A multi-sensor-based zinc-rich water flow sprinkler irrigation control system

Abstract

This invention relates to the field of intelligent control technology, specifically to a multi-sensor-based zinc-rich water flow sprinkler irrigation control system. The system includes: S1, real-time acquisition of spectral bandwidth distribution, local micro-electric field gradient, mycelial network fractal dimension, and cell membrane Zeta potential relaxation rate; S2, generation of a real-time state vector; S3, calculation of the resonance mismatch index; S4, comparison of the resonance mismatch index with a preset threshold to generate a comparison result; S5, generation of coordinated control commands for sprinkler flow, infrared spectrum, and micro-electric field; and S6, receiving and executing the coordinated control commands to perform closed-loop adjustment of the zinc-rich water flow, infrared spectral bandwidth, and local micro-electric field gradient. This invention solves the problems of unstable product quality and system control oscillation caused by unclear causes.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to a zinc-rich water flow sprinkler irrigation control system based on multiple sensors. Background Technology

[0002] Sprinkler irrigation is a common irrigation method in industrialized agriculture. It is a technology that atomizes nutrient solution through nozzles and applies it to the cultivated object. Zinc-enriched cultivation of white fungus involves using sprinkler irrigation equipment to spray zinc-rich nutrient solution onto the surface of the white fungus during the fruiting body growth stage, thereby achieving zinc enrichment in the fruiting body through absorption.

[0003] To ensure enrichment efficiency, precise control of the sprinkler irrigation process is usually required. The purpose of this control is to ensure stable mineral absorption. However, in actual production, complex changes in the cultivation environment (such as temperature, humidity, infrared spectrum, local micro-electric field, etc.) can easily lead to highly unstable mineral absorption, abnormally sharp drops in absorption efficiency, or even the active expulsion of minerals. Existing technologies include control strategies that monitor conventional environmental parameters such as temperature and humidity using sensors and adjust the sprinkler irrigation flow accordingly. However, in practical applications, traditional strategies fail to identify and monitor the influence of other implicit environmental factors on the absorption process, and the underlying causal relationship is unclear. If control is directly based on these conventional parameters, it is impossible to effectively quantify and predict the risk of sudden drops in mineral absorption efficiency caused by complex implicit factors, let alone take proactive avoidance measures, leading to technical problems such as unstable product quality and serious waste of nutrient solution. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention discloses a multi-sensor-based zinc-rich water flow sprinkler irrigation control system, the technical solution of which includes:

[0005] A multi-sensor-based method for controlling the flow rate of zinc-rich water in sprinkler irrigation, applied to a fungal cultivation process, includes the following steps:

[0006] S1. Real-time acquisition of spectral bandwidth distribution, local micro-electric field gradient, mycelial network fractal dimension and cell membrane Zeta potential relaxation rate through multi-dimensional state sensing module.

[0007] S2. Based on the spectral bandwidth distribution, the local micro-electric field gradient, the fractal dimension of the hyphal network, and the cell membrane Zeta potential relaxation rate, a real-time state vector is generated.

[0008] S3. Input the real-time state vector into the nonlinear resonance risk assessment module to calculate the resonance mismatch index;

[0009] S4. Compare the resonance mismatch index with a preset threshold to generate a comparison result;

[0010] S5. Based on the comparison results, generate coordinated control commands for sprinkler flow rate, infrared spectrum, and micro-electric field;

[0011] S6. The composite execution module receives and executes the cooperative control command to perform closed-loop adjustment of the zinc-rich water flow rate, infrared spectral bandwidth, and local micro-electric field gradient.

[0012] Preferably, S1 specifically includes:

[0013] The spectral bandwidth distribution was obtained using a Fourier transform infrared spectrometer.

[0014] The local micro-electric field gradient was calculated using a microelectrode array implanted inside the culture medium.

[0015] The fractal dimension of the mycelial network was calculated using a hyperspectral camera and a box-counting dimension method image processing algorithm.

[0016] The zeta potential relaxation rate of the cell membrane was measured using an online electro-acoustic spectrometer.

[0017] Preferably, the nonlinear resonance risk assessment module performs calculations based on a nonlinear coupling model;

[0018] The mechanism of the nonlinear coupling model is as follows: the realization of efficient transmembrane transport of mineral ions depends on the resonance matching between the collapse rate of the ion hydration shell driven by the external environment and the inherent polarization response rate of the hyphal cell membrane.

[0019] Preferably, the resonance mismatch index The calculation steps include:

[0020] Determine the environment-driven equivalent hydration shell collapse rate ;

[0021] Obtain the cell membrane zeta potential relaxation rate ;

[0022] Obtain the fractal dimension of the mycelial network ;

[0023] Based on dynamically updated historical maximum fractal dimension With the historical minimum fractal dimension In conjunction with the above Construct normalized growth stage regulatory factors;

[0024] According to the formula Combined with the growth stage regulatory factors, the With the Calculate the resonance mismatch index ;

[0025] in, Adjust the weights for the preset fractal dimension. To prevent extremely small positive numbers with a denominator of zero.

