Production quality control method and system for agriculture and animal husbandry waste fermented fertilizer

Abstract

The invention provides a production quality control method and system for agriculture and animal husbandry waste fermented fertilizer, and relates to the technical field of fertilizer preparation, and the method comprises the steps: uniformly distributing a monitoring sensor group, carrying out the time sequence monitoring, and building a real-time position data flow; positioning a frame insertion point, performing trend fitting, and updating frame insertion data to a real-time position data stream; carrying out adaptive clustering under the same time node, and carrying out adaptive clustering cluster correction by utilizing a preset time step length; constructing fermentation state partitions, wherein each fermentation state partition is provided with a code identifier of a state evaluation index set; establishing a mapping difference regulation and control strategy by using a multi-target deviation regulation and control model; and introducing a global regulation and control parameter optimization channel, executing global optimization, and performing production management and control. According to the invention, the technical problems of low efficiency and unstable quality in the fermentation process caused by neglect of the mutual relation and influence among different parameters by generally performing quality control based on a single or a small number of monitoring parameters in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of fertilizer preparation, and particularly relates to a production quality control method and system for fermented fertilizer from agricultural and animal husbandry wastes. Background Art

[0002] Agricultural and animal husbandry wastes, such as animal feces, plant residues, etc., as an important organic resource, have wide applications. Converting these wastes into fermented fertilizers can not only solve the problem of waste treatment, but also provide high-quality organic fertilizers for agriculture, improving the yield and quality of crops. However, the production process of fermented fertilizer is a complex biochemical reaction process involving multiple variables, such as temperature, humidity, oxygen concentration, pH value, etc. To ensure that the quality of the fermented fertilizer meets the standards, strict quality control must be carried out on the fermentation process. Existing technologies usually carry out regulation based on single or a small number of monitoring parameters, ignoring the mutual relationship and influence between different parameters, resulting in the inability to achieve global optimization of the fermentation process, the multi-dimensional interaction not being reasonably regulated, and thus the fermentation process being inefficient and the quality being unstable. Summary of the Invention

[0003] This application provides a production quality control method and system for fermented fertilizer from agricultural and animal husbandry wastes, aiming to solve the technical problems that existing technologies usually carry out quality control based on single or a small number of monitoring parameters, ignoring the mutual relationship and influence between different parameters, being unable to achieve global optimization of the fermentation process, and thus resulting in low efficiency and unstable quality of the fermentation process.

[0004] In the first aspect disclosed in this application, a production quality control method for fermented fertilizer from agricultural and animal husbandry wastes is provided. The method includes: evenly distributing a monitoring sensor group in the fermentation pile body, using the monitoring sensor group to perform time-series monitoring on the fermented fertilizer in the fermentation pile body, and establishing a real-time position data stream; after reading the control accuracy of the production quality, positioning the interpolation points with the control accuracy, and performing trend fitting on the real-time position data stream within a preset search area with the interpolation points as the interpolation centers, and updating the interpolation data to the real-time position data stream; performing adaptive clustering on the updated real-time position data stream at the same time node, and performing adaptive clustering cluster correction using a preset time step; constructing a fermentation state partition with the corrected adaptive clustering clusters, and each fermentation state partition is set with an encoded identifier of a state evaluation index set; establishing a mapping difference regulation strategy for each fermentation state partition based on the encoded identifier using a multi-objective deviation regulation model; introducing a global regulation parameter optimization channel, performing global optimization of the mapping difference regulation strategy, and performing production control with the global optimization result.

[0005] The second aspect disclosed in this application provides a production quality control system for agricultural and pastoral waste fermentation fertilizer. The system is used for the production quality control method of the above-mentioned agricultural and pastoral waste fermentation fertilizer. The system includes: a timing monitoring module, which is used to evenly distribute a monitoring sensor group in the fermentation heap body, use the monitoring sensor group to perform timing monitoring on the fermentation fertilizer in the fermentation heap body, and establish a real-time position data stream; a trend fitting module, which is used to read the control accuracy of production quality, locate the interpolation points with the control accuracy, and perform trend fitting on the real-time position data stream within a preset search area with the interpolation points as the interpolation centers, and update the interpolation data to the real-time position data stream; an adaptive clustering module, which is used to perform adaptive clustering on the updated real-time position data stream at the same time node, and perform adaptive clustering cluster correction using a preset time step; a state partition construction module, which is used to construct a fermentation state partition with the corrected adaptive clustering clusters, and each fermentation state partition is set with an encoding identifier of a state evaluation index set; a regulation strategy establishment module, which is used to establish a mapping difference regulation strategy for each fermentation state partition based on the encoding identifier using a multi-objective deviation regulation model; a global optimization module, which is used to introduce a global regulation parameter optimization channel, perform global optimization of the mapping difference regulation strategy, and perform production control with the global optimization result.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects: By evenly distributing a monitoring sensor group within the fermentation pile body, multiple important parameters during the fermentation process can be monitored in real time. These sensors continuously collect data and convert it into a real-time position data stream, providing the necessary basic data for subsequent fermentation state analysis and optimization. Through the frame interpolation technique, the gaps in the real-time data stream are filled to ensure the continuity and integrity of the data. By setting the control accuracy and performing trend fitting on the data within the preset search area, the data stream can be optimized and corrected, reducing data discontinuity and volatility, which makes subsequent analysis and decision-making more reliable. Through the adaptive clustering method, the data is intelligently classified according to time nodes, and regions with similar characteristics are clustered together, enabling more precise analysis of different regions and states during the fermentation process. Through the correction of the clustering results, the stability and accuracy of the clustering clusters are ensured, contributing to further improving the accuracy of fermentation state analysis. Based on the correction results of the adaptive clustering clusters, the fermentation pile body is divided into multiple fermentation state partitions, each partition being equipped with a unique coding identifier. This not only improves the regional management ability of the fermentation process but also facilitates the state tracking and quality control of different regions. Through the coding identifier, the fermentation state and data of each region can be effectively monitored, facilitating subsequent analysis and optimization. Through the multi-objective deviation regulation model, according to the coding identifier of each fermentation state partition, a targeted regulation strategy is formulated. This strategy can handle the differences in each partition on the basis of multi-objective optimization, ensuring that each fermentation region can reach the predetermined fermentation state under different conditions, ultimately improving the quality of the overall fermented fertilizer. By introducing a global regulation parameter optimization channel, the global optimization of the mapping difference regulation strategy is executed, and the control parameters in the production process are adjusted according to the optimization results. Through this global optimization, the differences between different partitions can be coordinated, the demands of each partition can be balanced, ensuring the high efficiency and stability of the overall fermentation process, optimizing resource utilization, reducing production costs, and improving the overall quality of the fermented fertilizer.

