Intelligent monitoring method and system for fumigation liquid production

Abstract

The application relates to the technical field of computers and discloses an intelligent monitoring method and system for fumigation liquid production, which comprises the following steps: collecting energy consumption data in a fumigation liquid production process in real time through a sensor and uploading the data to a block chain; verifying the legality of the uploaded data; when the data is verified as legal, triggering an intelligent contract to execute an optimization decision and automatically adjusting operation parameters of the fumigation liquid; feeding back adjustment results through the block chain and iteratively updating the intelligent contract according to real-time data to ensure continuous improvement of the system. Through the combination of Lagrange programming neural networks and simulated annealing algorithms, multistage constraints can be effectively processed, key parameters in the production process can be accurately controlled, and production efficiency can be improved. Through a continuous feedback mechanism and optimization iteration, the energy consumption in the fumigation liquid production process can always be in an optimal state, and dual optimization of energy saving and production benefit is realized.

Description

Technical Field

[0001] This invention relates to the field of computer technology, specifically to an intelligent monitoring method and system for fumigation liquid production. Background Technology

[0002] The production process of fumigation liquid typically involves several key stages, such as heating, ventilation, and humidity control. The energy consumption at each stage directly impacts production efficiency and energy costs. Traditional production control methods often rely on manual operation or automated control systems based on preset rules. These systems often have several problems. First, they cannot adjust various parameters in the production process in real time, especially when anomalies occur or external environmental changes occur, making it difficult for traditional control methods to respond quickly. Second, the lack of transparency and traceability of data during the production process limits the effectiveness of data analysis and optimization decision-making.

[0003] Currently, smart contracts based on blockchain technology and data-driven optimization control methods have become important directions for improving production efficiency and reducing energy consumption. The emergence of blockchain technology provides technical guarantees for data transparency, security, and immutability, while smart contracts enable automated decision-making and execution, reducing human intervention and improving the automation level of the system. In industrial production, an increasing number of applications are relying on blockchain technology for data storage and management, ensuring the integrity and reliability of production data. However, despite the significant advantages of blockchain technology and smart contracts in improving system transparency and efficiency, existing technologies still face challenges such as real-time data processing and multi-constraint optimization decision-making.

[0004] To address these challenges, the application of artificial intelligence, especially deep learning and optimization algorithms, in industrial control systems has gradually emerged in recent years. By combining neural networks with traditional optimization algorithms, it is possible to better handle multivariate and multi-constraint optimization problems in complex production processes, thereby improving the intelligence level of the system. However, existing AI-based optimization methods mostly rely on static models or simple constraints, which cannot be flexibly adjusted according to real-time data and are difficult to handle optimization problems with multiple levels of constraints and complex production environments.

[0005] Therefore, how to combine blockchain technology and smart contracts, and utilize advanced optimization algorithms (such as Lagrange programming neural networks, simulated annealing algorithms, etc.) to process multi-level constraints in real time and dynamically adjust operating parameters has become a technical challenge that urgently needs to be solved. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is: optimizing energy consumption, handling multi-level constraints, and adjusting operating parameters in real time during the fumigation liquid production process.

[0008] To address the aforementioned technical problems, the present invention provides the following technical solution: an intelligent monitoring method for fumigation liquid production, comprising: collecting energy consumption data during the fumigation liquid production process in real time via sensors and uploading it to a blockchain;

[0009] Verify the legality of the uploaded data. When the data is verified to be legal, trigger the smart contract to execute optimization decisions and automatically adjust the operating parameters of the fumigation liquid.

[0010] The system continuously improves by adjusting the results through blockchain feedback and iterating smart contracts based on real-time data.

[0011] The smart contract includes using an improved Lagrange programming neural network to process constraints in the fumigation liquid production process and initially optimize operating parameters; and using a simulated annealing algorithm to perform a global search of the operating parameters to obtain the optimal operating parameters for the fumigation liquid.

[0012] As a preferred embodiment of the intelligent monitoring method for fumigation liquid production described in this invention, the energy consumption data includes temperature data, humidity data, airflow speed, heating power, equipment load data, production time, evaporation rate, and energy efficiency data.

