High-precision dynamic ingredient and conveying control system for edible mushroom culture medium

Abstract

The application relates to the technical field of agricultural engineering, and discloses a high-precision dynamic ingredient and conveying control system for edible mushroom culture medium, which comprises a formula digital twin module, a raw material pretreatment and metering module, a dynamic ingredient optimization module, a multi-axis precise ingredient execution module, a flexible pneumatic conveying and homogenizing module and a central monitoring and data analysis module. The actual ingredients of raw materials are detected in real time through an online component spectrum analysis unit, and the benchmark ingredient instruction is corrected in real time through the dynamic ingredient optimization module, so that the batch differences of the water content and the nutrient ingredients of raw materials such as sawdust and bran can be directly responded to, the problem that a static formula model cannot adapt to the physical property fluctuation of raw materials is solved, the accuracy of the key proportioning of the carbon-nitrogen ratio in the final culture medium is ensured, and the quality stability of the culture medium is ensured.

Description

Technical Field

[0001] This invention relates to the field of agricultural engineering technology, specifically to a high-precision dynamic batching and conveying control system for edible fungi culture media. Background Technology

[0002] Edible fungi refer to large, edible fruiting bodies of mushrooms, commonly known as mushrooms.

[0003] Currently, due to the diverse sources and significant fluctuations in the physical properties of raw materials for edible fungi culture media, the static formulation model relied upon cannot respond in real time to the batch-to-batch differences in the components of the raw materials when dynamically mixing and blending various raw materials. When using preset proportions for feeding, the actual proportions of key nutrients will deviate from the target range, making it impossible to guarantee the accuracy of the final culture media formulation.

[0004] Therefore, a high-precision dynamic batching and conveying control system for edible fungi culture medium is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a high-precision dynamic batching and conveying control system for edible fungi culture media, which solves the problem of the actual proportion of key nutrients deviating from the target range mentioned in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a high-precision dynamic batching and conveying control system for edible fungi culture medium, the system comprising: The formulation digital twin module uses a material property database unit to store raw material property parameters, constructs a virtual batching model through a formulation analysis and modeling unit, and outputs a baseline batching instruction through a dynamic simulation calculation unit. The raw material pretreatment and metering module receives the reference batching instruction, outputs raw materials through the multi-channel raw material silo unit, detects the raw material composition using the online component spectral analysis unit, and outputs real-time raw material flow and composition data through the high-precision dynamic weighing unit. The dynamic ingredient optimization module receives the raw material flow and composition data and environmental sensor data, and corrects the baseline ingredient instruction in real time through the feedback compensation calculation unit and the model prediction control unit, and outputs the optimized ingredient instruction to be executed. The multi-axis precision dispensing execution module receives the dispensing instruction, positions the dispensing port through the multi-axis linkage robotic arm unit, performs precise dispensing and mixing of materials using the variable screw conveyor unit and airflow fine-tuning unit, and outputs a dispensing completion signal. The flexible pneumatic conveying and homogenizing module receives the batching completion signal and the batching mixture, fluidizes the mixture through the venturi tube negative pressure suction unit, performs secondary mixing during the conveying process using the ring tube homogenizing and stirring unit, and outputs conveying status data through the pressure and flow sensing unit. The central monitoring and data analysis module integrates and receives the execution instructions, raw material composition data, conveying status data, and production batch information. Through the data fusion processing unit and the process compliance judgment unit, it generates a visualized full-process production report and process optimization suggestions.

[0007] Preferably, the material property database unit in the formula digital twin module specifically includes: The raw material basic attribute sub-library stores the particle size distribution, true density, bulk density, angle of repose, and standard range of moisture content for different types of main and auxiliary materials. The nutrient composition mapping sub-library is associated with the raw material basic attribute sub-library and stores the standard content data of the raw material's carbon-nitrogen ratio, cellulose, lignin, crude protein, and ash content. The process parameter constraint sub-library stores the upper and lower limits of formula ratios, mixing uniformity standards, and target pH ranges for different edible fungi strains and different culture objectives. Based on the data from the material property database unit, the formula analysis and modeling unit parses the production formulas in text and data table formats into a structured formula model that includes material types, target weights, physical property parameter constraints, and mixing order.

[0008] Preferably, the dynamic simulation calculation unit includes: The discrete element method (DEM) simulation engine, based on the structured formula model and the physical parameters of the raw materials, simulates the mixing process under different feeding sequences and stirring speeds in a virtual environment. It predicts the mixing uniformity and the risk of agglomeration, and outputs an optimized feeding strategy. The mixing uniformity is determined by the sample variation coefficient of key components in the mixed material. To quantitatively assess this, the calculation formula is as follows: ; In the formula, This represents the sample standard deviation of the content of key components measured from multiple samples in a virtual mixture. This represents the average sample value of the key component content measured from the multiple samplings mentioned above. Represents the coefficient of variation. The smaller the value, the higher the mixing uniformity; The flow coupling simulation model, based on the bulk density and flowability parameters of each material, calculates the particle size segregation and premixing state that will occur in the shared conveying pipeline when multiple material flows are fed simultaneously, and outputs the initial benchmark batching command, which includes the timing start and stop commands of each raw material silo, the target flow curve and the tolerance range.

