A control system for a composite methanol evaporator

Abstract

A control system for a composite methanol evaporator is disclosed. The control system first obtains temperature values ​​at multiple predetermined time points within a predetermined time period collected by multiple temperature sensors deployed at multiple positions of a heat pipe, then extracts temperature time series characteristics of the temperature values ​​at multiple predetermined time points within a predetermined time period collected by each temperature sensor to obtain multiple temperature time series characteristic vectors, then extracts spatial topological characteristics of the multiple positions to obtain a spatial topological characteristic matrix, and finally, based on the multiple temperature time series characteristic vectors and the spatial topological characteristic matrix, determines whether to start a motor to drive the evaporation mechanism to rotate. In this way, it is possible to reasonably determine when to start the motor of the evaporation mechanism, reduce unnecessary energy consumption, and reduce production costs.

Description

Technical Field

[0001] The present application relates to the field of methanol evaporators, and more specifically, to a control system of a composite methanol evaporator. Background Art

[0002] Methanol is a colorless, transparent liquid with a pungent odor. Methanol is one of the main raw materials for producing formaldehyde. In the formaldehyde production system, methanol evaporates from liquid into gas, and then is used as one of the gases for preparing formaldehyde. It is added to other mixed gases and finally made into formaldehyde.

[0003] In the existing methanol evaporator, the heat pipe cannot move during evaporation, making it difficult to change the contact surface between the heat pipe and methanol. In addition, the heat on the surface of the heat pipe is used to evaporate methanol, so the temperature of the surface of the heat pipe decreases after a long period of contact with methanol, thereby affecting the practicality of the heat pipe, thereby reducing the evaporation efficiency of methanol and increasing production costs.

[0004] To solve the above problems, patent CN113521773B provides a solution, which can raise the temperature by driving the evaporation mechanism to rotate during use by a motor, thereby preventing liquid methanol from being sprayed toward one part of the evaporator and causing the temperature of the heat pipe to not be restored in time. However, if the evaporation mechanism moves too frequently, unnecessary energy consumption will increase. Therefore, in the control system, determining when to drive the evaporation mechanism is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to solve the above technical problems, this application is proposed. The embodiment of this application provides a control system for a composite methanol evaporator. It can reasonably determine when to start the motor of the evaporation mechanism, reduce unnecessary energy consumption, and reduce production costs.

[0006] According to one aspect of the present application, a control system for a composite methanol evaporator is provided, which includes: a temperature value acquisition module, used to acquire temperature values ​​at multiple predetermined time points within a predetermined time period collected by multiple temperature sensors deployed at multiple positions of a heat pipe; a temperature timing feature extraction module, used to extract temperature timing features of the temperature values ​​at multiple predetermined time points within a predetermined time period collected by each temperature sensor to obtain multiple temperature timing feature vectors; a spatial topological feature extraction module, used to extract spatial topological features of the multiple positions to obtain a spatial topological feature matrix; and a motor control module, used to determine whether to start the motor to drive the evaporation mechanism to rotate based on the multiple temperature timing feature vectors and the spatial topological feature matrix.

[0007] Compared with the prior art, the control system of the composite methanol evaporator provided by the present application first obtains the temperature values ​​of multiple predetermined time points within a predetermined time period collected by multiple temperature sensors deployed at multiple positions of the heat pipe, then extracts the temperature time series characteristics of the temperature values ​​of multiple predetermined time points within the predetermined time period collected by each temperature sensor to obtain multiple temperature time series characteristic vectors, then extracts the spatial topological characteristics of the multiple positions to obtain a spatial topological characteristic matrix, and finally, based on the multiple temperature time series characteristic vectors and the spatial topological characteristic matrix, determines whether to start the motor to drive the evaporation mechanism to rotate. In this way, it is possible to reasonably judge when to start the motor of the evaporation mechanism, reduce unnecessary energy consumption, and reduce production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present application.

[0009] Figure 1 It is a schematic block diagram of a control system of a compound methanol evaporator according to an embodiment of the present application.

[0010] Figure 2 It is a block diagram of the temperature time series feature extraction module in the control system of the compound methanol evaporator according to the embodiment of the present application.

[0011] Figure 3 It is a block diagram of the spatial topological feature extraction module in the control system of the compound methanol evaporator according to an embodiment of the present application.

[0012] Figure 4 It is a block diagram of the motor control module in the control system of the compound methanol evaporator according to an embodiment of the present application.

[0013] Figure 5 4 is a flow chart of a control method for a composite methanol evaporator according to an embodiment of the present application.

[0014] Figure 6 Schematic diagram of the system architecture of the control method of the compound methanol evaporator according to an embodiment of the present application.

[0015] Figure 7 This is an application scenario diagram of a control system of a composite methanol evaporator according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present application.

[0017] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0018] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0019] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0020] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0021] In response to the above technical problems, the technical concept of this application is to capture the temperature time series information of each position of the heat pipe, and combine the spatial topological characteristic distribution of each position to automatically determine whether to start the motor to drive the evaporation mechanism to rotate. In this way, it is possible to reasonably determine when to start the motor of the evaporation mechanism, reduce unnecessary energy consumption, and reduce production costs.

