Kelp drying energy consumption optimization method and system combined with big data comprehensive analysis

Abstract

The invention discloses a kelp drying energy consumption optimization method and system combined with big data comprehensive analysis, and relates to the field of kelp drying energy consumption, and the method comprises the steps: collecting equipment operation state data and environment parameters in real time through setting an edge data collection node; receiving data acquired by each edge data acquisition node in real time by utilizing an internet of things technology, and establishing a kelp drying energy consumption database; a kelp drying energy consumption evaluation model is constructed, and kelp drying energy consumption is comprehensively evaluated and predicted; according to an evaluation result of the kelp drying energy consumption evaluation model, obtaining a weight value of drying energy consumption of each edge data acquisition node; and the relevance of the drying energy consumption of each edge data acquisition node is obtained, and the kelp drying energy consumption is dynamically adjusted and optimized. The method has the advantages that the kelp drying energy consumption is dynamically evaluated and the waste heat recovery is optimized through the multi-mode sensing data and the hybrid machine learning model, and the kelp productivity loss is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy consumption in kelp drying, and specifically to an optimization method and system for kelp drying energy consumption combined with big data comprehensive analysis. Background Art

[0002] At present, with the continuous growth of global energy demand and the increasing prominence of environmental problems, the energy consumption problem in the kelp drying industry has attracted more and more attention. On the one hand, the high energy cost increases the drying cost of enterprises and reduces their market competitiveness. On the other hand, a large amount of energy consumption also poses great pressure on the environment. However, in the existing kelp drying process, the management of energy consumption mostly stays in the stage of post-statistical analysis, lacking real-time prediction and optimal control of energy consumption during the drying process, and unable to adjust the drying parameters and equipment operating status in time during the drying process to achieve energy consumption reduction. Therefore, it is of great practical significance to develop a method and system that can real-time predict the energy consumption of kelp drying and optimize it.

[0003] The existing kelp drying energy consumption management technology mainly focuses on the model construction of a single link, lacking the global perception data dimension of kelp drying, unable to comprehensively process time series dependence, spatial correlation and dynamic attention mechanism, and lacking the dynamic optimization ability based on the correlation between real-time weight and energy consumption, resulting in rigid waste heat recovery strategies, lagging system response, and difficulty in adapting to the dynamic changes of the drying environment and process, ultimately causing problems such as low energy consumption prediction accuracy, poor optimization effect and energy waste. Summary of the Invention

[0004] To solve the above technical problems, an optimization method and system for kelp drying energy consumption combined with big data comprehensive analysis are provided. This technical solution solves the problems proposed in the above background art, such as lacking the global perception data dimension of kelp drying, unable to comprehensively process time series dependence, spatial correlation and dynamic attention mechanism, and lacking the dynamic optimization ability based on the correlation between real-time weight and energy consumption, resulting in rigid waste heat recovery strategies, lagging system response, and difficulty in adapting to the dynamic changes of the drying environment and process, ultimately causing problems such as low energy consumption prediction accuracy, poor optimization effect and energy waste.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] An optimization method for kelp drying energy consumption combined with big data comprehensive analysis, comprising:

[0007] Deploy a multi-modal sensor array in each kelp drying process, and set up edge data acquisition nodes to collect equipment operating status data and environmental parameters in real time;

[0008] Utilize the Internet of Things technology to receive the data collected by each edge data acquisition node in real time and establish a kelp drying energy consumption database;

[0009] Based on the LSTM-Transformer-GNN hybrid machine learning algorithm, a kelp drying energy consumption evaluation model is constructed to comprehensively evaluate and predict the kelp drying energy consumption;

[0010] According to the evaluation results of the kelp drying energy consumption evaluation model, the weight values of the drying energy consumption of each edge data acquisition node are obtained;

[0011] Obtain the correlation of the drying energy consumption of each edge data acquisition node, and through waste heat recovery optimization, dynamically adjust and optimize the kelp drying energy consumption.

[0012] Preferably, the construction of the kelp drying energy consumption evaluation model based on the LSTM-Transformer-GNN hybrid machine learning algorithm to comprehensively evaluate and predict the kelp drying energy consumption specifically includes:

[0013] According to the kelp drying process cycle, set the window length of the LSTM layer time series, where the window length of the LSTM layer time series is within the range of the kelp drying process cycle;

[0014] According to the kelp drying energy consumption database, the data collected by each edge data acquisition node is used as the input quantity;

[0015] Based on the LSTM neural network, output the device operation state data and environmental parameters with time series, and use them as the input quantity of the Transformer model;

[0016] Through linear projection, map the data collected by each edge data acquisition node in real time to the same-dimensional space as Key / Value;

[0017] According to the Transformer model, calculate the correlation between each data in the input quantity and other data, and capture the dependency relationship between any data in the input quantity data;

[0018] According to the kelp process parameter knowledge graph, based on the mean square error formula, establish a constraint loss function for the LSTM-Transformer model;