[0026] Preferably, the environment-driven equivalent hydration shell collapse rate The calculation steps include:

[0027] Based on the spectral bandwidth distribution The equivalent collapse rate function driven by infrared spectroscopy was determined using a Gaussian function. ;

[0028] Based on the local micro-electric field gradient Determine the equivalent collapse rate function driven by the micro-electric field. ;

[0029] According to the formula Combined with preset contribution weighting coefficients and Calculate the .

[0030] Preferably, in step S5, the logic for generating the collaborative control command is as follows:

[0031] If the comparison result shows that the resonance mismatch index is less than the preset threshold, then an instruction in the normal control mode is generated;

[0032] If the comparison result shows that the resonance mismatch index is greater than or equal to the preset threshold, then an instruction in risk avoidance mode is generated.

[0033] Preferably, in the conventional control mode, the cooperative control command is used to make the irrigation flow rate positively correlated with the mycelial growth rate.

[0034] Preferably, the instructions in the risk avoidance mode are used to trigger the following actions:

[0035] Implement sprinkler suppression, setting the sprinkler flow rate to zero or maintaining a low flow rate;

[0036] The resonance mismatch index is set as the objective function;

[0037] A fast optimization algorithm is initiated to find the optimal combination of infrared spectral parameters and micro-electric field parameters that minimizes the objective function.

[0038] Generate coordinated control instructions containing the optimal parameter combinations.

[0039] A multi-sensor-based zinc-rich water flow sprinkler irrigation control system includes:

[0040] A multidimensional state sensing module is used to collect spectral bandwidth distribution, local micro-electric field gradient, mycelial network fractal dimension and cell membrane Zeta potential relaxation rate in real time, and generate a real-time state vector based on the collected parameters.

[0041] A nonlinear resonance risk assessment module is used to receive the real-time state vector and calculate the resonance mismatch index;

[0042] A multi-objective collaborative control decision module is used to compare the resonance mismatch index with a preset threshold to generate a comparison result, and generate collaborative control instructions based on the comparison result;

[0043] The composite execution module is used to receive and execute the cooperative control command to adjust the zinc-rich water flow rate, infrared spectral bandwidth, and local micro-electric field gradient.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. This invention reveals a core biophysical contact point for efficient transmembrane transport, namely, the collapse rate of the ionized hydration shell driven by the external environment must be consistent with the inherent strongest response rate of the hyphal cell membrane; based on this, this invention upgrades the control logic from traditional response to active triggering of risk causes, solving the problems of unstable product quality and system control oscillation caused by unclear causes.

[0046] 2. This invention provides a comprehensive and profound understanding of the physiological state of fungi through precise acquisition of multidimensional states. Unlike traditional control schemes that rely on macroscopic indirect parameters, this invention makes good use of a variety of precision instruments to acquire high-level biophysical parameters such as spectral bandwidth distribution, micro-electric field, fractal dimension of hyphal network, and relaxation rate of cell membrane surface potential in real time, providing a solid data foundation for accurate risk assessment and control decisions.

[0047] 3. This invention constructs a nonlinear risk assessment model that can quantify complex and variable biophysical states into a single, clear resonance mismatch index. The introduction of this index enables the system to accurately identify and judge the risk of a sudden drop in absorption efficiency, realizing a technological upgrade from delay compensation to predictive risk management.

[0048] 4. This invention sets up a dynamic adaptive dual-mode high-efficiency control strategy, which significantly improves resource utilization efficiency and system robustness. The system can intelligently switch between the conventional mode that promotes growth and the risk avoidance mode that avoids losses based on the real-time resonance mismatch risk assessment results. In the case of high risk, the system decisively implements sprinkler suppression and at the same time starts a fast optimization algorithm to actively adjust environmental parameters to restore the efficient absorption state, thus ensuring the stability and enrichment efficiency of the cultivation process. Attached Figure Description

[0049] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0050] Figure 1 This is a flowchart of the system of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0052] Example 1:

[0053] Please see Figure 1 A multi-sensor-based method for controlling the flow rate of zinc-rich water in sprinkler irrigation, applied to the fungal culture process, includes the following steps:

[0054] S1. Real-time acquisition of spectral bandwidth distribution, local micro-electric field gradient, mycelial network fractal dimension and cell membrane Zeta potential relaxation rate through multi-dimensional state sensing module.

[0055] S2. Based on the spectral bandwidth distribution, local micro-electric field gradient, mycelial network fractal dimension and cell membrane Zeta potential relaxation rate, a real-time state vector is generated.

[0056] S3. Input the real-time state vector into the nonlinear resonance risk assessment module to calculate the resonance mismatch index;

[0057] S4. Compare the resonance mismatch index with a preset threshold to generate a comparison result;

[0058] S5. Based on the comparison results, generate coordinated control commands for sprinkler flow rate, infrared spectrum, and micro-electric field;

[0059] S6. The composite execution module receives and executes the cooperative control command to perform closed-loop adjustment of the zinc-rich water flow rate, infrared spectral bandwidth and local micro-electric field gradient.

