Internet of things based intelligent control method for combined composting of waste mushroom sticks and livestock and poultry manure

Abstract

The application relates to the technical field of intelligent regulation and control, and discloses a kind of abandoned mushroom stick livestock and poultry manure combined compost intelligent regulation and control method based on Internet of Things.The method comprises the following steps: three-dimensionally monitoring compost site through Internet of Things sensor, constructing strongly connected directed topological graph based on mushroom stick cellulose content and livestock and poultry manure nitrogen-phosphorus ratio, generating feasibility detection report by using biochemical reaction adaptability detection algorithm, distributing multi-actuator equipment tasks and planning collaborative work path according to emergency degree, adjusting equipment control parameter combination to execute precise control by using mushroom stick-manure mixing ratio coefficient, and finally evaluating control effect and adaptively updating mixing ratio coefficient parameter.The application solves the problem that there is lack of intelligent collaborative control method based on Internet of Things in existing abandoned mushroom stick livestock and poultry manure composting technology, resulting in insufficient control precision, low equipment collaboration efficiency and lack of parameter optimization capability.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and in particular to an intelligent control method for the combined composting of waste mushroom substrate and livestock and poultry manure based on the Internet of Things. Background Technology

[0002] Current composting technologies primarily employ traditional methods of manual monitoring and timed turning of the compost pile for waste treatment. This involves periodically measuring environmental parameters such as temperature, humidity, and pH, and then manually adjusting the operation based on experience. For the combined composting of waste mushroom substrate and livestock manure, existing technologies typically use simple mixing ratios and fixed work cycles, relying on operator experience for parameter adjustments and equipment operation. Some advanced composting facilities have begun to introduce automated equipment, such as timed turning machines and spray systems; however, most of these devices operate on preset programs and lack the ability to respond to real-time environmental changes.

[0003] However, manual monitoring methods suffer from problems such as low monitoring frequency, limited coverage, and insufficient data accuracy, making it difficult to accurately grasp real-time changes in the composting process. Secondly, traditional experience-based judgment and control methods lack scientific basis, and control parameters are often not precise enough, which can easily lead to unstable composting effects. Thirdly, existing automated equipment lacks intelligent coordination mechanisms, and conflicts can easily occur when multiple devices operate simultaneously, affecting control efficiency. In addition, existing technologies lack in-depth analysis of the cellulose characteristics of waste mushroom substrate and the nutritional components of livestock and poultry manure, making it impossible to configure parameters accurately based on the characteristics of raw materials. Summary of the Invention

[0004] This application provides an intelligent control method for the combined composting of waste mushroom substrate and livestock manure based on the Internet of Things (IoT), which solves the problems of insufficient control accuracy, low equipment collaboration efficiency, and lack of parameter optimization capabilities in the existing waste mushroom substrate and livestock manure composting technology due to the lack of an IoT-based intelligent collaborative control method.

[0005] This application provides an intelligent control method for the combined composting of waste mushroom substrate and livestock manure based on the Internet of Things (IoT). The method includes: performing three-dimensional monitoring of the composting site using IoT sensors to obtain a composting environmental parameter matrix; generating a strongly connected directed topology graph based on the cellulose content of the mushroom substrate and the nitrogen-phosphorus ratio of the livestock manure; calculating the activity index using a biochemical reaction adaptability detection algorithm based on the strongly connected directed topology graph to obtain node control state classification results and generate a feasibility test report; assigning tasks according to the urgency of the composting reaction based on the feasibility test report to obtain a multi-actuator equipment control sequence and generate a collaborative operation path scheme; adjusting the control intensity of the collaborative operation path scheme using the mushroom substrate-manure mixing ratio coefficient to obtain equipment control parameter combinations and execute precise control commands; evaluating the composting quality based on the execution results of the equipment control parameter combinations to obtain control effect data and update the adaptive parameters of the mushroom substrate-manure mixing ratio coefficient.

[0006] The technical solution provided in this application utilizes IoT sensors to perform three-dimensional monitoring of the composting site and generate a strongly connected directed topology graph. This solves the problems of limited coverage and insufficient data accuracy in traditional composting monitoring. A spatialized data model based on the cellulose content of the mushroom substrate and the nitrogen-phosphorus ratio of livestock manure is established, making the acquisition of composting environmental parameters more comprehensive and accurate. The application of a biochemical reaction adaptability detection algorithm, through precise calculation of the compost biochemical reaction activity index and intelligent determination of the optimal fermentation range of 30-65, enables a scientific assessment of the fermentation status of waste mushroom substrate and livestock manure, effectively addressing the technical shortcomings of traditional experience-based control methods that lack scientific basis. The implementation of multi-actuator equipment collaborative path planning, through comprehensive evaluation of safety distance constraints and candidate path sets, eliminates the problem of operational conflicts among existing automated equipment, significantly improving the collaborative operation efficiency of the temperature-controlled turning machine, pH-adjusting spray truck, and oxygen supply device. The precise calculation of the mushroom substrate-manure mixing ratio coefficient and the adaptive adjustment of equipment control parameters fully consider the decomposition characteristics of lignocellulose in waste mushroom substrates and the buffering capacity characteristics of livestock and poultry manure. This enables precise control of turning depth, spraying volume, and oxygen supply rate, effectively improving the accuracy of composting control. The establishment of a composting quality assessment and adaptive parameter update mechanism for the mushroom substrate-manure mixing ratio coefficient, through feedback analysis of control effect data and parameter optimization, forms a continuously improving intelligent control system, solving the technical problem of the lack of parameter optimization capabilities in existing technologies.

[0007] In the co-composting of waste mushroom substrate and livestock manure, the biochemical reaction adaptability detection algorithm is specifically optimized for the biochemical characteristics of cellulose decomposition in mushroom substrates and nitrogen-phosphorus conversion in livestock manure. The weight coefficient configuration in the algorithm fully considers the special characteristics of the difficult degradation of lignocellulose in waste mushroom substrates and the easy volatilization of ammonia in livestock manure, making the calculation of the activity index more consistent with the actual fermentation law. The multi-actuator collaborative path planning algorithm, when handling mushroom substrate composting operations, specifically considers the differences in equipment characteristics such as the large operating radius of the turning machine, the precise positioning of the spray truck, and the centralized oxygen supply of the oxygen supply device. Through differentiated safety distance settings and operation sequence arrangements, it ensures the high efficiency of equipment collaboration. The mushroom substrate-manure mixing ratio coefficient calculation model is specifically modeled for the carbon-nitrogen ratio characteristics of waste mushroom substrates and the nutrient composition of livestock manure, making the calculation of control parameters more closely match the characteristics of the raw materials and the execution of control commands more precise and effective. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of an embodiment of the intelligent control method for combined composting of waste mushroom substrate and livestock manure based on the Internet of Things in this application. Detailed Implementation

[0010] This application provides an intelligent control method for the combined composting of waste mushroom substrate and livestock manure based on the Internet of Things. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0011] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the intelligent control method for combined composting of waste mushroom substrate and livestock manure based on the Internet of Things in this application includes:

[0012] Step S101: Perform three-dimensional monitoring of the composting site through IoT sensors to obtain a composting environmental parameter matrix, and generate a strongly connected directed topology graph based on the cellulose content of the mushroom sticks and the nitrogen-phosphorus ratio of livestock and poultry manure.

