A sensor-based real-time monitoring method for moisture content of organic fertilizer and related device

Abstract

This invention discloses a sensor-based method, device, and computer-readable storage medium for real-time monitoring of organic fertilizer moisture content. The method synchronously acquires raw data from the material pile, extracts density features from the material's physical structure dimension, and extracts biochemical evolution features from its biochemical state dimension. Then, based on a preset multidimensional state mapping rule library, it matches the corresponding environmental interference weight matrix to dynamically compensate and calibrate the initial moisture electrical signal, thereby obtaining the true moisture content after excluding combined interferences such as temperature drift, salt precipitation, and material compaction. Furthermore, it compares this true moisture content with a preset target threshold range, generating corresponding environmental intervention commands to achieve closed-loop control of fermentation moisture. This effectively solves the problem of insufficient adaptability of traditional fixed calibration curves in complex fermentation environments.

Description

Technical Field

[0001] This invention belongs to the field of solid waste resource utilization technology, and more specifically, relates to a sensor-based method for real-time monitoring of organic fertilizer moisture content, a sensor-based device for real-time monitoring of organic fertilizer moisture content, a real-time moisture content monitoring equipment, and a computer-readable storage medium. Background Technology

[0002] In the organic ecological agriculture and animal husbandry industry, the production of organic fertilizer through aerobic fermentation of livestock and poultry manure is an important way to realize the resource utilization of waste. Moisture content, as a key parameter affecting the activity of aerobic microorganisms, directly determines the fermentation efficiency and fertilizer quality. Therefore, accurate real-time monitoring of it is of great significance.

[0003] In related technologies, the industry commonly uses insertion-type capacitive or resistive sensors to directly measure the moisture content of materials. However, these methods have significant shortcomings in actual fermentation environments. On the one hand, the internal temperature of the fermentation pile changes drastically during fermentation, accompanied by the precipitation of a large amount of inorganic salts. Fluctuations in temperature and conductivity significantly alter the dielectric constant of the material, causing the moisture signal output by the sensor to deviate severely from the true value. On the other hand, as fermentation progresses, the material gradually matures and undergoes sedimentation and compaction. The contact state between the sensor probe and the material continuously changes, further triggering nonlinear drift in the measurement signal. These two types of interference factors dynamically superimpose throughout the entire fermentation cycle. Existing methods typically rely on factory-fixed calibration curves and lack adaptive compensation capabilities for the dynamic evolution of the material's physicochemical properties. Consequently, the monitoring data cannot accurately reflect the moisture content, making it difficult to provide reliable decision-making basis for on-site operations such as turning and replenishing water.

[0004] Therefore, there is an urgent need for a moisture content monitoring method that can comprehensively sense the multidimensional physical state of the fermentation environment and implement dynamic compensation calibration, so as to improve the accuracy and reliability of moisture monitoring under complex fermentation conditions. Summary of the Invention

[0005] The purpose of this application is to provide a sensor-based method for real-time monitoring of the moisture content of organic fertilizer, a sensor-based device for real-time monitoring of the moisture content of organic fertilizer, a real-time moisture content monitoring equipment, and a computer-readable storage medium, so as to improve the accuracy and reliability of moisture monitoring under complex fermentation conditions.

[0006] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a sensor-based method for real-time monitoring of the moisture content of organic fertilizers, comprising: S1, acquire multi-source physical data of the organic fertilizer fermentation pile; wherein, the multi-source physical data includes: initial moisture electrical signal, internal temperature value of the pile, lateral pressure value of the probe, and material conductivity; S2, based on the lateral pressure value of the probe and the preset depth pressure benchmark model, calculate the density characteristic value of the fermentation pile; according to the internal temperature value of the pile and the electrical conductivity of the material, extract the biochemical evolution characteristic quantity of the fermentation pile at the current fermentation stage; S3, in the pre-built multi-dimensional state mapping rule base, match the environmental disturbance weight matrix corresponding to the currently calculated density feature value and the biochemical evolution feature quantity; perform numerical compensation on the initial water electrochemical signal according to the environmental disturbance weight matrix, and calculate the calibrated true water content value. S4. The actual moisture content value is compared with the target moisture threshold range corresponding to the current fermentation stage. If the actual moisture content value deviates from the target moisture threshold range, a corresponding environmental intervention command is generated to control the operating equipment to perform the corresponding fermentation environment adjustment operation.

