A sensor data analysis method and system for a grain drying tower

Abstract

This invention provides a sensor data analysis method and system for grain drying towers, belonging to the field of sensor equipment technology. Specifically, it includes: determining a construction method for a basic building group within a monitoring group by utilizing sensor equipment data in a matched monitoring group, as well as the deviation between simulation data and monitoring data from different sensor equipment; determining activation processing strategies for sensor equipment of different grain types based on the correlation between the basic building group data, simulation deviation data in the basic building group, and the matched monitoring group; updating risky devices based on the activation processing strategies; and determining a monitoring analysis and optimization method for sensor equipment using the update processing results of risky devices and the activation processing strategies for different grain types, thereby improving the operational reliability of the sensor equipment.

Description

Technical Field

[0001] This invention belongs to the field of sensor equipment technology, and in particular relates to a sensor data analysis method and system for grain drying towers. Background Technology

[0002] During operation, grain drying towers require periodic sampling to determine changes in the moisture content of the drying target, allowing for targeted adjustments to the drying temperature. A similar technical solution is presented in invention patent application CN202411543852.5, "An Online Moisture Monitoring Method and System for Grain Drying." However, this technical solution has the following drawbacks: When performing temperature monitoring, turning on all sensors simultaneously increases power consumption, leading to poor monitoring reliability. Furthermore, prolonged operation of the sensors in harsh environments further degrades their reliability. Therefore, determining the appropriate temperature monitoring strategy within the tower based on the data input requirements of the tower's temperature field model and the monitoring reliability of different sensor combinations is crucial. This strategy aims to ensure reliable temperature monitoring while simultaneously improving the stability and reliability of the sensors, becoming a pressing technical challenge.

[0003] Specifically, this application provides a sensor data analysis method and system for grain drying towers. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a sensor data analysis method for grain drying towers, which includes: S1 uses sensor data from the grain drying tower to construct a temperature field model inside the grain drying tower. Based on the data input requirements of the sensor devices in the temperature field model inside the tower, the sensor devices are divided into different groups. Using the simulation data of the temperature field model inside the tower for each group, the matching monitoring group in the group is determined. S2 uses the sensor device data in the matched monitoring group, as well as the deviation between the simulation data and the monitoring data of different sensor devices, to determine the construction method of the basic construction group in the group; S3 uses the construction method to determine the basic construction group, and determines the activation processing strategy for sensor devices of different grain types based on the basic construction group data, the simulation deviation data in the basic construction group and the correlation between the matching monitoring group and the data. S4 updates the deviation risk equipment based on the activation processing strategy, and uses the update processing results of the deviation risk equipment and the activation processing strategies of sensor equipment for different grain types to determine the monitoring, analysis and optimization method of the sensor equipment.

[0005] The beneficial effects of this invention are as follows: By using simulation data from the temperature field model within the tower, matching monitoring groups are identified. Based on the simulation data from the temperature field model within the tower, the consistency between the simulation data and the monitoring data, with the monitoring data from different groups of sensors as input, is determined under the current temperature field model within the tower. By utilizing the consistency and the number of sensor devices, the matching monitoring groups that need to be activated during the drying process of all types of grains are identified, which also lays the foundation for reducing the number of sensor devices to be activated within the drying tower.

[0006] By utilizing the updated processing results of deviation risk equipment and the activation processing strategies of sensor equipment for different grain types, the monitoring and analysis optimization methods for sensor equipment are determined. Specifically, using the updated processing results of deviation risk equipment and the groups for constructing and processing the temperature field model inside the tower for different grain types, the reliability of using the current basic construction group to assist in temperature field monitoring and processing is determined. Based on the reliability of using the current basic construction group to assist in temperature field monitoring and processing, the activation management strategies for all sensor equipment are determined in a targeted manner. This ensures the operational reliability of the sensor equipment, improves the overall reliability of temperature monitoring and processing, and verifies the reliability of the existing temperature field model inside the tower.

[0007] Furthermore, the sensor data of the grain drying tower includes the number and location of the sensor devices in the grain drying tower.

[0008] Furthermore, the construction of the temperature field model inside the grain drying tower is carried out, specifically including: Based on the temperature monitoring data of the sensor equipment inside the grain drying tower, a temperature field model inside the grain drying tower is constructed.

[0009] Furthermore, the data input requirements of the sensor devices are determined based on the required number of sensor devices when solving for temperature using the temperature field model.

[0010] Furthermore, the sensor devices are divided into different groups, specifically including: Based on meeting the data input requirements of the sensor devices, combinations of sensor devices that meet the data input requirements of the sensor devices are grouped into the same group.

[0011] Furthermore, the simulation data of the temperature field model inside the tower for the group is determined based on the simulation results of the temperature field model inside the tower in different sensor devices, with the monitoring data of the sensor devices of the group as input.

[0012] Furthermore, the method for determining the matching monitoring group within the group is as follows: Based on the simulation data of the temperature field model inside the tower, the degree of consistency between the group and the monitoring data of different sensor devices in different simulation processes is determined. Using the aforementioned degree of consistency, the deviation sensor devices in different simulation processes are determined; By using the deviation sensor devices in different simulation processes and the sensor devices in the group, it is determined whether the group belongs to the matching monitoring group.

[0013] Furthermore, the deviation sensor device is a sensor device in which the average absolute value of the deviation rate of the monitoring data at different monitoring times during the simulation is greater than a preset deviation rate threshold.

[0014] Furthermore, by using the deviation sensor devices in different simulation processes and the sensor devices in the group, it is determined whether the group belongs to a matching monitoring group, specifically including: If the number of deviation sensor devices in the group is less than a preset threshold for the number of deviation sensor devices in different simulation processes, then the group is considered a potential matching group and the process proceeds to the next step. Otherwise, the group is determined not to belong to the matching monitoring group. Determine whether the number of sensor devices in the potential matching group is the minimum. If so, the group is determined to belong to the matching monitoring group; otherwise, the group is determined not to belong to the matching monitoring group.

[0015] Furthermore, the method for determining the monitoring, analysis, and optimization method for the sensor device is as follows: The number of deviation risk devices is determined using the update processing results of the deviation risk devices; Based on the activation processing strategy of sensor devices for different grain types, groups for constructing and processing the temperature field model inside the tower are determined for different grain types, and these groups are used as matching groups for construction. By utilizing the number of deviation risk devices and the degree of overlap between deviation risk devices and deviation influence devices in different grain types that form matching groups, the monitoring and analysis optimization method for the sensor devices is determined.

[0016] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the aforementioned sensor data analysis method for a grain drying tower when running the computer program.

[0017] 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.

[0018] 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

[0019] 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.

[0020] Figure 1 This is a flowchart of a sensor data analysis method for grain drying towers; Figure 2 This is a flowchart illustrating the method for determining the matching monitoring group within the group; Figure 3 This is a flowchart illustrating the method for determining the construction method of the basic building blocks of a group. Detailed Implementation

[0021] 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.

[0022] 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.

[0023] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a sensor data analysis method for a grain drying tower is provided, specifically including: The core objective of this embodiment is to effectively reduce the number of sensor devices in operation while ensuring the reliability of temperature field monitoring through systematic analysis of sensor data from grain drying towers, and to continuously optimize the monitoring strategy based on deviation risk analysis. Its core logic is as follows: first, matching monitoring groups are determined based on the grouped simulation data of the sensor devices; then, the construction method of the basic construction group is determined based on the simulation deviation; next, the operation handling strategy for different grain types is determined based on the correlation between the basic construction group data and the matching monitoring groups; finally, the monitoring analysis optimization method is determined based on the updated results of the deviation risk devices. The overall logic follows the process of "determining matching monitoring groups → determining the construction method of the basic construction group → determining the operation handling strategy → determining the monitoring analysis optimization method".