[0019] According to the LSTM-Transformer model, judge and predict the kelp drying energy consumption values and the total kelp drying energy consumption values of each edge data acquisition node within the window length;

[0020] According to the drying process of each edge data acquisition node in the kelp drying process, based on the CNN neural network, adjust the edge weights in real time to accurately reflect the impact of the drying schedule change on the energy consumption, and further correct and optimize the kelp drying energy consumption value;

[0021] The expression for setting the window length of the LSTM layer time series is:

[0022]

[0023] In the formula, is the window length of the LSTM layer time series, is the drying process cycle of kelp, is the dominant angular frequency in the drying data of kelp, is the floor function symbol to ensure that the window length is an integer time unit;

[0024] The expression of the constraint loss function of the LSTM-Transformer model is:

[0025]

[0026] In the formula, is the value of the constraint loss function of the LSTM-Transformer model, is the number of sample groups, is the predicted value of the kelp drying energy consumption of the model, is the target value of the kelp drying energy consumption, is the weight coefficient with constraint terms in the kelp process parameter knowledge graph, is the sample set with constraint terms in the kelp process parameter knowledge graph, is the standard boundary threshold in the kelp process parameter knowledge graph.

[0027] Preferably, obtaining the weight value of the drying energy consumption of each edge data acquisition node according to the evaluation result of the kelp drying energy consumption evaluation model specifically includes:

[0028] Obtaining the kelp drying energy consumption values of each edge data acquisition node within the window length according to the evaluation result of the kelp drying energy consumption evaluation model;

[0029] Extracting the weights in the LSTM-Transformer-GNN hybrid model respectively according to the kelp drying energy consumption evaluation model;

[0030] Among them, LSTM is the historical data weight of each edge data acquisition node, Transformer is the self-attention weight of each edge data acquisition node, and GNN is the spatial weight of each edge data acquisition node;

[0031] Obtaining the comprehensive weight value of the drying energy consumption of each edge data acquisition node according to the weighting formula;

[0032] Normalizing the comprehensive weight value of the drying energy consumption of each edge data acquisition node according to the Softmax normalization formula so that the sum of all comprehensive weight values is 1;

[0033] The expression for the comprehensive weight value of the drying energy consumption of each edge data acquisition node is as follows:

[0034]

[0035] In the formula, is the comprehensive weight value of the drying energy consumption of the edge data acquisition node, , , are respectively the historical data weight, the self-attention weight, and the spatial weight of the edge data acquisition node, , , are respectively the overweight coefficients of the historical data weight, the self-attention weight, and the spatial weight of the edge data acquisition node, which can be determined by cross-validation.

[0036] Preferably, obtaining the correlation of the drying energy consumption of each edge data acquisition node, optimizing through waste heat recovery, and dynamically adjusting and optimizing the kelp drying energy consumption specifically include:

[0037] According to the kelp drying energy consumption database, obtain the equipment operation status data and environmental parameters of each edge data acquisition node;

[0038] Based on the equipment operation status data, environmental parameters, and drying energy consumption data of each edge data acquisition node, and based on the Pearson correlation formula, determine the correlation of the drying energy consumption of each edge data acquisition node;

[0039] According to the correlation of the drying energy consumption of each edge data acquisition node and the correlation coupling of each node, establish a dynamic adjustment and optimization model for kelp drying energy consumption;

[0040] According to the dynamic adjustment and optimization model of kelp drying energy consumption, dynamically adjust the start / stop and parameters of waste heat recovery.

[0041] Furthermore, this solution proposes a kelp drying energy consumption optimization system combined with big data comprehensive analysis, which is used to implement the kelp drying energy consumption optimization method combined with big data comprehensive analysis as described above, including:

[0042] A data acquisition module, which is used to deploy a multi-modal sensor array in each kelp drying process, set edge data acquisition nodes to collect equipment operation status data and environmental parameters in real time; use the Internet of Things technology to receive the data collected by each edge data acquisition node in real time, and establish a kelp drying energy consumption database;

[0043] A prediction and optimization module, which is used to construct a kelp drying energy consumption evaluation model based on the LSTM-Transformer-GNN hybrid machine learning algorithm, comprehensively evaluate and predict the kelp drying energy consumption; according to the evaluation results of the kelp drying energy consumption evaluation model, obtain the weight values of the drying energy consumption of each edge data acquisition node; obtain the correlation of the drying energy consumption of each edge data acquisition node, and dynamically adjust and optimize the kelp drying energy consumption through waste heat recovery optimization.

[0044] Preferably, the data acquisition module includes:

[0045] A data acquisition unit, which is used to deploy a multi-modal sensor array in each kelp drying process, and set edge data acquisition nodes to collect equipment operation status data and environmental parameters in real time;

[0046] A data processing unit, which is used to use the Internet of Things technology to receive the data collected by each edge data acquisition node in real time and establish a kelp drying energy consumption database.

