A tobacco seedling raising whole-process intelligent management method and system

Abstract

The application is a kind of tobacco seedling whole process intelligent management method and system, relating to the field of tobacco seedling intelligent management and control technology, comprising: seedling environment parameter high-frequency sensing module; root zone substrate space-time grid discrete module; solute transport differential solution algorithm module; substrate osmotic pressure threshold constraint module; water and fertilizer ratio hard switching scheduling module. In the application, matrix operation mode deduces the distribution and change of inorganic salt ions in the substrate. The whole process organically combines environmental perception, spatial modeling and solute calculation, can continuously grasp the physicochemical state and solute transport trend of root zone substrate, the operation process has good adaptability and stability, can provide solid model and data support for subsequent control actions, and improve the fine level of seedling root zone environment state analysis.

Description

Technical Field

[0001] This invention relates to the field of intelligent management and control technology for tobacco seedling cultivation, and in particular to an intelligent management method and system for the entire process of tobacco seedling cultivation. Background Technology

[0002] Intensive and industrialized seedling production is a common method used in tobacco cultivation. With the development of intelligent agricultural technology, various environmental parameter monitoring devices have been gradually applied to the tobacco seedling production process, enabling the collection of basic physical signals such as substrate temperature, conductivity, and weight.

[0003] Currently, most monitoring solutions primarily output raw sensor data. The application of sophisticated processing methods, such as coupling error correction of multi-dimensional analog signals and standardized processing of time-series data, is relatively limited in seedling cultivation scenarios.

[0004] In the field of solute transport analysis in agricultural substrates, numerical calculation methods such as spatial grid discretization and finite difference have been studied and applied, and can be used to extrapolate the distribution and changing trends of water and fertilizer solutes within the medium.

[0005] For the root zone environment of tobacco seedlings, most existing technologies use basic computational logic to conduct concentration analysis independently, and rarely integrate grid division, adaptive adjustment of computational step size stability and matrix solving algorithms to complete the dynamic calculation of inorganic salt ion concentration in an integrated manner. There is still room for optimization and improvement in this technology.

[0006] At present, the regulation of water and fertilizer in tobacco seedling cultivation has gradually relied on the physical and chemical indicators of the substrate to achieve coordinated management, and can make corresponding adjustments to the water and fertilizer supply strategy based on the substrate salinity and moisture status.

[0007] Existing control schemes mostly adopt conventional on / off control modes. However, the combination of osmotic pressure and matrix potential to form total water potential as the basis for risk assessment, along with a complete set of control logics such as soft shutdown of liquid supply, quantitative rinsing with clean water, and slow-release return of concentration, is rarely used in intelligent tobacco seedling systems. Such schemes can further improve the precision and smoothness of water and fertilizer management during the seedling stage. Summary of the Invention

[0008] The purpose of this invention is to provide an intelligent management method and system for the entire process of tobacco seedling cultivation in order to solve the above-mentioned problems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] An intelligent management system for the entire tobacco seedling cultivation process, comprising:

[0011] The high-frequency sensing module for seedling environment parameters is used to acquire multi-dimensional physical quantity simulation signals and convert them into digital sequences with unified hardware timestamps, which serve as initial physical field data and dynamic boundary conditions.

[0012] The root zone matrix spatiotemporal grid discretization module is used to spatially discretize the physical seedling space to establish a one-dimensional addressable data array, set the computation time step and introduce numerical stability constraint conditions algorithm logic, fill the discrete nodes of the one-dimensional addressable data array with the initial physical attribute values ​​of multi-dimensional structures, and encapsulate the digital sequence as upper and lower boundary physical constraint parameters.

[0013] The solute transport differential solution module is used to receive a one-dimensional addressable data array and upper and lower boundary physical constraint parameters, calculate the hydrodynamic dispersion coefficient and source-sink term boundary data, construct a sparse tridiagonal matrix system, and solve for the absolute concentration values ​​of inorganic salt ions at each virtual space node in the future system time step.

[0014] The matrix osmotic pressure threshold constraint module is used to receive the absolute concentration values ​​of inorganic salt ions and convert them into osmotic pressure data, calculate matrix potential data, and superimpose the two to generate the total water potential resistance barrier data item. It extracts the limiting water potential short plate data index and injects an interrupt request level signal when the limiting water potential short plate data index falls below the dynamic safe water potential critical threshold constant.

[0015] The water-fertilizer ratio hard switching scheduling module is used to respond to the interruption request level signal to block the injection of mother liquor, calculate the absolute digital quantity of the total volume of clear water rinsing, execute micro-pulse frequency division drive physical execution, and enter the concentration slow release regression protection transition state after the osmotic pressure index falls back to within the safety constant to adjust the subsequent water-fertilizer mother liquor mixing injection ratio.

[0016] Preferably, the step of acquiring multidimensional physical quantity analog signals and converting them into digital sequences with unified hardware timestamps specifically includes:

[0017] The multidimensional physical quantity simulation signal includes the instantaneous value of uncompensated apparent conductivity and the real-time absolute Celsius temperature of the solution;

[0018] The microprocessor extracts the instantaneous value of the uncompensated apparent conductivity measured at the current actual temperature and simultaneously reads the real-time absolute Celsius temperature of the solution obtained after discrete solution;

[0019] The difference between the real-time absolute Celsius temperature of the solution and the standard reference temperature constant is calculated by executing a subtraction instruction in the floating-point arithmetic register. The difference is multiplied by the empirical constant of the temperature compensation coefficient. The product is then added to form the compensation denominator cache data. Finally, the instantaneous value of the uncompensated apparent conductivity is used as the numerator of the division and sent to the floating-point division assembly instruction. After eliminating thermodynamic coupling errors, the real-time digital quantity of absolute conductivity is generated as a component of the digital sequence with a unified hardware timestamp.

[0020] Preferably, the multidimensional physical quantity simulation signal further includes a signal containing minute deformation changes in physical gravity; this is converted into a digital sequence with a unified hardware timestamp, specifically including the use of weight time-series differential data processing logic.

[0021] Extract the total weight digital value read at the current moment and processed by low-pass digital filtering, and subtract the total weight digital value stored in memory at the previous sampling moment to obtain the weight loss difference data. In the floating-point unit, multiply the preset physical density constant of room temperature liquid water, the effective evaporation surface area constant, and the sampling time interval constant. Then divide the weight loss difference data by the multiplication result to generate the average water evaporation flux digital value within the current time step as a component of the digital sequence with a unified hardware timestamp.

[0022] Preferably, the root region matrix spatiotemporal grid discretization module introduces numerical stability constraint algorithm logic, and the specific steps include:

[0023] Extract the maximum absolute value of the water flow velocity within the current time slice, multiply it with the current calculation time step variable, divide the product by the spatial node distance step constant, and generate a dimensionless Corange ratio dynamic parameter in the floating-point arithmetic unit.

[0024] The underlying logic checker compares the dynamic parameter of the Courant number ratio with the theoretical critical threshold constant, and triggers an adaptive adjustment interrupt mechanism when the dynamic parameter of the Courant number ratio approaches the theoretical critical threshold constant, dynamically reducing the calculation time step variable to be used in the next difference cycle according to the preset decay polynomial.

[0025] Preferably, the process of constructing the sparse tridiagonal matrix system is as follows:

[0026] Parallel extraction of pore velocity data, spatial physical distance step constant, volumetric water content discrete state data, global computation time step, and calculation of hydrodynamic dispersion coefficient and source-sink term boundary data stored in the cache. After multiplication and accumulation operations within the arithmetic logic unit, the lower diagonal coefficient, main diagonal coefficient, and upper diagonal coefficient are generated. The known old concentration cache value at the current time is merged and added with the boundary source-sink constant terms to form a set of known constant terms. A sparse tridiagonal matrix system is assembled in the static random access memory heap area.

[0027] The equation for the fully implicit finite difference scheme is:

[0028] ;

[0029] Among them, subscript Indicates the internal spatial node index variable, superscript Indicates the next system computation time step, parameters This indicates that the target absolute concentration data for the currently addressed node at future time points is to be solved, and the parameters are... This represents the future concentration data of the neighboring node located above it in memory address, parameter This represents the future concentration data of the adjacent node located below it in memory address, parameter Represents the lower diagonal coefficient, parameter Represents the coefficients of the main diagonal, parameters Represents the upper diagonal coefficient, parameter This represents the set of known constants.

[0030] Preferably, the step of receiving the absolute concentration values ​​of inorganic salt ions and converting them into osmotic pressure data specifically includes:

[0031] The system executes high-speed memory access instructions to retrieve the numerical values ​​of absolute solute concentrations at spatial nodes, calculated iteratively at the latest computation time step, from the L1 cache in batches. It then reads constant parameters from the non-volatile data configuration area using addressing instructions. The hardware floating-point multiplier is then scheduled to sequentially load the floating-point variables and constants into registers for multiplication. Finally, the system calls the discretized and adapted osmotic pressure conversion formula.

[0032] ;

[0033] Among them, parameters Represents macroscopic physical pressure data, parameters This represents the empirical coefficient constant data of the overall degree of dissociation of the solution, and the parameters are... Represents ideal gas constant data, parameters This represents the real-time absolute temperature data of the root zone matrix, parameters. This represents the numerical value of the absolute concentration of solute at a spatial node.