[0009] Preferably, the online component spectral analysis unit in the raw material pretreatment and metering module is specifically a near-infrared spectrometer installed at the raw material silo outlet and a hyperspectral imager installed on the metering belt. It performs real-time scanning of raw materials such as sawdust, bran and corn cob during feeding, and quickly analyzes their current moisture content, lignin and cellulose content through the built-in chemometric model. It also compares the detected real-time component data with the standard data in the material characteristic database unit to generate a component deviation signal. The high-precision dynamic weighing unit adopts a loss-in-weight scale based on strain gauge sensors and a belt scale based on nuclear scales. It is linked with the online component spectral analysis unit to receive the component deviation signal and dynamically fine-tune its calibration coefficient according to a preset algorithm to compensate for the mass measurement error caused by the fluctuation of raw material components, thereby outputting the raw material flow rate and component data after component calibration.

[0010] Preferably, the feedback compensation calculation unit in the dynamic batching optimization module receives the raw material flow rate and composition data from the high-precision dynamic weighing unit in real time, and calculates the instantaneous deviation and cumulative deviation between the data and the target value in the current batching instruction. The model prediction control unit, embedded with a transfer function model derived from the virtual model in the formula digital twin module, receives instantaneous deviations, cumulative deviations, and temperature and humidity data from environmental sensors. Using a combination of feedforward and feedback, it continuously optimizes the target flow rate setpoints of each raw material silo over a future period, generating the optimized batching instructions to proactively suppress batching errors caused by fluctuations in raw material characteristics, mechanical delays, and environmental interference. The objective function of the continuous optimization... Minimize to: ; In the formula, Indicates the prediction time domain, Indicates control of the time domain. Represents the discrete time step. Indicates the first Step settings Indicates the model's prediction of the th The system output value at the time of step, The weighting coefficients represent the output error. Indicates the first Step control increment, This represents the weight matrix that controls the incremental changes. Represents the norm.

[0011] Preferably, the variable screw conveyor unit in the multi-axis precision batching execution module adopts a precision twin-screw structure driven by a servo motor, and its rotation speed is linearly related to the execution batching command output by the dynamic batching optimization module, so as to realize the precise metering and conveying of powdery and small granular auxiliary materials. The airflow fine-tuning unit includes an annular compressed air nozzle arranged around the discharge port of the variable screw conveyor unit. By adjusting the pressure and flow rate of the injected compressed air, it diffuses and guides the falling powdery material flow, preventing it from generating dust and sticking to the walls when it falls into the mixing chamber, and promoting the initial mixing with the main material.

[0012] Preferably, the multi-axis linkage robotic arm unit is a six-degree-of-freedom robotic arm mounted on a moving guide rail, and its end effector integrates the discharge port of the variable screw conveyor unit, the airflow fine-tuning unit, and a miniature high-speed stirring head; During the batching process, the robotic arm drives the end effector to perform a three-dimensional trajectory movement above the mixing chamber according to the recipe instructions, thereby achieving mobile and dispersed feeding instead of fixed-point feeding. This improves the initial uniformity of the distribution of different materials in the spatial dimension. The batching completion signal is issued by the robotic arm controller after completing the preset trajectory.

[0013] Preferably, the Venturi tube negative pressure suction unit in the flexible pneumatic conveying and homogenizing module has its air inlet connected to a high-pressure blower. It uses the negative pressure generated by the high-speed airflow at the throat to uniformly suck the batching mixture from the mixing chamber into the conveying pipeline, thereby achieving pneumatic conveying initialization with a low breakage rate. The ring-type homogenizing mixing unit consists of a coiled annular pipe and a built-in static mixer. When the material and gas flow through, continuous turbulent mixing occurs under the centrifugal force of the pipe bend and the splitting and reorganizing action of the static mixer blades, achieving "simultaneous conveying and mixing" during the conveying process. The pressure and flow sensing unit monitors the pressure difference and flow stability at the inlet and outlet of the annular pipe in real time, serving as an indirect evaluation index of the mixing and conveying effect.

[0014] Preferably, the process compliance judgment unit in the central monitoring and data analysis module has a built-in rule engine, which compares the received real-time data with preset process standards, and the rules include: Does the accuracy of individual raw material feeding continuously exceed the set threshold? Does pressure fluctuation during the transport of the mixture indicate pipeline blockage and uneven mixing? Does the calculated total carbon-to-nitrogen ratio of the batch production fall within the optimal range for the target strain? When any rule is triggered, the unit will generate alarm events of different levels and associate them with the visualized full-process production report.

[0015] Preferably, the data fusion processing unit uses timestamp synchronization technology to align and fuse instruction data from the dynamic batching optimization module, detection data from the raw material pretreatment and metering module, status data from the flexible pneumatic conveying and homogenization module, and batch data from the external production management system on a unified time axis to construct a batch production full-dimensional data cube. The process compliance judgment unit performs mining analysis on the data cube to identify key factors affecting the accuracy of ingredient mixing and the uniformity of mixing, as well as their coupling relationships. The process optimization suggestions specifically include: suggestions for fine-tuning the formula for specific batches of raw materials, calibration suggestions for model parameters in the dynamic simulation calculation unit, and preventive maintenance tips for key equipment.