[0022] Based on this, Figure 1 FIG. 1 is a block diagram of a control system of a composite methanol evaporator according to an embodiment of the present application. Figure 1As shown, the control system 100 of the compound methanol evaporator according to the embodiment of the present application includes: a temperature value acquisition module 110, which is used to obtain the temperature values ​​of multiple predetermined time points within a predetermined time period collected by multiple temperature sensors deployed at multiple positions of the heat pipe; a temperature time series feature extraction module 120, which is used to extract the temperature time series features of the temperature values ​​of multiple predetermined time points within the predetermined time period collected by each temperature sensor to obtain multiple temperature time series feature vectors; a spatial topological feature extraction module 130, which is used to extract the spatial topological features of the multiple positions to obtain a spatial topological feature matrix; and a motor control module 140, which is used to determine whether to start the motor to drive the evaporation mechanism to rotate based on the multiple temperature time series feature vectors and the spatial topological feature matrix.

[0023] Specifically, in the technical solution of the present application, firstly, temperature values ​​at multiple predetermined time points within a predetermined time period collected by multiple temperature sensors deployed at multiple positions of the heat pipe are obtained.

[0024] Then, the temperature time series characteristics of the temperature values ​​of multiple predetermined time points within the predetermined time period collected by each temperature sensor are extracted to obtain multiple temperature time series characteristic vectors. That is, the temperature time series change characteristic information of each position is extracted. Specifically, the temperature time series change characteristic information reflects the temperature change of the contact between the heat pipe and methanol at different positions, and can provide uniformity and stability information of the contact between the heat pipe and methanol.

[0025] In a specific example of the present application, the encoding process of extracting the temperature timing features of the temperature values ​​at multiple predetermined time points within a predetermined time period collected by each temperature sensor to obtain multiple temperature timing feature vectors includes: firstly arranging the temperature values ​​at multiple predetermined time points within a predetermined time period collected by each temperature sensor into temperature timing input vectors according to the time dimension to obtain multiple temperature timing input vectors; and then respectively passing the multiple temperature timing input vectors through a temperature timing feature extractor based on a one-dimensional convolutional layer to obtain multiple temperature timing feature vectors.

[0026] Next, the spatial topological features of the multiple positions are extracted to obtain a spatial topological feature matrix. Here, the spatial topological relationship describes the spatial distance and interaction between different positions. By constructing a spatial topological matrix, the correlation and spatial topological distribution characteristics between the heat pipes can be captured. These spatial topological features can provide the heat conduction path and heat exchange between the heat pipes, and further reveal the overall characteristics of the temperature distribution of the heat pipes.

[0027] In a specific example of the present application, the encoding process of extracting the spatial topological features of the multiple positions to obtain a spatial topological feature matrix includes: first constructing a spatial topological matrix of the multiple positions, wherein the eigenvalue of each position at a non-diagonal position of the spatial topological matrix is ​​the spatial distance between the corresponding two positions; and then passing the spatial topological matrix through a spatial topological feature extractor based on a convolutional neural network model to obtain a spatial topological feature matrix.

[0028] Furthermore, in order to illustrate the specific data form of the spatial topological matrix and the spatial topological feature matrix, an example is provided. Assume that there is a heat pipe system containing four temperature sensors, which are distributed at different locations and marked as A, B, C and D. These locations constitute the nodes of the spatial topological matrix. The spatial topological matrix will record the spatial distance between any two locations. Table 1 is an example of a spatial topological matrix.

[0029]

[0030] In this matrix, "0" represents the distance from a location to itself, and the remaining numbers represent the spatial distance between locations. For example, the distance from location A to location B is 10 units.

[0031] Next, this spatial topology matrix will be input into a spatial topology feature extractor based on a convolutional neural network (CNN), which will learn local patterns and structural information in the input data. Through the convolution operation, the model captures the spatial relationship and local features between different locations, and then gradually extracts higher-level feature representations through the stacking of multiple convolutional layers and pooling layers.

[0032] The spatial topological feature matrix is ​​the result of the above spatial topological matrix being processed by the convolutional neural network. This processing will generate a new matrix, each item of which represents the spatial feature representation of each position in the original spatial topological matrix. This feature matrix may no longer directly reflect the actual spatial distance, but reflects how the topological relationship and spatial configuration between positions affect the heat conduction path and heat exchange. It may contain multiple layers of abstract feature representations, which help to further reveal the overall characteristics of the temperature distribution of the heat pipe.

[0033] Since the spatial topological feature matrix is ​​the result of deep learning model processing, its specific form depends on the design and training of the model. It is usually a high-dimensional feature vector matrix, where each row corresponds to the feature vector representation of a position. Table 2 is an example of a spatial topological feature matrix.

[0034]

[0035] Considering that the graph neural network model has the ability to process graph structure data, the relationship between nodes can be modeled and learned. In the application scenario of this application, the location of the heat pipe can be regarded as a node in the graph data, and the spatial relationship between them can be represented as an edge in the graph data.

[0036] Specifically, in the technical solution of the present application, the multiple temperature time series feature vectors and the spatial topology feature matrix are passed through a graph neural network model to obtain a spatial topology heat pipe temperature time series global feature matrix. That is, by inputting the temperature time series features and the spatial topology features into the graph neural network model, the model can be used to learn the interaction and propagation rules between nodes to capture the temperature time series feature distribution information of the heat pipe containing spatial topology information.

