CN113048780A - Control method and device of dryer, computer equipment and storage medium - Google Patents
Control method and device of dryer, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a control method, a control device, computer equipment and a storage medium of a dryer, wherein the method comprises the steps of obtaining state attribute data of a material to be dried, a target moisture value and operation attribute parameters of the dryer, determining a linear regression function of a predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried and the operation attribute parameters of the dryer, carrying out optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain target control parameters of the dryer, and controlling the operation of the dryer by adopting the target control parameters.
Description
Technical Field
The present application relates to the field of automatic control technologies, and in particular, to a method and an apparatus for controlling a dryer, a computer device, and a storage medium.
Background
At present, in a continuous dryer for drying products such as feed and food on the market, an operator needs to set control parameters such as target temperature of each subarea in the dryer, fan rotating speed, humidity-discharging air door opening degree and conveyor belt rotating speed according to experience.
And because these parameters affect the drying rate of the product and ultimately the quality of the finished product. Therefore, in order to ensure that the produced product meets the quality requirements and the dryer can work in the best state, the operator needs to adjust all parameters according to experience, thereby making it extremely complicated to operate the dryer.
Disclosure of Invention
In view of the above, it is necessary to provide a control method, an apparatus, a computer device and a storage medium for a dryer, which automatically set control parameters, in order to solve the above-mentioned problem that manual debugging of the control parameters of the dryer is complicated.
A control method of a dryer, comprising:
acquiring state attribute data, a target moisture value and operation attribute parameters of a dryer of a material to be dried;
determining a linear regression function of the predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried and the operation attribute parameters of the dryer;
performing optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain a target control parameter of the dryer;
and controlling the dryer to operate by adopting the target control parameter.
In one embodiment, the decision tree regression model is constructed by: continuously acquiring sample data in the operation process of the dryer according to a preset sampling period, wherein the sample data comprises various state attribute data sets of sample materials to be dried in the operation process of the dryer, various operation attribute parameter sets of the dryer and a moisture sample set corresponding to the dried sample materials; generating a decision tree based on preset splitting conditions, various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer, wherein the decision tree is provided with a plurality of leaf nodes; and establishing a linear regression function of the water sample values corresponding to the leaf nodes according to the subset of the sample data corresponding to each leaf node to obtain the decision tree regression model.
In one embodiment, the preset splitting condition comprises a first splitting condition and a second splitting condition; the generating of the decision tree based on the preset splitting condition, the various state attribute data sets of the sample materials to be dried in the operation process of the dryer and the various operation attribute parameter sets of the dryer comprises the following steps: determining a root node of the decision tree based on various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer; segmenting the root node by adopting the first splitting condition to obtain a plurality of candidate sub-nodes of the decision tree and a subset of sample data corresponding to each candidate sub-node; and if the candidate child node meets the second splitting condition, determining the candidate child node as a leaf node.
In one embodiment, the method further comprises: if the candidate child node does not satisfy the second splitting condition, re-splitting the candidate child node based on the first splitting condition and the subset of the sample data corresponding to the candidate child node, and determining the split candidate child node as a leaf node when the split candidate child node satisfies the second splitting condition.
In one embodiment, the subset of sample data includes a moisture sample value of a corresponding dried sample material, various state attribute data of a corresponding sample material to be dried, and various operation attribute parameters of the dryer; the establishing a linear regression function of the water sample values corresponding to the leaf nodes according to the subset of the sample data corresponding to each leaf node includes: according to the subset of the sample data corresponding to each leaf node, establishing a linear relation among the moisture sample values of the dried sample materials in the subset of the sample data, various state attribute data of the corresponding to-be-dried sample materials and various operation attribute parameters of the dryer; determining a linear regression function of the water sample values corresponding to the leaf nodes based on the linear relationship.
In one embodiment, the performing an optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain the target control parameter of the dryer includes: and solving a minimum solution of the difference between the target moisture value and the linear regression function of the predicted moisture value based on function optimization to obtain a target control parameter of the dryer, wherein the function optimization adopts any one of a genetic optimization algorithm, a local minimization optimization algorithm, a directional acceleration optimization algorithm and a gradient optimization algorithm.
In one embodiment, the controlling the operation of the dryer using the target control parameter includes: acquiring a standard range corresponding to the target control parameter, wherein the standard range comprises a first boundary value and a second boundary value which correspond to each other; if the target control parameter is greater than or equal to the first boundary value and less than or equal to the second boundary value, controlling the dryer to operate by adopting the target control parameter; if the target control parameter is smaller than the first boundary value, the first boundary value is used as the target control parameter to control the dryer to operate; or, if the target control parameter is greater than the second boundary value, the second boundary value is used as the target control parameter to control the dryer to operate.
A control apparatus of a dryer, the apparatus comprising:
the data acquisition module is used for acquiring state attribute data of the material to be dried, a target moisture value and operation attribute parameters of the dryer;
the linear regression function determining module is used for determining a linear regression function of the predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried and the operation attribute parameters of the dryer;
the target control parameter acquisition module is used for carrying out optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain target control parameters of the dryer;
and the control module is used for controlling the dryer to operate by adopting the target control parameters.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as set forth above.
