Rhizome processing warehouse inventory intelligent control system and method

Abstract

The present application relates to the technical field of biomass raw material logistics scheduling and production forecast data processing, and discloses a root and stem processing inventory intelligent regulation system and method, which comprises: a material loss evaluation unit acquires dynamic dry matter loss rate data of materials in each storage unit; a scheduling decision unit separates high-frequency fluctuation residual values of downstream processing equipment operation power sequences, and solves processing resistance characteristic values in combination with fundamental wave power trend values; when a predicted load parameter exceeds a critical overload threshold, a concurrent scheduling matrix reconstruction module associates and reconstructs an over-temperature loss storage unit and a low-loss compensation storage unit into a hedging scheduling combination, calculates a concurrent distribution flow rate vector, generates a concurrent scheduling control matrix, and adjusts the material conveying speed; the present application breaks through the data island of storage attenuation and processing links, flattens the overload peak in advance, and realizes nonlinear decoupling of asset clearing and stable operation of the production line.

Description

Technical Field

[0001] This invention belongs to the field of biomass raw material logistics scheduling and production forecasting data processing technology, and particularly relates to an intelligent control system and method for root and tuber processing inventory. Background Technology

[0002] Current mainstream warehousing control solutions often employ time-scale-based first-in-first-out (FIFO) rules and static book quality indicators to construct digital inventory accounts. They utilize centralized databases to record the entry time nodes of each storage area and combine this with fixed unidirectional linear queues to output batch allocation control instructions. This satisfies the daily data recording and management requirements for the flow of basic materials under stable operating conditions. However, root and tuber materials, as biomass entities, undergo continuous in vivo respiratory and metabolic reactions during their circulation cycle. Their internal dry matter quality and tissue characteristics exhibit nonlinear decreases with fluctuations in stacking tension, ambient temperature, and humidity. The inherent steady-state assumptions of existing systems cannot establish a correlation path between ambient temperature gradients and the decline in material quality. This leads to a continuously accumulating data deviation between static book quality data and the actual material equivalent at the processing end. When driven by complex order demands, this can easily introduce the potential risk of delayed production scheduling decisions due to the delayed perception of raw material deterioration characteristics. Consequently, batches with latent decay may experience sudden changes in mechanical load and severe fluctuations in energy consumption when entering downstream continuous cutting or separation processes.

[0003] To address the scheduling disturbances caused by physical decay, conventional optimization methods often employ mechanical means such as increasing the rated power of downstream drive motors to enhance hardware load resistance, or manually increasing the sampling frequency to correct the data on paper. However, hardware redundancy increases system construction costs, while discrete offline detection, lacking spatiotemporal continuity, cannot dynamically track the long-term deterioration evolution path of materials. For example, Chinese invention patent application CN120595902A discloses an intelligent control system and method for the storage environment of traditional Chinese medicine materials. It distinguishes between moisture redistribution and microscopic degradation of active ingredients through neural networks and implements temperature and humidity compensation. In the continuous processing of high-throughput root and rhizome materials, material deterioration manifests as a surge in mechanical processing resistance caused by the loss of chemical components and tissue fibrosis. Existing technologies stop at maintaining a static match between the storage environment and physicochemical indicators, lacking a mechanism to offset the physical decay characteristics of storage and the nonlinear load of processing machinery, and cannot solve the load chain failure caused by inferior batches entering the production line.

[0004] Therefore, the technical problem to be solved by this invention is how to continuously and multidimensionally predict the deterioration and activity characteristics of biomass raw materials under multi-warehouse distributed non-constant temperature storage conditions, and how to combine downstream real-time processing impedance feedback to build a cross-warehouse concurrent mixed-flow reconstruction system with multi-feature and multi-mechanism collaborative interaction, so as to ensure the flow and computing efficiency of the data processing system while mitigating the potential risk of dynamic chain load failure in multi-warehouse asset control. Summary of the Invention

[0005] This invention aims to solve the problem of continuous processing line load failure caused by the lack of multi-dimensional continuous prediction of raw material deterioration characteristics and the lack of multi-dimensional concurrent mixed-flow reconstruction mechanism for multi-warehouse asset control under multi-warehouse distributed storage conditions.

[0006] In this technical solution, a root and tuber processing inventory intelligent control system includes: The scheduling decision unit is connected to the material loss assessment unit; the scheduling decision unit has a concurrent scheduling matrix reconstruction module; the scheduling decision unit is used to separate the high-frequency fluctuation residual value from the obtained operating power sequence of the downstream processing equipment, and calculate the processing resistance characteristic value according to the ratio of the high-frequency fluctuation residual value to the fundamental power trend value stored in the system. The material loss assessment unit is used to obtain dynamic dry matter loss rate data of materials in each storage unit. The concurrent scheduling matrix reconstruction module is used to reconstruct the overheating loss storage units in the system's memory space when the predicted load parameters extracted from the dynamic dry matter loss rate data exceed the critical overload threshold. These units are associated with low-loss compensation storage units that provide hedging capacity and are at risk of overload due to the rising dynamic dry matter loss rate data. The module is then used to reconstruct the hedging scheduling combination. Under the boundary constraint that the predicted synthetic processing resistance characteristic value is lower than the critical overload threshold, the module calculates the concurrent allocation flow velocity vector for each storage unit. This transforms the single-track timing scheduling parameters into a concurrent scheduling control matrix containing a set of multiple flow velocity setpoints, thereby adjusting the conveying speed of materials from the overheating loss storage unit to the downstream processing equipment.

[0007] Preferably, the material loss assessment unit is externally connected to sensor components arranged in each storage unit, and the material loss assessment unit internally includes a sensor spatiotemporal correlation verification module and a data substitution estimation module. The sensor spatiotemporal correlation verification module is used to extract the data feature differences between adjacent sensor nodes in the sensor components under a set heat transfer topology, thereby verifying the spatial data constraint relationship and locating the fault node. The data substitution estimation module is used to call the data substitution estimation rule based on the monotonic characteristics of thermophysical properties when the fault node is located, reconstruct the virtual temperature vector, and input the virtual temperature vector into the material loss assessment unit to correct the dynamic dry matter loss rate data.

[0008] Preferably, the scheduling decision unit further includes an adaptive degradation control module; the adaptive degradation control module is used to monitor the packet loss rate in the internal data network of the system in real time, and when the packet loss rate exceeds the preset safety threshold, the system interrupts the matrix calculation of the concurrent scheduling matrix reconstruction module under the hedging scheduling combination, and switches to the threshold interception control mode based on discrete resistance ladder.

[0009] Preferably, the adaptive degradation control module has a constant coefficient storage matrix inside; in the threshold interception control mode, the adaptive degradation control module reads the safety period reduction constant from the constant coefficient storage matrix and uses the safety period reduction constant to cut off the allowable disbursement flow rate of each storage unit, thereby suppressing the surge in processing resistance caused by the over-temperature loss storage unit.