[0060] A multi-sensor-based zinc-rich water flow sprinkler irrigation control system includes:

[0061] The multidimensional state sensing module is used to acquire spectral bandwidth distribution, local micro-electric field gradient, mycelial network fractal dimension and cell membrane Zeta potential relaxation rate in real time, and generate real-time state vectors based on the acquired parameters.

[0062] The nonlinear resonance risk assessment module is used to receive real-time state vectors and calculate the resonance mismatch index.

[0063] The multi-objective collaborative control decision module is used to compare the resonance mismatch index with a preset threshold to generate a comparison result, and generate collaborative control commands based on the comparison result;

[0064] The composite execution module is used to receive and execute cooperative control commands to adjust the zinc-rich water flow rate, infrared spectral bandwidth, and local micro-electric field gradient.

[0065] This embodiment provides a multi-sensor-based zinc-rich water flow sprinkler irrigation control system and method, applied to the cultivation process of high-value medicinal and edible fungi; the system and method constitute a complete and self-consistent technical closed loop, aiming to solve the problem of mineral absorption efficiency drop and system oscillation caused by the failure of traditional sprinkler irrigation strategies to identify and avoid the contradiction between hydration shell collapse and cell membrane polarization resonance.

[0066] A multi-sensor-based zinc-rich water flow sprinkler irrigation control system aims to achieve stable and efficient enrichment of minerals in fruiting bodies through real-time sensing, nonlinear risk assessment, and collaborative control of multi-dimensional states during the cultivation process. In this embodiment, the system includes a multi-dimensional state sensing module, a nonlinear resonance risk assessment module, a multi-objective collaborative control decision module, and a composite execution module. These modules are connected sequentially to form a dynamic closed-loop control system. Furthermore, the system includes a system fault tolerance and safety module to handle abnormal sensor data. This module monitors in real-time whether the parameters output by the multi-dimensional state sensing module are within reasonable physical or biological ranges. If abnormalities are detected, such as prolonged zero sensor readings, numerical overflow, or exceeding preset safety thresholds, the system will immediately suspend the current dual-mode control logic and switch to a preset safety mode. For example, the sprinkler flow rate will be set to a sustained low flow rate, and the infrared and micro-electric field modules will be turned off, while simultaneously issuing an alarm to the operator. This ensures that the system's behavior is predictable and safe in the event of unexpected situations, enhancing the robustness of the entire control system.

[0067] Correspondingly, a multi-sensor-based zinc-rich water flow sprinkler irrigation control method, executed by the aforementioned system, comprises the following steps that constitute the core logic of the entire control process:

[0068] S1. Real-time acquisition of spectral bandwidth distribution, local micro-electric field gradient, mycelial network fractal dimension and cell membrane Zeta potential relaxation rate through multi-dimensional state sensing module.

[0069] The multidimensional state perception module refers to an integrated data acquisition unit whose function is to acquire key parameters in multiple dimensions that affect the efficiency of mineral transmembrane transport in real time, providing a data basis for subsequent risk assessment and decision-making. In this embodiment, the module continuously acquires the physical and biological parameters of the culture environment and mycelium.

[0070] After S1 and before S2, the system fault tolerance and safety module performs real-time verification of the collected raw data. If the sensor reading exceeds the preset safety threshold or an anomaly such as data overflow occurs, the main control process is suspended and the system enters the preset safety mode, such as setting the irrigation flow rate to a sustained low flow rate and issuing an alarm to the operator. Only when the data returns to normal will S2 and subsequent steps continue to be executed. This ensures that the vectors input to the nonlinear model are always valid and safe.

[0071] S2. Based on the spectral bandwidth distribution, local micro-electric field gradient, mycelial network fractal dimension and cell membrane Zeta potential relaxation rate, a real-time state vector is generated.

[0072] In this embodiment, the multi-dimensional state perception module integrates the collected raw data into a structured real-time state vector; this vector is a set of multiple data points that comprehensively describes the key state of the system at any given time, and its function is to serve as the standard input for the next module.

[0073] S3. Input the real-time state vector into the nonlinear resonance risk assessment module to calculate the resonance mismatch index;

[0074] The nonlinear resonance risk assessment module is the core computing unit of this system. Its purpose is to quantify the risk of sudden changes in mineral absorption efficiency caused by latent influencing factors. In this embodiment, the module receives real-time state vectors and uses its internally constructed nonlinear coupling model to perform calculations and output a single risk assessment index, namely the resonance mismatch index.

[0075] S4. Compare the resonance mismatch index with a preset threshold to generate a comparison result;

[0076] The multi-objective collaborative control decision module, as the core of the system's decision-making, aims to dynamically switch control strategies and generate optimal execution instructions based on risk assessment results. In this embodiment, it compares the resonance mismatch index output by the nonlinear resonance risk assessment module with a preset threshold, which is a critical value that distinguishes between efficient and inefficient mineral absorption states.