[0007] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented in accordance with the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the specific embodiments of this application. Brief Description of the Drawings

[0008] Figure 1 It is a schematic flowchart of a method for controlling the production quality of fermented fertilizer from agricultural and animal husbandry waste provided by an embodiment of this application.

[0009] Figure 2 It is a schematic structural diagram of a system for controlling the production quality of fermented fertilizer from agricultural and animal husbandry waste provided by an embodiment of this application.

[0010] Explanation of the accompanying drawings: time series monitoring module 10, trend fitting module 20, adaptive clustering module 30, state partition construction module 40, control strategy establishment module 50, global optimization module 60. DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a production quality control method and system for agricultural and animal husbandry waste fermented fertilizer, which solves the technical problems that the prior art usually performs quality control based on a single or a small number of monitoring parameters, ignores the relationship and influence between different parameters, and cannot achieve global optimization of the fermentation process, thereby leading to low efficiency and unstable quality of the fermentation process.

[0012] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a production quality control method for agricultural and animal husbandry waste fermentation fertilizer, the method comprising: The monitoring sensor group is evenly distributed in the fermentation pile body, and the fermented fertilizer in the fermentation pile body is monitored in time series by using the monitoring sensor group to establish a real-time position data stream.

[0014] In the fermentation pile, monitoring sensor groups are evenly distributed. These sensor groups are used to monitor key parameters in the fermentation pile, such as temperature, humidity, pH value, etc., and use odor VOC sensor groups for corruption and decomposition gas identification. The location and number of these sensors are designed according to the size and shape of the pile to ensure that the fermentation process of the entire pile can be fully and accurately monitored. Each sensor group continuously collects and uploads real-time monitoring data, and records the data in chronological order to form a time series data stream. These time series data streams contain the data of each sensor at different time points, reflecting the change process in the fermentation pile. Among them, in the fermentation pile, the specific position of each monitoring sensor is known. Therefore, after obtaining the sensor data, these data are matched with the position in combination with the actual position of the sensor to form a real-time position data stream. This real-time position data stream not only records the time series data of each sensor, but also includes the spatial position of the sensor, that is, the specific coordinates of the monitoring point. Through these data, the fermentation status and change trend of different positions in the fermentation pile can be known at any time.

[0015] After reading the control accuracy of production quality, the interpolation point is located with the control accuracy, and the interpolation point is used as the interpolation center to perform trend fitting of the real-time position data stream in a preset search area, and the interpolation data is updated to the real-time position data stream.

[0016] In the actual production process, it is required to precisely control various parameters in the fermentation pile body. To ensure that the quality control achieves the expected effect, a control accuracy is first set. This means that during the production process, the quality monitoring of the fermented fertilizer will determine the requirements for data accuracy according to pre-set standards (such as the temperature and humidity fluctuation ranges).

[0017] Analyze the real-time data stream according to the control accuracy to find key interpolation points. An interpolation point refers to a data point in the time-series data stream that needs to be supplemented, updated, or analyzed in more detail based on the control accuracy requirements. The positioning of the interpolation point is determined according to the quality, change trend, and accuracy requirements of the current monitoring data. For example, when there are intervals or drastic changes in the real-time monitoring data, the corresponding data points are selected as interpolation points for more refined analysis and processing.

[0018] After determining the interpolation points, around the interpolation points, perform trend fitting based on the surrounding real-time data stream. Trend fitting is to predict the change trend during the fermentation process through mathematical models such as curve fitting and regression analysis. By fitting the interpolation points and the data around them, the data at other time points are estimated. Especially in the case of sparse or missing data, the interpolation points play a role in filling data gaps and complementing the time-series data. After the interpolation data is calculated, these data are inserted into the real-time position data stream. In this way, all the monitoring data will be updated to ensure that the real-time data stream is more complete and accurate.

[0019] Perform adaptive clustering on the updated real-time position data stream at the same time node and use a preset time step for adaptive clustering cluster correction.

[0020] Perform adaptive clustering on the updated real-time position data stream. The basic idea of adaptive clustering is to group the data according to time nodes, that is, the same time point or time window. Each time node corresponds to a group of data, and these data reflect the change trends of different regions or different parameters in the fermentation pile body. The purpose of adaptive clustering is to identify potential similarities or regularities in these data streams and aggregate the data with similar characteristics together. Adaptive clustering can adopt algorithms with dynamic adjustment, such as the K-means algorithm, DBSCAN, hierarchical clustering, etc. Adaptive clustering emphasizes dynamically adjusting the clustering parameters according to the actual distribution of the data, such as the number of clusters, distance metrics, etc., in order to obtain more accurate results. According to different characteristics in the real-time data stream, such as temperature, humidity, pH value, etc., these data are divided into different clusters. The data points in the same cluster have certain similarities, and the differences between different clusters are relatively large, corresponding to different fermentation states or regions in the fermentation pile body.

[0021] One challenge in the clustering process is how to ensure that the clustering results can accurately reflect the actual changes in the fermentation heap over time. To address this issue, a preset time step is used to correct the clustering clusters. The preset time step is a predefined time interval used to stabilize the clustering clusters and adjust the errors in the clustering process. Within each time step, the clustering clusters are re-evaluated and adjusted to ensure that their clustering results can accurately track the evolution of the fermentation heap state. For example, if the temperature and humidity in a certain area change rapidly, a smaller time step is required for correction; while for areas with slower changes, a longer time step is needed. These corrections ensure the accuracy of the data and the stability of the clustering results.

[0022] Construct a fermentation state partition based on the corrected adaptive clustering clusters, and each fermentation state partition is set with the coding identifier of the set of state evaluation indicators.