[0024] S1 uses sensor data from the grain drying tower to construct a temperature field model inside the grain drying tower. Based on the data input requirements of the sensor devices in the temperature field model inside the tower, the sensor devices are divided into different groups. Using the simulation data of the temperature field model inside the tower for each group, the matching monitoring group in the group is determined. S2 uses the sensor device data in the matched monitoring group, as well as the deviation between the simulation data and the monitoring data of different sensor devices, to determine the construction method of the basic construction group in the group; S3 uses the construction method to determine the basic construction group, and determines the activation processing strategy for sensor devices of different grain types based on the basic construction group data, the simulation deviation data in the basic construction group and the correlation between the matching monitoring group and the data. S4 updates the deviation risk device based on the activation processing strategy, and uses the update processing results of the deviation risk device and the activation processing strategies of sensor devices for different grain types to determine the monitoring, analysis and optimization method of the sensor device.

[0025] Furthermore, the sensor data of the grain drying tower includes the number and location of the sensor devices in the grain drying tower.

[0026] Furthermore, the construction of the temperature field model inside the grain drying tower is carried out, specifically including: Based on the temperature monitoring data of the sensor equipment inside the grain drying tower, a temperature field model inside the grain drying tower is constructed.

[0027] Furthermore, the data input requirements of the sensor devices are determined based on the required number of sensor devices when solving for temperature using the temperature field model.

[0028] Furthermore, the sensor devices are divided into different groups, specifically including: Based on meeting the data input requirements of the sensor devices, combinations of sensor devices that meet the data input requirements of the sensor devices are grouped into the same group.

[0029] Furthermore, the simulation data of the temperature field model inside the tower for the group is determined based on the simulation results of the temperature field model inside the tower in different sensor devices, with the monitoring data of the sensor devices of the group as input.

[0030] The sensor data refers to the temperature monitoring data collected by various sensor devices inside the grain drying tower, including the number and location information of the sensor devices; the tower temperature field model refers to the simulation model that uses the temperature monitoring data from the sensor devices as input to mathematically model the temperature distribution inside the drying tower; the data input requirement refers to the minimum number of sensor devices required for the temperature field model to solve for the temperature inside the tower; the group refers to the combination of sensor devices that meets the data input requirement, that is, the data input from all sensor devices in the same group can drive the temperature field model to complete a complete temperature solution; the simulation data refers to the simulation temperature results output by the temperature field model at all sensor device locations using the monitoring data from a certain group of sensor devices as input; the matched monitoring group refers to the group with the minimum number of sensor devices that meets the requirement in all simulations, representing the optimal combination that can achieve reliable simulation coverage with the fewest devices.

[0031] Suppose a grain drying tower is equipped with multiple sensor devices distributed at different heights and cross-sectional locations. Solving the temperature field model requires data from a minimum number of sensor devices as input. Therefore, all sensor devices that meet this requirement are grouped into groups, and the temperature field model is simulated one by one using the monitoring data of each group. The simulated temperature at each sensor device location is output and compared with the actual monitored temperature. The group with the fewest deviation sensor devices and whose entire simulation process meets the deviation requirement is identified as the matching monitoring group.

[0032] This step establishes a sensor group evaluation framework with both simulation accuracy and equipment economy as constraints. Its significance lies in identifying the group that requires the fewest number of sensor devices to be turned on while ensuring monitoring reliability through a systematic evaluation of the simulation coverage capability of each group. This lays the foundation for rationally controlling the number of sensor devices to be turned on under different drying conditions.

[0033] Specifically, such as Figure 2 As shown, the method for determining the matching monitoring group in the group is as follows: In this embodiment, based on the simulation data of the temperature field model inside the tower, the degree of consistency between the simulation data and the monitoring data with the monitoring data of different groups of sensors as input under the current temperature field model inside the tower is determined. By using the degree of consistency and the number of sensor devices, the matching monitoring groups that need to be activated during the drying process of all types of grains are determined, which also lays the foundation for reducing the number of sensor devices to be activated inside the drying tower.

[0034] S11 determines the degree of consistency between the group's simulation data in the tower temperature field model and the monitoring data of different sensor devices in different simulation processes. The degree of consistency refers to the degree of agreement between the simulated temperature value output by the model and the actual monitored temperature value of each sensor device when the temperature field model is driven by data from a group of sensor devices. It is usually quantified by the average absolute value of the deviation rate. The smaller the deviation rate, the higher the degree of consistency.

[0035] Assuming there are multiple sensor devices inside the drying tower, the data from a certain group of devices is used as the input to the temperature field model. The model simulates the temperature at the locations of the remaining sensor devices and outputs the simulated values. The simulated values ​​are compared with the actual monitored values ​​at these locations one by one. The absolute value of the deviation rate of each device at different monitoring times is calculated and averaged to obtain the consistency index of the device in this simulation process.

[0036] This step establishes a quantitative assessment basis for the simulation accuracy of each group. Its significance lies in providing objective data support for subsequent identification of deviation sensor devices and judgment of whether the group meets the matching requirements by systematically quantifying the deviation between simulation data and monitoring data, ensuring that the selection of matching monitoring groups is based on a rigorous simulation accuracy assessment.

[0037] S12 uses the degree of consistency to determine the deviation sensor devices in different simulation processes; The deviation sensor device refers to a sensor device in which, during the simulation process, the average absolute value of the deviation rate of the monitoring data at different monitoring times is greater than a preset deviation rate threshold. This indicates that there is a large deviation between the simulated temperature and the actual monitored temperature at the device location, and the current sensor combination is not accurate enough in simulating the temperature field at the device location.

[0038] Assuming that the consistency of multiple simulation processes is calculated one by one, and a preset deviation rate threshold is set to a fixed value, then sensor devices whose average absolute deviation rate exceeds the threshold are identified as deviation sensor devices, and the set and number of deviation sensor devices in each simulation process are recorded.

[0039] This step identifies the locations of sensors with insufficient temperature field model coverage in each simulation process. Its significance lies in the fact that by accurately locating the deviation sensor equipment, it quantifies the shortcomings of different groups in simulating the temperature field inside the tower, providing a direct basis for judging whether a group meets the admission criteria for a matching monitoring group.

[0040] S13 determines whether the group belongs to a matching monitoring group by using the deviation sensor devices in different simulation processes and the sensor devices in the group.

[0041] The determination of the matching monitoring group must meet two conditions simultaneously: first, the number of deviation sensor devices in the group is less than the preset threshold for the number of deviation sensor devices in all simulation processes; second, among all candidate groups (potential matching groups) that meet condition one, the group contains the fewest number of sensor devices, thereby minimizing the number of devices turned on while ensuring simulation accuracy.

[0042] If, after identification by S12, some groups have a number of deviation sensor devices exceeding a preset threshold in certain simulation processes, these groups are directly excluded. For groups whose number of deviation sensor devices does not exceed the threshold in all simulation processes, they are marked as potential matching groups. Finally, the group with the fewest number of sensor devices is selected as the matching monitoring group.

[0043] This step, with the premise of achieving the required simulation accuracy and the optimization goal of minimizing the number of devices, determines the optimal sensor activation scheme. Its significance lies in the fact that through a dual screening mechanism, it ensures that the global coverage capability of the temperature field simulation is not lower than the preset standard, while selecting the scheme with the fewest devices from the group that meets the conditions, thereby achieving the optimal allocation of sensor operating resources and laying the foundation for low-cost and reliable monitoring of the drying tower.

[0044] It should be noted that the deviation sensor device is a sensor device in which the average absolute value of the deviation rate of the monitoring data at different monitoring times during the simulation is greater than a preset deviation rate threshold.