[0077] S5. Generate coordinated control commands for sprinkler flow rate, infrared spectrum, and micro-electric field;

[0078] The multi-objective collaborative control decision module generates a set of collaborative control instructions, which include target set values ​​for irrigation flow rate, infrared spectral parameters, and micro-electric field parameters.

[0079] S6. The composite execution module receives and executes the cooperative control command to perform closed-loop adjustment of the zinc-rich water flow rate, infrared spectral bandwidth and local micro-electric field gradient.

[0080] The composite execution module is the physical execution terminal of the decision command, and its purpose is to precisely regulate various physical parameters of the culture environment. In this embodiment, the module receives and executes the collaborative control command. After the regulation is executed, the state of the culture environment and mycelium will change immediately. This change will be captured by the multi-dimensional state perception module to form a new round of real-time state vector, thereby entering the next control cycle and realizing the dynamic, real-time closed-loop control of the entire system.

[0081] This invention provides a system and method for significantly improving the efficiency and stability of mineral enrichment in the industrialized and standardized cultivation environment of high-value functional agricultural products. Under the premise of stable control of macroscopic environmental parameters such as temperature, humidity, and pH required for traditional cultivation, the system and method upgrade the control logic from traditional concentration-absorption linear feedback control based on macroscopic environmental parameters to nonlinear prediction and proactive avoidance control based on deep biophysical effects of environment-state-response. It can proactively identify and quantify the risk of a sharp drop in mineral absorption efficiency caused by two latent factors: infrared spectroscopy and micro-electric fields. This solves the problems of unstable mineral content in finished products, resource waste, and control system oscillations in the prior art, significantly improving the accuracy and efficiency of industrialized cultivation of high-value functional agricultural products.

[0082] Example 2:

[0083] S1 specifically includes:

[0084] The spectral bandwidth distribution was obtained using a Fourier transform infrared spectrometer.

[0085] The local micro-electric field gradient was calculated using a microelectrode array implanted inside the culture medium.

[0086] A hyperspectral camera was used, combined with a box-counting dimension method image processing algorithm, to calculate the fractal dimension of the mycelial network;

[0087] The relaxation rate of the cell membrane zeta potential was measured using an online electro-acoustic spectrometer.

[0088] This embodiment further defines the specific implementation of step S1 in embodiment 1; S1 specifically includes the coordinated operation of the following sensors and units, the purpose of which is to provide accurate and reliable multi-dimensional input data for the subsequent nonlinear model;

[0089] The spectral bandwidth distribution is obtained using a Fourier transform infrared spectrometer; in this embodiment, the spectral bandwidth distribution is denoted as... It is a quantitative description of the spectral characteristics of infrared light used for assisted culture; it is measured in real time using a Fourier transform infrared spectrometer, and its output is a data pair { , },in The peak wavelength is derived from real-time measurements taken by the spectrometer. The full width at half maximum (FWHM) is derived from the real-time measurements of the spectrometer.

[0090] A microelectrode array implanted inside the culture medium is used to calculate the local micro-electric field gradient; in this embodiment, the local micro-electric field gradient is denoted as... V / m² is the rate of change of electric field intensity in the microenvironment at the front of mycelial growth. It is measured by an array of microelectrodes implanted inside a mycelial stick or culture medium. It is calculated by monitoring the potential difference between adjacent microelectrodes in real time and based on the known spacing between the electrodes.

[0091] A hyperspectral camera is used, combined with a box-counting dimension image processing algorithm, to calculate the fractal dimension of the mycelial network; in this embodiment, the fractal dimension of the mycelial network is denoted as . , is a dimensionless parameter characterizing the complexity of hyphal growth morphology and surface coverage; it is calculated by the back-end image processing unit using the box-counting dimension algorithm by periodically capturing images of hyphal growth with a hyperspectral camera.

[0092] The zeta potential relaxation rate of the cell membrane was measured using an online electro-acoustic spectrometer; in this embodiment, the zeta potential relaxation rate of the cell membrane is denoted as . Electroacoustic charge (ECC) is a core biophysical parameter characterizing the dynamic response of the surface charge on the cell membrane of mycelia. It is measured in real-time by sending a small sample of mycelia into an online electroacoustic spectrometer via a microfluidic sampling system connected to the culture system. The unit is [unit missing]. Or Hz.

[0093] Example 3:

[0094] The nonlinear resonance risk assessment module is based on a nonlinear coupling model for calculations;

[0095] The mechanism of the nonlinear coupling model is as follows: the realization of efficient transmembrane transport of mineral ions depends on the resonance matching between the collapse rate of the ion hydration shell driven by the external environment and the inherent polarization response rate of the hyphal cell membrane.

[0096] This embodiment further elucidates the core mechanism upon which the nonlinear resonance risk assessment module of Embodiment 1 is based;

[0097] The nonlinear resonance risk assessment module is based on a nonlinear coupling model. This model is not built on simple empirical fitting, but rather on a deep understanding of deep biophysical processes.