According to the control method, the control device, the computer equipment and the storage medium of the dryer, the linear regression function of the predicted moisture value of the material to be dried is determined by acquiring the state attribute data, the target moisture value and the operation attribute parameter of the dryer, and based on the preset decision tree regression model, the state attribute data and the operation attribute parameter of the dryer, the optimal solution is carried out according to the target moisture value and the linear regression function of the predicted moisture value, the target control parameter of the dryer is obtained, the operation of the dryer is controlled by adopting the target control parameter, the operation complexity of the dryer is reduced, the dependence degree on experience operation workers is reduced, and the control precision is improved.
Drawings
Fig. 1 is a diagram illustrating an exemplary embodiment of a control method of a dryer;
fig. 2 is a flowchart illustrating a control method of a dryer in one embodiment;
FIG. 3 is a schematic flow chart diagram illustrating the steps of constructing a regression model of a decision tree in one embodiment;
FIG. 4 is a diagram illustrating the structure of a decision tree in one embodiment;
fig. 5 is a block diagram showing a control apparatus of a dryer in one embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment;
fig. 7 is an internal structural view of a computer device in another embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The application relates to a dryer which is mechanical equipment used for drying products such as feed and food so as to achieve the purpose of removing moisture in the products. The moisture requirement of the dried product is typically within a specified range to meet storage requirements. Taking pet food and feed as an example, if the moisture value of the material is too large, the material is easy to go moldy in the storage and transportation processes; conversely, if the moisture value of the material is too low, the palatability of the material is reduced (animals do not like to eat), and excessive drying leads to increased production costs. In order to ensure that the produced product meets the moisture requirement of the product, quality testing staff need to regularly measure and evaluate the moisture content of the product. If the product after the drying is finished through sampling inspection, whether the moisture reaches the standard is detected, however, the whole process needs about 1 hour from the product drying to the detection, and if the product is detected to be unqualified, the material in the 1 hour is changed into unqualified product. Therefore, at present, the evaluation of the moisture content of the product by a spot check method not only needs to consume a lot of time and labor, but also causes a lot of material waste.
And the thickness of the material entering the dryer is not uniform due to the unstable output of the equipment (such as the bulking machine, the granulator and the like) upstream of the dryer, while the current dryers are generally based on the assumption that the output of the machines is constant, and once the output of the machines changes, the total amount of moisture to be removed also changes, and the moisture content of the dried material also has a large difference due to the fact that the current dryers cannot recognize the change. At present, the continuous dryer for drying feed and food in the market needs an operator to set parameters such as target temperature, fan rotating speed, humidity-discharging air door opening and closing degree, conveying belt rotating speed and the like of each drying area in the dryer according to experience. And these parameters all affect the drying rate of the product and ultimately the moisture content of the finished product. Therefore, in order to ensure that the product produced meets the moisture requirement of the product and the dryer can work in the best condition, the operator needs to adjust these parameters according to experience, thereby making the operation of the dryer extremely complicated.
Based on this, the present application proposes a control method of a dryer, which can be applied to the application environment as shown in fig. 1. Wherein the terminal 110 is communicatively connected with the dryer 120. The terminal 110 obtains state attribute data of the material to be dried, a target moisture value and operation attribute parameters of the dryer, determines a linear regression function of a predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried and the operation attribute parameters of the dryer, and then performs optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain target control parameters of the dryer, and controls the operation of the dryer by adopting the target control parameters, so that not only is the operation complexity of the dryer reduced, but also the dependence degree on experience operators is reduced, and meanwhile, the control precision is improved. Specifically, the terminal 110 may be, but is not limited to, various servers, personal computers, notebook computers, smart phones, tablet computers, portable wearable devices, and the like.
In one embodiment, as shown in fig. 2, there is provided a control method of a dryer, which is described by taking an example of the method applied to the terminal of fig. 1, and includes the steps of:
step 210, obtaining state attribute data of the material to be dried, a target moisture value and operation attribute parameters of the dryer.
The state attribute data of the material to be dried is data for representing various states of the material to be dried, including but not limited to a moisture value of the material to be dried at a feeding port of the dryer, a mass flow rate of the material to be dried entering the drying area, and the like. And may also include the moisture content of the air entering the drying section of the dryer and the moisture content of the air exiting the drying section. The operation attribute parameters of the dryer comprise set temperature of each drying area of the dryer, rotating speed of a material conveying belt, rotating speed of a fan motor, rotating speed of a moisture-removing fan, opening and closing degree of a moisture-removing air door and the like, and can be detected by corresponding sensing devices.
Specifically, the target moisture value of the material to be dried refers to the moisture value which is expected to be reached after the material is dried, and can be obtained according to the target data of the material to be dried. The moisture value of the material to be dried at the feeding port of the dryer refers to the moisture before the material is dried, and can be obtained by detecting through a moisture detector arranged at an outlet of an upstream device of the dryer or at an inlet of the dryer, or can be obtained by calculating through related data of the upstream device, for example, if the upstream device is a bulking machine, the moisture value can be obtained by indirectly calculating through data such as the mass flow of the material of the bulking machine, the water adding amount of a modulator, the steam adding amount of the bulking machine and the like by combining with a corresponding calculation formula. The mass flow rate of the material to be dried is the mass of the material passing through the effective cross section of the dryer in unit time, and can be directly or indirectly obtained through the operation data of upstream equipment, for example, if the upstream equipment is a bulking machine, the mass flow rate can be obtained through conversion of the frequency of a feeder of a modulator of the bulking machine, and can also be obtained through detection of a flow sensor arranged at the outlet of the bulking machine or the inlet of the dryer. The moisture content of the air entering the drying zone of the dryer refers to the humidity of the air entering the drying zone, and the moisture content of the air discharged from the drying zone refers to the humidity of the air discharged from the dryer, which can be detected by humidity sensors disposed at the air inlet and the air outlet of the dryer. The humidity increase of the drying zone may be determined based on the moisture content of the air entering the drying zone of the dryer and the moisture content of the air discharged from the drying zone.