[0007] Optionally, the calculation of the compaction characteristic value of the fermentation pile based on the lateral pressure value of the probe and a preset depth pressure benchmark model includes: The burial depth information of the sensor that collects the lateral pressure value of the probe in the fermentation pile is obtained; In the depth pressure benchmark model, query the initial standard pressure value corresponding to the burial depth information; The ratio of the lateral pressure value of the probe to the initial standard pressure value is calculated. If the calculated ratio is greater than a preset threshold, it is determined that the fermentation pile has settled and compacted, and the ratio is used as the density characteristic value.

[0008] Optionally, the step of extracting the biochemical evolution characteristics of the fermentation pile at its current fermentation stage based on the internal temperature value of the pile and the electrical conductivity of the material includes: Calculate the rate of change of the internal temperature of the pile and the rate of change of the conductivity of the material during the current monitoring period; The internal temperature value of the pile, the rate of temperature change, and the rate of change of electrical conductivity are input into a preset logic decision tree model to determine the current fermentation stage of the fermentation pile. Based on the fermentation stage determined by the judgment, the corresponding stage identifier and feature vector are extracted, and the biochemical evolution feature quantity is generated.

[0009] Optionally, the construction process of the multidimensional state mapping rule base includes: Obtain calibration test data of various organic fertilizer samples under different temperature, humidity, density and salinity conditions; Extract the moisture signal attenuation and gain benchmarks from the calibration test data, establish a lookup table or relational mapping matrix indexed by density features and biochemical evolution features, and obtain the multidimensional state mapping rule base.

[0010] Optionally, the environmental disturbance weight matrix may include at least: The physical contact compensation coefficient is used to compensate for the artificially high moisture signal caused by the increase in material density; and the temperature-salt dielectric compensation coefficient is used to compensate for the polarization error of the moisture signal caused by high temperature and high conductivity.

[0011] Optionally, the step of numerically compensating the initial moisture content signal based on the environmental interference weight matrix to calculate the calibrated true moisture content value includes: The initial water electroelectric signal is multiplied by the physical contact compensation coefficient and the temperature-salinity dielectric compensation coefficient respectively to remove the false response signal caused by environmental interference and extract the true dielectric response signal. The actual dielectric response signal is substituted into a preset reference conversion formula to calculate the actual moisture content value.

[0012] Optionally, in step S4, generating the corresponding environmental intervention command to control the operating equipment to perform corresponding fermentation environment adjustment operations includes: If the actual moisture content value is lower than the lower limit of the target moisture threshold range, it is determined that the moisture loss of the fermentation pile is too fast, and a water replenishment and turning instruction is generated. The water replenishment and turning instructions are sent to the trough-type compost turner and automatic sprinkler system on site via industrial bus. The trough-type compost turner turns the material and the automatic sprinkler system sprays water in a quantitative manner until the actual moisture content value calculated in the next monitoring cycle returns to the target moisture threshold range.

[0013] This application also provides a sensor-based real-time monitoring device for the moisture content of organic fertilizer, comprising: The data acquisition module is used to acquire multi-source physical data of the organic fertilizer fermentation pile; wherein, the multi-source physical data includes: initial moisture electrical signal, internal temperature value of the pile, probe lateral pressure value, and material conductivity; The feature extraction module is used to calculate the density feature value of the fermentation pile based on the lateral pressure value of the probe and the preset depth pressure benchmark model; and to extract the biochemical evolution feature quantity of the fermentation pile at the current fermentation stage based on the internal temperature value of the pile and the electrical conductivity of the material. The dynamic compensation module is used to match the environmental disturbance weight matrix corresponding to the currently calculated density feature value and the biochemical evolution feature value in a pre-built multi-dimensional state mapping rule library; and to perform numerical compensation on the initial water electrochemical signal according to the environmental disturbance weight matrix to calculate the calibrated true water content value. The adaptive control module is used to compare the actual moisture content value with the target moisture threshold range corresponding to the current fermentation stage. If the actual moisture content value deviates from the target moisture threshold range, a corresponding environmental intervention command is generated to control the operating equipment to perform corresponding fermentation environment adjustment operations.

[0014] This application also provides a real-time moisture content monitoring device, including: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the method for real-time monitoring of the moisture content of organic fertilizer as described above.

[0015] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for real-time monitoring of the moisture content of organic fertilizer as described above.