[0098] The mechanism of the nonlinear coupling model is as follows: the realization of efficient transmembrane transport of mineral ions depends on the resonance matching between the collapse rate of the ion hydration shell driven by the external environment and the inherent polarization response rate of the hyphal cell membrane.

[0099] Specifically, in aqueous solutions, mineral ions such as zinc exist as hydrated ions, which are surrounded by a layer of tightly packed water molecules. For ions to pass through the cell membrane and be absorbed, they need to shed this hydrated shell. This invention has found that specific infrared radiation and micro-electric field environments can work synergistically to accelerate this dehydration process. This dehydration rate driven by the external environment is defined as the environment-driven equivalent hydrated shell collapse rate. At the same time, the cell membrane of hyphae has specific bioelectric response characteristics. Under external electric field disturbance, the polarization state of its surface charge relaxes at a characteristic rate, which is the inherent polarization response rate of the hyphal cell membrane.

[0100] When these two rates—one determined by the external environment and the other by the organism itself—are close to or equal, the system resonates, resulting in the highest efficiency of energy and matter exchange, and mineral ions can be transported smoothly across the membrane. Conversely, when the two rates deviate significantly, a mismatch occurs, which not only greatly reduces absorption efficiency but may also trigger the cell's active efflux mechanism, leading to mineral loss.

[0101] Example 4:

[0102] Resonance Mismatch Index The calculation steps include:

[0103] Determine the environment-driven equivalent hydration shell collapse rate ;

[0104] Acquiring the relaxation rate of the cell membrane zeta potential ;

[0105] Obtaining the fractal dimension of the mycelial network ;

[0106] Based on dynamically updated historical maximum fractal dimension With the historical minimum fractal dimension , combined Construct normalized growth stage regulatory factors;

[0107] According to the formula Combined with growth stage regulators, and Calculate the resonance mismatch index ;

[0108] in, Adjust the weights for the preset fractal dimension. To prevent extremely small positive numbers with a denominator of zero;

[0109] Environment-driven equivalent hydration shell collapse rate The calculation steps include:

[0110] Based on spectral bandwidth distribution The equivalent collapse rate function driven by infrared spectroscopy was determined using a Gaussian function. ;

[0111] Based on local micro electric field gradient Determine the equivalent collapse rate function driven by the micro-electric field. ;

[0112] According to the formula Combined with preset contribution weighting coefficients and ,calculate ;

[0113] This embodiment elaborates on the specific mathematical implementation of the nonlinear coupling model in Embodiment 3, and defines the resonance mismatch index in detail. and its core variables The calculation steps;

[0114] Resonance Mismatch Index The calculation steps are as follows, the purpose of which is to quantify the complex biophysical resonance mismatch risk into a single, clear dimensionless indicator.

[0115] To dynamically assess the risk of resonance mismatch, this embodiment introduces a resonance mismatch index. The calculation method is as follows:

[0116]

[0117] in:

[0118] : Resonance mismatch index, a dimensionless pure number, calculated by this module; this index integrates the degree of mismatch between mycelial growth stage and core rate. The larger the value, the higher the risk of resonance mismatch and the lower the mineral absorption efficiency.

[0119] : Cell membrane zeta potential relaxation rate, in units of The data is obtained directly from the online electro-acoustic spectrometer in the multi-dimensional state perception module.

[0120] Environment-driven equivalent hydration shell collapse rate, in units of Its source is calculated by this module according to the following steps;

[0121] The fractal dimension of the hyphal network is a dimensionless pure number, which is obtained in real time from the hyphal morphology characterization unit in the multidimensional state perception module.

[0122] , The historical maximum and minimum fractal dimensions are dynamically updated, dimensionless pure numbers, derived from records and updates in system memory; this item It is a normalized growth stage regulator; its technical significance lies in the fact that the more mature and complex the mycelial growth, the larger its total absorption surface area, and the more severe the overall loss caused by any resonance mismatch. Therefore, this factor needs to be given a higher weight.

[0123] To ensure the feasibility of the program in the initial stage, at the beginning of each new culture cycle, it is necessary to... and Initialization is performed. The initial values ​​are based on extensive preliminary culture observation data of the strains used; for example, for a specific variety of *Tremella fuciformis*, the fractal dimension of the mycelium when it first forms a visible network on the culture medium surface is approximately 1.1. It can be initialized to 1.1; to ensure the stability of the normalization factor in the initial stage, It can be initialized to a value that ensures sufficient dynamic range, such as 1.2;

[0124] To ensure the robustness of the model, and The dynamic update logic is as follows: In each calculation cycle, the system updates the current... With memory recorded and Compare. If Then update ;like Then update Furthermore, to prevent the denominator from being zero, a protective judgment is added when calculating the growth stage regulation factor: if... Less than a very small threshold (e.g.) If the growth rate is 1.0, the adjustment factor is set to a preset stable value (e.g., 1.0) to reflect that the growth has entered the mature and stable period.