[0013] Step S102: Based on the strongly connected directed topology graph, the activity index is calculated using the biochemical reaction adaptability detection algorithm to obtain the node regulation state classification results and generate a feasibility detection report.

[0014] Step S103: The feasibility test report is processed by task allocation according to the urgency of composting reaction to obtain the control sequence of multi-actuator equipment and generate a collaborative operation path plan;

[0015] Step S104: Adjust the intensity of the collaborative operation path scheme by using the mixing ratio coefficient of mushroom sticks and feces to obtain the combination of equipment control parameters and execute precise control commands.

[0016] Step S105: Perform composting quality assessment on the execution results of the equipment control parameter combination to obtain control effect data and update the adaptive parameters of the mushroom stick-manure mixing ratio coefficient.

[0017] It is understood that the implementing entity of this application can be an intelligent control system for the combined composting of waste mushroom substrate and livestock manure based on the Internet of Things, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0018] Specifically, the three-dimensional monitoring and processing using IoT sensors is achieved by deploying temperature, humidity, and pH sensors in a 5m x 5m grid at the composting site. Each sensor transmits its collected data to the central control unit via the LoRa wireless communication protocol. The temperature sensor uses a PT100 platinum resistance thermometer with a measurement range of -50℃ to 150℃ and an accuracy of ±0.1℃; the humidity sensor uses a capacitive humidity sensor with a measurement range of 0-100%RH and an accuracy of ±2%RH; the pH sensor uses a glass electrode method with a measurement range of 0-14pH and an accuracy of ±0.02pH. Data acquisition is set to once every 5 minutes, forming a numerical matrix corresponding to the three-dimensional coordinates. The composting environmental parameter matrix is ​​constructed by associating the spatial coordinates of each sensor node with the corresponding temperature, humidity, and pH values. Each element in the matrix contains location information and environmental parameter information. The cellulose content parameter is obtained by acid washing and cellulose determination of waste mushroom substrate samples. Specific operations include sample drying, crushing and sieving, acid detergent treatment, neutral detergent treatment, drying, and weighing. The nitrogen-phosphorus ratio was determined by measuring total nitrogen content in livestock manure using the Kjeldahl method and total phosphorus content using the molybdenum-antimony colorimetric method, and the nitrogen-phosphorus mass ratio was calculated. The biochemical reaction conduction coefficient was calculated based on the product of cellulose content and the nitrogen-phosphorus ratio, reflecting the diffusion intensity of microbial metabolites between adjacent nodes. A strongly connected directed topological graph was constructed by treating each monitoring node as a vertex in graph theory and the biochemical reaction conduction coefficient as the weight of directed edges; a directed path connects any two nodes in the graph.

[0019] The biochemical reaction adaptability detection algorithm first reads the temperature, humidity, and pH values ​​of each node in the strongly connected directed topology graph, forming a set of node environmental state data. The algorithm calculates the compost biochemical reaction activity index using a weighted summation method. The weight coefficients are determined based on the biochemical characteristics of the waste mushroom substrate and livestock manure: temperature weight coefficient is 0.3, humidity weight coefficient is 0.2, pH weight coefficient is 0.25, cellulose content weight coefficient is 0.15, and nitrogen content weight coefficient is 0.1. The activity index is calculated as the sum of the products of each parameter value and its corresponding weight coefficient. The optimal fermentation range threshold of 30-65 is determined based on the optimal growth conditions for compost microorganisms; an activity index below 30 indicates insufficient microbial activity, while an index above 65 indicates over-fermentation. Node state classification is achieved through numerical comparison; nodes within the 30-65 range are marked as controllable, while nodes outside the range are marked as requiring urgent control. The control priority sequence is ordered according to the degree to which the activity index deviates from the 30-65 range, with greater deviation indicating higher priority. The microbial activity time window is determined based on the metabolic cycle of composting microorganisms, generally ranging from 2 to 4 hours. Exceeding this time window significantly reduces the control effect. The feasibility study report integrates node status classification, priority sequences, and time window data to form structured control decision-making information.

[0020] Task allocation is based on the priority sequence in the feasibility study report: temperature anomaly control is prioritized as level 1, pH adjustment as level 2, and oxygen supplementation as level 3. The composting control task types are arranged in descending order of urgency, forming an ordered task execution queue. Equipment matching and allocation are achieved through the correspondence between task type and equipment function: temperature anomaly control is assigned to the temperature-controlled turning machine, pH adjustment to the pH-adjusting spray truck, and oxygen supplementation to the oxygen supply device. The multi-actuator equipment control sequence includes equipment identification, target node coordinates, and task type information. The path planning algorithm uses an improved A* search algorithm, considering equipment physical size constraints: the temperature-controlled turning machine has an operating radius of 3 meters, the pH-adjusting spray truck has an operating radius of 2 meters, and the oxygen supply device has an operating radius of 1.5 meters. Collision detection is achieved by predicting the position trajectory of each device on the time axis; when the predicted trajectories of two devices overlap in the spatiotemporal dimension, a potential collision is identified. The safety distance constraint is determined based on the maximum operating radius of the equipment plus a 1-meter safety buffer distance. The candidate path set is generated by searching for feasible paths that satisfy the safe distance constraint in a strongly connected directed topology graph. The collaborative operation path scheme selects the optimal scheme by comprehensively scoring the candidate paths based on their path length, estimated operation time, and equipment collaboration efficiency.

[0021] The mushroom substrate-manure mixing ratio coefficient is calculated based on the correlation between the cellulose content parameter and the nitrogen-phosphorus ratio parameter at each work node, reflecting the optimal mixing ratio of waste mushroom substrate and livestock manure at that node. The adaptive calculation of the turning depth parameter is based on the degree of cellulose decomposition: when the cellulose decomposition rate is below 40%, the material structure is compact and requires deep turning, with a depth set at 80 cm; when the decomposition rate is between 40% and 70%, the material begins to loosen, and the depth is adjusted to 60 cm; when the decomposition rate exceeds 70%, the material is fully decomposed, and the depth is reduced to 40 cm to avoid excessive disturbance. The spraying volume parameter is calculated by multiplying the mushroom substrate-manure mixing ratio coefficient by the difference between the target pH value and the current value. The target pH value is set according to different composting stages: 7.5-8.0 during the warming period, 8.0-8.5 during the high-temperature period, 7.0-7.5 during the cooling period, and 6.5-7.0 during the maturation period. The livestock manure buffering capacity coefficient is determined based on the organic acid and carbonate content in the manure, reflecting the manure's buffering capacity against pH changes. The oxygen supply parameter is calculated based on the degradation rate of compost organic matter, which is obtained by measuring the change rate of chemical oxygen demand (COD) in the compost material. The compost volume correction factor takes into account the density changes and volume shrinkage of the compost material, with the volume gradually decreasing as fermentation progresses. The equipment control parameter combination packages the specific operating parameters of each device into a unified instruction set, which is then sent to the corresponding actuator devices via the industrial Ethernet protocol.