[0016] Compared with the prior art, the present invention provides a high-precision dynamic batching and conveying control system for edible fungi culture medium, which has the following beneficial effects: 1. In this invention, when performing high-precision dynamic batching of edible fungi culture medium, the actual composition of the raw materials is detected in real time by an online component spectral analysis unit, and the benchmark batching instructions are corrected in real time by a dynamic batching optimization module. This can directly respond to batch-to-batch differences in moisture content and nutrient composition of raw materials such as sawdust and bran, solving the problem that static formula models cannot adapt to fluctuations in the physical properties of raw materials, ensuring the accuracy of the key carbon-nitrogen ratio in the final culture medium, and ensuring the stability of the culture medium quality.

[0017] 2. In this invention, when performing dynamic metering and feeding of multi-component raw materials, the flow deviation is calculated in real time by the feedback compensation calculation unit, and the future control command is optimized by the model prediction control unit. This can actively suppress the instantaneous proportioning imbalance caused by fluctuations in the feeding mechanism and mechanical delays, and solve the problem of easy accumulation of errors under traditional open-loop control. This enables the multi-axis precision batching execution module to achieve accurate tracking and synchronization of flow, and improves the consistency and reliability of the entire batching process.

[0018] 3. In this invention, during the conveying and homogenization process after the ingredients are prepared, the flexible pneumatic conveying and the ring-tube homogenizing and stirring unit in the homogenization module continuously mix the mixture during conveying. This can counteract the particle size segregation phenomenon caused by materials of different densities and particle sizes in the pipeline, solve the problem of decreased uniformity caused by the conveying process, and ensure the mixing uniformity of the culture medium before it is loaded into the cultivation container, laying the foundation for the uniformity of subsequent fermentation and the uniformity of mushroom growth. Attached Figure Description

[0019] Figure 1This is a schematic diagram of the high-precision dynamic batching and conveying control system for edible fungi culture medium of the present invention; Figure 2 This is a schematic diagram of the digital twin module for the formula of the present invention; Figure 3 This is a structural diagram of the multi-axis precision batching execution module of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figures 1-3 This is a high-precision dynamic batching and conveying control system for edible fungi culture medium. The system includes: The formulation digital twin module uses a material property database unit to store raw material property parameters, constructs a virtual batching model through a formulation analysis and modeling unit, and outputs a baseline batching instruction through a dynamic simulation calculation unit. The raw material pretreatment and metering module receives the baseline batching instruction, outputs the raw materials through the multi-channel raw material silo unit, detects the raw material composition using the online component spectral analysis unit, and outputs real-time raw material flow and composition data through the high-precision dynamic weighing unit. The dynamic ingredient optimization module receives raw material flow and composition data as well as environmental sensor data. It then uses a feedback compensation calculation unit and a model prediction control unit to correct the baseline ingredient instructions in real time and outputs the optimized ingredient instructions for execution. The multi-axis precision dispensing execution module receives dispensing instructions, positions the dispensing port through a multi-axis linkage robotic arm unit, uses a variable screw conveyor unit and an airflow fine-tuning unit to perform precise dispensing and mixing of materials, and outputs a dispensing completion signal. The flexible pneumatic conveying and homogenizing module receives the batching completion signal and the batching mixture, fluidizes the mixture through the venturi tube negative pressure suction unit, performs secondary mixing during the conveying process using the ring tube homogenizing and stirring unit, and outputs conveying status data through the pressure and flow sensing unit. The central monitoring and data analysis module integrates and receives batching instructions, raw material composition data, conveying status data, and production batch information. Through the data fusion processing unit and process compliance judgment unit, it generates a visualized full-process production report and process optimization suggestions.

[0022] The material property database unit in the formulation digital twin module specifically includes: The raw material basic attribute sub-library stores the particle size distribution, true density, bulk density, angle of repose, and standard range of moisture content for different types of main and auxiliary materials. The nutrient composition mapping sub-library is linked to the raw material basic attribute sub-library, storing standard content data of raw material carbon-nitrogen ratio, cellulose, lignin, crude protein and ash content; The process parameter constraint sub-library stores the upper and lower limits of formula ratios, mixing uniformity standards, and target pH ranges for different edible fungi strains and different culture objectives. Based on the data from the material property database unit, the formula analysis and modeling unit parses the production formulas in text and data table formats into a structured formula model that includes material types, target weights, physical property parameter constraints, and mixing order.