[0013] Uploading the energy consumption data to the blockchain includes: attaching a timestamp and device identifier to each piece of energy consumption data, and transmitting it to the blockchain for storage via wireless communication.

[0014] As a preferred embodiment of the intelligent monitoring method for fumigation liquid production described in this invention, the verification of the legality of the uploaded data includes: the smart contract receiving energy consumption data with timestamps and device identifiers from the blockchain, and performing data rationality verification, consistency verification, and time verification;

[0015] The data rationality verification is to determine whether the energy consumption data meets the preset data standards; the consistency verification is to check whether there are abnormal fluctuations in the data collected by different sensors; the time verification is to verify whether the timestamp of the energy consumption data is reasonable.

[0016] When the energy consumption data uploaded to the blockchain meets all verification conditions, the smart contract confirms the data's validity and triggers the smart contract to execute optimization decisions.

[0017] When the energy consumption data uploaded to the blockchain does not meet all the verification conditions, the smart contract records the abnormal data and triggers an alarm to notify the operators.

[0018] As a preferred embodiment of the intelligent monitoring method for fumigation liquid production described in this invention, the improved Lagrange programming neural network introduces a multi-level constraint mechanism, optimizes the constraint conditions in the fumigation liquid production process through the Lagrange multiplier method, and controls the influence of each constraint on the objective function by setting first-level, second-level and third-level constraint Lagrange multipliers, thereby initially optimizing the operating parameters in the fumigation liquid production process.

[0019] The improved Lagrange programming neural network includes an input layer, a hidden layer, and an output layer;

[0020] The input layer receives valid energy consumption data;

[0021] Hidden layer: Consists of three layers of neural network, each containing 32 neurons;

[0022] The first hidden layer receives data from the input layer, performs preliminary feature extraction, learns the correlation in the input data, and uses the ReLU activation function to perform a non-linear mapping on the data.

[0023] The second hidden layer receives the data output from the first layer, performs further feature extraction, and uses the ReLU activation function for non-linear mapping.

[0024] The third hidden layer receives the features output from the first two layers, performs feature concatenation and optimization, and then passes them to the output layer.

[0025] Output layer: Outputs preliminary optimized operating parameters, including heating temperature, airflow speed, humidity control, equipment load, production time, evaporation rate, equipment power, and cooling temperature and speed.

[0026] As a preferred embodiment of the intelligent monitoring method for fumigation liquid production described in this invention, the multi-level constraint mechanism includes primary constraint, secondary constraint and tertiary constraint;

[0027] Each constraint is introduced into the objective function via Lagrange multipliers, where , Represents the Lagrange multipliers with first-order constraints; Represents a Lagrange multiplier with second-order constraints; Representing Lagrange multipliers with third-order constraints;

[0028] The objective function is to minimize energy consumption;

[0029] The loss function consists of an objective function and penalty terms for constraints. The penalty term for each constraint is multiplied by the corresponding Lagrange multiplier, and the influence of the constraints on the optimization objective is controlled by weighting.

[0030] As a preferred embodiment of the intelligent monitoring method for fumigation liquid production described in this invention, the method of using the simulated annealing algorithm to perform a global search on the preliminary optimized operating parameters includes: using the preliminary optimized operating parameters as the initial solution of the simulated annealing algorithm; setting an initial temperature value, which gradually decreases as the iteration proceeds; generating new candidate solutions by making small perturbations to the initial solution, and performing multiple rounds of iteration.

[0031] Calculate the energy consumption difference between the current solution and the new solution. If the energy consumption of the new candidate solution is lower than that of the current solution, the new solution is accepted unconditionally. If the energy consumption of the new candidate solution is higher than that of the current solution, the acceptance probability is used to determine whether to accept the new solution.

[0032] When the temperature drops to the set low value or the maximum number of iterations is reached, the search stops and the optimal operating parameters of the fumigation liquid are output.

