A multi-temperature zone and stage temperature monitoring management and alarm system for kelp drying

Abstract

The present invention discloses a multi-temperature zone, stage-by-stage temperature monitoring management and alarm system and method for kelp drying, belonging to the field of temperature data processing technology. The system comprises a temperature zone division module for dividing the drying area into four temperature zones and monitoring the temperature in real time through temperature sensors; a data acquisition module for deploying distributed optical fiber sensors, collecting temperature data, and performing dynamic error correction; a central control module comprising a data fusion unit, a dynamic threshold calculation unit, and a control instruction generation unit for performing multi-scale noise reduction processing, dynamically updating the temperature thresholds of each stage, and generating control instructions; an alarm module comprising a probability analysis unit and a thermodynamic constraint unit for analyzing the risk of temperature exceeding the limit; and a power control module for adjusting the heating power of the temperature exceeding the limit zone and regulating the conveyor belt operating speed. The present invention solves the bottlenecks of traditional drying systems in terms of accuracy, stability, and energy efficiency, and realizes the intelligence and high reliability of the kelp drying process.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature data processing, and in particular to a multi-temperature zone staged temperature monitoring management and alarm system for kelp drying. Background Art

[0002] As a crucial step in agricultural product processing, kelp drying has evolved from traditional natural air-drying to modern mechanical drying. With advancements in sensor technology, automated control technology, and data processing technology, kelp drying technology is gradually moving toward intelligent and sophisticated processes. However, existing kelp drying systems still have numerous deficiencies in temperature monitoring and management. First, traditional drying systems often employ a single temperature control system, which cannot meet the differentiated temperature requirements of kelp during different drying stages. This leads to problems such as uneven drying, high energy consumption, and unstable product quality. Second, existing systems employ relatively simple temperature monitoring methods, resulting in low data accuracy and a lack of effective dynamic correction mechanisms, making precise temperature control difficult. Furthermore, existing alarm systems are mostly simple threshold alarms that cannot predict temperature over-limit risks in advance and lack compliance verification of temperature field distribution, leading to delayed alarms and significant safety hazards. Summary of the Invention

[0003] In view of the above-mentioned existing problems, the purpose of the present invention is to achieve refined and intelligent control of the kelp drying process through multi-temperature zone division, distributed fiber optic sensor data acquisition, multi-scale noise reduction and dynamic threshold calculation of the central control module, probability analysis and thermodynamic constraint verification of the alarm module, and dynamic adjustment of PID parameters and sliding mode control algorithm optimization of the power control module.

[0004] In order to solve the above technical problems, a multi-temperature zone and stage-by-stage temperature monitoring management and alarm system for kelp drying is proposed, including a temperature zone division module, which is used to divide the drying area into four temperature zones, control the heating power through independent heating units, and monitor the temperature in real time through temperature sensors; a data acquisition module, which is used to collect real-time temperature data of each temperature zone, and perform dynamic error correction on the main sensor data through a redundant correction unit; a central control module, which includes a data fusion unit, a dynamic threshold calculation unit and a control instruction generation unit, which is used to perform multi-scale noise reduction processing on the temperature data, dynamically update the temperature threshold of each stage, and generate control instructions; an alarm module, which includes a probability analysis unit and a thermodynamic constraint unit, which is used to analyze the temperature over-limit risk through a hidden Markov model, and verify the compliance of the temperature field distribution in combination with the heat conduction equation to trigger an alarm signal; a power control module, which is used to adjust the heating power of the over-limit temperature zone according to the control instruction, and synchronously adjust the running speed of the kelp conveyor belt through linkage control.

[0005] As a preferred solution of the multi-temperature zone staged temperature monitoring management and alarm system for kelp drying according to the present invention, wherein: in the temperature zone division module, the preset temperature threshold range of each temperature zone is a nonlinear increasing sequence;

[0006] The drying area includes preheating zone, balanced dehumidification zone, heating and drying zone and cooling and shaping zone;

[0007] The threshold range setting steps include fitting the exponential decay threshold interval of the preheating zone using the least squares method according to the initial moisture content of kelp, and the threshold interval of the balanced dehumidification zone using a quadratic function model. The threshold intervals of the heating and drying zone and the cooling and fixing zone are dynamically adjusted through the kelp drying rate and heat capacity parameters, and are associated with the first two temperature zones.