[0045] This embodiment achieves the systematic determination of matching monitoring groups through steps S11 to S13. Its core value is reflected in three aspects: First, by quantitatively evaluating the consistency between simulation data and monitoring data of each group, an objective simulation accuracy measurement system is established; second, by identifying and constraining the number of deviation sensor devices, an entry threshold for the simulation coverage capability of the group is established; and third, by selecting the scheme with the fewest number of sensor devices among the candidate groups, the optimal balance between monitoring reliability and equipment economy is achieved.

[0046] Suppose a grain drying tower has 20 sensor devices installed, numbered T1 to T20, distributed at different heights and cross-sectional locations within the tower. The temperature field model requires data from at least 6 sensor devices as input when solving for the temperature inside the tower. Therefore, all combinations that satisfy the condition of "exactly containing 6 sensor devices" are grouped into separate groups, generating several groups in total.

[0047] In S11, the actual monitoring data of the six sensor devices in each group are used as input to drive the temperature field model for simulation, and the simulated temperature values ​​at the locations of the remaining 14 sensor devices are output. For each simulation process, the difference between the simulated temperature and the actual monitored temperature of each sensor device at different monitoring times is calculated and divided by the actual monitored temperature (i.e., the deviation rate). The absolute values ​​of the deviation rates at all monitoring times are averaged to obtain the average absolute value of the deviation rate of each device in this simulation process, which serves as a quantitative indicator of the degree of consistency.

[0048] In S12, if the preset deviation rate threshold is set to 8%, then in a certain simulation process, sensor devices with an average absolute deviation rate greater than 8% are identified as deviation sensor devices in that simulation process. Taking group G1 (including T1, T3, T5, T8, T12, and T16) as an example, after multiple simulation processes, the set of deviation sensor devices in each simulation process is identified, and their number is recorded.

[0049] In S13, the preset threshold for the number of deviation sensor devices is set to 3. After checking, the number of deviation sensor devices in group G1 does not exceed 3 in all simulations, which meets condition one and is listed as a potential matching group. After comparing the number of sensor devices in all potential matching groups that meet condition one, the number of sensor devices in group G1 (6) is the same as other potential matching groups, but its total number of deviation sensor devices is the smallest in all simulations. It performs best among potential matching groups with the same number of devices. Finally, group G1 is determined to be the matching monitoring group, and the corresponding sensor devices are T1, T3, T5, T8, T12, and T16.

[0050] Specifically, such as Figure 3 As shown, the method for determining the construction method of the basic building group in the group is as follows: In this embodiment, the reliability of the matching monitoring group in the temperature monitoring process is determined based on the number of sensor devices in the matching monitoring group and the degree of overlap of the deviation sensor devices in different simulation processes. The reliability is then used to determine the construction method of the basic building group for temperature field monitoring and processing together with the matching monitoring group. This further improves the reliability of temperature monitoring and processing while reducing the number of sensor devices that need to be turned on.

[0051] S21 uses the sensor device data in the matching monitoring group to determine the number of sensor devices in the matching monitoring group; The number of sensor devices in the matching monitoring group refers to the total number of sensor devices included in the final determined matching monitoring group; the basic matching coefficient refers to the proportion of the number of sensor devices in the matching monitoring group to the total number of sensor devices in the drying tower, reflecting the coverage of the matching monitoring group to all sensor devices.

[0052] Assuming that the drying tower is equipped with multiple sensor devices, and the determined matching monitoring group includes several of these devices, the ratio of the number of devices in the matching monitoring group to the total number of all devices is calculated to obtain the basic matching coefficient. This coefficient is then compared with a preset matching coefficient threshold. If the basic matching coefficient is less than the preset matching coefficient threshold, the preset construction method is directly used to determine the construction method of the basic construction group. If it is not less than the threshold, the process proceeds to S22 for further deviation overlap analysis.

[0053] This step uses the coverage ratio of the devices in the matching monitoring group as the first-level judgment indicator for selecting the group construction method. Its significance is that when the proportion of the number of sensor devices in the matching monitoring group is extremely low, it indicates that its coverage is limited. At this time, there is no need to perform complex deviation and overlap analysis, and the preset construction method can be directly adopted, thereby simplifying the decision-making process and improving processing efficiency.

[0054] S22 determines the degree of overlap of the deviation sensor devices of the matching monitoring group in different simulation processes based on the deviation between the simulation data and the monitoring data of different sensor devices; The overlap of the deviation sensor devices refers to the proportion of times a certain sensor device is identified as a deviation sensor device in all simulation processes out of the total number of simulation processes, which is the simulation deviation factor of the device. The simulation deviation factor reflects the frequency of continuous deviation of a specific sensor device in multiple simulation processes. The higher the simulation deviation factor, the more difficult it is for the device to be accurately covered by the simulation model of the matching monitoring group under each simulation condition, which is a location with a high risk of deviation.

[0055] Assuming the matching monitoring group undergoes multiple simulation processes, the number of times each sensor device is identified as a deviation sensor device in all simulation processes is counted, and the result is divided by the total number of simulation processes to obtain the simulation deviation factor of that device. Devices with higher simulation deviation factors are consistently identified as deviation positions in all simulations and should be given special consideration in the basic construction group.

[0056] This step, by statistically analyzing the frequency of deviations of each sensor device during multiple simulations, characterizes the stability distribution of the simulation accuracy of the temperature field inside the tower at different locations. Its significance lies in identifying the locations of high-risk devices that continuously exhibit deviations in the simulation model driven by the matching monitoring group, providing accurate deviation hotspot information for the subsequent construction target of the basic construction group, and ensuring that the addition of the basic construction group can effectively compensate for the simulation coverage shortcomings of the matching monitoring group.

[0057] It should be noted that the basic construction method is to construct the basic construction group with the constraint that the device with the largest simulation deviation factor and the sensor device of the matching monitoring group belong to the device with the largest simulation deviation factor and the sensor device of the matching monitoring group.

[0058] The preset construction method is to construct the basic construction group with the deviation-affecting device and the sensor device of the matching monitoring group as constraints, and with the goal that all sensor devices in the basic construction group belong to the deviation-affecting device and the sensor device of the matching monitoring group.

[0059] The above steps include the following: S221 determines the proportion of the simulation process in which the sensor device belongs to the deviation sensor device based on the degree of overlap of the deviation sensor devices in different simulation processes of the matching monitoring group, and uses it as the simulation deviation factor of the sensor device. It then determines whether there are sensor devices whose simulation deviation factor is greater than the preset deviation factor threshold. If yes, proceed to step S222; otherwise, adopt the basic construction method to determine the construction method of the basic construction group in the group. The simulation deviation factor refers to the ratio of the number of times a specific sensor device is identified as a deviation sensor device in the entire simulation process to the total number of simulations. The preset deviation factor threshold is a critical value for judging whether a sensor device belongs to a high-frequency deviation position. Exceeding this threshold indicates that the device continues to show deviations under multiple simulation conditions and has a high risk of deviation.

[0060] Assuming the matched monitoring group undergoes multiple simulation processes, the simulation deviation factor is calculated for all sensor devices in the tower that are not part of the matched monitoring group. If the simulation deviation factor of all devices does not exceed the preset deviation factor threshold, it indicates that the simulation coverage capability of the matched monitoring group is relatively balanced, and the basic construction method is directly used to determine the basic construction group. If there are devices whose simulation deviation factor exceeds the threshold, then proceed to S222 to screen and determine the number of devices affected by the deviation.

[0061] This step uses the threshold judgment of the simulation deviation factor as the diversion node for identifying the deviation-affected equipment. Its significance lies in distinguishing between the uniform situation of temperature field simulation coverage and the situation of local high deviation. For the situation of local high deviation, a more refined deviation-affected equipment analysis process is initiated, thereby ensuring that the construction method of the basic building group can be differentiated according to the specific deviation distribution characteristics.