[0033] As a preferred embodiment of the intelligent monitoring method for fumigation liquid production described in this invention, the step of iterating the smart contract based on real-time data includes: the optimal operating parameters being fed back to the system via blockchain as input for the next round of smart contract optimization.

[0034] Intelligent monitoring systems for fumigation liquid production include:

[0035] The data module collects energy consumption data in real time during the fumigation liquid production process through sensors and uploads it to the blockchain;

[0036] The blockchain module verifies the legality of uploaded data. When the data is verified to be legal, it triggers a smart contract to execute optimization decisions and automatically adjust the operating parameters of the fumigation solution.

[0037] The iteration module uses blockchain feedback to adjust results and iterates smart contracts based on real-time data to ensure continuous system improvement.

[0038] A computer device includes: a memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of the method described in any one of the present invention.

[0039] A computer-readable storage medium having a computer program stored thereon, characterized in that: when the computer program is executed by a processor, it implements the steps of the method described in any one of the present invention.

[0040] The beneficial effects of this invention are as follows: The intelligent monitoring method for fumigation liquid production provided by this invention collects and uploads energy consumption data to the blockchain in real time, ensuring data transparency and immutability, thus improving data reliability. Smart contracts automatically trigger optimization decisions based on verified legitimate data, adjusting operating parameters in the fumigation liquid production process in real time, optimizing energy consumption, and reducing manual intervention. The combination of Lagrange programming neural networks and simulated annealing algorithms effectively handles multi-level constraints, ensuring precise control of key parameters in the production process and improving production efficiency. Through continuous feedback mechanisms and iterative optimization, the energy consumption in the fumigation liquid production process is always kept at an optimal level, achieving a dual optimization of energy saving and production efficiency. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0042] Figure 1 The overall flowchart of the intelligent monitoring method for fumigation liquid production provided in the first embodiment of the present invention is shown. Detailed Implementation

[0043] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0044] Example 1, referring to Figure 1 As one embodiment of the present invention, an intelligent monitoring method for fumigation liquid production is provided, comprising:

[0045] S1: Collect energy consumption data in real time during the fumigation liquid production process using sensors and upload it to the blockchain.

[0046] Energy consumption data includes temperature data, humidity data, airflow speed, heating power, equipment load data, production time, evaporation rate, and energy efficiency data.

[0047] Sensors are installed at key stages of the fumigation liquid production process (heating, ventilation, humidity control) to collect real-time data on energy consumption. The sensors continuously monitor and update data every minute to ensure data timeliness.

[0048] The collected energy consumption data is transmitted to the blockchain network via wireless communication (such as LoRa, NB-IoT, etc.).

[0049] Energy consumption data is stored on the blockchain, with each data point accompanied by a timestamp and device identifier, ensuring the immutability and transparency of the data.

[0050] By collecting energy consumption data in real time and uploading it to the blockchain, all critical data in the fumigation liquid production process is ensured to be accurately recorded, transparently stored, and tamper-proof. Sensors installed in key areas such as heating, ventilation, and humidity control continuously monitor energy consumption data such as temperature, humidity, airflow speed, heating power, and equipment load, updating every minute to ensure data timeliness and accuracy. Utilizing wireless communication technologies (such as LoRa and NB-IoT) to transmit the collected data to the blockchain network in real time not only provides an efficient data transmission mechanism but also ensures the immutability and transparency of all data, providing a reliable foundation for subsequent smart contract optimization and decision-making. Simultaneously, this design provides strong protection for data security and quality control during the production process, avoiding potential data leakage or tampering issues in traditional methods, and improving the traceability and compliance of the production process.

[0051] S2: Verify the legality of the uploaded data. When the data is verified to be legal, the smart contract is triggered to execute optimization decisions and automatically adjust the operating parameters of the fumigation liquid.

[0052] The smart contract receives uploaded energy consumption data from the blockchain and performs legality verification, including:

[0053] Data validity verification: According to the predetermined standards, the temperature should be between 18°C ​​and 22°C, and the humidity should be between 50% and 60%. Check whether the temperature and humidity in the data meet the specified range; and verify whether the equipment load and power data are within the allowable operating range of the equipment.