[0016] This application provides a sensor-based real-time monitoring method for the moisture content of organic fertilizer, comprising: S1, acquiring multi-source physical data of the organic fertilizer fermentation pile; wherein, the multi-source physical data includes: initial moisture electrical signal, internal temperature value of the pile, lateral pressure value of the probe, and material conductivity; S2, calculating the density characteristic value of the fermentation pile based on the lateral pressure value of the probe and a preset depth pressure benchmark model; extracting the biochemical evolution characteristic quantity of the fermentation pile at its current fermentation stage based on the internal temperature value of the pile and the material conductivity; S3, in the pre- In the first constructed multidimensional state mapping rule base, an environmental disturbance weight matrix is ​​matched with the currently calculated density feature value and the biochemical evolution feature value; the initial moisture electrical signal is numerically compensated according to the environmental disturbance weight matrix to calculate the calibrated true moisture content value; S4, the true moisture content value is compared with the target moisture threshold range corresponding to the current fermentation stage. If the true moisture content value deviates from the target moisture threshold range, a corresponding environmental intervention command is generated to control the operating equipment to perform corresponding fermentation environment adjustment operations.

[0017] It has the following beneficial effects: By simultaneously collecting initial moisture electrical signals, internal temperature values ​​of the pile, lateral pressure values ​​of the probe, and material conductivity, density feature values ​​and biochemical evolution feature quantities are extracted from the dimensions of physical structure and biochemical state, respectively. The initial moisture electrical signals are dynamically compensated by matching the environmental interference weight matrix using a multi-dimensional state mapping rule base. This effectively eliminates the combined interference of temperature fluctuations, salt precipitation, and material compaction on moisture content measurement during fermentation, significantly improving monitoring accuracy. At the same time, the calibrated true moisture content is compared with the target threshold range and environmental intervention instructions are generated, realizing precise closed-loop control of fermentation moisture content. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a sensor-based real-time monitoring method for the moisture content of organic fertilizer provided in this application embodiment; Figure 2 A schematic diagram of a sensor-based real-time monitoring device for the moisture content of organic fertilizer provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a real-time moisture content monitoring device provided in an embodiment of this application. Detailed Implementation

[0020] The purpose of this application is to provide a sensor-based method for real-time monitoring of the moisture content of organic fertilizer, a sensor-based device for real-time monitoring of the moisture content of organic fertilizer, a real-time moisture content monitoring equipment, and a computer-readable storage medium, so as to improve the accuracy and reliability of moisture monitoring under complex fermentation conditions.

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] The following embodiment illustrates a sensor-based method for real-time monitoring of the moisture content of organic fertilizer provided in this application.

[0023] Please refer to Figure 1 , Figure 1This is a flowchart illustrating a sensor-based method for real-time monitoring of the moisture content of organic fertilizer, as provided in an embodiment of this application.

[0024] In this embodiment, the method may include: S1, acquire multi-source physical data of the organic fertilizer fermentation pile; the multi-source physical data includes: initial moisture electrical signal, internal temperature value of the pile, lateral pressure value of the probe, and material conductivity; This embodiment takes the aerobic fermentation scenario of cattle and sheep manure in a high-altitude pastoral area as an example, specifically applied to the organic fertilizer fermentation workshop of an organic ecological agricultural and livestock technology enterprise in Zeku County, Qinghai Province. Inside the organic fertilizer fermentation tank, several composite sensing nodes are deployed in a grid layout. Each composite sensing node integrates four types of core sensing elements: an FDR (Frequency Domain Reflectometry) moisture probe, a thermocouple temperature sensor, a pressure film sensor, and a conductivity electrode. Specifically, the FDR moisture probe is used to sense the dielectric response intensity of water molecules in the material to electromagnetic waves; the thermocouple is used to measure the real-time temperature inside the pile; the pressure film sensor is attached to the probe sidewall to sense the lateral compressive force of the material on the probe; and the conductivity electrode is used to measure the conductivity of the material.

[0025] During fermentation, the system synchronously collects a set of multi-source physical data at a preset sampling frequency. In this embodiment, the sampling frequency is set to once every 10 minutes. Each collection of multi-source physical data includes the following four dimensions of raw measurement values. The first is the initial moisture electrical signal, i.e., the voltage value fed back by the FDR moisture probe. This signal directly reflects the dielectric response intensity of water molecules in the material to electromagnetic waves, but it is also inevitably mixed with interference components from factors such as temperature, salinity, and material density. The second is the internal temperature value of the pile, i.e., the real-time temperature measured by the thermocouple inside the material. In this acquisition cycle, this value is 55℃. The third is the probe lateral pressure value, i.e., the lateral squeezing force of the material on the probe sensed by the pressure film sensor. In this acquisition cycle, this value is 12 kPa. This parameter can indirectly characterize the dynamic changes in porosity and density of the material during fermentation and sedimentation. Fourthly, the material conductivity, which is the electrical conductivity value of the material measured by the conductivity electrode. In this collection period, this value was 3.5 mS / cm. This parameter reflects the degree of precipitation of inorganic salt ions during the decomposition of organic matter.

