Animal carcass hydrolysis treatment and peptide liquid recovery intelligent system based on Internet of Things

Abstract

The invention relates to the technical field of biological resource recovery, in particular to an intelligent animal carcass hydrolysis treatment and peptide liquid recovery system based on the Internet of Things. The system comprises an Internet of Things data acquisition module, an intelligent decision control module, an execution module, a linkage safety module and a remote monitoring and data tracing module. The internet-of-things data acquisition module acquires multi-dimensional data in real time and transmits the multi-dimensional data to the intelligent decision control module, operation constraints are dynamically adjusted through long-term processing planning, short-term reaction prediction, tactical optimization and feedback correction, an optimization problem is solved by adopting a sequential quadratic programming algorithm, and an accurate hydrolysis and peptide liquid recovery parameter control instruction is generated; the linkage safety module is used for realizing safety interlocking protection and blocking instruction execution in an unsafe state; and the remote monitoring and data tracing module provides full-process data storage, tracing and remote access functions. The treatment efficiency, the peptide liquid quality and the system operation safety and traceability are improved.

Description

Technical Field

[0001] This invention relates to the field of biological resource recycling technology, and more specifically, to an intelligent system for the hydrolysis treatment of animal carcasses and the recycling of peptide liquid based on the Internet of Things. Background Technology

[0002] The harmless treatment of animal carcasses (including diseased animals and slaughter waste) is a key link in ensuring public health and safety and preventing environmental pollution. At the same time, animal carcasses are rich in biological resources such as protein. By using hydrolysis technology to recover products such as peptide liquid, resources can be recycled, which has important economic and environmental value.

[0003] Traditional methods for handling animal carcasses mainly include incineration, landfill, and simple hydrolysis. However, with increasing environmental protection requirements and growing demand for resource recycling, traditional methods have gradually revealed many limitations: incineration consumes a lot of energy and easily produces harmful gases that pollute the atmosphere; landfill occupies land resources, may cause soil and groundwater pollution, and cannot achieve resource recycling; simple hydrolysis relies on fixed process parameters and lacks the ability to dynamically adapt to the characteristics of animal carcasses and the reaction process. Therefore, an intelligent system for animal carcass hydrolysis treatment and peptide liquid recovery based on the Internet of Things was designed.

[0004] The existing technology has the following shortcomings, specifically:

[0005] 1. Rigid parameters and insufficient coordination between processing and recovery: The use of fixed process parameters cannot adapt to the characteristics of animal carcasses and the dynamic changes of the reaction. Furthermore, the hydrolysis reaction and peptide recovery are independent of each other, and parameter adjustments are not linked, resulting in low peptide recovery rate and large fluctuations in purity.

[0006] 2. Lack of safety protection and weak risk management: There is a lack of real-time interlock protection for abnormalities in high-temperature and high-pressure equipment (leakage, pressure / temperature exceeding the standard), and there is no targeted control for pathogens, which can easily lead to secondary pollution or safety accidents.

[0007] 3. Lack of full-process traceability and chaotic multi-batch processing: The lack of a standardized data storage and traceability system makes it difficult to trace quality problems to their source; the lack of a scientific resource allocation mechanism during multi-batch processing easily leads to cross-contamination and low efficiency. Summary of the Invention

[0008] The purpose of this invention is to provide an intelligent system for the hydrolysis treatment of animal carcasses and the recovery of peptide liquid based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention aims to provide an intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things (IoT), comprising: an IoT data acquisition module for real-time acquisition of animal carcass attribute data, hydrolysis reaction process data, peptide recovery process data, and equipment operating status data, and transmitting the data to an intelligent decision control module via the IoT.

[0010] The intelligent decision control module is used to generate long-term processing plans and short-term reaction predictions based on collected data, optimize hydrolysis and recovery parameters, and generate control commands; at the same time, it monitors the deviation between predicted values ​​and actual data in real time and triggers constraint adjustment strategies.

[0011] The execution module is used to execute the modified hydrolysis parameter control instructions and peptide liquid recovery parameter control instructions, driving the hydrolysis reaction equipment, peptide liquid recovery equipment and auxiliary equipment to complete the corresponding actions.

[0012] The linkage safety module is used to receive correction signals and equipment status data in real time. When the safety linkage conditions are met, it triggers a safety command to block the execution of control commands in unsafe conditions.

[0013] The remote monitoring and data traceability module is used to store all collected data, control commands and processing results, and provides remote access, data traceability, fault warning and report generation functions.

[0014] As a further improvement to this technical solution, the intelligent decision control module includes a long-term processing planning unit, a short-term response prediction unit, a tactical optimization unit, and a feedback correction unit.

[0015] The long-term processing planning unit generates a long-term processing plan based on historical processing data using a random forest algorithm. This plan includes the optimal processing batch for different types of animal carcasses, the total hydrolysis cycle, peptide recovery rate targets, and purity targets.

[0016] The short-term reaction prediction unit uses real-time data from the IoT data acquisition module and a neural network model to generate short-term prediction information for a preset short period of time. The prediction information includes predicted values ​​for hydrolysis reaction progress, peptide concentration, peptide purity, and equipment operating load.

[0017] The tactical optimization unit takes long-term processing plans as a reference and short-term prediction results and real-time collected data as input. Based on the correction signal generated by the feedback correction unit, it dynamically adjusts the initial operating constraints according to the preset adjustment strategy to obtain optimized constraints. It uses a preset algorithm to solve the optimization problem and generates corrected hydrolysis parameter control instructions and peptide liquid recovery parameter control instructions for the current rolling cycle.

[0018] The feedback correction unit calculates the deviation between the short-term predicted value and the real-time acquired data, including the deviation of hydrolysis temperature, peptide concentration, and purity. It generates a deviation feature vector containing the deviation type and duration, and matches it with a predefined rule base to generate a correction signal.

[0019] As a further improvement to this technical solution, the optimization objectives in the tactical optimization unit include maximizing peptide recovery rate, maximizing peptide purity, and minimizing energy consumption and processing time.

[0020] Initial operational constraints include:

[0021] Constraints on hydrolysis reaction, peptide solution recovery, and equipment protection.