[0010] Preferably, when the concurrent scheduling matrix reconstruction module constructs the hedging scheduling combination in the system's running memory space, it pairs the deterioration risk weight corresponding to the over-temperature loss storage unit with the inventory availability corresponding to the low-loss compensation storage unit to generate a collaborative hedging constraint matrix.

[0011] Preferably, the scheduling decision unit is externally connected to flow regulating valves distributed on each storage unit; the scheduling decision unit is used to convert the concurrent scheduling control matrix into underlying register adjustment instructions and send them to the flow regulating valves, thereby adjusting the output material ratio of each storage unit.

[0012] Preferably, the sensor assembly includes a temperature sensor array and a humidity sensor array arranged inside each storage unit; the material loss assessment unit is used to import the temperature distribution data collected by the temperature sensor array and the humidity distribution data collected by the humidity sensor array, and calculate the dynamic dry matter loss rate data based on the temperature distribution data and the humidity distribution data.

[0013] Preferably, the scheduling decision unit is externally connected to a processing equipment power consumption monitoring module; the processing equipment power consumption monitoring module is used to collect the active power of the motors of downstream processing equipment in real time, thereby generating the operating power sequence of downstream processing equipment.

[0014] Preferably, the scheduling decision unit further includes an extended vector generation module and a production scheduling optimization module; the extended vector generation module is used to extract the attenuation acceleration inflection point of the loss and deterioration stage in the time domain based on the dynamic dry matter loss rate data, and generate a production scheduling extended vector accordingly; the extended vector generation module is used to input the production scheduling extended vector as a time-series constraint boundary into the production scheduling optimization module to update and optimize the production scheduling time slice allocation matrix, thereby eliminating the production line flow rate stagnation caused by the high-loss batches leaving the warehouse under the premise of adjusting the processing resistance characteristic value to be stable within the preset safety range.

[0015] A method for intelligent control of root and tuber processing inventory, used to operate an intelligent control system for root and tuber processing inventory, includes the following steps: Step S1: Use the material loss assessment unit to obtain dynamic dry matter loss rate data of materials in each storage unit. Step S2: The high-frequency fluctuation residual value is separated from the obtained operating power sequence of the downstream processing equipment by the scheduling decision unit, and the processing resistance characteristic value is calculated according to the ratio of the high-frequency fluctuation residual value to the fundamental power trend value stored in the system. Step S3: When the predicted load parameter in the dynamic dry matter loss rate data exceeds the critical overload threshold, the concurrent scheduling matrix reconstruction module inside the scheduling decision unit is used to associate and reconstruct the over-temperature loss storage units in each storage unit that are at risk of overload due to the rise in dynamic dry matter loss rate data with the low loss compensation storage units used to provide hedging capacity in the system running memory space, and reconstruct them into a hedging scheduling combination. Step S4: Under the boundary constraint condition that the predicted synthetic processing resistance characteristic value is lower than the critical overload threshold, the concurrent allocation flow velocity vector of each storage unit is calculated using the concurrent scheduling matrix reconstruction module. The single-track timing scheduling parameters are transformed into a concurrent scheduling control matrix containing a set of multiple flow velocity settings, thereby adjusting the conveying speed of materials in the over-temperature loss storage unit to the downstream processing equipment.

[0016] Compared with existing technologies, the intelligent control system for root and stem processing inventory of the present invention has the following advantages: 1. In the intelligent control of root and tuber processing inventory, the material loss assessment unit connects the storage unit environmental monitoring matrix with the external wall heat infiltration counter-gating module, collects the spatiotemporal heat dispersion vector sequence, extracts the wall heat transfer gradient vector caused by external solar radiation, and filters out external meteorological disturbance noise before inputting the vector sequence into the solution operator. This in-situ correlation processing and signal stripping mechanism of multi-source spatial temperature gradient data solves the problem of the delay in perceiving the physiological deterioration trajectory of materials caused by traditional storage control relying solely on static book quality indicators. It transforms single-point environmental changes into the dry matter loss rate distribution characteristics that characterize the decline in inventory activity, providing accurate material decay characteristic constraint data for downstream scheduling, improving the data spatial adaptation accuracy of inventory instructions under distributed non-constant temperature conditions, and avoiding deep hidden deterioration of materials remaining in the warehouse.

[0017] 2. The scheduling decision unit connects to the communication interface of the frequency converter of the cutting motor in the processing station, periodically reads the discrete active power data sequence, extracts the statistical high-frequency fluctuation residual value within the set time window through the processing impedance solution operator, and calculates the dimensionless processing impedance coefficient corresponding to the current processing condition. The coefficient interacts with the dry matter loss rate distribution characteristics output from the storage end in the data decision space to derive the global residence time extension vector, breaking down the data silos between the storage attenuation process and the physical processing link. The system uses the extension vector as a rigid time-series constraint boundary to optimize the tensor update of the production scheduling time slice allocation, eliminates the surge in processing impedance caused by the forced release of high-loss batches, suppresses dynamic chain failures in multi-warehouse collaboration, and improves the global material flow rate.

[0018] 3. The scheduling decision unit constructs a virtual hedging asset portfolio in memory space by using a cross-warehouse concurrent mixed-flow tensor reconstruction mechanism to combine high-risk overheating storage units and low-loss compensation storage units. Using an impedance hedging solver with the expectation that the combined impedance is lower than the critical shock impedance threshold as a rigid constraint, it calculates the concurrent allocation flow velocity vector of each physical unit. The single-track timing scheduling is reconstructed into a concurrent control instruction tensor containing multiple flow velocity setpoints. By utilizing the weighted average characteristics of the combined impedance of materials in different storage units, the peak physical impedance is smoothed out in advance during the information flow stage, allowing high-loss materials to continuously penetrate into the processing flow at a controlled flow rate. This balances the system conflict between clearing deteriorated assets and continuous operation of the production line, and improves the actual processing yield of assets. Attached Figure Description

[0019] Figure 1 This is a flowchart of the root and stem processing inventory control method based on resistance characteristic calculation and concurrent reconstruction of the present invention; Figure 2 This is the multimodal adaptive state diagram of the root and stem processing inventory control system of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] A smart inventory control system for root and tuber processing includes: The scheduling decision unit is connected to the material loss assessment unit; the scheduling decision unit has a concurrent scheduling matrix reconstruction module; the scheduling decision unit is used to separate the high-frequency fluctuation residual value from the obtained operating power sequence of the downstream processing equipment, and calculate the processing resistance characteristic value according to the ratio of the high-frequency fluctuation residual value to the fundamental power trend value stored in the system. The material loss assessment unit is used to obtain dynamic dry matter loss rate data of materials in each storage unit. The concurrent scheduling matrix reconstruction module is used to reconstruct the overheating loss storage units in each storage unit that are at risk of overload due to the rise in dynamic dry matter loss rate data, and the low loss compensation storage units that provide hedging capacity, in the system's running memory space when the predicted load parameters extracted from the dynamic dry matter loss rate data exceed the critical overload threshold. This module is then associated with and reconstructed into a hedging scheduling combination. Under the boundary constraint condition that the predicted synthetic processing resistance characteristic value is lower than the critical overload threshold, the module calculates the concurrent allocation flow velocity vector of each storage unit and transforms the single-track timing scheduling parameters into a concurrent scheduling control matrix containing a set of multiple flow velocity setpoints, thereby adjusting the conveying speed of materials from the overheating loss storage unit to the downstream processing equipment.