[0023] Divide the fermentation heap into different fermentation state partitions according to the corrected adaptive clustering clusters. Each partition represents an area or a set of areas in the fermentation heap with similar fermentation states. The division of the fermentation state partitions can be based on different fermentation parameters, such as temperature, humidity, pH value, oxygen content, etc., and their change trends at different time nodes. For example, one partition represents an area with a higher temperature and a lower humidity, and another partition represents an area with a higher humidity and a lower temperature. The state of each area can reflect its corresponding fermentation environment, which in turn affects the production quality of the fertilizer.

[0024] Each fermentation state partition is set with the coding identifier of the set of evaluation indicators for the state of that area. The set of state evaluation indicators includes the fermentation temperature and humidity gradient index, the oxygen stability index, the acid-base buffering index, and the biological activity trend factor. These indicators are calculated based on the actual monitoring data of the fermentation heap to form specific values to help determine whether the fermentation state of each partition is within the ideal range.

[0025] Use the multi-objective deviation regulation model to establish a mapping difference regulation strategy for each fermentation state partition based on the coding identifier.

[0026] Use the multi-objective deviation regulation model to regulate each fermentation state partition. The multi-objective deviation regulation model analyzes multiple objectives, such as temperature, humidity, pH value, etc., and their interactions. The ultimate goal is to minimize the deviations of these objectives to achieve an ideal fermentation environment. Deviation regulation means taking corresponding regulation measures according to the gap, that is, the deviation, between the actual monitoring data of each fermentation state partition and the set target value.

[0027] Each fermentation state partition has a corresponding coding identifier. Based on these coding identifiers, each partition is mapped to a specific regulation strategy. These strategies adjust the parameters of the fermentation pile according to the fermentation state and corresponding quality indicators of each partition to ensure that the state of each partition is as close as possible to the preset ideal state. The mapping difference regulation strategy takes into account the different characteristics and requirements of each fermentation state partition, so the regulation scheme will be different. For example, a region with higher humidity needs to strengthen ventilation to reduce humidity, while a region with too low temperature needs to be heated to increase temperature.

[0028] Introduce a global regulation parameter optimization channel to perform global optimization of the mapping difference regulation strategy, and conduct production control based on the global optimization results.

[0029] To improve the effect of overall production control, a global regulation parameter optimization channel is introduced. The core of this channel is to globally optimize the regulation parameters of the entire fermentation process, rather than being limited to local regulation of a single partition. The main task of this optimization channel is to integrate the regulation strategies of each fermentation state partition and coordinate their interactions. Specifically, globally optimize the control parameters of all partitions (such as temperature, humidity, gas flow, etc.) to ensure that the overall environment of the fermentation pile reaches the optimal state. Global optimization can be achieved through intelligent algorithms such as genetic algorithms, particle swarm optimization, simulated annealing, etc. These algorithms can find the best combination of regulation parameters in a multi-dimensional space.

[0030] In the global regulation parameter optimization channel, not only is each fermentation state partition regulated, but also global strategy optimization is carried out to ensure the maximization of the quality of the entire fermentation pile. The goal of global optimization is to reduce the control deviation between partitions and optimize the overall stability of each target variable (such as temperature, humidity, gas concentration, pH value, etc.). During this process, the mapping difference regulation strategy is executed, and the control parameters of each partition are continuously optimized through an iterative method. For example, when the temperature in a certain region is too high, the temperature inside the fermentation pile is reduced overall by adjusting the ventilation, heating, etc. of other regions to achieve global control.

[0031] After global optimization is completed, final production control strategies are formulated according to the optimization results. These strategies are directly applied to the production process to guide the real-time regulation of each partition in the fermentation pile. The optimized production control strategies ensure that all links in the fermentation process are coordinated, minimize the deviation of various quality indicators, and thus improve the quality and yield of the final product.

[0032] Furthermore, the introduction of the global regulation parameter optimization channel to perform global optimization of the mapping difference regulation strategy includes: After the mapping difference control strategy is input into the global control parameter optimization channel, the global optimization objective function is called, and the evaluation characteristics of the global optimization objective function include regional state target approximation characteristics, control cost characteristics, and synergistic influence characteristics between partitions; after the mapping difference control strategy is evaluated by the global optimization objective function, the adjustment focus items of the current mapping difference control strategy are identified; the adjustment focus items are used as iteration indexes, iterative optimization is performed, and the global optimization result is established with the iterative optimization result.

[0033] The mapping difference control strategy is input into the global control parameter optimization channel. These control strategies have been refined according to the characteristics of each fermentation state partition. Now these strategies need to be adjusted globally to ensure that the production process of the entire fermentation pile reaches the optimal state. After the mapping difference control strategy is input, the global optimization objective function is called. The core task of this objective function is to evaluate the effect of the mapping difference control strategy and provide guidance for the global optimization process.

[0034] The global optimization objective function evaluates the input control strategy through three key evaluation features, including the regional state target approximation feature, the control cost feature, and the synergistic influence feature between partitions. Among them, the regional state target approximation feature evaluates the gap between the actual state and the target state of each fermentation state partition, that is, the target approximation degree. This indicator measures whether each partition has achieved the expected temperature, humidity, pH value and other targets; the control cost feature evaluates the cost brought by the control measures, including energy consumption, material consumption, etc. If a lot of energy or cost is required to adjust the state of certain areas, then the evaluation value of this feature will be higher; the synergistic influence feature between partitions evaluates the interaction between different partitions. The partitions in the fermentation pile may affect each other, and the regulation of a partition may affect the state of other partitions. For example, the excessive temperature of a partition may affect the humidity of the adjacent partitions. Therefore, the control strategy needs to consider these synergistic influences to avoid systematic errors.

[0035] The input mapping difference control strategy is comprehensively evaluated using the global optimization objective function. The current control strategy is evaluated based on the three evaluation features in the objective function. Each score is combined to determine whether the current strategy has reached the best optimization state. After the evaluation, the adjustment focus items in the current mapping difference control strategy are identified. Adjustment focus items refer to those parts that need special attention in the current state. For example, the state of some partitions deviates far from the target value, or the control cost is too high, or there is a strong synergistic impact problem. These parts are adjustment focus items.