[0047] Preferably, the prediction and optimization module includes:

[0048] An evaluation and prediction unit, which is used to construct a kelp drying energy consumption evaluation model based on the LSTM-Transformer-GNN hybrid machine learning algorithm, and comprehensively evaluate and predict the kelp drying energy consumption;

[0049] A node weight unit, which is used to obtain the weight values of the drying energy consumption of each edge data acquisition node according to the evaluation results of the kelp drying energy consumption evaluation model;

[0050] An energy consumption adjustment unit, which is used to obtain the correlation of the drying energy consumption of each edge data acquisition node, and dynamically adjust and optimize the kelp drying energy consumption through waste heat recovery optimization.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] The present invention provides an optimized method for the energy consumption of kelp drying combined with big data comprehensive analysis. By deploying a multi-modal sensor array, synchronizing multi-dimensional data of equipment operation status and environmental parameters, and combining with edge data acquisition nodes to achieve real-time data acquisition, a kelp drying energy consumption database covering all technological links is constructed, providing a richer data basis for subsequent energy consumption analysis. At the same time, based on the LSTM-Transformer-GNN hybrid machine learning algorithm, a kelp drying energy consumption evaluation model is constructed to comprehensively evaluate and predict the energy consumption of kelp drying, realizing in-depth mining and accurate prediction of energy consumption data, and significantly improving the reliability of the evaluation results. Secondly, according to the kelp drying energy consumption evaluation model, the weights in the LSTM-Transformer-GNN hybrid model are extracted respectively, and according to the weighted formula, the comprehensive weight value of the energy consumption of each edge data acquisition node for drying is obtained, innovatively integrating the time series modeling ability of LSTM, the self-attention mechanism of Transformer, and the spatial correlation analysis ability of GNN. Finally, through the correlation between the comprehensive weight value and energy consumption, combined with the heat-mass coupling characteristics of the waste heat recovery system, the real-time dynamic adjustment of the start / stop and parameters of the recovery is realized, enabling the system to adapt to the fluctuations of the drying schedule, maximizing the waste heat utilization rate and reducing the total energy consumption, so as to achieve the purpose of dynamically evaluating the energy consumption of kelp drying and optimizing the waste heat recovery through multi-modal perception data and hybrid machine learning models, and significantly reducing the production loss of kelp. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flowchart of the optimized method for the energy consumption of kelp drying combined with big data comprehensive analysis according to the present invention;

[0054] Figure 2 is a flowchart of constructing a kelp drying energy consumption evaluation model based on the LSTM-Transformer-GNN hybrid machine learning algorithm of the present invention to comprehensively evaluate and predict the energy consumption of kelp drying;

[0055] Figure 3 is a flowchart of obtaining the weight value of the energy consumption of each edge data acquisition node for drying according to the evaluation result of the kelp drying energy consumption evaluation model of the present invention;

[0056] Figure 4 is a flowchart of obtaining the correlation of the energy consumption of each edge data acquisition node for drying, and dynamically adjusting and optimizing the energy consumption of kelp drying through waste heat recovery optimization of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations.

[0058] Refer to Figure 1As shown, an optimization method for the energy consumption of kelp drying combined with comprehensive big data analysis includes:

[0059] Deploy a multi-modal sensor array in each kelp drying process. By setting edge data acquisition nodes, real-time acquisition of equipment operation status data and environmental parameters is carried out;

[0060] Utilize Internet of Things technology to receive the data collected by each edge data acquisition node in real time and establish a kelp drying energy consumption database;

[0061] Based on the LSTM-Transformer-GNN hybrid machine learning algorithm, construct a kelp drying energy consumption evaluation model to comprehensively evaluate and predict the energy consumption of kelp drying;

[0062] According to the evaluation results of the kelp drying energy consumption evaluation model, obtain the weight values of the drying energy consumption of each edge data acquisition node;

[0063] Obtain the correlation of the drying energy consumption of each edge data acquisition node, and through waste heat recovery optimization, dynamically adjust and optimize the energy consumption of kelp drying.

[0064] It can be explained that in this solution, by deploying a multi-modal sensor array, synchronizing multi-dimensional data such as equipment operation status data and environmental parameters, and combining edge data acquisition nodes to achieve real-time data acquisition, a kelp drying energy consumption database covering the entire process link is constructed, providing a richer data basis for subsequent energy consumption analysis. At the same time, based on the LSTM-Transformer-GNN hybrid machine learning algorithm, a kelp drying energy consumption evaluation model is constructed to comprehensively evaluate and predict the energy consumption of kelp drying, realizing in-depth mining and accurate prediction of energy consumption data, and significantly improving the reliability of the evaluation results. Secondly, according to the kelp drying energy consumption evaluation model, the weights in the LSTM-Transformer-GNN hybrid model are extracted respectively, and according to the weighted formula, the comprehensive weight values of the drying energy consumption of each edge data acquisition node are obtained, innovatively integrating the time series modeling ability of LSTM, the self-attention mechanism of Transformer, and the spatial correlation analysis ability of GNN. Finally, through the comprehensive weight values and energy consumption correlation, combined with the heat and mass coupling characteristics of the waste heat recovery system, the real-time dynamic adjustment of the recovery start and stop and parameters is realized, enabling the system to adapt to the drying schedule fluctuations, maximizing the waste heat utilization rate and reducing the total energy consumption, so as to achieve the purpose of dynamically evaluating the energy consumption of kelp drying and optimizing waste heat recovery through multi-modal perception data and hybrid machine learning models, and significantly reducing the production loss of kelp.