[0022] Preferably, the material loss assessment unit is externally connected to sensor components arranged in each storage unit, and the material loss assessment unit internally includes a sensor spatiotemporal correlation verification module and a data substitution estimation module. The sensor spatiotemporal correlation verification module is used to extract the data feature differences between adjacent sensor nodes in the sensor components under a set heat transfer topology, thereby verifying the spatial data constraint relationship and locating the fault node. The data substitution estimation module is used to call the data substitution estimation rule based on the monotonic characteristics of thermophysical properties when the fault node is located, reconstruct the virtual temperature vector, and input the virtual temperature vector into the material loss assessment unit to correct the dynamic dry matter loss rate data.

[0023] Preferably, the scheduling decision unit further includes an adaptive degradation control module; the adaptive degradation control module is used to monitor the packet loss rate in the internal data network of the system in real time, and when the packet loss rate exceeds the preset safety threshold, the system interrupts the matrix calculation of the concurrent scheduling matrix reconstruction module under the hedging scheduling combination, and switches to the threshold interception control mode based on discrete resistance ladder.

[0024] Preferably, the adaptive degradation control module has a constant coefficient storage matrix inside; in the threshold interception control mode, the adaptive degradation control module reads the safety period reduction constant from the constant coefficient storage matrix and uses the safety period reduction constant to cut off the allowable disbursement flow rate of each storage unit, thereby suppressing the surge in processing resistance caused by the over-temperature loss storage unit.

[0025] Preferably, when the concurrent scheduling matrix reconstruction module constructs the hedging scheduling combination in the system's running memory space, it pairs the deterioration risk weight corresponding to the over-temperature loss storage unit with the inventory availability corresponding to the low-loss compensation storage unit to generate a collaborative hedging constraint matrix.

[0026] Preferably, the scheduling decision unit is externally connected to flow regulating valves distributed on each storage unit; the scheduling decision unit is used to convert the concurrent scheduling control matrix into underlying register adjustment instructions and send them to the flow regulating valves, thereby adjusting the output material ratio of each storage unit.

[0027] Preferably, the sensor assembly includes a temperature sensor array and a humidity sensor array arranged inside each storage unit; the material loss assessment unit is used to import the temperature distribution data collected by the temperature sensor array and the humidity distribution data collected by the humidity sensor array, and calculate the dynamic dry matter loss rate data based on the temperature distribution data and the humidity distribution data.

[0028] Preferably, the scheduling decision unit is externally connected to a processing equipment power consumption monitoring module; the processing equipment power consumption monitoring module is used to collect the active power of the motors of downstream processing equipment in real time, thereby generating the operating power sequence of downstream processing equipment.

[0029] Preferably, the scheduling decision unit further includes an extended vector generation module and a production scheduling optimization module; the extended vector generation module is used to extract the attenuation acceleration inflection point of the loss and deterioration stage in the time domain based on the dynamic dry matter loss rate data, and generate a production scheduling extended vector accordingly; the extended vector generation module is used to input the production scheduling extended vector as a time-series constraint boundary into the production scheduling optimization module to update and optimize the production scheduling time slice allocation matrix, thereby eliminating the production line flow rate stagnation caused by the high-loss batches leaving the warehouse under the premise of adjusting the processing resistance characteristic value to be stable within the preset safety range.

[0030] A method for intelligent control of root and tuber processing inventory includes the following steps: Step S1: Use the material loss assessment unit to obtain dynamic dry matter loss rate data of materials in each storage unit. Step S2: The high-frequency fluctuation residual value is separated from the obtained operating power sequence of the downstream processing equipment by the scheduling decision unit, and the processing resistance characteristic value is calculated according to the ratio of the high-frequency fluctuation residual value to the fundamental power trend value stored in the system. Step S3: When the predicted load parameter in the dynamic dry matter loss rate data exceeds the critical overload threshold, the concurrent scheduling matrix reconstruction module inside the scheduling decision unit is used to associate and reconstruct the over-temperature loss storage units in each storage unit that are at risk of overload due to the rise in dynamic dry matter loss rate data with the low loss compensation storage units used to provide hedging capacity in the system running memory space, and reconstruct them into a hedging scheduling combination. Step S4: Under the boundary constraint condition that the predicted synthetic processing resistance characteristic value is lower than the critical overload threshold, the concurrent allocation flow velocity vector of each storage unit is calculated using the concurrent scheduling matrix reconstruction module. The single-track timing scheduling parameters are transformed into a concurrent scheduling control matrix containing a set of multiple flow velocity settings, thereby adjusting the conveying speed of materials in the over-temperature loss storage unit to the downstream processing equipment.