[0036] The adjustment focus items are used as iteration indexes. The iteration index indicates which parts of the optimization process are adjusted first, and optimization will be performed on these focus items. Iterative optimization is the process of multiple adjustments to the control strategy through optimization algorithms, such as gradient descent method, genetic algorithm, etc. In each iteration, the relevant control parameters will be adjusted according to the evaluation results of the optimization objective function until a global optimal solution is found. After multiple rounds of iterative optimization, a global optimization result is finally obtained. This result represents the optimal combination of control strategies, covers the optimal control parameters of each fermentation state partition, and ensures the minimization of the control cost, while taking into account the synergistic effects between partitions.

[0037] Furthermore, the performing iterative optimization using the adjustment focus item as an iteration index includes: Acquire the attention data of the adjustment focus item; use the attention data to configure the mapping random perturbation, use the random perturbation and the adjustment focus item to search and update the control parameters, generate a first round of search update results, and identify the actual search direction of the first round of search update results; perform search evaluation of multiple rounds of search update results, if the search evaluation result is an update state inactive state, and multiple rounds of search are random perturbation searches, generate random perturbation suppression, use the random perturbation suppression to adjust the mapping random perturbation, and continue iterative optimization.

[0038] Obtain the attention data of the adjustment focus items. The attention data refers to the degree to which each adjustment focus item needs to be focused on during the global optimization process. These data reflect the impact of each adjustment focus item on the fermentation state and the final result during the optimization process. The attention data can be obtained through historical data, model simulation or real-time monitoring data. For example, if the temperature of a fermentation state partition deviates greatly from the target, it will cause a higher degree of attention, because the temperature deviation has a greater impact on the fermentation process.

[0039] Random perturbations are mapped according to the configuration of attention data. This perturbation is a random search mechanism used to explore the combination of different control parameters. By introducing perturbations, different control schemes can be extensively tried in the search space to find the optimal combination of control parameters. The attention data affects the intensity and direction of the perturbation. If the attention of a certain adjustment item is high, the corresponding perturbation will be stronger in order to more finely control the parameter; if the attention is low, the perturbation will be weaker, thereby avoiding unnecessary over-adjustment.

[0040] With the introduction of disturbances, a search and update is performed in the control parameter space. This process changes the control parameters (such as temperature, humidity, oxygen flow, etc.) to find the optimal solution that can reduce deviations and improve fermentation quality. According to the disturbance effect of each adjustment item, the current control strategy is updated to generate the first round of search and update results, which includes a new set of control parameters.

[0041] After the first round of search updates, the actual search direction of the first round of search update results is identified. This step is to determine whether the current control parameters are optimized in the right direction. If the search results fail to effectively approach the optimal solution, the search direction needs to be adjusted.

[0042] Perform multiple rounds of search updates. After each round of search, evaluate the results to ensure that the optimization direction of the control parameters is correct. The purpose of this search evaluation process is to evaluate whether the search process is effective and whether it can drive the system to gradually approach the global optimal solution. If the search evaluation result shows that the current update status is "inactive", that is, the search process has failed to find an effective optimization direction, and multiple search rounds are based on random perturbations, then the random perturbation suppression mechanism will be executed. The purpose of random perturbation suppression is to reduce invalid perturbations and avoid over-exploration of search directions that have been proven to be invalid. The suppression mechanism can be achieved by adjusting the range, frequency or intensity of the perturbations. In this way, resources can be concentrated on directions that can effectively improve the control results and avoid unnecessary random changes.

[0043] After the disturbance suppression mechanism is generated and applied, the mapping random disturbance is adjusted to make it more refined and effective, and the next round of iterative optimization process is continued. The adjusted disturbance will be more concentrated on the search area that is most likely to lead to optimization, thereby improving the optimization efficiency.

[0044] Furthermore, the step of locating the interpolation point with the control accuracy, performing trend fitting of the real-time position data stream in a preset search area with the interpolation point as the interpolation center, and updating the interpolation data to the real-time position data stream includes: A matching evaluation is performed according to the control accuracy and the distribution of the monitoring sensor group, and the interpolation point is located using the matching evaluation result; after determining the preset search area, any two monitoring sensor groups in the preset search area are combined to establish a combination set; sensor groups are connected for each combination in the combination set, and after configuring trend trust weights based on the point-line distance between the sensor group connection and the interpolation point, trend fitting is performed on all combination sets, and the interpolation data of the interpolation point is calculated using the trend fitting results and the trend trust weights.

[0045] Matching evaluation is performed based on the control accuracy and the distribution of the monitoring sensor group. The control accuracy refers to the accuracy requirements in monitoring and adjusting the fermentation process, and the sensor group distribution refers to the specific location of each sensor in the fermentation pile. The matching evaluation process includes evaluating whether the current sensor group position can meet the accuracy requirements of the production process, and evaluating whether each sensor can collect enough information within its coverage range to support accurate process control. When the evaluation results confirm which locations have accuracy or data gaps, the interpolation points are located based on these evaluation results. Interpolation points refer to those time or space locations in the data stream where new data needs to be inserted. Through interpolation points, areas with missing or incomplete data can be supplemented to enhance the accuracy and continuity of the overall data stream.

[0046] Define a preset search area, which is set in the fermentation pile based on the distribution range of monitoring sensors and actual needs. The preset search area is a space for data analysis and optimization. Data analysis and interpolation operations will be performed in this area. The selection of the preset search area is based on the existing monitoring data and the structural characteristics of the fermentation pile to ensure that the search area includes areas that have a greater impact on the fermentation process while avoiding redundant data analysis. In the search area, any two monitoring sensor groups are combined to establish a combination set. These sensor group combinations are used to analyze and compare the data differences and relationships between different monitoring points.

[0047] Based on the combination set, sensor group connections are performed for each combination. Sensor group connection means connecting their monitoring data by establishing connections between each pair of sensors in the combination set, for example, calculating distances, establishing spatial relationships, etc. These connections help analyze the relationships between different sensors, especially their physical locations, monitoring parameters, etc., which can help better understand the changing trends in different areas.

[0048] After connecting the sensor groups, the relationship between the sensor groups and the interpolation points is analyzed by calculating each point-line distance. The point-line distance refers to the spatial distance between the sensor group and the interpolation point. According to the point-line distance, a trend trust weight is configured for the relationship between each sensor group and the interpolation point. The weight measures the credibility of each sensor group data for the interpolation point data. For example, the sensor group closer to the interpolation point has a higher trust and a larger weight; while the sensor group farther away from the interpolation point has a lower trust and a smaller weight.