[0022] Compost quality assessment is achieved by comparing changes in environmental parameters before and after equipment regulation. Temperature changes reflect the heat balance, humidity changes reflect the effectiveness of moisture regulation, and pH changes reflect the effectiveness of acid-base balance regulation. The comprehensive compost quality evaluation index is calculated using a weighted average method, with each weight coefficient determined according to composting process requirements: temperature compliance rate weight 0.25, pH stability weight 0.2, organic matter degradation rate weight 0.3, compost cycle shortening rate weight 0.15, and energy consumption reduction rate weight 0.1. Quality benchmark thresholds are set according to compost product quality standards: a comprehensive evaluation index above 80 is excellent, 60-80 is good, and below 60 requires improvement. Regulation effect data records include regulation time, regulation parameters, environmental change amplitude, and quality evaluation results. The regulation operation-effect response database adopts a relational database structure, storing the correlation between historical regulation records and corresponding effects, supporting subsequent data mining and pattern recognition. The adaptive parameter update of the mushroom substrate-manure mixing ratio coefficient is achieved by analyzing the parameter combinations with the highest effect contribution in historical data, and using a gradient descent algorithm to progressively optimize the coefficient values.

[0023] In one specific embodiment, the process of performing step S101 may specifically include the following steps:

[0024] Temperature, humidity, and pH sensors are deployed at the composting site using a 5m x 5m grid density. Real-time data acquisition and processing are performed at each monitoring node to obtain the temperature, humidity, and pH values ​​corresponding to the three-dimensional coordinate positions.

[0025] Temperature, humidity, and pH values ​​are processed into a matrix according to spatial coordinate sequence to obtain a composting environmental parameter matrix containing spatial distribution information.

[0026] The lignocellulose content of the waste mushroom sticks was quantitatively analyzed to obtain the cellulose content parameter, and the nitrogen, phosphorus and potassium elements of the livestock and poultry manure samples were chemically detected to obtain the nitrogen and phosphorus ratio parameter.

[0027] Numerical calculations were performed on the biochemical reaction diffusion intensity between adjacent monitoring nodes based on cellulose content parameters and nitrogen-phosphorus ratio parameters to obtain quantified biochemical reaction conduction coefficients.

[0028] By using the biochemical reaction conduction coefficient as the directed edge weight, the composting environmental parameter matrix is ​​transformed using graph theory to obtain a strongly connected directed topological graph that represents the node connection relationship of the composting site.

[0029] Specifically, the 5m x 5m grid density deployment refers to arranging sensor positions within the composting site according to a regular square grid layout, with each grid unit having a side length of 5 meters, forming a uniformly distributed monitoring array. The temperature sensor uses a digital temperature probe, converting temperature changes into electrical signals through thermistor or thermocouple principles. The humidity sensor uses a capacitive humidity probe, measuring ambient humidity based on the principle of dielectric constant change. The pH sensor uses the glass electrode method, determining acidity or alkalinity by measuring the potential difference generated by hydrogen ion concentration. Real-time data acquisition and processing involves converting analog signals into digital signals through the sensor's built-in analog-to-digital converter, and then transmitting the digitized temperature, humidity, and pH values ​​to the data receiving terminal via a LoRa wireless communication module. Each monitoring node is equipped with a unique device identifier and three-dimensional spatial coordinate information.

[0030] Matrix assembly processing is a data structuring process that arranges environmental parameter data collected by each monitoring node in an ordered manner according to their corresponding spatial coordinates. Specifically, it involves establishing a three-dimensional array structure, where the first dimension represents the X-axis coordinate, the second the Y-axis coordinate, and the third the Z-axis height coordinate. Each array element contains the temperature, humidity, and pH values ​​for that location. The composting environmental parameter matrix is ​​formed by establishing a one-to-one correspondence between spatial coordinates and environmental parameters. Each element in the matrix carries environmental state information for a specific spatial location, allowing the environmental distribution of the entire composting site to be stored and processed in the form of a data structure.

[0031] Quantitative analysis of lignocellulose content in waste mushroom substrate samples was performed using the acid-washed fiber determination method. First, the substrate samples were dried to constant weight, then pulverized and passed through a 40-mesh sieve. A representative sample was treated with an acidic detergent under boiling conditions for 60 minutes, filtered, washed with distilled water until neutral, defatted with acetone, dried, and weighed to obtain the weight of the acid-washed fibers. The cellulose content parameter was calculated from the weight difference. Chemical analysis of nitrogen, phosphorus, and potassium in livestock and poultry manure samples employed a combination of wet digestion and colorimetric determination. After digestion with sulfuric acid and hydrogen peroxide, nitrogen content was determined using the Kjeldahl method, phosphorus content using the molybdenum-antimony colorimetric method, and potassium content using flame photometry. The nitrogen-phosphorus ratio parameter was calculated by the mass ratio of nitrogen to phosphorus content.

[0032] The numerical calculation of biochemical reaction diffusion intensity is based on the propagation patterns of metabolic products such as organic acids and ammonia nitrogen generated during cellulose decomposition in compost materials. Cellulose content reflects the substrate concentration during organic matter decomposition, while the nitrogen-phosphorus ratio reflects the nutrient balance of microbial metabolism; both together determine the activity level of biochemical reactions and the rate of metabolite production. The biochemical reaction conduction coefficient is obtained by numerically calculating the cellulose content and nitrogen-phosphorus ratio parameters. Specifically, the percentage of cellulose content is multiplied by the nitrogen-phosphorus ratio, and then divided by a standardization factor to obtain a dimensionless conduction coefficient. This coefficient reflects the ease with which microbial metabolites diffuse between adjacent monitoring nodes.

[0033] Graph theory structure transformation is the process of converting matrix-form environmental parameter data into a graph theory model. A strongly connected directed topological graph is a special type of directed graph structure where any two nodes are connected by a directed path. The transformation process first treats each data element in the composting environmental parameter matrix as a vertex in the graph. Then, directed edges are established between adjacent vertices based on spatial adjacency. The direction of the directed edges is determined by the magnitude of the biochemical reaction conduction coefficient; nodes with larger conduction coefficients are connected to nodes with smaller conduction coefficients via directed edges. The weight of each directed edge is the corresponding biochemical reaction conduction coefficient value.