[0031] Example 1: In the application of distributed and non-constant temperature control for large-scale continuous storage and regulation of root and tuber processing materials, due to the diurnal and seasonal changes in external environmental temperature, the biomass entities within each storage unit continuously undergo endogenous respiration and metabolic activities. This leads to a non-linear decay in the dry matter mass and physical activity of the stacked large volume of root and tuber materials. Furthermore, downstream manufacturing lines frequently encounter uncertain and high-frequency fluctuations in order demand. Traditional management systems based on static book inventory indicators and a single time axis first-in-first-out queuing rule lack a direct causal relationship between the data flow of the storage environment and the physical processing links. They cannot perceive in real time the interference of microscopic metabolic losses within the materials on the actual effective processing equivalent, thus mistakenly retaining materials with latent decay in the warehouse or excessively consuming fresh materials. When batches of raw materials with high loss rates and deep latent decay are concentrated and allocated to the processing line, the fibrosis of their tissues or the release of sugars causes sudden changes in cutting resistance in downstream cutting equipment or high-frequency clogging of solid-liquid separation screens, leading to increased load on the cutting motors of the processing station. The increased load and the forced reduction of effective throughput rate in processing impedance effects, this efficiency loss, in the continuous production line management system, inversely prolongs the actual residence time of the remaining batches waiting to be processed in the warehouse, inducing a secondary cascading accelerated decay of distributed materials throughout the warehouse area and a systemic supply chain crisis of large-scale asset scrapping. In order to mitigate the dynamic chain load failure in the above-mentioned multi-warehouse collaboration, the root and stem processing inventory intelligent control system, when operating under the above-mentioned distributed long-cycle conditions, connects its internal material loss assessment unit to the sensor components arranged in each storage unit, including temperature sensor arrays and humidity sensor arrays. It imports the temperature distribution data collected by the temperature sensor array and the humidity distribution data collected by the humidity sensor array, and the internal sensor spatiotemporal correlation verification module extracts the data feature difference between adjacent sensor nodes under the set heat transfer topology to locate the fault node. Specifically, the set heat transfer topology refers to the spatial adjacency matrix and one-dimensional steady-state local heat conduction network topology model constructed based on the spatial physical three-dimensional coordinates of each temperature sensor node in each storage unit.The data substitution estimation rule based on the monotonic characteristics of thermal properties refers to the following: When a sensor node is located as an abnormal zero-point drift or damaged node due to hardware failure, the data substitution estimation module, based on the continuity and local monotonic variation characteristics of the temperature field distribution within a continuous physical medium in Fourier's law of thermal conduction, retrieves real-time temperature measurement data from several healthy adjacent sensor nodes spatially closest to the faulty node in the heat transfer topology. It then uses a three-dimensional spatial linear equal-length interpolation algorithm to back-calculate the virtual temperature scalar value at the fault location, thereby reconstructing a complete virtual temperature vector and completing the in-situ substitution of the loop data. When a fault occurs, the data substitution estimation module calls the data substitution estimation rule based on the monotonic characteristics of thermal properties to reconstruct the virtual temperature vector for correction, thereby calculating in real-time the dynamic dry matter loss rate data M, which characterizes the material activity decay within each storage unit. Specifically, the material loss assessment unit imports the three-dimensional spatial data of each storage unit... After collecting temperature and humidity distribution data, high-frequency interference noise is filtered out. Based on the preset water activity balance kinetic model and Arrhenius biochemical reaction principle, the absolute temperature value and relative humidity fluctuation of the local stacked area are mapped in real time to the endogenous respiratory metabolic activity intensity index of the corresponding root and stem material. The calculation process of dynamic dry matter loss rate data M is as follows: within the set system flow evaluation cycle, the respiratory metabolic activity intensity index is integrated in the time domain to characterize the ratio of the cumulative loss of organic dry matter mass caused by endogenous respiration to the initial total mass of the material entering the warehouse. Its output form is a dynamic dry matter loss rate scalar in percentage form, which is stored in the system main control running memory space in real time for subsequent scheduling decision unit to call. At the same time, the scheduling decision unit connects to the communication interface of the downstream processing station cutting motor frequency converter as the power consumption monitoring module of the processing equipment, and reads the discrete active power data sequence of the cutting motor at a fixed sampling period. Its internally configured processing impedance calculation operator extracts the active power data sequence. Within the set time window The statistical high-frequency fluctuation residual values ​​are obtained, and the processing resistance characteristic value is calculated based on the ratio of the high-frequency fluctuation residual values ​​to the fundamental power trend values ​​stored in the system. Processing resistance characteristic value The calculation is based on the load torque fluctuation law of cutting dynamics. The dispersion of the transient active power of the motor from the mean active power shows a monotonically positive correlation with the cutting resistance. The machining impedance solution operator is in length of time window Within, calculate the active power data sequence. The root mean square residual value is used as the high-frequency fluctuation residual value. This root mean square residual value is divided by the fundamental power trend value to obtain a dimensionless ratio, which is then multiplied by a preset conversion coefficient. Obtain the characteristic value of processing resistance The calculation formula is: Processing resistance characteristic value This is a dimensionless parameter, taking values ​​from zero to one hundred real numbers; preset conversion coefficients. The dimensionless amplification factor is constant at fifty; the root mean square residual of active power. Indicates within the time window The square root of the variance of the power fluctuation after removing the fundamental component is a real number that is greater than zero; the fundamental power trend value. This represents the average power of downstream processing equipment under stable no-load operation, and its value is greater than zero; time window. With 300 sampling periods, the machining impedance calculation operator converts discrete power signal fluctuations into characteristic parameters that characterize the mechanical cutting resistance of the material.

[0032] When the predicted load parameter extracted by the material loss assessment unit from the dynamic dry matter loss rate data M exceeds the critical overload threshold Furthermore, when the system faces the boundary threat of concentrated outbound shipments from local high-risk storage units, the specific rules for extracting the predicted load parameter from the dynamic dry matter loss rate data M in actual operation are as follows: The scheduling decision unit retrieves the time-series data sequence of the dry matter loss rate of each storage unit within the current specific sliding time window in real time. The absolute value of the time-domain change rate of the current loss rate is calculated using a first-order forward difference operator. The latest absolute value of the loss rate in the current period is then weighted and summed with the absolute value of the change rate. The resulting scalar comprehensive evaluation benchmark is defined as the predicted load parameter, which is used as the basis for future production scheduling. The instruction predicts the core input indicators of mechanical impact pressure in the continuous processing production line segment during the forward assessment phase. The concurrent scheduling matrix reconstruction module within the scheduling decision unit automatically intercepts the independent priority outbound action of this single unit in the system's runtime memory space, forcibly constructs a hedging scheduling combination, selects low-loss compensation storage units in the remaining healthy storage queue with processing resistance characteristic values ​​in the low range, and reconstructs them in the logical topology network in association with overheat loss storage units at risk of overload. It also calls the internal impedance hedging solution operator, ensuring that the predicted composite processing resistance characteristic value is below the critical overload threshold. Under rigid boundary constraints, the concurrent disbursement velocity vector of each storage unit is calculated using the weighted average characteristics of the combined impedance of materials in different storage units. The specific formula for calculating the characteristic value of the combined processing resistance is as follows: ,in, To predict the characteristic value of the synthetic processing resistance, This refers to the component velocity value in the concurrent allocation velocity vector of the over-temperature loss storage unit. This represents the independent processing resistance characteristic value of the over-temperature loss storage unit. To compensate for low-loss delivery of the component velocity value in the concurrent allocation velocity vector of the storage unit, To compensate for the independent processing resistance characteristics of storage units with low loss, the concurrent scheduling matrix reconstruction module outputs a set of values ​​that satisfy the optimal convergence condition. The established solution for maximizing the overall flow velocity ratio transforms the single-track timing scheduling parameters into a concurrent scheduling control matrix containing a set of multiple flow velocity setpoints. The concurrent scheduling matrix reconstruction module, based on multi-constraint convex optimization theory and the law of mass conservation, maximizes the total conveying mass flow rate through boundary gradient search, provided that the synthetic processing resistance does not exceed the equipment's safe bearing limit. The concurrent scheduling matrix reconstruction module also incorporates the independent processing resistance characteristic values ​​of the over-temperature loss storage unit. Independent processing resistance characteristic value of low-loss compensation storage unit As a constant input to the computational core, the critical overload threshold for the current cycle is also read. The calculation steps are as follows: The objective function is set as the component flow rate value of the over-temperature loss storage unit. Component flow rate value of low-loss compensation storage unit The sum is maximized; boundary constraints are introduced to maximize the eigenvalue of the synthetic processing resistance. Less than or equal to the critical overload threshold ,and The search is confined to a bounded closed interval between 0.1 kg / s and 0.4 kg / s; a cyclically increasing search method is employed, with a search step size of 0.01 kg / s, increasing from zero. The numerical value is used to calculate the allowable value based on the formula derived from the synthetic processing resistance characteristic value. Maximum boundary value; among all flow velocity combinations that satisfy the constraints, select the unique real solution that maximizes the objective function value as the concurrent flow velocity vector, limit the underlying register instructions of the flow regulating valve, and realize the control of the conveying speed. The scheduling decision unit converts the concurrent scheduling control matrix into underlying register adjustment instructions and sends them to the flow regulating valves allocated to each storage unit to adjust the output material ratio of each storage unit. At the physical level, it controls the proportional mixing of heterogeneous raw materials to adaptively smooth the high-frequency residual fluctuations of the processing resistance characteristic value at the production line end; in order to cope with the non-ideal situation of random drops in communication network bandwidth. Under normal operating conditions, when the adaptive degradation control module inside the scheduling decision unit detects in real time that the packet loss rate in the internal data network of the system exceeds the preset safety threshold, the automatic control system interrupts the matrix calculation of the concurrent scheduling matrix reconstruction module under the hedging scheduling combination, and switches to the threshold interception control mode based on discrete resistance ladder. The adaptive degradation control module reads the safety period reduction constant from the internally configured constant coefficient storage matrix, and uses the safety period reduction constant to cut off the allowable disbursement flow rate of each storage unit, thereby suppressing the surge in processing resistance caused by the over-temperature loss storage unit and maintaining the basic low data bandwidth regulation function closed loop.