[0037] Accordingly, if Figure 2 As shown, the temperature time series feature extraction module 120 includes: a vectorization unit 121, which is used to arrange the temperature values ​​of multiple predetermined time points within a predetermined time period collected by each temperature sensor into temperature time series input vectors according to the time dimension to obtain multiple temperature time series input vectors; and a time series feature extraction unit 122, which is used to pass the multiple temperature time series input vectors through a temperature time series feature extractor based on a one-dimensional convolution layer to obtain the multiple temperature time series feature vectors. It is worth mentioning that the one-dimensional convolution layer is a neural network layer commonly used in deep learning for processing sequence data. It performs a sliding window operation on the time dimension and extracts the features of the input sequence by learning a trainable convolution kernel (filter). The role of the one-dimensional convolution layer in the temperature time series feature extraction module is to extract features from multiple temperature time series input vectors. It can capture local patterns, trends and correlation information in the input sequence and encode them into feature vectors. The input of the one-dimensional convolution layer is a one-dimensional vector sequence, such as time series data. It performs convolution operations on the input sequence through a sliding window, convolves the local features in the window with a trainable convolution kernel, and obtains a new feature representation. This operation can be performed on the entire input sequence to extract the overall time series features. The output of the one-dimensional convolution layer can be multiple feature maps, each of which corresponds to a convolution kernel. Each feature map contains the feature representation of the input sequence at different positions, which can capture patterns and association information at different scales. These feature maps can be further used for subsequent tasks such as classification, regression or other data analysis tasks. In other words, the role of the one-dimensional convolution layer in the temperature time series feature extraction module is to extract the features of multiple temperature time series input vectors through convolution operations for subsequent analysis and processing. It can help the model understand the patterns, trends and association information in the temperature series, thereby improving the understanding and prediction capabilities of temperature data.

[0038] Accordingly, if Figure 3 As shown, the spatial topological feature extraction module 130 includes: a matrixing unit 131, which is used to construct the spatial topological matrix of the multiple positions, wherein the eigenvalues ​​of each position at the non-diagonal position of the spatial topological matrix are the spatial distances between the corresponding two positions; and a topological feature extraction unit 132, which is used to pass the spatial topological matrix through a spatial topological feature extractor based on a convolutional neural network model to obtain the spatial topological feature matrix. It is worth mentioning that the convolutional neural network (CNN) is a deep learning model, which is particularly suitable for processing data with a grid structure. The core idea of ​​the convolutional neural network is to extract local features in the input data through convolution operations, and reduce the spatial dimensions of the features through pooling operations, and finally input the extracted features into the fully connected layer for classification, regression or other tasks. The role of the convolutional neural network model in the spatial topological feature extraction module is to extract spatial topological features by processing the spatial topological matrix. The spatial topological matrix is ​​a matrix representing the spatial relationship between positions, wherein the eigenvalues ​​at the non-diagonal positions represent the spatial distances between the corresponding two positions. The topological feature extraction unit processes the spatial topological matrix through a spatial topological feature extractor based on a convolutional neural network model to obtain a spatial topological feature matrix. In spatial topological feature extraction, the convolutional neural network model can learn local patterns and structural information in the input data. Through the convolution operation, the model can capture the spatial relationship and local features between different positions, and gradually extract higher-level feature representations through the stacking of multiple convolutional layers and pooling layers. These feature representations can be used for subsequent tasks such as classification, segmentation, target detection, etc. In other words, the role of the convolutional neural network model in the spatial topological feature extraction module is to extract features in the spatial topological matrix through convolution operations, helping the model understand the spatial relationship and local structure between positions, thereby improving the ability to understand and analyze spatial data.

[0039] Then, the spatial topological heat pipe temperature time series global feature matrix is ​​passed through a classifier to obtain a classification result, and the classification result is used to indicate whether to start the motor to drive the evaporation mechanism to rotate.

[0040] Accordingly, if Figure 4 As shown, the motor control module 140 includes: a joint encoding unit 141, which is used to jointly encode the multiple temperature time series feature vectors and the space topology feature matrix to obtain a space topology heat pipe temperature time series global feature matrix; and a start confirmation unit 142, which is used to determine whether to start the motor to drive the evaporation mechanism to rotate based on the space topology heat pipe temperature time series global feature matrix.

[0041] More specifically, the joint encoding unit 141 is further used to: pass the multiple temperature time series feature vectors and the spatial topological feature matrix through the graph neural network model to obtain the spatial topological heat pipe temperature time series global feature matrix. It is worth mentioning that the graph neural network (GNN) is a deep learning model for processing graph structure data. It can learn and represent the features of nodes and edges, and capture the relationship and global structure between nodes through local connection and aggregation operations of the graph. The role of the graph neural network model in the joint encoding unit is to take multiple temperature time series feature vectors and spatial topological feature matrices as input, and generate a spatial topological heat pipe temperature time series global feature matrix by learning the features of nodes and edges in the graph structure. In the graph neural network, each node represents a sample or data point, and each edge represents the relationship or connection between nodes. The model updates the features of the node in an iterative manner, while considering the features of the node itself and the features of the neighboring nodes. In this way, the representation of the node gradually integrates local and global information, so that the relationship and overall structure between nodes can be better understood. The graph neural network model usually includes two main operations: node update and message passing. Node update generates an updated node representation by aggregating the node's own features and the features of neighboring nodes. Message passing is to pass the node's representation information to neighboring nodes and perform aggregation operations on neighboring nodes. Through multiple rounds of node updates and message passing, the model can gradually extract higher-level graph feature representations. The advantage of the graph neural network model is that it can process data with complex topological structures, such as social networks, molecular structures, recommendation systems, etc. It can learn the relationship and global structure between nodes, so as to perform tasks such as classification, regression, and clustering of nodes. In this application, the graph neural network model is used to combine the temperature time series feature vector and the spatial topological feature matrix, learn the relationship and global structure between nodes (positions) in the heat pipe system, and generate a global feature matrix of the heat pipe temperature time series, so that the temperature changes in the heat pipe system can be more comprehensively understood and analyzed.