[0049] After calculating the point-line distance and trend trust weight between each sensor group and the interpolation point, trend fitting is performed on the sensor group data in all combination sets. Trend fitting fits the sensor group data through mathematical models, such as regression analysis, curve fitting, etc., to generate a trend curve that describes the data change. After completing trend fitting, the interpolation data of the interpolation point is calculated based on the fitting results and trend trust weight. This data is generated through weighted averaging and conforms to the trend and logic of the overall data flow. The calculated interpolation data will be inserted into the real-time location data stream to fill the data gap and optimize the continuity and accuracy of the data.

[0050] Furthermore, the adaptive clustering of the updated real-time location data stream at the same time node and the adaptive clustering cluster correction using a preset time step includes: After treating the data of the same time node as the data of the same dimension, adaptive clustering of the real-time location data stream under the same dimension data is performed to establish an adaptive clustering cluster; the preset time step is used to perform a stability search for the origin and development of the adaptive clustering cluster of each dimension, and the stability search result is used to update the adaptive clustering cluster to complete the adaptive clustering cluster correction.

[0051] The monitoring data at the same time node is treated as data of the same dimension for processing. This means that if multiple sensors collect data at the same time, these data will be merged into a unified time dimension data set. These data can be measurement values ​​of different sensors, such as temperature, humidity, pH value, etc., but they all belong to the same time node and will be processed as a unified data level during the analysis process.

[0052] After organizing the data into the same dimension, adaptive clustering is performed based on these data. The goal of adaptive clustering is to aggregate monitoring data with similar characteristics into the same cluster. For example, multiple data dimensions such as temperature and humidity can be used to identify which areas have similar fermentation states at the same time node through cluster analysis. Adaptive clustering algorithms can use methods such as K-means, DBSCAN, hierarchical clustering, etc. to dynamically assign data to different clusters based on the similarity of the data. Each cluster represents a group of areas with similar fermentation characteristics at a time node. After the clustering process is completed, a set containing multiple clusters is obtained, each cluster corresponds to a group of data, which show similar change trends or characteristics at the time node. The purpose of adaptive clustering is to aggregate the fermentation characteristics of the monitoring points and provide meaningful data grouping for subsequent analysis.

[0053] The stability of each adaptive cluster is searched and evaluated using a preset time step. The preset time step refers to the time interval or time window set during the clustering process to track the evolution and changes of the cluster in the time dimension. During the stability search process, check whether each adaptive cluster remains stable within different time steps. For example, if the characteristics of a cluster change dramatically, the cluster needs to be re-evaluated or adjusted. The stability search process helps determine whether the change trend of the cluster in the time dimension is reasonable and whether it conforms to the actual situation of the fermentation pile.

[0054] Based on the results of the stability search, each cluster is updated. This update is intended to ensure that the fermentation state reflected by each cluster is more consistent with the actual situation. For example, if the stability of a cluster is poor, it is necessary to increase or decrease the data points in the cluster, or adjust the cluster division criteria. Through this stability correction, the structure of the cluster can be dynamically adjusted so that each cluster can better adapt to changes in the environment and state in the fermentation pile.

[0055] Furthermore, the state evaluation index set includes fermentation temperature and humidity gradient index, oxygen stability index, acid-base buffering index, and biological activity trend factor.

[0056] The set of status evaluation indicators includes fermentation temperature and humidity gradient index, oxygen stability index, acid-base buffering index, and biological activity trend factor. They are used to measure different physical and chemical characteristics of the fermentation process. These indicators help to comprehensively evaluate the fermentation status of different areas in the fermentation pile, ensure that the fermentation process is carried out under ideal conditions, and ultimately obtain fermented fertilizer products that meet expectations.

[0057] Among them, the fermentation temperature and humidity gradient index is used to measure the changing gradient of temperature and humidity in the fermentation pile. Temperature and humidity are key factors affecting the fermentation process. Too high or too low temperature and humidity will affect the activity of microorganisms and the fermentation effect. By calculating the gradient of temperature and humidity at different positions in the pile, that is, the rate of change of temperature and humidity, the uniformity of the fermentation pile can be evaluated. Ideally, temperature and humidity should be evenly distributed to promote balanced microbial activity and avoid local overheating or overhumidity.

[0058] The oxygen stability index indicates the oxygen content and stability in the fermentation pile. During the fermentation process, the oxygen content is crucial to the activity of aerobic microorganisms. Too high or too low oxygen concentration will affect the fermentation process. By monitoring the changes and stability of oxygen in the fermentation pile, the normal progress of the aerobic fermentation reaction can be ensured. For example, if the index is too low, it means that the oxygen supply is insufficient and ventilation needs to be increased; if the index is too high, it is necessary to restrict air circulation or reduce ventilation to avoid unnecessary energy consumption.

[0059] The acid-base buffering index indicates the stability of the acidity (pH value) in the fermentation pile. During the fermentation process, microbial activity will produce acidic substances, causing the pH value to drop. Too low a pH value will inhibit the activity of microorganisms and affect the fermentation effect. If the pH value in the pile is stable and maintained within an appropriate range (usually between 4-7), it means that the fermentation process is good; if the acid-base buffering is not insufficient, the pH value can be adjusted by adding alkaline substances (such as lime) to avoid excessive acidification.

[0060] The bioactivity trend factor indicates the trend of microbial activity in the fermentation pile. Bioactivity is directly related to the efficiency of fermentation. Therefore, monitoring bioactivity helps predict the progress of fermentation and the quality of the final product. By tracking the trend of microbial activity, it is possible to identify whether there is microbial inhibition or death during the fermentation process, so that timely measures can be taken to adjust the fermentation conditions. If the value of this factor decreases, it means that the microbial activity has weakened, and environmental conditions such as temperature, humidity or oxygen content need to be adjusted to promote microbial activity.