[0022] As a further improvement to this technical solution, the preset adjustment strategy in the tactical optimization unit dynamically adjusts the initial operational constraints. Specifically, when a "hydrolysis reaction is too slow" signal is received, that is, the deviation of hydrolysis temperature and the deviation of hydrolysis rate both exceed the preset tolerance and the duration exceeds the preset threshold, the upper limit of hydrolysis temperature is increased by a preset ratio, but not exceeding the equipment safety limit.

[0023] Increase the upper limit of enzyme addition according to the preset ratio, but do not exceed the preset safety threshold.

[0024] The reactor's continuous operating time constraint is extended according to a preset ratio.

[0025] When a "peptide purity insufficient" signal is received, meaning that the peptide purity deviation exceeds the preset tolerance and the impurity content exceeds the threshold, and the duration exceeds the preset threshold: increase the filtration pressure according to the preset ratio, but do not exceed the equipment safety limit.

[0026] A new constraint on the amount of adsorbent added has been introduced, ensuring that the amount of adsorbent added is within a preset reasonable range.

[0027] Increase the lower limit of the concentration ratio according to the preset ratio, and do not lower than the preset minimum concentration standard.

[0028] When a "equipment overload" signal is received, that is, the pump vibration value exceeds the preset tolerance or the energy consumption exceeds the preset proportion of the predicted value, and the duration exceeds the preset threshold: the upper limit of hydrolysis temperature and the upper limit of pressure are reduced by the preset proportion.

[0029] Reduce the stirring speed and peptide liquid recovery flow rate according to the preset ratio.

[0030] When a "pathogen risk warning" signal is received, i.e., the pathogen detection sensor outputs a positive result: raise the lower limit of the hydrolysis temperature and the lower limit of the pressure to the preset safety standard.

[0031] The hydrolysis reaction cycle and disinfection time are extended according to the preset ratio.

[0032] As a further improvement to this technical solution, the correction signals of the feedback correction unit include: a signal indicating that the hydrolysis reaction is too slow, a signal indicating that the peptide solution is not pure enough, a signal indicating that the equipment load exceeds the standard, a signal indicating that the pathogen risk is warning, and a signal indicating that the peptide solution recovery rate meets the standard.

[0033] As a further improvement to this technical solution, the execution module includes a hydrolysis reaction execution subunit, a peptide liquid recovery execution subunit, and a disinfection and sterilization subunit.

[0034] The hydrolysis reaction execution subunit is used to adjust the temperature, pressure, enzyme addition amount, and stirring speed of the hydrolysis reactor.

[0035] The peptide liquid recovery execution subunit is used to adjust the filtration pressure and concentration factor to achieve the linkage of peptide liquid filtration, concentration and purification.

[0036] The disinfection and sterilization subunit is used to perform disinfection operations on hydrolyzed residues, peptide solutions, and the interior of equipment according to preset disinfection standards.

[0037] As a further improvement to this technical solution, the specific implementation steps for the feedback correction unit to generate a deviation feature vector containing the deviation type and the duration of the deviation are as follows:

[0038] Step 1: Data input. Obtain the predicted values ​​of hydrolysis temperature, peptide concentration, and peptide purity output by the short-term reaction prediction unit, as well as the corresponding real-time data synchronously collected by the IoT data acquisition module.

[0039] Step 2: Deviation calculation. Calculate the difference between the predicted hydrolysis temperature and the real-time hydrolysis temperature to obtain the hydrolysis temperature deviation. Calculate the difference between the predicted peptide concentration and the real-time peptide concentration to obtain the peptide concentration deviation. Calculate the difference between the predicted peptide purity and the real-time peptide purity to obtain the peptide purity deviation.

[0040] Step 3: Deviation type determination. Compare each deviation with the preset tolerance to determine whether the deviation is positively exceeded, negatively exceeded, or within a reasonable range, and form a deviation type identifier.

[0041] Step 4: Duration statistics: Start the timing mechanism to record the duration of each deviation type identifier, and obtain the duration of the deviation.

[0042] Step 5: Feature vector construction. Combine the deviation type identifier, deviation values ​​of each dimension, and deviation duration in a preset order to form a deviation feature vector containing the deviation type and deviation duration.

[0043] As a further improvement to this technical solution, the specific implementation steps for the tactical optimization unit to generate the corrected hydrolysis parameter control instructions and peptide liquid recovery parameter control instructions for the current rolling cycle are as follows:

[0044] Step 1: Data preprocessing. Receive prediction information from the short-term response prediction unit, real-time data from the IoT data acquisition module, and correction signals from the feedback correction unit. Clean, denoise, and standardize the data.

[0045] Step 2: Optimize target adaptation. Based on the pathogen risk level of animal carcasses determined by the IoT data acquisition module, dynamically adjust the weight allocation of maximizing peptide recovery rate, maximizing peptide purity, minimizing energy consumption, and minimizing processing time.

[0046] Step 3: Constraint adjustment. Based on the correction signal from the feedback correction unit, the hydrolysis reaction constraint, peptide liquid recovery constraint, and equipment protection constraint in the initial operation constraints are dynamically adjusted according to the preset adjustment strategy to obtain the optimized constraint boundary.

[0047] Step 4: Optimize problem solving. Guided by the optimization objective and constrained by the optimized boundary conditions, construct and solve the optimization model using a preset algorithm to generate the control sequence for the next rolling cycle.

[0048] Step 5: Command interception and verification. The control parameters of the current rolling cycle are intercepted from the control sequence as the initial control command. After the safety compliance is verified by the linkage safety module, the corrected hydrolysis parameter control command and peptide liquid recovery parameter control command are obtained.

[0049] Step 6: Instruction output. The corrected control instructions are transmitted to the execution module and simultaneously fed back to the feedback correction unit for record-keeping.