In this embodiment, when the dryer is to be automatically controlled to reduce the complexity of the operation of the dryer so that the moisture value of the final dried product can meet the requirement, it is first necessary to obtain the above-mentioned various necessary data.
The following specific process for indirectly calculating the moisture value of the material to be dried at the feeding port of the dryer and the mass flow rate of the material by using a calculation formula, for example, specifically, firstly, collecting the parameters of the upstream equipment as follows: for the adjuster part, the maximum dry mixing ratio MR, the dry mixing humidity M and the steam quantity S are collectedPWater amount WPAnd the amount of injected FatPThe units are percentages; for the bulking machine partThen collect the steam quantity SEAnd water amount WEThe units are all percentages; for other parts, the acquisition of melting point data T is also neededmeltEvaporation temperature TevaVaporization heat dissipation HvaAnd a specific heat adjustment coefficient delta. And then the moisture value of the material at the feeding port of the dryer is obtained by indirect calculation of the parameters as follows:
wherein the content of the first and second substances,
for the calculated result, defined as the moisture value of the material at the feeding port of the dryer, MM is the melting moisture,
the water evaporation per kg was estimated. Specifically, MM ═ TWR/TMR, where TWR is the total water amount, calculated by the following formula: TWR ═ MR × (M + S)P+WP+SE+WE) TMR is the total flow of the puffing substances and is calculated by the following formula: TMR + MR × (S)P+WP+FatP+SE+WE)。
Then it is calculated by the following formula:
wherein HlossThe heat loss per kg of extrudate is calculated by the following formula:
while
As an estimate of the specific heat in the extruder barrel,
estimation value of material mass flow
Then it is calculated by the following formula:
and step 220, determining a linear regression function of the predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried and the operation attribute parameters of the dryer.
The decision tree regression model is a linear model represented by a tree structure, in the tree structure, a root node is a node without a father node, leaf nodes are nodes without child nodes, each leaf node has a corresponding linear regression function (also called as a linear model), and the father node of each leaf node is provided with a determination condition, that is, a decision basis. The decision tree regression model in this embodiment is obtained based on performing a grouping modeling on a large amount of sample data, so that a plurality of simple models are used to solve a complex problem. For example, if the moisture value of the dried material is in a curve relationship with the operation property parameters of the dryer, an error is too large if fitting is performed by only one linear model, but if fitting is performed by a plurality of piecewise linear functions (i.e., linear models corresponding to each leaf node), the error is relatively reduced and the accuracy is improved.
Specifically, each leaf node in the preset decision tree structure in the application has a corresponding linear regression function for predicting the moisture value, and the father node of each leaf node is provided with a decision basis, so that the matched leaf node can be determined based on the state attribute data of the material to be dried and the operation attribute parameters of the dryer through the decision basis of each node in the preset decision tree regression model, and the linear regression function for predicting the moisture value corresponding to the leaf node is obtained. It should be noted that the linear regression function is a linear relationship between the moisture sample value of the dried sample material, various state attribute data of the corresponding sample material, and various operation attribute parameters of the dryer, which are established based on the sample data corresponding to the leaf node in the modeling process of the decision tree regression model.
And step 230, performing optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain the target control parameter of the dryer.
Wherein the target control parameter is a control parameter for controlling the dryer. Because the linear regression function is established based on a certain decision basis, and the linear relation between the moisture sample value of the dried sample material in the corresponding sample data, the various state attribute data of the corresponding sample material and the various operation attribute parameters of the dryer, the linear regression function can also be understood as a function related to the various operation attribute parameters of the dryer, and the result of the function is the predicted moisture value, therefore, under the condition that the target moisture value is known, the linear regression function can be optimized and solved, namely, the solution which is closest to the result of the linear regression function and the target moisture value is solved based on the optimization algorithm, and the obtained solution is the target control parameter of the dryer.
And step 240, controlling the operation of the dryer by adopting the target control parameters.
Specifically, the operation of the dryer is controlled based on the obtained target control parameters, so that the dried material capable of meeting the target moisture value is obtained.
According to the control method of the dryer, the state attribute data, the target moisture value and the operation attribute parameters of the dryer of the material to be dried are obtained, the linear regression function of the predicted moisture value of the material to be dried is determined based on the preset decision tree regression model, the state attribute data and the operation attribute parameters of the dryer of the material to be dried, then the optimization solution is carried out according to the target moisture value and the linear regression function of the predicted moisture value, the target control parameters of the dryer are obtained, the operation of the dryer is controlled by adopting the target control parameters, the operation complexity of the dryer is reduced, the dependence degree on experience operation workers is reduced, and the control precision is improved.