[0033] Through the coordinated adjustment of cross-warehouse concurrent mixed flow hedging scheduling in the aforementioned data space, the over-temperature loss storage unit and the low-loss compensation storage unit have completed the associated reconstruction in the memory space. This allows high-loss raw materials that would otherwise be forcibly scrapped or downgraded due to the processing equipment breaking through the critical overload threshold caused by concentrated outbound shipments to continuously and safely penetrate into the normal low-resistance material flow with a controlled concurrent allocation flow vector. Without causing physical overload shutdowns of downstream continuous processing equipment, the resistance characteristic value exhibited at the processing end is diluted in advance in the data processing flow and the overload peak is smoothed out. This resolves the conflict between the accumulation of deterioration activity of distributed raw materials in multiple warehouses and the continuous high-throughput operation of the production line. It also adjusts the material conveying speed from the over-temperature loss storage unit to the downstream processing equipment, so that the actual processing yield of the global assets and the material flow speed of the entire production line are improved simultaneously. This achieves the decoupling control objective of asset clearing and stable continuous operation of the production line, demonstrating the distributed information collaborative construction principle of translating the underlying physical degradation constraints into a nonlinear hedging matrix in the management predictive data processing space.

[0034] Example 2: In a semi-industrial verification platform comprising multiple node computing servers, a distributed warehouse test array, and a multi-task root and stem cutting workshop, the verification operation uses spatiotemporal feature datasets collected by temperature and humidity sensor arrays within 24 physical warehouse zones as the original input data source. The temperature sensor array has a measurement resolution of 0.1℃. The scheduling decision unit reads the discrete active power data sequence from the internal register of the cutting motor inverter of the downstream processing equipment through a standard industrial communication bus interface. Its data sampling frequency is set to 50Hz, and the key timing input parameter in the calibration control loop is the fixed sampling period. At that time, its control constraint is to offset the dynamic deviation between the processing load of the central processing unit of the computing host and the timely detection of abnormal transitions in inventory loss status when multiple data sources are input concurrently. Its deterministic control rule is manifested in the fact that the spatiotemporal correlation verification module inside the system calculates the absolute value of the first derivative of the temperature spatial gradient of adjacent temperature measurement nodes. When the absolute value of the first derivative of the spatial gradient exceeds the preset deterioration acceleration threshold, the judgment rule controls the fixed sampling period. The sampling period is fixed when the global temperature gradient is within a stable range and converges to the lower limit of 1.0s to capture transient respiratory thermal peaks. To reduce the bus data packet throughput pressure, the sampling period is extended to the upper limit of its numerical range of 10.0s. Therefore, under the current verification conditions, the above judgment rule is applied to fix the sampling period. The setpoint was fixed at 5.0s. To offset environmental interference and construct a comparative verification of different data intensities, the experimental scheme actively superimposed a non-stationary heat infiltration noise disturbance with an amplitude of 1.5℃ onto the input data source to simulate data distortion caused by real meteorological changes. The experimental objects were divided into three experimental groups with equally spaced gradients in the initial deterioration characteristic data. The initial dry matter loss rate data M of the first experimental group was set to 2.1% to represent the normal working condition, the initial dry matter loss rate data M of the second experimental group was set to 9.4% to represent the mid-term mild deterioration working condition, and the initial dry matter loss rate data M of the third experimental group was set to 18.6% to represent the late-term severe deterioration working condition. At the same time, a missing feature control group was added, which removed the concurrent scheduling matrix reconstruction module and only used the single-dimensional time-series outbound queuing rule, and a critical overload threshold was added. Set as an out-of-range control group exceeding the equipment's rated safe load limit, after system startup, the memory unit inside the material loss assessment unit receives the input sequence from the temperature measurement node and calls the external wall infiltration heat counter-gating module to extract the wall heat transfer gradient vector. According to the signal stripping rules, subtraction is performed on the original sequence containing noise to filter out the influence of heat seepage from the outer wall. The processor inside the scheduling decision unit reads the cutting motor within a fixed time window. Active power data sequence within The high-frequency fluctuation residual value with a root mean square value of 42.6W was extracted, and the intermediate processing resistance characteristic value, which characterizes the current physical properties of the raw material, was calculated in real time based on the ratio of the high-frequency fluctuation residual value to the fundamental power trend value. At this point, in the control group with missing features and no concurrent reconstruction, as the strength of raw material degradation increases, the intermediate machining resistance characteristic value at the cutting motor increases. It exhibits a linear upward trend. Under the severe deterioration condition corresponding to the third test group, the measured processing resistance characteristic value directly rises to a dimensionless value of 86.4. However, in the out-of-range control group, the overload threshold is set too wide, and the control action fails to intercept the outbound action of the over-temperature loss storage unit in time. As a result, when the outbound gate is opened, the cutting motor current instantly breaks through the rated safe overload boundary, causing the verification platform to experience physical overload shutdown.