[0061] Furthermore, the global optimization objective function is as follows:

[0062] in, Characterize the global optimization objective function, The control parameter set that represents all fermentation state partitions, N is the total number of fermentation state partitions, i is the fermentation state partition index, is the regional state target approximation weight of the i-th fermentation state partition, For control parameters The actual state of the next i-th fermentation state partition, is the target state of the i-th fermentation state partition, To adjust the weight coefficient of the cost item, is the control cost function of the ith fermentation state partition, V is the weight of the synergistic influence term, represents the set of state partitions that have coupling effects on the i-th fermentation state partition, j represents the index of the state partition that has coupling effects on the i-th fermentation state partition, Characterizes the state coupling strength of the i-th fermentation state partition and the j-th fermentation state partition d, For control parameters The actual state of the next j-th fermentation state partition.

[0063] Specifically, the global optimization objective function is used to optimize the various control parameters in the fermentation process to achieve the ideal fermentation state. The formula is as follows: Among them, the first is the error term, which measures the actual state of each fermentation state partition With target status The gap between them is calculated by weighting the sum of squared errors, which aims to minimize the difference between each partition and its target state.

[0064] Item 2 is the control cost item, which is used to balance the adjustment cost of the control parameters , avoid excessive control input and ensure that the use of control parameters is economical.

[0065] Item 3 It is a synergistic influence term, which measures the synergistic influence between different fermentation status partitions. By considering the relationship and synergistic effect between partitions, the goal is to reduce unnecessary cross-regional regulatory differences, thereby making the state of the entire fermentation pile more stable.

[0066] The purpose of the global optimization objective function is to find a set of optimal control parameters based on minimizing the target error of each fermentation state partition, the control cost and the synergistic effect between partitions. , so that the fermentation process as a whole reaches the optimal state.

[0067] Furthermore, the production control based on the global optimization result includes: The control parameters of the fermentation process are recorded, and the control parameter recording results are embedded in the origin code as a production label; the origin code embedded in the production label is used to build a traceable fermentation history for fermentation fertilizer management.

[0068] Record the control parameters of the fermentation process, which usually include temperature, humidity, pH value, oxygen concentration, etc. These parameters play a key role in the entire fermentation process and can be used to trace any problems in the fermentation process. The control parameter record results are used as production labels and embedded in the origin code. The origin code is a unique identifier used to identify the production location and production batch of fermented fertilizer. By embedding the control parameter record into the origin code, the digital storage of production information is realized, so that each batch of fermented fertilizer can be traced back to the specific production process. This practice not only ensures the integrity of production data, but also enhances production transparency and facilitates subsequent quality inspections and production audits.

[0069] The origin code embedded in the production label is used to build a traceable fermentation history, which contains detailed information on the entire production process from raw material input to final product delivery, including all key production parameters, production time, production batches, raw materials used, records of control parameters, and measures related to quality control.

[0070] Building a traceable fermentation history not only improves the transparency of the production process, but also helps with the management of fermented fertilizers. In this way, each batch of fermented fertilizers produced can be accurately traced, and each step in the production process can be audited and verified. For example, if a batch of fermented fertilizers has quality problems, the traceability system can quickly identify the root cause of the problem and find out the changes in control parameters that caused the quality fluctuations, thereby improving the production process.

[0071] Furthermore, after the production control is performed based on the global optimization result, the following steps are also included: The clustering results of each round of fermentation, the global optimization results and the output effect are recorded and stored in a production database; and the production database is used to update and optimize the strategy of subsequent fermentation fertilizer production management.

[0072] Record the clustering results of each round of fermentation. Each round of fermentation clustering results reflects the state classification of different areas in the fermentation pile. Through clustering, the fermentation characteristics of different areas at a certain time node can be identified and grouped. Recording these clustering results helps to understand the dynamic changes of each area in the fermentation pile; record the global optimization results. The global optimization results are the results of global adjustment of the control parameters in the entire fermentation process based on the optimization algorithm. These results represent the best control strategy optimized according to the current fermentation state. After each round of fermentation, record the corresponding output effect, that is, the final product quality and output of the fermented fertilizer. These data are the actual effect of the fermentation process and reflect whether the production has achieved the expected goals. These data provide an important basis for subsequent quality control and production optimization. All the above recorded data are stored in a unified production database. These data will serve as historical records and provide a reference for future production management.

[0073] Subsequent fermentation fertilizer production management strategies are updated and optimized based on the data stored in the production database. By analyzing historical data, patterns, trends, and potential problems in the production process can be identified, thereby improving production management strategies. For example, by analyzing the clustering results and global optimization results of each round of fermentation, it can be determined which control measures have a positive effect on fermentation quality and which measures are ineffective. This data-driven management model achieves continuous improvement and optimization of the production process, thereby improving production efficiency, product quality, and management accuracy.

[0074] In summary, the production quality control method of agricultural and animal husbandry waste fermented fertilizer provided in the embodiment of the present application has the following technical effects: By evenly distributing monitoring sensor groups in the fermentation pile, multiple important parameters in the fermentation process can be monitored in real time. These sensors continuously collect data and convert it into real-time position data streams, providing the necessary basic data for subsequent fermentation status analysis and optimization; through interpolation technology, the gaps in the real-time data stream are filled to ensure the continuity and integrity of the data. By setting the control accuracy and performing trend fitting on the data in the preset search area, the data stream can be optimized and corrected to reduce the discontinuity and volatility of the data, which makes subsequent analysis and decision-making more reliable; through the adaptive clustering method, the data is intelligently classified according to the time node, and the areas with similar characteristics are clustered together, so as to more accurately analyze the different areas and states in the fermentation process. By correcting the clustering results, the stability and accuracy of the clustering clusters are ensured, which helps to further improve the accuracy of the fermentation status analysis; based on the correction results of the adaptive clustering clusters, the fermentation pile is divided into multiple fermentation status partitions. Each partition is equipped with a unique coding identification, which not only improves the regional management capability of the fermentation process, but also facilitates the status tracking and quality control of different areas. Through the coding identification, the fermentation status and data of each area can be effectively monitored, which is convenient for subsequent analysis and optimization; through the multi-objective deviation control model, a targeted control strategy is formulated according to the coding identification of each fermentation status partition. This strategy can deal with the differences between the partitions on the basis of multi-objective optimization, ensure that each fermentation area can reach the predetermined fermentation state under different conditions, and ultimately improve the overall quality of the fermented fertilizer; introduce a global control parameter optimization channel, perform global optimization of the mapping difference control strategy, and adjust the control parameters in the production process according to the optimization results. Through this global optimization, the differences between different partitions can be coordinated and the needs of each partition can be balanced, thereby ensuring the efficiency and stability of the overall fermentation process, optimizing resource utilization, reducing production costs, and improving the overall quality of fermented fertilizer.