[0034] The matrix potential data is calculated, and the two are superimposed to generate the total potential resistance barrier data item, which specifically includes:

[0035] Real-time discrete values ​​of volumetric water content are extracted using a base-address plus indexed addressing method. An instruction is executed by calling the base-exponential function from the standard hardware mathematical function library. The matrix potential data is then calculated using the inverse function of the water characteristic equation, as shown in the formula: ;

[0036] Among them, parameters This represents negative matrix potential numerical data, parameters Represents the discrete numerical value of real-time volumetric water content, parameters Represents the physical constant of saturated water content, parameter Represents the residual moisture constant, parameter This represents empirical parameters representing the morphology of porous media. With parameters All represent shape parameters;

[0037] After calculating the negative matrix potential data, an addition instruction is executed in the loop program that traverses the effective root system nodes to perform algebraic superposition and merging of the absolute values ​​of macroscopic physical pressure data and negative matrix potential data to generate the total water potential resistance barrier data item.

[0038] Preferably, the water-fertilizer ratio hard switching scheduling module responds to the interrupt request level signal to block the injection of mother liquor, specifically including executing soft-stop drive electromechanical control logic based on a negative exponential decay function:

[0039] The initial steady-state drive analog voltage value being received by the fertilizer metering pump driver at the moment the interrupt is triggered is extracted. A dedicated high-speed countdown hardware timer is started, and the absolute time accumulated variable since the interrupt occurs is retrieved and multiplied with the empirical constant of the hydraulic damping buffer attenuation coefficient by an instruction. The product is negative and then the natural exponent is calculated by calling the dedicated assembly instruction for solving the natural exponent. The result is then multiplied a second time with the initial steady-state drive analog voltage value by an instruction. The final product result is continuously written into the data holding output register of the digital-to-analog converter.

[0040] Preferably, the specific steps for calculating the absolute value of the total volume of the clean water rinse include:

[0041] The concentration constant data that meets the physiological safety target of the developmental stage is extracted and pushed into the working register as the common denominator of the division instruction. The high concentration absolute value variables from the outermost physical node to the node where the extreme water potential disaster warning occurs are extracted as the numerator and the division operation is performed one by one. The logarithm core library function instruction is called to obtain the logarithm value. The generated logarithm value is multiplied by the real-time volume water content discrete value variable of the corresponding node and the thickness step size constant variable of the spatial virtual node. The product results are accumulated and summed using a high bit width data accumulator. Finally, the sum data item is multiplied by the physical surface area floating point constant parameter and the rinsing efficiency empirical correction coefficient constant parameter.

[0042] Preferably, the step of entering the concentration-slow-release regression protection transition state to adjust the subsequent water-fertilizer mother liquor mixing and injection ratio specifically includes performing nonlinear target concentration control:

[0043] Calculate the difference between the cumulative increase of the regular drip irrigation cycle counter value since the crisis was resolved and the constant of the number of times the midpoint was offset. Multiply the difference by the slope control constant and take the negative sign. Calculate the natural exponent of the negative variable and add the real number one to the result as the common denominator.

[0044] Simultaneously, the difference between the standard high target concentration constant and the basic concentration safety constant is used as the numerator to perform floating-point division. Finally, the quotient of the division is added to the basic concentration safety constant to generate nonlinear target concentration control data, and the pulse width modulation parameters of the fertilizer injection variable frequency metering pump motor are dynamically adjusted.

[0045] A method for intelligent management of the entire tobacco seedling cultivation process includes:

[0046] The high-frequency sensing module for seedling environment parameters collects multi-dimensional physical quantity simulation signals and converts them into digital sequences with unified hardware timestamps, which serve as basic field data and boundary conditions.

[0047] Based on the root zone matrix spatiotemporal grid discretization module, the seedling space is discretized by grid and the time step is set. Combined with numerical stability constraints, the parameters are dynamically adjusted to complete the assignment of node attributes and the encapsulation of boundary parameters.

[0048] The solute transport differential solution module combines grid data and boundary parameters to construct a sparse tridiagonal matrix and solve for the inorganic salt ion concentration at each node.

[0049] Using the matrix osmotic pressure threshold constraint module, the ion concentration is converted into osmotic pressure and matrix potential, and the total water potential data is synthesized. Once the index falls below the safety threshold, an interruption signal is issued.

[0050] The water-fertilizer ratio hard switching scheduling module responded to the interruption, shut down the mother liquor injection, calculated the volume of clean water rinsing, and performed the rinsing operation. After the indicators returned to the safe range, the water-fertilizer ratio was gradually restored through nonlinear control to achieve a smooth transition.

[0051] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0052] 1. This invention uses matrix operations to deduce the distribution and changes of inorganic salt ions within the matrix. The entire process organically combines environmental perception, spatial modeling, and solute calculation, enabling continuous monitoring of the physicochemical state and solute transport trends of the root zone matrix. The calculation process exhibits good adaptability and stability, providing solid model and data support for subsequent control actions and improving the precision of root zone environmental state analysis in seedling cultivation.

[0053] 2. This invention integrates osmotic pressure and matrix potential to form a comprehensive water potential evaluation dimension, which serves as the basis for environmental risk identification. It can promptly capture abnormal states of the root zone growth environment and trigger an early warning mechanism. It can dynamically adjust the water and fertilizer ratio according to the environmental recovery, gradually bringing the supply state back to a reasonable range. The entire set of control logic is smoothly connected, further improving the coordination and adaptability of seedling water and fertilizer management, and helping to maintain the root zone growth environment of tobacco seedlings in a suitable state in the long term. Attached Figure Description

[0054] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0055] Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0056] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0057] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0058] Example 1

[0059] Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0060] Appendix Figure 1 This invention provides a structural block diagram of an intelligent management system for the entire process of tobacco seedling cultivation, which shows the connection relationship between the high-frequency sensing module for seedling environmental parameters and the hard switching scheduling module for water and fertilizer ratio, and marks the main functional interaction flow of each module.

[0061] In this embodiment, it includes:

[0062] The high-frequency sensing module for seedling environment parameters is used to acquire multi-dimensional physical quantity simulation signals and convert them into digital sequences with unified hardware timestamps, which serve as initial physical field data and dynamic boundary conditions.

[0063] This module continuously and at high resolution acquires multidimensional physical quantity simulation signals related to solute transport and water evaporation, and converts these simulation signals into high-frequency data sequences with unified hardware timestamps and digital cleaning.

[0064] These structured digital sequences will bypass the central processing unit and be directly stored in the microcontroller's static random access memory via the direct memory access controller.

[0065] At the ends of the main water and fertilizer pipelines and each drip irrigation microtube, this system is equipped with a composite conductivity sensing array that includes AC excitation electrodes and high-precision negative temperature coefficient thermistors.

[0066] Electrical conductivity is a key physical parameter characterizing the total concentration of inorganic salt soluble ions in a water-fertilizer solution, and it directly determines the initial mass boundary of the solute input into the seedling substrate.

[0067] The sensing array is equipped with a waveform generator, which measures the impedance characteristics of the solution to alternating current by applying a high-frequency AC excitation voltage of a specific frequency to the solution.

[0068] The microprocessor acquires a tiny analog voltage signal representing impedance through its internal high-speed analog-to-digital converter (ADC) according to a set sampling clock cycle.

[0069] Since the activity of inorganic salt ions increases with increasing solution physical temperature, in order to obtain accurate baseline concentration characterization, the system must perform a digital temperature compensation step in the arithmetic logic unit of the microprocessor.

[0070] The specific data processing memory operation process is as follows:

[0071] The microprocessor extracts the instantaneous value of the uncompensated apparent conductivity measured at the current actual temperature through the address bus, and simultaneously reads the real-time absolute Celsius temperature of the solution obtained by synchronously acquiring data from the negative temperature coefficient thermistor and discretizing it using the Steinhardt equation.

[0072] The system extracts the standard reference temperature constant (usually set to 25 degrees Celsius) and the empirical constant of the temperature compensation coefficient of the specific water-fertilizer mother liquor, which are pre-burned into specific sectors of non-volatile flash memory.

[0073] In the processor's floating-point register, a subtraction instruction is first executed to calculate the difference between the current real-time absolute Celsius temperature and the standard reference temperature;

[0074] Then, a multiplication instruction is executed to multiply the difference by the empirical constant of the temperature compensation coefficient; then, the product result is added with the number one to form the compensation denominator buffer data.

[0075] The instantaneous value of apparent conductivity is used as the numerator of the division operation, and the underlying floating-point division assembly instruction is invoked.

[0076] After this algebraic operation, the system eliminates thermodynamic coupling errors and outputs a real-time digital value of absolute conductivity. This value is mapped to a static memory address dedicated to the input concentration variable, waiting for the main program to call it at any time.

[0077] The transport depth and diffusion range of solutes in porous seedling substrates are highly dependent on the total input volume of water in the carrier and the input pore flow rate.

[0078] This module embeds a small flow sensing device based on the Hall effect into the fluid nodes of the water and fertilizer transmission and distribution pipeline network;

[0079] When the water-fertilizer solution flows through the pipe cavity, the fluid kinetic energy drives the internal micro impeller with magnetic components to rotate, and the externally sealed Hall element senses the alternating magnetic field, thereby outputting a continuous square wave pulse electrical signal.

[0080] The microcontroller unit is equipped with a dedicated 32-bit advanced hardware timer, which configures specific general-purpose input / output pins to input capture mode to continuously detect the rising edge state transition of the square wave signal.

[0081] In a separately configured hardware timer interrupt service function, the system accumulates the number of triggered pulses into an unsigned long integer variable within a preset capture time window.

[0082] The steps for processing the cumulative volume data of a single drip irrigation event are as follows:

[0083] The central processing unit extracts the total number of accumulated Hall square wave pulses from the general-purpose registers and reads the factory calibration instrument constant of the sensor from the read-only configuration parameter area (this constant represents the number of physical pulses generated per fixed volume of liquid flowing through).