In one embodiment, in particular, since the linear regression function predicts the moisture value of the drying material, what is actually required is the target control parameter of the dryer. The linear regression function is based on the linear relationship between the moisture sample value of the dried sample material in the corresponding sample data, the various state attribute data of the corresponding sample material and the various operation attribute parameters of the dryer, and can be equivalently in the form of y ═ f (x), where y is the predicted moisture value of the material, x is the input various state attribute data of the material and the various operation attribute parameters of the dryer, and f is the corresponding linear relationship. If the currently acquired state attribute data of the materials to be dried and the operation attribute parameter of the dryer are x*If the currently obtained target moisture value is y _ target, performing optimization solution according to the target moisture value and a linear regression function of the predicted moisture value to obtain a target control parameter of the dryer, and specifically performing optimization solution through the following formula:
min(y_target-f(x*))2
that is, a minimum solution of a difference between the linear regression function of the target moisture value and the predicted moisture value is solved, thereby obtaining a target control parameter of the dryer. In this embodiment, the function optimization algorithm used in the optimization solution process may be any one of a genetic optimization algorithm, a local minimization optimization algorithm, a directional acceleration optimization algorithm, and a gradient optimization algorithm.
In the embodiment, the optimal solution is performed based on the target moisture value, so that a solution closest to the target moisture value is obtained, that is, the target control parameter of the dryer is obtained, and the dryer is controlled to operate through the target control parameter, so that the dried material meeting the target moisture value can be finally obtained, and the quality of the dried material is improved.
In one embodiment, as shown in FIG. 3, the decision tree regression model is constructed by:
and 310, continuously collecting sample data in the operation process of the dryer according to a preset sampling period.
The sampling period may be a preset time interval for acquiring data. The sample data comprises various state attribute data sets of the sample materials to be dried, various operation attribute parameter sets of the dryer and moisture sample sets corresponding to the dried sample materials, which are continuously acquired based on a preset sampling period in the operation process of the dryer. Specifically, the various state attribute data sets of the sample material to be dried include, but are not limited to, a data set of moisture values of the sample material to be dried at the feed inlet of the dryer, a data set of mass flow rates of the sample material to be dried entering the drying zone, and the like. A moisture content data set of air entering the drying zone of the dryer and a moisture content data set of air discharged from the drying zone may also be included and a humidity increase data set for the drying zone determined based thereon. The various operation attribute parameter sets of the dryer comprise a data set of set temperature of each drying area of the dryer, a data set of rotating speed of a material conveying belt, a data set of rotating speed of a fan motor, a data set of rotating speed of a moisture exhaust fan, a data set of opening and closing degree of a moisture exhaust air door and the like. It should be noted that each data set represents corresponding data attributes, and is composed of sample data acquired at different times. For example, for a data set with data attributes being set temperatures for a drying zone, the data set may comprise set temperatures for different moments in the drying zone.
And 320, generating a decision tree based on preset splitting conditions, various state attribute data sets of the sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer.
The splitting condition includes a first splitting condition and a second splitting condition, specifically, the first splitting condition may be a condition for splitting data to form nodes in the decision tree, and the second splitting condition may be a condition for stopping splitting to determine leaf nodes. In this embodiment, a root node of the decision tree is determined based on various state attribute data sets of a sample material to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer, and the root node is divided by adopting a first division condition, so that a plurality of candidate sub-nodes of the decision tree and a subset of sample data corresponding to each candidate sub-node are obtained, and if the candidate sub-nodes meet a second division condition, the candidate sub-nodes are determined to be leaf nodes. And if the candidate child node does not meet the second splitting condition, re-splitting the candidate child node based on the first splitting condition and the subset of the sample data corresponding to the candidate child node, and determining the split candidate child node as a leaf node when the split candidate child node meets the second splitting condition, wherein generally, the generated decision tree has a plurality of leaf nodes.
Specifically, according to the subset of the sample data corresponding to each leaf node, a linear relation among the moisture sample values of the dried sample materials in the subset of the sample data, various state attribute data of the sample materials corresponding to the to-be-dried sample materials and various operation attribute parameters of the dryer is established, and a linear regression function of the moisture sample values corresponding to the leaf nodes is determined based on the linear relation. In this embodiment, the leaf nodes are nodes without child nodes, each leaf node has a corresponding linear regression function (also referred to as a linear model), and the parent node of each leaf node is provided with a determination condition, that is, a decision basis, so as to obtain a decision tree regression model.
The method for constructing a regression model of decision tree in this application is further described by the following specific examples, specifically, assuming that the collected sample data is shown in the following table, where Y is a moisture sample set (which is usually label data) of a dried sample material, X is a feature data set, including various state attribute data sets of the sample material to be dried and various operation attribute parameter sets of a dryer, and if the feature data X in this embodiment includes a data set of moisture values of the sample material to be dried at a feeding inlet of the X1 dryer and a data set of set temperature of a drying zone of X2, the whole sample data set can be denoted as { X1, X2, Y }.
| Item | X1 | X2 | Y |
| Data of | 3 | 4 | 5 |
| Data of | 1 | 2 | 3 |
| Mean value of | 2 | 3 |
It can be understood that, since the data properties of various data are different, for example, the moisture value of the sample material and the set temperature of the drying area have different dimensional orders, if the data are directly used for model modeling, the effect of the index with a higher value in the comprehensive analysis can be highlighted, and the effect of the index with a lower value level can be relatively weakened, so that the comprehensive results of different acting forces can not be correctly reflected, therefore, the change of the data properties of the inverse index needs to be considered first, so that the acting forces of all the indexes on the evaluation scheme are the same and chemotactic, and the data with different properties need to be standardized. Based on this, the sample data in the above table is obtained by normalizing the collected original data.