[0054] Consistency verification: Check the consistency of data from different sensors to ensure that parameters such as temperature, humidity, and airflow do not fluctuate abnormally, avoiding data errors caused by sensor malfunctions. If energy consumption data fluctuates by more than m times the standard deviation over a certain period, the data for that period is considered to have abnormal fluctuations.

[0055] Time verification: The smart contract verifies whether the timestamp of the data is reasonable and ensures that each piece of data is accompanied by a device identifier to prevent data forgery or tampering.

[0056] When the uploaded data meets all the verification conditions, the smart contract confirms the data is valid and triggers subsequent optimization decisions.

[0057] When data fails to meet all validation criteria, the smart contract records the abnormal data and triggers an alarm to prevent erroneous data from affecting subsequent decisions.

[0058] The smart contract includes using an improved Lagrange programming neural network to handle constraints in the fumigation liquid production process and optimize energy consumption in the process; using a simulated annealing algorithm to perform a global search for operating parameters, and combining Newton's method to perform local optimization of the global search results to obtain the optimal operating parameters for the fumigation liquid.

[0059] Operating parameters include heating temperature, airflow speed, humidity control, equipment load, production time, evaporation rate, equipment power, and cooling temperature and speed.

[0060] Heating temperature: Controlling the temperature parameters during the fumigation liquid production process affects the evaporation rate and energy consumption. Appropriate temperature settings ensure optimal production efficiency and energy use.

[0061] Airflow velocity: In the production process of fumigation liquid, the airflow velocity has an important impact on heating and humidity control.

[0062] Humidity control: Control the humidity of the fumigation liquid production environment to ensure that the humidity is maintained within the specified range.

[0063] Equipment load: Equipment load refers to the working load of equipment (heaters, ventilation equipment, pumps). Equipment load affects energy consumption and equipment operating efficiency.

[0064] Production time: The duration of the production process, i.e., the time required to produce each batch. The longer the production time, the higher the energy consumption may be.

[0065] Evaporation rate: The rate at which liquids evaporate during the production process, which is usually closely related to temperature, airflow, and humidity. By controlling the evaporation rate, energy consumption can be optimized and product quality can be controlled.

[0066] Equipment power: Controls the power output of various production equipment (heaters, fans, pumps). Proper power allocation improves energy efficiency and prevents equipment overload or inefficient operation.

[0067] An improved Lagrange programming neural network introduces a multi-level constraint mechanism, optimizing the constraints in the fumigation liquid production process using the Lagrange multiplier method. Adjustments to the Lagrange multipliers for first-, second-, and third-level constraints dynamically weight and control the impact of each constraint on the objective function, thereby initially optimizing the operating parameters in the fumigation liquid production process.

[0068] A Lagrange programming neural network consists of an input layer, hidden layers, and an output layer.

[0069] Input layer: Receives valid energy consumption data, consisting of 6 neurons.

[0070] There are 3 hidden layers, with 32 neurons in each layer.

[0071] The first hidden layer: Data received from the input layer enters the first hidden layer for initial feature extraction, learning the correlations in the input data. The ReLU activation function is used to perform a non-linear mapping on the data, allowing the model to learn complex patterns. The output of the first hidden layer is a non-linear transformation of the original features, which will be further processed and optimized in subsequent layers.

[0072] The second hidden layer further learns more complex patterns and features from the data output by the first layer. The ReLU activation function is used to non-linearly map the data, ensuring the network can learn non-linear relationships. The output of the second layer is an optimization and transformation of the results from the previous layer, designed to strengthen the correlations between data points.

[0073] The third hidden layer further concatenates the features output from the first two layers and uses the ReLU activation function to maintain non-linear feature learning, generating an optimized feature representation, which is finally passed to the output layer.

[0074] Output layer: Generates and outputs optimized preliminary operation parameters.