[0084] The processor executes a long integer division instruction to calculate the total volume of irrigation liquid.

[0085] Subsequently, the system retrieves the total number of individual holes in the seedling trays within the currently controlled greenhouse area, as well as the geometric parameters of the physical surface area of ​​each hole. In the arithmetic logic unit, the total volume is calculated using a continuous division method that distributes the surface area evenly.

[0086] The final calculated irrigation flux density per unit area is pushed into the transmission queue buffer of the system's main data bus. This parameter constitutes an indispensable upper boundary input for constructing a microscopic water flow downward driving dynamics model.

[0087] Inside the seedling greenhouse, the core physical processes that lead to water loss and subsequently the accumulation of inorganic salt residues in the substrate are the evaporation of water from the substrate surface and the transpiration of tobacco seedling leaves through the stomata.

[0088] To extract this dynamic water consumption flux, this module deployed a representative miniature weighing evaporation percolation physics device within the greenhouse.

[0089] The device is an independent seedling tray unit filled with the same seedling substrate and planted with standard tobacco seedlings. Its bottom is physically suspended and supported on a high-precision strain gauge weighing sensor array.

[0090] The sensor converts the minute deformation changes of physical gravity into a differential analog voltage signal at the microvolt level. This signal is then digitally processed by an external 24-bit high-resolution programmable gain amplifier and a high-precision analog-to-digital converter to generate a high-precision time series of the total weight of the substrate and the plant.

[0091] To extract the pure water loss rate from the total weight change, the microprocessor employs weight time-series differential data processing logic for adjacent time steps:

[0092] The system extracts the total weight digital value read at the current moment and processed by low-pass digital filtering, subtracts the total weight digital value stored in memory at the previous sampling moment, and executes the subtraction instruction to obtain the weight loss difference data.

[0093] The system further extracts the preset physical density constant of room temperature liquid water, the effective evaporation surface area constant of the evaporator exposed to air, and the time interval constant between two adjacent weighing samples from the memory.

[0094] The processor multiplies the physical density constant, surface area constant, and sampling time interval in the floating-point unit, and then divides the weight loss difference data by the multiplication result.

[0095] The average water evaporation flux within the current time step is then generated. This value is subsequently written to a specific memory segment as core data for the source and sink terms in subsequent calculations of the matrix water uplift dynamics.

[0096] The sensing subsystem acquired heterogeneous data from multiple sources, including conductivity, temperature, flow pulses, and minute weight changes. These raw digital signals often exhibit high-frequency white noise or large spike errors due to inconsistent response frequencies of the various sensors and radio frequency electromagnetic interference from the operation of agricultural machinery in the field.

[0097] If these raw variables that have not been cleaned are directly pushed into the partial differential difference solution matrix of the next level, it is easy to cause data divergence in iterative calculations or even cause microprocessor hardware errors.

[0098] Therefore, this module incorporates digital signal conditioning and timing alignment processes:

[0099] The system's main control chip utilizes an internal real-time hardware clock generator driven by an independent low-frequency crystal oscillator to bind a globally unified high-precision timestamp to the sampling result of each analog-to-digital conversion. The timestamp recording accuracy reaches the millisecond level, thereby solving the data timing phase deviation between different physical quantities.

[0100] For each sensor data stream, the system allocates an independent circular buffer in the dynamic random access memory and performs outlier removal and weighted moving average smoothing filtering algorithms.

[0101] Taking the filtering and cleaning of small-weight data streams as an example, the system continuously saves the data array of the ten most recent sampling points, calls the internally embedded statistical mathematics standard library, and quickly calculates the arithmetic mean and standard deviation of the local array.

[0102] The system makes logical judgments based on the Raida statistical criteria, and identifies isolated jump data that deviates from the arithmetic mean by more than three standard deviations as gross errors, and forcibly erases the corresponding array elements from the circular buffer.

[0103] For the remaining valid physical quantity data, the processor performs the weighted moving average calculation step:

[0104] Before performing the accumulation operation, the processor first forces the weight normalization verification logic to traverse the decay weight coefficients assigned to each time series node by the system, perform the accumulation operation, and verify whether the sum of all decay weight coefficients involved in the calculation is exactly equal to one, thereby ensuring that the physical authenticity of the output data is not distorted.

[0105] After confirming that normalization is successful, the processor multiplies each historical valid sample value in the window by its corresponding attenuation weight coefficient (the rule is that the more recent the data is, the larger the weight value is assigned), and sums all the products in the accumulator register.

[0106] After this time-series fusion, normalization verification, and algebraic filtering process, this module finally outputs a discrete digital state array with equal time intervals and a high signal-to-noise ratio to the computing kernel.

[0107] The root zone matrix spatiotemporal grid discretization module is connected to the high-frequency sensing module for seedling environment parameters. It is used to spatially discretize the physical seedling space to establish a one-dimensional addressable data array, set the calculation time step and introduce numerical stability constraint algorithm logic, fill the discrete nodes of the one-dimensional addressable data array with the initial physical attribute value data of the multi-dimensional structure, and encapsulate the digital sequence with a unified hardware timestamp as the upper and lower boundary physical constraint parameters.

[0108] The processes of water flow and solute ion transport within the root zone matrix of plants are characterized by continuous spatial distribution and dynamic, continuous evolution over time in the real physical world.

[0109] The central processing unit inside a digital microcontroller can only perform discrete data frame capture and arithmetic operations, and cannot directly process continuous physical fields.

[0110] This module performs precise data structuring and dimensionality reduction processing on the limited physical three-dimensional space within the seedling tray, establishes a virtual discrete spatiotemporal coordinate array system that maps physical entities, and transforms the solution domain of complex partial differential equations into a regular multidimensional data linked list in the microprocessor memory.

[0111] For the standardized multi-hole trays widely used in centralized tobacco seedling cultivation, the horizontal cross-sectional area of ​​each independent seedling hole is relatively small, and the water and fertilizer fluids are mainly dripped from the top of the hole and vertically infiltrated downwards by gravity. Therefore, the vertical solute concentration gradient inside the substrate is much greater than the weak diffusion in the horizontal direction.

[0112] To maximize the conservation of the microprocessor's precious random access memory resources and ensure the ultra-high real-time response rate of the core operation, this system removes the horizontal diffusion parameter when modeling the underlying data structure, and abstracts the three-dimensional inverted frustum-shaped seedling holes as an equivalent one-dimensional linear depth data linked list inside the processor.

[0113] When the system starts, it reads the absolute value of the total physical depth of the actual seedling tray holes set in the non-volatile configuration parameter area and establishes a unidirectional virtual spatial coordinate system along this depth direction. The system then spatially discretizes this total depth according to equally spaced geometric partitioning steps.

[0114] The specific microprocessor execution process is as follows:

[0115] The arithmetic unit divides the total physical depth value by the system's preset constant for the number of spatial grid subdivisions to obtain the fixed physical distance step size between two adjacent discrete spatial nodes. Subsequently, the microprocessor invokes memory allocation instructions to dynamically allocate a one-dimensional array space in static random access memory, with each element having a length exactly equal to the number of subdivisions. Each index element of this array represents the geometric center computation node of a tiny physical volume unit.

[0116] The system initialization controller assigns the index value of the first array element at the top to one, representing the surface node closest to the air-matrix interface;

[0117] The largest index value is assigned to the last element of the array, representing a deep leakage node located at the bottom of the pit. The originally continuous and indivisible real matrix space is thus rigorously transformed into a one-dimensional addressable data array arranged sequentially according to a specific physical address within the microcontroller.

[0118] The evolution of solute concentration over time must also be sliced ​​and discretized within the clock cycle of the microprocessor.

[0119] The system sets a uniform discrete time step for matrix calculation, and the background data processing time axis of the entire seedling cycle is divided into countless discrete micro system time points.

[0120] The key challenge in ensuring the long-term stable operation of the system lies in the fact that the values ​​of the computation time step and the spatial distance step cannot be arbitrarily assigned in memory.

[0121] If, within a single computation iteration cycle, the downward movement distance of the water flow in physical deduction exceeds the physical thickness of a spatial grid, it will cause the underlying differential computing array to skip data steps, resulting in severe numerical oscillations. This will cause the calculated ion concentration value to overflow negatively, leading to a microcontroller entering an infinite loop failure.

[0122] To ensure absolute convergence of the computing engine throughout the entire seedling stage, this module forcibly embeds the Courant-Friedrich-Levy (CFL) numerical stability constraint algorithm logic into a high-priority background timer task.

[0123] The specific execution steps of this logic are as follows:

[0124] The processor extracts the maximum absolute value of the water flow velocity within the current time slice, performs a multiplication instruction with the current computation time step variable, and then divides the product by the spatial node distance step constant, thereby generating a dimensionless Courant ratio dynamic parameter in the floating-point arithmetic unit.

[0125] The underlying logic checker of the system compares and judges this dimensionless ratio dynamic parameter with the preset stability theory critical threshold constant (usually burned by the engineer as a positive real number less than one) at a very high frequency.

[0126] Once the comparator detects that the ratio is approaching the theoretical critical threshold, the system bus immediately triggers an adaptive adjustment interrupt mechanism to dynamically reduce the computation time step variable to be used in the next differential cycle according to a preset decay polynomial.

[0127] After completing the array allocation of the basic spatiotemporal grid, the system needs to fill the corresponding physical initial attribute value data for each virtual node address in memory.