[0065] The deployment of a multi-modal sensor array in each kelp drying process, and the real-time acquisition of equipment operation status data and environmental parameters by setting edge data acquisition nodes specifically include:

[0066] According to the drying process of kelp, a multi-modal sensor array is deployed in each drying process to collect equipment operation status data and environmental parameters in real time during each drying process;

[0067] In each drying process, an edge data acquisition node is set up to perform filtering and normalization preprocessing on the equipment operation status data and environmental parameters collected in real time through the edge data acquisition node;

[0068] Adopt the simultaneous acquisition mode to make each edge data acquisition node operate synchronously under the unified time reference, ensuring the time consistency and synchronization of the collected data;

[0069] For each edge data acquisition node, a standardized communication format specification is uniformly set to ensure a high degree of consistency in the format and structure of the data uploaded by different nodes.

[0070] It can be explained that deploying a multi-modal sensor array and edge data acquisition nodes in kelp drying and constructing a unified data acquisition and communication mechanism are the basis for building an efficient energy consumption management system. Its core significance lies in solving the analysis failure problems caused by one-sided data, noise interference, and heterogeneity in traditional solutions through full-dimensional data perception and standardized processing. At the same time, the simultaneous acquisition mode is adopted to ensure that each node operates synchronously under the unified time reference, avoiding data misalignment caused by clock drift or transmission delay, and ensuring time consistency, thus providing an accurate, real-time, and standardized data basis for kelp drying energy consumption management and being the key support for improving the accuracy of energy consumption prediction and optimization effect.

[0071] The use of Internet of Things technology to receive the data collected by each edge data acquisition node in real time and establish a kelp drying energy consumption database specifically includes:

[0072] Use Internet of Things technology to receive the data collected by each edge data acquisition node in real time;

[0073] Apply interpolation and data downsampling techniques to process the data of each edge data acquisition node to achieve alignment and position consistency of the data in time or space;

[0074] According to the technological requirements of kelp drying, extract the boundary constraints of the data collected by each edge data acquisition node, thereby establishing a kelp process parameter knowledge graph;

[0075] Based on the historical data of kelp drying and the kelp process parameter knowledge graph, establish a kelp drying energy consumption database.

[0076] It can be explained that in the energy consumption management of kelp drying, the multi-source data of kelp drying from edge data collection nodes is received in real time through Internet of Things technology and stored in the kelp drying energy consumption database, which is the core foundation for supporting accurate energy consumption assessment and optimization decision-making of kelp drying. Through interpolation and data downsampling techniques, the deviation problem corresponding to data positions can be effectively solved. By using the timestamps and spatial positions of the multi-source data of kelp drying, the consistency of data in the time and space dimensions is ensured, providing reliable input for subsequent analysis. On this basis, by extracting data boundary constraints in combination with the technological requirements of kelp and constructing a knowledge graph of kelp process parameters, the boundary constraints between parameters can be explicitly expressed, avoiding energy consumption anomalies caused by parameter overstepping, thereby improving the accuracy and reliability of data.

[0077] Refer to Figure 2 As shown, the construction of an energy consumption assessment model for kelp drying based on the LSTM-Transformer-GNN hybrid machine learning algorithm and the comprehensive assessment and prediction of the energy consumption of kelp drying specifically include:

[0078] According to the technological cycle of kelp drying, set the window length of the LSTM layer time series, where the window length of the LSTM layer time series is within the range of the technological cycle of kelp drying;

[0079] According to the kelp drying energy consumption database, use the data collected by each edge data collection node as input variables;

[0080] Based on the LSTM neural network, output the device operation status data and environmental parameters with time series and use them as the input variables of the Transformer model;

[0081] Through linear projection, map the data collected by each edge data collection node in real time to the same-dimensional space as Key / Value;

[0082] According to the Transformer model, calculate the correlation between each data in the input variables and other data, and capture the dependency relationships between any data in the input variable data;

[0083] According to the knowledge graph of kelp process parameters and based on the mean square error formula, establish a constraint loss function for the LSTM-Transformer model;

[0084] According to the LSTM-Transformer model, judge and predict the kelp drying energy consumption values and the total kelp drying energy consumption values of each edge data collection node within the window length;

[0085] According to the drying processes of each edge data collection node in the drying process of kelp and based on the CNN neural network, adjust the edge weights in real time to accurately reflect the impact of drying schedule changes on energy consumption, and further correct and optimize the kelp drying energy consumption values;

[0086] The expression for the window length setting the time sequence of the LSTM layer is as follows:

[0087]

[0088] In the formula, is the window length of the time sequence of the LSTM layer, is the drying process cycle of kelp, is the dominant angular frequency in the drying data of kelp, is the floor function symbol to ensure that the window length is an integer time unit;

[0089] The expression for the constraint loss function of the LSTM-Transformer model is as follows:

[0090]

[0091] In the formula, is the value of the constraint loss function of the LSTM-Transformer model, is the number of sample groups, is the predicted value of the energy consumption of kelp drying by the model, is the target value of the energy consumption of kelp drying, is the weight coefficient with constraint terms in the knowledge graph of kelp process parameters, is the sample set with constraint terms in the knowledge graph of kelp process parameters, is the standard boundary threshold in the knowledge graph of kelp process parameters.