[0026] The simultaneous acquisition of the above four types of data provides a complete multi-dimensional sensing foundation for subsequent physical state feature extraction and dynamic compensation calibration.

[0027] S2, based on the probe's lateral pressure value and the preset depth pressure benchmark model, calculates the compaction characteristic value of the fermentation pile; based on the internal temperature value and material conductivity of the pile, extracts the biochemical evolution characteristic quantity of the fermentation pile at its current fermentation stage.

[0028] This step includes two parallel feature extraction processes, which quantify the current state of the fermentation pile from the dimensions of physical structure and biochemical state, respectively.

[0029] The first process involves calculating the density characteristic value. The system first acquires the burial depth information of the pressure film sensor within the fermentation pile, which measures the lateral pressure value of the sensor probe. Since the burial depth of each sensor node was accurately recorded and written into the system database during the initial pile-building stage of fermentation, this information can be directly retrieved. Subsequently, the system queries the preset depth-pressure benchmark model for the initial standard pressure value corresponding to this burial depth information. This depth-pressure benchmark model records the standard lateral pressure exerted by the material on the sensor probe at each depth in the initial pile-building state; it is the benchmark information calibrated and stored during the first data acquisition after pile building. During the fermentation and degradation of cattle and sheep manure, large organic molecules gradually decompose into smaller molecules, the material volume shrinks and settles, and the porosity decreases, leading to a gradual increase in the lateral pressure exerted on the sensor probe. The system calculates the ratio between the currently acquired probe lateral pressure value and the retrieved initial standard pressure value. If the calculated ratio is greater than a preset threshold, it is determined that the fermentation pile has undergone settling and compaction, and this ratio is output as the density characteristic value. Taking this embodiment as an example, the current lateral pressure value of the probe is 12 kPa, while the initial standard pressure value corresponding to this burial depth is 8 kPa. The ratio of the two is 1.5, which is greater than the preset threshold of 1.0, indicating that the material has undergone significant settling and compaction. At this time, the density characteristic value is 1.5. The introduction of the density characteristic value effectively quantifies the porosity change caused by the material's decay and collapse during fermentation, thereby overcoming the signal nonlinear drift problem caused by changes in the physical structure of the material contact surface in traditional sensors, and significantly improving the physical robustness of the monitoring.

[0030] The second process involves extracting biochemical evolution characteristics. The system first calculates the rate of change of the internal temperature of the compost pile and the rate of change of the material's electrical conductivity within the current monitoring period. The rate of change of temperature is obtained by subtracting the temperature value of the current period from the temperature value of the previous period and dividing by the sampling interval; the rate of change of electrical conductivity is calculated similarly. The rate of change of temperature reflects the intensity of microbial metabolic activity within the compost pile, while the rate of change of electrical conductivity indirectly reflects the rate at which organic matter mineralizes and decomposes, releasing inorganic salt ions. The combination of these two factors provides crucial dynamic trend information for determining the fermentation stage. Subsequently, the system inputs the current internal temperature of the compost pile, the rate of change of temperature, and the rate of change of electrical conductivity into a preset logical decision tree model. This model then comprehensively determines the current fermentation stage of the compost pile based on preset branch decision conditions. Taking this embodiment as an example, when the internal temperature of the fermentation pile is in a rapidly rising range, i.e., from 20°C to 50°C, the rate of temperature change is positive and the value is large. Simultaneously, the conductivity also increases synchronously due to the dissolution of a large amount of inorganic salts along with the decomposition of organic matter, and the rate of conductivity change is also positive. Based on the above combination of features, the logical decision tree model determines the current fermentation state as a thermophilic warming phase. The system extracts the corresponding stage identifier and feature vector based on the determined fermentation stage and combines them to generate a biochemical evolution feature quantity. This biochemical evolution feature quantity not only contains the identifier information of the current fermentation stage but also contains a vectorized representation of the dynamic changes in temperature and conductivity, providing a reliable retrieval basis for subsequent accurate matching in the multidimensional state mapping rule base.

[0031] S3. In the pre-built multidimensional state mapping rule base, match the environmental disturbance weight matrix corresponding to the currently calculated density feature value and biochemical evolution feature value; perform numerical compensation on the initial water electrical signal according to the environmental disturbance weight matrix, and calculate the calibrated true water content value.