[0128] Given that agricultural seedling substrate is a three-phase mixed heterogeneous medium composed of solid porous particles, liquid free water and gaseous pore air, the system constructs a composite data structure containing multiple physical member variables in the microprocessor memory.

[0129] For each allocated space node index variable, the system defines and executes the initialization assignment of the following core state members in the corresponding structure memory block:

[0130] First, initialize the discrete numerical member variable of volumetric water content, which represents the percentage of physical volume occupied by liquid water at the current depth node;

[0131] Second, initialize the soil dry bulk density and saturated porosity member variables. Due to the gravity settling and the impact of dripping water inside the seedling tray, the substrate bulk density usually exhibits a non-uniform physical gradient change with a loose upper layer and a compact lower layer.

[0132] The system calls a nonlinear spatial weight allocation algorithm to write porosity values ​​with differentiated gradients to array nodes at different depths, thus highly restoring the anisotropic physical resistance characteristics of the artificial matrix at the digital processing level.

[0133] Third, the most crucial data preparation step is to initialize the solute concentration physical field, that is, to assign an initial ion concentration scalar to each structure node.

[0134] During the system's cold start-up and power-on operation or the initial reset phase of seedling cultivation and sowing, the microprocessor uses direct memory write instructions to uniformly fill the concentration memory addresses corresponding to all spatial nodes with a small positive real constant representing the background value of the base fertilizer.

[0135] All physical initial state quantities are neatly arranged and fixed in static random access memory in double-precision floating-point data format, forming an initial calculation sandbox, waiting to be retrieved by the core computing unit at any time.

[0136] Discrete solutions to partial differential equations after they have been transformed into algebraic matrices must rely on the exact boundary conditions of the external input.

[0137] This module translates and encapsulates the output external environment digital stream into the upper and lower boundary physical constraint parameters required by the internal differential array matrix.

[0138] For the top boundary of a one-dimensional spatial array (i.e., the virtual region above the first element of the memory array), perform typical physical flux boundary data encapsulation operations.

[0139] When the status register detects that the irrigation pump start flag is valid, the system extracts the drip irrigation flow conversion data transmitted from the sensing module, assigns its value to the downward liquid phase permeation rate variable, and converts the absolute conductivity of the water and fertilizer mother liquor after temperature compensation into a constant solute injection source concentration.

[0140] The system's processing unit constructs the upper boundary input flux in memory:

[0141] The downward permeation velocity value is extracted and multiplied by the injection source concentration constant. The product is then used as the solute mass boundary flux flowing into the matrix system and pushed onto the top of the difference array. In the discrete time step where there is no drip irrigation and there is light radiation, the system reads the water evaporation flux value calculated by the weight time-series difference. At this time, the computation kernel will hardware-invert the downward velocity sign (representing that the fluid motion has switched from gravity-dominated to capillary suction-dominated upward migration) and forcibly call the zeroing instruction to forcibly reset the solute concentration boundary constant flowing into the system to zero.

[0142] For the bottom physical boundary of the spatial array (i.e., the virtual area below the last element of the memory array), the system sets it to represent the free drainage boundary logic of the bottom of the porous array. When the microprocessor maps memory addresses, it logically forces the solute concentration variable at the virtual out-of-bounds position below the bottom node to be equal to the real-time concentration variable of the current bottom-most real array element.

[0143] The solute transport differential solution module is connected to the root region matrix spatiotemporal grid discretization module. It is used to receive the one-dimensional addressable data array and the physical constraint parameters of the upper and lower boundaries, calculate the hydrodynamic dispersion coefficient and source-sink term boundary data, construct a sparse tridiagonal matrix system with a fully implicit finite difference scheme, and execute the Thomas pursuit method hardware-level execution program to solve the absolute concentration values ​​of inorganic salt ions at each virtual space node in the future system time step.

[0144] When the water and fertilizer solution is dripped into the seedling trays from the drip irrigation network, the inorganic salt ions will not only undergo macroscopic physical displacement (i.e., convection) as the liquid water seeps downwards, but will also diffuse and kineticly disperse due to the microscopic molecular thermal motion and uneven flow velocity in the porous pores. At the same time, the roots of the tobacco seedlings will also absorb some water and nutrients according to their physiological rhythms during this process.

[0145] Microprocessors need to transform the continuous partial differential equations characterizing these highly coupled physical dynamics into a set of discrete data instructions executable by a digital controller, thereby deriving the absolute value of the inorganic salt concentration for each discrete spatial virtual node in a future time slice within the digital space.

[0146] Before constructing the core differential sparse matrix for solving, the microcontroller’s central processing unit must first calculate the dynamic variables, namely the dynamic dispersion coefficient and the source-sink term, which are highly dependent on the real-time physical state, and store them in the internal high-speed static register for later use.

[0147] The dynamic dispersion coefficient is not a fixed constant. The system's computational kernel uses the following digital conditioning steps based on the underlying hardware for real-time calibration calculation:

[0148] The system processes the mapping and conversion of the tortuosity factor of porous media. The system calls the underlying fixed mathematical standard function library. The floating-point unit of the microprocessor extracts the discrete value of the volumetric water content of the current computing node through memory address mapping, executes the floating-point exponentiation assembly instruction, calculates the cube seventh power of the water content value, and temporarily pushes the result into the working stack area.

[0149] The data bus is used to extract the pre-programmed physical constant data of the saturated water content of the specific matrix from the read-only protected sector of the read-only memory, and execute the square multiplication instruction of itself.

[0150] The arithmetic logic unit retrieves the result of the cube-seventh power operation stored in the stack area and puts it into the dividend register, puts the result of the square of the saturated moisture content into the divisor register, and executes the hardware-level floating-point division instruction to calculate the dimensionless absolute value of the tortuosity factor of the porous medium.

[0151] After obtaining the tortuosity factor, the processor extracts the absolute value of the current actual pore water flow velocity and performs a multiplication instruction operation with the longitudinal dispersion empirical parameter pre-stored in the non-volatile memory to generate convection drive component data.

[0152] Meanwhile, the calculated bending factor value is multiplied by the physical constant of the diffusion coefficient of inorganic salt ions in pure water molecules to generate molecular diffusion component data.

[0153] The microprocessor executes an arithmetic addition instruction to add the aforementioned convection-driven component data to the molecular diffusion component data, thus formally generating the hydrodynamic dispersion coefficient value of the current node, which characterizes the overall diffusion capability of ions.

[0154] For root absorption sinks, the system establishes a data conditioning and follow-up processing logic that is deeply coupled with water consumption.

[0155] The microprocessor reads the total water loss flux data of the matrix surface, which has been measured and smoothed by the first sensing module, from a specific memory segment. According to the preset root density vertical distribution polynomial function, it performs continuous multiplication and division machine instructions to distribute the total flux to each effective root node according to the physical depth ratio, generating the root volume absorption rate value at each depth.

[0156] Based on the assumption of the passive absorption mechanism at the micro level in agriculture, the microprocessor directly retrieves the real-time digital value of the water and fertilizer solution concentration of the current node, performs a multiplication instruction with the newly calculated root volume absorption rate value, and finally generates source and sink term boundary data representing the mass of inorganic salt ions consumed and extracted by the root system per unit time and per unit volume, which is stored in the specified cache address for subsequent matrix assembly.

[0157] To ensure absolute numerical stability during long-term seedling cultivation operations lasting tens of days and to prevent divergent oscillations in concentration calculations that do not conform to physical principles (such as negative concentrations), this module constructs a large-scale linear algebraic sparse equation system in the processor's static random access memory.

[0158] The microprocessor uses Taylor series expansion algorithm logic from the underlying mathematical library to replace the time dimension derivative of the continuous equation with forward difference numerical substitution and the spatial dimension derivative with central difference numerical substitution, thereby indexing variables (denoted as subscripts) for each internal spatial node. The following one-dimensional discrete difference digital computation equation is generated:

[0159] ;

[0160] Among them, all those with superscript The parameters all represent the next system computation time step (the time step size uses a uniform and globally unique numerical symbol). Unknown target concentration variables awaiting processor solution;

[0161] in To solve for the absolute concentration data of the target at future time steps for the currently addressed node, For the future concentration data of the adjacent nodes located above it in memory address, This refers to the future concentration data of the adjacent nodes located below it in memory address.

[0162] The set of coefficients of the equation formed by both sides of the equal sign These are not static constants; they are all generated in real time by the microprocessor's processing core through high-frequency calls to the preceding data within the current time slice.

[0163] The specific memory generation and assembly steps are as follows:

[0164] The processor extracts current macroscopic pore velocity data, spatial physical distance step constant, volumetric water content discrete state data, and global computation time step in parallel. , as well as the hydrodynamic dispersion coefficient and source-sink boundary data that were just calculated and stored in the cache in the previous section.

[0165] After massive high-speed calculations by the multiply-accumulate unit inside the arithmetic logic unit, the system generates the lower diagonal coefficients respectively. (Physically, this represents the downward gravitational and fluid-driven weight of the upper physical node on this node), main diagonal coefficient. (The physical meaning combines the node's own capacity hysteresis, time step decay, and root absorption and consumption) and the upper diagonal coefficient. (Physically, this represents the diffusion-impeding weight of the reverse concentration gradient generated by the node below this node.)

[0166] The set of known constants on the right-hand side of the equation The microprocessor then combines the known old concentration cache value of the node at the current moment with the boundary source and sink constant terms to form the value.