[0075] Embodiment 2, based on the same inventive concept as the production quality control method of agricultural and animal husbandry waste fermentation fertilizer in the above embodiment, Figure 2 As shown, the embodiment of the present application provides a production quality control system for agricultural and animal husbandry waste fermentation fertilizer, the system comprising: The time sequence monitoring module 10 is used to evenly distribute the monitoring sensor group in the fermentation pile, use the monitoring sensor group to perform time sequence monitoring on the fermented fertilizer in the fermentation pile, and establish a real-time position data stream.

[0076] The trend fitting module 20 is used to read the control accuracy of production quality, locate the interpolation point with the control accuracy, and use the interpolation point as the interpolation center to perform trend fitting of the real-time position data stream in a preset search area, and update the interpolation data to the real-time position data stream.

[0077] The adaptive clustering module 30 is used to adaptively cluster the updated real-time location data stream at the same time node, and to perform adaptive clustering cluster correction using a preset time step.

[0078] The state partition construction module 40 is used to construct fermentation state partitions with the corrected adaptive clustering clusters, and each fermentation state partition is provided with a coding identifier of a state evaluation index set.

[0079] The control strategy establishment module 50 is used to establish a mapping difference control strategy for each fermentation state partition based on the coding identifier using a multi-objective deviation control model.

[0080] The global optimization module 60 is used to introduce a global control parameter optimization channel, perform global optimization of the mapping difference control strategy, and perform production control based on the global optimization results.

[0081] Furthermore, the global optimization module 60 is used to perform the following operation steps: After the mapping difference control strategy is input into the global control parameter optimization channel, the global optimization objective function is called, and the evaluation characteristics of the global optimization objective function include regional state target approximation characteristics, control cost characteristics, and synergistic influence characteristics between partitions; after the mapping difference control strategy is evaluated by the global optimization objective function, the adjustment focus items of the current mapping difference control strategy are identified; the adjustment focus items are used as iteration indexes, iterative optimization is performed, and the global optimization result is established with the iterative optimization result.

[0082] Furthermore, the global optimization module 60 is used to perform the following operation steps: Acquire the attention data of the adjustment focus item; use the attention data to configure the mapping random perturbation, use the random perturbation and the adjustment focus item to search and update the control parameters, generate a first round of search update results, and identify the actual search direction of the first round of search update results; perform search evaluation of multiple rounds of search update results, if the search evaluation result is an update state inactive state, and multiple rounds of search are random perturbation searches, generate random perturbation suppression, use the random perturbation suppression to adjust the mapping random perturbation, and continue iterative optimization.

[0083] Furthermore, the trend fitting module 20 is used to perform the following operation steps: A matching evaluation is performed according to the control accuracy and the distribution of the monitoring sensor group, and the interpolation point is located using the matching evaluation result; after determining the preset search area, any two monitoring sensor groups in the preset search area are combined to establish a combination set; sensor groups are connected for each combination in the combination set, and after configuring trend trust weights based on the point-line distance between the sensor group connection and the interpolation point, trend fitting is performed on all combination sets, and the interpolation data of the interpolation point is calculated using the trend fitting results and the trend trust weights.

[0084] Furthermore, the adaptive clustering module 30 is used to perform the following operation steps: After treating the data of the same time node as the data of the same dimension, adaptive clustering of the real-time location data stream under the same dimension data is performed to establish an adaptive clustering cluster; the preset time step is used to perform a stability search for the origin and development of the adaptive clustering cluster of each dimension, and the stability search result is used to update the adaptive clustering cluster to complete the adaptive clustering cluster correction.

[0085] Furthermore, the state evaluation index set includes fermentation temperature and humidity gradient index, oxygen stability index, acid-base buffering index, and biological activity trend factor.

[0086] Furthermore, the global optimization objective function is as follows:

[0087] in, Characterize the global optimization objective function, The control parameter set that represents all fermentation state partitions, N is the total number of fermentation state partitions, i is the fermentation state partition index, is the regional state target approximation weight of the i-th fermentation state partition, For control parameters The actual state of the next i-th fermentation state partition, is the target state of the i-th fermentation state partition, To adjust the weight coefficient of the cost item, is the control cost function of the ith fermentation state partition, V is the weight of the synergistic influence term, represents the set of state partitions that have coupling effects on the i-th fermentation state partition, j represents the index of the state partition that has coupling effects on the i-th fermentation state partition, Characterizes the state coupling strength of the i-th fermentation state partition and the j-th fermentation state partition d, For control parameters The actual state of the next j-th fermentation state partition.

[0088] Furthermore, the global optimization module 60 is used to perform the following operation steps: The control parameters of the fermentation process are recorded, and the control parameter recording results are embedded in the origin code as a production label; the origin code embedded in the production label is used to build a traceable fermentation history for fermentation fertilizer management.

[0089] Furthermore, the global optimization module 60 is further configured to perform the following operation steps: The clustering results of each round of fermentation, the global optimization results and the output effect are recorded and stored in a production database; and the production database is used to update and optimize the strategy of subsequent fermentation fertilizer production management.

[0090] Through the above-mentioned detailed description of the production quality control method of agricultural and animal husbandry waste fermented fertilizer in this specification, those skilled in the art can clearly know the production quality control system of agricultural and animal husbandry waste fermented fertilizer in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0091] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A production quality control method for agricultural and animal husbandry waste fermentation fertilizer, characterized in that: The method comprises: Evenly distribute monitoring sensor groups in the fermentation pile, use the monitoring sensor groups to perform time-series monitoring on the fermented fertilizer in the fermentation pile, and establish a real-time position data stream; After reading the control accuracy of production quality, the interpolation point is located with the control accuracy, and the interpolation point is used as the interpolation center to perform trend fitting of the real-time position data stream in a preset search area, and the interpolation data is updated to the real-time position data stream; Adaptively cluster the updated real-time location data stream at the same time node, and use the preset time step to perform adaptive clustering cluster correction; The fermentation state partitions are constructed by using the corrected adaptive clustering clusters, and each fermentation state partition is provided with a coding identifier of a state evaluation index set; Using a multi-objective deviation control model to establish a mapping difference control strategy for each fermentation state partition based on the coding identifier; Introduce a global control parameter optimization channel to perform global optimization of the mapping difference control strategy, and use the global optimization results for production control.