[0050] As a further improvement to this technical solution, the tactical optimization unit solves the optimization problem in the following specific process: the objective function is approximated as a quadratic function, the inequality constraints in the optimized constraint boundary are approximated as linear constraints, a quadratic programming subproblem is constructed, and the optimal solution of the quadratic programming subproblem is obtained to obtain a set of candidate control parameters. The set of candidate control parameters is checked to see if it satisfies all optimized constraint boundaries. If it does, it is considered a valid solution. If it does not, the constraint weights are adjusted and the subproblem is reconstructed. The solution is repeated until a valid solution is obtained. The valid solution is mapped to the control parameters corresponding to the actual process parameter range to form a control sequence for a future rolling cycle.

[0051] As a further improvement to this technical solution, the system has a multi-batch collaborative processing function: when processing multiple types of animal carcasses at the same time, the Internet of Things data acquisition module determines the pathogen risk level of the animal carcasses based on the pathogen detection data and preset risk grading standards, prioritizes the animal carcasses based on their risk level and processing difficulty, and dynamically allocates hydrolysis reactor and recycling equipment resources to avoid cross-contamination between different batches.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] 1. Adaptive linkage optimization to improve processing and recovery quality: By dynamically adjusting operational constraints through deviation feature vectors and combining risk level to optimize target weights, adaptive linkage adjustment of hydrolysis and recovery parameters is achieved, significantly improving peptide recovery rate and purity stability, and solving the problems of parameter rigidity and insufficient synergy.

[0054] 2. Multiple safety interlocks to eliminate operational risks: The linkage safety module monitors the equipment status and correction signals in real time, triggering interlock commands such as emergency shutdown, pressure relief, and disinfection to block operations under unsafe conditions, effectively preventing equipment failure and pathogen spread risks, and making up for the shortcomings in safety protection.

[0055] 3. Full-process traceability + multi-batch collaboration to meet regulatory and efficiency requirements: The remote monitoring module enables full-process data storage and traceability, and supports fault warning and report generation; the multi-batch collaboration function dynamically allocates resources according to risk level to avoid cross-contamination, while improving equipment utilization and solving the problems of missing traceability and chaotic multi-batch processing. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0058] Figure 2 This is a schematic diagram of the intelligent decision control module of the present invention.

[0059] Figure 3 This is a schematic diagram of the execution module of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Example: Please refer to Figure 1As shown, an intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things (IoT) is provided, including: an IoT data acquisition module, which is used to collect animal carcass attribute data, hydrolysis reaction process data, peptide recovery process data and equipment operating status data in real time, and transmit them to the intelligent decision control module via the IoT.

[0062] In another embodiment, the IoT data acquisition module includes a sensor network and a data transmission unit:

[0063] Sensor networks include:

[0064] Animal carcass property sensor: used to collect data on the type, weight, moisture content, pH value, and pathogen detection of animal carcasses (achieved through a real-time PCR sensor).

[0065] Hydrolysis reaction sensor: used to collect data on temperature (accuracy ±0.5℃), pressure (accuracy ±0.01MPa), pH value (accuracy ±0.05), enzyme concentration, reaction liquid level and stirring speed in the hydrolysis reactor.

[0066] Peptide solution recovery sensor: Used to collect data on peptide solution concentration (via UV spectrophotometer), purity (via HPLC sensor), flow rate, filtration pressure difference, and concentration factor.

[0067] Equipment status sensors: used to collect the operating status (start / stop, load), vibration values ​​(accuracy ±0.1mm / s), sealing leakage signals and fault alarm data of hydrolysis reactors, filters, concentrators, pumps and valves.

[0068] The data transmission unit adopts 5G+LoRa dual-mode transmission to realize real-time data uploading and downlink transmission of control commands, supports edge computing preprocessing (data noise reduction, outlier removal), and ensures stable communication in weak network environments.

[0069] The intelligent decision control module is used to generate long-term processing plans and short-term reaction predictions based on collected data, optimize hydrolysis and recovery parameters, and generate control commands; at the same time, it monitors the deviation between predicted values ​​and actual data in real time and triggers constraint adjustment strategies.

[0070] In one specific embodiment, the intelligent decision control module includes a long-term processing planning unit, a short-term response prediction unit, a tactical optimization unit, and a feedback correction unit.

[0071] The long-term processing planning unit generates a long-term processing plan based on historical processing data using a random forest algorithm. This plan includes the optimal processing batch for different types of animal carcasses, the total hydrolysis cycle, peptide recovery rate targets, and purity targets.

[0072] The short-term reaction prediction unit uses real-time data from the IoT data acquisition module and a neural network model to generate short-term prediction information for a preset short period of time. The prediction information includes predicted values ​​for hydrolysis reaction progress, peptide concentration, peptide purity, and equipment operating load.

[0073] The tactical optimization unit takes long-term processing plans as a reference and short-term prediction results and real-time collected data as input. Based on the correction signal generated by the feedback correction unit, it dynamically adjusts the initial operating constraints according to the preset adjustment strategy to obtain optimized constraints. It uses a preset algorithm to solve the optimization problem and generates corrected hydrolysis parameter control instructions and peptide liquid recovery parameter control instructions for the current rolling cycle.

[0074] The preset algorithm includes, but is not limited to, the sequential quadratic programming algorithm.

[0075] The feedback correction unit calculates the deviation between the short-term predicted value and the real-time acquired data, including the deviation of hydrolysis temperature, peptide concentration, and purity. It generates a deviation feature vector containing the deviation type and duration, and matches it with a predefined rule base to generate a correction signal.

[0076] In one specific embodiment, the optimization objectives in the tactical optimization unit include maximizing peptide recovery rate, maximizing peptide purity, and minimizing energy consumption and processing time.

[0077] Initial operational constraints include:

[0078] Hydrolysis reaction constraints: temperature range, pressure range, enzyme dosage range, pH range, and stirring speed range.

[0079] Peptide solution recovery constraints: filtration pressure range, concentration factor range, lower limit of peptide solution purity, and flow rate limit.

[0080] Equipment protection constraints: upper limit of reactor continuous operation time, upper limit of pump vibration value, and seal leakage threshold.

[0081] In another embodiment, the hydrolysis reaction is constrained by the following parameters: temperature range [100-150℃], pressure range [0.6-1.2MPa], enzyme addition range [0.3%-1.0%], pH range [6.0-8.0], and stirring speed range [30-80r / min].