Specifically, the data can be normalized by using the following transformation function:
wherein x is the original data before standardization, including various state attribute data sets of the sample material to be dried in the operation process of the dryer, various operation attribute parameter sets of the dryer and the moisture sample set corresponding to the dried sample material, x*Mu is the mean value of the corresponding data in the sample data set, and sigma is the standard deviation of the corresponding data in the sample data set.
In the present embodiment, it is assumed that the predefined first splitting conditions are as follows:
if the feature data set X is based on the feature data set X, the feature data set X is used as a root node, and the data set with the attribute of X1 is firstly split to obtain two sets T0 and T1, where the T0 set is { X1: 3, and the set of T1 is { X1: 1, X2: {4, 2} }, calculating Δ error according to the formula, there are:
since there is only one sample in the T0 set, the result of Δ error can be obtained by substituting | T0| ═ 1 and | T1| ═ 3 in the same way as above.
And then, performing a second cycle, namely performing different splitting based on the characteristic data set X, and if the T0 set obtained this time is { X1: {3, 1} }, set of T1 is { X2: {4, 2} }, then | T0| ═ 2, | T1| ═ 2, and Δ error is also calculated according to the formula. The significance of Δ error is to determine a specific splitting point, that is, to perform different splitting on the feature data set X based on different permutation and combination, and then calculate Δ error corresponding to the splitting result, and to use the two sets corresponding to Δ error with the largest median value of all Δ error as the splitting point, so as to obtain two split sets, that is, to obtain two candidate child nodes.
And judging the split set based on a second splitting condition, and if one set in the split set meets the second splitting condition, determining the corresponding candidate child node as a leaf node. If one of the split sets does not satisfy the second splitting condition, the above steps are repeated to further split the set, and the split set is judged until all leaf nodes are determined, so that the structure of the decision tree shown in fig. 4 is obtained.
Specifically, the second splitting condition may be a threshold of the number of samples in the set, for example, when the number of samples in a certain set after splitting is less than a preset threshold, splitting of the set may be stopped, that is, the set is determined as a corresponding leaf node. The second splitting condition may also be a threshold of the number of attribute categories of the samples in the set, that is, when there are fewer attribute categories corresponding to the samples in a certain set after splitting, the splitting of the set may be stopped, that is, the set is determined as a corresponding leaf node. While a threshold for the number of sample attribute categories may be determined, in general, by the ratio of the standard deviation of the sample attribute categories in the split set to the standard deviation of the overall sample attribute categories, thereby limiting tree growth.
After the leaf nodes are determined through the steps, linear regression is further performed according to the subset of the sample data corresponding to the leaf nodes (namely, the set corresponding to the leaf nodes), so that linear regression functions corresponding to the nodes are obtained, and the establishment of the decision tree regression model is completed.
Further, in the process of splitting the set to establish the decision tree, Δ error may be further optimized, so as to avoid the situation of overall error distortion. Specifically, Δ error may be optimized based on an optimization coefficient, where the optimization coefficient is (n + v)/(n-v), where n is the number of samples in the set to be split, and v is the number of sample attribute classes in the set to be split. The optimized Δ error is Δ error × (n + v)/(n-v).
Further, when the decision tree regression model is applied to predict the moisture value of the material, a linear model corresponding to a leaf node in the tree structure is usually used for prediction, but when the tree is excessively cut or sample data for generating the tree structure is less, the whole regression is discontinuous, and based on the discontinuous linear model, prediction data of the leaf node can be optimized along a path of a root node in a smooth mode, so that the data can be fitted more accurately.
Specifically, the following formula can be adopted for smooth optimization:
taking fig. 4 as an example, if the leaf node linearmode _1 at the bottom left corner in the graph is used to predict the moisture value of the material, the output value of the linear model in linearmode _1 is directly output without smoothing optimization. The smoothing process is calculated from LinearModel _1 to the direction along the root node according to the above formula, and the non-leaf nodes are predicted based on the corresponding linear models.
Now, suppose that the set corresponding to the leaf node (parent node corresponding to the leaf nodes LinearModel _1 and LinearModel _2 in the graph) smoothed to the split point Temperature is Si, and the sample number is recorded as SiniS is a subset of Si, i.e., data used to train LinearModel _ 1. M (S)i) The predicted value of the parent node Temperature of the LinearModel _1, m(s) the predicted value of the LinearModel _1, pv(s) the value propagated to the parent node, can be calculated by the above formula, K is a constant, generally between 10 and 20, and is preferably 15. After PV (S) is calculated, the value is used as a child node of the FanRotationSpeed node and is continuously propagated to the root node until the root node stops smoothing, so that a final optimized value is obtained, and more accurate fitting data is realized.
