggered sprinkler irrigation treatment. "Preset screening ratio threshold" is a critical value set by the system to determine whether the proportion of screened affected devices is too low.

[0078] When the proportion of screened devices is less than a preset screening threshold, it indicates that only a few devices participated in the sprinkler irrigation triggering process, and the impact of these devices on sprinkler irrigation is relatively concentrated. In this case, if these devices exhibit monitoring anomalies, it will significantly affect the timeliness of sprinkler irrigation; therefore, all of them are identified as devices at risk of deviation impact. The significance of this approach is that it directly identifies risky devices in scenarios where a few devices dominate sprinkler irrigation triggering, simplifying subsequent analysis procedures.

[0079] If there are a number of IoT devices in the target area that have a related impact process, and the proportion of these devices to the total number of IoT devices is less than the preset screening proportion threshold, then the system determines that these devices have a concentrated impact on sprinkler irrigation and directly identifies the IoT devices with related impact processes as devices with deviation impact risk.

[0080] S232 uses different screening methods to determine the number of associated impact processes of the device and whether there are IoT devices whose proportion of the number of associated impact processes is greater than the preset threshold for the proportion of the number of impact processes. If yes, proceed to step S233. If no, IoT devices whose proportion of the number of associated impact processes is greater than the target proportion and whose number of associated impact processes is greater than the preset threshold for the number of associated processes are considered as devices with deviation impact risk. "The percentage of associated impact processes" refers to the proportion of the number of associated impact processes of a certain IoT device to the total number of sprinkler irrigation processes in the area where the device is located, reflecting the device's contribution to sprinkler irrigation. "Preset threshold for the percentage of associated impact processes" is a critical value set by the system to determine whether the percentage of associated impact processes of a certain device is too high. "Target percentage" is a lower limit set by the system to identify the percentage of associated impact processes of devices at risk of deviation impact. "Preset threshold for the number of associated processes" is a lower limit set by the system to identify the number of associated impact processes of devices at risk of deviation impact.

[0081] When the proportion of screened devices affecting the sprinkler irrigation is not less than the preset screening proportion threshold, it indicates that a large number of devices are involved in the sprinkler irrigation triggering process, and further analysis of the participation degree of each device is needed. If there are devices whose proportion of associated affecting processes is greater than the preset threshold for the proportion of associated affecting processes, it indicates that these devices contribute significantly to the sprinkler irrigation treatment and require further analysis; if none exist, devices with a certain impact on the sprinkler irrigation treatment are screened using both the target proportion and the preset threshold for the number of associated processes. The significance of this is to conduct refined screening based on the degree of device participation, avoiding the omission of devices with a moderate degree of impact.

[0082] Assuming the proportion of screened affected devices is not less than a preset screening proportion threshold, the system calculates the percentage of associated impact processes for each screened affected device. If the percentage of associated impact processes for any device is greater than the preset threshold for the percentage of associated impact processes, the system proceeds to the next step; otherwise, devices with a percentage of associated impact processes greater than the target percentage and a number of associated impact processes greater than the preset threshold for the number of associated impact processes are considered as devices at risk of deviation impact.

[0083] S233 identifies IoT devices whose proportion of associated impact processes exceeds a preset threshold for the proportion of impact processes as clustering devices. It then determines whether the proportion of the clustering devices among the screened impact devices is less than a preset threshold for the proportion of clustering devices. If so, IoT devices whose proportion of associated impact processes exceeds the target proportion and whose number of associated impact processes exceeds the target threshold for the number of processes are identified as devices at risk of deviation impact. Otherwise, IoT devices with associated impact processes are identified as devices at risk of deviation impact.

[0084] "Aggregating devices" refer to IoT devices whose proportion of associated influencing processes exceeds a preset threshold for the proportion of influencing processes. These devices contribute significantly to sprinkler irrigation. The "preset aggregated device proportion threshold" is a critical value set by the system to determine whether the proportion of aggregated devices among the screened influencing devices is too low. The "target process quantity threshold" is a lower limit set by the system to identify the number of associated influencing processes of devices at risk of deviation impact. This threshold is lower than the preset associated process quantity threshold.

[0085] The presence of clustered devices indicates that the contribution of some devices to sprinkler irrigation is particularly concentrated. If the proportion of clustered devices is low, it means that only a few devices contribute significantly; anomalies in these devices would have a substantial impact, but they still require screening under dual conditions. If the proportion of clustered devices is high, it means that most devices contribute significantly to sprinkler irrigation, indicating high overall participation. Therefore, all IoT devices with related influence processes are considered as devices at risk of deviation impact. The significance of this approach is to differentiate treatment based on the concentration of device participation, thereby improving the accuracy of identification.

[0086] Assume that a number of IoT devices with a proportion of associated impact processes exceeding a preset threshold are identified as clustered devices. The system calculates the proportion of clustered devices among the screened impacting devices. If this proportion is less than the preset clustered device proportion threshold, then devices with a proportion of associated impact processes exceeding the target proportion and the target process number threshold are considered as devices at risk of deviation impact. If this proportion is not less than the preset clustered device proportion threshold, then all IoT devices with associated impact processes are considered as devices at risk of deviation impact.

[0087] It is understood that the target process number threshold is less than the preset associated process number threshold.

[0088] This explanation clarifies the relative size of the two thresholds: the target process number threshold is less than the preset associated process number threshold. This means that when the proportion of clustered devices is low, the requirement for the number of associated impact processes is relatively reduced to avoid omitting devices that have some impact on sprinkler irrigation but whose contribution is not particularly large.