[0082] Peptide solution recovery constraints: filtration pressure range [0.2-0.5MPa], concentration factor range [2-5 times], lower limit of peptide solution purity ≥85%, flow rate limit [5-20L / min];

[0083] Equipment protection constraints: reactor continuous operation time ≤ 24 hours, pump vibration value ≤ 5.0 mm / s, seal leakage threshold ≤ 0.01 MPa / h;

[0084] Taking the single-control cycle optimization problem with "maximizing peptide recovery rate" as the main objective as an example, a specific mathematical model is constructed.

[0085] Taking the single-control cycle optimization problem with "maximizing peptide recovery rate" as the main objective as an example, a specific mathematical model is constructed:

[0086] (1) Decision Variables Let X be the vector of decision variables that need to be optimized in the current control period, which includes the following 6 variables: ;in: Indicates the hydrolysis temperature (T), in °C; This indicates the hydrolysis pressure (P), measured in MPa. This indicates the amount of enzyme added (F), expressed as a percentage by mass; This indicates the stirring speed (S), in r / min. Indicates filtration pressure ( ), unit MPa; The concentration factor (C) is dimensionless.

[0087] (2) The objective function aims to maximize the peptide recovery rate R(X) while minimizing the energy consumption E(X). A weighted sum method is used to construct a single objective function. Let the weight coefficients be... and ,satisfy , ,and The optimization problem is transformed into a minimization problem, with the objective function... Defined as:

[0088] Where R(X) and E(X) are the peptide recovery rate prediction function and energy consumption prediction function, respectively.

[0089] (3) Constraints a) Variable boundary constraints:

[0090] b) Lower limit constraint on peptide purity: in, This is a function for predicting peptide purity. This is to set a minimum purity requirement.

[0091] c) Equipment protection constraints (taking pump vibration value as an example): in, This is a function for predicting the vibration value of a critical pump. This is the vibration safety threshold.

[0092] d) Reaction process constraints (optional): in, This is a function for predicting the hydrolysis rate. This represents the minimum hydrolysis rate requirement for the current cycle.

[0093] 2. Examples of specific forms of prediction functions

[0094] Each prediction function can be established using data-driven methods such as multiple linear regression, neural networks, or response surface methodology. Taking a quadratic polynomial as an example, the peptide recovery rate prediction function... It can be represented as: ;in, These are the regression coefficients; This is the error term.

[0095] In practice, data is collected through preliminary process experiments, and the values ​​of each coefficient are obtained by fitting and stored in the system knowledge base.

[0096] 3. Steps for solving the sequential quadratic programming algorithm

[0097] For the aforementioned nonlinear programming problem, a sequential quadratic programming algorithm is used for iterative solution, with the following steps:

[0098] Step 1: Initialize the given initial feasible point X0 (usually the optimal value of the previous cycle or a set value), and set the iteration counter. Set convergence tolerance (like ).

[0099] Step 2: Calculate the gradient and Hessian matrix to calculate the current iteration point. gradient of the objective function at point The Jacobian matrix of the constraint function is also given. The Hessian matrix approximation of the Lagrangian function is updated using the BFGS quasi-Newton method. .

[0100] Step 3: Constructing the quadratic programming subproblem At this point, the original problem is approximated as a quadratic programming subproblem: Minimize: Constraints: ;in, Indicating the search direction; This is an inequality constraint function; This is the equality constraint function; and These represent the number of inequalities and equality constraints, respectively.

[0101] Step 4: Solve the quadratic programming subproblem. Use the effective set method or interior point method to solve the above quadratic programming problem and obtain the search direction d_k.

[0102] Step 5: One-dimensional line search along the direction Perform a one-dimensional search to find the step size. This makes the new iteration point While satisfying the constraints, the objective function value should be reduced sufficiently (satisfying the Armijo conditions).

[0103] Step 6: Convergence test If the constraint violation rate is less than the tolerance, then stop the iteration and output the result. As the optimal solution; otherwise, let Return to step 2.

[0104] In one specific embodiment, the preset adjustment strategy in the tactical optimization unit dynamically adjusts the initial operational constraints. Specifically, when a "hydrolysis reaction is too slow" signal is received, that is, the deviation of hydrolysis temperature and the deviation of hydrolysis rate both exceed the preset tolerance and the duration exceeds the preset threshold, the upper limit of hydrolysis temperature is increased by a preset ratio, but not exceeding the equipment safety limit.

[0105] Increase the upper limit of enzyme addition according to the preset ratio, but do not exceed the preset safety threshold.

[0106] The reactor's continuous operating time constraint is extended according to a preset ratio.

[0107] When a "peptide purity insufficient" signal is received, meaning that the peptide purity deviation exceeds the preset tolerance and the impurity content exceeds the threshold, and the duration exceeds the preset threshold: increase the filtration pressure according to the preset ratio, but do not exceed the equipment safety limit.

[0108] A new constraint on the amount of adsorbent added has been introduced, ensuring that the amount of adsorbent added is within a preset reasonable range.

[0109] Increase the lower limit of the concentration ratio according to the preset ratio, and do not lower than the preset minimum concentration standard.

[0110] When a "equipment overload" signal is received, meaning the pump vibration value exceeds the preset tolerance or the energy consumption exceeds the preset proportion of the predicted value, and the duration exceeds the preset threshold:

[0111] The upper limits of hydrolysis temperature and pressure are lowered according to the preset ratio.

[0112] Reduce the stirring speed and peptide liquid recovery flow rate according to the preset ratio.

[0113] When a "pathogen risk warning" signal is received, i.e., the pathogen detection sensor outputs a positive result: raise the lower limit of the hydrolysis temperature and the lower limit of the pressure to the preset safety standard.

[0114] The hydrolysis reaction cycle and disinfection time are extended according to the preset ratio.

[0115] The preset ratio, preset tolerance, and preset threshold are all set by professionals. For example, in another embodiment, when a "hydrolysis reaction is too slow" signal is received:

[0116] The upper limit of the hydrolysis temperature is adjusted to the original upper limit × (1+β) (β is the temperature rise coefficient, 0.05-0.1), but not exceeding 150℃.