[0034] In one specific embodiment, the process of performing step S102 may specifically include the following steps:

[0035] The temperature, humidity and pH values ​​of each monitoring node in the strongly connected directed topology graph are input into the biochemical reaction adaptability detection algorithm for data reading and processing to obtain a set of node environmental status data.

[0036] The environmental status data set of each node is weighted and calculated based on preset weighting coefficients for temperature, humidity, pH, cellulose content, and nitrogen content to obtain the composting biochemical reaction activity index value of each node.

[0037] The biochemical reaction activity index of compost is compared with the optimal fermentation range threshold of 30-65 to obtain the node activity status determination result. Nodes with activity indices within the range are marked as controllable, and nodes outside the range are marked as requiring urgent control.

[0038] Nodes marked as requiring urgent regulation are prioritized according to the degree of deviation of their activity index to obtain a regulation priority sequence. The timing of regulation for each node is calculated based on the microbial activity time window to obtain the estimated regulation time window data.

[0039] The node regulation status classification results, regulation priority sequence, and estimated regulation time window data are integrated and processed into a report format to obtain a feasibility test report containing regulation decision information.

[0040] Specifically, the data reading and processing of the biochemical reaction adaptability detection algorithm is achieved by traversing the attribute information of all nodes in a strongly connected directed topology graph. The algorithm first accesses the node list in the graph structure, extracting the temperature, humidity, and pH values ​​stored by each monitoring node. The data reading process adopts a depth-first search approach, starting from the root node of the topology graph and sequentially visiting all connected nodes along the directed edges, storing the environmental parameter data of each node in an array structure according to the node number. The node environmental state data set is a multi-dimensional data structure containing node identifiers, spatial coordinates, temperature values, humidity values, pH values, and corresponding cellulose and nitrogen content parameters. Each element in the data set represents the complete environmental state information of a monitoring node.

[0041] The weighted calculation is based on a linear weighted summation method. The weighting coefficients for temperature, humidity, pH, cellulose content, and nitrogen content are determined according to the influence of various environmental factors on microbial activity during the composting of waste mushroom substrate and livestock manure. The temperature weighting coefficient is set to 0.3 because temperature directly affects microbial enzyme activity and metabolic rate; the humidity weighting coefficient is set to 0.25 because suitable humidity conditions maintain normal microbial physiological activities; the pH weighting coefficient is set to 0.2 because pH affects microbial cell membrane stability; the cellulose content weighting coefficient is set to 0.15 because cellulose is the main substrate for microbial decomposition; and the nitrogen content weighting coefficient is set to 0.1 because nitrogen is an essential element for microbial protein synthesis. The compost biochemical reactivity index is calculated by multiplying each environmental parameter value by its corresponding weighting coefficient and then summing the results. The formula is: temperature value multiplied by temperature weighting coefficient, humidity value multiplied by humidity weighting coefficient, pH value multiplied by pH weighting coefficient, cellulose content value multiplied by cellulose weighting coefficient, and nitrogen content value multiplied by nitrogen weighting coefficient.

[0042] The optimal fermentation range threshold of 30-65 is determined based on the optimal growth conditions and metabolic activity range of the composting microbial community. This range reflects the ideal state of microbial activity in the mixture of waste mushroom substrate and livestock manure. Numerical comparison is implemented through conditional statements. The algorithm compares the calculated activity index value with the two thresholds of 30 and 65. When the activity index is less than 30, it indicates insufficient microbial activity requiring further fermentation promotion; when the activity index is greater than 65, it indicates over-fermentation requiring a reduction in intensity; and when the activity index is within the 30-65 range, it indicates a suitable fermentation state requiring only maintenance and regulation. The node activity state determination result is generated through Boolean logic. A controllable state corresponds to nodes with an activity index within the suitable range, while a state requiring urgent regulation corresponds to nodes with an activity index exceeding the suitable range. The determination result is stored in the node attributes in the form of a status marker.

[0043] Priority sorting employs a numerical difference-based sorting algorithm. The deviation of the activity index is determined by calculating the minimum distance between the activity index value and the boundary value of the 30-65 range. When the activity index is less than 30, the deviation equals 30 minus the activity index value; when the activity index is greater than 65, the deviation equals the activity index value minus 65. A larger deviation indicates a higher urgency for regulation. The regulation priority sequence is generated by arranging all nodes requiring urgent regulation in descending order of deviation, using a quicksort algorithm to reduce time complexity. The microbial activity time window is determined based on the metabolic cycle and response time of composting microorganisms, generally set to 2 to 4 hours, reflecting the time required for microorganisms to make physiological adjustments to environmental changes. The estimated regulation time window data is calculated by adding the microbial activity time window to the current time, representing the upper limit of time for each node to perform regulation operations.

[0044] The report format integration process organizes and encapsulates different types of data according to a predetermined structural template. The feasibility assessment report includes key components such as a report header, a summary of node status, a priority list, and a time window table. The node control status classification results are recorded in tabular form, showing each node's number, coordinates, activity index, and status marker. The control priority sequence is recorded in an ordered list, showing the order of nodes requiring control. The estimated control time window data is recorded in timestamp format, showing the control deadline for each node. The report integration process uses data serialization technology to convert multiple data types into a unified document format.

[0045] In one specific embodiment, the process of executing step S103 may specifically include the following steps:

[0046] The control priority sequence in the feasibility test report was classified into three categories: temperature anomaly control, pH adjustment and oxygen supplementation, resulting in a compost control task type sequence ordered by urgency.

[0047] Based on the composting control task type sequence, the temperature-controlled turning machine, pH-adjusting spray truck and oxygen supply device are matched and allocated to obtain a multi-actuator equipment control sequence that includes the correspondence between equipment type and target node.

[0048] The physical size constraint parameters and estimated control time window data of each device in the multi-actuator device control sequence are input into the path planning algorithm for collision detection processing to obtain the safe distance constraint conditions between devices.

[0049] Based on the safety distance constraint, the traversable paths in the strongly connected directed topology graph are filtered and calculated to obtain a set of candidate paths that meet the requirements of multi-device collaborative operation.

[0050] The candidate path set is comprehensively evaluated based on path length, operation time cost, and equipment coordination efficiency to obtain a collaborative operation path scheme that includes the optimal operation path and time scheduling for each device.