[0167] Since the calculation of the future state of each node index redundantly depends on the unknown states of its adjacent nodes both upwards and downwards, when the microprocessor has traversed all the preset... After partitioning the space into nodes, the system successfully assembled and constructed a large-scale space in the heap region of the static random access memory, where only the main diagonal and its upper and lower sides contain non-zero elements. Sparse tridiagonal matrix system.

[0168] From a professional perspective of digital signal processing, directly calling the standard inverse function on a large sparse matrix will instantly exhaust the computing clock cycles of the edge IoT microcontroller, resulting in excessive control latency.

[0169] To achieve millisecond-level latency-free real-time computation on low-power hardware devices, a hardware-level execution program for the Thomas chase method, specifically designed for sparse tridiagonal matrix structures, is embedded in the microprocessor's internal firmware.

[0170] The microcontroller follows the following irreversible two-step memory pointer operation logic:

[0171] The first step is to perform a forward elimination and catching-up process of the memory pointer. The operation kernel initializes a register loop counter, and the instruction pointer traverses downwards according to the virtual space coordinates from the top surface node (the array index pointer is equal to one) to the bottom leak node.

[0172] Within each cycle, the arithmetic logic unit simultaneously retrieves the main diagonal coefficient data, the upper diagonal coefficient data, and the known constant term data from memory. It then executes algebraic elimination division and continuous subtraction instructions within the floating-point unit to generate a corrected auxiliary coefficient array data after eliminating the lower diagonal dependency. Finally, it uses a memory overwrite instruction to directly write this data back to the original physical memory address. This continuous high-speed data erasure and write operation mathematically reduces the previously highly coupled complex matrix to a simple upper triangular matrix containing only the main and upper diagonals, effectively severing the massive redundant dependency chain of the underlying equation system on the top-level input parameters.

[0173] The second step involves performing a backward substitution of memory pointers to solve the problem. Since the boundary mapping mechanism in the previous module has already provided the lowest-level nodes with clear boundary conditions for free drainage, the microprocessor forcibly resets the loop counter, reverses the memory addressing direction, and starts from the deepest node at the bottom (where the array index pointer equals...). Starting from the top, traverse upwards in reverse order until the first surface node is reached.

[0174] During each processor clock cycle of the reverse traversal, since the matrix has been reduced in dimension, the microprocessor only needs to execute one instruction operation for solving a single-variable linear equation with extremely low computational overhead:

[0175] That is, extract the corrected constant term data, add the sum of the products of the upper diagonal coefficients and the known concentrations of the adjacent nodes below, and then feed this sum into the divider to divide by the corrected main diagonal coefficient data.

[0176] Each successful execution of a division assembly instruction and the raising of the flag bit signifies that the absolute concentration value of inorganic salt ions of a virtual space node at a future system time step has been successfully extracted and parsed. This high-precision floating-point data is immediately refreshed and written into the corresponding extended member variable of the structure array.

[0177] By performing thousands of cyclic differential calculations within a single physical drip irrigation cycle, the system depicts a thermodynamic digital model of the dynamic accumulation of inorganic salt ions within the matrix profile at extremely high resolution.

[0178] The matrix osmotic pressure threshold constraint module is connected to the solute transport differential solution calculation module. It is used to receive the absolute concentration values ​​of inorganic salt ions and convert them into osmotic pressure data. It calculates the matrix potential data by combining the inverse function of the water characteristic equation. The two are superimposed to generate the total water potential resistance barrier data item. It executes the spatial extreme value search addressing algorithm to extract the limit water potential short plate data index. When the limit water potential short plate data index falls below the dynamic safe water potential critical threshold constant, it forcibly injects a hardware-level external interrupt request level signal.

[0179] The core physical principle of plant roots absorbing water in porous media is that the water potential generated by transpiration pull within the plant must be greater than the physical binding force and chemical adsorption force of the surrounding substrate on the liquid water.

[0180] At agricultural seedling nurseries, the root cause of irreversible physiological water shortage and wilting in seedlings (commonly known as seedling burn) is:

[0181] The combined water potential resistance caused by substrate drought, coupled with the osmotic resistance caused by the enrichment of high-concentration inorganic salts, exceeds the maximum tensile force limit that the root hair cells of tobacco seedlings can generate.

[0182] The function of this module is to extract the concentration structure digital array refreshed by the previous module, convert it into real physical and chemical pressure data through instruction set operations, and combine it with the plant development calendar to perform critical state verification and comparison and hardware-level over-limit interrupt alarm requests.

[0183] In order to make state comparisons in a unified dimension, the microprocessor needs to convert the digital quantity of volumetric molar concentration, which represents quantity, into osmotic pressure data with the physical dimension of macroscopic pressure.

[0184] The system's computational kernel forces the invocation of the following discretized and adapted van der Hoff osmotic pressure physical formula: ;

[0185] The specific underlying steps of the microcontroller executing this pressure conversion formula are as follows:

[0186] The central processing unit first executes high-speed memory access instructions, retrieving in batches the numerical values ​​of the absolute solute concentrations at each spatial node obtained from the latest computation time step iteration calculation from the L1 cache. .

[0187] The processor reads three key constant parameters from the non-volatile data configuration area using addressing instructions:

[0188] The empirical coefficient constant data of the overall solution dissociation degree (i.e., parameters) input manually. Characterizing the multi-ion ionization effect of electrolytes), and ideal gas constant data (parameters) solidified in the bottom layer. ), and the real-time absolute temperature data of the root zone matrix after high-frequency noise has been filtered out by moving average and transmitted from the first sensing module (parameters). (The units have been converted from Celsius to absolute Kelvin). The arithmetic logic unit of the controller is equipped with a hardware floating-point multiplier. The processor schedules this peripheral to sequentially load the four floating-point variables and the constant into the register to perform a multiplication operation.

[0189] Through this series of millisecond-level continuous multiplication machine instructions, the system successfully and precisely converted the microscopic chemical accumulation characteristics of inorganic salts into macroscopic physical pressure data that hinders root water absorption. (Its dimension is uniformly set to kilopascal), and the result is persistently stored in the osmotic pressure extension member field of the corresponding spatial node structure.

[0190] In addition to the osmotic pressure barrier caused by the chemical potential of inorganic salts, the microstructure of the solid particles on the surface of the porous seedling substrate itself will generate strong capillary adsorption force and physical surface tension (i.e., substrate potential) on liquid water molecules.

[0191] The drier the matrix becomes under evaporation, the more exponentially this physical adsorption force increases. To accurately quantify this solid particle binding force, the core formula of the inverse function of the internationally recognized nonlinear van Gnukten moisture characteristic equation, designed for heterogeneous media, is loaded into the read-only segment of the system memory:

[0192] ;

[0193] The instruction decomposition and execution steps of this inverse function equation within the microprocessor are as follows:

[0194] The system extracts the real-time discrete values ​​of volumetric water content from the current node structure using a base-address plus index addressing method. ;

[0195] Simultaneously, extract a series of specific matrix physical property parameters (including the physical constant of saturated water content) pre-existing in the read-only segment of the read-only memory. Residual moisture content constant Empirical parameters of porous media morphology Shape parameters And the parameters derived from the derivation ).

[0196] The arithmetic unit first executes the subtraction instruction and floating-point division operation within the deepest brackets of the formula to calculate dimensionless data representing the effective usable water ratio.

[0197] The system calls the base-exponential function execution instruction from the standard hardware math function library (i.e., the underlying pow function logic encapsulation) to calculate the negative first power of the dimensionless proportional data divided by... Intermediate results of powers.

[0198] After subtracting the constant 1 from the intermediate result, the base exponentiation function is called again to calculate the total expression. One-eighths power of a constant.

[0199] Multiply the result of this operation by a negative one and divide by the empirical morphological parameter. The reciprocal coefficient constant is ultimately used to generate negative matrix potential data representing the resistance of porous physics in the floating-point arithmetic unit. .

[0200] After successfully calculating the physical matrix potential, the microprocessor executes a simple arithmetic addition operation (since both represent negative pressures representing suction, it is actually an algebraic addition of the absolute values ​​of the negative pressures) in the loop program traversing each effective root space node, and performs algebraic superposition and merging of the osmotic pressure negative pressure value representing chemical resistance and the matrix potential negative pressure value representing physical resistance:

[0201] ;

[0202] The final result of this addition equation This generates a total water potential resistance barrier data item that the plant root system must overcome by its own physiological mechanisms under the current discrete depth slice, which characterizes the total pressure of the combined physiological stress environment imposed on fragile seedlings by both drought and water shortage and excessive fertilization and salinization.

[0203] After obtaining the total water potential resistance digital array containing all depth subdivision nodes, the processor cannot use a simple full-scale averaging algorithm to assess the risk because the barrel effect occurs when a local root system encounters extreme resistance, which can cause the entire plant to wilt.

[0204] At this stage, this module initiates the underlying spatial extremum search and addressing algorithm to find dangerous extreme value bottlenecks:

[0205] The microcontroller's workflow is as follows:

[0206] Initialize a dedicated comparison register to store the current minimum negative pressure value and clear its initial data bits to zero.

[0207] The processor iterates through the addresses of all node array elements within the effective physical distribution depth of the root system in a loop-step manner.

[0208] The total water potential resistance value is read from a new node via the bus. The arithmetic unit uses its internal hardware-level data comparator to compare the value with the current value stored in a dedicated comparison register.

[0209] Since the water potential is a negative pressure value representing the pulling force, if the newly read floating-point value is smaller on the number axis (that is, the physical quantity of the absolute value of the negative pressure is larger, which means that the water absorption resistance at that depth is more extreme), the processor immediately triggers a data transfer instruction to overwrite the newly read extreme value into the dedicated comparison register.