[0089] Setting two different thresholds for the number of associated influence processes allows for the use of different screening criteria in different scenarios. When the proportion of clustered devices is low, it indicates that there are relatively few devices making particularly large contributions, requiring a slight reduction in screening criteria to cover more potentially risky devices. Conversely, when the proportion of clustered devices is high, it indicates that most devices contribute significantly, allowing for a more lenient screening strategy. The significance lies in flexibly adjusting screening criteria based on actual circumstances to improve the comprehensiveness of identification.

[0090] In one possible specific embodiment: S21: Based on the sprinkler irrigation treatment strategy, the system determines the interval between two adjacent sprinkler irrigation processes. Since the sprinkler irrigation treatment strategy is a strict sprinkler irrigation strategy, the system calculates the average interval between all adjacent sprinkler irrigation processes within the target area. A total of 5 sprinkler irrigation processes occurred within the set time, with intervals of 32 hours, 38 hours, 35 hours, 40 hours, and 36 hours respectively. The average interval is calculated as (32+38+35+40+36)÷5=36.2 hours≈36 hours. The system compares the average interval of 36 hours with the preset interval threshold of 48 hours. The average interval is less than the preset threshold, indicating a high sprinkler irrigation frequency. Further identification of equipment at risk of deviation is needed, and S22 is executed.

[0091] If the average interval duration is not less than the preset interval duration threshold of 48 hours, it indicates that the sprinkler irrigation frequency is moderate or low. The system skips S22 and directly executes S23, adopting a simplified deviation impact risk equipment identification strategy. However, in this embodiment, the average interval duration of 36 hours is less than the preset threshold of 48 hours, so S22 is executed.

[0092] S22: The system determines the associated impact processes of IoT devices in the target area. Based on the monitoring data of IoT devices and the irrigation process data, the system identifies the associated impact processes of IoT devices in the irrigation process. An associated impact process refers to the data change process triggered or affected by the data monitored by the IoT device falling within the target humidity range. The system identifies associated impact processes for each IoT device, identifying a total of 120 associated impact processes. Among them, devices numbered D1 to D40 have a relatively large number of associated impact processes, with an average of more than 3 associated impact processes per device.

[0093] S23: The system determines the deviation impact risk device identification strategy based on the correlation influence process. The system first filters out IoT devices with a number of correlation influence processes exceeding a preset threshold of 3, resulting in 40 devices, accounting for 40% of the total number of IoT devices. These IoT devices with a number of correlation influence processes exceeding a preset threshold of 5 are then considered clustered devices. The system calculates the proportion of clustered devices to the total number of screened devices as 15 ÷ 40 = 37.5%. Comparing this 37.5% proportion with the preset clustering proportion threshold of 30%, the system determines that the deviation impact risk device identification strategy is a strict identification strategy, meaning all IoT devices exhibiting correlation influence processes are considered deviation impact risk devices.

[0094] If the proportion of clustered devices is not greater than the preset clustering proportion threshold of 30%, the system determines that the deviation impact risk device identification strategy is a lenient identification strategy, and only identifies clustered devices and devices with a number of associated impact processes greater than the preset high associated impact process number threshold of 4 as deviation impact risk devices.

[0095] This embodiment achieves accurate identification of equipment at risk of deviation impact through steps S21 to S23. Its core value lies in two aspects: first, by analyzing the interval duration, it determines whether the frequency of sprinkler irrigation treatment needs in-depth analysis of the associated impact processes; second, by identifying and clustering the associated impact processes, it scientifically screens out equipment that has potential deviation impacts on the sprinkler irrigation treatment process, providing key support for determining subsequent sprinkler irrigation optimization targets.

[0096] Specifically, the method for determining the sprinkler irrigation optimization target in the aforementioned plot is as follows: The application determines the sprinkler irrigation optimization target based on the sprinkler irrigation treatment strategy and the number of deviation-affected risk devices in the plot. Specifically, it assesses the impact of the number of deviation-affected risk devices on the timeliness of sprinkler irrigation treatment in the plot based on the sprinkler irrigation treatment strategy, and uses the degree of impact to determine whether the plot belongs to the sprinkler irrigation optimization target. Targeted optimization measures for sprinkler irrigation treatment are then determined, further improving the reliability of identification and handling when there are monitoring anomalies in the deviation-affected risk devices in the aforementioned plot.

[0097] S31 uses the distribution data of deviation-affected risk equipment in the plot to determine the number of deviation-affected risk equipment in the plot; "Plot" refers to a sub-region within the target area, divided according to land characteristics or management needs. "Distribution data of deviation-affected risk equipment" refers to the distribution information such as the location and quantity of deviation-affected risk equipment in each plot. "Number of deviation-affected risk equipment" refers to the number of deviation-affected risk equipment units contained in each plot.

[0098] This step aims to quantify the distribution of equipment at risk of deviation impact in each plot, providing a quantitative basis for determining subsequent sprinkler irrigation optimization targets. Plots with more equipment at risk of deviation impact have a higher risk of monitoring anomalies and a greater impact on the timeliness of sprinkler irrigation treatment. Its significance lies in transforming the spatial distribution of equipment at risk of deviation impact into quantitative indicators for each plot, laying the foundation for developing differentiated sprinkler irrigation optimization strategies.

[0099] Assuming the target area is divided into several plots, the system counts the number of risky devices affected by deviations in each plot, which serves as the basis for subsequent analysis.

[0100] S32 determines whether the plot of land belongs to the sprinkler irrigation optimization target based on the sprinkler irrigation treatment strategy of the target area and the number of deviation-affected risk devices in the plot.

[0101] "Sprinkler irrigation optimization targets" refer to plots of land that require sprinkler irrigation optimization treatment. These plots have deviation risks, equipment or sprinkler irrigation treatment strategies that have strict requirements, and additional monitoring or control measures are needed to ensure the timeliness and reliability of sprinkler irrigation treatment.