[0023] The dynamic simulation calculation unit includes: The discrete element method (DEM) simulation engine simulates the mixing process under different feeding sequences and stirring speeds in a virtual environment based on a structured formula model and the physical parameters of the raw materials. It predicts mixing uniformity and the risk of agglomeration, and outputs an optimized feeding strategy. Mixing uniformity is measured by the sample variation coefficient of key components in the mixed material. To quantitatively assess this, the calculation formula is as follows: ; In the formula, This represents the sample standard deviation of the content of key components measured from multiple samples in a virtual mixture. This represents the average sample value of the key component content measured from the multiple samplings mentioned above. Represents the coefficient of variation. The smaller the value, the higher the mixing uniformity; A flow coupling simulation model, based on the bulk density and flowability parameters of each material, calculates the particle size segregation and premixing state that will occur in a shared conveying pipeline when multiple material flows are fed simultaneously. The specific implementation method is as follows: First, a dynamic model based on the coupling of discrete element method and computational fluid dynamics is established. When multiple materials from different raw material bins converge at the inlet of a common conveying pipe, the system needs to calculate the initial mixing state of each material flow under the action of airflow. According to Newton's second law, the kinematic equations of the particles in the axial and radial directions of the pipe are established: ; in, For the first The mass of a single particle of the material For the first The acceleration vector of the material particles. For the first The fluid drag force vector acting on the material For the first The gravitational vector acting on the particles of this material. It is the gravitational acceleration vector. For the first The resultant force vector of the contact forces when a material particle comes into contact with other particles. For indexing; By iteratively calculating the equation, the initial trajectory of the particles in the pipe is obtained, and the spatial distribution probability of particles of different sizes is initially determined. At the confluence of multiple material flows, particle size segregation is prone to occur due to differences in the physical properties of the particles. This step introduces a dimensionless segregation index. To quantify The degree of separation of the materials is determined by this index, which is calculated based on the bulk density of the materials. and characteristic particle size Establish the formula for calculating the particle size segregation index: ; in, For the first Particle size segregation index of the material. For the first The bulk density of the material For the first The characteristic particle size of the material For the first The bulk density of the material For the first The characteristic particle size of the material For empirical shape coefficients, The total number of raw material types involved in the mixing. This is the flow field disturbance correction factor. For indexing, For indexing; when near When the material is evenly distributed, it indicates that the material is uniformly distributed. If the concentration is greater than that of other materials, it indicates that the material tends to aggregate. As materials are transported within the pipeline, particles continuously collide and diffuse, reaching a dynamic equilibrium and forming a premixed state. The system assesses the premixing uniformity by statistically analyzing the particle concentration distribution across the pipeline cross-section, defining a time-averaged mixing ratio. With instantaneous mixing ratio variance between As an evaluation indicator: ; Among them, instantaneous mixing ratio The volume of a certain infinitesimal element in the pipeline The quality of specific materials inside With total mass The ratio yields: ; in, For mixed variance, For the statistical time window, For a moment The instantaneous mixing ratio, The average mixing ratio over the entire time period. For a moment The mass of a specific material within a specific micro-element volume. For a moment The total mass of material within a specific infinitesimal volume. For a moment Spatial location First The density of the material For a moment Spatial location The total material density at the location, For the infinitesimal volume, It is a spatial coordinate vector. Indicates time, For indexing; It also outputs the initial baseline batching command, which includes the timing start / stop commands for each raw material silo, the target flow rate curve, and the tolerance range.

[0024] The online component spectral analysis unit in the raw material pretreatment and metering module specifically consists of a near-infrared spectrometer installed at the raw material silo outlet and a hyperspectral imager mounted on the metering conveyor belt. This unit scans the raw materials such as sawdust, bran, and corn cobs in real time, rapidly analyzing their current moisture content, lignin, and cellulose content using a built-in chemometric model. The detected real-time component data is then compared with standard data in the material characteristic database unit to generate a component deviation signal. The specific implementation method is as follows: Near-infrared spectrometers transmit near-infrared light of specific wavelengths through material layers, and detectors receive the transmitted and reflected light signals. First, the fundamental physical relationship between spectral absorbance and material concentration is established, and then calculations are performed using a modified form of the classic Lambert-Beer law. ; in, At wavelength absorbance at that point Let be the initial intensity of the incident light. The intensity of transmitted light after passing through the material layer. At wavelength The molar absorptivity at that point The molar concentration of the chemical component to be measured. The effective optical path thickness of the material layer. For baseline drift and scattering correction terms, Indicates wavelength; Since a single wavelength is insufficient to accurately distinguish complex organic components, the system employs a chemometric model to process massive amounts of data across the entire spectral range, extracting features and establishing a mapping relationship between spectral response and principal component content. The model predicts values... The calculation formula is as follows: ; in, The model represents the first time. The predicted concentration value of a specific component at each sampling time. This is the intercept constant term of the model. The total number of effective spectral variables for modeling, No. The weights of the regression coefficients corresponding to each spectral variable It is in the At the sampling time, the first Spectral absorbance values ​​at each wavelength point For indexing, For indexing; To generate a component deviation signal that can drive the control system's actions, the system compares the actual component values ​​predicted by the model in real time with the target values ​​of the standard formulation, thus defining the component deviation signal. The calculation formula is: ; in, Indicates the first The component deviation signal generated at each sampling time. The system detects and calculates the actual content of raw materials in real time. The target component content of this raw material is set in the material property database. For indexing; When calculated When the system's preset threshold is exceeded, the component deviation signal will be activated and transmitted to the dynamic ingredient optimization module. The system will then adjust the feed flow rate of the corresponding raw material silo and correct the ingredient model to ensure that the final mixed culture medium formula accuracy meets the process requirements. The high-precision dynamic weighing unit uses a loss-in-weight scale based on strain gauge sensors and a belt scale based on nuclear scales. It is linked with an online component spectral analysis unit to receive component deviation signals and dynamically fine-tune its calibration coefficients according to a preset algorithm to compensate for the mass measurement error caused by fluctuations in raw material composition, thereby outputting raw material flow rate and component data after component calibration.