[0008] As a preferred solution of the multi-temperature zone and staged temperature monitoring management and alarm system for kelp drying according to the present invention, the data acquisition module comprises distributed optical fiber sensors arranged according to a preset node distribution pattern;

[0009] The distributed fiber optic sensor deployment includes placing main sensors along the conveyor belt according to the Legendre polynomial node distribution pattern. The node spacing decreases nonlinearly with the length of the temperature zone. Redundant sensors are added at the end of the temperature zone. The main sensor data is dynamically corrected using the Kalman filter algorithm. The redundant sensor data is preferentially used to correct the measurement deviation of the main sensor. The corrected temperature data is uploaded to the data fusion unit of the central control module in real time.

[0010] The node spacing is expressed as:

[0011]

[0012] in, is the spacing between the nth nodes, L is the length of the conveyor belt, N is the total number of nodes, and n is the sequence number of the current node;

[0013] The dynamic correction formula for the main sensor data is expressed as:

[0014]

[0015] in, is the estimated value of the corrected temperature state at the current moment k, k and k-1 are the moment values, is the estimated value of the current temperature state based on the prediction at the previous moment, is the Kalman gain, is the actual temperature measurement value of the redundant sensor at time k, and H is the observation matrix.

[0016] As a preferred solution of the multi-temperature zone and staged temperature monitoring management and alarm system for kelp drying according to the present invention, the central control module includes a data fusion unit, a dynamic threshold calculation unit and a control instruction generation unit;

[0017] The data fusion unit performs wavelet transform on multi-sensor data in the same temperature zone, decomposing it into high-frequency noise and low-frequency signals of different scales. It then filters the effective signal components based on the adaptive threshold, reconstructs the noise-reduced temperature data, and inputs the reconstructed data into the dynamic threshold calculation unit, which combines the fuzzy logic algorithm to update the temperature threshold range of each stage.

[0018] The control instruction generation unit includes calculating the difference between the real-time temperature of each temperature zone and the dynamic threshold to generate a temperature deviation signal. Based on historical data, when the temperature difference is greater than or equal to the temperature difference threshold, an emergency cooling instruction is generated; when the temperature difference is less than the temperature difference threshold, a fine-tuning heating instruction is generated;

[0019] The formula for reconstructing the temperature data after noise reduction is:

[0020]

[0021]

[0022] in, is the original temperature signal, is the temperature data after noise reduction, is the wavelet basis function, is the wavelet coefficient, is the indicator function, j is the scale parameter, g is the translation parameter, t is the time variable, is the adaptive threshold, is the noise standard deviation, U is the signal length, and J is the total number of scales.

[0023] As a preferred solution of the multi-temperature zone staged temperature monitoring management and alarm method for kelp drying according to the present invention, the central control module further includes integrating and training a spatiotemporal convolutional neural network;

[0024] Training and applying spatiotemporal convolutional neural networks involves inputting historical temperature series and control records from multiple temperature zones, constructing a training dataset, extracting spatiotemporal features through convolutional layers, predicting future temperature trends, and dynamically optimizing temperature thresholds and control response parameters based on the prediction results.

[0025] The loss optimization is expressed as:

[0026]

[0027] in, is the loss function that measures the error between the predicted temperature and the actual temperature. is the predicted temperature series, is the actual temperature series, is the norm of the vector, is the regularization parameter, is the total variation regularization term of the weight matrix W, W is the weight matrix, For time point.

[0028] As a preferred embodiment of the multi-temperature zoned, staged temperature monitoring, management, and alarm system for kelp drying according to the present invention, the alarm module includes: calculating the probability of the current temperature exceeding the limit based on the hidden Markov model, verifying whether the temperature gradient satisfies the constraints of the heat conduction equation, discretely solving the temperature field distribution within the temperature zone using the finite element method, detecting local overheating or cold zone anomalies, and triggering an alarm signal and generating a control instruction when any of the conditions of exceeding the limit probability or thermodynamic violation are met;

[0029] The state transition probability is generated by training historical drying data, and the current temperature exceeding limit probability is fitted with the real-time temperature distribution through the Gaussian mixture model;

[0030] The formula for calculating the probability of the current temperature exceeding the limit is:

[0031]

[0032] Where M is the number of states, is the mixture weight of the Gaussian mixture model, is the mean matrix of the mth Gaussian component, i is the state index, m is the Gaussian component index, is the covariance matrix of the mth Gaussian component, is the Gaussian probability density function, is the temperature at time r, r is the time, is the probability of the current temperature exceeding the limit;

[0033] Thermodynamic verification involves discretely solving the heat conduction equation using the finite element method to detect whether the local temperature gradient is abnormal. The formula is expressed as:

[0034]

[0035] in, is the rate of change of temperature with time, is the thermal conductivity coefficient, is the Laplace operator of temperature, is the internal heat source term, Z is the temperature, and r is the time; when When the threshold is set, it is judged as abnormal local temperature gradient. When the value is less than or equal to the set threshold, the local temperature gradient is determined to be normal.

[0036] As a preferred solution of the multi-temperature zone staged temperature monitoring management and alarm system for kelp drying described in the present invention, the power control module includes calculating proportional, integral and differential control quantities based on temperature deviation, dynamically adjusting PID parameters to adapt to the heat capacity characteristics of the temperature zone, introducing a sliding mode control algorithm to optimize the power adjustment quantity, and controlling the conveyor belt speed in a linked manner to synchronize the residence time of the kelp in the temperature zone with the change of heating power;

[0037] The formula for dynamically adjusting PID parameters is expressed as:

[0038]

[0039] in, is the control quantity, that is, the control signal output by the PID controller. is the proportional gain, i.e. the proportional term coefficient of PID control, is the integral gain, i.e. the integral term coefficient of PID control, is the differential gain, i.e. the differential term coefficient of PID control, is the deviation between the real-time temperature and the threshold, i.e. the error signal, r is the time, is the rate of change of the error signal, that is, the differential of the error, is the variable index;

[0040] The sliding mode control algorithm is expressed as:

[0041]

[0042] in, is the gain coefficient, s is the sliding surface, i.e. the combination of error and integral, is the power adjustment amount, is a symbolic function;

[0043] The linkage control conveyor belt speed is expressed as:

[0044]

[0045] in, To adjust the conveyor belt speed, is the initial conveyor belt speed, is the maximum power, that is, the maximum power output allowed by the system. is the power adjustment amount.

[0046] Another object of the present invention is to provide a multi-temperature zoned, staged temperature monitoring, management and alarm method for kelp drying. The present invention solves the problems of insufficient temperature control accuracy, temperature fluctuation and instability, excessive energy consumption, low production efficiency, safety and reliability in traditional kelp drying systems, and provides a more efficient, stable and safe solution for the kelp drying industry.

[0047] As a preferred embodiment of the multi-temperature zone staged temperature monitoring management and alarm method for kelp drying according to the present invention, it is characterized by comprising: dividing into four independent temperature zones, initializing the heating unit and sensor parameters of each temperature zone, and collecting temperature data at a preset node spacing through distributed optical fiber sensors;

[0048] The central control module performs multi-scale decomposition and reconstruction on the original data, calculates the dynamic temperature threshold, and compares the real-time temperature with the threshold. When the limit is exceeded, an alarm is triggered and a control instruction is generated. The heating power is adjusted through the adaptive controller, and the speed of the kelp conveyor belt is controlled synchronously.

[0049] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of a multi-temperature zone staged temperature monitoring management and alarm system for kelp drying.

[0050] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the multi-temperature zone staged temperature monitoring management and alarm system for kelp drying are implemented.