[0210] When the entire root system effective distribution array has been scanned, the single minimum value remaining in the dedicated register is extracted. The system defines this value as the most extremely dangerous comprehensive limit water potential short-board data index in the current global space, stores it in the address of the global monitoring flag variable, and records it as... .

[0211] Another background subroutine running concurrently is responsible for extracting the absolute number of days the current batch of seedlings has grown since sowing, based on the real-time seedling calendar clock maintained by the crystal oscillator within the system. The microprocessor uses this number of days as input and substitutes it into the segmented physiological tolerance threshold mapping polynomial calculation program pre-programmed into the non-volatile memory protected area.

[0212] After rapid table lookup and linear interpolation completion operations, the processor calculates a dynamic safe water potential critical threshold constant that closely matches the current developmental stage of the plant's cell wall suberization tolerance, and records it as... And push it into the comparison cache.

[0213] Simultaneously, the ultimate water potential short-plate data indicators, representing the real-time spatial state, were acquired within the same time step. And the critical threshold constant for developmental stage safety, which represents the bottom line of plant physiological safety. Afterwards, this module performs the final system state determination and control transfer operation.

[0214] The microcontroller's arithmetic logic unit (ALU) retrieves the data from these two registers and directly executes the hardware subtraction instruction (i.e., calculation). ).

[0215] The system determines the subsequent logic flow by checking the sign bit in the status condition code register inside the arithmetic logic unit. If the result is determined... The value is greater than on the number line. (This means that the absolute amount of actual physical water absorption resistance has not exceeded the physiological safety limit of the plant.) The central processing unit determines that the current water and salt transport status of the substrate microenvironment is within the normal safe envelope range.

[0216] At this point, the module does not perform any hardware intervention. It simply packages the current array data in a unified timestamp format and archives it in the system background cache. Then, the microprocessor clears the temporary stack area and continues to silently execute the next round of sequential instruction fetching and partial differential iteration calculation.

[0217] If a rapid evaporation of moisture occurs due to slightly high temperatures in the greenhouse, or if excessive fertilization in a certain area leads to a sudden increase in salt accumulation in the substrate at a specific depth, causing the real-time calculated values ​​to be affected... The value fell below the negative direction When the constant (i.e., the absolute amount of total water absorption resistance exceeds the critical point of the safe physical limit for cell plasmolysis) is exceeded, the system's logical judgment result immediately reverses.

[0218] The control logic circuit inside the microprocessor not only immediately sets the Boolean flag representing the emergency disaster alarm in the global status register word to a logic high level, but also directly injects a hardware-level external interrupt request level signal into the highest priority interrupt controller (NVIC) of the control core through the programmable crossbus control network inside the chip.

[0219] The high response of this non-maskable interrupt signal indicates that the data sandbox warning driven by the underlying thermodynamics and fluid physics laws has been successfully captured by the system. All routine irrigation and display scheduling processes currently being executed by the main processor will be immediately suspended and terminated. The system execution authority will be directly transferred to the next execution module to carry out forced cut-off and physical hydraulic rescue control.

[0220] The water-fertilizer ratio hard switching scheduling module is connected to the matrix osmotic pressure threshold constraint module. It is used to respond to hardware-level external interrupt request level signals to block the injection of mother liquor. Based on the steady-state reverse deduction physical model, it calculates the absolute digital quantity of the total volume of clear water rinsing, executes micro-pulse frequency division driven physical execution, and enters the concentration slow release regression protection transition state after the osmotic pressure index falls back to within the safety constant to adjust the subsequent water-fertilizer mother liquor mixing injection ratio.

[0221] When the hardware interrupt flag on the system bus, which represents the danger of plant physiological dehydration, is pulled high without being masked, the central microprocessor must immediately abandon routine irrigation calendar tasks that are based on daily experience or preset clocks.

[0222] The system control was forcibly switched to an emergency dispatch mode based on the law of conservation of fluid mass and convection flushing logic;

[0223] This module blocks the source of the high-concentration mother liquor that causes disaster within a millisecond-level time window, and drives the waveform by accurately outputting a clean water rinsing sequence. It uses the physical shear force of the water flow to expel the inorganic salt ions that have accumulated excessively around the root hairs into the deep, non-sensitive areas of the matrix.

[0224] When the highest priority hardware over-limit interrupt request level is triggered in the kernel, the nested vector interrupt controller inside the microprocessor will immediately make a hardware-level response.

[0225] The processor first performs an assembly-level push operation, pushing the program counter address and status register snapshot content of the currently executing low-priority routine polling task (such as LCD character refresh, non-critical environmental temperature and humidity data packaging and reporting, etc.) into the memory stack area for context protection. The system control flow is then forcibly jumped to the emergency interrupt service routine instruction segment pre-burned into the underlying flash memory protection area.

[0226] Upon entering this emergency service routine, the primary electromechanical instruction issued by the system is to halt the continued injection of high-concentration fertilizer solution into the main pipeline.

[0227] If the system uses a simple and crude hard-shutdown solid-state relay to directly cut off the main power supply to the fertilizer metering pump, the hydraulic pipeline will experience a severely destructive physical fluid water hammer phenomenon due to the instantaneous obstruction of fluid momentum.

[0228] A set of soft-stop drive electromechanical control logic based on a negative exponential decay function is executed through the digital-to-analog converter (DAC) channel peripheral deployed within the microcontroller:

[0229] The microprocessor retrieves from the cache the initial steady-state drive analog voltage value that the fertilizer metering pump driver is receiving at the moment the interrupt is triggered.

[0230] The processor starts a dedicated high-speed countdown hardware timer to begin system timing in millisecond-level time units.

[0231] Within each tiny time slice period triggered by the timer, the arithmetic logic unit of the central processing unit retrieves the absolute time accumulation variable that has elapsed since the moment the interrupt occurred, performs a multiplication instruction on it with the empirical constant of the hydraulic damping buffer attenuation coefficient extracted from the configuration area, and takes the negative value of the product.

[0232] The processor then calls the underlying assembly instructions for solving the natural exponent to calculate the natural exponent of the negative variable, and then performs a second multiplication instruction with the previously extracted initial steady-state driving analog voltage value.

[0233] The system writes the final product result data into the data retention output register of the digital-to-analog converter in real time.

[0234] Through this extremely high frequency of background data refresh operations, the analog control voltage waveform output to the variable frequency fertilizer metering pump drive board gradually decreases to absolute zero volts within a short time window of a few hundred milliseconds, following a preset smooth exponential decay curve.

[0235] This mechanism, while ensuring the absolute integrity and safety of the physical pipeline electromechanical structure, seals off the fluid channels through which inorganic salt solutes can continue to invade the seedling microenvironment.

[0236] After successfully blocking the injection path of the new fertilizer solution, the system must initiate a rescue procedure, which involves calculating the total physical volume of pure water (such as deionized water treated by reverse osmosis equipment) that is completely free of chemical fertilizers to be injected into the dangerous pit, with the intention of using the strong physical kinetic energy of the downward water flow to dilute and wash away the solidified and enriched salts.

[0237] To prevent excessive rinsing water from causing plant roots to suffocate due to prolonged water immersion and lack of oxygen, the microcontroller must calculate the precise total rinsing demand based on a steady-state inverse physical model.

[0238] The microprocessor invokes the inverse piston flow algebraic control expression of the built-in one-dimensional convection dispersion control model and executes the following solution sequence instructions within the arithmetic unit:

[0239] The processor reads the floating-point constant parameter representing the physical surface area of ​​a single-cell seedling tray from the non-volatile configuration parameter table, as well as the system-set empirical correction coefficient constant parameter for rinsing efficiency (used to compensate for water flow detour short-circuit loss).

[0240] The processor extracts pre-stored concentration constant data that conforms to the physiological safety target of the current plant development stage and pushes it into the working register as the common denominator of the division instruction. At the same time, the processor starts a high-speed array loop traversal, sequentially extracting the high concentration absolute value variables corresponding to all discrete nodes from the outermost physical node (array index 1) down to the danger node where the extreme water disaster alarm occurs, and using them as numerators, sending them one by one into the divider to execute high-precision division operation instructions.

[0241] For each node's division quotient, the system calls the underlying natural logarithm core library function to obtain the logarithm value of the quotient.

[0242] The generated logarithm is multiplied sequentially by the discrete numerical variable of the real-time volumetric water content of the corresponding node and the constant variable of the thickness step size of the spatial virtual node. The microprocessor uses its internal high-bit-width data accumulator to sum the above continuous multiplication results of all nodes within this disaster depth range.

[0243] The system multiplies this summed data item by the previously extracted physical surface area floating-point constant parameter and the rinsing efficiency empirical correction coefficient constant parameter;

[0244] At this point, the floating-point coprocessor generates a core data in the final output register, which is the absolute digital quantity of the total volume of clean water required to eliminate this extreme salt content crisis (the system automatically converts its unit of measurement to cubic centimeters or international standard liters). This data is pushed onto the main control stack.

[0245] After obtaining the total volume required for rescue from the stack, if the control circuit keeps the main pipeline water solenoid valve open for a one-time flooding, the short-term infiltration rate of the fluid surface will be greatly limited, which will inevitably cause lateral liquid overflow on the substrate surface, leading to cross-infection of pathogenic microorganisms between different seedling holes.

[0246] Therefore, at the output end, the total rinsing volume is forcibly cut and broken into multiple discrete, small, intermittent irrigation pulse-driven timing waveforms for output.