[0102] This step aims to scientifically determine which plots require sprinkler irrigation optimization based on the number of equipment at risk of deviation impact in each plot and the sprinkler irrigation treatment strategy for the target area. Different sprinkler irrigation strategies have varying tolerances for equipment at risk of deviation impact; strict strategies are more sensitive, while lenient strategies have higher tolerance. Its significance lies in achieving precise screening of sprinkler irrigation optimization targets, providing target plots for the subsequent development of differentiated sprinkler irrigation optimization strategies.

[0103] Assuming the sprinkler irrigation strategy for the target area is a strict sprinkler irrigation strategy, and there are several devices in a certain plot that pose a risk of deviation, the system determines whether the plot belongs to the sprinkler irrigation optimization target based on the strategy requirements and the number of devices.

[0104] It is understandable that if there are no equipment at risk of deviation affecting the plot, then the plot is determined not to be a sprinkler irrigation optimization target.

[0105] This regulation clarifies the handling method for plots of land without deviation-affected equipment: when there are no deviation-affected equipment in a plot, the plot does not need to undergo additional sprinkler optimization treatment and is directly identified as a non-sprinkler optimization target.

[0106] If no equipment at risk of deviation affects the site, it indicates that the monitoring data for that site is highly reliable, and there is no risk that the timeliness of sprinkler irrigation will be affected by abnormal equipment monitoring. Therefore, no sprinkler optimization treatment is needed. This simplifies the processing flow for risk-free sites, allowing resources to be concentrated on sites with potential risks, thus improving processing efficiency.

[0107] If a plot of land is statistically found to have no bias affecting risk equipment, the system will directly determine that the plot is not a target for sprinkler irrigation optimization and no further optimization is required.

[0108] Additionally, it should be noted that if there are equipment at risk of deviation affecting the site, this includes the following: Determine whether the sprinkler irrigation treatment strategy for the target area belongs to a strict sprinkler irrigation strategy. If so, as long as there are deviation-affected equipment in the plot, in order to ensure the accuracy of the monitoring data of the deviation-affected equipment, determine that the plot belongs to the sprinkler irrigation optimization target. If not, proceed to the next step. This regulation clarifies the handling of equipment at risk of deviation impact under a strict sprinkler irrigation strategy: as long as there is equipment at risk of deviation impact in a plot, it will be directly identified as a sprinkler irrigation optimization target to ensure that the monitoring data of these devices are given extra attention and verification.

[0109] When the sprinkler irrigation strategy for the target area is a strict sprinkler irrigation strategy, it indicates that the system has high requirements for the reliability of monitoring data and the sprinkler irrigation triggering conditions are relatively stringent. In this case, the presence of any equipment at risk of deviation can significantly impact the timeliness of sprinkler irrigation. Therefore, it is necessary to optimize all plots with such equipment. The significance lies in employing a comprehensive optimization strategy under the strict strategy to ensure the accuracy of sprinkler irrigation.

[0110] Assuming the sprinkler irrigation strategy for the target area is a strict sprinkler irrigation strategy, and there are several equipment with deviation risks in a certain plot, the system directly determines that the plot belongs to the sprinkler irrigation optimization target.

[0111] Based on the number of equipment at risk of deviation in the plot and the sprinkler irrigation treatment strategy for the target area, the optimization requirement value of the plot is determined. It is then determined whether the optimization requirement value of the plot is greater than a preset requirement threshold. If so, the plot is determined to be a sprinkler irrigation optimization target; otherwise, the plot is determined not to be a sprinkler irrigation optimization target.

[0112] "Optimization Demand Value" is a quantitative indicator that comprehensively reflects the degree of optimization demand for sprinkler irrigation in a plot of land. Its calculation takes into account factors such as the number of equipment at risk of deviation and the sprinkler treatment strategy for the target area. "Preset Demand Threshold" is a critical value set by the system to determine whether the optimization demand value of a plot of land has met the optimization requirements.

[0113] When the sprinkler irrigation strategy for the target area is not a strict sprinkler irrigation strategy, it indicates that the system's requirements for the reliability of monitoring data are relatively low. In this case, it is necessary to quantitatively assess the optimization needs based on the specific conditions of the site. The optimization requirement value comprehensively considers the number of devices at risk of deviation and the strictness of the sprinkler irrigation strategy, and can objectively reflect the urgency of the site's sprinkler irrigation optimization. Its significance lies in achieving quantitative screening of sprinkler irrigation optimization targets and avoiding the arbitrariness of subjective judgment.

[0114] Assuming the sprinkler irrigation strategy for the target area is a lenient or basic sprinkler irrigation strategy, and several equipment in a certain plot of land have potential risks due to deviations, the system calculates the optimization requirement value for that plot. If the value is greater than a preset requirement threshold, the plot is determined to be a sprinkler irrigation optimization target; if the value is not greater than the preset requirement threshold, the plot is determined not to be a sprinkler irrigation optimization target.

[0115] It is understandable that the more deviation-affected devices there are in the plot, the more monitoring devices are required within the target humidity range during the sprinkler irrigation treatment strategy of the target area, and the higher the optimization requirement value of the plot, that is, the higher the requirement to ensure the reliability of monitoring of deviation-affected devices in the plot.

[0116] This explanation clarifies the calculation logic for the optimization demand value: the more devices at risk of deviation impact in a plot, the more devices at risk of monitoring anomalies exist in that plot; the more monitoring devices within the target humidity range required by the sprinkler irrigation strategy for the target area during irrigation, the more stringent the irrigation triggering conditions and the higher the reliability requirements for monitoring data. These two factors work together to increase the optimization demand value.

[0117] The calculation logic for optimized demand values ​​reflects the basic principles of risk assessment: the more risk factors there are, the higher the risk level, and the higher the corresponding optimization demand. The number of risky equipment and the strictness of the sprinkler irrigation strategy are two main factors affecting the timeliness of sprinkler irrigation treatment in a given plot. By comprehensively quantifying these two factors, the optimization needs of each plot can be objectively assessed. Its significance lies in establishing a scientific optimization demand assessment mechanism, providing an objective basis for determining sprinkler irrigation optimization objectives.