[0051] Beneficial effects of the present invention: The present invention realizes refined zoning control of the drying area through the temperature zone division module. Each temperature zone independently controls the heating power and monitors the temperature in real time, ensuring that the kelp can obtain the optimal temperature environment in different drying stages, thereby improving the drying efficiency and quality, and avoiding the problems of over-drying or under-drying; the data acquisition module improves the accuracy and reliability of temperature data through the optimized layout and dynamic error correction of distributed optical fiber sensors, provides high-quality data input for the central control module, and ensures the accuracy and effect of subsequent processing; the data fusion unit of the central control module effectively removes high-frequency noise and retains low-frequency effective signals through wavelet transform noise reduction processing of multi-sensor data, improves the signal-to-noise ratio of temperature data, and provides more accurate basic data for dynamic threshold calculation and control instruction generation; the dynamic threshold calculation unit dynamically updates the temperature threshold of each stage in combination with the fuzzy logic algorithm to achieve temperature The flexibility and adaptability of temperature control can adjust the control strategy according to real-time conditions, further optimize the drying process, and improve the drying quality of kelp; the integrated spatiotemporal convolutional neural network realizes the prediction of future temperature change trends, and dynamically optimizes the temperature threshold and control response parameters according to the prediction results, enhancing the foresight and initiative of the system, and improving the accuracy and stability of temperature control; the probability analysis unit and thermodynamic constraint unit of the alarm module realize real-time analysis of temperature over-limit risks and compliance verification of temperature field distribution, and can timely detect and deal with local overheating or cold zone anomalies, ensuring the safety and stability of the drying process; the power control module optimizes the power adjustment amount through the dynamic adjustment of PID parameters and the introduction of sliding mode control algorithm, and controls the conveyor belt speed in a linked manner, synchronizing the residence time of kelp in the temperature zone with the heating power change, realizing the refinement and intelligence of power control, and further improving the drying efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts. Among them:

[0053] Figure 1 A schematic diagram of a module of a multi-temperature zoned staged temperature monitoring management and alarm system for kelp drying provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0054] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it individually or selectively refer to an embodiment that is mutually exclusive of other embodiments.

[0057] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0058] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0059] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0060] Example 1, with reference to Figure 1, which is the first embodiment of the present invention, provides a multi-temperature zone divided stage temperature monitoring management and alarm system for kelp drying, including: a temperature zone division module, a data acquisition module, a central control module, an alarm module and a power control module.

[0061] The temperature zone division module is used to divide the drying area into a preheating zone, a balanced dehumidification zone, a heating and drying zone, and a cooling and setting zone. Each temperature zone controls the heating power through an independent heating unit and monitors the temperature in real time through a temperature sensor.

[0062] In the temperature zone division module, the preset temperature threshold range of each temperature zone is a nonlinear increasing sequence, preheating zone (zone 1): temperature 50-55℃, kelp center temperature 50℃, high box humidity, and consistent temperature inside and outside the kelp to prevent the formation of a dense film on the surface, which is convenient for the next step of dehumidification; balanced dehumidification zone (zone 2): temperature 55-60℃, adjust the air valve to control the wind speed, use weak wind to dehumidify, keep the box humidity low, dehumidify, and form tiny channels; heating and drying zone (zone 3): temperature 65-70℃, adjust the air valve to control the wind speed, use strong wind to dehumidify and dry, and realize waste heat recovery and enter the waste heat heating zone in zone 1 for recycling; cooling and shaping zone (zone 4): temperature 50-55℃, adjust the air valve to control the wind speed, cool and shape, and avoid deformation due to excessive dehydration.

[0063] The threshold range setting steps include fitting the exponential decay threshold interval of the preheating zone using the least squares method according to the initial moisture content of kelp, and the threshold interval of the balanced dehumidification zone using a quadratic function model. The threshold intervals of the heating and drying zone and the cooling and fixing zone are dynamically adjusted through the kelp drying rate and heat capacity parameters, and are associated with the first two temperature zones.

[0064] It should be noted that by using a nonlinear increasing threshold, the cracking of the kelp surface or internal moisture residue caused by sudden temperature changes can be avoided, and the problem of uneven drying caused by traditional linear heating can be solved; the post-stabilization temperature zone is associated with the thresholds of the first two temperature zones to ensure a smooth temperature transition, reduce the thermal stress caused by temperature zone switching, and improve the kelp yield.

[0065] The data acquisition module is used to deploy distributed optical fiber sensors according to a preset node distribution pattern, collect real-time temperature data of each temperature zone, and perform dynamic error correction on the main sensor data through the redundancy correction unit;

[0066] In the data acquisition module, distributed optical fiber sensors are arranged according to a preset node distribution rule;

[0067] The distributed fiber optic sensor deployment includes placing main sensors along the conveyor belt according to the Legendre polynomial node distribution pattern. The node spacing decreases nonlinearly with the length of the temperature zone. Redundant sensors are added at the end of the temperature zone. The main sensor data is dynamically corrected using the Kalman filter algorithm. The redundant sensor data is preferentially used to correct the measurement deviation of the main sensor. The corrected temperature data is uploaded to the data fusion unit of the central control module in real time.