[0125] The fractal dimension adjusts the weights, which are dimensionless and are obtained through the offline experimental calibration process described below.

[0126] ϵ is set to prevent the denominator from being zero, and is related to... Minimal positive numbers with the same dimensions, for example This ensures that the model can output finite values ​​that conform to physical meaning under any input, thus guaranteeing the numerical stability of the algorithm;

[0127] To ensure the robustness of the model, and to prevent... A calculation error occurred in a physics scenario where the physics value is zero or close to zero; a very small positive number was introduced into the denominator of the formula. This parameter is much smaller than the conventional one. The value of is solely for the purpose of preventing the denominator from being zero, ensuring that the model can output a physically meaningful and finite mismatch exponent under any input, thereby guaranteeing the numerical stability of the control algorithm.

[0128] Environment-driven equivalent hydration shell collapse rate The calculation steps are as follows, the purpose of which is to quantify the influence of different physical fields on the same microscopic physical process into a rate with the same physical dimensions.

[0129]

[0130] The linear weighted sum model here is constructed on the assumption that the two physical field effects are independent under a first-order approximation. This simplification aims to ensure the simplicity, robustness, and ease of parameter calibration of the model. It should be noted that this linear model is constructed based on experimental observations that the two physical field effects exhibit weak coupling within a specific process window, which significantly improves engineering feasibility while ensuring control accuracy. Although higher-order physical models can introduce cross-action terms to describe potential nonlinear coupling, within the operating range set by this invention, this linear model has been verified by experimental data to provide sufficient prediction accuracy, thereby achieving stable and efficient closed-loop control, achieving the best balance between engineering practicality and physical fidelity.

[0131] in: , Contribution weight coefficient, dimensionless, its source is related to The results were identical and obtained through the offline experimental calibration process described below;

[0132] The equivalent collapse rate function driven by infrared spectroscopy represents the rate at which a specific spectral distribution affects water molecule vibrations and, consequently, the stability of the hydrated shell. Its response is strongest at a certain optimal wavelength and weakens as it deviates from this wavelength; therefore, a Gaussian function is used for modeling.

[0133]

[0134] The output dimension of this function is Among them, the peak response vibration frequency and optimal resonance wavelength All model parameters were obtained through offline experimental calibration; response bandwidth Then, the full width at half maximum (FWHM) of the spectrum is measured in real time by the multi-dimensional state sensing module. Directly related, their relationship can be expressed through physical formulas. Online calculations show that this allows the model to respond not only to changes in spectral peaks but also to changes in bandwidth in real time.

[0135] The equivalent collapse rate function driven by the micro-electric field is physically defined as the rate of directional traction and perturbation effect of the electric field gradient on charged ions and their hydrated shells. Experiments show that within the working electric field gradient range set by this system, the perturbation effect exhibits a good linear positive correlation with the magnitude of the electric field gradient. Therefore, a linear model can be used to simplify the calculation, which can accurately characterize the dose-effect relationship of the system within the preset working range while ensuring computational efficiency.

[0136]

[0137] The output dimension of this function is ;

[0138] To ensure the rigor of the physical formulas, The unit is V / m²; to ensure The output dimension is Electric field influence coefficient The unit must be m²· · The coefficient Similarly, the calibration was obtained through offline experiments.

[0139] To further clarify the source of the undetermined parameters in the model, the offline calibration process is explained in detail; parameter group { All datasets were calibrated using data obtained from multiple prior orthogonal experiments. To ensure the independence of variable domains, the variables used to construct the calibration dataset were explicitly defined during the calibration process. Specifically, this was achieved through conducting... Independent culture experiments were conducted in the first group. Group( In the experiment, constant infrared spectral parameters were set. and micro electric field gradient Throughout the entire cultivation cycle, the time series of process data was recorded, and the zinc content of the final product, dried white fungus blocks, was measured and recorded as follows: ;

[0140] Thus, a complete calibration dataset containing input, process, and output was constructed; the inherent logic of the calibration process lies in adjusting the parameter set { using numerical optimization algorithms such as nonlinear least squares method}. The objective function can be defined as follows: To minimize the error between the model's predictions and the actual measurements, the value of} is determined.

[0141] ,

[0142] in It is a function that maps the final zinc content to a domain comparable to the model's cumulative mismatch; based on the biophysical assumption that the final zinc enrichment effect is inversely proportional to the cumulative resonance mismatch, a mapping function is constructed. The specific form is .in, It is a dimensional fitting constant whose function is to unify the dimensions on both sides of the objective function and adjust the numerical scale. It is related to the parameter set. It is optimized and determined during the calibration process.

[0143] Through this calibration process, a set of calibration methods can be obtained that enables... The optimal parameter value that has the strongest negative correlation with the final zinc content.

[0144] Example 5:

[0145] In S5, the logic for generating cooperative control instructions is as follows:

[0146] If the comparison result shows that the resonance mismatch index is less than the preset threshold, then the command in the normal control mode is generated;

[0147] If the comparison results show that the resonance mismatch index is greater than or equal to the preset threshold, then an instruction in risk avoidance mode is generated.