[0118] If some deviations in plot A affect risky equipment, and plot B has more deviations affecting risky equipment, then the optimization requirement value for plot B is higher than that for plot A. If the sprinkler irrigation treatment strategy for the target area is more stringent, the optimization requirement value for each plot will also increase accordingly.

[0119] This embodiment achieves the systematic determination of sprinkler irrigation optimization targets through the series of steps S31 to S32 described above. Its core value lies in three aspects: First, the evaluation basis is comprehensive, considering both the number of equipment at risk of deviation in the plot and the sprinkler irrigation treatment strategy for the target area, thus achieving a comprehensive assessment of the impact on the timeliness of sprinkler irrigation treatment in the plot; second, the screening strategy is differentiated, employing different screening criteria for different sprinkler irrigation treatment strategies, with comprehensive coverage under strict strategies and quantitative evaluation under non-strict strategies, achieving adaptive adjustment of the screening strategy; third, the decision-making process is scientific, achieving objectivity and standardization in determining sprinkler irrigation optimization targets through quantitative calculation of optimization demand values ​​and judgment of preset demand thresholds, providing clear target plots for the subsequent formulation of sprinkler irrigation optimization strategies.

[0120] In one possible specific embodiment: S31: Based on the distribution data of deviation-affected equipment and the sprinkler irrigation treatment strategy, the system determines the number of deviation-affected equipment in each plot. The target area is divided into 6 plots with the following areas: Plot 1: 8 hectares; Plot 2: 10 hectares; Plot 3: 7 hectares; Plot 4: 9 hectares; Plot 5: 8 hectares; Plot 6: 8 hectares. The system counts the number of deviation-affected equipment in each plot: Plot 1 contains 8 deviation-affected equipment, Plot 2 contains 10 deviation-affected equipment, Plot 3 contains 7 deviation-affected equipment, Plot 4 contains 9 deviation-affected equipment, Plot 5 contains 4 deviation-affected equipment, and Plot 6 contains 2 deviation-affected equipment. The distribution of the 40 deviation-affected equipment across the plots is as follows: Plots 1 to 4 are relatively concentrated, while Plots 5 and 6 have fewer.

[0121] S32: The system determines whether each plot of land is a target for sprinkler irrigation optimization based on the number of equipment at risk of deviation impact and the sprinkler irrigation treatment strategy in each plot.

[0122] Strict Sprinkler Irrigation Strategy: Since the sprinkler irrigation strategy is strict, the system employs a rigorous judgment method, directly identifying plots with a number of devices at risk of deviation exceeding the preset threshold of 5 as sprinkler irrigation optimization targets. The system compares the number of devices at risk of deviation in each plot with the preset threshold of 5: Plot 1 has 8 devices at risk of deviation, exceeding the preset threshold of 5, thus qualifying as a sprinkler irrigation optimization target. Therefore, the system ultimately identifies four plots—Plot 1, Plot 2, Plot 3, and Plot 4—as sprinkler irrigation optimization targets.

[0123] For non-strict sprinkler irrigation strategies: If the sprinkler irrigation strategy is a basic sprinkler irrigation strategy or a lenient sprinkler irrigation strategy, the system uses an optimization demand value judgment method to calculate the optimization demand value for each plot. The optimization demand value is calculated as the product of the proportion of the number of devices at risk of deviation impact to the total number of IoT devices in that plot and the average number of associated impact processes of the devices at risk of deviation impact. The system compares the optimization demand value of each plot with a preset optimization demand value threshold. Plots with optimization demand values ​​greater than the preset optimization demand value threshold are identified as sprinkler irrigation optimization targets. However, in this embodiment, the sprinkler irrigation strategy is a strict sprinkler irrigation strategy, therefore a strict judgment method is used, and there is no need to calculate the optimization demand value.

[0124] This embodiment achieves accurate identification of sprinkler irrigation optimization targets through steps S31 and S32. Its core value lies in two aspects: First, by statistically analyzing the number of risky equipment affected by deviations in various plots, it clearly presents the regional characteristics of equipment distribution; second, by combining the type of sprinkler irrigation treatment strategy, it adopts the corresponding judgment method to scientifically identify sprinkler irrigation optimization targets, ensuring that the identification results of optimization targets match the actual sprinkler irrigation treatment requirements.

[0125] Specifically, the method for determining the sprinkler optimization strategy for the sprinkler optimization objective is as follows: In this embodiment, based on the number of irrigation optimization targets and the proportion of risky devices affected by deviations in the irrigation optimization targets, an irrigation optimization strategy for anomaly identification and processing of IoT monitoring devices is determined. By performing irrigation optimization processing in a timely manner, the efficiency of identifying and processing IoT monitoring devices with anomalies in the irrigation optimization targets is ensured, and a foundation is laid for further ensuring the timeliness and reliability of irrigation treatment in the target area.

[0126] S41 determines the number of sprinkler irrigation optimization targets based on the sprinkler irrigation optimization target data; In the above steps, the number of irrigation optimization targets in the target area is obtained, and it is determined whether the number of irrigation optimization targets in the target area is greater than the preset optimization target number threshold. If so, the irrigation optimization strategy of the irrigation optimization target is determined to be that as long as the interval between the last irrigation process is greater than the preset irrigation duration threshold, that is, in the irrigation optimization target, when the deviation impact risk device falls into the target humidity range, a specified amount of irrigation is given, and it is determined whether the deviation impact risk device under the specified amount of irrigation is reliably monitored. If not, proceed to step S42.