[0075] In the training of LPNN, a multi-level constraint mechanism is introduced to better control the fumigation liquid production process. The constraints are divided into three levels:

[0076] Primary constraints (critical constraints): These constraints directly affect product quality and production process safety and must be strictly controlled. These include: Heating temperature: Should be maintained within the set range to ensure production efficiency and product quality. Humidity control: Humidity should be within the empirical range to avoid affecting evaporation rate and product quality. Equipment load: Equipment load must be maintained within safe operating ranges to prevent equipment overload.

[0077] Secondary constraints (optimization constraints): These constraints affect production efficiency and system stability, but their impact on product quality is less direct than that of primary constraints. These include: Production rate: Controlled within a fixed hourly fumigation liquid demand range to ensure high production efficiency. Concentrate volume: The final concentrate volume should be maintained within a set range to ensure production process stability.

[0078] Level 3 constraints (sustainability constraints): These constraints have a relatively small direct impact on production, but may affect costs and sustainability in the long term. These include: Ambient temperature fluctuations: These should be controlled between 20°C and 35°C to avoid the indirect impact of drastic changes on production efficiency. Water quality: Use purified water to ensure that the water quality meets the fumigation liquid production standards.

[0079] Each constraint is incorporated into the objective function using the Lagrange multiplier method. Each constraint corresponds to a Lagrange multiplier. The magnitude of the multiplier determines the degree to which the constraint affects the optimization of the objective function. The Lagrange multiplier formula is expressed as:

[0080] ;

[0081] in, These are first-order constraint Lagrange multipliers, which play a dominant role in the objective function optimization process, ensuring that the production process is carried out under strict control. This represents the Lagrange multiplier for second-order constraints; although second-order constraints are not as strict as first-order constraints, they still need to be optimized during the training process. The Lagrange multipliers representing third-level constraints have the least impact on the objective function and are typically related to cost and sustainability. To avoid over-constraining the fumigation liquid production process, the Lagrange multipliers for third-level constraints are minimized. The Lagrange multipliers for first-level constraints are more than 10 times larger than those for second-level constraints, while the Lagrange multipliers for second-level constraints are more than 50 times larger than those for third-level constraints.

[0082] The loss function consists of the objective function (minimizing energy consumption) and penalty terms for constraints. The penalty term for each constraint is multiplied by its corresponding Lagrange multiplier, and the weighted average controls the impact of the constraints on the optimization objective. The formula is:

[0083] ;

[0084] in, This represents the objective function for minimizing energy consumption. This represents the penalty term for the j-th constraint. It is the Lagrange multiplier of the j-th constraint, and its size is dynamically adjusted to control the influence of the constraint on the optimization objective. Let represent the Lagrange function, and j represent the index.

[0085] The training objective of a Lagrange programming neural network is to minimize the loss function while ensuring that all constraints are satisfied. The training process includes: inputting energy consumption data into the Lagrange programming neural network through the input layer, performing feature learning and constraint processing through the hidden layers, and finally calculating the optimal operating parameters through the output layer.

[0086] The loss function is calculated using backpropagation, minimized using gradient descent, and the constraints are ensured to be met. The Lagrange multipliers are dynamically adjusted based on the actual impact of each constraint to ensure a balance between the objective function and the constraints during optimization.

[0087] The initial optimized operating parameters will be used as the initial solution for the simulated annealing; an initial temperature value will be set. initial temperature The initial temperature determines the extent of the algorithm's exploration during the search process. A higher initial temperature allows the algorithm to more readily accept poorer solutions, helping to explore a wider solution space. As iterations progress, the temperature gradually decreases.

[0088] The temperature will gradually decrease. As the temperature drops, the search will become more refined, avoiding large-scale searches and ensuring that the algorithm can remain stable near the global optimum.

[0089] Simulated annealing generates new candidate solutions by making small perturbations to the initial solution, and iterates through multiple rounds. The energy consumption difference ΔE between the current solution and the new solution is calculated, expressed by the formula:

[0090] ;

[0091] in, This is the energy consumption of the new solution. This represents the energy consumption of the current solution.