[0092] It can be explained that traditional LSTM or neural network models are difficult to balance the influence of time series fluctuations and spatial layout, resulting in a large deviation between the prediction results and the actual situation, problems such as insufficient prediction accuracy, and the disconnection of time series dependence and spatial correlation. Therefore, in this solution, the LSTM layer is used to extract the time series features of the device operating state and environmental parameters, which are used as the input of the Transformer model. The self-attention mechanism is utilized to capture any dependence relationship between data. At the same time, a constraint loss function is constructed by combining the knowledge graph of kelp process parameters to ensure that the model output conforms to process constraints, significantly improving the prediction accuracy. On this basis, the edge weights are dynamically adjusted through the CNN neural network, which can reflect the impact of the drying schedule change on energy consumption in real time, further correcting the prediction results and avoiding error accumulation caused by static model assumptions. Thus, the model can not only output the local energy consumption values of each edge node, but also comprehensively evaluate the total energy consumption of the whole process, providing data support for subsequent dynamic waste heat recovery optimization and significantly improving the energy efficiency management level of kelp drying. Among them, an LSTM-Transformer hybrid model is adopted to process sensor time series data, predict the energy consumption at the process level in the future window length, and achieve short-term prediction of kelp drying energy consumption. The GNN neural network is used to model the energy consumption dependence relationship between processes, combined with the drying schedule, to predict the energy consumption trend of the whole batch and achieve long-term prediction of kelp drying energy consumption;

[0093] The expression for the total kelp drying energy consumption value is:

[0094]

[0095] In the formula, is the total kelp drying energy consumption value, is the number of edge data acquisition nodes, is the th kelp drying energy consumption value of the edge data acquisition node.

[0096] Referring to Figure 3 shown, the specific steps for obtaining the weight values of the drying energy consumption of each edge data acquisition node according to the evaluation results of the kelp drying energy consumption evaluation model are as follows:

[0097] According to the evaluation results of the kelp drying energy consumption evaluation model, obtain the kelp drying energy consumption values of each edge data acquisition node within the window length;

[0098] According to the kelp drying energy consumption evaluation model, extract the weights in the LSTM-Transformer-GNN hybrid model respectively;

[0099] Among them, LSTM is the historical data weight of each edge data acquisition node, Transformer is the self-attention weight of each edge data acquisition node, and GNN is the spatial weight of each edge data acquisition node;

[0100] According to the weighted formula, obtain the comprehensive weight value of the drying energy consumption of each edge data acquisition node;

[0101] According to the Softmax normalization formula, normalize the comprehensive weight value of the drying energy consumption of each edge data acquisition node so that the sum of all comprehensive weight values is 1;

[0102] The expression of the comprehensive weight value of the drying energy consumption of each edge data acquisition node is:

[0103]

[0104] In the formula, is the comprehensive weight value of the drying energy consumption of the edge data acquisition node, , , are respectively the historical data weight, the self-attention weight, and the spatial weight of the edge data acquisition node, , , are respectively the overweight coefficients of the historical data weight, the self-attention weight, and the spatial weight of the edge data acquisition node, which can be determined by cross-validation.

[0105] It can be explained that the drying of kelp involves multi-device collaboration and complex process coupling. The energy consumption contributions of different edge data acquisition nodes are significantly affected by historical operation rules, real-time correlation relationships, and spatial layouts. Due to the multi-dimensionality and complexity of the data collected by each edge data acquisition node, the difficulty of accurately positioning the key drying energy consumption of kelp increases. To solve the problems of fuzzy node contribution degrees and scattered optimization objectives in traditional energy consumption analysis, this solution extracts the historical data weights of each edge data acquisition node through LSTM, extracts the self-attention weights of each edge data acquisition node through Transformer, and extracts the spatial weights of each edge data acquisition node through GNN. Thus, through the three types of weight weighting methods of historical data weights, self-attention weights, and spatial weights, the temporal dependence, data correlation, and spatial correlation are fused to obtain the comprehensive weight value. Thus, by quantifying the energy consumption priority of nodes, such as high-weight nodes participating in waste heat recovery first, the accurate quantification of the energy consumption contribution of kelp drying is realized, which is the key support for constructing an energy consumption optimization closed-loop and improving the fineness of energy efficiency management, and provides data correlation analysis for subsequent waste heat recovery optimization.