[0148] In the conventional control mode, the coordinated control command is used to make the irrigation flow rate positively correlated with the mycelial growth rate;

[0149] In risk-averse mode, the command is used to trigger the following actions:

[0150] Implement sprinkler suppression, setting the sprinkler flow rate to zero or maintaining a low flow rate;

[0151] The resonance mismatch index is set as the objective function;

[0152] A fast optimization algorithm is initiated to find the optimal combination of infrared spectral parameters and micro-electric field parameters that minimizes the objective function;

[0153] Generate coordinated control instructions containing the optimal parameter combinations.

[0154] This embodiment is a detailed explanation of the collaborative control instruction generation logic in step S5 of embodiment 1, and describes in detail how the system adopts drastically different control strategies under different risk levels;

[0155] The multi-objective collaborative control decision module calculates in real time... Value and preset threshold Based on the comparison results, a dual-mode control strategy is implemented; a preset threshold is used. The setup logic is as follows: by analyzing the experimental data of each group in the calibration dataset... Time series and zinc content of final product Statistical correlation analysis was conducted, and samples significantly correlated with the inflection point of the zinc content decline trend were selected. Value is the baseline value Then, fine-tune the settings according to the preset system control sensitivity requirements: for systems requiring high sensitivity and early risk avoidance, the settings can be adjusted. ,in The sensitivity coefficient (e.g., 0.1) ensures that the system activates avoidance mode when the risk index reaches 90% of the inflection point. Conversely, the threshold can be appropriately increased; for example, if the baseline value is 0.22, setting a 10% sensitivity lead time will ultimately determine... ;

[0156] If the comparison result shows that the resonance mismatch index is less than the preset threshold, then the command in the normal control mode is generated;

[0157] In this mode, the system is determined to be in the high-efficiency absorption zone, and the main control objective is to meet the nutrients and water required for normal mycelial growth.

[0158] In conventional control mode, the coordinated control command is used to make the irrigation flow rate positively correlated with the mycelial growth rate; specifically, this is achieved by adjusting the irrigation flow rate of the variable frequency irrigation unit. It is set to be proportional to the rate of change of the fractal dimension of the hyphal network:

[0159]

[0160] in, It is a growth-flow conversion coefficient, whose physical meaning is 'the volume of zinc-rich water required for a unit fractal dimension increase', and the unit is usually L or This coefficient is derived from the principle of matter-energy balance, comprehensively considering factors such as the water content of the culture medium, evaporation rate, mycelial water content, and zinc ion concentration in zinc-rich water. It was calibrated using data from multiple previous batches of culture experiments to ensure that the sprinkler flow rate accurately matches the actual needs of mycelial growth. In this mode, the system will also tentatively adjust the infrared spectral parameters with a small, preset perturbation. and micro electric field gradient and continuously monitor The changes, in order to explore whether there is a way to make A lower optimal operating point;

[0161] If the comparison results show that the resonance mismatch index is greater than or equal to the preset threshold ( If the risk is avoided, then instructions in the risk-avoidance mode will be generated.

[0162] In this mode, the system determines that resonance mismatch has occurred or is about to occur, and mineral absorption efficiency faces the risk of a sharp drop; the control objective immediately switches from promoting growth to minimizing it. In order to proactively avoid losses;

[0163] In risk-averse mode, commands trigger a series of actions, including: Instructions in this mode trigger sprinkler suppression, reducing sprinkler flow. Setting the flow rate to zero or a maintenance low flow rate sufficient only to keep the mycelia alive aims to directly prevent resource waste when mineral absorption channels are blocked; the system sets the resonance mismatch index as the objective function, i.e. Furthermore, a fast optimization algorithm with low computational overhead and high convergence speed, such as particle swarm optimization (PSO) or simulated annealing, is initiated to determine the peak wavelength of the infrared light source. With half-peak full width and micro electric field gradient As an adjustable independent variable, the search is conducted within a preset safety and physical feasibility range to find the optimal combination of parameters that minimizes the objective function. The search space for the independent variables in the optimization process is pre-defined; infrared spectral parameters. The search range is determined by the performance parameters of the infrared light source used; micro-electric field gradient The search range is set based on the upper limit of the safe electric field strength that will not damage the hyphae and the lower limit that can produce an effective biological effect, as determined by previous experiments. Finally, a cooperative control command containing this optimal parameter combination is generated and sent to the composite execution module. After the composite execution module executes the command, the system does not lock onto this parameter combination, but continues to minimize it within each control cycle. To achieve the target, the infrared spectrum and micro-electric field parameters are dynamically optimized and adjusted in real time; until... The value steadily falls back to the preset threshold. After a preset stabilization period, the control logic automatically switches back to the normal control mode. This continuous optimization strategy ensures that the system can respond to changes in mycelial state as quickly as possible and rapidly move away from high-risk areas.