[0127] "Sprinkler irrigation optimization target data" refers to data that records the basic information of each sprinkler irrigation optimization target, including plot number, number of equipment at risk of deviation impact, optimization requirement value, etc. "Number of sprinkler irrigation optimization targets" refers to the total number of plots in the target area that have been identified as sprinkler irrigation optimization targets.

[0128] This step aims to quantify the number of plots in the target area that require sprinkler irrigation optimization, providing a quantitative basis for determining subsequent sprinkler irrigation optimization strategies. A larger number of sprinkler irrigation optimization targets indicates a greater risk of monitoring anomalies in the target area, necessitating more comprehensive optimization measures. Its significance lies in assessing the overall optimization needs of the target area through quantitative statistics, providing fundamental data for developing reasonable optimization strategies.

[0129] Assuming that several sprinkler irrigation optimization targets are determined in the target area through step S2, the system counts the number of these targets as the basis for subsequent analysis.

[0130] "Preset optimization target quantity threshold" is a critical value set by the system to determine whether the number of irrigation optimization targets is too large. "Preset irrigation duration threshold" is a critical value set by the system to determine whether the time interval since the last irrigation process is long enough. "Specified irrigation amount" refers to the irrigation amount preset by the system to verify the reliability of risk equipment monitoring for deviations. This irrigation amount is insufficient to have a significant impact on crops, but is sufficient to produce a measurable change in soil moisture.

[0131] When the number of irrigation optimization targets exceeds a preset threshold, it indicates that there are many plots in the target area requiring irrigation optimization, resulting in a high overall optimization demand. In this case, a unified simplified optimization strategy is adopted: as long as the interval between the last irrigation treatment and the current treatment exceeds a preset irrigation duration threshold, a verification irrigation treatment is performed on the irrigation optimization target. This is significant because it ensures the reliability of monitoring data as much as possible when there are many optimization targets, i.e., when the demand is high.

[0132] Assuming that the number of irrigation optimization targets in the target area is greater than the preset threshold for the number of optimization targets, the system determines the irrigation optimization strategy for all irrigation optimization targets as follows: as long as the interval between the last irrigation process and the last irrigation process is greater than the preset irrigation duration threshold, when the equipment at risk of deviation falls into the target humidity range, a specified amount of irrigation is applied to verify whether the equipment is reliable in monitoring.

[0133] S42 determines the proportion of the number of devices at risk of deviation impact under different sprinkler irrigation optimization objectives based on the proportion of the number of devices at risk of deviation impact under all deviations. "The proportion of equipment at risk of deviation impact in different sprinkler irrigation optimization objectives" refers to the proportion of the number of equipment at risk of deviation impact in a certain sprinkler irrigation optimization objective to the total number of all equipment at risk of deviation impact in the target area, reflecting the degree of concentration of equipment at risk of deviation impact in the sprinkler irrigation optimization objective.

[0134] This step aims to quantify the concentration of equipment at risk of deviation impact within each sprinkler irrigation optimization objective, providing a basis for determining subsequent differentiated sprinkler irrigation optimization strategies. A higher percentage of equipment at risk of deviation impact indicates a more concentrated concentration of equipment at risk of monitoring anomalies within that sprinkler irrigation optimization objective, requiring a more aggressive optimization strategy; a lower percentage indicates a relatively dispersed distribution of equipment, allowing for a more lenient optimization strategy. Its significance lies in characterizing each sprinkler irrigation optimization objective through percentage analysis, providing a quantitative basis for formulating differentiated strategies.

[0135] Assume there are several devices at risk of deviation impact in the target area, distributed across several sprinkler irrigation optimization targets. The system calculates the proportion of devices at risk of deviation impact in each sprinkler irrigation optimization target relative to the total number, thus obtaining the proportion of each sprinkler irrigation optimization target.

[0136] S43 determines the sprinkler optimization strategy for the sprinkler optimization target based on the number of sprinkler optimization targets and the proportion of risky equipment affected by deviations in different sprinkler optimization targets.

[0137] "Sprinkler irrigation optimization strategy" refers to the sprinkler irrigation control and monitoring verification rules formulated for sprinkler irrigation optimization objectives. These rules include different types such as basic optimization strategies and secondary optimization strategies, and are used to verify the monitoring reliability of equipment at risk of deviation and to ensure the timeliness of sprinkler irrigation treatment.

[0138] This step aims to scientifically determine differentiated sprinkler irrigation optimization strategies based on the number of optimization targets and the distribution of equipment at risk of deviation affecting each target. Different optimization targets have varying degrees of concentration of equipment at risk of deviation affecting them, necessitating different optimization intensities. Its significance lies in achieving precision and differentiation in sprinkler irrigation optimization strategies, and rationally allocating optimization resources according to actual needs.

[0139] Assuming that the number of irrigation optimization targets in the target area is no greater than the preset threshold for the number of optimization targets, the system determines differentiated irrigation optimization strategies based on the proportion of risky equipment affected by the deviation of each irrigation optimization target.

[0140] Furthermore, based on the number of irrigation optimization targets and the proportion of equipment at risk of deviation from different irrigation optimization targets, the irrigation optimization strategy for each target is determined, specifically including: S431 determines the sum of the proportions of the number of risky devices affected by deviations in different sprinkler optimization targets, and judges whether the sum of the proportions of the number of risky devices affected by deviations in different sprinkler optimization targets is less than a preset threshold for the proportion of risky devices. If so, the sprinkler optimization strategy for all sprinkler optimization targets is determined to be that as long as the interval between the last sprinkler treatment process is greater than a preset sprinkler duration threshold, that is, in the sprinkler optimization target, when the risky device affected by deviations falls into the target humidity range, a specified amount of sprinkler is given. It is then determined whether the risky device affected by deviations under the specified amount of sprinkler is reliably monitored. If not, proceed to step S432. "The sum of the proportions of risky equipment affected by deviations in different sprinkler irrigation optimization objectives" refers to the sum of the proportions of risky equipment affected by deviations in all sprinkler irrigation optimization objectives, reflecting the overall coverage of risky equipment affected by deviations in sprinkler irrigation optimization objectives. "Preset risky equipment quantity proportion threshold" is a critical value set by the system to determine whether risky equipment affected by deviations is mainly distributed in sprinkler irrigation optimization objectives.