[0032] This step is crucial for eliminating environmental interference and achieving accurate moisture content calibration. In traditional moisture content monitoring schemes, the system directly converts the initial moisture electrical signal into a moisture percentage using a factory-fixed calibration curve. However, this fixed curve cannot detect or adapt to the drastic dynamic changes in temperature, salt concentration, and material density during fermentation. Consequently, the conversion result often deviates significantly from the actual moisture content, rendering the monitoring data ineffective in guiding turning and water replenishment operations.

[0033] Before matching, a multidimensional state mapping rule base needs to be constructed. The construction process is as follows: In the initial calibration experiment phase, calibration test data of various organic fertilizer samples under different temperature, humidity, density, and salinity conditions were obtained. Specifically, researchers systematically simulated the environmental conditions of different fermentation stages using cow and sheep manure samples in the laboratory, changing key variables such as temperature, humidity, density, and salinity. Under each set of conditions, the electrical signal value output by the FDR moisture probe and the standard value of the actual moisture content measured using the oven drying method were recorded simultaneously. Subsequently, the moisture signal attenuation and gain benchmarks under each set of conditions were extracted from all calibration test data, i.e., the systematic deviation patterns of sensor readings relative to the actual moisture content. Finally, using density characteristics and biochemical evolution characteristics as two-dimensional indexes, the above deviation patterns were organized into a lookup table or relational mapping matrix, forming a complete multidimensional state mapping rule base.

[0034] In actual monitoring, the system uses the density feature value and biochemical evolution feature value calculated in step S2 as search indexes to match in the multidimensional state mapping rule base to determine the environmental interference weight matrix for the current monitoring period. This environmental interference weight matrix contains at least two core compensation parameters. The first parameter is the physical contact compensation coefficient, which is used to offset the influence of falsely high moisture signals caused by the closer contact between the sensor probe and the material due to increased material density. As the fermentation process progresses, the material settling and compaction increases the material density around the probe, and the dielectric constant detected by the FDR probe contains more false response components caused by the close contact of solid materials. The physical contact compensation coefficient is used to eliminate this component. The second parameter is the temperature-salinity dielectric compensation coefficient, which is used to offset the influence of the polarization error of the moisture signal caused by the shift in the dielectric constant of the material due to the combined effect of high temperature and high conductivity. During the high-temperature fermentation period, the material temperature can reach 60 to 70°C, accompanied by the precipitation of a large number of inorganic salt ions. The superposition of these two factors will significantly change the apparent dielectric constant of the material, causing the sensor to misinterpret the dielectric change caused by non-moisture factors as an increase in moisture content. The temperature-salt dielectric compensation coefficient is used to eliminate this polarization error.

[0035] After determining the environmental interference weight matrix, the system performs specific numerical compensation calculations. The system multiplies the initial moisture content signal obtained in step S1 with the physical contact compensation coefficient and the temperature-salinity dielectric compensation coefficient, respectively. By peeling away the spurious contact response signal caused by changes in material density and the spurious polarization response signal caused by changes in temperature and salinity layer by layer, the system extracts the true dielectric response signal caused solely by the dielectric effect of pure water molecules. Subsequently, the system substitutes the true dielectric response signal into a preset benchmark conversion formula to calculate the calibrated true moisture content value. This benchmark conversion formula establishes a standard correspondence between the pure dielectric response signal and the percentage moisture content, and is calibrated for the dielectric properties of pure water molecules after eliminating all known interference factors.

[0036] Taking this embodiment as an example, the initial moisture electrical signal directly output by the sensor, after being converted by the factory-fixed curve, represents a moisture content reading of 65%. After the aforementioned multi-dimensional dynamic compensation process, the system accurately calibrates it to the actual 48%. This calibration result effectively eliminates the superimposed influence of the triple interference factors of high temperature, high salinity, and high density on the measurement results, truly reflecting the actual moisture content of the pile material. It achieves dynamic decoupling from the drastic heat fluctuations and salt ion precipitation effects during fermentation, significantly improving the absolute accuracy of moisture monitoring.

[0037] S4 compares the actual moisture content value with the target moisture threshold range corresponding to the current fermentation stage. If the actual moisture content value deviates from the target moisture threshold range, a corresponding environmental intervention command is generated to control the operating equipment to perform the corresponding fermentation environment adjustment operation.