[0042] More specifically, the start confirmation unit 142 is further used to: pass the spatial topological heat pipe temperature time series global feature matrix through a classifier to obtain a classification result, and the classification result is used to indicate whether to start the motor to drive the evaporation mechanism to rotate.

[0043] More specifically, the start confirmation unit 142 is further used to: expand the spatial topological heat pipe temperature time series global feature matrix into a classification feature vector according to a row vector or a column vector; use the fully connected layer of the classifier to fully connect the classification feature vector to obtain an encoded classification feature vector; and input the encoded classification feature vector into the Softmax classification function of the classifier to obtain the classification result.

[0044] That is, in the technical solution disclosed in the present invention, the labels of the classifier include starting the motor to drive the evaporation mechanism to rotate (first label), and not starting the motor to drive the evaporation mechanism to rotate (second label), wherein the classifier determines which classification label the spatial topological heat pipe temperature time series global feature matrix belongs to through a soft maximum function. It is worth noting that the first label p1 and the second label p2 here do not contain artificially set concepts. In fact, during the training process, the computer model does not have the concept of "whether to start the motor to drive the evaporation mechanism to rotate". It only has two classification labels and the probability of the output feature under these two classification labels, that is, the sum of p1 and p2 is one. Therefore, the classification result of whether to start the motor to drive the evaporation mechanism to rotate is actually converted into a binary class probability distribution that conforms to the laws of nature through the classification label. In essence, the physical meaning of the natural probability distribution of the label is used, rather than the linguistic text meaning of "whether to start the motor to drive the evaporation mechanism to rotate".

[0045] It should be understood that the role of the classifier is to use the given categories and known training data to learn classification rules and classifiers, and then classify (or predict) unknown data. Logistic regression, SVM, etc. are often used to solve binary classification problems. For multi-class classification problems, logistic regression or SVM can also be used, but multiple binary classifications are required to form a multi-classification, but this is prone to errors and inefficient. Commonly used multi-classification methods include the Softmax classification function.

[0046] In the technical solution of the present application, each temperature time series feature vector represents the local time series correlation feature of the temperature values ​​at each position of the heat pipe collected by the corresponding temperature sensor. Therefore, after passing it together with the spatial topological feature matrix through the graph neural network model, the topological correlation based on the spatial topology of multiple temperature sensors can be further performed. That is, the spatial topological heat pipe temperature time series global feature matrix simultaneously includes the feature representation of the time-space mixed correlation dimension of the temperature value in a diverse time-space mixed space context, so that the spatial topological heat pipe temperature time series global feature matrix has a diversified feature distribution representation in the entire time-space feature domain. Therefore, it is expected to improve the balance between the regression mapping accuracy and completeness when the spatial topological heat pipe temperature time series global feature matrix is ​​input into the classifier for classification regression, thereby improving the accuracy of the classification results.

[0047] In a preferred example, passing the spatial topological heat pipe temperature time series global feature matrix through a classifier to obtain a classification result includes: determining the spatial topological heat pipe temperature time series global maximum eigenvalue and the spatial topological heat pipe temperature time series global minimum eigenvalue of the spatial topological heat pipe temperature time series global feature matrix, and calculating the spatial topological heat pipe temperature time series global mean and the spatial topological heat pipe temperature time series global standard deviation of the feature set of the spatial topological heat pipe temperature time series global feature matrix; calculating the quotient of the spatial topological heat pipe temperature time series global mean and the spatial topological heat pipe temperature time series global standard deviation to obtain the spatial topological heat pipe temperature time series global statistical normalized value; calculating the inverse of each eigenvalue of the spatial topological heat pipe temperature time series global feature matrix, and comparing it with the global maximum eigenvalue of the spatial topological heat pipe temperature time series. After multiplying the difference between the global minimum eigenvalue of the spatial topological heat pipe temperature time series and the global statistical standardized value of the spatial topological heat pipe temperature time series, perform point subtraction from the global statistical standardized value of the spatial topological heat pipe temperature time series to obtain the global distribution approximation matrix of the spatial topological heat pipe temperature time series; calculate the exponential function of each eigenvalue of the global distribution approximation vector of the spatial topological heat pipe temperature time series with the natural constant as the base to obtain the global distribution class approximation matrix of the spatial topological heat pipe temperature time series; perform point addition on the global distribution class approximation matrix of the spatial topological heat pipe temperature time series and the global statistical standardized value of the spatial topological heat pipe temperature time series, and calculate the logarithm of the absolute value of each eigenvalue of the point addition matrix with the base two to obtain the optimized global feature matrix of the spatial topological heat pipe temperature time series; pass the optimized global feature matrix of the spatial topological heat pipe temperature time series through a classifier to obtain a classification result.

