CN116227367B - Back pressure prediction model construction method, back pressure prediction method and back pressure prediction device of direct air cooling system - Google Patents
Back pressure prediction model construction method, back pressure prediction method and back pressure prediction device of direct air cooling system Download PDFInfo
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Abstract
The invention relates to the technical field of air cooling power generation, and discloses a back pressure prediction model construction method, a back pressure prediction method and a back pressure prediction device of a direct air cooling system. The method comprises the steps of obtaining historical operation data of target parameters of a target direct air cooling system and preprocessing the historical operation data; constructing a sparse LS-SVR model by adopting a sparse LS-SVR algorithm based on dictionary learning; randomly extracting part of data from the preprocessed data as a training sample to train the sparse LS-SVR model, taking the average relative error between the predicted value and the actual value of the data in the sliding window as a model updating index, and updating the training sample to re-train the model when the average relative error is greater than a preset model updating threshold value so as to complete the updating of the backpressure prediction model; and the back pressure value is predicted based on the currently obtained back pressure prediction model. The prediction model provided by the invention can avoid larger errors under the influence of the operating condition and environmental changes.
Description
Technical Field
The invention relates to the technical field of air cooling power generation, in particular to a back pressure prediction model construction method, a back pressure prediction method and a back pressure prediction device of a direct air cooling system.
Background
Air cooling systems of direct air-cooled thermal power plants generally use air as a cooling medium for condensers, and direct air cooling refers to condensing turbine exhaust directly from air provided by mechanical ventilation. When the exhaust pressure of the low pressure cylinder of the steam turbine (i.e., the back pressure of the steam turbine) rises, the load of the corresponding unit is reduced. Therefore, if the back pressure can be reasonably predicted, the back pressure optimizing control system is beneficial to providing accurate operation guidance for operators and effective real-time information for the back pressure optimizing control strategy, so that the performance of a direct air cooling system and the operation economy of the cold end of the thermal power unit are effectively improved.
In the prior art, a data-driven backpressure prediction model is constructed based on an artificial intelligence algorithm, and the backpressure prediction is realized through the model. The back pressure prediction model can be established to provide accurate and quick operation guidance for operators, but timeliness of the model is not considered in the existing research. The back pressure of the steam turbine in the direct air cooling system is easily influenced by the operation working condition and environmental change, and when a new working condition occurs, the existing back pressure prediction model is easy to have larger error.
Disclosure of Invention
The invention provides a method for constructing a back pressure prediction model of a direct air cooling system, a prediction method and a device thereof, which solve the technical problem that the back pressure prediction model of the existing direct air cooling system is easy to have larger error under the influence of the operation condition and environmental change.
The first aspect of the invention provides a back pressure prediction model construction method of a direct air cooling system, comprising the following steps:
acquiring historical operation data of target parameters of a target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
preprocessing the historical operation data to obtain preprocessed data;
constructing a sparse LS-SVR model by adopting a dictionary learning-based sparse LS-SVR (least squares support vector regression) algorithm; the sparse LS-SVR model takes the target parameter as an input variable and takes the back pressure of the target direct air cooling system as an output variable;
randomly extracting part of data from the preprocessed data as a training sample to train the sparse LS-SVR model, so as to obtain a backpressure prediction model of the target direct air cooling system;
determining a predicted value of the training sample based on the backpressure prediction model, and calculating an average relative error between the predicted value and an actual value of the training sample;
and updating a training sample when the average relative error is greater than a preset model updating threshold value, and re-performing model training based on the updated training sample so as to update the backpressure prediction model.
According to one implementation manner of the first aspect of the present invention, the preprocessing the historical operation data includes:
removing outliers in the historical operation data;
removing unsteady state data in the historical operation data;
and/or, carrying out data standardization processing on the historical operation data.
According to one implementation manner of the first aspect of the present invention, the removing the outliers in the historical operating data includes:
calculating the local density value and the dispersion of each data point in the historical operation data; the dispersion is a distance value from a corresponding data point to a nearest data point with higher local density;
calculating the product of the local density value and the dispersion of the data points as a global representative index of the corresponding data points;
and when the global representative index is lower than a preset outlier screening threshold, removing the corresponding data point as an outlier.
According to one implementation manner of the first aspect of the present invention, the removing unsteady state data in the historical operating data includes:
intercepting window data corresponding to preset important parameters serving as steady-state screening representation from the historical operation data by adopting a first preset sliding window, and calculating standard deviation of the intercepted window data; the preset important parameters comprise unit load and main steam flow;
And if the standard deviation is smaller than a preset steady-state working condition screening threshold value, removing the corresponding window data as unsteady-state data.