[0038] The system retrieves the current fermentation stage, i.e., the thermophilic warming period, determined in step S2, and queries the corresponding target moisture threshold range. For aerobic fermentation of cattle and sheep manure, the optimal moisture content during the thermophilic warming period is usually maintained between 50% and 60%. This range provides sufficient moisture for aerobic microorganisms to maintain their efficient metabolic activity, while preventing the pile from entering an anaerobic state due to obstructed oxygen diffusion caused by excessive moisture content. The system compares the actual moisture content value of 48% calculated in step S3 with this target moisture threshold range. Since 48% is lower than the lower limit of the target range (50%), the system determines that the current fermentation pile has a problem of excessively rapid moisture loss. In high-altitude and cold regions such as Zeku County in Qinghai Province, fermentation piles are constantly exposed to strong winds and dry climates. The evaporation rate of surface moisture is much higher than the rate of internal moisture migration. In addition, during the high-temperature fermentation stage, the internal heat drives a large amount of water vapor to escape outward. Therefore, low moisture content is a common abnormal state in the fermentation process of organic fertilizer in this region.

[0039] Based on the comparison results, the system automatically generates a water replenishment and turning command. This command is sent via the industrial bus to the trough-type turning machine and automatic spraying system deployed on-site. Upon receiving the command, the trough-type turning machine initiates the turning operation, moving longitudinally along the fermentation trough and thoroughly mixing the materials in the pile to ensure uniform mixing of the inner and outer layers, while simultaneously promoting aeration within the pile to maintain the activity of aerobic microorganisms. Simultaneously, the automatic spraying system starts synchronously during the turning process, spraying water quantitatively according to a preset dosage, ensuring that the replenished water penetrates evenly into all layers of the material during the turning action, rather than remaining only on the surface of the pile. After the above turning and water replenishment operation is completed, the system re-executes the data acquisition, feature extraction, and dynamic compensation calibration process of steps S1 to S3 in the next monitoring cycle to recalculate the true moisture content value. If this value has recovered to within the target moisture threshold range, the system maintains the current fermentation state without further intervention; if it still deviates from the target range, the system will continue to generate and send environmental intervention commands until the true moisture content value recovers to within the target moisture threshold range.

[0040] Through the aforementioned closed-loop control mechanism, this embodiment accurately transforms the raw multi-source physical data collected by the underlying composite sensing nodes into environmental intervention commands that can directly guide on-site fermentation operations after density feature extraction, biochemical evolution feature extraction, and multi-dimensional dynamic compensation calibration. This enables real-time and precise monitoring of moisture content and unmanned, refined control during the organic fertilizer fermentation process, effectively shortening the composting cycle of organic fertilizer in high-altitude and cold regions and significantly improving the quality stability of the final product.

[0041] In summary, this embodiment simultaneously collects initial moisture electrical signals, internal temperature values ​​of the pile, lateral pressure values ​​of the probe, and material conductivity. It extracts density feature values ​​and biochemical evolution feature quantities from the dimensions of physical structure and biochemical state, respectively. Furthermore, it uses a multi-dimensional state mapping rule base to match the environmental interference weight matrix to dynamically compensate for the initial moisture electrical signals. This effectively eliminates the combined interference of temperature fluctuations, salt precipitation, and material compaction on moisture content measurement during fermentation, significantly improving monitoring accuracy. At the same time, it compares the calibrated true moisture content with the target threshold range and generates environmental intervention commands, achieving precise closed-loop control of fermentation moisture content.

[0042] The following describes a sensor-based real-time monitoring device for the moisture content of organic fertilizer provided in an embodiment of this application. The sensor-based real-time monitoring device for the moisture content of organic fertilizer and the sensor-based real-time monitoring method for the moisture content of organic fertilizer described below can be referred to in correspondence with each other.

[0043] Please refer to Figure 2 , Figure 2 This is a schematic diagram of a sensor-based real-time monitoring device for the moisture content of organic fertilizer provided in an embodiment of this application.

[0044] In this embodiment, the device may include: The data acquisition module 100 is used to acquire multi-source physical data of the organic fertilizer fermentation pile; the multi-source physical data includes: initial moisture electrical signal, internal temperature value of the pile, lateral pressure value of the probe, and material conductivity. The feature extraction module 200 is used to calculate the compaction feature value of the fermentation pile based on the lateral pressure value of the probe and the preset depth pressure benchmark model; and to extract the biochemical evolution feature of the fermentation pile at the current fermentation stage based on the internal temperature value and material conductivity of the pile. The dynamic compensation module 300 is used to match the environmental disturbance weight matrix corresponding to the currently calculated density feature value and biochemical evolution feature value in a pre-built multi-dimensional state mapping rule library; and to perform numerical compensation on the initial water electrochemical signal according to the environmental disturbance weight matrix to calculate the calibrated true water content value. The adaptive control module 400 is used to compare the actual moisture content value with the target moisture threshold range corresponding to the current fermentation stage. If the actual moisture content value deviates from the target moisture threshold range, a corresponding environmental intervention command is generated to control the operating equipment to perform the corresponding fermentation environment adjustment operation.