[0117] The upper limit for enzyme addition is adjusted to the original upper limit × (1 + 0.2), but not exceeding 1.0%.

[0118] Extend the reactor continuous operation time constraint to 1.2 times the original upper limit.

[0119] When a "peptide solution purity insufficient" signal is received:

[0120] Adjust the filter pressure to the original set value × (1 + 0.15), but not exceeding 0.5 MPa.

[0121] The new adsorbent addition amount is subject to a constraint (0.1%-0.3%).

[0122] The lower limit of the concentration factor is adjusted to 1.2 times the original lower limit, but not less than 3 times.

[0123] When a "equipment overload" signal is received:

[0124] The upper limit of hydrolysis temperature is lowered to 0.95 times the original upper limit, and the upper limit of pressure is lowered to 0.9 times the original upper limit.

[0125] Reduce the stirring speed to 0.8 times the original setting and adjust the peptide liquid recovery flow rate to 0.7 times the original setting.

[0126] When a "pathogen risk warning" signal is received (pathogen test positive):

[0127] The lower limit of the hydrolysis temperature is adjusted to 120℃, and the lower limit of the pressure is adjusted to 0.8MPa.

[0128] The hydrolysis reaction cycle is extended to 1.5 times the original cycle, and the disinfection and sterilization time is increased by 30 minutes.

[0129] In one specific embodiment, the correction signals of the feedback correction unit include: a signal indicating that the hydrolysis reaction is too slow, a signal indicating that the peptide solution is not pure enough, a signal indicating that the equipment load exceeds the standard, a signal indicating that the pathogen risk is warning, and a signal indicating that the peptide solution recovery rate meets the standard.

[0130] The signal for a slow hydrolysis reaction is triggered when the deviation in hydrolysis temperature and the deviation in hydrolysis rate both exceed the preset tolerance and the duration exceeds the preset threshold.

[0131] The signal indicates insufficient peptide purity. The triggering conditions are that the peptide purity deviation exceeds the preset tolerance and the impurity content exceeds the threshold.

[0132] The equipment overload signal is triggered when the vibration value exceeds the preset tolerance or the energy consumption exceeds the preset proportion of the predicted value and the duration exceeds the preset threshold.

[0133] The pathogen risk warning signal is triggered when the pathogen detection sensor outputs a positive result.

[0134] The peptide solution recovery rate meets the target signal, which is triggered when the peptide solution recovery rate reaches the preset target value and the peptide solution concentration deviation is within the preset reasonable range; among them, the preset tolerance of each deviation and the preset threshold of the duration can be adaptively adjusted by machine learning.

[0135] In one specific embodiment, the specific steps for the feedback correction unit to generate a deviation feature vector containing the deviation type and the duration of the deviation are as follows:

[0136] Step 1: Data input. Obtain the predicted values ​​of hydrolysis temperature, peptide concentration, and peptide purity output by the short-term reaction prediction unit, as well as the corresponding real-time data synchronously collected by the IoT data acquisition module.

[0137] Step 2: Deviation calculation. Calculate the difference between the predicted hydrolysis temperature and the real-time hydrolysis temperature to obtain the hydrolysis temperature deviation. Calculate the difference between the predicted peptide concentration and the real-time peptide concentration to obtain the peptide concentration deviation. Calculate the difference between the predicted peptide purity and the real-time peptide purity to obtain the peptide purity deviation.

[0138] Step 3: Deviation type determination. Compare each deviation with the preset tolerance to determine whether the deviation is positively exceeded, negatively exceeded, or within a reasonable range, and form a deviation type identifier.

[0139] Step 4: Duration statistics: Start the timing mechanism to record the duration of each deviation type identifier, and obtain the duration of the deviation.

[0140] Step 5: Feature vector construction. Combine the deviation type identifier, deviation values ​​of each dimension, and deviation duration in a preset order to form a deviation feature vector containing the deviation type and deviation duration.

[0141] In one specific embodiment, the tactical optimization unit generates the modified hydrolysis parameter control command and peptide solution recovery parameter control command for the current rolling cycle in the following specific steps:

[0142] Step 1: Data preprocessing. Receive prediction information from the short-term response prediction unit, real-time data from the IoT data acquisition module, and correction signals from the feedback correction unit. Clean, denoise, and standardize the data.

[0143] Step 2: Optimize target adaptation. Based on the pathogen risk level of animal carcasses determined by the IoT data acquisition module, dynamically adjust the weight allocation of maximizing peptide recovery rate, maximizing peptide purity, minimizing energy consumption, and minimizing processing time.

[0144] Step 3: Constraint adjustment. Based on the correction signal from the feedback correction unit, the hydrolysis reaction constraint, peptide liquid recovery constraint, and equipment protection constraint in the initial operation constraints are dynamically adjusted according to the preset adjustment strategy to obtain the optimized constraint boundary.

[0145] Step 4: Optimize problem solving. Guided by the optimization objective and constrained by the optimized boundary conditions, construct and solve the optimization model using a preset algorithm to generate the control sequence for the next rolling cycle.

[0146] Step 5: Command interception and verification. The control parameters of the current rolling cycle are intercepted from the control sequence as the initial control command. After the safety compliance is verified by the linkage safety module, the corrected hydrolysis parameter control command and peptide liquid recovery parameter control command are obtained.

[0147] Step 6: Instruction output. The corrected control instructions are transmitted to the execution module and simultaneously fed back to the feedback correction unit for record-keeping.

[0148] In one specific embodiment, the tactical optimization unit solves the optimization problem by approximating the objective function as a quadratic function, approximating the inequality constraints in the optimized constraint boundary as linear constraints, constructing a quadratic programming subproblem, obtaining a candidate control parameter set by solving the optimal solution of the quadratic programming subproblem, verifying whether the candidate control parameter set satisfies all optimized constraint boundaries, if it does, it is considered a valid solution, if it does not, the constraint weights are adjusted and the subproblem is reconstructed, and the solution is repeated until a valid solution is obtained, mapping the valid solution to the control parameters corresponding to the actual process parameter range, forming a control sequence for a future rolling cycle.