[0141] When the sum of the proportions of risky devices affected by deviations across different sprinkler irrigation optimization objectives is less than a preset threshold for the proportion of risky devices, it indicates that risky devices affected by deviations in sprinkler irrigation optimization objectives only account for a portion of the total, and a considerable proportion of risky devices are distributed across non-sprinkler irrigation optimization objectives. In this case, a unified simplified optimization strategy is adopted to standardize all sprinkler irrigation optimization objectives. The significance of this strategy lies in improving processing efficiency and avoiding overly complex differentiated processing when risky devices are relatively dispersed.

[0142] Assuming that the sum of the proportions of risky devices affected by deviations in all sprinkler optimization targets is less than the preset threshold for the proportion of risky devices, the system determines the sprinkler optimization strategy for all sprinkler optimization targets as follows: as long as the interval between the last sprinkler treatment process and the last sprinkler treatment process is longer than the preset sprinkler duration threshold, when a risky device affected by deviations falls into the target humidity range, a specified amount of sprinkler volume is applied to verify whether the device is reliable in monitoring.

[0143] S432 Based on the proportion of deviation-affected risk devices in the sprinkler optimization target and the proportion of deviation-affected risk devices in the IoT devices of the sprinkler optimization target, determine the optimization demand coefficient of the sprinkler optimization target, and determine whether the optimization demand coefficient of the sprinkler optimization target is greater than the preset demand coefficient threshold. If so, determine the sprinkler optimization strategy of the sprinkler optimization target as the basic optimization strategy. That is, as long as the interval between the last sprinkler treatment process is greater than the preset sprinkler duration threshold, then in the sprinkler optimization target, when the deviation-affected risk device falls into the target humidity range, a specified amount of sprinkler is given. Determine whether the deviation-affected risk device is reliably monitored under the specified amount of sprinkler. If not, proceed to step S433. "Percentage of IoT devices at risk of deviation impact in the sprinkler optimization target" refers to the proportion of devices at risk of deviation impact in a sprinkler optimization target to the total number of IoT devices in that target, reflecting the density of such devices within the target. "Optimization demand coefficient" is a quantitative indicator comprehensively reflecting the optimization demand of a sprinkler optimization target, calculated considering factors such as the percentage and density of devices at risk of deviation impact. "Preset demand coefficient threshold" is a system-set critical value used to determine whether the optimization demand coefficient of a sprinkler optimization target is high. "Basic optimization strategy" refers to the optimization strategy formulated for sprinkler optimization targets with high optimization demand, requiring verification sprinkler treatment after a sufficiently long time interval since the last sprinkler treatment.

[0144] When the sum of the proportions of risky equipment affected by deviations in different sprinkler irrigation optimization objectives is not less than a preset threshold for the proportion of risky equipment, it indicates that risky equipment affected by deviations is mainly distributed within the sprinkler irrigation optimization objectives, requiring further analysis of the optimization needs of each objective. The optimization demand coefficient comprehensively considers the proportion of risky equipment affected by deviations in all sprinkler irrigation optimization objectives and their density within a single objective, thus fully reflecting the urgency of optimization for that objective. When the optimization demand coefficient exceeds a preset threshold, it indicates a high optimization demand for that objective, necessitating focused processing using basic optimization strategies. This is significant in achieving refined classification of sprinkler irrigation optimization objectives, allowing for focused processing of objectives with high optimization demands.

[0145] If the optimization demand coefficient of a certain sprinkler irrigation optimization target is calculated to be greater than the preset demand coefficient threshold, the system determines the sprinkler irrigation optimization strategy for the target as the basic optimization strategy: as long as the interval between the last sprinkler irrigation process is greater than the preset sprinkler irrigation duration threshold, when the deviation-affected equipment falls into the target humidity range, a specified amount of sprinkler irrigation is given to verify whether the equipment is reliable in monitoring.

[0146] S433 determines whether the number of irrigation optimization targets in the basic optimization strategy is greater than the preset threshold for the number of optimization targets. If so, the remaining optimization targets do not need to be optimized. If not, the irrigation optimization strategy for the irrigation optimization targets is determined to be a type II optimization strategy. That is, if the interval between the last irrigation process and the last irrigation process is greater than the preset irrigation duration threshold, and the average interval between adjacent irrigation processes within the most recent preset duration is greater than the preset irrigation duration threshold, then in the irrigation optimization targets, when the deviation-affected risk equipment falls into the target humidity range, a specified amount of irrigation is given, and it is determined whether the deviation-affected risk equipment is reliably monitored under the specified amount of irrigation.

[0147] "Number of irrigation optimization targets under the basic optimization strategy" refers to the number of irrigation optimization targets identified as using the basic optimization strategy. "Preset optimization target number threshold" is a critical value set by the system to determine whether the number of irrigation optimization targets using the basic optimization strategy is excessive. "Type II optimization strategy" refers to an optimization strategy formulated for irrigation optimization targets with lower optimization needs. This strategy adds additional conditions to the basic optimization strategy, requiring not only a sufficiently long time interval since the last irrigation treatment but also a low irrigation treatment frequency in the recent period before conducting verification irrigation treatment. "Most recent preset duration" is a time window set by the system for calculating the irrigation treatment frequency. "Average interval duration between adjacent irrigation treatments" refers to the average interval duration between all adjacent irrigation treatments within the most recent preset duration, reflecting the irrigation treatment frequency within that time window.