[0106] Refer to Figure 4 As shown, the acquisition of the correlation of the drying energy consumption of each edge data acquisition node, and the dynamic adjustment and optimization of the drying energy consumption of kelp through waste heat recovery optimization specifically include:

[0107] Obtain the device operation status data and environmental parameters of each edge data acquisition node according to the kelp drying energy consumption database;

[0108] Based on the device operation status data, environmental parameters and drying energy consumption data of each edge data acquisition node, determine the correlation of drying energy consumption of each edge data acquisition node based on the Pearson correlation formula;

[0109] Establish a dynamic adjustment and optimization model for kelp drying energy consumption according to the correlation of drying energy consumption of each edge data acquisition node and the correlation coupling of each node;

[0110] Dynamically adjust the start and stop of waste heat recovery and the kelp drying energy consumption according to the dynamic adjustment and optimization model of kelp drying energy consumption.

[0111] It can be explained that due to the inability to perceive the energy consumption coupling relationship between nodes, the traditional static waste heat recovery scheme often suffers from insufficient recovery or waste of heat sources. In the management of kelp drying energy consumption, by quantifying the energy consumption correlation of each edge data acquisition node and dynamically adjusting the waste heat recovery strategy, it is a key breakthrough to break through the isolation of traditional energy efficiency optimization means and achieve collaborative energy saving in the whole process. This solution establishes a dynamic adjustment and optimization model for kelp drying energy consumption through the correlation between data and energy consumption correlation, and dynamically adjusts the start and stop of waste heat recovery and the kelp drying energy consumption through the model. Among them, the conditional formula for the dynamic start and stop of waste heat recovery is:

[0112] The start condition formula is:

[0113] The stop condition formula is:

[0114] In the formula, is the comprehensive weight value of the drying energy consumption of the edge data acquisition node, is the correlation of the drying energy consumption of each edge data acquisition node and the correlation coupling of each node, and are the boundary thresholds of the comprehensive weight value of the drying energy consumption of the edge data acquisition node and the correlation of the drying energy consumption of each edge data acquisition node and the correlation coupling of each node respectively, is the predicted waste heat recovery efficiency of kelp drying energy consumption, is the minimum value of the predicted waste heat recovery efficiency of kelp drying energy consumption, is the real-time waste heat recovery efficiency of kelp drying energy consumption;

[0115] Obtain the maximum recovery efficiency of the waste heat of kelp drying energy consumption according to the correlation between data and energy consumption correlation. Among them, the expression of the maximum recovery efficiency of the waste heat of kelp drying energy consumption is:

[0116]

[0117] In the formula, is the waste heat recovery flow rate from the th edge data acquisition node to the th edge data acquisition node, is the maximum value of the waste heat recovery flow rate of the edge data acquisition node, , are respectively two nodes of the waste heat supply-demand relationship during the kelp drying process. The recovery pair refers to the set of all waste heat recovery paths during the kelp drying process, is the comprehensive weight value of the drying energy consumption of the th edge data acquisition node, is the relevance of the drying energy consumption between the th node and the th node of the waste heat supply-demand relationship, as well as the correlation coupling relevance of each node;

[0118] According to the maximum recovery efficiency of the waste heat of the kelp drying energy consumption, a dynamic adjustment and optimization model of the kelp drying energy consumption is established. Among them,

[0119] The expression of the dynamic adjustment and optimization model of the kelp drying energy consumption is:

[0120]

[0121] In the formula, is the minimum energy consumption for kelp drying, is the total kelp drying energy consumption value, is the recovery efficiency, which is a constant term, is the heat fusion ratio, is the outlet temperature of the waste heat recovery of the kelp drying energy consumption, is the required temperature of the waste heat recovery node of the kelp drying energy consumption.

[0122] Furthermore, based on the same inventive concept as the above-mentioned kelp drying energy consumption optimization method combined with big data comprehensive analysis, this solution proposes a kelp drying energy consumption optimization system combined with big data comprehensive analysis, including:

[0123] A data acquisition module, which is used to deploy a multi-modal sensor array in each kelp drying process, and set edge data acquisition nodes to collect equipment operation status data and environmental parameters in real time; use the Internet of Things technology to receive the data collected by each edge data acquisition node in real time, and establish a kelp drying energy consumption database;

[0124] A prediction and optimization module, which is used to build a kelp drying energy consumption evaluation model based on the LSTM-Transformer-GNN hybrid machine learning algorithm, comprehensively evaluate and predict the kelp drying energy consumption; obtain the weight values of the drying energy consumption of each edge data acquisition node according to the evaluation results of the kelp drying energy consumption evaluation model; obtain the correlation of the drying energy consumption of each edge data acquisition node, and dynamically adjust and optimize the kelp drying energy consumption through waste heat recovery optimization;

[0125] The data acquisition module includes:

[0126] A data acquisition unit, which is used to deploy a multi-modal sensor array in each kelp drying process, and set edge data acquisition nodes to collect equipment operation status data and environmental parameters in real time;

[0127] A data processing unit, which is used to use Internet of Things technology to receive the data collected by each edge data acquisition node in real time and establish a kelp drying energy consumption database;

[0128] The prediction and optimization module includes:

[0129] An evaluation and prediction unit, which is used to build a kelp drying energy consumption evaluation model based on the LSTM-Transformer-GNN hybrid machine learning algorithm, and comprehensively evaluate and predict the kelp drying energy consumption;

[0130] A node weight unit, which is used to obtain the weight values of the drying energy consumption of each edge data acquisition node according to the evaluation results of the kelp drying energy consumption evaluation model;

[0131] An energy consumption adjustment unit, which is used to obtain the correlation of the drying energy consumption of each edge data acquisition node, and dynamically adjust and optimize the kelp drying energy consumption through waste heat recovery optimization.