In one embodiment, when the target control parameter is used to control the operation of the dryer, in order to avoid system oscillation caused by a large control amplitude, the operation of the dryer may be controlled in combination with a corresponding control standard range. Specifically, by obtaining a standard range corresponding to the target control parameter, for example, if the target control parameter is a target drying temperature of a certain drying zone, a pre-configured standard drying temperature range of the drying zone is obtained. And if the target control parameter is the target fan rotating speed of a certain drying area, acquiring a preset standard fan rotating speed range of the drying area. And if the target control parameter is greater than or equal to the first boundary value and less than or equal to the second boundary value, the target control parameter is adopted to control the operation of the dryer. If the target control parameter is smaller than the first boundary value, the first boundary value is used as the target control parameter to control the operation of the dryer; or, if the target control parameter is greater than the second boundary value, controlling the operation of the dryer by using the second boundary value as the target control parameter. So as to limit the target control parameter within the corresponding standard range, thereby reducing the system oscillation.
It should be understood that although the various steps in the flowcharts of fig. 1-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-4 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps or stages.
In one embodiment, as shown in fig. 5, there is provided a control apparatus of a dryer, including a data acquisition module 502, a linear regression function determination module 504, a target control parameter acquisition module 506, and a control module 508, wherein:
a data obtaining module 502, configured to obtain state attribute data of a material to be dried, a target moisture value, and an operation attribute parameter of the dryer;
a linear regression function determining module 504, configured to determine a linear regression function of the predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried, and the operation attribute parameters of the dryer;
a target control parameter obtaining module 506, configured to perform optimization solution according to the target moisture value and the linear regression function of the predicted moisture value, so as to obtain a target control parameter of the dryer;
a control module 508 for controlling the operation of the dryer using the target control parameter.
In one embodiment, the apparatus further comprises a decision tree regression model generation module configured to: continuously acquiring sample data in the operation process of the dryer according to a preset sampling period, wherein the sample data comprises various state attribute data sets of sample materials to be dried in the operation process of the dryer, various operation attribute parameter sets of the dryer and a moisture sample set corresponding to the dried sample materials; generating a decision tree based on preset splitting conditions, various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer, wherein the decision tree is provided with a plurality of leaf nodes; and establishing a linear regression function of the water sample values corresponding to the leaf nodes according to the subset of the sample data corresponding to each leaf node to obtain the decision tree regression model.
In one embodiment, the preset splitting conditions comprise a first splitting condition and a second splitting condition; the decision tree regression model generation module is further configured to: determining a root node of the decision tree based on various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer; segmenting the root node by adopting the first splitting condition to obtain a plurality of candidate sub-nodes of the decision tree and a subset of sample data corresponding to each candidate sub-node; and if the candidate child node meets the second splitting condition, determining the candidate child node as a leaf node.
In one embodiment, the decision tree regression model generation module is further configured to: if the candidate child node does not satisfy the second splitting condition, re-splitting the candidate child node based on the first splitting condition and the subset of the sample data corresponding to the candidate child node, and determining the split candidate child node as a leaf node when the split candidate child node satisfies the second splitting condition.
In one embodiment, the subset of sample data includes moisture sample values of corresponding dried sample materials, various state attribute data of corresponding to the sample materials to be dried, and various operation attribute parameters of the dryer; the decision tree regression model generation module is further configured to: according to the subset of the sample data corresponding to each leaf node, establishing a linear relation among the moisture sample values of the dried sample materials in the subset of the sample data, various state attribute data of the corresponding to-be-dried sample materials and various operation attribute parameters of the dryer; determining a linear regression function of the water sample values corresponding to the leaf nodes based on the linear relationship.
In one embodiment, the target control parameter obtaining module is specifically configured to: and solving a minimum solution of the difference between the target moisture value and the linear regression function of the predicted moisture value based on function optimization to obtain a target control parameter of the dryer, wherein the function optimization adopts any one of a genetic optimization algorithm, a local minimization optimization algorithm, a directional acceleration optimization algorithm and a gradient optimization algorithm.
In one embodiment, the control module is specifically configured to: acquiring a standard range corresponding to the target control parameter, wherein the standard range comprises a first boundary value and a second boundary value which correspond to each other; if the target control parameter is greater than or equal to the first boundary value and less than or equal to the second boundary value, controlling the dryer to operate by adopting the target control parameter; if the target control parameter is smaller than the first boundary value, the first boundary value is used as the target control parameter to control the dryer to operate; or, if the target control parameter is greater than the second boundary value, the second boundary value is used as the target control parameter to control the dryer to operate.
For specific limitations of the control device of the dryer, reference may be made to the above limitations of the control method of the dryer, which are not described herein again. The respective modules in the control device of the above dryer may be wholly or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data of the decision tree regression model. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a control method of a dryer.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 7. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a control method of a dryer. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the configurations shown in fig. 6 or 7 are merely block diagrams of some configurations relevant to the present disclosure, and do not constitute a limitation on the computing devices to which the present disclosure may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring state attribute data, a target moisture value and operation attribute parameters of a dryer of a material to be dried;
determining a linear regression function of the predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried and the operation attribute parameters of the dryer;
performing optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain a target control parameter of the dryer;
and controlling the dryer to operate by adopting the target control parameter.
In one embodiment, the processor, when executing the computer program, further performs the steps of: continuously acquiring sample data in the operation process of the dryer according to a preset sampling period, wherein the sample data comprises various state attribute data sets of sample materials to be dried in the operation process of the dryer, various operation attribute parameter sets of the dryer and a moisture sample set corresponding to the dried sample materials; generating a decision tree based on preset splitting conditions, various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer, wherein the decision tree is provided with a plurality of leaf nodes; and establishing a linear regression function of the water sample values corresponding to the leaf nodes according to the subset of the sample data corresponding to each leaf node to obtain the decision tree regression model.