According to one implementation manner of the first aspect of the present invention, during the training process of the sparse LS-SVR model, searching is performed on the optimal kernel function subscript of the sparse LS-SVR model in a random subset of the given kernel function subscript set;
the number of samples of the random subset is calculated according to the following equation:
；
in the method, in the process of the invention,sample number representing random subset, +.>For the relative error of the model, +.>For the maximum number of samples of said random subset, < >>The value of (2) is the number of samples of the kernel index set,/I>A minimum value of the number of samples that is the random subset;
wherein the minimum value of the number of samples of the random subset is based on a given probability valueSum function estimate +.>Performing calculation if the value is +.>The probability obtained value of (2) is +.>Function estimation of>。
According to one implementation manner of the first aspect of the present invention, the updating the training samples when the average relative error is greater than a preset model update threshold value includes:
when the average relative error is larger than a preset model updating threshold value, sample data reflecting a new operation condition is intercepted from the training sample based on a second preset sliding window to serve as first new sample data;
Screening historical sample data corresponding to preset main parameters from historical training samples; the preset main parameters comprise ambient temperature and ambient wind speed;
calculating the distance between each screened historical sample data and the first new sample data of the corresponding parameter type, and taking the historical sample data meeting the preset distance condition as second new sample data;
and constructing a new training sample based on the first new sample data and the second new sample data, thereby completing updating of the training sample.
According to one implementation manner of the first aspect of the present invention, the preset distance condition is:
；
in the method, in the process of the invention,indicating the selected->Historical sample data->Is->First new sample data of corresponding parameter type, < ->And screening a threshold value for the preset distance.
The second aspect of the present invention provides a back pressure prediction model construction apparatus for a direct air cooling system, including:
the first acquisition module is used for acquiring historical operation data of target parameters of the target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
The preprocessing module is used for preprocessing the historical operation data to obtain preprocessed data;
the construction module is used for constructing a sparse LS-SVR model by adopting a sparse LS-SVR algorithm based on dictionary learning; the sparse LS-SVR model takes the target parameter as an input variable and takes the back pressure of the target direct air cooling system as an output variable;
the training module is used for randomly extracting part of data from the preprocessed data as a training sample to train the sparse LS-SVR model, so as to obtain a backpressure prediction model of the target direct air cooling system;
the calculation module is used for determining a predicted value of the training sample based on the backpressure prediction model and calculating an average relative error between the predicted value and an actual value of the training sample;
and the updating module is used for updating the training sample when the average relative error is larger than a preset model updating threshold value, and re-carrying out model training based on the updated training sample so as to update the backpressure prediction model.
According to one manner in which the second aspect of the present invention can be implemented, the preprocessing module includes:
the first preprocessing unit is used for removing outliers in the historical operation data;
The second preprocessing unit is used for removing unsteady state data in the historical operation data;
and/or a third preprocessing unit, configured to perform data normalization processing on the historical operation data.
According to one possible manner of the second aspect of the present invention, the first preprocessing unit is specifically configured to:
calculating the local density value and the dispersion of each data point in the historical operation data; the dispersion is a distance value from a corresponding data point to a nearest data point with higher local density;
calculating the product of the local density value and the dispersion of the data points as a global representative index of the corresponding data points;
and when the global representative index is lower than a preset outlier screening threshold, removing the corresponding data point as an outlier.
According to one possible manner of the second aspect of the present invention, the second preprocessing unit is specifically configured to:
the second aspect of the present invention provides a back pressure prediction model construction apparatus for a direct air cooling system, including:
the first acquisition module is used for acquiring historical operation data of target parameters of the target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
The preprocessing module is used for preprocessing the historical operation data to obtain preprocessed data;
the construction module is used for constructing a sparse LS-SVR model by adopting a sparse LS-SVR algorithm based on dictionary learning; the sparse LS-SVR model takes the target parameter as an input variable and takes the back pressure of the target direct air cooling system as an output variable;
the training module is used for randomly extracting part of data from the preprocessed data as a training sample to train the sparse LS-SVR model, so as to obtain a backpressure prediction model of the target direct air cooling system;
the calculation module is used for determining a predicted value of the training sample based on the backpressure prediction model and calculating an average relative error between the predicted value and an actual value of the training sample;
and the updating module is used for updating the training sample when the average relative error is larger than a preset model updating threshold value, and re-carrying out model training based on the updated training sample so as to update the backpressure prediction model.
According to one manner in which the second aspect of the present invention can be implemented, the preprocessing module includes:
the first preprocessing unit is used for removing outliers in the historical operation data;
The second preprocessing unit is used for removing unsteady state data in the historical operation data;
and/or a third preprocessing unit, configured to perform data normalization processing on the historical operation data.