[0051] Specifically, the priority sequence classification for control is achieved by analyzing the deviations in the node activity index and the corresponding anomalies in environmental parameters recorded in the feasibility study report. The classification algorithm categorizes control tasks into three types based on the main reasons for the excessive activity index. Temperature anomaly control tasks correspond to nodes where the activity index deviation is primarily caused by excessively high or low temperatures, determined by temperatures exceeding the optimal composting temperature range, leading to decreased or excessive microbial activity. pH adjustment tasks correspond to nodes where the activity index deviation is primarily caused by acid-base imbalance, determined by pH values ​​deviating from the optimal pH range for composting microorganisms, resulting in reduced enzyme activity. Oxygen replenishment tasks correspond to nodes where the activity index deviation is primarily caused by insufficient oxygen, determined by excessively dense compost material or poor ventilation leading to the formation of an anaerobic environment. The compost control task type sequence is generated by sorting similar tasks from highest to lowest activity index deviation, ensuring that the most urgent control needs are addressed first.

[0052] Equipment matching and allocation are based on matching rules established according to the correspondence between task type and equipment function. Temperature anomaly control tasks are assigned to temperature-controlled turning machines because turning operations can adjust heat dissipation and internal temperature distribution by changing the material's stacking state. pH adjustment tasks are assigned to pH-adjusting spray trucks because spray equipment can precisely control the dosage and location of regulators, quickly changing the local pH environment. Oxygen supplementation tasks are assigned to oxygen supply devices because forced oxygen supply can directly improve the oxygen concentration and ventilation of compost materials. The multi-actuator equipment control sequence establishes a data structure to record key information such as equipment type, equipment number, target node coordinates, and task priority for each control task, forming the basic data for equipment scheduling.

[0053] The collision detection processing of the path planning algorithm predicts potential spatial conflicts by analyzing the physical size constraints and motion trajectories of each device. Physical size constraints include the length, width, height, and operating radius of the device. The operating radius of a temperature-controlled turning machine is typically 3 meters because turning operations require a large operating space for material mixing and relocation. The operating radius of a pH-adjusting spray truck is 2 meters because spraying operations require maintaining an appropriate distance to ensure uniform distribution of the regulator. The operating radius of an oxygen supply device is 1.5 meters because oxygen supply operations are relatively concentrated and the equipment is relatively small. Collision detection determines whether a conflict exists by calculating the motion paths and overlapping operating areas of each device within the estimated control time window. When the operating areas of two devices overlap in the time dimension and the spatial distance is less than a safety threshold, a potential collision is identified. The safety distance constraint is calculated by adding the maximum operating radii of each device and then adding a 1-meter safety buffer distance.

[0054] The path selection calculation process involves traversing all possible node connection paths in the strongly connected directed topology graph, eliminating path options that do not meet the safe distance constraint. The selection algorithm employs a depth-first search method, starting from the current position of the device and searching along the directed edges of the topology graph for all possible paths to the target node. During the search, it checks whether nodes on each path conflict with the predetermined paths of other devices. The candidate path set contains all feasible paths that meet the safe distance constraint and can be completed within the estimated control time window. Each candidate path records information such as node sequence, total path length, and estimated travel time.

[0055] The comprehensive evaluation process scores each path in the candidate path set using a multi-objective evaluation function. Path length reflects the distance cost of equipment movement, calculated by summing the Euclidean distances between adjacent nodes along the path. Operation time cost includes equipment movement time and operation execution time. Movement time is calculated based on path length and equipment speed, while operation execution time is determined by the complexity of the control task and equipment efficiency. Equipment coordination efficiency reflects the degree of mutual influence when multiple devices operate simultaneously; coordination efficiency is higher when equipment operating areas are adjacent and lower when their operating areas are dispersed. The collaborative operation path scheme is generated by selecting the path combination with the highest comprehensive score, containing detailed scheduling information such as the specific travel routes of each device, arrival times at target nodes, and start and end times of the operation.

[0056] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0057] The operation nodes of each device in the collaborative operation path scheme are matched with the cellulose content parameters and nitrogen-phosphorus ratio parameters to obtain the node-specific mushroom stick-feces mixing ratio coefficient value.

[0058] The turning depth of the temperature-controlled turning machine is adaptively calculated based on the ratio coefficient of the mushroom stick-manure mixture, and the turning depth parameter is determined according to the degree of cellulose decomposition. The turning depth parameter is set to one of 80 cm, 60 cm or 40 cm.

[0059] The precise spraying volume parameters of the pH-adjusting spray truck are obtained by multiplying the mushroom stick-manure mixing ratio coefficient with the difference between the pH target value and the current pH value. The spraying volume parameters are then corrected based on the livestock and poultry manure buffer capacity coefficient to obtain the final spraying volume value.

[0060] The oxygen supply rate of the oxygen supply device is dynamically adjusted based on the ratio coefficient of the mushroom stick-manure mixture to obtain the oxygen supply parameters that match the degradation rate of compost organic matter. The oxygen supply parameters are then multiplied with the compost volume correction coefficient to obtain the final oxygen supply rate value.

[0061] The parameters of turning depth, final spray volume, and final oxygen supply rate are combined and packaged to obtain a combination of equipment control parameters containing the specific operating parameters of each device. Control commands are then sent to the corresponding actuators for precise control operations.

[0062] Specifically, data matching processing is achieved by associating the target nodes assigned to each device in the collaborative operation path scheme with the raw material composition information of the corresponding nodes. The matching algorithm extracts the cellulose content parameter and nitrogen-phosphorus ratio parameter of the target node from the composting environmental parameter matrix. The cellulose content parameter reflects the concentration of lignocellulose in the waste mushroom substrate at that node, expressed as a mass percentage value obtained by the acid washing fiber determination method. The nitrogen-phosphorus ratio parameter reflects the nutrient element ratio of livestock and poultry manure at that node, expressed as the nitrogen to phosphorus mass ratio obtained by chemical analysis. The mushroom substrate-manure mixing ratio coefficient is calculated by associating the cellulose content parameter and the nitrogen-phosphorus ratio parameter. The calculation process involves multiplying the cellulose content percentage value by the nitrogen-phosphorus ratio value and then dividing by a standardization constant to obtain a dimensionless coefficient. This coefficient reflects the optimal mixing ratio of waste mushroom substrate and livestock and poultry manure at a specific node location.

[0063] The adaptive calculation of turning depth is based on the correlation between the mixing ratio of mushroom substrate and manure and the degree of cellulose decomposition. The algorithm determines the corresponding turning depth parameter by judging the stage of cellulose decomposition. The degree of cellulose decomposition is calculated by comparing the initial cellulose content with the current cellulose content. A decomposition degree below 40% indicates that the compost is in the initial decomposition stage, the material structure is compact, and deep turning is needed to promote oxygen penetration. The turning depth parameter is set to 80 cm. A decomposition degree between 40% and 70% indicates that the compost has entered the active decomposition stage, the material is becoming loose, but moderate turning is still needed to maintain ventilation. The turning depth parameter is set to 60 cm. A decomposition degree above 70% indicates that the compost is close to the mature stage, the material has been fully decomposed, and excessive turning will damage the pile structure. The turning depth parameter is set to 40 cm.