[0035] In the prototype of this invention, which maintains the integrity of its technical features, the concurrent scheduling matrix reconstruction module intercepts the independent full-volume outbound actions of overheated loss storage units in the system's runtime memory space. For the first, second, and third experimental groups, it directly pairs the corresponding deterioration risk weights with the inventory availability of low-loss compensation storage units to reconstruct the production scheduling time slice allocation matrix, thereby generating a collaborative hedging constraint matrix. Specifically, when constructing the hedging scheduling combination in the system's runtime memory space, the concurrent scheduling matrix reconstruction module performs Kronecker product algebra operations on the deterioration risk weight vectors of each high-risk physical partition and the inventory availability vectors of each healthy partition, thereby constructing a collaborative hedging constraint matrix containing multi-dimensional concurrent resource allocation boundaries. Each row of this matrix corresponds to an overheated loss storage unit, and each column corresponds to a low-loss compensation storage unit. The cross-columns within the matrix... The meta-numerical characterization represents the evolution weight of cross-hedging risk during pairwise mixed flow. In the subsequent flow rate optimization solution stage, the main control central processor introduces maximizing the total conveying mass flow rate as the objective function, and uses the equipment safety bearing limit mapped by the collaborative hedging constraint matrix as the rigid constraint boundary condition. Through the boundary gradient search method, the high-dimensional matrix logical constraint projection is reduced to a one-dimensional scalar control space, thus transforming it into finding a real number solution for the comprehensive flow rate ratio that satisfies the requirement that the combined resistance does not exceed the limit. The internal impedance hedging solution operator is called, and the concurrent allocation flow rate vector is solved in real time based on the weighted average characteristics of the combined impedance of materials in each storage unit. The concurrent scheduling control matrix containing multiple flow rate setpoints is automatically output to the flow regulating valve register on each storage unit. The mathematical relationship used by the concurrent scheduling matrix reconstruction module to calculate the characteristic value of the combined processing resistance is as follows: ,in, To predict the characteristic value of the synthetic processing resistance, This refers to the component velocity value in the concurrent allocation velocity vector of the over-temperature loss storage unit. This represents the independent processing resistance characteristic value of the over-temperature loss storage unit. To compensate for low-loss delivery of the component velocity value in the concurrent allocation velocity vector of the storage unit, To compensate for the independent processing resistance characteristics of storage units with low loss, the concurrent scheduling matrix reconstruction module outputs a set of values ​​that satisfy the optimal convergence condition. The established integrated flow rate ratio solution transforms the single-track timing scheduling parameters into a concurrent scheduling control matrix containing multiple flow rate setpoints. The scheduling decision unit converts the concurrent scheduling control matrix into underlying register adjustment instructions and sends them to the flow regulation valves allocated to each storage unit to adjust the output material ratio of each storage unit. This physically controls the proportional mixing of heterogeneous raw materials, smoothing out high-frequency residual fluctuations in the processing resistance characteristic value at the production line end. In the event of a random drop in the bandwidth of the line communication network, the adaptive degradation control module within the scheduling decision unit will... When the packet loss rate in the internal data network of the monitoring system exceeds the preset safety threshold, the automatic control system interrupts the matrix calculation of the concurrent scheduling matrix reconstruction module under the hedging scheduling combination, and switches to the threshold interception control mode based on discrete resistance ladder. The adaptive degradation control module reads the safety period reduction constant from the internally stored constant coefficient storage matrix and uses the safety period reduction constant to cut off the allowable disbursement flow rate of each storage unit, thereby suppressing the surge in processing resistance caused by the over-temperature loss storage unit and maintaining the basic low data bandwidth regulation function closed loop.

[0036] Through the coordinated adjustment of cross-warehouse concurrent mixed flow hedging scheduling in the aforementioned data space, the over-temperature loss storage unit and the low-loss compensation storage unit complete the associated reconstruction in the memory space. This allows high-loss raw materials that would otherwise face forced scrapping or degradation due to the processing equipment breaking through the critical overload threshold caused by concentrated outbound shipments to continuously and safely penetrate into the normal low-resistance material flow with a controlled concurrent allocation flow vector. Without causing physical overload shutdowns of downstream continuous processing equipment, the resistance characteristic value exhibited at the processing end is diluted in advance in the data processing flow and the overload peak is smoothed out. This resolves the conflict between the accumulation of deterioration activity of distributed raw materials in multiple warehouses and the continuous high-throughput operation of the production line. It also adjusts the material conveying speed from the over-temperature loss storage unit to the downstream processing equipment, simultaneously improving the actual processing yield of global assets and the material flow speed of the entire production line. It controls the decoupling of asset clearing and stable continuous operation of the production line, and establishes distributed information coordination rules that translate the underlying physical degradation constraints into a nonlinear hedging matrix in the management and prediction data processing space.

[0037] Example 3: In a distributed processing inventory control scenario for root and tuber materials, when the system faces an abnormal fluctuation in the current load of the cutting motor due to the fibrosis of the raw material, the intelligent inventory control system for root and tuber processing will use the discrete active power data sequence output by the frequency converter of the cutting motor. As input features, to eliminate the interference of nonlinear degradation processes on the system control sensitivity, the processing impedance solution operator is applied to the active power data sequence. A first-order forward numerical difference operator is performed, combined with a weighted moving average filter within a specific time window, to identify the characteristics of the material fiber resistance signal. The closed-loop engineering execution parameters of this process are based on the input vector. Perform, where the input vector Depend on It consists of discrete power sample points, and the processing logic is to extract... The system calculates the magnitude of the numerical variation between adjacent data points and outputs a processed vector containing high-frequency fluctuation residuals. By comparing the root mean square value of the processed vector with the fundamental power trend value, the system adaptively determines the cutting resistance state of the current material composition. This determination logic applies when the root mean square value exceeds a preset threshold. The data points are identified as critical zones of processing load. By outputting a load over-limit state trigger signal to the concurrent scheduling matrix reconstruction module, the offset scheduling is initiated, thereby proportionally distributing high-resistance raw materials and low-resistance raw materials at the material flow level to achieve a smooth transition of the processing equipment load.

[0038] To address the engineering challenge of zero-point drift in sensors due to prolonged exposure to high humidity and dust environments, the adaptive degradation control module incorporates a standardized calibration procedure. This procedure involves offline setting of temperature and humidity reference standards for each storage unit, comparing the voltage output values ​​of each temperature sensor node with the ideal response curve under the standard reference environment, calculating the deviation drift, and generating a drift compensation factor. Adjusted critical overload threshold Dynamically follow formula: ,in, This is the adjusted critical overload threshold. The preset constant benchmark threshold, As a time-dependent drift compensation factor calculated based on the statistical variance of temperature residuals, the adaptive degradation control module dynamically updates the time-dependent drift compensation factor according to the real-time feedback value of the temperature sensor array during each 24-hour global scan cycle. The value of this parameter ensures that the system can still accurately determine the machining resistance characteristic value even when the reference drift is caused by sensor aging. Maintaining a reasonable dynamic control envelope range ensures the stability of the production line's throughput, enabling long-term, highly reliable prediction and intelligent, precise control of the deterioration state of distributed warehouse materials.