[0025] The feedback compensation calculation unit in the dynamic batching optimization module receives raw material flow and composition data from the high-precision dynamic weighing unit in real time, and calculates the instantaneous and cumulative deviations between these data and the target values ​​in the currently executed batching command. The specific implementation method is as follows: To address the instantaneous flow rate fluctuations caused by mechanical vibration and material impact during the feeding process, the system employs a filtering algorithm within a sliding time window to calculate the instantaneous flow rate at the current moment and compares it with the target flow rate in the feeding instruction. The instantaneous deviation is then recorded. The calculation formula is as follows: ; in, For a moment Instantaneous flow deviation, The actual mass flow rate after low-pass filtering. For a moment The actual mass of material within a specific infinitesimal volume. For a moment The target flow rate set in the ingredient dispensing instruction Indicates time; Since changes in raw material composition can affect the actual feed rate, the system introduces a composition deviation correction factor to compensate for deviations in the basic flow rate, defining a comprehensive composition deviation correction amount. : ; in, For a moment The overall component deviation correction flow rate, This represents the total number of raw material types involved in the mixing process. For the first The difference between the actual raw material composition and the standard value at the current moment. For the first The standard raw material composition of the raw materials For the first The component sensitivity coefficient of the raw material. For a moment The target traffic, Indicates time, For indexing; During the long-term operation of the batching process, although instantaneous fluctuations can be quickly corrected, the accumulation of positive and negative deviations will lead to a deviation in the final total weight. The system monitors this long-term cumulative deviation through integral calculations. : ; in, Indicates the time from the start of ingredient preparation to the time specified. Total cumulative deviation The symbol for definite integral is . For integration variables, Indicates time, For instantaneous flow deviation, Overall component deviation correction flow rate; Through the above three steps, the feedback compensation calculation unit not only outputs the deviation signal reflecting the current instantaneous state, but also outputs the deviation signal reflecting the historical cumulative state. These signals together constitute the input of the subsequent model prediction control unit, enabling the system to achieve high-precision closed-loop feedback regulation based on feedforward control. The model prediction control unit, embedded with a transfer function model derived from the virtual model in the formula digital twin module, receives instantaneous deviations, cumulative deviations, and temperature and humidity data from environmental sensors. Using a combination of feedforward and feedback mechanisms, it continuously optimizes the target flow setpoints for each raw material silo over a future period, generating optimized batching instructions to proactively suppress batching errors caused by fluctuations in raw material characteristics, mechanical delays, and environmental interference. The objective function of this continuous optimization... Minimize to: ; In the formula, Indicates the prediction time domain, Indicates control of the time domain. Represents the discrete time step. Indicates the first Step settings Indicates the model's prediction of the th The system output value at the time of step, The weighting coefficients represent the output error. Indicates the first Step control increment, This represents the weight matrix that controls the incremental changes. Represents the norm; Taking a bran silo screw conveyor as an example, its transfer function model can be obtained by linearizing the mechanistic model in the digital twin module at the rated operating point, and it takes the form of a first-order inertial plus pure time-delay system: ; in, For transfer functions, For complex frequency variables, For system gain, Represents the time constant. For the time lag, The base of the natural constant; For the system, the transfer function parameters of each raw material silo Pre-calibrated and stored in the model.

[0026] The variable screw conveyor unit in the multi-axis precision batching execution module adopts a precision twin-screw structure driven by a servo motor. Its rotation speed is linearly related to the execution batching command output by the dynamic batching optimization module, so as to realize the precise metering and conveying of powdery and small granular auxiliary materials. The airflow fine-tuning unit includes an annular compressed air nozzle arranged around the discharge port of the variable screw conveyor unit. By adjusting the pressure and flow rate of the injected compressed air, it diffuses and guides the falling powdery material flow, preventing it from generating dust and sticking to the walls when it falls into the mixing chamber, and promoting the initial mixing with the main material.

[0027] The multi-axis linkage robotic arm unit is a six-degree-of-freedom robotic arm mounted on a moving guide rail. Its end effector integrates the discharge port of the variable screw conveyor unit, the airflow fine-tuning unit, and a miniature high-speed mixing head. During the batching process, the robotic arm drives the end effector to perform three-dimensional trajectory movement above the mixing chamber according to the formula instructions, realizing mobile and dispersed feeding instead of fixed-point feeding. This improves the initial uniformity of the distribution of different materials in the spatial dimension. The batching completion signal is issued by the robotic arm controller after completing the preset trajectory. The preset trajectory includes: a grid scanning trajectory covering the surface of the mixing chamber, a spiral trajectory from the outside to the inside, a large-area sparse trajectory for light auxiliary materials, and a small-area dense trajectory for heavy main materials. The robotic arm controller has pre-stored trajectory programs corresponding to different formulas, and the formula instructions include trajectory program selection indicators.

[0028] The Venturi tube negative pressure suction unit in the flexible pneumatic conveying and homogenization module has an air inlet connected to a high-pressure blower. It uses the negative pressure generated by the high-speed airflow at the throat to uniformly suck the batching mixture from the mixing chamber into the conveying pipeline, thereby achieving pneumatic conveying initialization with a low breakage rate. The ring-type homogenizing mixing unit consists of a coiled annular pipe and a built-in static mixer. When the material and gas flow through, continuous turbulent mixing occurs under the centrifugal force of the pipe bend and the splitting and reorganizing action of the static mixer blades, achieving "simultaneous conveying and mixing" during the conveying process. The pressure and flow sensing unit monitors the pressure difference and flow stability at the inlet and outlet of the annular pipe in real time, serving as an indirect evaluation index of the mixing and conveying effect.