[0149] The sequential quadratic programming algorithm solves the problem by "approaching the optimal solution step by step":

[0150] Step 1: Approximate the nonlinear objective function (such as the nonlinear relationship between recovery rate and temperature) as a quadratic function, and approximate the inequality constraints (such as pressure ≤ 0.5MPa) as linear constraints;

[0151] The second step is to solve the quadratic programming subproblem to obtain the "candidate control sequence" for the current iteration (e.g., the temperature rises from 55℃ to 58℃ and the ultrasonic power increases from 0.3w / cm² to 0.4w / cm² in the next cycle).

[0152] Step 3: Verify whether the candidate sequence satisfies all constraints (such as whether the power increase exceeds the device safety limit). If it does, output the result; otherwise, adjust the iteration direction and solve the problem again.

[0153] The execution module is used to execute the modified hydrolysis parameter control instructions and peptide liquid recovery parameter control instructions, driving the hydrolysis reaction equipment, peptide liquid recovery equipment and auxiliary equipment to complete the corresponding actions.

[0154] In one specific embodiment, the execution module includes a hydrolysis reaction execution subunit, a peptide liquid recovery execution subunit, and a disinfection and sterilization subunit.

[0155] The hydrolysis reaction execution subunit includes an intelligent temperature controller, a pressure regulating valve, an enzyme addition pump, and a stirring motor, which are used to regulate the temperature, pressure, enzyme addition amount, and stirring speed of the hydrolysis reactor.

[0156] The peptide solution recovery execution subunit includes a filter pressure regulating valve, a concentrator, a peptide solution delivery pump, and a purity detector, which are used to adjust the filter pressure and concentration ratio to achieve the linkage of peptide solution filtration, concentration, and purification.

[0157] The disinfection and sterilization subunit includes an ultraviolet sterilizer and a high-temperature steam generator, which are used to perform disinfection operations on hydrolyzed residues, peptide solutions, and the interior of the equipment according to preset disinfection standards.

[0158] The linkage safety module is used to receive correction signals and equipment status data in real time. When the safety linkage conditions are met, it triggers a safety command to block the execution of control commands in unsafe conditions.

[0159] The safety linkage conditions of the linkage safety module include a first condition and a second condition, which trigger a safety command when both are met. The first condition is receiving a high-risk correction signal from the feedback correction unit, which includes pathogen risk warning signals, equipment overload signals, and abnormal pressure or temperature signals. The second condition is that the equipment status data shows a critical equipment failure, which includes reactor seal leakage, pressure exceeding the safe range, temperature exceeding the safe range, and disinfection and sterilization subunit failure. The safety command includes sending a blocking signal to the intelligent decision control module to pause control command output, sending an emergency shutdown command to the execution module to shut down the heating or pressurizing device and enzyme addition pump, opening the pressure relief valve and releasing pressure at a preset safe rate, starting the disinfection and sterilization subunit for emergency disinfection, and sending an audible and visual alarm and message notification to the remote monitoring and data traceability module. When the equipment failure is resolved and the parameters return to the safe range, the linkage safety module sends a release signal, and the intelligent decision control module resumes control command output.

[0160] The remote monitoring and data traceability module is used to store all collected data, control commands and processing results, and provides remote access, data traceability, fault warning and report generation functions.

[0161] In another embodiment, the remote monitoring and data traceability module includes:

[0162] Data storage unit: adopts dual storage of edge server + cloud database, with a storage period of ≥1 year. The data includes animal carcass attributes, processing parameters, peptide liquid quality test results, equipment operation logs, and alarm records.

[0163] Remote access unit: Supports access via web and mobile app, displays processing progress, equipment status, and peptide solution quality data in real time, and supports remote adjustment of control parameters (requires authorization verification).

[0164] Fault warning unit: Based on equipment operation data and deviation feature vectors, it uses machine learning models to predict potential faults (such as abnormal pump vibration trends and seal aging warnings) and sends warning notifications 24 hours in advance.

[0165] Traceability Report Unit: Automatically generates traceability reports for each batch of processing, including batch number, animal carcass information, processing parameter curves, peptide solution quality test reports, and disinfection records, and supports one-click export.

[0166] In one specific embodiment, the system has a multi-batch collaborative processing function: when processing multiple types of animal carcasses at the same time, the Internet of Things data acquisition module determines the pathogen risk level of the animal carcasses based on the pathogen detection data and preset risk grading standards, prioritizes the animal carcasses based on their risk level and processing difficulty, and dynamically allocates hydrolysis reactor and recycling equipment resources to avoid cross-contamination between different batches.

[0167] In another embodiment, applied to the disposal of carcasses of diseased and dead pigs, the specific workflow is as follows:

[0168] IoT data acquisition phase:

[0169] Animal carcass property sensors collected data on the weight of a 250kg dead pig carcass, moisture content of 68%, and pH value of 6.5. The pathogen detection result was "negative for swine fever virus".

[0170] The hydrolysis reaction sensor collected real-time data on the reactor's initial temperature of 100℃, pressure of 0.6MPa, pH value of 6.5, enzyme addition of 0.3%, and stirring speed of 30r / min.

[0171] The equipment status sensors showed that the reactor was sealed without leakage and the pump vibration value was 2.5 mm / s, indicating that all equipment was operating normally.

[0172] The data is uploaded to the intelligent decision-making and control module via the 5G transmission module, and simultaneously stored in the remote monitoring and data traceability module.

[0173] Intelligent decision-making and control stage:

[0174] The long-term processing planning unit generates a processing plan based on historical data: total hydrolysis cycle T1 = 8 hours, peptide recovery rate target 88%, purity target 92%.

[0175] The short-term reaction prediction unit uses an LSTM model to predict that within the next hour (T2=1 hour), the hydrolysis rate can reach 35%, the peptide concentration is 18g / L, and the equipment load is normal.