[0048] Among them, the spatial topological heat pipe temperature time series global characteristic matrix is ​​recorded as The optimization expression is: ;in, represents the global maximum eigenvalue of the temperature time series of the spatial topological heat pipe, Represents the global minimum eigenvalue of the temperature time series of the spatial topological heat pipe, represents the global mean of the temperature time series of the spatial topological heat pipe, represents the global standard deviation of the temperature time series of the spatial topological heat pipe, is the global statistical normalized value of the spatial topological heat pipe temperature time series, and is the difference between the global maximum eigenvalue of the spatial topological heat pipe temperature time series and the global minimum eigenvalue of the spatial topological heat pipe temperature time series, represents the global characteristic matrix of the temperature time series of the spatial topological heat pipe, It means to calculate the inverse of each eigenvalue of the matrix. It means point multiplication by position. Add by position, Subtract by position, Represents the approximate matrix of the global distribution of the temperature time series of the spatial topological heat pipe, Represents the global characteristic matrix of the optimized spatial topology heat pipe temperature time series.

[0049] In this preferred embodiment, by performing random statistical normalization on the diversified feature set of the spatial topological heat pipe temperature series global feature matrix, a standardized continuous probability density distribution is approximated for the response hypothesis test of the confidence space constructed based on the overall eigenvalue of the spatial topological heat pipe temperature series global feature matrix relative to each eigenvalue of the spatial topological heat pipe temperature series global feature matrix, thereby establishing the target reachability of the diversified feature distribution of the spatial topological heat pipe temperature series global feature matrix to a unified regression target, so as to achieve a balanced executableness between mapping accuracy and mapping completeness in the classification regression process based on the diversified feature distribution of the spatial topological heat pipe temperature series global feature matrix, and improve the accuracy of the classification result obtained by inputting the spatial topological heat pipe temperature series global feature matrix into the classifier.

[0050] In summary, the control system 100 of the compound methanol evaporator based on the embodiment of the present application is explained, which can reasonably determine when to start the motor of the evaporation mechanism, reduce unnecessary energy consumption, and reduce production costs.

[0051] As described above, the control system 100 of the compound methanol evaporator based on the embodiment of the present application can be implemented in various terminal devices, such as a server having a control algorithm of the compound methanol evaporator based on the embodiment of the present application. In one example, the control system 100 of the compound methanol evaporator based on the embodiment of the present application can be integrated into the terminal device as a software module and / or a hardware module. For example, the control system 100 of the compound methanol evaporator based on the embodiment of the present application can be a software module in the operating system of the terminal device, or can be an application developed for the terminal device; of course, the control system 100 of the compound methanol evaporator based on the embodiment of the present application can also be one of the many hardware modules of the terminal device.

[0052] Alternatively, in another example, the control system 100 of the compound methanol evaporator based on the embodiment of the present application and the terminal device may also be separate devices, and the control system 100 of the compound methanol evaporator may be connected to the terminal device via a wired and / or wireless network, and transmit interactive information in accordance with an agreed data format.

[0053] Figure 5 4 is a flow chart of a control method for a composite methanol evaporator according to an embodiment of the present application. Figure 6Schematic diagram of the system architecture of the control method of the composite methanol evaporator according to the embodiment of the present application. Figure 5 and Figure 6 As shown, according to the control method of the compound methanol evaporator of the embodiment of the present application, it includes: S110, obtaining temperature values ​​at multiple predetermined time points within a predetermined time period collected by multiple temperature sensors deployed at multiple positions of the heat pipe; S120, extracting temperature time series characteristics of the temperature values ​​at multiple predetermined time points within the predetermined time period collected by each temperature sensor to obtain multiple temperature time series characteristic vectors; S130, extracting spatial topological characteristics of the multiple positions to obtain a spatial topological characteristic matrix; and, S140, based on the multiple temperature time series characteristic vectors and the spatial topological characteristic matrix, determining whether to start the motor to drive the evaporation mechanism to rotate.

[0054] In a specific example, in the control method of the above-mentioned composite methanol evaporator, the temperature time series characteristics of the temperature values ​​at multiple predetermined time points within a predetermined time period collected by each temperature sensor are extracted to obtain multiple temperature time series feature vectors, including: arranging the temperature values ​​at multiple predetermined time points within a predetermined time period collected by each temperature sensor as temperature time series input vectors according to the time dimension to obtain multiple temperature time series input vectors; and, respectively passing the multiple temperature time series input vectors through a temperature time series feature extractor based on a one-dimensional convolutional layer to obtain the multiple temperature time series feature vectors.

[0055] Here, those skilled in the art will appreciate that the specific operations of each step in the control method of the composite methanol evaporator have been described in the above reference. Figures 1 to 4 The control system 100 of the compound methanol evaporator has been described in detail, and therefore, its repeated description will be omitted.

[0056] Figure 7 FIG. 1 is an application scenario diagram of a control system of a composite methanol evaporator according to an embodiment of the present application. Figure 7 As shown, in this application scenario, first, the temperature values ​​(for example, Figure 7 D1 as shown in FIG), and multiple locations of multiple temperature sensors (e.g., Figure 7 Then, the behavior monitoring video is input into a server in which a control algorithm of a composite methanol evaporator is deployed (for example, Figure 7S), wherein the server can use the control algorithm of the compound methanol evaporator to process the temperature values ​​of multiple predetermined time points within a predetermined time period collected by each temperature sensor and the multiple positions of the multiple temperature sensors to obtain a classification result indicating whether to start the motor to drive the evaporation mechanism to rotate.