[0029] The process compliance judgment unit in the central monitoring and data analysis module has a built-in rule engine that compares the received real-time data with preset process standards. The rules include: Whether the accuracy of feeding a single raw material continuously exceeds the set threshold, wherein the set threshold is determined comprehensively based on the tolerance range allowed by the production process, the control limits in historical production statistics, and the equipment accuracy indicators; Does pressure fluctuation during the transport of the mixture indicate pipeline blockage and uneven mixing? Does the calculated total carbon-to-nitrogen ratio of the batch production fall within the optimal range for the target strain? When any rule is triggered, the unit will generate alarm events of different levels and associate them with the visualized full-process production report. The alarm levels are divided into Level 1, Level 2, and Level 3 alarms based on the severity and urgency of the deviation.

[0030] The data fusion processing unit employs timestamp synchronization technology to align and fuse instruction data from the dynamic batching optimization module, detection data from the raw material pretreatment and metering module, status data from the flexible pneumatic conveying and homogenization module, and batch data from the external production management system on a unified timeline, constructing a full-dimensional data cube for batch production. The specific implementation method is as follows: The system first collects data streams from each module through a data fusion processing unit. Due to the different sampling frequencies of different sensors, the raw data are misaligned on the time axis. To ensure the accuracy of the analysis, the system uniformly maps all external input data to a standard time axis provided by a high-precision clock source. For missing timestamp data, the system uses a resampling algorithm based on Lagrange interpolation to fill in the gaps. For event-type and state-type data, the nearest neighbor hold method is used for alignment. The system's standard synchronization time interval is set to [value missing]. And it is the reciprocal of the highest sampling frequency among all data sources, for any acquisition channel The original data sequence Its interpolated reconstructed value on a unified time axis The calculation is as follows: ; in, Indicates the first Standard time points Place, No. Reconstructed alignment values ​​for each data source. This represents the total number of data points contained in the original data sequence. For the first The data source in the first The actual recorded value at each original sampling time. To unify the standard timeline of the system A discrete time point, For the first The first data source The timestamp of the original sampling time For the first The first data source The timestamp of the original sampling time For indexing, For indexing, For indexing, For indexing; After achieving strict alignment of the timeline, the system reorganizes the corrected instruction data, real-time detection data, status data, and batch management data according to the "time-space-attribute" dimension within a unified time window. The system packages the discrete aligned data into a multi-dimensional matrix, thereby achieving the fusion of all-dimensional data and defining the core tensor of the batch production all-dimensional data cube at a certain moment. The expression is: ; in, For a moment Batch production full-dimensional data cube, For a moment The target flow command output by the dynamic ratio optimization module. For a moment The actual weighing flow rate fed back by the raw material pretreatment and metering module For a moment The concentration of key chemical components detected by the raw material pretreatment and metering module For a moment The pipeline pressure and flow velocity status data of the flexible pneumatic conveying and homogenization module. Batch static metadata vectors provided to external production management systems; The process compliance assessment unit performs data cube mining and analysis to identify key factors affecting ingredient accuracy and mixing uniformity and their coupling relationships. The specific process optimization suggestions include: formula fine-tuning suggestions for specific raw material batches, calibration suggestions for model parameters in the dynamic simulation calculation unit, and preventive maintenance tips for key equipment.

[0031] The operation steps of a high-precision dynamic batching and conveying control system for edible fungi culture medium are as follows: Step 1: Virtual Modeling and Simulation Pre-play: The system first constructs a digital twin model of the formula in a virtual environment. It stores the physical properties and nutritional standards of raw materials such as sawdust and bran in a database and parses the text formula into a structured digital model. Then, using discrete element simulation technology, it simulates the mixing process under different feeding sequences and stirring speeds in the computer in advance, predicts the risk of agglomeration and the uniformity of mixing, and calculates the optimal initial feeding sequence and flow rate baseline command, providing a theoretically optimal "battle plan" for actual production.

[0032] Step 2: Real-time sensing and calibration of raw material components: During the actual feeding process, the system uses a near-infrared spectrometer and a hyperspectral imager installed at the discharge port and on the metering belt to scan the flowing raw material in real time. Using a chemometrics model, the system quickly analyzes the current real-time moisture content and lignin of the raw material and compares it with the standard values ​​in the database to generate a "composition deviation signal". The high-precision dynamic weighing unit receives this signal and dynamically calibrates its own measurement coefficient, thereby outputting accurate and reliable raw material flow and composition data after composition correction.

[0033] Step 3: Dynamic Optimization and Closed-Loop Control The dynamic batching optimization module is the "intelligent decision-making center" of the system. It receives real-time data from the previous step and continuously monitors the instantaneous deviation and cumulative error between the actual feed flow rate and the target instruction through the feedback compensation calculation unit. At the same time, its built-in model prediction control unit continuously optimizes the flow rate setpoint of each raw material silo for a period of time based on the system model and future trends. This process actively suppresses interference caused by factors such as raw material characteristic fluctuations and mechanical delays by combining feedforward and feedback, and generates and outputs precise optimized execution instructions in real time.