[0068] The node spacing is expressed as:

[0069]

[0070] in, is the spacing between the nth nodes, L is the length of the conveyor belt, N is the total number of nodes, and n is the sequence number of the current node;

[0071] The dynamic correction formula for the main sensor data is expressed as:

[0072]

[0073] in, is the estimated value of the corrected temperature state at the current moment k, k and k-1 are the moment values, is the estimated value of the current temperature state based on the prediction at the previous moment, is the Kalman gain, is the actual measurement value of the redundant sensor at time k, and H is the observation matrix.

[0074] Furthermore, the Legendre node layout optimizes the balance between dense sampling at the front end of the temperature zone and sparse sampling at the end, solving the problem of insufficient end data caused by traditional equidistant layout; Kalman filter correction uses redundant sensor data to prioritize correcting end errors, improving the reliability of end temperature zone data (the end is easily affected by the environment) and reducing the risk of miscontrol.

[0075] The central control module includes a data fusion unit, a dynamic threshold calculation unit, and a control instruction generation unit, which are used to perform multi-scale noise reduction processing on the collected temperature data, dynamically update the temperature thresholds of each stage based on the fuzzy logic algorithm, and generate control instructions;

[0076] The central control module includes a data fusion unit, a dynamic threshold calculation unit and a control instruction generation unit;

[0077] The data fusion unit performs wavelet transform on multi-sensor data in the same temperature zone, decomposing it into high-frequency noise and low-frequency signals of different scales. It then filters the effective signal components based on the adaptive threshold, reconstructs the noise-reduced temperature data, and inputs the reconstructed data into the dynamic threshold calculation unit, which combines the fuzzy logic algorithm to update the temperature threshold range of each stage.

[0078] The control instruction generation unit includes calculating the difference between the real-time temperature of each temperature zone and the dynamic threshold to generate a temperature deviation signal. Based on historical data, when the temperature difference is greater than or equal to the temperature difference threshold, an emergency cooling instruction is generated; when the temperature difference is less than the temperature difference threshold, a fine-tuning heating instruction is generated;

[0079] The formula for reconstructing the temperature data after noise reduction is:

[0080]

[0081]

[0082] in, is the original temperature signal, is the temperature data after noise reduction, is the wavelet basis function, is the wavelet coefficient, is the indicator function, j is the scale parameter, g is the translation parameter, t is the time variable, is the adaptive threshold, is the noise standard deviation, U is the signal length, and J is the total number of scales.

[0083] Furthermore, the central control module also includes integrating a spatiotemporal convolutional neural network and performing training;

[0084] Training and applying spatiotemporal convolutional neural networks involves inputting historical temperature series and control records from multiple temperature zones, constructing a training dataset, extracting spatiotemporal features through convolutional layers, predicting future temperature trends, and dynamically optimizing temperature thresholds and control response parameters based on the prediction results.

[0085] The loss optimization is expressed as:

[0086]

[0087] in, is the loss function that measures the error between the predicted temperature and the actual temperature. is the predicted temperature series, is the actual temperature series, is the norm of the vector, is the regularization parameter, is the total variation regularization term of the weight matrix W, W is the weight matrix, For time point.

[0088] It should be noted that wavelet denoising separates high-frequency noise from effective signals, solving the performance degradation problem of traditional filtering algorithms in non-stationary signals; and predicts future temperature changes through spatiotemporal characteristics, adjusts the threshold and power in advance, and avoids overshoot or undershoot caused by delayed response.