[0132] In summary, the advantages of the present invention are as follows: By using multi-modal perception data and a hybrid machine learning model, the kelp drying energy consumption is dynamically evaluated and the waste heat recovery is optimized, significantly reducing the production loss of kelp.

[0133] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. An optimization method for the energy consumption of kelp drying combined with comprehensive big data analysis, characterized in that, Including: Deploy a multi-modal sensor array in each kelp drying process. By setting up edge data acquisition nodes, collect the device operation status data and environmental parameters in real time. Utilize Internet of Things technology to receive the data collected by each edge data acquisition node in real time and establish a kelp drying energy consumption database. Based on the LSTM-Transformer-GNN hybrid machine learning algorithm, construct a kelp drying energy consumption evaluation model to comprehensively evaluate and predict the kelp drying energy consumption. According to the evaluation results of the kelp drying energy consumption evaluation model, obtain the weight values of the drying energy consumption of each edge data acquisition node. Obtain the correlation of the drying energy consumption of each edge data acquisition node, and through waste heat recovery optimization, dynamically adjust and optimize the kelp drying energy consumption.

2. The kelp drying energy consumption optimization method combined with big data comprehensive analysis according to claim 1, characterized in that, The deployment of a multi-modal sensor array in each kelp drying process, and the real-time collection of device operation status data and environmental parameters by setting up edge data acquisition nodes specifically include: According to the kelp drying process, deploy a multi-modal sensor array in each drying process to collect the device operation status data and environmental parameters in real time during each drying process. In each drying process, set up edge data acquisition nodes, and perform filtering and normalization preprocessing on the device operation status data and environmental parameters collected in real time by the edge data acquisition nodes. Adopt a simultaneous sampling mode to make each edge data acquisition node operate synchronously under a unified time reference to ensure the time consistency and synchronization of the collected data. For each edge data acquisition node, uniformly set the standardized communication format specification to ensure a high degree of consistency in the format and structure of the data uploaded by different nodes.

3. The kelp drying energy consumption optimization method combined with big data comprehensive analysis according to claim 2, characterized in that, The utilization of Internet of Things technology to receive the data collected by each edge data acquisition node in real time and establish a kelp drying energy consumption database specifically includes: Utilize Internet of Things technology to receive the data collected by each edge data acquisition node in real time. Apply interpolation and data downsampling techniques to process the data of each edge data acquisition node to achieve alignment and position consistency of the data in time or space. According to the technological requirements of kelp drying, extract the boundary constraints of the data collected by each edge data acquisition node to establish a kelp process parameter knowledge graph. Based on the historical data of kelp drying and the kelp process parameter knowledge graph, establish a kelp drying energy consumption database.

4. An optimized method for the energy consumption of kelp drying combined with big data comprehensive analysis according to claim 3, characterized in that, The construction of a kelp drying energy consumption evaluation model based on the LSTM-Transformer-GNN hybrid machine learning algorithm to comprehensively evaluate and predict the kelp drying energy consumption specifically includes: According to the kelp drying process cycle, set the window length of the LSTM layer time series, where the window length of the LSTM layer time series is within the range of the kelp drying process cycle. According to the kelp drying energy consumption database, use the data collected by each edge data acquisition node as the input quantity. Based on the LSTM neural network, output the device operation status data and environmental parameters with time series and use them as the input quantity of the Transformer model. Through linear projection, map the data collected by each edge data acquisition node in real time to the same-dimensional space as Key / Value. According to the Transformer model, calculate the correlation between each data in the input quantity and other data, and capture the dependence relationship between any data in the input quantity data; According to the kelp process parameter knowledge graph, based on the mean square error formula, establish a constraint loss function for the LSTM-Transformer model; According to the LSTM-Transformer model, judge and predict the kelp drying energy consumption value and the total kelp drying energy consumption value of each edge data acquisition node within the window length; According to the drying process of each edge data acquisition node in the kelp drying process, based on the CNN neural network, adjust the edge weights in real time to accurately reflect the impact of the drying schedule change on the energy consumption, and further correct and optimize the kelp drying energy consumption value; The expression of the window length of the LSTM layer time series is as follows: In the formula, is the window length of the LSTM layer time series, is the drying process cycle of kelp, is the dominant angular frequency in the drying data of kelp, is the floor function symbol to ensure that the window length is an integer time unit; The expression of the constraint loss function of the LSTM-Transformer model is as follows: In the formula, is the value of the constraint loss function of the LSTM-Transformer model, is the number of sample groups, is the predicted value of the energy consumption for drying kelp by the model, is the target value of the energy consumption for drying kelp, is the weight coefficient with constraint terms in the knowledge graph of kelp process parameters, is the sample set with constraint terms in the knowledge graph of kelp process parameters, is the standard boundary threshold in the knowledge graph of kelp process parameters.