[0062] S222 identifies sensor devices with simulation deviation factors greater than a preset deviation factor threshold as deviation-affecting devices and determines whether the number of such devices meets the requirements. If yes, proceed to step S23; otherwise, use a preset construction method to determine the construction method of the basic construction group in the group.

[0063] The deviation-affecting devices refer to sensor devices whose simulation deviation factor exceeds the preset deviation factor threshold and which continuously exhibit deviations during each simulation; the requirement that the number of deviation-affecting devices meets the requirements means that the number of deviation-affecting devices is within a reasonable range, i.e., not excessive.

[0064] Assuming that devices whose simulation deviation factors exceed the preset deviation factor threshold are identified as deviation-affected devices, the total number of deviation-affected devices is counted. If the number of deviation-affected devices is too large, it exceeds the applicable scope of construction based on deviation-affected device constraints. Instead, a preset construction method is adopted, using all deviation-affected devices plus the matching monitoring group devices as the constraint target to construct the basic construction group. If the number of deviation-affected devices meets the requirements, then proceed to S23 for comprehensive judgment.

[0065] This step verifies the reasonableness of the number of devices affected by the deviation, preventing the construction constraints of the basic building group from failing due to an excessively large scale of devices affected by the deviation. Its significance lies in ensuring the applicability of subsequent simulation matching value calculation and relaxed construction methods, and achieving a more accurate basic building group design when the deviation scale is controllable, thereby effectively improving the pertinence and reliability of temperature field monitoring.

[0066] S23 uses the number of sensor devices in the matching monitoring group and the degree of overlap of the deviation sensor devices in different simulation processes to determine the construction method of the basic building group in the group.

[0067] Furthermore, based on the basic matching coefficient of the matching monitoring group and the simulation deviation factor of different sensor devices, the simulation matching value of the matching monitoring group is determined. It is then determined whether the simulation matching value of the matching monitoring group is greater than a preset matching threshold. If so, a relaxed construction method is used to determine the construction method of the basic construction group in the group. If not, a preset construction method is used to determine the construction method of the basic construction group in the group.

[0068] Specifically, the relaxed construction method involves constructing a basic construction group with the constraint of a preset number of deviation-affected devices with the largest simulation deviation factor and the sensor devices of the matching monitoring group, and with the objective that all sensor devices in the basic construction group belong to the preset number of deviation-affected devices with the largest simulation deviation factor and the sensor devices of the matching monitoring group.

[0069] The relaxed construction method involves using a preset number of devices with the largest simulation deviation factor and the sensor devices of the matched monitoring group as constraints, and aiming to construct the basic construction group so that all sensor devices in the basic construction group belong to the range of the aforementioned constrained devices. The simulation matching value refers to a comprehensive index reflecting the overall simulation coverage capability of the matched monitoring group, calculated by combining the basic matching coefficient and the simulation deviation factor of each sensor device. The preset matching threshold is a critical value for determining whether the simulation matching value meets the conditions for using the relaxed construction method.

[0070] Assuming the number of devices affected by the deviation meets the requirements, the simulation matching value is calculated by combining the basic matching coefficient and the simulation deviation factor of the devices affected by the deviation. If the simulation matching value is greater than the preset matching threshold, a relaxed construction method is adopted, and only the preset number of devices affected by the deviation with the largest simulation deviation factor (not all devices affected by the deviation) plus the matching monitoring group devices are used as the constraint target. If the simulation matching value is not greater than the preset matching threshold, the preset construction method is still adopted, and all devices affected by the deviation plus the matching monitoring group devices are used as the constraint target.

[0071] This step, through a comprehensive evaluation of the simulation matching values, further distinguishes the construction strictness of the basic construction group on the basis that the scale of the deviation-affected equipment is controllable. Its significance lies in the fact that when the overall simulation coverage capability of the matching monitoring group is strong, it allows for more relaxed construction constraints (introducing only a few deviation-affected equipment with the highest risk), achieving effective supplementation of temperature field monitoring with fewer equipment activations, and achieving the optimal balance between overall reliability and economy.

[0072] This embodiment, through steps S21 to S23, achieves a systematic determination of the construction method for the basic building group. Its core value is reflected in three aspects: First, by pre-judging the basic matching coefficient, it quickly filters out scenarios applicable to the preset construction method, avoiding unnecessary complex analysis; second, by calculating the simulation deviation factor on a device-by-device basis, it accurately identifies high-risk deviation locations in the temperature field simulation, providing an objective basis for the construction target of the basic building group; and third, by comprehensively evaluating the simulation matching value, it achieves adaptive adjustment of the strictness of the construction of the basic building group under different simulation coverage quality conditions.

[0073] Continuing with the specific scenario of S1, the matching monitoring group G1 includes sensor devices T1, T3, T5, T8, T12, and T16, a total of 6 devices. The drying tower has a total of 20 sensor devices from T1 to T20.

[0074] In S21, the number of sensor devices in the matching monitoring group G1 is 6, and the basic matching coefficient = 6 ÷ 20 = 0.30.

[0075] Set the preset matching coefficient threshold to 0.25. If 0.30 ≥ 0.25, proceed to S22.

[0076] In S22, the number of times the non-matching monitoring group sensor devices (T2, T4, T6, T7, T9, T10, T11, T13, T14, T15, T17, T18, T19, T20, a total of 14) were identified as deviation sensor devices in all simulation processes. Assuming a total of 10 simulation processes were performed, the simulation deviation factor for each device (number of times identified as a deviation sensor device ÷ 10 times) is as follows: In S221, if the preset deviation factor threshold is set to 0.40, then the sensor devices with a simulation deviation factor greater than 0.40 are: T6 (0.70), T9 (0.80), T13 (0.60), and T18 (0.50), a total of 4. Since there are sensor devices with a simulation deviation factor greater than the preset deviation factor threshold, proceed to S222.

[0077] In S222, the devices affected by the deviation are T6, T9, T13, and T18, a total of 4. Assuming the requirement for the number of devices affected by the deviation is no more than 6, and 4 meet the requirement, proceed to S23.

[0078] In S23, the simulation matching value is calculated: Let the simulation matching value = basic matching coefficient ÷ (sum of simulation deviation factors of devices affected by deviation ÷ number of devices affected by deviation), the sum of simulation deviation factors of devices affected by deviation = 0.70 + 0.80 + 0.60 + 0.50 = 2.60, the average simulation deviation factor of devices affected by deviation = 2.60 ÷ 4 = 0.65; the simulation matching value = 0.30 ÷ 0.65 ≈ 0.462; Set the preset matching threshold to 0.40. Since 0.462 > 0.40, a relaxed construction method is adopted, which means that the preset number of deviation-affected devices with the largest simulation deviation factor and the sensor devices of the matching monitoring group are used as constraints.

[0079] Assuming a preset quantity of 2, the two devices with the largest simulation deviation factors are selected as T9 (0.80) and T6 (0.70). The constraint devices of the relaxed construction method are: the matching monitoring group {T1, T3, T5, T8, T12, T16} and the priority deviation-affected devices {T9, T6}, for a total of 8 devices. All sensor devices in the basic construction group must belong to the combination range of these 8 devices. The basic construction group is constructed with this as the goal.

[0080] Furthermore, the method for determining the activation processing strategy of the grain-type sensor device is as follows: In the above steps, based on the number of basic building groups and the correlation between the simulation deviation data in the basic building groups and the matching monitoring groups, the difficulty and suitability of using basic building groups to assist in temperature monitoring are determined. Based on the difficulty and suitability of using basic building groups to assist in temperature monitoring, the activation strategy of the sensor devices is determined. This not only ensures the reliability of temperature monitoring for relatively discrete particle distribution, but also reduces the difficulty of temperature monitoring analysis and the operational reliability of the sensor devices by reducing the number of basic building groups and the number of activated sensor devices.