[0045] This application also provides a real-time moisture content monitoring device; please refer to [reference needed]. Figure 3 , Figure 3 This is a schematic diagram of the structure of a real-time moisture content monitoring device provided in an embodiment of this application. The real-time moisture content monitoring device may include: Memory, used to store computer programs; The processor, when executing a computer program, can implement the steps of any of the sensor-based real-time monitoring methods for the moisture content of organic fertilizers described above.

[0046] like Figure 3 The diagram shows the structural composition of a real-time moisture content monitoring device. This device may include a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, memory 11, and communication interface 12 all communicate with each other via the communication bus 13.

[0047] In this embodiment, the processor 10 may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic devices.

[0048] The processor 10 can call the program stored in the memory 11. Specifically, the processor 10 can execute the operations in the embodiment of the abnormal IP identification method.

[0049] The memory 11 is used to store one or more programs. The programs may include program code, which includes computer operation instructions. In this embodiment, the memory 11 stores at least a program for implementing the following functions: S1, acquire multi-source physical data of the organic fertilizer fermentation pile; the multi-source physical data includes: initial moisture electrical signal, internal temperature value of the pile, lateral pressure value of the probe, and material conductivity; S2, based on the probe's lateral pressure value and the preset depth pressure benchmark model, calculates the compaction characteristic value of the fermentation pile; based on the internal temperature value and material conductivity of the pile, extracts the biochemical evolution characteristic quantity of the fermentation pile at its current fermentation stage; S3. In the pre-built multi-dimensional state mapping rule base, match the environmental disturbance weight matrix corresponding to the currently calculated density feature value and biochemical evolution feature quantity; perform numerical compensation on the initial water electrical signal according to the environmental disturbance weight matrix, and calculate the calibrated true water content value. S4 compares the actual moisture content value with the target moisture threshold range corresponding to the current fermentation stage. If the actual moisture content value deviates from the target moisture threshold range, a corresponding environmental intervention command is generated to control the operating equipment to perform the corresponding fermentation environment adjustment operation.

[0050] In one possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; and the data storage area may store data created during use.

[0051] In addition, memory 11 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0052] Communication interface 12 can be an interface for the communication module, used to connect with other devices or systems.

[0053] Of course, it should be noted that, Figure 3 The structure shown does not constitute a limitation on the real-time moisture content monitoring device in the embodiments of this application. In practical applications, a real-time moisture content monitoring device may include more than Figure 3 More or fewer components as shown, or combinations of certain components.

[0054] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps of any of the sensor-based real-time monitoring methods for organic fertilizer moisture content described above.

[0055] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0056] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0057] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0058] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0059] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0060] The foregoing has provided a detailed description of a sensor-based real-time monitoring method for the moisture content of organic fertilizer, a sensor-based real-time monitoring device for the moisture content of organic fertilizer, a real-time moisture content monitoring equipment, and a computer-readable storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A sensor-based method for real-time monitoring of the moisture content of organic fertilizer, characterized in that, include: S1, acquire multi-source physical data of the organic fertilizer fermentation pile; wherein, the multi-source physical data includes: initial moisture electrical signal, internal temperature value of the pile, lateral pressure value of the probe, and material conductivity; S2, based on the lateral pressure value of the probe and the preset depth pressure benchmark model, calculate the density characteristic value of the fermentation pile; according to the internal temperature value of the pile and the electrical conductivity of the material, extract the biochemical evolution characteristic quantity of the fermentation pile at the current fermentation stage; S3, in the pre-built multi-dimensional state mapping rule base, match the environmental disturbance weight matrix corresponding to the currently calculated density feature value and the biochemical evolution feature quantity; perform numerical compensation on the initial water electrochemical signal according to the environmental disturbance weight matrix, and calculate the calibrated true water content value. S4. The actual moisture content value is compared with the target moisture threshold range corresponding to the current fermentation stage. If the actual moisture content value deviates from the target moisture threshold range, a corresponding environmental intervention command is generated to control the operating equipment to perform the corresponding fermentation environment adjustment operation.