[0247] To scientifically define the safety boundary of a single pulse, the system executes a key multiplication logic instruction (this is a supplementary calculation step based on the omission): the processor reads the constant variable of the surface matrix saturation hydraulic conductivity coefficient from the physical attribute configuration area, multiplies it by the physical surface area parameter of the single-hole seedling tray in the arithmetic logic unit, and then multiplies it by the constant of the maximum allowable continuous opening duration of the pulse set in the system safety threshold table, thereby deriving the absolute value of the maximum safe water injection volume that a single water injection pulse can physically tolerate.

[0248] The microprocessor performs a division operation, dividing the total volume digital value by the calculated maximum safe water injection volume per cycle, to obtain the required number of physical pulse frequency division triggers.

[0249] During the actual hardware execution phase, the microcontroller's general purpose input / output (GPIO) pins output high-level control signals to the outside, driving the power-stage solid-state relay to open the main pipeline water solenoid valve and simultaneously activating the internal high-frequency pulse counter.

[0250] When the injection volume data fed back by the flow meter sensor at the front end of the pipeline reaches the threshold of the maximum safe injection volume for a single injection, the control pin is quickly pulled low to forcibly close the solenoid valve. Between two consecutive injection pulse waveforms, the system forcibly inserts a static gravity physical infiltration dormancy time window, which is strictly controlled by an internal countdown timer.

[0251] During this execution cycle, which alternates between the intermittent opening and closing of the hardware valves, the underlying computing and early warning sandbox in the system's backend maintains a state of extremely high-frequency iterative monitoring.

[0252] Under the premise that the solute concentration input at the upper boundary is forcibly cleared to zero by the software, the differential operation module continuously calculates the dynamic process of the high-concentration salt accumulation peak being forced to physically move and diffuse into a deeper, non-sensitive area in the virtual space array.

[0253] The microcontroller cyclically extracts real-time updated osmotic pressure assessment indicators from each new node within the entire root system distribution area. When the digital comparator determines that all osmotic pressure indicators within the monitoring range have safely fallen back to within the critical physiological threshold constant data range, the system declares that the physical fluid flushing target has been achieved.

[0254] The processor then clears the hardware-level emergency control interrupt flag via the bus, resets the zero pulse counter, and cuts off the control power supply to the water solenoid valve, thus declaring the successful and safe termination of this emergency physical rinsing process.

[0255] After experiencing the drastic shock of localized hypertonic salinity stress and subsequent large-scale desalination and washing, the basal cells of the seedling root system are temporarily damaged due to a large number of transmembrane ion pumps. They urgently need a period of gentle physicochemical buffering to reconstruct the active absorption function of the cells.

[0256] If, after the interruption alarm is cleared, the system immediately outputs a high-concentration water-fertilizer drive signal at full power, conforming to normal development standards, the resulting secondary massive osmotic pressure drop will cause irreversible mechanical damage to the cell wall.

[0257] After the emergency interruption is successfully cleared, the system is forced into a special protective transition state called "concentration slow-release return".

[0258] The system extracts a pre-stored nonlinear S-shaped algebraic growth curve calculation program at the system bottom layer, and gradually adjusts the mixing ratio of water and fertilizer mother liquor in each subsequent round in a stepless speed regulation manner.

[0259] The microprocessor-level execution steps of its target concentration control function are as follows:

[0260] The processor extracts the extremely low base concentration safety constant set at the moment the rinsing action ends, the standard high target concentration constant that conforms to the current tobacco seedling growth and development calendar, the regression curve ramp slope control constant preset by the system experience, and the irrigation number constant that is expected to be offset when reaching the midpoint of the ramp.

[0261] Before each new routine drip irrigation task cycle begins, the processor reads the cumulative increment of the routine drip irrigation cycle counter since the crisis was resolved as the input variable.

[0262] The arithmetic logic unit calculates the difference between the counter value and the midpoint offset number constant, multiplies the difference by the slope control constant, and then takes a negative sign.

[0263] The system calls the natural exponent instruction to obtain the nonlinear exponent result of the negative value, adds a real number one to the result, and sends it to the division instruction as a common denominator.

[0264] At the same time, the difference between the standard high target concentration constant and the basic concentration safety constant is used as the numerator and placed into the work area to perform a floating-point division operation.

[0265] Add the basic concentration safety constant to the quotient of this division operation;

[0266] Based on the final calculated, smoothly and gradually changing nonlinear target concentration control data, the microprocessor dynamically and continuously adjusts the operating frequency pulse width modulation parameters of the fertilizer injection variable frequency metering pump motor, thereby realizing a nonlinear variable speed intervention and protection action in the actual physical water pipe where the fertilizer solution concentration can be continuously increased from extremely dilute to concentrated.

[0267] After all physical hydraulic rescue actions and slow-release cycles have been executed flawlessly, the microcontroller needs to comprehensively collect and organize the underlying system status of this major intervention event.

[0268] The microprocessor sends a feed reset signal to the watchdog timer via the internal bus to prevent the program from crashing after long-term intervention.

[0269] The nested vector interrupt controller module automatically clears all relevant interrupt dangling flags, pops the previously saved regular task pointers from the processor stack to restore them, and re-transfers the hardware-level control to the main loop thread of the first module for the regular polling scan of multi-source physical quantities.

[0270] To deeply respond to the system-level information processing needs of tamper-proof evidence storage and full life-cycle historical traceability of agricultural operations in the modern agricultural Internet of Things environment, the microprocessor activates the built-in high-speed direct memory access controller.

[0271] The system extracts and packages the core process data generated before and after this abnormal event, including the absolute timestamp variable at the moment of triggering the interruption, the limit space water potential data index at the moment of warning, the absolute digital quantity of the total volume of clean water rinsing calculated by the arithmetic unit and finally physically executed, and the characteristic parameters of the concentration regression smoothing curve that was finally called, into a continuous data block of a memory structure with a fixed byte width.

[0272] The microprocessor calls the internally embedded hardware cryptographic calculation unit to perform a cyclic redundancy check algorithm or a secure hash operation on the combined structure block, generating an absolutely unique hexadecimal check signature code to be appended to the end of the data packet.

[0273] Through high-speed peripheral interfaces such as the Universal Serial Bus, an uninterruptible write operation command is initiated to the designated protected sector of the locally mounted high-capacity non-volatile flash memory chip, and the data packet with the verification anti-counterfeiting code is physically solidified.

[0274] Once the locally mounted wireless radio frequency communication module (such as a narrowband IoT or 4G / 5G module) detects that the network link resources are idle, the system immediately pushes this encrypted and solidified data packet to the remote cloud server platform, ensuring that every microscopic physical ion enrichment intervention process at the edge is recorded in the system with traceable digital credentials.

[0275] Example 2

[0276] A fully intelligent management method for tobacco seedling cultivation includes the following components:

[0277] The high-frequency sensing module for seedling environment parameters collects multi-dimensional physical quantity simulation signals and converts them into digital sequences with unified hardware timestamps, which serve as basic field data and boundary conditions.

[0278] Based on the root zone matrix spatiotemporal grid discretization module, the seedling space is discretized by grid and the time step is set. Combined with numerical stability constraints, the parameters are dynamically adjusted to complete the assignment of node attributes and the encapsulation of boundary parameters.

[0279] The solute transport differential solution module combines grid data and boundary parameters to construct a sparse tridiagonal matrix and solve for the inorganic salt ion concentration at each node.

[0280] Using the matrix osmotic pressure threshold constraint module, the ion concentration is converted into osmotic pressure and matrix potential, and the total water potential data is synthesized. Once the index falls below the safety threshold, an interruption signal is issued.

[0281] The water-fertilizer ratio hard switching scheduling module responded to the interruption, shut down the mother liquor injection, calculated the volume of clean water rinsing, and performed the rinsing operation. After the indicators returned to the safe range, the water-fertilizer ratio was gradually restored through nonlinear control to achieve a smooth transition.

[0282] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0283] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does 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 the element.

[0284] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0285] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0286] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent management system for the entire process of tobacco seedling cultivation, characterized in that, include: The high-frequency sensing module for seedling environment parameters is used to acquire multi-dimensional physical quantity simulation signals and convert them into digital sequences with unified hardware timestamps, which serve as initial physical field data and dynamic boundary conditions. The root zone matrix spatiotemporal grid discretization module is used to spatially discretize the physical seedling space to establish a one-dimensional addressable data array, set the computation time step and introduce numerical stability constraint conditions algorithm logic, fill the discrete nodes of the one-dimensional addressable data array with the initial physical attribute values ​​of multi-dimensional structures, and encapsulate the digital sequence as upper and lower boundary physical constraint parameters. The solute transport differential solution module is used to receive a one-dimensional addressable data array and upper and lower boundary physical constraint parameters, calculate the hydrodynamic dispersion coefficient and source-sink term boundary data, construct a sparse tridiagonal matrix system, and solve for the absolute concentration values ​​of inorganic salt ions at each virtual space node in the future system time step. The matrix osmotic pressure threshold constraint module is used to receive the absolute concentration values ​​of inorganic salt ions and convert them into osmotic pressure data, calculate matrix potential data, and superimpose the two to generate the total water potential resistance barrier data item. It extracts the limiting water potential short plate data index and injects an interrupt request level signal when the limiting water potential short plate data index falls below the dynamic safe water potential critical threshold constant. The water-fertilizer ratio hard switching scheduling module is used to respond to the interruption request level signal to block the injection of mother liquor, calculate the absolute digital quantity of the total volume of clear water rinsing, execute micro-pulse frequency division drive physical execution, and enter the concentration slow release regression protection transition state after the osmotic pressure index falls back to within the safety constant to adjust the subsequent water-fertilizer mother liquor mixing injection ratio.