[0092] When the energy consumption of the new solution is lower than that of the current solution ( If the energy consumption of the new solution is higher than that of the current solution, then the new solution is accepted unconditionally; if the energy consumption of the new solution is higher than that of the current solution, then the new solution is accepted unconditionally. When a new solution is found to be acceptable, the decision to accept it is based on the acceptance probability. The formula for the probability of accepting a new solution is as follows:

[0093] ;

[0094] in, This represents the probability of accepting the new solution. This indicates the current temperature.

[0095] Generate a random number It is a random number between [0, 1]; when Then a new interpretation will be accepted.

[0096] when If the new solution is rejected, the current solution is retained.

[0097] As the search progresses, the temperature gradually decreases, reducing the probability of accepting inferior solutions, and eventually converges to a stable solution. The formula for temperature decay is typically:

[0098] ;

[0099] in, It is a constant less than 1 that determines the rate of temperature decay. This indicates the temperature in the new iteration; This indicates the temperature in the current iteration.

[0100] The algorithm stops when the temperature drops to the set low value; the annealing algorithm stops searching when it reaches the maximum number of iterations, thus obtaining the optimal operating parameters.

[0101] Furthermore, simulated annealing algorithms play a crucial role in optimizing operational parameters (such as heating temperature, airflow velocity, humidity control, and equipment load) during fumigation liquid production. Through global search, simulated annealing effectively avoids local optima problems that may occur in traditional optimization methods, ensuring that parameter optimization reaches the global optimum. This process is achieved through dynamic temperature control, which not only explores a broad solution space but also ensures that the final operational parameters meet the stringent constraints of the production process (such as heating temperature and humidity control). Simulated annealing provides an efficient and robust solution to complex multi-parameter optimization problems, guaranteeing the minimization of energy consumption and the maximization of production efficiency.

[0102] Furthermore, the advantage of using simulated annealing lies in its ability to explore a wide parameter space, avoiding the common problem of relying solely on local searches. By setting an initial temperature value, the algorithm can accept poor solutions during the search process, thus escaping local optima and gradually finding the global optimum. As the temperature gradually decreases, the algorithm converges to the optimal solution, ensuring that the operating parameters in the production process have the highest energy efficiency and production stability. In this way, key operating parameters such as temperature, humidity, and equipment load in the fumigation liquid production process can be precisely optimized, ensuring the high efficiency and sustainability of the production process.

[0103] Furthermore, a Lagrange Programmed Neural Network (LPNN) is used to process the constraints in the fumigation liquid production process, and these constraints are introduced as penalty terms into the optimization objective function. This ensures optimization while avoiding violations of important constraints in the production process (such as temperature, humidity, and equipment load). Simulated annealing algorithm, as a global search tool, further guarantees that the optimal solution can be found globally, avoiding the problem of local optima.

[0104] Building upon simulated annealing, a multi-level constraint mechanism is incorporated. By dynamically adjusting the size of the Lagrange multiplier, the constraints at different levels are ensured to be reasonably controlled. For example, first-level constraints (such as temperature and humidity) have a significant impact on the optimization process, second-level constraints (such as production rate and concentrate volume) have a smaller impact but cannot be ignored, while third-level constraints (such as ambient temperature fluctuations and water quality) focus more on the sustainability of production. The introduction of this hierarchical constraint ensures that the optimization process not only focuses on minimizing energy consumption but also takes into account the safety, stability, and long-term sustainability of the production process.

[0105] The simulated annealing algorithm's global search capability ensures that it can escape local optima in complex optimization problems and find globally optimal operating parameters, thereby ensuring the optimization of the production process. By introducing multi-level constraints into the objective function using the Lagrange multiplier method, and dynamically adjusting the weight of each constraint in the optimization process based on its actual impact, it ensures that each constraint is properly considered. This flexible constraint handling effectively balances the minimization of energy consumption with the safety and stability of the production process. As the temperature gradually decreases, the simulated annealing algorithm not only reduces the randomness in the search process but also finely adjusts the parameters, ensuring that the optimal operating parameters are ultimately obtained. This gradual cooling process enhances the stability and accuracy of the optimization results. By precisely optimizing the operating parameters, energy consumption is effectively reduced while ensuring production efficiency, and the sustainability of production is improved.