[0039] Example 4: In the application of distributed and non-constant temperature control for large-scale continuous storage and regulation of root and tuber processed materials over long periods, in order to ensure that the system can accurately record the dry matter loss rate data caused by material metabolism in complex industrial environments... For stable calculation, the material loss assessment unit has a pre-set standardized pre-calibration procedure. Under the physical boundary of the compartment being in an empty state with a constant internal airflow velocity of 0.5m / s and no external heat source interference, the processor reads the original voltage sequence of each temperature sensing node. When the temperature fluctuation variance value is continuously kept below 0.05 within a continuous sampling period of 600.0s, this state is defined as the trigger point for the construction of the intrinsic noise model of the hardware environment. The processor extracts the voltage amplitude of each node and stores it in the reference electrical noise matrix. All physical measurement signals in subsequent operation are de-offset processed based on this reference. This calibration method based on the no-load steady-state physical environment eliminates the reference error introduced by the sensor circuit itself with temperature drift, ensuring that the logical starting point of loss rate calculation has absolute technical rigor.

[0040] To establish an engineering-quantitative closed loop between the change in respiration heat released by materials during storage and the actual dry matter loss, the system embeds probes in the deep core area of ​​the material to measure the local temperature gradient and introduces the respiration load index. As a core data decision parameter, its physical implementation relies on a material heat dissipation kinetic model. It extracts two discrete local temperature rise data points with a time interval of 180.0 s and convolves them with the material's thermal impedance coefficient to obtain specific biochemical metabolic dissipation rate indicators and respiratory load indexes. The calculation logic is as follows: ,in, The respiratory load index, The bulk density of the material. This represents the local temperature rise value within a discrete sampling time interval. For discrete sampling time intervals, The effective thermal conductivity damping coefficient of the material.

[0041] The system reads the temperature data of the core area and calculates the local temperature rise rate. Under calibration conditions, the local temperature rise rate in the deep core area of ​​the material is measured to be 0.003℃ / s. By substituting this parameter into the model, the system outputs the breathing load index. The value is 3.4, and it is automatically written into the constant coefficient storage matrix as a boundary constraint index. When the scheduling decision unit detects that this value causes the predicted load to exceed the processing threshold, it immediately adjusts the component of the storage unit in the concurrent allocation flow vector, transforming the underlying physical quantity into a flow coordination allocation instruction between the storage space and the manufacturing space, thus realizing closed-loop control of material endogenous metabolic dissipation and manufacturing throughput stability.

[0042] Example 5: Before the continuous root and tuber material control system is put into production, each storage unit performs a standardized calibration procedure to eliminate the cumulative reference error caused by the zero-point electrical signal offset of the multi-source sensors under long-term operating conditions in the calculation of dry matter loss rate data M. The calibration procedure is performed in a constant environment where the compartment is empty and there is no external power or heat source coupling. It requires that the basic floating-point computing power of the enabling environment be maintained at 128 MFLOPS, the data bus throughput rate be no less than 115200 bps, and the compartment space envelope size meet the physical reference of 12.0m × 6.0m × 4.0m.

[0043] Under the aforementioned boundary requirements, the material loss assessment unit calls the spatiotemporal correlation verification module to continuously read the voltage output values ​​of each sensor node. When the sampling duration spans 600.0 s and the temperature difference fluctuation variance remains less than 0.05, the system is determined to have entered a physical steady state. The system extracts the voltage amplitude of each node and stores it in the hardware intrinsic electrical noise register, which is used to perform subtraction and zeroing correction on the subsequently acquired physical signals. This electrical reference calibration mechanism based on a physical static field decouples the electrical noise correlation between the sensor itself and external physical quantities, establishes the physical traceability basis for loss rate calculation, and introduces a respiratory load index to correlate biomass respiratory metabolic intensity with material quality deterioration rate. And based on the monitoring data of the local temperature rise rate, the respiratory load index is calculated. The calculation formula is as follows: ,in, The respiratory load index, The bulk density of the material. This represents the local temperature rise value within a discrete sampling time interval. For discrete sampling time intervals, The effective thermal conductivity damping coefficient of the material is used to eliminate the dimensional non-equivalence caused by the direct product of the aforementioned physical parameters, ensuring that it is precisely nested within the theoretical orbit of dimensionless scalars. From a physical perspective, the calculation formula includes a physical reference denominator term for normalization. Specifically, when the material loss assessment unit calculates the breathing load index I, it divides the product of the material bulk density, local temperature rise rate, and effective thermal conductivity damping coefficient by the standard physical property reference scalar Pref, which is preset in the constant coefficient storage matrix. The physical dimensions of the standard physical property reference scalar Pref are completely equivalent and matched with the dimensions after the direct product of the aforementioned three parameters. Its value is the intrinsic heat dissipation reference power density of the material in a completely static cold storage without any endogenous biochemical respiratory metabolic activity, as pre-determined by the system under the same storage space envelope size. This normalization mapping rule completely cancels out the macroscopic physical dimensions, thereby ensuring that the final output breathing load index I is a pure numerical dimensionless scalar.

[0044] During baseline calibration, the system embedded a thermal response probe at a core depth of 2.5m in the material stack. The local temperature rise rate at a measurement interval of 180.0s was 0.003℃ / s. Based on the preset property transfer function, the system outputs the respiratory load index. The scalar value is 3.4, and this value is stored in the constant coefficient storage matrix of the scheduling decision unit, which serves as the critical overload threshold under multi-task conditions. The calibration benchmark, respiratory load index With critical overload threshold The corresponding relationship is based on the thermodynamic tissue degradation law. The cumulative heat generated by the respiration metabolism of materials has a deterministic positive correlation with the degradation rate of cellulose in root and stem tissue, thereby reducing the load boundary that the processing equipment can withstand; the working range of the respiration load index is 1.0 to 5.0; when the respiration load index When the value is less than or equal to 2.0, the material is hard, the equipment's active power is stable, and the corresponding constant reference threshold is... It is eighty; when the respiratory load index When the pH is in the range of 2.0 to 4.0, the tissue partially softens. When it is 3.4, the corresponding constant benchmark threshold It is 65; when the respiratory load index When the value is greater than 4.0, the material exhibits deep degradation and is prone to tool adhesion; constant reference threshold. Below forty; the constant coefficient storage matrix stores the corresponding relationship array, and the system calculates the respiratory load index in real time. Retrieve the unique constant benchmark threshold And combined with drift compensation factor The adjusted critical overload threshold is calculated by multiplication. This provides boundary constraints for subsequent reconstruction calculations. In subsequent operation, the system calculates the drift compensation factor based on the statistical variance of the temperature residuals measured in real time. The value is 0.92, and this compensation factor is used to dynamically correct the critical overload threshold. This enables dynamic parameter correction of zero-point inaccuracy caused by sensor aging. Finally, the experiment verified that when the flow rate of high-loss raw materials is limited to the window range of 0.1 kg / s to 0.4 kg / s, the load fluctuation of the cutting motor of the downstream processing equipment is stable, thus confirming the effective matching of parameter optimization logic with heterogeneous physical raw materials and production line throughput capacity control.