2. The method for real-time monitoring of the moisture content of organic fertilizer according to claim 1, characterized in that, The density characteristic value of the fermentation pile is calculated based on the lateral pressure value of the probe and a preset depth pressure benchmark model, including: The burial depth information of the sensor that collects the lateral pressure value of the probe in the fermentation pile is obtained; In the depth pressure benchmark model, query the initial standard pressure value corresponding to the burial depth information; The ratio of the lateral pressure value of the probe to the initial standard pressure value is calculated. If the calculated ratio is greater than a preset threshold, it is determined that the fermentation pile has settled and compacted, and the ratio is used as the density characteristic value.

3. The method for real-time monitoring of the moisture content of organic fertilizer according to claim 1, characterized in that, The step of extracting biochemical evolution characteristics of the fermentation pile at its current fermentation stage based on the internal temperature and electrical conductivity of the material includes: Calculate the rate of change of the internal temperature of the pile and the rate of change of the conductivity of the material during the current monitoring period; The internal temperature value of the pile, the rate of temperature change, and the rate of change of electrical conductivity are input into a preset logic decision tree model to determine the current fermentation stage of the fermentation pile. Based on the fermentation stage determined by the judgment, the corresponding stage identifier and feature vector are extracted, and the biochemical evolution feature quantity is generated.

4. The method for real-time monitoring of the moisture content of organic fertilizer according to claim 1, characterized in that, The construction process of the multidimensional state mapping rule base includes: Obtain calibration test data of various organic fertilizer samples under different temperature, humidity, density and salinity conditions; Extract the moisture signal attenuation and gain benchmarks from the calibration test data, establish a lookup table or relational mapping matrix indexed by density features and biochemical evolution features, and obtain the multidimensional state mapping rule base.

5. The method for real-time monitoring of the moisture content of organic fertilizer according to claim 1, characterized in that, The environmental interference weight matrix includes at least: The physical contact compensation coefficient is used to compensate for the artificially high moisture signal caused by the increase in material density; and the temperature-salt dielectric compensation coefficient is used to compensate for the polarization error of the moisture signal caused by high temperature and high conductivity.

6. The method for real-time monitoring of the moisture content of organic fertilizer according to claim 5, characterized in that, The step of numerically compensating the initial water content signal based on the environmental interference weight matrix to calculate the calibrated true water content value includes: The initial water electroelectric signal is multiplied by the physical contact compensation coefficient and the temperature-salinity dielectric compensation coefficient respectively to remove the false response signal caused by environmental interference and extract the true dielectric response signal. The actual dielectric response signal is substituted into a preset reference conversion formula to calculate the actual moisture content value.

7. The method for real-time monitoring of the moisture content of organic fertilizer according to claim 1, characterized in that, In step S4, generating the corresponding environmental intervention command to control the operating equipment to perform corresponding fermentation environment adjustment operations includes: If the actual moisture content value is lower than the lower limit of the target moisture threshold range, it is determined that the moisture loss of the fermentation pile is too fast, and a water replenishment and turning instruction is generated. The water replenishment and turning instructions are sent to the trough-type compost turner and automatic sprinkler system on site via industrial bus. The trough-type compost turner turns the material and the automatic sprinkler system sprays water in a quantitative manner until the actual moisture content value calculated in the next monitoring cycle returns to the target moisture threshold range.

8. A sensor-based real-time monitoring device for the moisture content of organic fertilizer, characterized in that, include: The data acquisition module is used to acquire multi-source physical data of the organic fertilizer fermentation pile; wherein, the multi-source physical data includes: initial moisture electrical signal, internal temperature value of the pile, probe lateral pressure value, and material conductivity; The feature extraction module is used to calculate the density feature value of the fermentation pile based on the lateral pressure value of the probe and the preset depth pressure benchmark model; and to extract the biochemical evolution feature quantity of the fermentation pile at the current fermentation stage based on the internal temperature value of the pile and the electrical conductivity of the material. The dynamic compensation module is used to match the environmental disturbance weight matrix corresponding to the currently calculated density feature value and the biochemical evolution feature value in a pre-built multi-dimensional state mapping rule library; and to perform numerical compensation on the initial water electrochemical signal according to the environmental disturbance weight matrix to calculate the calibrated true water content value. The adaptive control module is used to compare the actual moisture content value with the target moisture threshold range corresponding to the current fermentation stage. If the actual moisture content value deviates from the target moisture threshold range, a corresponding environmental intervention command is generated to control the operating equipment to perform corresponding fermentation environment adjustment operations.

9. A real-time moisture content monitoring device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the real-time monitoring method for the moisture content of organic fertilizer as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the real-time monitoring method for the moisture content of organic fertilizer as described in any one of claims 1 to 7.

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