[0081] S31 uses the basic building group data to determine the number of basic building groups; In the above steps, if the number of basic building groups is less than the preset threshold for the number of basic building groups, all sensor devices in the basic building groups are turned on in different grain types, thereby realizing the construction of simulation models of basic building groups and matching building groups. This reduces the number of sensor devices that need to be turned on while ensuring the reliability of monitoring and processing.

[0082] The number of basic building groups refers to the total number of all basic building groups determined in stage S2 that can cooperate with the matching monitoring groups for temperature field monitoring. The threshold for the number of basic building groups refers to the critical value for judging whether the number of basic building groups is within a manageable range. When the number of basic building groups is less than the threshold, the sensor devices of all basic building groups are turned on in each type of grain, which can ensure monitoring reliability at a relatively low cost.

[0083] Assuming that after the analysis in stage S2, the system identifies multiple available basic building groups and counts their total number, if the number is less than the preset threshold for the number of basic building groups, then the sensor devices of all basic building groups will be activated in the drying process of all grain types, without the need to judge the differentiated activation strategy; if the number is not less than the preset threshold for the number of basic building groups, then proceed to stage S32 for deviation overlap analysis.

[0084] This step uses the total number of basic construction groups as the first-level judgment criterion for the activation strategy. Its significance lies in the fact that when the number of basic construction groups is limited, the management of full activation is simpler than the differentiated strategy, and it can directly ensure complete coverage of temperature field monitoring for each type of grain. When the number is large, full activation consumes a lot of equipment, and further screening is required through deviation overlap analysis to achieve refined control of the number of activated equipment.

[0085] It should also be noted that if the number of basic building groups is not less than the preset basic group number threshold, proceed to step S32; S32 determines the proportion of the number of deviation-affected devices in the basic construction group to the number of deviation-affected devices in the matching monitoring group based on the correlation between the simulation deviation data in the basic construction group and the matching monitoring group, and uses the proportion of the number of deviation-affected devices in the basic construction group to the number of deviation-affected devices in the matching monitoring group as the deviation overlap value of the basic construction group. The deviation overlap value refers to the proportion of the number of deviation-affected devices in a certain basic construction group to the total number of deviation-affected devices in the matching monitoring group. This ratio reflects the coverage of the deviation-affected locations of the basic construction group to the matching monitoring group. The lower the deviation overlap value, the lower the overlap between the basic construction group and the matching monitoring group in terms of deviation-affected locations, and the weaker the contribution to compensating for simulation deviations.

[0086] Assuming there are several devices affected by the deviation in the matching monitoring group, for each basic building group, count the number of sensor devices that belong to the deviation affected by the matching monitoring group, divide the number by the total number of devices affected by the deviation in the matching monitoring group, obtain the deviation overlap value of each basic building group, and compare it with the preset deviation overlap threshold.

[0087] This step quantifies the ability of each basic building group to compensate for the shortcomings in the deviation coverage of the matching monitoring group. Its significance lies in the fact that basic building groups with higher deviation overlap values ​​contain more deviation hotspot devices of the matching monitoring group, which can more effectively supplement the insufficient simulation coverage of the matching monitoring group. By calculating the deviation overlap value, a quantitative basis is provided for the differentiated judgment of the subsequent activation strategy, ensuring that basic building groups with strong deviation compensation capabilities are activated first.

[0088] S33 uses the number of the basic building blocks and the deviation overlap value of different basic building blocks to determine the activation processing strategy of the sensor device for the grain type.

[0089] The activation processing strategy includes a strict activation control strategy and a differentiated activation strategy based on aggregation type. The distribution aggregation value refers to the maximum percentage of the number of grains distributed in different diameter ranges during the historical drying process. The higher the value, the more concentrated the diameter distribution of the grain particles. The first type of aggregation refers to grains with the highest distribution aggregation value, where the particle distribution is highly concentrated and the temperature field distribution is relatively uniform. The second type of aggregation refers to grains with the middle distribution aggregation value, where the particle distribution is moderately concentrated. The third type of aggregation refers to grains with the lowest distribution aggregation value, where the particle distribution is relatively dispersed and the temperature field distribution is the most complex, requiring the highest sensor monitoring coverage. The reliable supplementary group refers to the basic construction group with a deviation overlap value less than the preset deviation overlap threshold, i.e., the group with weak coverage of the deviation position of the matching monitoring group.

[0090] Suppose that it is necessary to determine whether to activate all sensor devices of the basic construction group or only the sensor devices of the matching monitoring group in different grain types based on the deviation overlap value of each basic construction group and the distribution and clustering of grain types. Then, based on the judgment logic of S331 and S332, combined with the distribution and clustering type, the sensor activation strategy corresponding to each grain type is determined.

[0091] This step integrates the bias coverage capability of the basic construction group with the grain particle distribution characteristics to achieve a refined design of sensor activation strategies for different grain types. Its significance lies in the fact that for grain types with discrete particle distribution (type 1 clustering), the temperature field distribution is complex, requiring more sensor devices to participate in monitoring. On the other hand, for grain types with concentrated particle distribution (type 3 clustering), the temperature field is relatively uniform, and the number of sensors can be appropriately reduced. By combining with the clustering type, the number of activation devices for different grain types can be optimized while ensuring monitoring reliability.

[0092] The above steps include the following: S331 uses the deviation overlap values ​​of different basic construction groups to determine whether there are basic construction groups with deviation overlap values ​​less than a preset deviation overlap threshold. If so, proceed to step S332; otherwise, determine the activation processing strategy for the sensor device of the grain type according to the strict activation control strategy.

[0093] Specifically, based on the strict activation control strategy, the activation processing strategy for the sensor devices of the aforementioned grain type is determined, including: The distribution clustering value of the grain type is determined by the maximum percentage of different diameter ranges distributed during the historical drying process. This distribution clustering value is then used to determine the distribution clustering type of the grain type. When the distribution clustering type belongs to the third type, all sensor devices in the basic construction group are activated for each grain type, thereby constructing the tower temperature field model for both the basic construction group and the matching construction group. This reduces the number of sensor devices required while ensuring the reliability of monitoring and processing. When the distribution clustering type belongs to the first or second type, only all sensor devices in the matching construction group need to be activated to construct the tower temperature field model for both the basic construction group and the matching construction group. This again reduces the number of sensor devices required while ensuring the reliability of monitoring and processing.

[0094] The strict activation control strategy refers to determining the activation strategy based on the distribution and clustering type of grain when the deviation overlap value of all basic building blocks is not less than the preset deviation overlap threshold (i.e., the deviation positions of each basic building block group and the matching monitoring group are highly overlapped): for grains with three clustering types, the sensor devices of all basic building blocks are activated in all grain types; for grains with one or two clustering types, only the sensor devices of the matching monitoring group need to be activated, and there is no need to activate additional basic building blocks. In cases where the improvement is poor, the number of activated sensor devices should be reduced as much as possible.

[0095] Assuming that the deviation overlap values ​​of each basic building group exceed the preset deviation overlap threshold, the distribution and aggregation type of the currently dried grain is determined. If it belongs to the third type of aggregation (highly discrete particles), all basic building group devices and matching monitoring group devices are activated. If it belongs to the first or second type of aggregation, only the matching monitoring group devices are activated, and the temperature field monitoring is completed using the simulation model that already has sufficient coverage.

[0096] This step, through a rapid judgment path when all deviation overlap values ​​meet the standard, combined with the grain particle distribution characteristics, realizes differentiated sensor activation decisions. Its significance lies in avoiding the use of the largest-scale sensor activation scheme for all grain types, and effectively reducing the number of sensor devices operating during the drying process of Class I and Class II aggregated grains while ensuring monitoring reliability.