[0057] Further, the composite methanol evaporator is described. In a specific embodiment of the present application, it includes a box body, a filter screen is arranged below the inner wall of the box body, two layers of evaporation mechanisms are arranged above the filter screen, the two layers of evaporation mechanisms are staggered and evenly distributed, an exhaust pipe is arranged on the top inner wall of the box body and in the middle position, a plurality of groups of circular tubes are arranged on the inner wall of the box body near the exhaust pipe, the two ends of the plurality of groups of circular tubes are respectively fixedly connected to the two sides of the inner wall of the box body, a plurality of groups of atomizing nozzles are arranged below the circular tubes, the liquid inlet ends of the plurality of groups of atomizing nozzles are connected to the inner wall of the circular tubes, and the left side of the circular tubes penetrates the inner wall of the box body and extends to the box body. A T-tube is fixedly connected to the outside of the box, and a liquid pump is fixedly connected to the other end of the T-tube. A liquid outlet pipe is fixedly connected to the side of the lower end of the liquid pump close to the box body, and a liquid inlet pipe is fixedly connected to the side of the lower end of the liquid pump away from the box body. A fixing plate is fixedly connected to the outer surface of the box body and below the liquid pump, and the other end of the liquid outlet pipe penetrates the lower outer surface of the fixing plate and extends to the bottom inner wall of the box body, and the other end of the liquid inlet pipe penetrates the lower outer surface of the fixing plate and extends to the bottom of the fixing plate. A protective cover is fixedly connected to the front outer surface of the box body, a fixing frame is fixedly connected to the right outer surface of the box body, and a motor is fixedly connected to the upper end of the fixing frame.

[0058] By adopting the above technical solution, when the user needs to evaporate liquid methanol, the liquid methanol is first pumped from the liquid inlet pipe into the T-tube through the liquid pump, and then diverted to each circular tube through the T-tube, and then the liquid methanol is sprayed on the evaporation mechanism in the form of mist through the atomizing nozzle, and finally the liquid methanol is evaporated into gas through the evaporation mechanism, and then the gas is collected into the collection device through the exhaust pipe, and the liquid methanol that has not evaporated will be stored at the bottom of the box after filtering impurities through the filter net, and then the liquid methanol that has not evaporated will continue to be circulated and processed through the liquid pump, and the evaporation mechanism can be driven to rotate by the motor, so that the evaporation mechanism can be heated up during use, so as to avoid the liquid methanol being sprayed toward one place of the evaporation mechanism, resulting in the temperature of the heat transfer tube not being able to recover in time and reducing the evaporation efficiency of the methanol.

[0059] As an embodiment of the present application, the evaporation mechanism includes a first fixed block, a second fixed block, a heat conduction pipe, a rotating shaft, a connecting rod, a primary gear, a secondary gear, a primary pulley and a primary belt, the rear end outer surface of the box body is fixedly connected to the first fixed block, the front end outer surface of the box body is fixedly connected to the second fixed block, the first fixed block and the second fixed block are both provided with a plurality of grooves on a side close to the box body, and the plurality of grooves are annularly distributed on one side of the fixed block, the first fixed block is provided with an air inlet on a side away from the box body, the other ends of the air inlet holes are respectively connected to the inner walls of the grooves, the inner walls of the grooves are rotatably connected with the heat conduction pipe, one end of the heat conduction pipe close to the second fixed block is fixedly connected with the connecting rod, the other end of the connecting rod passes through the outer surface of the second fixed block and extends to the outside of the second fixed block and is fixedly connected with the primary gear, the outer surface of the connecting rod is rotatably connected to the inner wall of the second fixed block, the side of the connecting rod away from the second fixed block is provided with a through hole, and the other end of the through hole is connected to the inner wall of the heat conduction pipe. By adopting the above technical solution, when the evaporation mechanism needs to work, water vapor is introduced into the heat pipe through the air inlet, and then the motor is started. The motor drives the primary pulley to rotate through the belt drive, the primary pulley drives the rotating shaft to rotate, the rotating shaft drives the secondary gear to rotate, and the secondary gear is engaged with the primary gear to drive the connecting rod to rotate. The connecting rod drives the heat pipe to rotate so that the contact surface between the heat pipe and the mist methanol is always changing, thereby ensuring that the heated surface of the heat pipe gets a certain buffer time for the temperature to rise, thereby improving the evaporation efficiency of the mist methanol.

[0060] As an embodiment of the present application, a first bearing groove is provided on a side of the first fixed block close to the housing, a first bearing is fixedly connected to the inner wall of the first bearing groove, a rotating shaft is fixedly connected to the inner wall of the first bearing, a second bearing groove is provided on a side of the second fixed block close to the housing, a second bearing is fixedly connected to the inner wall of the second bearing groove, the inner wall of the second bearing is fixedly connected to the outer surface of the rotating shaft, the other end of the rotating shaft passes through the outer surface of the second fixed block and extends to the outside of the second fixed block and is fixedly connected to a secondary gear, a primary pulley is fixedly connected to the outer surface of the rotating shaft and located on the right side of the secondary gear, and a primary belt is sleeved between the outer surfaces of two adjacent primary pulleys. By adopting the above technical solution, the wear of the rotating shaft can be reduced and the transmission efficiency can be enhanced by setting the first bearing and the second bearing.