[0148] When the optimization demand coefficient of a sprinkler irrigation optimization target is not greater than a preset demand coefficient threshold, it indicates that the optimization demand for that target is relatively low, but certain optimization measures are still required. At this point, it is necessary to further determine the number of sprinkler irrigation optimization targets covered by existing basic optimization strategies. If the number is large, it means that most sprinkler irrigation optimization targets have been prioritized, and the remaining targets with lower optimization demands do not require additional optimization. If the number is small, it means that certain measures need to be taken for these targets with lower optimization demands, but a more stringent second-type optimization strategy should be adopted to balance optimization effectiveness and resource input. Its significance lies in flexibly adjusting strategies based on existing optimization coverage to avoid over-optimization or resource waste.

[0149] Assuming the optimization demand coefficient of a certain sprinkler irrigation optimization target is not greater than a preset demand coefficient threshold, the system determines the number of sprinkler irrigation optimization targets in the basic optimization strategy. If the number is greater than the preset optimization target number threshold, then the sprinkler irrigation optimization target does not need to be optimized; if the number is not greater than the preset optimization target number threshold, then the sprinkler irrigation optimization strategy for the target is determined to be a Class II optimization strategy: it needs to simultaneously satisfy that the interval between the last sprinkler irrigation process is greater than a preset sprinkler irrigation duration threshold, and that the average interval of adjacent sprinkler irrigation processes within the most recent preset duration is greater than the preset sprinkler irrigation duration threshold, before a specified amount of sprinkler irrigation is applied for verification when the equipment at risk of deviation falls within the target humidity range.

[0150] In one possible specific embodiment: The system determines the number of sprinkler irrigation optimization objectives based on these objectives. The system counts four objectives, including plots 1, 2, 3, and 4. The system compares these four objectives with a preset threshold of five. Since the number of objectives exceeds the threshold, further analysis is needed to determine the proportion of equipment at risk of deviation and to identify differentiated sprinkler irrigation optimization strategies.

[0151] If the number of irrigation optimization targets exceeds the preset threshold of 5, the system determines the irrigation optimization strategy as a unified optimization strategy, meaning that the same optimization method is applied to all irrigation optimization targets. However, in this embodiment, the number of irrigation optimization targets is 4, which is not greater than the preset threshold of 5, therefore further analysis is required.

[0152] S42: The system determines the proportion of equipment at risk of deviation impact. The system calculates the proportion of equipment at risk of deviation impact in each sprinkler irrigation optimization objective out of the total number of equipment at risk of deviation impact. The system calculates that the sum of the proportions of equipment at risk of deviation impact for all sprinkler irrigation optimization objectives equals 0.5. The system compares this sum of 0.5 with a preset threshold of 0.4. If the sum of the proportions of equipment at risk of deviation impact is greater than the preset threshold, it indicates a high concentration of equipment at risk of deviation impact in each sprinkler irrigation optimization objective, necessitating the calculation of optimization demand coefficients to determine differentiated sprinkler irrigation optimization strategies.

[0153] S43: The system determines the sprinkler irrigation optimization strategy based on the number of target devices for sprinkler irrigation optimization and the proportion of equipment at risk of deviation.

[0154] S431: The system calculates the optimization demand coefficient for each sprinkler optimization target based on the proportion of devices at risk of deviation impact. The optimization demand coefficient measures the urgency of sprinkler optimization for each target and is calculated as the average of the proportion of devices at risk of deviation impact and the proportion of IoT devices at risk of deviation impact within the target. The optimization demand coefficients for each plot are as follows: Plot 1 = 0.1; Plot 2 = 0.2; Plot 3 = 0.13; Plot 4 = 0.3. The system compares the optimization demand coefficients for each target with a preset threshold of 0.2. Specifically, it determines that the basic optimization strategy for Plot 4 is to apply a specified amount of irrigation when the interval between the last irrigation treatment is greater than 7 days, and when the devices at risk of deviation impact fall within the target humidity range. The system then determines whether the monitoring of these devices is reliable under the specified irrigation amount.

[0155] S432: The system counts the number of basic optimization strategy targets, that is, the number of sprinkler optimization targets with an optimization demand coefficient greater than the preset optimization demand coefficient threshold of 0.55 is 1. The system compares the number of basic optimization strategy targets of 1 with the preset basic optimization strategy target number threshold of 3. The number of basic optimization strategy targets is less than the preset threshold of 3.

[0156] S433: The system determines a differentiated sprinkler irrigation optimization strategy based on the comparison between the number of basic optimization strategy objectives and the preset threshold. Since the number of basic optimization strategy objectives (1) is less than the preset threshold (3), the system determines the differentiated sprinkler irrigation optimization strategy as follows: if the interval since the last sprinkler irrigation process is greater than the preset sprinkler irrigation duration threshold, and the average interval of adjacent sprinkler irrigation processes within the most recent preset duration is greater than the preset sprinkler irrigation duration threshold (e.g., 40 hours).

[0157] This embodiment achieves the scientific determination of differentiated sprinkler irrigation optimization strategies through steps S41 to S43. Its core value is reflected in three aspects: First, by statistically analyzing the number of sprinkler irrigation optimization targets, it determines whether differentiated treatment is needed; second, by calculating the proportion of equipment at risk of deviation impact, it quantifies the urgency of optimizing each optimization target; and third, by calculating the optimization demand coefficient and comparing the number of basic optimization strategy targets, it scientifically determines the type of optimization strategy corresponding to each sprinkler irrigation optimization target, thereby achieving targeted and differentiated sprinkler irrigation optimization treatment.