[0097] S332 designates the basic building group with a deviation overlap value less than a preset deviation overlap threshold as a reliable supplementary group. It then determines whether the proportion of the reliable supplementary group in the basic building group is greater than a preset supplementary group proportion threshold. If so, all sensor devices in the basic building group are activated for different grain types, thereby constructing the tower temperature field model for both the basic building group and the matching building group. This reduces the number of activated sensor devices while ensuring the reliability of monitoring and processing. If not, when the distribution clustering type belongs to type three or type two, all sensor devices in the basic building group are activated for each grain type, thus constructing the tower temperature field model for both the basic building group and the matching building group. This reduces the number of activated sensor devices while ensuring the reliability of monitoring and processing. When the distribution clustering type belongs to type one, only all sensor devices in the matching building group need to be activated to construct the tower temperature field model for both the basic building group and the matching building group, reducing the number of activated sensor devices while ensuring the reliability of monitoring and processing.

[0098] The reliable supplementary group refers to the basic construction group whose deviation overlap value is less than the preset deviation overlap threshold. Its deviation coverage capability has little overlap with the matching construction group, and it can effectively supplement the equipment affected by the deviation of the matching construction group. The preset supplementary group proportion threshold refers to the critical value for judging whether the proportion of the number of reliable supplementary groups to the total number of basic construction groups is too high. When the proportion of reliable supplementary groups exceeds the threshold, it indicates that the deviation coverage capability of most basic construction groups is strong. At this time, the overall supplementation capability is high, and it is necessary to fully activate the basic construction group equipment for all grain types, thereby improving the monitoring reliability. When the proportion does not exceed the threshold, the basic construction group equipment is activated only for grain types with relatively dispersed particle distribution (three or two types of aggregation). The grain type with the most dispersed particle distribution (one type of aggregation) does not need to activate additional basic construction groups, and the monitoring needs can be met by matching monitoring groups alone.

[0099] Assuming that some basic construction groups with deviation overlap values ​​below the preset deviation overlap threshold are identified as reliable supplementary groups, the proportion of the number of reliable supplementary groups to the total number of basic construction groups is counted. If it exceeds the preset supplementary group proportion threshold, all basic construction group devices are activated regardless of the grain type's aggregation type. If it does not exceed the preset supplementary group proportion threshold, all basic construction group devices are activated for grain types with three and two aggregation types, and only the matching monitoring group devices are activated for grain types with one aggregation type.

[0100] This step, through the dual assessment of the proportion of reliable supplementary groups and the type of grain grain aggregation, enables differentiated activation decisions when constructing groups with insufficient coverage due to bias. Its significance lies in the fact that when the proportion of reliable supplementary groups is high (strong overall supplementation capacity), a conservative strategy of full activation is adopted to ensure monitoring reliability. When the proportion is low (weak overall supplementation capacity, and activation is not very meaningful for all grain types), the activation range is flexibly adjusted for different aggregation types, achieving a dynamic balance between monitoring accuracy and equipment economy.

[0101] This embodiment, through steps S31 to S332, systematically determines the sensor activation processing strategy for different grain types. Its core value lies in three aspects: First, it quickly determines the full activation scheme in a simple scenario by pre-judging the number of basic building groups; second, it quantifies the supplementary contribution of each basic building group to the deviation coverage of the matching monitoring group by calculating the deviation overlap value; and third, it realizes the design of sensor activation strategies based on grain type differentiation under different supplementary capacity states by combining the deviation overlap threshold judgment with the grain aggregation type.

[0102] Continuing with the specific scenario of S2, the constrained devices of the relaxed construction method are {T1, T3, T5, T8, T12, T16 (matching monitoring group)} and {T9, T6 (the two devices with the largest simulation deviation factors)}, totaling 8 devices. Based on the fundamental constraint that each basic construction group must contain at least 6 sensor devices, at least 6 sensor devices are selected from these 8 devices to form a group. The total number of possible combinations is: C(8,6) = 28 combinations for selecting 6 devices, C(8,7) = 8 combinations for selecting 7 devices, and C(8,8) = 1 combination for selecting 8 devices, for a total of 37 basic construction groups, labeled B1 to B37. B1 to B28 are groups with 6 devices, B29 to B36 are groups with 7 devices, and B37 is a group with 8 devices.

[0103] In S31, the number of basic building groups is 37. The preset threshold for the number of basic groups is 20. If 37 ≥ 20, proceed to S32.

[0104] In S32, the devices that affect the deviation of the matched monitoring group G1 (devices whose simulated deviation factor exceeds the preset deviation factor threshold of 0.40) are T6 (0.70), T9 (0.80), T13 (0.60), and T18 (0.50), a total of 4.

[0105] In S33, proceed to S331: Set the preset deviation overlap threshold to 0.30. The basic building groups with deviation overlap values ​​less than 0.30 are B11 to B28 (containing only T6 or only T9, with a deviation overlap value of 0.25) and B33 to B36 (containing only T6 or only T9, with a deviation overlap value of 0.25), totaling 24 groups. There are basic building groups with deviation overlap values ​​less than the preset deviation overlap threshold, so proceed to S332.

[0106] In S332, reliable supplementary groups are B11 to B28 and B33 to B36, totaling 24 groups, accounting for approximately 0.649% of the total number of basic building groups (37). Assuming a preset supplementary group percentage threshold of 0.50 (0.649 > 0.50), all sensor devices in the basic building groups are activated for different grain types. Assume there are three types of grain currently being dried: grain type X1 has a distribution clustering value of 0.72 (type 1 clustering), grain type X2 has a distribution clustering value of 0.55 (type 2 clustering), and grain type X3 has a distribution clustering value of 0.38 (type 3 clustering). Based on the judgment result of S332, for grain types X1, X2, and X3, all sensor devices in the basic building groups (B1 to B37) are activated during the drying process.

[0107] Furthermore, the deviation risk device is a sensor device whose number of simulation processes where the simulation data of the matching build group and the basic build group are inconsistent is greater than a preset value.

[0108] Furthermore, the method for determining the monitoring, analysis, and optimization method for the sensor device is as follows: In the above steps, based on the updated processing results of the deviation risk equipment and the different grain types, groups are constructed and processed for the temperature field model inside the tower. The reliability of using the current basic construction group to assist in the temperature field monitoring and processing is determined. Based on the reliability of using the current basic construction group to assist in the temperature field monitoring and processing, the activation management strategy of all sensor equipment is determined in a targeted manner. This ensures the operational reliability of the sensor equipment and also improves the overall reliability of temperature monitoring and processing.

[0109] S41 uses the update processing results of the deviation risk devices to determine the number of deviation risk devices; The deviation risk device refers to a sensor device whose simulation data of the matching monitoring group and the basic construction group are inconsistent, and the number of such devices is greater than the preset number. In other words, if a device continuously appears in the simulation results of the matching monitoring group and the basic construction group in multiple simulations, it indicates that there is a high risk of continuous simulation deviation at the location of the device. The update processing result of the deviation risk device refers to the set and number of sensor devices that still have simulation deviation risk after performing targeted update processing on the deviation risk device.

[0110] In the above steps, if the number of deviation risk devices is greater than the preset risk device number threshold, then all sensor devices in all grain types are activated to construct a new matching monitoring group.

[0111] Assuming that after the update process, the number of sensor devices that still meet the definition conditions for deviation risk devices (the number of inconsistent simulation processes is greater than the preset value of the constituent quantity) is counted, if the number exceeds the preset threshold for the number of risk devices, it indicates that the overall deviation risk of the current temperature field simulation monitoring system is high, and it is necessary to start all sensor devices in all grain types to redetermine the matching monitoring group; if it does not exceed the threshold, proceed to S42 to determine the construction of the matching group.