5. An energy consumption optimization method for kelp drying combined with big data comprehensive analysis according to claim 4, characterized in that The specific steps of obtaining the weight value of the drying energy consumption of each edge data acquisition node according to the evaluation result of the kelp drying energy consumption evaluation model include: According to the evaluation result of the kelp drying energy consumption evaluation model, obtain the kelp drying energy consumption value of each edge data acquisition node within the window length; According to the kelp drying energy consumption evaluation model, extract the weights in the LSTM-Transformer-GNN hybrid model respectively; Among them, LSTM is the historical data weight of each edge data acquisition node, Transformer is the self-attention weight of each edge data acquisition node, and GNN is the spatial weight of each edge data acquisition node; According to the weighting formula, obtain the comprehensive weight value of the drying energy consumption of each edge data acquisition node; According to the Softmax normalization formula, normalize the comprehensive weight value of the drying energy consumption of each edge data acquisition node so that the sum of all comprehensive weight values is 1; The expression of the comprehensive weight value of the drying energy consumption of each edge data acquisition node is as follows: wherein, is the comprehensive weight value of the drying energy consumption of the edge data acquisition node, , , are respectively the historical data weight, the self-attention weight, and the spatial weight of the edge data acquisition node, , , are respectively the bias coefficients of the historical data weight, the self-attention weight, and the spatial weight of the edge data acquisition node, which can be determined by cross-validation.

6. The kelp drying energy consumption optimization method combined with big data comprehensive analysis according to claim 5, characterized in that, The specific steps of obtaining the correlation of the drying energy consumption of each edge data acquisition node and dynamically adjusting and optimizing the kelp drying energy consumption through waste heat recovery optimization include: According to the kelp drying energy consumption database, obtain the equipment operation status data and environmental parameters of each edge data acquisition node; According to the equipment operation status data and environmental parameters of each edge data acquisition node and the drying energy consumption data of each edge data acquisition node, based on the Pearson correlation formula, determine the correlation of the drying energy consumption of each edge data acquisition node; According to the correlation of the drying energy consumption of each edge data acquisition node and the associated coupling correlation of each node, establish a dynamic adjustment and optimization model for the kelp drying energy consumption; According to the dynamic adjustment and optimization model of the kelp drying energy consumption, dynamically adjust the start and stop and parameters of the waste heat recovery; 7. An energy consumption optimization system for kelp drying combined with comprehensive big data analysis, characterized in that For implementing the kelp drying energy consumption optimization method combined with big data comprehensive analysis as described in any one of claims 1-6, including: Data acquisition module, which is used to deploy a multi-modal sensor array in each kelp drying process, and by setting edge data acquisition nodes, to collect device operation status data and environmental parameters in real time; using Internet of Things technology, to receive the data collected by each edge data acquisition node in real time, and establish a kelp drying energy consumption database; Prediction and optimization module, which is used to build a kelp drying energy consumption evaluation model based on the LSTM-Transformer-GNN hybrid machine learning algorithm, comprehensively evaluate and predict the kelp drying energy consumption; according to the evaluation results of the kelp drying energy consumption evaluation model, obtain the weight values of the drying energy consumption of each edge data acquisition node; obtain the correlation of the drying energy consumption of each edge data acquisition node, and dynamically adjust and optimize the kelp drying energy consumption through waste heat recovery optimization.

8. A kelp drying energy consumption optimization system integrated with big data comprehensive analysis according to claim 7, characterized in that, The data acquisition module includes: Data acquisition unit, which is used to deploy a multi-modal sensor array in each kelp drying process, and by setting edge data acquisition nodes, to collect device operation status data and environmental parameters in real time; Data processing unit, which is used to use Internet of Things technology to receive the data collected by each edge data acquisition node in real time, and establish a kelp drying energy consumption database.

9. The kelp drying energy consumption optimization system integrated with big data comprehensive analysis according to claim 8, characterized in that, The prediction and optimization module includes: Evaluation and prediction unit, which is used to build a kelp drying energy consumption evaluation model based on the LSTM-Transformer-GNN hybrid machine learning algorithm, and comprehensively evaluate and predict the kelp drying energy consumption; Node weight unit, which is used to obtain the weight values of the drying energy consumption of each edge data acquisition node according to the evaluation results of the kelp drying energy consumption evaluation model; Energy consumption adjustment unit, which is used to obtain the correlation of the drying energy consumption of each edge data acquisition node, and dynamically adjust and optimize the kelp drying energy consumption through waste heat recovery optimization.

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