[0045] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A smart control system for root and tuber processing inventory, characterized in that, include: The scheduling decision unit is connected to the material loss assessment unit; the scheduling decision unit has a concurrent scheduling matrix reconstruction module; the scheduling decision unit is used to separate the high-frequency fluctuation residual value from the obtained operating power sequence of the downstream processing equipment, and calculate the processing resistance characteristic value according to the ratio of the high-frequency fluctuation residual value to the fundamental power trend value stored in the system. The material loss assessment unit is used to obtain dynamic dry matter loss rate data of materials in each storage unit. The concurrent scheduling matrix reconstruction module is used to reconstruct the overheating loss storage units in the system's memory space when the predicted load parameters extracted from the dynamic dry matter loss rate data exceed the critical overload threshold. These units are associated with low-loss compensation storage units that provide hedging capacity and are at risk of overload due to the rising dynamic dry matter loss rate data. The module is then used to reconstruct the hedging scheduling combination. Under the boundary constraint that the predicted synthetic processing resistance characteristic value is lower than the critical overload threshold, the module calculates the concurrent allocation flow velocity vector for each storage unit. This transforms the single-track timing scheduling parameters into a concurrent scheduling control matrix containing a set of multiple flow velocity setpoints, thereby adjusting the conveying speed of materials from the overheating loss storage unit to the downstream processing equipment.

2. The intelligent control system for root and stem processing inventory according to claim 1, characterized in that, The material loss assessment unit is externally connected to sensor components arranged in each storage unit, and internally includes a sensor spatiotemporal correlation verification module and a data substitution estimation module. The sensor spatiotemporal correlation verification module is used to extract the data feature differences between adjacent sensor nodes in the sensor components under a set heat transfer topology, thereby verifying the spatial data constraint relationship and locating the fault node. When the fault node is located, the data substitution estimation module is used to call the data substitution estimation rule based on the monotonic characteristics of thermophysical properties to reconstruct the virtual temperature vector, and input the virtual temperature vector into the material loss assessment unit to correct the dynamic dry matter loss rate data.

3. The intelligent control system for root and tuber processing inventory according to claim 1, characterized in that, The scheduling decision unit also includes an adaptive degradation control module; The adaptive degradation control module is used to monitor the packet loss rate in the internal data network of the system in real time. When the packet loss rate exceeds the preset safety threshold, the control system interrupts the matrix calculation of the concurrent scheduling matrix reconstruction module under the hedging scheduling combination and switches to the threshold interception control mode based on discrete resistance ladder.

4. The intelligent control system for root and tuber processing inventory according to claim 3, characterized in that, The adaptive degradation control module has a constant coefficient storage matrix inside. In the threshold interception control mode, the adaptive degradation control module reads the safety period reduction constant from the constant coefficient storage matrix and uses the safety period reduction constant to cut off the allowable disbursement flow rate of each storage unit, thereby suppressing the surge in processing resistance caused by the over-temperature loss storage unit.

5. The intelligent control system for root and tuber processing inventory according to claim 1, characterized in that, When constructing hedging scheduling combinations in the system's running memory space, the concurrent scheduling matrix reconstruction module pairs the deterioration risk weight corresponding to the over-temperature loss storage unit with the inventory availability corresponding to the low-loss compensation storage unit to generate a collaborative hedging constraint matrix.

6. The intelligent control system for root and tuber processing inventory according to claim 1, characterized in that, The scheduling decision unit is externally connected to flow regulating valves distributed on each storage unit; the scheduling decision unit is used to convert the concurrent scheduling control matrix into underlying register adjustment instructions and send them to the flow regulating valves, thereby adjusting the output material ratio of each storage unit.

7. The intelligent control system for root and tuber processing inventory according to claim 2, characterized in that, The sensor assembly includes temperature and humidity sensor arrays arranged inside each storage unit; The material loss assessment unit is used to import temperature distribution data collected by the temperature sensor array and humidity distribution data collected by the humidity sensor array, and calculate the dynamic dry matter loss rate data based on the temperature distribution data and humidity distribution data.

8. The intelligent control system for root and tuber processing inventory according to claim 1, characterized in that, The scheduling decision unit is externally connected to a processing equipment power consumption monitoring module; the processing equipment power consumption monitoring module is used to collect the active power of the motors of downstream processing equipment in real time, thereby generating the operating power sequence of downstream processing equipment.

9. The intelligent control system for root and tuber processing inventory according to claim 1, characterized in that, The scheduling decision unit also includes an extended vector generation module and a production scheduling optimization module. The extended vector generation module is used to extract the attenuation acceleration inflection point of the loss and deterioration stage in the time domain based on dynamic dry matter loss rate data, and generate a production scheduling extended vector accordingly. The extended vector generation module is used to input the production scheduling extended vector as a time-series constraint boundary into the production scheduling optimization module to update and optimize the production scheduling time slice allocation matrix, thereby eliminating the production line flow rate stagnation caused by the release of high-loss batches while stabilizing the processing resistance characteristic value within the preset safety range.

10. A method for intelligent control of root and tuber processing inventory, used to operate the intelligent control system for root and tuber processing inventory as described in claim 1, characterized in that, Includes the following steps: Step S1: Use the material loss assessment unit to obtain dynamic dry matter loss rate data of materials in each storage unit. Step S2: The high-frequency fluctuation residual value is separated from the obtained operating power sequence of the downstream processing equipment by the scheduling decision unit, and the processing resistance characteristic value is calculated according to the ratio of the high-frequency fluctuation residual value to the fundamental power trend value stored in the system. Step S3: When the predicted load parameter in the dynamic dry matter loss rate data exceeds the critical overload threshold, the concurrent scheduling matrix reconstruction module inside the scheduling decision unit is used to associate and reconstruct the over-temperature loss storage units in each storage unit that are at risk of overload due to the rise in dynamic dry matter loss rate data with the low loss compensation storage units used to provide hedging capacity in the system running memory space, and reconstruct them into a hedging scheduling combination. Step S4: Under the boundary constraint condition that the predicted synthetic processing resistance characteristic value is lower than the critical overload threshold, the concurrent allocation flow velocity vector of each storage unit is calculated using the concurrent scheduling matrix reconstruction module. The single-track timing scheduling parameters are transformed into a concurrent scheduling control matrix containing a set of multiple flow velocity settings, thereby adjusting the conveying speed of materials in the over-temperature loss storage unit to the downstream processing equipment.

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