[0064] The precise calculation of the spraying volume is achieved by multiplying the mushroom substrate-manure mixing ratio coefficient by the pH deviation value. The pH deviation value is calculated as the difference between the target pH value and the current pH value. The target pH value is determined based on the optimal growth conditions of microorganisms at different fermentation stages of composting: 7.5 to 8.0 during the warming period, 8.0 to 8.5 during the high-temperature period, 7.0 to 7.5 during the cooling period, and 6.5 to 7.0 during the maturation period. The spraying volume parameter is calculated by multiplying the mushroom substrate-manure mixing ratio coefficient by the pH deviation value, reflecting the amount of regulator required to adjust the pH value at that point. The livestock and poultry manure buffering capacity coefficient reflects the manure's resistance to pH changes and is determined by measuring the ratio of organic acid content to alkaline substance content in the manure. The correction calculation is achieved by dividing the spraying volume parameter by the buffering capacity coefficient. The corrected spraying volume value can overcome the buffering effect of the manure to achieve the expected pH adjustment effect.

[0065] The dynamic adjustment of oxygen supply rate is based on the correlation between the mixing ratio of mushroom substrate and manure and the organic matter degradation rate. The organic matter degradation rate is calculated by measuring the change rate of the chemical oxygen demand (COD) of the compost material. The degradation rate reflects the intensity of microbial metabolic activity; the more active the metabolism, the greater the oxygen demand. The oxygen supply parameter is calculated by multiplying the basic oxygen supply by the mixing ratio of mushroom substrate and manure, which is determined based on the theoretical oxygen demand of the compost material. The compost volume correction factor considers the influence of material density changes and volume shrinkage on oxygen distribution during composting and is determined by measuring the ratio of the actual volume to the theoretical volume of the compost pile. The oxygen supply rate value is calculated by multiplying the oxygen supply parameter by the volume correction factor, ensuring that oxygen can fully penetrate into the compost pile to meet the metabolic needs of the microorganisms.

[0066] The parameter combination encapsulation process integrates the control parameters of various devices through a unified data structure. Each device control parameter combination includes key information such as device identifier, parameter type, parameter value, and execution time. The encapsulation process uses a key-value pair data format, storing parameters such as turning depth, spray rate, and oxygen supply rate as different parameter keys. Control command generation is achieved by converting the parameter combinations into control signals recognizable by the devices. The command format includes components such as device address, operation type, parameter value, and checksum. Commands are transmitted to the corresponding actuator devices via industrial Ethernet or CAN bus protocols. Upon receiving the command, the device executes the corresponding control operation based on the parameter values.

[0067] In one specific embodiment, the process of dynamically adjusting the oxygen supply rate of the oxygen supply device based on the mixing ratio coefficient of the mushroom substrate and feces can specifically include the following steps:

[0068] The organic matter concentration data of the work node is processed by biochemical reaction rate calculation to obtain the compost organic matter degradation rate value of the current node.

[0069] Based on the standard COD degradation rate benchmark value, the ratio of the compost organic matter degradation rate values ​​is calculated to obtain the organic matter degradation rate ratio factor.

[0070] The oxygen demand correction coefficient is obtained by multiplying the organic matter degradation rate ratio factor with the mushroom stick-feces mixing ratio coefficient.

[0071] The basic oxygen supply value is adjusted by multiples according to the oxygen supply demand correction coefficient to obtain the preliminary oxygen supply parameters.

[0072] The oxygen supply parameters are obtained by multiplying the initial oxygen supply parameters with the reactor volume correction factor.

[0073] Specifically, the biochemical reaction rate calculation is achieved by analyzing the changing trends of organic matter concentration data at different operational nodes. Organic matter concentration data is obtained through chemical oxygen demand (COD) measurement, reflecting the total amount of organic matter in the compost material that can be decomposed by microorganisms. The calculation employs time-series analysis, calculating the rate of change in organic matter concentration by comparing COD values ​​at different time points. The compost organic matter degradation rate is calculated by dividing the difference between two consecutive COD measurements by the time interval, expressed in milligrams per liter per hour. This value directly reflects the intensity of microbial metabolic activity and the speed of organic matter decomposition. A higher degradation rate indicates stronger microbial activity and a correspondingly higher oxygen demand, while a lower degradation rate indicates weaker microbial activity and a relatively lower oxygen demand.

[0074] The standard COD degradation rate benchmark is a reference value determined based on the degradation characteristics of waste mushroom substrate mixed with livestock and poultry manure under ideal conditions. The benchmark value comprehensively considers the theoretical degradation rate of lignocellulose in the mushroom substrate and the standard decomposition rate of organic matter in the livestock and poultry manure. The ratio calculation is achieved by dividing the measured organic matter degradation rate of the current node by the standard COD degradation rate benchmark. The result is a dimensionless organic matter degradation rate ratio factor. When the ratio factor is greater than 1, it indicates that the organic matter degradation rate at that node exceeds the standard level, indicating strong microbial activity and a need for increased oxygen supply. When the ratio factor is less than 1, it indicates that the organic matter degradation rate at that node is below the standard level, indicating weak microbial activity and a need for moderately reduced oxygen supply. When the ratio factor equals 1, it indicates that the degradation state at that node is at an ideal level.

[0075] The product calculation of the oxygen demand correction coefficient combines the interaction between the organic matter degradation rate proportionality factor and the mushroom substrate-manure mixing ratio coefficient. The mushroom substrate-manure mixing ratio coefficient reflects the mixing ratio of waste mushroom substrates and livestock manure at that node; different mixing ratios correspond to different oxygen consumption characteristics. The product calculation is performed by multiplying the organic matter degradation rate proportionality factor by the mushroom substrate-manure mixing ratio coefficient. The result is the oxygen demand correction coefficient, which comprehensively reflects the combined impact of the node's biological activity level and raw material ratio characteristics on oxygen demand. A larger correction coefficient indicates a more urgent oxygen demand at that node, while a smaller correction coefficient indicates a relatively lower oxygen demand.

[0076] The baseline oxygen supply value is a standard oxygen supply calculated based on composting theory, determined by the theoretical amount of oxygen required for the complete oxidation of a unit mass of organic matter. The adjustment is achieved by multiplying the baseline oxygen supply value by an oxygen demand correction factor. This adjustment amplifies or reduces the standard oxygen supply value according to actual demand. When the oxygen demand correction factor is greater than 1, the preliminary oxygen supply parameter will be greater than the baseline oxygen supply value, indicating a need to increase oxygen supply intensity. When the oxygen demand correction factor is less than 1, the preliminary oxygen supply parameter will be less than the baseline oxygen supply value, indicating a need to decrease oxygen supply intensity. The preliminary oxygen supply parameter reflects the oxygen supply demand after correction for biological activity and proportioning characteristics.