[0106] S3: Adjust results through blockchain feedback and iterate smart contracts based on real-time data to ensure continuous system improvement;

[0107] The final optimized operating parameters are returned to the system via the blockchain. Each adjusted operating parameter is monitored during production, and real-time data is continuously uploaded to the blockchain for verification. Based on the new real-time data, the smart contract will continue to optimize and adjust, ensuring that every operation in the production process is continuously improved.

[0108] Smart contracts verify the effectiveness of each adjustment based on real-time data feedback from the blockchain and trigger new optimization decisions to ensure that operating parameters are always in an optimal state. Through continuous data updates and feedback, smart contracts constantly adjust operating parameters to cope with changes that may occur during production, ensuring long-term system optimization.

[0109] Each optimized and adjusted operating parameter is fed back to the system as data input for the next round of optimization, forming a closed-loop feedback mechanism. By recording the results of each optimization through blockchain, the system can continuously optimize based on constantly changing production conditions, ensuring that energy consumption in each production run is at its optimal level.

[0110] Furthermore, by utilizing smart contracts, blockchain, and real-time data feedback, traditional fumigation liquid production control methods have been significantly improved. Traditional methods rely on manually setting fixed control parameters, which cannot be dynamically adjusted in real time according to changes in production, and lack transparency and data traceability. In contrast, this invention collects energy consumption data during the production process in real time, uploads it to the blockchain, and automatically triggers optimization decisions after ensuring the data's legitimacy. Smart contracts are used to adjust production parameters, improving the flexibility and efficiency of the production process.

[0111] Furthermore, by recording each operational parameter adjustment using blockchain, this invention enhances the transparency and traceability of the production process, meeting industry requirements for data security and compliance. Compared to traditional methods, this invention not only improves energy consumption optimization and production efficiency but also enables continuous system improvement, avoiding errors caused by human intervention, thereby providing more reliable and efficient production process management.

[0112] Example 2, an embodiment of the present invention, provides an intelligent monitoring system for fumigation liquid production, comprising:

[0113] The data module collects energy consumption data in real time during the fumigation liquid production process through sensors and uploads it to the blockchain.

[0114] The blockchain module verifies the legality of uploaded data. When the data is verified to be legal, it triggers a smart contract to execute optimization decisions and automatically adjusts the operating parameters of the fumigation solution.

[0115] The iteration module uses blockchain feedback to adjust results and iterates smart contracts based on real-time data to ensure continuous system improvement.

[0116] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0118] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program is printed, because the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0119] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent monitoring of fumigant production, characterized in that, The application relates to a smudging liquid production process optimization system based on improved Lagrange programming neural network and blockchain. Real-time energy consumption data of the smudging liquid production process is collected by sensors and uploaded to the blockchain; The legality of the uploaded data is verified, and when the data is verified as legal, the intelligent contract executes an optimization decision to automatically adjust the operation parameters of the smudging liquid; The adjustment results are fed back through the blockchain, and the intelligent contract is iterated according to real-time data to ensure continuous improvement of the system; The intelligent contract includes using an improved Lagrange programming neural network to process constraint conditions in the smudging liquid production process and preliminarily optimizing operation parameters; using a simulated annealing algorithm to perform global search on the operation parameters to obtain optimal operation parameters of the smudging liquid; The improved Lagrange programming neural network introduces a multi-level constraint mechanism to optimize constraint conditions in the smudging liquid production process by using a Lagrange multiplier method; by setting the Lagrange multipliers of primary, secondary and tertiary constraints, the influence of each constraint on the objective function is weighted controlled, thereby preliminarily optimizing the operation parameters in the smudging liquid production process; The improved Lagrange programming neural network includes an input layer, a hidden layer and an output layer; The input layer receives legal energy consumption data; The hidden layer is composed of three layers of neural networks, and each layer includes 32 neurons; The first hidden layer receives the input layer data, performs preliminary feature extraction, learns the correlation in the input data, and uses a ReLU activation function to perform nonlinear mapping on the data; The second hidden layer receives the data output by the first layer, further performs feature extraction, and uses a ReLU activation function for nonlinear mapping; The third hidden layer receives the features output by the previous two layers, performs feature splicing and optimization, and is transmitted to the output layer; The output layer outputs the preliminarily optimized operation parameters, including heating temperature, air flow speed, humidity control, equipment load, production time, evaporation rate, equipment power and cooling temperature and speed; The multi-level constraint mechanism includes primary constraints, secondary constraints and tertiary constraints; Each constraint is introduced into the objective function by a Lagrange multiplier, where , represents the Lagrange multiplier of the primary constraint; represents the Lagrange multiplier of the secondary constraint; represents the Lagrange multiplier of the tertiary constraint; The objective function is to minimize energy consumption; The loss function is composed of the objective function and the penalty term of the constraint condition, and the penalty term of each constraint condition is multiplied by the corresponding Lagrange multiplier to control the influence of the constraint on the optimization target through weighting.