[0158] Example 2 Secondly, the present invention provides an Internet of Things (IoT) collaborative control system for agricultural production, applied to the aforementioned IoT collaborative control method for agricultural production, specifically including: Risk identification module, optimized target identification module, sprinkler optimization module; The risk identification module is responsible for determining the identification strategy for risky devices affected by deviations in the IoT devices. The optimization target identification module is responsible for determining the sprinkler irrigation optimization targets in the plot; The sprinkler optimization module is responsible for determining the sprinkler optimization strategy for the sprinkler optimization target.

[0159] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0160] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0161] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. An Internet of Things collaborative control method for agricultural production, characterized in that, Specifically, it includes: Based on the distribution data of IoT devices in the target area and the consistency of changes in monitoring data between different IoT devices, the sprinkler irrigation treatment strategy for the target area is determined. The sprinkler irrigation treatment strategy is used to determine the sprinkler irrigation treatment data. Based on the correlation between IoT devices and changes in different sprinkler irrigation treatment processes, the identification strategy for the deviation impact risk devices in the IoT devices is determined. Based on the distribution data of deviation-affected risk equipment in different plots of land in the target area, and the sprinkler irrigation treatment strategy in the target area, the sprinkler irrigation optimization target in the plots is determined; Based on the sprinkler optimization target data and the composition of risky equipment due to deviations in different sprinkler optimization targets, the sprinkler optimization strategy for the sprinkler optimization target is determined.

2. The IoT collaborative control method for agricultural production of claim 1, wherein, The distribution data of the IoT devices includes the number of IoT devices.

3. The IoT collaborative control method for agricultural production of claim 1, wherein, The consistency of changes in monitoring data among the IoT devices is determined based on the deviation rate of monitoring data among the IoT devices at different monitoring times.

4. The IoT collaborative control method for agricultural production as described in claim 1, characterized in that, The method for determining the sprinkler irrigation strategy for the target area is as follows: Using the distribution data of IoT devices in the target area, determine the average number of IoT devices per unit area in the target area, and use this as the distribution density; Based on the consistency of changes in monitoring data between different IoT devices, the proportion of monitoring times when the deviation rate of the monitoring data between the IoT device and other IoT devices is less than a preset deviation rate threshold is determined, and the proportion of monitoring times when the deviation rate is less than the preset deviation rate threshold is used as the similarity coefficient of the monitoring data between the IoT device and other IoT devices. The sprinkler irrigation strategy for the target area is determined based on the distribution density in the target area and the similarity coefficient of monitoring data between different IoT devices.

5. The IoT collaborative control method for agricultural production of claim 4, wherein, If the distribution density in the target area is less than the preset distribution density threshold, then in order to ensure the timeliness of the sprinkler irrigation treatment for crops, the sprinkler irrigation treatment strategy for the target area is a relaxed sprinkler irrigation strategy. When the proportion of IoT devices in the target area that reach the target humidity range is above the first proportion, sprinkler irrigation treatment is carried out.

6. The IoT collaborative control method for agricultural production of claim 4, wherein, If the distribution density in the target area is less than a preset distribution density threshold, the following situations also apply: Based on the similarity coefficient of monitoring data between different IoT devices, IoT devices whose monitoring data similarity coefficient is greater than a preset similarity coefficient threshold are grouped into the same device group; If there is a group of devices with a proportion of IoT devices greater than a preset proportion threshold, the reliability of the monitoring data of IoT devices can be determined based on the degree of consistency. Therefore, in order to ensure the timeliness of the sprinkler irrigation treatment for crops, the sprinkler irrigation treatment strategy for the target area is a strict sprinkler irrigation strategy. When the proportion of IoT devices in the target area that reach the target humidity range is above a preset proportion, sprinkler irrigation treatment is carried out.

7. The IoT collaborative control method for agricultural production of claim 1, wherein, The method for determining the identification strategy for risky devices affected by deviations in the aforementioned IoT devices is as follows: Using the sprinkler irrigation treatment data, determine the interval between adjacent sprinkler irrigation processes in the target area; Based on the correlation between the IoT device and different irrigation processes, the irrigation process caused by the IoT device falling into the target humidity range is determined, and the irrigation process caused by the IoT device falling into the target humidity range is regarded as the associated influencing process. Based on the time interval between adjacent sprinkler irrigation processes in the target area and the data of IoT devices that have related influence processes, a strategy for identifying devices with deviation impact risks in the IoT devices is determined.

8. The IoT collaborative control method for agricultural production of claim 7, wherein, Based on the average interval between adjacent irrigation processes in the target area, the average interval is determined. If the average interval exceeds a preset interval threshold, the IoT device with the associated influence process is considered as a device with deviation influence risk.

9. The IoT collaborative control method for agricultural production of claim 1, wherein, The method for determining the sprinkler optimization strategy for the aforementioned sprinkler optimization objective is as follows: Based on the sprinkler irrigation optimization target data, determine the number of sprinkler irrigation optimization targets; The proportion of the number of risky devices affected by deviations in different sprinkler irrigation optimization objectives is determined based on the proportion of the number of risky devices affected by deviations in all deviations. Based on the number of sprinkler irrigation optimization targets and the proportion of risky equipment affected by deviations in different sprinkler irrigation optimization targets, the sprinkler irrigation optimization strategy for the sprinkler irrigation optimization targets is determined.

10. An Internet of Things collaborative control system for agricultural production, applied to the Internet of Things collaborative control method for agricultural production according to any one of claims 1-9, characterized in that, Specifically, it includes: Risk identification module, optimized target identification module, sprinkler optimization module; The risk identification module is responsible for determining the identification strategy for risky devices affected by deviations in the IoT devices. The optimization target identification module is responsible for determining the sprinkler irrigation optimization targets in the plot; The sprinkler optimization module is responsible for determining the sprinkler optimization strategy for the sprinkler optimization target.

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