[0089] The alarm module includes a probability analysis unit and a thermodynamic constraint unit, which is used to analyze the temperature over-limit risk through a probability model, verify the compliance of the temperature field distribution in combination with the heat conduction equation, and trigger an alarm signal;

[0090] The alarm module includes calculating the probability of the current temperature exceeding the limit based on the hidden Markov model, verifying whether the temperature gradient meets the constraints of the heat conduction equation, discretely solving the temperature field distribution in the temperature zone through the finite element method, detecting local overheating or cold zone anomalies, and triggering an alarm signal and generating a control instruction when any of the conditions of exceeding the limit probability or thermodynamic violation are met;

[0091] The state transition probability is generated by training historical drying data, and the current temperature exceeding limit probability is fitted with the real-time temperature distribution through the Gaussian mixture model;

[0092] The formula for calculating the probability of the current temperature exceeding the limit is:

[0093]

[0094] Where M is the number of states, is the mixture weight of the Gaussian mixture model, is the mean matrix of the mth Gaussian component, i is the state index, m is the Gaussian component index, is the covariance matrix of the mth Gaussian component, is the Gaussian probability density function, is the temperature at time r, r is the time, is the probability of the current temperature exceeding the limit;

[0095] Thermodynamic verification involves discretely solving the heat conduction equation using the finite element method to detect whether the local temperature gradient is abnormal. The formula is expressed as:

[0096]

[0097] in, is the rate of change of temperature with time, is the thermal conductivity coefficient, is the Laplace operator of temperature, is the internal heat source term, Z is the temperature, and r is the time; when When the threshold is set, it is judged as abnormal local temperature gradient. When the value is less than or equal to the set threshold, the local temperature gradient is determined to be normal.

[0098] It should be noted that the probability of the current temperature exceeding the limit is calculated based on the hidden Markov model to solve the single triggering problem of the traditional threshold alarm. The heat conduction equation constrains the detection of local temperature gradient anomalies (such as overheating at the edge or cold area in the center) to avoid the global average value masking local risks.

[0099] The power control module is used to adjust the heating power of the over-limit temperature zone according to the control instruction, and synchronously adjust the conveyor belt running speed through the linkage control unit.

[0100] The power control module includes calculating the proportional, integral and differential control quantities according to the temperature deviation, dynamically adjusting the PID parameters to adapt to the heat capacity characteristics of the temperature zone, introducing a sliding mode control algorithm to optimize the power adjustment quantity, and controlling the conveyor belt speed in a linked manner to synchronize the residence time of the kelp in the temperature zone with the change of the heating power;

[0101] The formula for dynamically adjusting PID parameters is expressed as:

[0102]

[0103] in, is the control quantity, that is, the control signal output by the PID controller. is the proportional gain, i.e. the proportional term coefficient of PID control, is the integral gain, i.e. the integral term coefficient of PID control, is the differential gain, i.e. the differential term coefficient of PID control, is the deviation between the real-time temperature and the threshold, i.e. the error signal, r is the time, is the rate of change of the error signal, that is, the differential of the error, is the variable index;

[0104] The sliding mode control algorithm is expressed as:

[0105]

[0106] in, is the gain coefficient, s is the sliding surface, i.e. the combination of error and integral, is the power adjustment amount, is a symbolic function;

[0107] The linkage control conveyor belt speed is expressed as:

[0108]

[0109] in, To adjust the conveyor belt speed, is the initial conveyor belt speed, is the maximum power, that is, the maximum power output allowed by the system. is the power adjustment amount.

[0110] It should also be noted that the sliding mode control quickly converges to a stable state when the temperature changes suddenly, solving the oscillation problem of traditional PID in nonlinear systems; the conveyor belt speed linkage ensures that the kelp residence time matches the power change, avoiding over-drying or under-drying of some areas due to control delays.

[0111] 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 the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0112] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that:

[0113] If the 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 the present invention, or the portion that contributes to the prior art, or the portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0114] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the 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 (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0115] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0116] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0117] Example 3 is the third embodiment of the present invention. This embodiment provides a multi-temperature zone divided into stages temperature monitoring management and alarm method for kelp drying, including dividing into four independent temperature zones, initializing the heating units and sensor parameters of each temperature zone, and collecting temperature data through distributed optical fiber sensors at preset node spacing.

[0118] The central control module performs multi-scale decomposition and reconstruction on the original data, calculates the dynamic temperature threshold, and compares the real-time temperature with the threshold. When the limit is exceeded, an alarm is triggered and a control instruction is generated. The heating power is adjusted through the adaptive controller, and the speed of the kelp conveyor belt is controlled synchronously.