In one embodiment, the preset splitting conditions comprise a first splitting condition and a second splitting condition; the processor, when executing the computer program, further performs the steps of: determining a root node of the decision tree based on various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer; segmenting the root node by adopting the first splitting condition to obtain a plurality of candidate sub-nodes of the decision tree and a subset of sample data corresponding to each candidate sub-node; and if the candidate child node meets the second splitting condition, determining the candidate child node as a leaf node.
In one embodiment, the processor, when executing the computer program, further performs the steps of: if the candidate child node does not satisfy the second splitting condition, re-splitting the candidate child node based on the first splitting condition and the subset of the sample data corresponding to the candidate child node, and determining the split candidate child node as a leaf node when the split candidate child node satisfies the second splitting condition.
In one embodiment, the subset of sample data includes moisture sample values of corresponding dried sample materials, various state attribute data of corresponding to sample materials to be dried, and various operation attribute parameters of the dryer; the processor, when executing the computer program, further performs the steps of: according to the subset of the sample data corresponding to each leaf node, establishing a linear relation among the moisture sample values of the dried sample materials in the subset of the sample data, various state attribute data of the corresponding to-be-dried sample materials and various operation attribute parameters of the dryer; determining a linear regression function of the water sample values corresponding to the leaf nodes based on the linear relationship.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and solving a minimum solution of the difference between the target moisture value and the linear regression function of the predicted moisture value based on function optimization to obtain a target control parameter of the dryer, wherein the function optimization adopts any one of a genetic optimization algorithm, a local minimization optimization algorithm, a directional acceleration optimization algorithm and a gradient optimization algorithm.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a standard range corresponding to the target control parameter, wherein the standard range comprises a first boundary value and a second boundary value which correspond to each other; if the target control parameter is greater than or equal to the first boundary value and less than or equal to the second boundary value, controlling the dryer to operate by adopting the target control parameter; if the target control parameter is smaller than the first boundary value, the first boundary value is used as the target control parameter to control the dryer to operate; or, if the target control parameter is greater than the second boundary value, the second boundary value is used as the target control parameter to control the dryer to operate.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring state attribute data, a target moisture value and operation attribute parameters of a dryer of a material to be dried;
determining a linear regression function of the predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried and the operation attribute parameters of the dryer;
performing optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain a target control parameter of the dryer;
and controlling the dryer to operate by adopting the target control parameter.
In one embodiment, the computer program when executed by the processor further performs the steps of: continuously acquiring sample data in the operation process of the dryer according to a preset sampling period, wherein the sample data comprises various state attribute data sets of sample materials to be dried in the operation process of the dryer, various operation attribute parameter sets of the dryer and a moisture sample set corresponding to the dried sample materials; generating a decision tree based on preset splitting conditions, various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer, wherein the decision tree is provided with a plurality of leaf nodes; and establishing a linear regression function of the water sample values corresponding to the leaf nodes according to the subset of the sample data corresponding to each leaf node to obtain the decision tree regression model.
In one embodiment, the preset splitting conditions comprise a first splitting condition and a second splitting condition; the computer program when executed by the processor further realizes the steps of: determining a root node of the decision tree based on various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer; segmenting the root node by adopting the first splitting condition to obtain a plurality of candidate sub-nodes of the decision tree and a subset of sample data corresponding to each candidate sub-node; and if the candidate child node meets the second splitting condition, determining the candidate child node as a leaf node.
In one embodiment, the computer program when executed by the processor further performs the steps of: if the candidate child node does not satisfy the second splitting condition, re-splitting the candidate child node based on the first splitting condition and the subset of the sample data corresponding to the candidate child node, and determining the split candidate child node as a leaf node when the split candidate child node satisfies the second splitting condition.
In one embodiment, the subset of sample data includes moisture sample values of corresponding dried sample materials, various state attribute data of corresponding to sample materials to be dried, and various operation attribute parameters of the dryer; the computer program when executed by the processor further realizes the steps of: according to the subset of the sample data corresponding to each leaf node, establishing a linear relation among the moisture sample values of the dried sample materials in the subset of the sample data, various state attribute data of the corresponding to-be-dried sample materials and various operation attribute parameters of the dryer; determining a linear regression function of the water sample values corresponding to the leaf nodes based on the linear relationship.
In one embodiment, the computer program when executed by the processor further performs the steps of: and solving a minimum solution of the difference between the target moisture value and the linear regression function of the predicted moisture value based on function optimization to obtain a target control parameter of the dryer, wherein the function optimization adopts any one of a genetic optimization algorithm, a local minimization optimization algorithm, a directional acceleration optimization algorithm and a gradient optimization algorithm.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a standard range corresponding to the target control parameter, wherein the standard range comprises a first boundary value and a second boundary value which correspond to each other; if the target control parameter is greater than or equal to the first boundary value and less than or equal to the second boundary value, controlling the dryer to operate by adopting the target control parameter; if the target control parameter is smaller than the first boundary value, the first boundary value is used as the target control parameter to control the dryer to operate; or, if the target control parameter is greater than the second boundary value, the second boundary value is used as the target control parameter to control the dryer to operate.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A control method of a dryer, characterized in that the method comprises:
acquiring state attribute data, a target moisture value and operation attribute parameters of a dryer of a material to be dried;
determining a linear regression function of the predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried and the operation attribute parameters of the dryer;
performing optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain a target control parameter of the dryer;
and controlling the dryer to operate by adopting the target control parameter.