[0112] This step uses the number of devices at risk of deviation as the core indicator for judging the overall reliability of the monitoring system. Its significance lies in quickly determining whether the current monitoring system needs to be completely reconstructed by assessing the number of devices that still have deviation risks after the update process. When the overall deviation risk exceeds the limit, the full sensor activation process can be initiated in a timely manner to prevent the temperature field monitoring from becoming inaccurate due to the accumulation of continuous deviation risks.

[0113] It is also understood that if the number of the deviation risk devices is not greater than the preset risk device number threshold, then proceed to step S42. S42 determines the groups for simulation model construction processing in different grain types based on the activation processing strategies of sensor devices for different grain types, and uses these groups as construction matching groups. The constructed matching group refers to the group that actually participates in the construction of the simulation model in a specific grain type according to the activation processing strategy determined in stage S3. This includes the matching monitoring group and the activated basic construction group. The more constructed matching groups there are, the more groups the temperature field monitoring of that grain type depends on, and the more comprehensive the monitoring coverage. However, it also means that the scale of sensor devices being activated is larger. The preset matching group number threshold refers to the critical value for judging whether the number of constructed matching groups is too large and needs to be optimized.

[0114] Assuming that different grain types adopt different activation strategies due to different aggregation types, some grain types require activating all basic construction groups plus matching monitoring groups, resulting in a large number of matching groups being built; while some grain types only activate matching monitoring groups, resulting in a smaller number of matching groups being built; then the number of matching groups built for each grain type is counted and compared with the preset threshold for the number of matching groups.

[0115] This step maps the actual group activation scale of each grain type to the number of matching groups constructed. Its significance lies in quantifying the temperature field monitoring coverage complexity of different grain types by statistically analyzing the number of matching groups constructed. This provides basic data for subsequent judgment on whether there are grain types with an excessive number of matching groups constructed, as well as the proportion of reliable grain types, thereby guiding the selection of monitoring and analysis optimization methods.

[0116] The above steps include the following: S421 determines whether there is a grain type whose number of matching groups exceeds a preset threshold based on the number of matching groups constructed in different grain types. If yes, proceed to step S422; otherwise, determine that the monitoring and analysis optimization method of the sensor device is to turn on all sensor devices in the three types of aggregated grain types, or to turn on all sensor devices when there is no period in the recent preset time when all sensor devices are turned on, thereby determining the reliability of the current temperature field monitoring and processing. The preset matching group number threshold refers to the critical value for determining whether the number of matching groups for a certain grain type is too large and whether it belongs to a reliable grain type; the preset duration refers to the time window used to determine whether a full sensor activation has been performed recently. If a full activation has not been performed within this duration, it is considered that a full activation is required to refresh the baseline monitoring data of the temperature field.

[0117] Assuming that the number of matching groups for all grain types does not exceed the preset threshold for the number of matching groups, it indicates that the monitoring coverage of each grain type is relatively small. A simpler optimization method can be adopted: for the three types of aggregated grains, the sensors are turned on in full periodically to verify the monitoring accuracy; if the sensors have not been turned on in full within the recent preset time period, then a full turn-on is performed for all grain types to refresh the baseline monitoring data and confirm the current reliability of the temperature field monitoring.

[0118] This step uses the presence or absence of an excessively high number of matching groups as a criterion for triage. Its significance lies in the fact that when the monitoring coverage of all grain types is within a reasonable range, there is no need to initiate complex and reliable grain type proportion analysis. The long-term reliability of temperature field monitoring can be maintained by using a targeted full-scale activation strategy, thus avoiding unnecessary systemic optimization.

[0119] S422 defines a grain type whose number of matching groups is greater than a preset threshold for the number of matching groups as a reliable grain type. It then determines whether the proportion of the reliable grain type in the grain type is greater than a preset threshold for the proportion of the reliable grain type. If so, it determines that the monitoring and analysis optimization method of the sensor device is to turn on all sensor devices in the three types of aggregated grain types to determine the reliability of the current temperature field monitoring and processing. If not, it proceeds to step S43. The reliable grain type refers to the grain type in which the number of matching groups exceeds the preset threshold for the number of matching groups. That is, in the drying process of this grain type, the number of groups participating in the simulation model construction is large, the temperature field monitoring coverage is more comprehensive, and the monitoring reliability is high. The preset threshold for the proportion of reliable grain types refers to the critical value for judging whether the proportion of reliable grain types in all grain types is too high. Exceeding this threshold indicates that the demand for high reliability monitoring is generally strong in the overall grain type structure.

[0120] Assuming that the number of matching groups for some grain types exceeds the preset threshold for the number of matching groups, the proportion of these reliable grain types to all grain types is calculated. If the proportion exceeds the preset threshold for the proportion of reliable grain types, a strategy of activating all sensors is adopted for all three types of clustered grains to ensure monitoring reliability in high-demand scenarios. If the proportion does not exceed the preset threshold for the proportion of reliable grain types, then proceed to S43 for more refined deviation impact value analysis.

[0121] This step distinguishes between overall high-reliability demand scenarios and local high-reliability demand scenarios by judging the proportion of reliable grain types. Its significance lies in the fact that when most grain types have high-reliability monitoring, a unified full-scale activation strategy for the three cluster types is adopted, simplifying the decision-making logic of the optimization method; when only a few grain types have high-reliability monitoring, a more detailed deviation impact value analysis is entered to achieve precise resource allocation.

[0122] S43 uses the number of deviation risk devices and the degree of overlap between the deviation risk devices and the deviation influence devices in the constructed matching groups of different grain types to determine the monitoring and analysis optimization method of the sensor devices.

[0123] The deviation impact value refers to a comprehensive indicator that reflects the degree of influence of deviation risk devices on the overall monitoring system, calculated by comprehensively considering the overlap between deviation risk devices and deviation impact devices in the matching groups of different grain types. The screening matching group refers to a matching group in a specific grain type where the deviation impact devices and deviation risk devices overlap (i.e., the deviation risk devices belong to the deviation impact devices of the matching group). The deviation impact value is calculated by the average proportion of the screening matching group in the matching groups of each grain type, reflecting the degree of penetration of deviation risk devices into the monitoring system of each grain type.

[0124] Assuming that the number of devices at risk of deviation does not exceed the preset threshold for the number of risky devices, and the proportion of reliable grain types does not exceed the preset threshold for the proportion of reliable grain types, then for each grain type, we will count which groups in their constructed matching groups have overlapping deviation-affected devices with deviation-risk devices. These groups will be used as screening matching groups. We will calculate the proportion of the screening matching groups in the total number of constructed matching groups for that grain type, take the average of the proportions of all grain types to obtain the deviation impact value, and compare it with the preset impact threshold.

[0125] This step, through comprehensive calculation of the deviation impact value, accurately assesses the penetration depth of deviation risk equipment into the monitoring system for each grain type. Its significance lies in the fact that a higher deviation impact value indicates that the deviation risk equipment has an impact in more matching groups for more grain types, indicating that the overall monitoring system is widely interfered with by deviation risk, and it is necessary to initiate a full-scale activation process to reconstruct the matching monitoring groups. When the deviation impact value does not exceed the threshold, by expanding the full-scale activation scope of the three and two cluster types (without requiring full-scale activation of all types), the deviation risk can be effectively repaired with controllable resource input, and the overall monitoring reliability can be maintained.

[0126] This embodiment, through steps S41 to S43, systematically determines the optimization method for monitoring and analyzing sensor equipment. Its core value lies in three aspects: First, by pre-judging the number of devices at risk of deviation, a rapid early warning mechanism for the reliability of the overall monitoring system is established; second, by constructing a hierarchical judgment of the number of matching groups and the proportion of reliable grain types, a refined stratification of the selection of optimization methods is achieved; and third, by comprehensively calculating the deviation impact value, the actual depth of the impact of devices at risk of deviation on the monitoring system for each grain type is accurately assessed, ensuring the pertinence of the optimization method and the rationality of resource investment.