[0034] Step 4: Precise Execution Through Multi-Axis Collaboration The optimized execution command is sent to the multi-axis precision feeding execution module. The multi-axis linkage robotic arm carries an end effector that integrates a variable screw conveyor head and an airflow fine-tuning nozzle. It performs precise trajectory movement above the mixing chamber to achieve "mobile dispersion feeding". This improves the initial distribution of materials in space. The screw driven by the servo motor precisely controls the feeding amount according to the command, while the surrounding airflow diffuses and guides the powder to prevent dust and sticking to the wall, ensuring that the material is accurately and evenly fed into the mixing chamber.

[0035] Step 5: Online homogenization during transport: After initial mixing, the materials enter the flexible pneumatic conveying and homogenizing module. The Venturi tube uses negative pressure to fluidize the materials and draw them into the pipeline, achieving low-breakage conveying. Subsequently, the material-air mixture enters the core "ring-type homogenizing and mixing unit". In the coiled pipeline, continuous turbulent secondary mixing is carried out during the conveying process by means of the centrifugal force of the bend and the cutting and reorganizing action of the built-in static mixer. This step counteracts the "particle size segregation" phenomenon caused by the difference in settling velocity of materials of different densities and particle sizes in the pipeline, ensuring the mixing uniformity at the conveying terminal.

[0036] Step 6: Full-process monitoring and decision optimization: The central monitoring and data analysis module, acting as the "command center," integrates instruction data, testing data, status data, and batch information from the entire process onto a unified timeline using high-precision timestamp synchronization technology, constructing a "data cube." The process compliance judgment unit monitors the production status in real time and issues automatic alarms based on preset rules. Finally, the system analyzes and mines massive amounts of data to identify key factors affecting quality and automatically generates optimization reports that include formula fine-tuning suggestions, model parameter calibration, and maintenance tips, driving continuous optimization of the production process.

[0037] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-precision dynamic batching and conveying control system for edible fungi culture medium, characterized in that, The system includes: The formulation digital twin module uses a material property database unit to store raw material property parameters, constructs a virtual batching model through a formulation analysis and modeling unit, and outputs a baseline batching instruction through a dynamic simulation calculation unit. The raw material pretreatment and metering module receives the reference batching instruction, outputs raw materials through the multi-channel raw material silo unit, detects the raw material composition using the online component spectral analysis unit, and outputs real-time raw material flow and composition data through the high-precision dynamic weighing unit. The dynamic ingredient optimization module receives the raw material flow and composition data and environmental sensor data, and corrects the baseline ingredient instruction in real time through the feedback compensation calculation unit and the model prediction control unit, and outputs the optimized ingredient instruction to be executed. The multi-axis precision dispensing execution module receives the dispensing instruction, positions the dispensing port through the multi-axis linkage robotic arm unit, performs precise dispensing and mixing of materials using the variable screw conveyor unit and airflow fine-tuning unit, and outputs a dispensing completion signal. The variable screw conveyor unit in the multi-axis precision batching execution module adopts a precision twin-screw structure driven by a servo motor, and its rotation speed is linearly related to the execution batching command output by the dynamic batching optimization module. The airflow fine-tuning unit includes an annular compressed air nozzle arranged around the discharge port of the variable screw conveyor unit. By adjusting the pressure and flow rate of the injected compressed air, it diffuses and guides the falling powdery material flow. The flexible pneumatic conveying and homogenizing module receives the batching completion signal and the batching mixture, fluidizes the mixture through the venturi tube negative pressure suction unit, performs secondary mixing during the conveying process using the ring tube homogenizing and stirring unit, and outputs conveying status data through the pressure and flow sensing unit. The Venturi tube negative pressure suction unit in the flexible pneumatic conveying and homogenizing module has an air inlet connected to a high-pressure blower. It uses the negative pressure generated by the high-speed airflow at the throat to uniformly suck the mixture from the mixing chamber into the conveying pipeline. The ring-type homogenizing mixing unit consists of a coiled annular pipe and a built-in static mixer. When the material and gas flow through, continuous turbulent mixing occurs under the centrifugal force of the pipe bend and the splitting and reorganizing action of the static mixer blades. The pressure and flow sensing unit monitors the pressure difference and flow stability at the inlet and outlet of the annular pipe in real time, serving as an indirect evaluation index of the mixing and conveying effect. The central monitoring and data analysis module integrates and receives the execution instructions, raw material composition data, conveying status data, and production batch information. Through the data fusion processing unit and the process compliance judgment unit, it generates a visualized full-process production report and process optimization suggestions.

2. The high-precision dynamic batching and conveying control system for edible fungi culture medium according to claim 1, characterized in that, The material property database unit in the formula digital twin module specifically includes: The raw material basic attribute sub-library stores the particle size distribution, true density, bulk density, angle of repose, and standard range of moisture content for different types of main and auxiliary materials. The nutrient composition mapping sub-library is associated with the raw material basic attribute sub-library and stores the standard content data of the raw material's carbon-nitrogen ratio, cellulose, lignin, crude protein, and ash content. The process parameter constraint sub-library stores the upper and lower limits of formula ratios, mixing uniformity standards, and target pH ranges for different edible fungi strains and different culture objectives. Based on the data from the material property database unit, the formula analysis and modeling unit parses the production formulas in text and data table formats into a structured formula model that includes material types, target weights, physical property parameter constraints, and mixing order.