2. The method of claim 1, wherein the decision tree regression model is constructed by:
continuously acquiring sample data in the operation process of the dryer according to a preset sampling period, wherein the sample data comprises various state attribute data sets of sample materials to be dried in the operation process of the dryer, various operation attribute parameter sets of the dryer and a moisture sample set corresponding to the dried sample materials;
generating a decision tree based on preset splitting conditions, various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer, wherein the decision tree is provided with a plurality of leaf nodes;
and establishing a linear regression function of the water sample values corresponding to the leaf nodes according to the subset of the sample data corresponding to each leaf node to obtain the decision tree regression model.
3. The method of claim 2, wherein the preset splitting conditions comprise a first splitting condition and a second splitting condition; the generating of the decision tree based on the preset splitting condition, the various state attribute data sets of the sample materials to be dried in the operation process of the dryer and the various operation attribute parameter sets of the dryer comprises the following steps:
determining a root node of the decision tree based on various state attribute data sets of sample materials to be dried in the operation process of the dryer and various operation attribute parameter sets of the dryer;
segmenting the root node by adopting the first splitting condition to obtain a plurality of candidate sub-nodes of the decision tree and a subset of sample data corresponding to each candidate sub-node;
and if the candidate child node meets the second splitting condition, determining the candidate child node as a leaf node.
4. The method of claim 3, further comprising:
if the candidate child node does not satisfy the second splitting condition, re-splitting the candidate child node based on the first splitting condition and the subset of the sample data corresponding to the candidate child node, and determining the split candidate child node as a leaf node when the split candidate child node satisfies the second splitting condition.
5. The method of claim 2, wherein the subset of sample data includes moisture sample values of corresponding dried sample material, various status attribute data of corresponding sample material to be dried, and various operational attribute parameters of the dryer; the establishing a linear regression function of the water sample values corresponding to the leaf nodes according to the subset of the sample data corresponding to each leaf node includes:
according to the subset of the sample data corresponding to each leaf node, establishing a linear relation among the moisture sample values of the dried sample materials in the subset of the sample data, various state attribute data of the corresponding to-be-dried sample materials and various operation attribute parameters of the dryer;
determining a linear regression function of the water sample values corresponding to the leaf nodes based on the linear relationship.
6. The method of claim 1, wherein said performing an optimization solution based on a linear regression function of said target moisture value and said predicted moisture value to obtain target control parameters for said dryer comprises:
and solving a minimum solution of the difference between the target moisture value and the linear regression function of the predicted moisture value based on function optimization to obtain a target control parameter of the dryer, wherein the function optimization adopts any one of a genetic optimization algorithm, a local minimization optimization algorithm, a directional acceleration optimization algorithm and a gradient optimization algorithm.
7. The method of any one of claims 1 to 6, wherein said using said target control parameter to control said dryer operation comprises:
acquiring a standard range corresponding to the target control parameter, wherein the standard range comprises a first boundary value and a second boundary value which correspond to each other;
if the target control parameter is greater than or equal to the first boundary value and less than or equal to the second boundary value, controlling the dryer to operate by adopting the target control parameter;
if the target control parameter is smaller than the first boundary value, the first boundary value is used as the target control parameter to control the dryer to operate; alternatively, the first and second electrodes may be,
and if the target control parameter is larger than the second boundary value, controlling the dryer to operate by taking the second boundary value as the target control parameter.
8. A control apparatus of a dryer, characterized in that the apparatus comprises:
the data acquisition module is used for acquiring state attribute data of the material to be dried, a target moisture value and operation attribute parameters of the dryer;
the linear regression function determining module is used for determining a linear regression function of the predicted moisture value of the material to be dried based on a preset decision tree regression model, the state attribute data of the material to be dried and the operation attribute parameters of the dryer;
the target control parameter acquisition module is used for carrying out optimization solution according to the target moisture value and the linear regression function of the predicted moisture value to obtain target control parameters of the dryer;
and the control module is used for controlling the dryer to operate by adopting the target control parameters.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113722908A (en) * | 2021-08-30 | 2021-11-30 | 湖南工商大学 | Textile drying time judgment method based on multiple nonlinear regression |
| CN113741402A (en) * | 2021-09-23 | 2021-12-03 | 广东电网有限责任公司 | Equipment control method and device, computer equipment and storage medium |
| CN113945087A (en) * | 2021-10-15 | 2022-01-18 | 青岛海尔空调电子有限公司 | Method and device for dehumidification, electronic equipment and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113722908A (en) * | 2021-08-30 | 2021-11-30 | 湖南工商大学 | Textile drying time judgment method based on multiple nonlinear regression |
| CN113722908B (en) * | 2021-08-30 | 2022-04-29 | 湖南工商大学 | Textile drying time judgment method based on multiple nonlinear regression |
| CN113741402A (en) * | 2021-09-23 | 2021-12-03 | 广东电网有限责任公司 | Equipment control method and device, computer equipment and storage medium |
| CN113945087A (en) * | 2021-10-15 | 2022-01-18 | 青岛海尔空调电子有限公司 | Method and device for dehumidification, electronic equipment and storage medium |
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