2. The intelligent monitoring method for fumigant production as claimed in claim 1 wherein: The energy consumption data includes temperature data, humidity data, air flow speed, heating power, equipment load data, production time, evaporation rate and energy efficiency data; The energy consumption data uploaded to the blockchain includes: each piece of energy consumption data is attached with a time stamp and a device identifier, and is transmitted to the blockchain for storage through wireless communication.

3. The intelligent monitoring method for fumigant production as claimed in claim 2, wherein: The legality verification of the uploaded data includes: the intelligent contract receives the energy consumption data attached with the time stamp and the device identifier from the blockchain, and performs data rationality verification, consistency verification and time verification; The data rationality verification is to judge whether the energy consumption data meets the preset data standard; the consistency verification is to check whether the data collected by different sensors has abnormal fluctuation; and the time verification is to verify whether the time stamp of the energy consumption data is reasonable; When the energy consumption data uploaded to the blockchain meets all the verification conditions, the intelligent contract confirms that the data is legal, and triggers the intelligent contract to execute an optimization decision. When the energy consumption data uploaded to the blockchain does not meet all the verification conditions, the smart contract records the abnormal data and triggers an alarm to notify the operator.

4. The intelligent monitoring method for fumigant production as claimed in claim 3, wherein: The global search of the preliminary optimized operation parameters using the simulated annealing algorithm includes: taking the preliminary optimized operation parameters as the initial solution of the simulated annealing algorithm; setting an initial temperature value, and gradually reducing the temperature as the iteration proceeds; generating a new candidate solution by slightly perturbing the initial solution, and performing multiple rounds of iteration; Calculate the energy consumption difference between the current solution and the new solution, and unconditionally accept the new solution when the energy consumption of the new candidate solution is lower than that of the current solution; when the energy consumption of the new candidate solution is higher than that of the current solution, decide whether to accept the new solution according to the acceptance probability; When the temperature drops to a set low value or reaches the maximum number of iterations, stop searching and output the optimal operation parameters of the fumigating liquid.

5. The intelligent monitoring method for fumigant production as claimed in claim 4, wherein: The iterative smart contract according to the real-time data includes: the optimal operation parameters are fed back to the system through the blockchain as the input of the next round of smart contract optimization.

6. An intelligent monitoring system for fumigation fluid production using the method according to any one of claims 1 to 5, characterized by: The intelligent monitoring system includes: A data module that collects energy consumption data in real time during the production of fumigating liquid through sensors and uploads it to the blockchain; A blockchain module that verifies the legality of the uploaded data, and when the data is verified to be legal, triggers the smart contract to execute optimization decisions and automatically adjusts the operation parameters of the fumigating liquid; An iteration module that adjusts the results through the blockchain and iterates the smart contract according to real-time data to ensure continuous improvement of the system. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the intelligent monitoring method for fumigating liquid production according to any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the intelligent monitoring method for fumigating liquid production according to any one of claims 1-5.

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