[0119] 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 the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-temperature zone and staged temperature monitoring management and alarm system for kelp drying, characterized by: Including temperature zone division module, data acquisition module, central control module, alarm module and power control module; The temperature zone division module is used to divide the drying area into a preheating zone, a balanced dehumidification zone, a heating and drying zone, and a cooling and setting zone. Each temperature zone controls the heating power through an independent heating unit and monitors the temperature in real time through a temperature sensor. The data acquisition module is used to deploy distributed optical fiber sensors according to a preset node distribution pattern, collect real-time temperature data of each temperature zone, and perform dynamic error correction on the main sensor data through the redundancy correction unit; The central control module includes a data fusion unit, a dynamic threshold calculation unit, and a control instruction generation unit, which are used to perform multi-scale noise reduction processing on the collected temperature data, dynamically update the temperature thresholds of each stage based on the fuzzy logic algorithm, and generate control instructions; The alarm module includes a probability analysis unit and a thermodynamic constraint unit, which is used to analyze the temperature over-limit risk through the hidden Markov model, and verify the compliance of the temperature field distribution in combination with the heat conduction equation to trigger the alarm signal; The power control module is used to adjust the heating power of the over-limit temperature zone according to the control instructions, and synchronously adjust the running speed of the kelp conveyor belt through linkage control; The central control module includes a data fusion unit, a dynamic threshold calculation unit and a control instruction generation unit; The data fusion unit performs wavelet transform on multi-sensor data in the same temperature zone, decomposing it into high-frequency noise and low-frequency signals of different scales. It then filters the effective signal components based on the adaptive threshold, reconstructs the noise-reduced temperature data, and inputs the reconstructed data into the dynamic threshold calculation unit, which combines the fuzzy logic algorithm to update the temperature threshold range of each stage. The control instruction generation unit includes calculating the difference between the real-time temperature of each temperature zone and the dynamic threshold to generate a temperature deviation signal. Based on historical data, when the temperature difference is greater than or equal to the temperature difference threshold, an emergency cooling instruction is generated; when the temperature difference is less than the temperature difference threshold, a fine-tuning heating instruction is generated; The formula for reconstructing the temperature data after noise reduction is: ； ； in, is the original temperature signal, is the temperature data after noise reduction, is the wavelet basis function, is the wavelet coefficient, is the indicator function, j is the scale parameter, g is the translation parameter, t is the time variable, is the adaptive threshold, is the noise standard deviation, U is the signal length, and J is the total number of scales; The alarm module includes calculating the probability of the current temperature exceeding the limit based on the hidden Markov model, verifying whether the temperature gradient meets the constraints of the heat conduction equation, discretely solving the temperature field distribution in the temperature zone through the finite element method, detecting local overheating or cold zone anomalies, and triggering an alarm signal and generating a control instruction when any of the conditions of exceeding the limit probability or thermodynamic violation are met; The state transition probability is generated by training historical drying data, and the current temperature exceeding limit probability is fitted with the real-time temperature distribution through the Gaussian mixture model; The formula for calculating the probability of the current temperature exceeding the limit is: ； Where M is the number of states, is the mixture weight of the Gaussian mixture model, is the mean matrix of the mth Gaussian component, i is the state index, m is the Gaussian component index, is the covariance matrix of the mth Gaussian component, is the Gaussian probability density function, is the temperature at time r, r is the time, is the probability of the current temperature exceeding the limit; Thermodynamic verification involves discretely solving the heat conduction equation using the finite element method to detect whether the local temperature gradient is abnormal. The formula is expressed as: ； in, is the rate of change of temperature with time, is the thermal conductivity coefficient, is the Laplace operator of temperature, is the internal heat source term, Z is the temperature, and r is the time; when When the threshold is set, it is judged as abnormal local temperature gradient. When the value is less than or equal to the set threshold, the local temperature gradient is determined to be normal.

2. The multi-temperature zone and staged temperature monitoring management and alarm system for kelp drying according to claim 1, characterized in that: In the temperature zone division module, the preset temperature threshold range of each temperature zone is a nonlinear increasing sequence; The threshold range setting steps include fitting the exponential decay threshold interval of the preheating zone using the least squares method according to the initial moisture content of kelp, and the threshold interval of the balanced dehumidification zone using a quadratic function model. The threshold intervals of the heating and drying zone and the cooling and fixing zone are dynamically adjusted through the kelp drying rate and heat capacity parameters, and are associated with the first two temperature zones.