[0176] The tactical optimization unit determines the optimization target weights: recovery rate 40%, purity 30%, energy consumption 20%, time 10%, and initial operating constraints: temperature 100-140℃, pressure 0.6-1.0MPa, enzyme addition 0.3%-0.8%, filtration pressure 0.2-0.4MPa, and concentration factor 2-4 times.

[0177] Using a sequential quadratic programming algorithm, control commands for the current period T3=20 minutes are generated: temperature rises to 115℃, pressure is maintained at 0.7MPa, enzyme addition is increased to 0.5%, stirring speed is 40r / min; filtration pressure is 0.3MPa, concentration factor is 2.5 times.

[0178] Execution and feedback correction phase:

[0179] The execution module receives control commands, the hydrolysis reaction execution subunit adjusts the temperature controller to raise the temperature to 115℃, the pressure regulating valve maintains 0.7MPa, and the enzyme addition pump adds enzyme solution quantitatively; the peptide solution recovery execution subunit adjusts the filtration pressure to 0.3MPa, and the concentrator starts to concentrate to 2.5 times.

[0180] Twenty minutes later, the IoT data acquisition module collected real-time data: hydrolysis temperature 114.8℃ ( ), peptide concentration 17.2 g / L ( ), purity 91.5% ( The duration of the deviation is t=5 minutes.

[0181] Indicates the deviation in hydrolysis temperature. This indicates a deviation in peptide concentration. This indicates a deviation in the purity of the peptide solution.

[0182] The feedback correction unit generates a deviation feature vector [-0.2, -0.8, -0.5, 5]. After matching the rule base, no high-risk correction signal is generated. Instead, a "fine-tuning signal" is sent to the tactical optimization unit.

[0183] The tactical optimization unit adjusts the control commands for the next cycle: maintain the temperature at 115℃, increase the enzyme addition to 0.6%, and adjust the filtration pressure to 0.35MPa.

[0184] Safety interlock trigger scenario (simulation):

[0185] If, during processing, the equipment status sensor detects a reactor seal leak (leakage rate 0.02 MPa / h > threshold 0.01 MPa / h), and simultaneously the feedback correction unit sends a "pressure anomaly signal" (pressure drops to 0.5 MPa), ).

[0186] When the linkage safety module meets both the first and second conditions, it immediately sends a blocking signal to the tactical optimization unit and an emergency shutdown command to the execution module: shut down the heating device and enzyme addition pump, open the pressure relief valve (pressure relief rate 0.03MPa / min), and start the disinfection and sterilization subunit for high-temperature steam sterilization (134℃, 20 minutes).

[0187] The remote monitoring unit sends audible and visual alarms and SMS notifications to the maintenance personnel. After the maintenance personnel troubleshoot the leak, they send a "troubleshoot" command through the APP, which triggers the safety module to send a release signal, and the system resumes operation.

[0188] Data traceability and report generation:

[0189] After batch processing is completed, the remote monitoring and data traceability module automatically generates traceability reports: batch number 2024052001, animal carcass information (dead pig, 250kg, moisture content 68%), processing parameter curves (changes in temperature, pressure, and enzyme addition), peptide solution quality test report (recovery rate 89%, purity 92.3%), disinfection record (134℃, 20 minutes), and supports export and archiving.

[0190] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. An intelligent system for the hydrolysis treatment and peptide recovery of animal carcasses based on the Internet of Things, characterized in that, include: The Internet of Things (IoT) data acquisition module is used to collect real-time data on animal carcass attributes, hydrolysis reaction process data, peptide liquid recovery process data, and equipment operating status data, and transmit them to the intelligent decision control module via the IoT. The intelligent decision control module is used to generate long-term processing plans and short-term reaction predictions based on collected data, optimize hydrolysis and recovery parameters, and generate control commands; at the same time, it monitors the deviation between predicted values ​​and actual data in real time and triggers constraint adjustment strategies. The execution module is used to execute the modified hydrolysis parameter control instructions and peptide liquid recovery parameter control instructions, driving the hydrolysis reaction equipment, peptide liquid recovery equipment and auxiliary equipment to complete the corresponding actions. The linkage safety module is used to receive correction signals and equipment status data in real time. When the safety linkage conditions are met, it triggers a safety command to block the execution of control commands in unsafe states. The remote monitoring and data traceability module is used to store all collected data, control commands and processing results, and provides remote access, data traceability, fault warning and report generation functions.

2. The intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things as described in claim 1, characterized in that: The intelligent decision control module includes a long-term processing planning unit, a short-term response prediction unit, a tactical optimization unit, and a feedback correction unit. The long-term processing planning unit generates a long-term processing plan based on historical processing data using the random forest algorithm, including the optimal processing batch for different types of animal carcasses, the total hydrolysis cycle and peptide recovery rate target, and the purity target. The short-term reaction prediction unit uses real-time data from the IoT data acquisition module and a neural network model to generate short-term prediction information for a preset short period of time in the future. The prediction information includes predicted values ​​for hydrolysis reaction progress, peptide concentration, peptide purity, and equipment operating load. The tactical optimization unit takes long-term processing plans as a reference and short-term prediction results and real-time collected data as input. Based on the correction signal generated by the feedback correction unit, it dynamically adjusts the initial operation constraints according to the preset adjustment strategy to obtain the optimized constraints. It uses a preset algorithm to solve the optimization problem and generates the corrected hydrolysis parameter control instructions and peptide liquid recovery parameter control instructions for the current rolling cycle. The feedback correction unit calculates the deviation between the short-term predicted value and the real-time acquired data, including the deviation of hydrolysis temperature, peptide concentration, and purity. It generates a deviation feature vector containing the deviation type and duration, and matches it with a predefined rule base to generate a correction signal.

3. The intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things as described in claim 2, characterized in that: The optimization objectives in the tactical optimization unit include maximizing peptide recovery rate, maximizing peptide purity, and minimizing energy consumption and processing time. Initial operational constraints include: hydrolysis reaction constraints, peptide solution recovery constraints, and equipment protection constraints.