[0164] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention; any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0165] It should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-sensor based zinc rich water flow sprinkler control method, characterized by, Be applied to the fungus culture process, it includes the following steps: S1, through the multi-dimensional state perception module, real-time acquisition spectrum bandwidth distribution, local micro electric field gradient, mycelium network fractal dimension and cell membrane Zeta potential relaxation rate; S2, based on the spectrum bandwidth distribution, the local micro electric field gradient, the mycelium network fractal dimension and the cell membrane Zeta potential relaxation rate, generate real-time state vector; S3, inputting the real-time state vector into a nonlinear resonance risk assessment module to calculate a resonance mismatch index; the resonance mismatch index comprises: Determining environment-driven equivalent hydration shell collapse rates ; acquiring the cell membrane zeta potential relaxation rate ; obtaining the fractal dimension of the mycelial network ; Dynamic update-based historical maximum fractal dimension Dynamic update-based historical minimum fractal dimension , combining the , to construct a normalized growth stage adjustment factor According to the formula , in combination with the growth phase adjustment factor, the and the , the resonance mismatch index is calculated; wherein, is a preset fractal dimension adjustment weight, is a small normal number to prevent the denominator from being zero; S4, the resonance mismatch index is compared with the preset threshold, and the comparison result is generated; S5, based on the comparison result, generate the coordinated control instruction of the spray irrigation flow, infrared spectrum and micro electric field; S6, by the composite execution module receives and executes the coordinated control instruction, adjusts the zinc-rich water flow, infrared spectrum bandwidth and local micro electric field gradient.

2. The method of claim 1, wherein, The S1 specifically includes: Adopt fourier transform infrared spectrometer, obtain the spectrum bandwidth distribution; Adopt the microelectrode array implanted in the culture medium, calculate the local micro electric field gradient; Adopt hyperspectral camera, combine the box dimension method image processing algorithm, calculate the mycelium network fractal dimension; Adopt on-line electro-acoustic spectrometer, measure the cell membrane Zeta potential relaxation rate.

3. The method of claim 1, wherein, The non-linear resonance risk assessment module is calculated based on the non-linear coupling model; Wherein, the mechanism of the non-linear coupling model is: the realization of mineral ion high-efficiency transmembrane transport depends on the resonance matching of the ion hydration shell collapse rate driven by the external environment and the inherent polarization response rate of the mycelium cell membrane.

4. The method of claim 1, wherein, The environment-driven equivalent hydration shell collapse rate The calculation step comprises: based on the spectral bandwidth distribution , determining an infrared spectrum driven equivalent collapse rate function with a Gaussian function ; based on the local microelectric field gradient , determining an equivalent collapse rate function of the microelectric field driven ; According to the formula , in combination with the preset contribution weight coefficient and , the is calculated.

5. The method of claim 1, wherein, In the S5, the generation logic of the coordinated control instruction is: If the comparison result shows that the resonance mismatch index is less than the preset threshold, the instruction under the normal control mode is generated; If the comparison result shows that the resonance mismatch index is greater than or equal to the preset threshold, the instruction under the risk avoidance mode is generated.

6. The method of claim 5, wherein, Under the normal control mode, the coordinated control instruction is used to make the spray irrigation flow positively correlated with the mycelium growth rate.

7. The method of claim 5, wherein, The instruction under the risk avoidance mode is used to trigger the following actions: Perform spray irrigation inhibition, set the spray irrigation flow to zero or maintain low flow; Set the resonance mismatch index as the objective function; Start the fast optimization algorithm to solve the optimal infrared spectrum parameter and micro electric field parameter combination that can minimize the objective function; Generate the coordinated control instruction containing the optimal parameter combination.

8. A multi-sensor based zinc-rich water flow sprinkling control system applied to the multi-sensor based zinc-rich water flow sprinkling control method of any one of claims 1-7, characterized in that, Including: Multi-dimensional state perception module, for real-time acquisition spectrum bandwidth distribution, local micro electric field gradient, mycelium network fractal dimension and cell membrane Zeta potential relaxation rate, and generate real-time state vector based on the acquisition parameters; a non-linear resonance risk assessment module for receiving the real-time state vector and calculating a resonance mismatch index; the resonance mismatch index comprises the steps of: Determining environment-driven equivalent hydrate shell collapse rates ; acquiring the cell membrane zeta potential relaxation rate ; obtaining the fractal dimension of the mycelial network ; Dynamic update-based historical maximum fractal dimension and historical minimum fractal dimension , in combination with the , to construct a normalized growth stage adjustment factor According to the formula , in combination with the growth phase adjustment factor, the and the , the resonance mismatch index is calculated; wherein, is a preset fractal dimension adjustment weight, is a very small normal number to prevent the denominator from being zero; Multi-objective coordinated control decision module, for comparing the resonance mismatch index with the preset threshold to generate the comparison result, and generating the coordinated control instruction based on the comparison result; Composite execution module, for receiving and executing the coordinated control instruction, adjusting zinc-rich water flow, infrared spectrum bandwidth and local micro electric field gradient.

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