[0077] The compost pile volume correction factor considers the impact of material volume changes on oxygen distribution during composting. Compost materials undergo volume shrinkage and density changes during fermentation, affecting oxygen penetration and distribution within the pile. The volume correction factor is calculated by measuring the ratio of the actual pile volume to the theoretical volume. The actual volume is determined using laser ranging or image recognition technology, while the theoretical volume is calculated based on the initial material input and standard density. The product calculation is performed by multiplying the initial oxygen supply parameter by the compost pile volume correction factor. The result is the oxygen supply parameter, which considers the influence of pile geometry on oxygen distribution, ensuring that the supplied oxygen can effectively penetrate into the pile to meet the metabolic needs of microorganisms.

[0078] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0079] The composting environment parameter matrix before and after the combined execution of equipment control parameters was numerically compared to obtain temperature change data, humidity change data, and pH change data.

[0080] The temperature, humidity, and pH change data are weighted and processed based on the weights of temperature compliance rate, pH stability, organic matter degradation rate, composting cycle shortening rate, and energy consumption reduction rate to obtain a comprehensive evaluation index for compost quality.

[0081] The comprehensive evaluation index of compost quality is compared with the preset quality benchmark threshold to obtain the control effect level judgment result, and the control effect level judgment result is recorded as control effect data.

[0082] The historical control operation-effect response database is updated based on the control effect data to obtain a control knowledge base containing current control experience.

[0083] Based on the effect contribution analysis in the regulation knowledge base, the value of the mushroom stick-manure mixing ratio coefficient was optimized to obtain the updated adaptive parameter of the mushroom stick-manure mixing ratio coefficient.

[0084] Specifically, numerical comparison processing is achieved by establishing a time-series data comparison mechanism. The composting environment parameter matrix before the execution of the equipment control parameter combination is used as the baseline data, and the environmental parameter matrix after execution is used as the comparison data for difference calculation. Temperature change data is obtained by calculating the difference between the temperature value after control and the temperature value before control. A positive difference indicates a temperature increase, and a negative difference indicates a temperature decrease; the absolute value of the difference reflects the magnitude of the temperature change. Humidity change data uses the same calculation method, reflecting the effect of humidity control through the difference between the humidity values ​​before and after control. pH change data is also calculated by the difference between the pH values ​​before and after control; the direction and magnitude of pH change directly reflect the accuracy of pH control. The comparison processing also includes time-dimensional analysis, recording the time required from the start of control to parameter stabilization, reflecting the speed of control response.

[0085] The weighted calculation process establishes a weight allocation mechanism based on the composting process quality evaluation system. The weight of temperature compliance rate reflects the importance of temperature control to composting success; the weight of pH stability reflects the impact of pH balance on microbial activity; the weight of organic matter degradation rate reflects the decisive role of decomposition efficiency in the composting cycle; the weight of composting cycle shortening rate reflects the contribution of time efficiency to economic benefits; and the weight of energy consumption reduction rate reflects the significance of energy-saving effects on cost control. Each weight coefficient is determined based on the specific characteristics of composting waste mushroom substrate and livestock manure. The decomposition of cellulose in mushroom substrate requires a relatively long time, hence the cycle weight is relatively high; livestock manure easily produces ammonia volatilization, therefore the pH stability weight is relatively important. The comprehensive composting quality evaluation index is calculated by multiplying temperature change data by the temperature compliance rate weight, humidity change data by the corresponding weight, and pH change data by the pH stability weight, and then summing all weighted results. This index comprehensively reflects the overall impact of control operations on compost quality.

[0086] The comparison of quality benchmark threshold values ​​is achieved by establishing a tiered evaluation standard. The benchmark thresholds are determined based on composting industry standards and the quality requirements of waste mushroom substrate and livestock manure mixtures. The comparison process involves comparing the calculated comprehensive compost quality evaluation index with multiple preset thresholds level by level. Values ​​above the excellent threshold are marked as excellent, those within the good threshold range are marked as good, and those below the qualified threshold are marked as needing improvement. The determination of the control effect level is achieved through numerical interval mapping, with different evaluation index values ​​corresponding to different level labels. The control effect data record includes multi-dimensional information such as control time, control parameters, environmental change magnitude, level determination results, and duration, forming a structured effect evaluation record.

[0087] The historical control operation-effect response database update process is implemented through relational database insertion and join operations. The database adopts a multi-table structure: the operation table records the specific parameters and execution status of each control measure, the effect table records the corresponding environmental changes and quality evaluation results, and the response table establishes the relationship between operations and effects. Data update processing writes the current control operation parameters, effect data, and time information into the corresponding data tables, while establishing primary key relationships to ensure data integrity and consistency. The control knowledge base extracts regular information from historical data using data mining algorithms, including the success rate of different control parameter combinations, optimal control strategies under different environmental conditions, and methods for handling various abnormal situations.

[0088] The contribution analysis of the effect uses statistical methods to calculate the contribution of each regulatory parameter to quality improvement. The analysis algorithm traverses all records in the regulatory knowledge base and calculates the correlation coefficient between the change of each parameter and the change of the quality index. The parameter optimization of the mushroom substrate-manure mixing ratio coefficient is based on the contribution analysis results. Gradient descent or genetic algorithms are used to find the optimal parameter value that maximizes the quality evaluation index. The optimization algorithm considers the constraints of the cellulose content of waste mushroom substrate and the nutrient composition of livestock and poultry manure to ensure the feasibility of the optimized parameters in practical applications. Adaptive parameter updating is achieved by replacing the original parameter values ​​with the new optimized parameter values. The update process includes parameter verification, boundary checks, and historical comparisons to ensure the rationality of the parameter update.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart control method for combined composting of waste mushroom substrate and livestock manure based on the Internet of Things, characterized in that, The method includes: The composting site is monitored in three dimensions using IoT sensors to obtain a composting environmental parameter matrix. A strongly connected directed topological graph is generated based on the cellulose content of the mushroom substrate and the nitrogen-phosphorus ratio of livestock manure. This process includes: deploying temperature, humidity, and pH sensors at a 5m x 5m grid density on the composting site to collect and process real-time data from each monitoring node, obtaining temperature, humidity, and pH values ​​corresponding to the three-dimensional coordinates; assembling the temperature, humidity, and pH values ​​into a matrix according to the spatial coordinate sequence to obtain a composting environmental parameter matrix containing spatial distribution information; quantitatively analyzing the lignocellulose content of discarded mushroom substrate samples to obtain cellulose content parameters; chemically detecting nitrogen, phosphorus, and potassium elements in livestock manure samples to obtain nitrogen-phosphorus ratio parameters; numerically calculating the biochemical reaction diffusion intensity between adjacent monitoring nodes based on the cellulose content and nitrogen-phosphorus ratio parameters to obtain quantified biochemical reaction conduction coefficients; and using these biochemical reaction conduction coefficients as directed edge weights to perform graph theory transformation on the composting environmental parameter matrix to obtain a strongly connected directed topological graph representing the node connectivity relationships of the composting site. Based on the strongly connected directed topology graph, the biochemical reaction adaptability detection algorithm is used to calculate the activity index, obtain the node regulation state classification results, and generate a feasibility test report. This includes: inputting the temperature, humidity, and pH values ​​of each monitoring node in the strongly connected directed topology graph into the biochemical reaction adaptability detection algorithm for data reading and processing to obtain a set of node environmental state data; and performing weighted calculation on the node environmental state data set based on preset weight coefficients for temperature, humidity, pH, cellulose content, and nitrogen content to obtain the compost biochemical reaction activity index value of each node. The biochemical reaction activity index of compost is compared with the optimal fermentation range threshold of 30-65 to determine the node activity status. Nodes with activity indices within the range are marked as controllable, while those outside the range are marked as requiring urgent control. Nodes marked as requiring urgent control are prioritized according to the degree of deviation of their activity indices to obtain a control priority sequence. The timing of control for each node is calculated based on the microbial activity time window to obtain estimated control time window data. The node control status classification results, control priority sequence, and estimated control time window data are integrated into a report format to obtain a feasibility test report containing control decision information. The feasibility test report is processed by assigning tasks according to the urgency of the composting reaction, obtaining the control sequence of multi-actuator equipment, and generating a collaborative operation path plan. The intensity of the collaborative operation path scheme is adjusted by using the mixing ratio coefficient of mushroom sticks and feces to obtain the combination of equipment control parameters and execute precise control commands. The results of the equipment control parameter combination are processed to evaluate compost quality, obtain control effect data, and update the adaptive parameters of the mushroom stick-manure mixing ratio coefficient.