2. The production quality control method of a fermented fertilizer of agricultural and animal husbandry waste according to claim 1, characterized in that: The introduction of the global control parameter optimization channel to perform global optimization of the mapping difference control strategy includes: After the mapping difference control strategy is input into the global control parameter optimization channel, a global optimization objective function is called, and the evaluation characteristics of the global optimization objective function include regional state target approximation characteristics, control cost characteristics, and synergistic influence characteristics between partitions; After evaluating the mapping difference regulation strategy using the global optimization objective function, identifying the regulation focus items of the current mapping difference regulation strategy; The adjustment focus item is used as an iteration index to perform iterative optimization, and a global optimization result is established based on the iterative optimization result.

3. The production quality control method of a fermented fertilizer of agricultural and animal husbandry waste according to claim 2, characterized in that: The step of performing iterative optimization using the adjustment focus item as an iteration index includes: Obtaining attention data of the adjusted attention item; Using the attention data to configure the mapping random disturbance, searching and updating the control parameters with the random disturbance and the adjustment attention item, generating a first round of search and update results, and identifying an actual search direction of the first round of search and update results; Perform search evaluation of multiple rounds of search update results. If the search evaluation result is that the update state is inactive, and the multiple rounds of search are all random perturbation searches, generate random perturbation suppression, use the random perturbation suppression to adjust the mapping random perturbation, and continue iterative optimization.

4. The production quality control method of a fermented fertilizer made from agricultural and animal husbandry waste according to claim 1, characterized in that: The step of locating the interpolation point with the control accuracy, performing trend fitting of the real-time position data stream in a preset search area with the interpolation point as the interpolation center, and updating the interpolation data to the real-time position data stream includes: Performing a matching evaluation according to the control accuracy and the distribution of the monitoring sensor group, and locating the insertion point using the matching evaluation result; After determining the preset search area, any two monitoring sensor groups within the preset search area are combined to establish a combination set; Sensor groups are connected for each combination in the combination set, and after the trend trust weight is configured based on the point-line distance between the sensor group connection and the interpolation point, trend fitting is performed on all combination sets, and the interpolation data of the interpolation point is calculated using the trend fitting result and the trend trust weight.

5. The production quality control method of a fermented fertilizer of agricultural and animal husbandry waste according to claim 1, characterized in that: The adaptive clustering of the updated real-time location data stream at the same time node and adaptive cluster correction using a preset time step includes: After treating the data of the same time node as the same dimension data, adaptive clustering of the real-time location data stream under the same dimension data is performed to establish an adaptive clustering cluster; The preset time step is used to perform stability search for the origin and development of each dimension's adaptive clustering cluster, and the stability search result is used to update the adaptive clustering cluster to complete the adaptive clustering cluster correction.

6. The method for controlling the production quality of agricultural and animal husbandry waste fermented fertilizer according to claim 1, characterized in that: The state evaluation index set includes a fermentation temperature and humidity gradient index, an oxygen stability index, an acid-base buffering index, and a biological activity trend factor.

7. The production quality control method of a fermented fertilizer of agricultural and animal husbandry waste according to claim 2, characterized in that: The global optimization objective function is as follows: in, Characterize the global optimization objective function, The control parameter set that represents all fermentation state partitions, N is the total number of fermentation state partitions, i is the fermentation state partition index, is the regional state target approximation weight of the i-th fermentation state partition, For control parameters The actual state of the next i-th fermentation state partition, is the target state of the i-th fermentation state partition, To adjust the weight coefficient of the cost item, is the control cost function of the ith fermentation state partition, V is the weight of the synergistic influence term, represents the set of state partitions that have coupling effects on the i-th fermentation state partition, j represents the index of the state partition that has coupling effects on the i-th fermentation state partition, Characterizes the state coupling strength of the i-th fermentation state partition and the j-th fermentation state partition d, For control parameters The actual state of the next j-th fermentation state partition.

8. The method for controlling the production quality of agricultural and animal husbandry waste fermented fertilizer according to claim 1, characterized in that: After the production control is performed based on the global optimization results, it includes: Record the control parameters of the fermentation process and embed the control parameter record results into the production code as a production label; Use the origin code embedded in the production label to build a traceable fermentation history for fermentation fertilizer management.

9. The method for controlling the production quality of agricultural and animal husbandry waste fermented fertilizer according to claim 1, characterized in that: After the production control is performed based on the global optimization result, the following is also included: Record and store the clustering results, global optimization results and output effects of each round of fermentation in the production database; The production database is used to update and optimize the strategy for subsequent fermentation fertilizer production management.

10. A production quality control system for agricultural and animal husbandry waste fermentation fertilizer, characterized in that: A method for controlling the production quality of an agricultural and animal husbandry waste fermented fertilizer according to any one of claims 1 to 9, the system comprising: A time-series monitoring module is used to evenly distribute a monitoring sensor group in the fermentation pile, use the monitoring sensor group to perform time-series monitoring on the fermented fertilizer in the fermentation pile, and establish a real-time position data stream; A trend fitting module is used to read the control accuracy of production quality, locate the interpolation point with the control accuracy, and use the interpolation point as the interpolation center to perform trend fitting of the real-time position data stream in a preset search area, and update the interpolation data to the real-time position data stream; An adaptive clustering module is used to adaptively cluster the updated real-time location data stream at the same time node, and perform adaptive clustering cluster correction using a preset time step; A state partition construction module is used to construct fermentation state partitions with the corrected adaptive clustering clusters, and each fermentation state partition is provided with a coding identifier of a state evaluation index set; A control strategy establishment module, used to establish a mapping difference control strategy for each fermentation state partition based on the coding identifier using a multi-objective deviation control model; The global optimization module is used to introduce the global control parameter optimization channel, perform global optimization of the mapping difference control strategy, and perform production control based on the global optimization results.

Images