[0127] This embodiment achieves systematic analysis and monitoring optimization of sensor data from grain drying towers through the complete process from S1 to S4. Its core value lies in four aspects: First, by accurately selecting matching monitoring groups, the number of regularly activated sensor devices is minimized while ensuring simulation coverage accuracy, effectively reducing the operational load on the sensor devices. Second, through differentiated design of the basic construction group construction method driven by simulation deviation factors, appropriate construction strategies are selected for different deviation distribution characteristics, ensuring that the basic construction groups can effectively compensate for the simulation coverage shortcomings of the matching monitoring groups. Third, through the linkage judgment of deviation overlap values ​​and grain particle aggregation types, refined customization of temperature field monitoring strategies for different grain types is achieved, achieving a dynamic optimal balance between monitoring reliability and equipment economy. Fourth, through continuous monitoring of deviation-risk devices and multi-level optimization methods, a dynamic perception and adaptive repair mechanism for the health status of the monitoring system is established, promoting continuous self-improvement of the drying tower temperature monitoring and management system during operation.

[0128] Continuing with the specific scenario of S3, the drying tower has three types of grains (X1 type I aggregation, X2 type II aggregation, and X3 type III aggregation). The current activation strategy determined for S3 is: all basic construction groups (B1 to B37) and matching monitoring group (G1) are activated for X1, X2, and X3. After the deviation risk device update processing, let's assume that the sensor devices that still meet the deviation risk device definition are T6 and T9, a total of 2.

[0129] In S41, the number of deviation risk devices is 2. The preset threshold for the number of risk devices is 3. 2≤3, then proceed to S42.

[0130] In S42, based on the S3 activation strategy, the matching groups for each grain type are constructed as follows: X1 (Category I cluster) activates G1 (matching monitoring group) and B1 to B37 (all basic construction groups), constructing a total of 38 matching groups (1 matching monitoring group + 37 basic construction groups); X2 (Category II cluster) is the same as X1, constructing a total of 38 matching groups; X3 (Category III cluster) is the same as X1, constructing a total of 38 matching groups.

[0131] In S421, the preset threshold for the number of matching groups is set to 25. The number of matching groups constructed by X1 is 38 > 25, and the number of matching groups constructed by X2 is 38 > 25. There are grain types with a number of matching groups constructed that are greater than the preset threshold for the number of matching groups. Proceed to S422.

[0132] In S422, there are three reliable grain types: X1, X2, and X3. The total number of grain types is three, and the percentage of reliable grain types is 3 ÷ 3 = 1.00. Assuming a preset threshold for the percentage of reliable grain types is 0.60 (1.00 > 0.60), the optimization method for monitoring and analyzing the sensor equipment is to activate all sensor equipment in the three clustered grain types (X3) to determine the reliability of the current temperature field monitoring and processing.

[0133] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the aforementioned sensor data analysis method for a grain drying tower when running the computer program.

[0134] 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.

[0135] 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.

[0136] 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. A sensor data analysis method for a grain drying tower, characterized in that, Specifically, it includes: Using sensor data from the grain drying tower, a temperature field model of the grain drying tower is constructed. Based on the data input requirements of the sensor devices in the temperature field model, the sensor devices are divided into different groups. Using the simulation data of the temperature field model in the tower, the matching monitoring groups in the groups are determined. By using the sensor device data in the matched monitoring group, and the deviation between the simulation data and the monitoring data of different sensor devices, the construction method of the basic construction group in the group is determined; The basic construction group is determined using the construction method described above. Based on the basic construction group data, the simulation deviation data in the basic construction group, and the correlation between the basic construction group and the matching monitoring group, the activation processing strategy for sensor devices of different grain types is determined. Based on the aforementioned activation processing strategy, the deviation risk equipment is updated. Using the update processing results of the deviation risk equipment and the activation processing strategies of sensor equipment for different grain types, the monitoring, analysis, and optimization method for the sensor equipment is determined.

2. The sensor data analysis method for a grain drying tower as described in claim 1, characterized in that, The sensor data of the grain drying tower includes the number and location of the sensor devices in the grain drying tower.

3. The sensor data analysis method for a grain drying tower as described in claim 1, characterized in that, The construction of the temperature field model inside the grain drying tower specifically includes: Based on the temperature monitoring data of the sensor equipment inside the grain drying tower, a temperature field model inside the grain drying tower is constructed.

4. The sensor data analysis method for a grain drying tower as described in claim 1, characterized in that, The data input requirements of the sensor devices are determined based on the required number of sensor devices when solving for temperature using the temperature field model.

5. The sensor data analysis method for a grain drying tower as described in claim 4, characterized in that, The sensor devices are divided into different groups, specifically including: Based on meeting the data input requirements of the sensor devices, combinations of sensor devices that meet the data input requirements of the sensor devices are grouped into the same group.

6. The sensor data analysis method for a grain drying tower as described in claim 1, characterized in that, The method for determining the matching monitoring group in the group is as follows: Based on the simulation data of the temperature field model inside the tower, the degree of consistency between the group and the monitoring data of different sensor devices in different simulation processes is determined. Using the aforementioned degree of consistency, the deviation sensor devices in different simulation processes are determined; By using the deviation sensor devices in different simulation processes and the sensor devices in the group, it is determined whether the group belongs to the matching monitoring group.

7. The sensor data analysis method for a grain drying tower as described in claim 6, characterized in that, Determining whether a group belongs to a matching monitoring group is based on deviation sensor devices in different simulation processes and sensor devices within the group, specifically including: If the number of deviation sensor devices in the group is less than a preset threshold for the number of deviation sensor devices in different simulation processes, then the group is considered a potential matching group and the process proceeds to the next step. Otherwise, the group is determined not to belong to the matching monitoring group. Determine whether the number of sensor devices in the potential matching group is the minimum. If so, the group is determined to belong to the matching monitoring group; otherwise, the group is determined not to belong to the matching monitoring group.

8. The sensor data analysis method for a grain drying tower as described in claim 1, characterized in that, The method for determining the monitoring, analysis, and optimization method for the sensor device is as follows: The number of deviation risk devices is determined using the update processing results of the deviation risk devices; Based on the activation processing strategy of sensor devices for different grain types, groups for constructing and processing the temperature field model inside the tower are determined for different grain types, and these groups are used as matching groups for construction. By utilizing the number of deviation risk devices and the degree of overlap between deviation risk devices and deviation influence devices in different grain types that form matching groups, the monitoring and analysis optimization method for the sensor devices is determined.

9. The sensor data analysis method for a grain drying tower as described in claim 8, characterized in that, Based on the degree of overlap between the deviation risk device and the deviation-affecting devices in the constructed matching groups of different grain types, it is determined that the deviation risk device in the grain type belongs to the constructed matching group of deviation-affecting devices, and this group is used as a screening matching group. The deviation impact value is determined based on the average proportion of the screening matching group in the constructed matching group in different grain types. It is then determined whether the deviation impact value is greater than a preset impact threshold. If so, all sensor devices are turned on in all grain types to construct a new matching monitoring group. If not, the monitoring and analysis optimization method of the sensor devices is determined to be to turn on all sensor devices in grain types with three clustering types and two clustering types, or to turn on all sensor devices if there is no period in the recent preset time when all sensor devices are turned on, thereby determining the reliability of the current temperature field monitoring and processing.

10. A computer system, comprising: A memory and processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a sensor data analysis method for a grain drying tower as described in any one of claims 1-9.

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