2. The intelligent control method for combined composting of waste mushroom substrate and livestock manure based on the Internet of Things as described in claim 1, characterized in that, The process of allocating tasks based on the urgency of composting reactions according to feasibility test reports, obtaining multi-actuator equipment control sequences, and generating collaborative operation path schemes includes: The control priority sequence in the feasibility test report was classified into three categories: temperature anomaly control, pH adjustment and oxygen supplementation, resulting in a compost control task type sequence ordered by urgency. Based on the composting control task type sequence, the temperature-controlled turning machine, pH-adjusting spray truck and oxygen supply device are matched and allocated to obtain a multi-actuator equipment control sequence that includes the correspondence between equipment type and target node. The physical size constraint parameters and estimated control time window data of each device in the multi-actuator device control sequence are input into the path planning algorithm for collision detection processing to obtain the safe distance constraint conditions between devices. Based on the safety distance constraint, the traversable paths in the strongly connected directed topology graph are filtered and calculated to obtain a set of candidate paths that meet the requirements of multi-device collaborative operation. The candidate path set is comprehensively evaluated based on path length, operation time cost, and equipment coordination efficiency to obtain a collaborative operation path scheme that includes the optimal operation path and time scheduling for each device.

3. The intelligent control method for combined composting of waste mushroom substrate and livestock manure based on the Internet of Things as described in claim 1, characterized in that, The process of adjusting the intensity of the collaborative operation path scheme by using the mixing ratio coefficient of mushroom sticks and feces to obtain a combination of equipment control parameters and execute precise control commands includes: The operation nodes of each device in the collaborative operation path scheme are matched with the cellulose content parameters and nitrogen-phosphorus ratio parameters to obtain the node-specific mushroom stick-feces mixing ratio coefficient value. The turning depth of the temperature-controlled turning machine is adaptively calculated based on the ratio coefficient of the mushroom stick-manure mixture, and the turning depth parameter is determined according to the degree of cellulose decomposition. The turning depth parameter is set to one of 80 cm, 60 cm or 40 cm. The precise spraying volume parameters of the pH-adjusting spray truck are obtained by multiplying the mushroom stick-manure mixing ratio coefficient with the difference between the pH target value and the current pH value. The spraying volume parameters are then corrected based on the livestock and poultry manure buffer capacity coefficient to obtain the final spraying volume value. The oxygen supply rate of the oxygen supply device is dynamically adjusted based on the ratio coefficient of the mushroom stick-manure mixture to obtain the oxygen supply parameters that match the degradation rate of compost organic matter. The oxygen supply parameters are then multiplied with the compost volume correction coefficient to obtain the final oxygen supply rate value. The parameters of turning depth, final spray volume, and final oxygen supply rate are combined and packaged to obtain a combination of equipment control parameters containing the specific operating parameters of each device. Control commands are then sent to the corresponding actuators for precise control operations.

4. The intelligent control method for combined composting of waste mushroom substrate and livestock manure based on the Internet of Things as described in claim 3, characterized in that, The process of dynamically adjusting the oxygen supply rate of the oxygen supply device based on the mixing ratio coefficient of the mushroom substrate and the feces to obtain oxygen supply parameters that match the degradation rate of compost organic matter includes: The organic matter concentration data of the work node is processed by biochemical reaction rate calculation to obtain the compost organic matter degradation rate value of the current node. Based on the standard COD degradation rate benchmark value, the ratio of the compost organic matter degradation rate values ​​is calculated to obtain the organic matter degradation rate ratio factor. The oxygen demand correction coefficient is obtained by multiplying the organic matter degradation rate ratio factor with the mushroom stick-feces mixing ratio coefficient. The basic oxygen supply value is adjusted by multiples according to the oxygen supply demand correction coefficient to obtain the preliminary oxygen supply parameters. The oxygen supply parameters are obtained by multiplying the initial oxygen supply parameters with the reactor volume correction factor.

5. The intelligent control method for combined composting of waste mushroom substrate and livestock manure based on the Internet of Things as described in claim 1, characterized in that, The process of evaluating the composting quality of the results of the equipment control parameter combination to obtain control effect data and updating the adaptive parameters of the mushroom substrate-manure mixing ratio coefficient includes: The composting environment parameter matrix before and after the combined execution of equipment control parameters was numerically compared to obtain temperature change data, humidity change data, and pH change data. The temperature, humidity, and pH change data are weighted and processed based on the weights of temperature compliance rate, pH stability, organic matter degradation rate, composting cycle shortening rate, and energy consumption reduction rate to obtain a comprehensive evaluation index for compost quality. The comprehensive evaluation index of compost quality is compared with the preset quality benchmark threshold to obtain the control effect level judgment result, and the control effect level judgment result is recorded as control effect data. The historical control operation-effect response database is updated based on the control effect data to obtain a control knowledge base containing current control experience. Based on the effect contribution analysis in the regulation knowledge base, the value of the mushroom stick-manure mixing ratio coefficient was optimized to obtain the updated adaptive parameter of the mushroom stick-manure mixing ratio coefficient.

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