2. The intelligent management system for the entire tobacco seedling cultivation process according to claim 1, characterized in that, Acquiring analog signals of multidimensional physical quantities and converting them into digital sequences with unified hardware timestamps specifically includes: The multidimensional physical quantity simulation signal includes the instantaneous value of uncompensated apparent conductivity and the real-time absolute Celsius temperature of the solution; The microprocessor extracts the instantaneous value of the uncompensated apparent conductivity measured at the current actual temperature and simultaneously reads the real-time absolute Celsius temperature of the solution obtained after discrete solution; The difference between the real-time absolute Celsius temperature of the solution and the standard reference temperature constant is calculated by executing a subtraction instruction in the floating-point arithmetic register. The difference is multiplied by the empirical constant of the temperature compensation coefficient. The product is then added to form the compensation denominator cache data. Finally, the instantaneous value of the uncompensated apparent conductivity is used as the numerator of the division and sent to the floating-point division assembly instruction. After eliminating thermodynamic coupling errors, the real-time digital quantity of absolute conductivity is generated as a component of the digital sequence with a unified hardware timestamp.

3. The intelligent management system for the entire tobacco seedling cultivation process according to claim 2, characterized in that, Multidimensional physical quantity simulation signals also include signals containing minute deformation changes due to physical gravity; these are converted into digital sequences with unified hardware timestamps, specifically using weight-time differential data processing logic. Extract the total weight digital value read at the current moment and processed by low-pass digital filtering, and subtract the total weight digital value stored in memory at the previous sampling moment to obtain the weight loss difference data. In the floating-point unit, multiply the preset physical density constant of room temperature liquid water, the effective evaporation surface area constant, and the sampling time interval constant. Then divide the weight loss difference data by the multiplication result to generate the average water evaporation flux digital value within the current time step as a component of the digital sequence with a unified hardware timestamp.

4. The intelligent management system for the entire tobacco seedling cultivation process according to claim 1, characterized in that, The algorithm logic for introducing numerical stability constraints into the root region matrix spatiotemporal grid discretization module includes the following steps: Extract the maximum absolute value of the water flow velocity within the current time slice, multiply it with the current calculation time step variable, divide the product by the spatial node distance step constant, and generate a dimensionless Corange ratio dynamic parameter in the floating-point arithmetic unit. The underlying logic checker compares the dynamic parameter of the Courant number ratio with the theoretical critical threshold constant, and triggers an adaptive adjustment interrupt mechanism when the dynamic parameter of the Courant number ratio approaches the theoretical critical threshold constant, dynamically reducing the calculation time step variable to be used in the next difference cycle according to the preset decay polynomial.

5. The intelligent management system for the entire tobacco seedling cultivation process according to claim 4, characterized in that, The process of constructing a sparse tridiagonal matrix system is as follows: Parallel extraction of pore velocity data, spatial physical distance step constant, volumetric water content discrete state data, global computation time step, and calculation of hydrodynamic dispersion coefficient and source-sink term boundary data stored in the cache. After multiplication and accumulation operations within the arithmetic logic unit, the lower diagonal coefficient, main diagonal coefficient, and upper diagonal coefficient are generated. The known old concentration cache value at the current time is merged and added with the boundary source-sink constant terms to form a set of known constant terms. A sparse tridiagonal matrix system is assembled in the static random access memory heap area. The equation for the fully implicit finite difference scheme is: ； Among them, subscript Indicates the internal spatial node index variable, superscript Indicates the next system computation time step, parameters This indicates that the target absolute concentration data for the currently addressed node at future time points is to be solved, and the parameters are... This represents the future concentration data of the neighboring node located above it in memory address, parameter This represents the future concentration data of the adjacent node located below it in memory address, parameter Represents the lower diagonal coefficient, parameter Represents the coefficients of the main diagonal, parameters Represents the upper diagonal coefficient, parameter This represents the set of known constants.

6. The intelligent management system for the entire process of tobacco seedling cultivation according to claim 1, characterized in that, It receives absolute concentration values ​​of inorganic salt ions and converts them into osmotic pressure data, specifically including: The system executes high-speed memory access instructions to retrieve the numerical values ​​of absolute solute concentrations at spatial nodes, calculated iteratively at the latest computation time step, from the L1 cache in batches. It then reads constant parameters from the non-volatile data configuration area using addressing instructions. The hardware floating-point multiplier is then scheduled to sequentially load the floating-point variables and constants into registers for multiplication. Finally, the system calls the discretized and adapted osmotic pressure conversion formula. ； Among them, parameters Represents macroscopic physical pressure data, parameters This represents the empirical coefficient constant data of the overall degree of dissociation of the solution, and the parameters are... Represents ideal gas constant data, parameters This represents the real-time absolute temperature data of the root zone matrix, parameters. This represents the numerical value of the absolute concentration of solute at a spatial node. The matrix potential data is calculated, and the two are superimposed to generate the total potential resistance barrier data item, which specifically includes: Real-time discrete values ​​of volumetric water content are extracted using a base-address plus indexed addressing method. An instruction is executed by calling the base-exponential function from the standard hardware mathematical function library. The matrix potential data is then calculated using the inverse function of the water characteristic equation, as shown in the formula: ; Among them, parameters This represents negative matrix potential numerical data, parameters Represents the discrete numerical value of real-time volumetric water content, parameters Represents the physical constant of saturated water content, parameter Represents the residual moisture constant, parameter This represents empirical parameters representing the morphology of porous media. With parameters All represent shape parameters; After calculating the negative matrix potential data, an addition instruction is executed in the loop program that traverses the effective root system nodes to perform algebraic superposition and merging of the absolute values ​​of macroscopic physical pressure data and negative matrix potential data to generate the total water potential resistance barrier data item.

7. The intelligent management system for the entire tobacco seedling cultivation process according to claim 1, characterized in that, The water-fertilizer ratio hard switching scheduling module responds to the interrupt request level signal to block the injection of mother liquor, specifically by executing soft-stop drive electromechanical control logic based on a negative exponential decay function: The initial steady-state drive analog voltage value being received by the fertilizer metering pump driver at the moment the interrupt is triggered is extracted. A dedicated high-speed countdown hardware timer is started, and the absolute time accumulated variable since the interrupt occurs is retrieved and multiplied with the empirical constant of the hydraulic damping buffer attenuation coefficient by an instruction. The product is negative and then the natural exponent is calculated by calling the dedicated assembly instruction for solving the natural exponent. The result is then multiplied a second time with the initial steady-state drive analog voltage value by an instruction. The final product result is continuously written into the data holding output register of the digital-to-analog converter.

8. The intelligent management system for the entire tobacco seedling cultivation process according to claim 1, characterized in that, The specific steps for calculating the absolute volume of the total clean water rinsing volume include: The concentration constant data that meets the physiological safety target of the developmental stage is extracted and pushed into the working register as the common denominator of the division instruction. The high concentration absolute value variables from the outermost physical node to the node where the extreme water potential disaster warning occurs are extracted as the numerator and the division operation is performed one by one. The logarithm core library function instruction is called to obtain the logarithm value. The generated logarithm value is multiplied by the real-time volume water content discrete value variable of the corresponding node and the thickness step size constant variable of the spatial virtual node. The product results are accumulated and summed using a high bit width data accumulator. Finally, the sum data item is multiplied by the physical surface area floating point constant parameter and the rinsing efficiency empirical correction coefficient constant parameter.

9. The intelligent management system for the entire tobacco seedling cultivation process according to claim 8, characterized in that, Entering a concentration-slow-release regression protection transition state to adjust the subsequent water-fertilizer mother liquor mixing and injection ratio, specifically including implementing nonlinear target concentration control: Calculate the difference between the cumulative increase of the regular drip irrigation cycle counter value since the crisis was resolved and the constant of the number of times the midpoint was offset. Multiply the difference by the slope control constant and take the negative sign. Calculate the natural exponent of the negative variable and add the real number one to the result as the common denominator. Simultaneously, the difference between the standard high target concentration constant and the basic concentration safety constant is used as the numerator to perform floating-point division. Finally, the quotient of the division is added to the basic concentration safety constant to generate nonlinear target concentration control data, and the pulse width modulation parameters of the fertilizer injection variable frequency metering pump motor are dynamically adjusted.

10. A method for intelligent management of the entire process of tobacco seedling cultivation, comprising an intelligent management system for the entire process of tobacco seedling cultivation according to any one of claims 1-9, characterized in that, include: The high-frequency sensing module for seedling environment parameters collects multi-dimensional physical quantity simulation signals and converts them into digital sequences with unified hardware timestamps, which serve as basic field data and boundary conditions. Based on the root zone matrix spatiotemporal grid discretization module, the seedling space is discretized by grid and the time step is set. Combined with numerical stability constraints, the parameters are dynamically adjusted to complete the assignment of node attributes and the encapsulation of boundary parameters. The solute transport differential solution module combines grid data and boundary parameters to construct a sparse tridiagonal matrix and solve for the inorganic salt ion concentration at each node. Using the matrix osmotic pressure threshold constraint module, the ion concentration is converted into osmotic pressure and matrix potential, and the total water potential data is synthesized. Once the index falls below the safety threshold, an interruption signal is issued. The water-fertilizer ratio hard switching scheduling module responded to the interruption, shut down the mother liquor injection, calculated the volume of clean water rinsing, and performed the rinsing operation. After the indicators returned to the safe range, the water-fertilizer ratio was gradually restored through nonlinear control to achieve a smooth transition.

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