3. The high-precision dynamic batching and conveying control system for edible fungi culture medium according to claim 2, characterized in that, The dynamic simulation calculation unit includes: The discrete element method (DEM) simulation engine, based on the structured formula model and the physical parameters of the raw materials, simulates the mixing process under different feeding sequences and stirring speeds in a virtual environment. It predicts the mixing uniformity and the risk of agglomeration, and outputs an optimized feeding strategy. The mixing uniformity is determined by the sample variation coefficient of key components in the mixed material. To quantitatively assess this, the calculation formula is as follows: ； In the formula, This represents the sample standard deviation of the content of key components measured from multiple samples in a virtual mixture. This represents the average sample value of the key component content measured from the multiple samplings mentioned above. Represents the coefficient of variation. The smaller the value, the higher the mixing uniformity; The flow coupling simulation model, based on the bulk density and flowability parameters of each material, calculates the particle size segregation and premixing state that will occur in the shared conveying pipeline when multiple material flows are fed simultaneously, and outputs the initial benchmark batching command, which includes the timing start and stop commands of each raw material silo, the target flow curve and the tolerance range.

4. The high-precision dynamic batching and conveying control system for edible fungi culture medium according to claim 1, characterized in that, The online component spectral analysis unit in the raw material pretreatment and metering module specifically consists of a near-infrared spectrometer installed at the raw material silo outlet and a hyperspectral imager installed on the metering belt. It performs real-time scanning of raw materials such as sawdust, bran, and corn cobs during feeding, and quickly analyzes their current moisture content, lignin, and cellulose content through a built-in chemometric model. The detected real-time component data is then compared with the standard data in the material characteristic database unit to generate a component deviation signal. The high-precision dynamic weighing unit adopts a loss-in-weight scale based on strain gauge sensors and a belt scale based on nuclear scales. It is linked with the online component spectral analysis unit to receive the component deviation signal and dynamically fine-tune its calibration coefficient according to a preset algorithm, thereby outputting the raw material flow rate and component data after component calibration.

5. The high-precision dynamic batching and conveying control system for edible fungi culture medium according to claim 1, characterized in that, The feedback compensation calculation unit in the dynamic batching optimization module receives the raw material flow rate and composition data from the high-precision dynamic weighing unit in real time, and calculates the instantaneous deviation and cumulative deviation between the data and the target value in the current batching instruction. The model prediction control unit, embedded with a transfer function model derived from the virtual model in the formula digital twin module, receives instantaneous deviations, cumulative deviations, and temperature and humidity data from environmental sensors. Using a combination of feedforward and feedback mechanisms, it continuously optimizes the target flow rate setpoints for each raw material silo over a future period, generating the optimized batching instruction. The objective function of this continuous optimization is... Minimize to: ； In the formula, Indicates the prediction time domain, Indicates control of the time domain. Represents the discrete time step. Indicates the first Step settings Indicates the model's prediction in the th... The system output value at the time of step, The weighting coefficients represent the output error. Indicates the first Step control increment, This represents the weight matrix that controls the incremental changes. Represents the norm.

6. The high-precision dynamic batching and conveying control system for edible fungi culture medium according to claim 1, characterized in that, The multi-axis linkage robotic arm unit is a six-degree-of-freedom robotic arm mounted on a moving guide rail. Its end effector integrates the discharge port of the variable screw conveyor unit, the airflow fine-tuning unit, and a miniature high-speed stirring head. During the batching process, the robotic arm drives the end effector to perform a three-dimensional trajectory movement above the mixing chamber according to the recipe instructions. The batching completion signal is issued by the robotic arm controller after the preset trajectory is completed.

7. The high-precision dynamic batching and conveying control system for edible fungi culture medium according to claim 1, characterized in that, The process compliance judgment unit in the central monitoring and data analysis module has a built-in rule engine that compares the received real-time data with preset process standards. The rules include: Does the accuracy of individual raw material feeding continuously exceed the set threshold? Does pressure fluctuation during the transport of the mixture indicate pipeline blockage and uneven mixing? Does the calculated total carbon-to-nitrogen ratio of the batch production fall within the optimal range for the target strain? When any rule is triggered, the unit will generate alarm events of different levels and associate them with the visualized full-process production report.

8. The high-precision dynamic batching and conveying control system for edible fungi culture medium according to claim 1, characterized in that, The data fusion processing unit uses timestamp synchronization technology to align and merge instruction data from the dynamic batching optimization module, detection data from the raw material pretreatment and metering module, status data from the flexible pneumatic conveying and homogenization module, and batch data from the external production management system on a unified timeline to construct a batch production full-dimensional data cube. The process compliance judgment unit performs mining analysis on the data cube to identify key factors affecting the accuracy of ingredient mixing and the uniformity of mixing, as well as their coupling relationships. The process optimization suggestions specifically include: suggestions for fine-tuning the formula for specific batches of raw materials, calibration suggestions for model parameters in the dynamic simulation calculation unit, and preventive maintenance tips for key equipment.

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