[0061] As an implementation mode of the present application, the inner wall of the heat pipe is provided with a plurality of barrier sheets, and the plurality of barrier sheets are staggered and distributed on both sides of the heat pipe. By adopting the above technical solution, by providing a plurality of barrier sheets on the inner wall of the heat pipe, and the plurality of barrier sheets are staggered and distributed on both sides of the heat pipe, the flow rate of water vapor in the heat pipe can be reduced, thereby improving the utilization rate of water vapor, reducing production costs, and saving resources.

[0062] As an implementation of the present application, a protective cover is fixedly connected to the outer surface of the box body and located outside the second fixed block. A plurality of annular grooves are provided on the side of the protective cover away from the box body. Fan blades are provided in the plurality of annular grooves. The inner wall of the fan blades is fixedly connected to the outer surface of the rotating shaft. By adopting the above technical solution, the provision of a protective cover can reduce safety hazards and avoid danger to the user during use; the provision of fan blades can quickly discharge the discharged water vapor from the protective cover to avoid water vapor from damaging the parts in the protective cover.

[0063] As an implementation of the present application, a secondary pulley is fixedly connected to the outer surface of the output shaft of the motor, and a secondary belt is sleeved on the outer surface of the secondary pulley and the outer surface of the primary pulley near the lower right corner of the box. By adopting the above technical solution, by setting the secondary pulley and the secondary belt, the motor can provide power to the evaporation mechanism during the rotation process.

[0064] As an implementation mode of the present application, the heat pipe and the barrier sheet are both made of aluminum, and the wall thickness of the heat pipe is 0.5-0.8 mm. By adopting the above technical solution, by setting the heat pipe and the barrier sheet to be made of aluminum, and the wall thickness of the heat pipe is 0.5-0.8 mm, the heat exchange efficiency of the heat pipe can be improved.

[0065] As an implementation of the present application, the primary gear and the secondary gear located on the right side of the same second fixed block are meshed with each other. By adopting the above technical solution, by setting the primary gear and the secondary gear located on the right side of the same second fixed block to mesh with each other, the heat pipes in the fixed block can be rotated at the same time, which can ensure that the heating surface of the heat pipe has a certain buffer time, so that the temperature of the heating surface can be recovered, thereby improving the evaporation effect.

[0066] As an implementation of the present application, an elastic sleeve is provided on the outer surface of the heat pipe, and the outer surface of the elastic sleeve is slidably connected to the inner wall of the box and the inner wall of the groove respectively, and the elastic sleeve is made of wear-resistant material. By adopting the above technical solution, the wear of the outer surface of the heat pipe can be reduced by providing the elastic sleeve, thereby improving the utilization rate of the heat pipe.

[0067] As an implementation of the present application, an opening and closing door is provided at the lower front end of the box, a handle is fixedly connected to the left side of the opening and closing door, and a circle of sealing gasket is provided on the inner wall of the opening and closing door. By adopting the above technical solution, the opening and closing door is provided to facilitate cleaning of the inside of the box, the handle is provided to facilitate control of the opening and closing of the opening and closing door, and the sealing gasket can enhance the sealing of the box.

[0068] Working principle: When the user needs to evaporate liquid methanol, firstly, the liquid methanol is pumped from the liquid inlet pipe into the T-tube through the liquid pump, and then diverted to each round tube through the T-tube, and then the liquid methanol is sprayed on the evaporation mechanism in the form of mist through the atomizing nozzle, and then the water vapor is introduced into the heat transfer tube through the air inlet hole, and then the motor is started, and the output shaft of the motor drives the secondary pulley to rotate, the secondary pulley drives the primary pulley to rotate through the secondary belt, the primary pulley drives the rotating shaft to rotate, the rotating shaft drives the secondary gear to rotate, and then the secondary gear and the primary gear are meshed with each other to drive the connecting rod to rotate. The connecting rod drives the heat pipe to rotate so that the contact surface between the heat pipe and the misty methanol is always changing. The misty methanol is vaporized and converted into gas after being heated, and then the gas is collected in the collection device through the exhaust pipe. The liquid methanol that has not evaporated will be stored at the bottom of the box after filtering out impurities through the filter net, and then the liquid methanol that has not evaporated will continue to be circulated through the liquid pump, thereby ensuring that the heated surface of the heat pipe has a certain buffer time, so that the heat pipe can be heated up in time during use, avoiding the liquid methanol from being sprayed towards one place of the heat pipe, resulting in the temperature of the heat pipe not being able to recover in time and reducing the evaporation efficiency of the methanol.

[0069] In the present application, when the user needs to evaporate liquid methanol, the liquid methanol is first pumped from the liquid inlet pipe into the T-tube through a liquid pump, and then diverted to each round tube through the T-tube, and then the liquid methanol is sprayed on the evaporation mechanism in the form of mist through an atomizing nozzle, and then water vapor is introduced into the heat conduction tube through an air inlet hole, and then the motor is started, the output shaft of the motor drives the secondary pulley to rotate, the secondary pulley drives the primary pulley to rotate through the secondary belt, the primary pulley drives the rotating shaft to rotate, the rotating shaft drives the secondary gear to rotate, and then the secondary gear and the primary gear are meshed with each other to drive the connecting rod to rotate. The connecting rod drives the heat pipe to rotate so that the contact surface between the heat pipe and the misty methanol is always changing. The misty methanol is vaporized and converted into gas after being heated, and then the gas is collected into the collection device through the exhaust pipe. The liquid methanol that has not evaporated will be stored at the bottom of the box, and then the liquid methanol that has not evaporated will continue to be circulated through the liquid pump, thereby ensuring that the heated surface of the heat pipe has a certain buffer time, so that the heat pipe can be heated up in time during use, avoiding the liquid methanol from being sprayed towards one place of the heat pipe, resulting in the temperature of the heat pipe unable to recover in time, thereby reducing the evaporation efficiency of the methanol, thereby reducing the production cost.