4. The intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things as described in claim 2, characterized in that: The preset adjustment strategy in the tactical optimization unit dynamically adjusts the initial operational constraints, specifically as follows: When a "hydrolysis reaction is too slow" signal is received: increase the upper limit of the hydrolysis temperature by a preset ratio, but do not exceed the equipment safety limit; Increase the maximum amount of enzyme added according to the preset ratio, but do not exceed the preset safety threshold; Extend the reactor's continuous operating time constraint by a preset ratio; When a "peptide solution purity is insufficient" signal is received: increase the filtration pressure according to the preset ratio, but do not exceed the equipment safety limit; A new constraint on the amount of adsorbent added has been introduced, ensuring that the amount of adsorbent added is within a preset reasonable range. Increase the lower limit of the concentration ratio according to the preset ratio, and do not lower than the preset minimum concentration standard; When a "equipment overload" signal is received: the upper limit of hydrolysis temperature and the upper limit of pressure are lowered according to the preset ratio; Reduce the stirring speed and peptide liquid recovery flow rate according to the preset ratio; When a "pathogen risk warning" signal is received: raise the lower limit of the hydrolysis temperature and the lower limit of the pressure to the preset safety standard; The hydrolysis reaction cycle and disinfection time are extended according to the preset ratio.

5. The intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things as described in claim 4, characterized in that: The correction signals of the feedback correction unit include: a signal indicating that the hydrolysis reaction is too slow, a signal indicating that the peptide solution is not pure enough, a signal indicating that the equipment load is too high, a signal indicating that the pathogen risk is warning, and a signal indicating that the peptide solution recovery rate meets the standard.

6. The intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things as described in claim 1, characterized in that: The execution module includes a hydrolysis reaction execution subunit, a peptide liquid recovery execution subunit, and a disinfection and sterilization subunit; The hydrolysis reaction execution subunit is used to adjust the temperature, pressure, enzyme addition amount, and stirring speed of the hydrolysis reactor; The peptide liquid recovery execution subunit is used to adjust the filtration pressure and concentration factor to achieve the linkage of peptide liquid filtration, concentration and purification. The disinfection and sterilization subunit is used to perform disinfection operations on hydrolyzed residues, peptide solutions, and the interior of equipment according to preset disinfection standards.

7. The intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things as described in claim 1, characterized in that: The specific implementation steps for the feedback correction unit to generate a deviation feature vector containing the deviation type and the duration of the deviation are as follows: Step 1: Data input, obtain the predicted values ​​of hydrolysis temperature, peptide concentration, and peptide purity output by the short-term reaction prediction unit, as well as the corresponding real-time data synchronously collected by the IoT data acquisition module; Step 2: Deviation calculation. Calculate the difference between the predicted hydrolysis temperature and the real-time hydrolysis temperature to obtain the hydrolysis temperature deviation; calculate the difference between the predicted peptide concentration and the real-time peptide concentration to obtain the peptide concentration deviation; calculate the difference between the predicted peptide purity and the real-time peptide purity to obtain the peptide purity deviation. Step 3: Deviation type determination. Compare each deviation with the preset tolerance to determine whether the deviation is positively exceeded, negatively exceeded, or within a reasonable range, and form a deviation type identifier. Step 4: Duration statistics: Start the timing mechanism to record the duration of each deviation type identifier to obtain the duration of the deviation. Step 5: Feature vector construction. Combine the deviation type identifier, deviation values ​​of each dimension, and deviation duration in a preset order to form a deviation feature vector containing the deviation type and deviation duration.

8. The intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things as described in claim 1, characterized in that: The specific steps for the tactical optimization unit to generate the corrected hydrolysis parameter control instructions and peptide solution recovery parameter control instructions for the current rolling cycle are as follows: Step 1: Data preprocessing. Receive prediction information from the short-term response prediction unit, real-time data from the IoT data acquisition module, and correction signals from the feedback correction unit. Clean, denoise, and standardize the data. Step 2: Optimize target adaptation. Based on the pathogen risk level of animal carcasses determined by the IoT data acquisition module, dynamically adjust the weight allocation of maximizing peptide recovery rate, maximizing peptide purity, minimizing energy consumption, and minimizing processing time. Step 3: Constraint adjustment. Based on the correction signal from the feedback correction unit, the hydrolysis reaction constraint, peptide liquid recovery constraint, and equipment protection constraint in the initial operation constraints are dynamically adjusted according to the preset adjustment strategy to obtain the optimized constraint boundary. Step 4: Optimize problem solving. Guided by the optimization objective and constrained by the optimized boundary conditions, construct and solve the optimization model using a preset algorithm to generate the control sequence for the next rolling cycle. Step 5: Command interception and verification. The control parameters of the current rolling cycle are intercepted from the control sequence as the initial control command. After the safety compliance is verified by the linkage safety module, the corrected hydrolysis parameter control command and peptide liquid recovery parameter control command are obtained. Step 6: Instruction output. The corrected control instructions are transmitted to the execution module and simultaneously fed back to the feedback correction unit for record-keeping.

9. The intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things as described in claim 1, characterized in that: The tactical optimization unit solves the optimization problem in the following specific process: The objective function is approximated as a quadratic function, and the inequality constraints in the optimized boundary conditions are approximated as linear constraints. A quadratic programming subproblem is constructed. By solving the optimal solution of the quadratic programming subproblem, a set of candidate control parameters is obtained. The candidate control parameter set is checked to see if it satisfies all optimized boundary conditions. If it does, it is considered a valid solution. If it does not, the constraint weights are adjusted and the subproblem is reconstructed. The solution is repeated until a valid solution is obtained. The valid solution is mapped to the control parameters corresponding to the actual process parameter range to form the control sequence for the next rolling cycle.

10. The intelligent system for animal carcass hydrolysis and peptide recovery based on the Internet of Things as described in claim 1, characterized in that: The system has a multi-batch collaborative processing function: when processing multiple types of animal carcasses at the same time, the Internet of Things data acquisition module determines the pathogen risk level of the animal carcasses based on the pathogen detection data and preset risk classification standards, prioritizes the animal carcasses based on their risk level and processing difficulty, and dynamically allocates hydrolysis reactor and recycling equipment resources to avoid cross-contamination between different batches.

Images