3. The multi-temperature zone and staged temperature monitoring management and alarm system for kelp drying according to claim 2, characterized in that: In the data acquisition module, distributed optical fiber sensors are arranged according to a preset node distribution rule; The distributed fiber optic sensor deployment includes placing main sensors along the conveyor belt according to the Legendre polynomial node distribution pattern. The node spacing decreases nonlinearly with the length of the temperature zone. Redundant sensors are added at the end of the temperature zone. The main sensor data is dynamically corrected using the Kalman filter algorithm. The redundant sensor data is preferentially used to correct the measurement deviation of the main sensor. The corrected temperature data is uploaded to the data fusion unit of the central control module in real time. The node spacing is expressed as: ； in, is the spacing between the nth nodes, L is the length of the conveyor belt, N is the total number of nodes, and n is the sequence number of the current node; The dynamic correction formula for the main sensor data is expressed as: ； in, is the estimated value of the corrected temperature state at the current moment k, k and k-1 are the moment values, is the estimated value of the current temperature state based on the prediction at the previous moment, is the Kalman gain, is the actual temperature measurement value of the redundant sensor at time k, and H is the observation matrix.

4. The multi-temperature zone and staged temperature monitoring management and alarm system for kelp drying according to claim 3, characterized in that: The central control module also includes integrating a spatiotemporal convolutional neural network and performing training; Training and applying spatiotemporal convolutional neural networks involves inputting historical temperature series and control records from multiple temperature zones, constructing a training dataset, extracting spatiotemporal features through convolutional layers, predicting future temperature trends, and dynamically optimizing temperature thresholds and control response parameters based on the prediction results. The loss optimization is expressed as: ； in, is the loss function that measures the error between the predicted temperature and the actual temperature. is the predicted temperature series, is the actual temperature series, is the norm of the vector, is the regularization parameter, is the total variation regularization term of the weight matrix W, W is the weight matrix, For time point.

5. The multi-temperature zone and staged temperature monitoring management and alarm system for kelp drying according to claim 4, characterized in that: The power control module includes calculating the proportional, integral and differential control quantities according to the temperature deviation, dynamically adjusting the PID parameters to adapt to the heat capacity characteristics of the temperature zone, introducing a sliding mode control algorithm to optimize the power adjustment quantity, and controlling the conveyor belt speed in a linked manner to synchronize the residence time of the kelp in the temperature zone with the change of the heating power; The formula for dynamically adjusting PID parameters is expressed as: ； in, is the control quantity, that is, the control signal output by the PID controller. is the proportional gain, i.e. the proportional term coefficient of PID control, is the integral gain, i.e. the integral term coefficient of PID control, is the differential gain, i.e. the differential term coefficient of PID control, is the deviation between the real-time temperature and the threshold, i.e. the error signal, r is the time, is the rate of change of the error signal, that is, the differential of the error, is the variable index; The sliding mode control algorithm is expressed as: ； in, is the gain coefficient, s is the sliding surface, i.e. the combination of error and integral, is the power adjustment amount, is a symbolic function; The linkage control conveyor belt speed is expressed as: ； in, To adjust the conveyor belt speed, is the initial conveyor belt speed, is the maximum power, that is, the maximum power output allowed by the system. is the power adjustment amount.

6. A multi-temperature zoned, staged temperature monitoring, management, and alarm method for kelp drying, applied to a multi-temperature zoned, staged temperature monitoring, management, and alarm system for kelp drying according to any one of claims 1 to 5, characterized in that: This includes dividing the system into four independent temperature zones, initializing the heating unit and sensor parameters in each zone, and collecting temperature data using distributed fiber optic sensors at preset node spacing. The central control module performs multi-scale decomposition and reconstruction on the original temperature data, calculates the dynamic temperature threshold, and compares the real-time temperature with the threshold. When the limit is exceeded, an alarm is triggered and a control instruction is generated. The heating power is adjusted through the adaptive controller, and the speed of the kelp conveyor belt is controlled synchronously.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-temperature zone staged temperature monitoring management and alarm system for kelp drying according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a multi-temperature zoned staged temperature monitoring management and alarm system for kelp drying according to any one of claims 1 to 5 are implemented.

Images