[0070] The present application uses specific words to describe the embodiments of the present application. For example, "first / second embodiment", "one embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or multiple times in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0071] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0072] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0073] The above is an explanation of the present application and should not be considered as a limitation thereof. Although several exemplary embodiments of the present application are described, it will be readily understood by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present application. Therefore, all of these modifications are intended to be included within the scope of the present application as defined by the claims. It should be understood that the above is an explanation of the present application and should not be considered to be limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present application is defined by the claims and their equivalents.

Claims

1. A control system for a composite methanol evaporator, characterized in that: include: A temperature value acquisition module, used to acquire temperature values ​​at multiple predetermined time points within a predetermined time period collected by multiple temperature sensors deployed at multiple positions of the heat pipe; A temperature time series feature extraction module, used to extract temperature time series features of temperature values ​​at multiple predetermined time points within a predetermined time period collected by each temperature sensor to obtain multiple temperature time series feature vectors; A spatial topological feature extraction module, used to extract the spatial topological features of the plurality of positions to obtain a spatial topological feature matrix; as well as A motor control module, used for determining whether to start the motor to drive the evaporation mechanism to rotate based on the multiple temperature time series feature vectors and the spatial topological feature matrix; Wherein, the motor control module comprises: A joint encoding unit, used for jointly encoding the multiple temperature time series feature vectors and the space topology feature matrix to obtain a space topology heat pipe temperature time series global feature matrix; and A start confirmation unit, used to determine whether to start the motor to drive the evaporation mechanism to rotate based on the spatial topological heat pipe temperature time series global characteristic matrix; Wherein, the start confirmation unit is used to: The spatial topological heat pipe temperature time series global feature matrix is ​​passed through a classifier to obtain a classification result, wherein the classification result is used to indicate whether to start the motor to drive the evaporation mechanism to rotate; Wherein, passing the spatial topological heat pipe temperature time series global feature matrix through a classifier to obtain a classification result includes: Determine the global maximum eigenvalue and the global minimum eigenvalue of the spatial topological heat pipe temperature time series of the spatial topological heat pipe temperature time series global characteristic matrix, and calculate the global mean and the global standard deviation of the spatial topological heat pipe temperature time series of the feature set of the spatial topological heat pipe temperature time series global characteristic matrix; Calculating the quotient of the global mean value of the spatial topological heat pipe temperature time series and the global standard deviation of the spatial topological heat pipe temperature time series to obtain a global statistical normalized value of the spatial topological heat pipe temperature time series; Calculate the inverse of each eigenvalue of the global characteristic matrix of the spatial topological heat pipe temperature time series, multiply it by the difference between the global maximum eigenvalue of the spatial topological heat pipe temperature time series and the global minimum eigenvalue of the spatial topological heat pipe temperature time series, and then perform point subtraction with the global statistical normalized value of the spatial topological heat pipe temperature time series to obtain a global distribution approximation matrix of the spatial topological heat pipe temperature time series; Calculate an exponential function with a natural constant as the base, with each eigenvalue of the spatial topological heat pipe temperature time series global distribution approximation vector as an exponent, to obtain a spatial topological heat pipe temperature time series global distribution class approximation matrix; Performing point addition on the spatial topology heat pipe temperature time series global distribution class approximation matrix and the spatial topology heat pipe temperature time series global statistical normalized value, and calculating the base-two logarithm of the absolute value of each eigenvalue of the point addition matrix to obtain an optimized spatial topology heat pipe temperature time series global characteristic matrix; The optimized spatial topology heat pipe temperature time series global feature matrix is ​​passed through a classifier to obtain a classification result.

2. The control system of the composite methanol evaporator according to claim 1, characterized in that: The temperature time series feature extraction module comprises: a vectorization unit, configured to arrange the temperature values ​​at a plurality of predetermined time points within a predetermined time period collected by each temperature sensor into temperature time series input vectors according to a time dimension to obtain a plurality of temperature time series input vectors; and The time series feature extraction unit is used to pass the multiple temperature time series input vectors through a temperature time series feature extractor based on a one-dimensional convolution layer to obtain the multiple temperature time series feature vectors.

3. The control system of the composite methanol evaporator according to claim 2, characterized in that: The spatial topological feature extraction module comprises: A matrixing unit, configured to construct a spatial topological matrix of the plurality of positions, wherein the eigenvalue of each position at a non-diagonal position of the spatial topological matrix is ​​a spatial distance between two corresponding positions; and A topological feature extraction unit is used to obtain the spatial topological feature matrix by passing the spatial topological matrix through a spatial topological feature extractor based on a convolutional neural network model.

4. The control system of the composite methanol evaporator according to claim 3, characterized in that: The joint coding unit is further configured to: The multiple temperature time series feature vectors and the spatial topological feature matrix are passed through a graph neural network model to obtain the spatial topological heat pipe temperature time series global feature matrix.

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