According to one possible manner of the second aspect of the present invention, the first preprocessing unit is specifically configured to:
calculating the local density value and the dispersion of each data point in the historical operation data; the dispersion is a distance value from a corresponding data point to a nearest data point with higher local density;
calculating the product of the local density value and the dispersion of the data points as a global representative index of the corresponding data points;
and when the global representative index is lower than a preset outlier screening threshold, removing the corresponding data point as an outlier.
According to one possible manner of the second aspect of the present invention, the second preprocessing unit is specifically configured to:
；
in the method, in the process of the invention,sample number representing random subset, +.>For the relative error of the model, +.>For the maximum number of samples of said random subset, < >>The value of (2) is the number of samples of the kernel index set,/I>A minimum value of the number of samples that is the random subset;
wherein the minimum value of the number of samples of the random subset is based on a given probability value Sum function estimate +.>Performing calculation if the value is +.>The probability obtained value of (2) is +.>Function estimation of>。
According to one manner of implementation of the second aspect of the present invention, the updating module includes:
the first screening unit is used for intercepting sample data reflecting new operation conditions from the training samples based on a second preset sliding window to serve as first new sample data when the average relative error is larger than a preset model updating threshold value;
the second screening unit is used for screening historical sample data corresponding to preset main parameters from historical training samples; the preset main parameters comprise ambient temperature and ambient wind speed;
the calculation unit is used for calculating the distance between each screened historical sample data and the first new sample data of the corresponding parameter type, and taking the historical sample data meeting the preset distance condition as the second new sample data;
and the construction unit is used for constructing a new training sample based on the first new sample data and the second new sample data so as to finish updating the training sample.
According to one manner of implementation of the second aspect of the present invention, the preset distance condition is:
；
in the method, in the process of the invention,indicating the selected- >Historical sample data->Is->First new sample data of corresponding parameter type, < ->And screening a threshold value for the preset distance.
The third aspect of the present invention provides a back pressure prediction method for a direct air cooling system, including:
acquiring real-time operation data of target parameters of a target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
inputting the real-time operation data into a current back pressure prediction model to obtain a predicted back pressure value; the current back pressure prediction model is constructed according to the back pressure prediction model construction method of the direct air cooling system in any mode.
A fourth aspect of the present invention provides a back pressure prediction apparatus for a direct air cooling system, comprising:
the second acquisition module is used for acquiring real-time operation data of target parameters of the target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
the prediction module is used for inputting the real-time operation data into a current back pressure prediction model to obtain a predicted back pressure value; the current back pressure prediction model is constructed according to the back pressure prediction model construction method of the direct air cooling system in any mode.
A fifth aspect of the present invention provides an electronic device, comprising:
a memory for storing instructions; the instruction is used for realizing the method for constructing the back pressure prediction model of the direct air cooling system in the mode capable of being realized in any one of the above modes, or the instruction is used for realizing the method for predicting the back pressure of the direct air cooling system in the mode;
and the processor is used for executing the instructions in the memory.
A sixth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the method for constructing a back pressure prediction model of a direct air-cooling system according to any one of the modes described above, or that, when executed by a processor, implements the method for predicting back pressure of a direct air-cooling system as described above.
From the above technical scheme, the invention has the following advantages:
the method comprises the steps of obtaining historical operation data of target parameters of a target direct air cooling system, and preprocessing the historical operation data; constructing a sparse LS-SVR model by adopting a sparse LS-SVR algorithm based on dictionary learning; intercepting corresponding window data from the obtained preprocessed data by adopting a second preset sliding window, taking the intercepted window data as a training sample to train the sparse LS-SVR model to obtain a backpressure prediction model, determining a predicted value of the training sample based on the backpressure prediction model, calculating an average relative error between the predicted value and an actual value of the training sample, updating the training sample when the average relative error is greater than a preset model updating threshold value, and carrying out model training again based on the updated training sample to realize updating of the backpressure prediction model; the invention also realizes the prediction of the back pressure value based on the back pressure prediction model obtained by the construction method; in order to avoid failure of a trained data model due to working condition change, the invention adopts average relative error as an index of model update, and retrains the model through a new training sample when the average relative error is larger than a preset model update threshold value, thereby completing the update of the model under the new working condition, considering the timeliness of the model, being beneficial to enhancing the capability of the back pressure prediction model to adapt to different working conditions and ensuring the accuracy of a prediction result, and further solving the technical problem that the back pressure prediction model of the existing direct air cooling system is easy to have larger error under the influence of the operation working condition and environmental change.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for constructing a back pressure prediction model of a direct air cooling system according to an alternative embodiment of the present invention;
FIG. 2 is a block diagram illustrating the structural connection of a back pressure prediction model building device for a direct air cooling system according to an alternative embodiment of the present invention;
FIG. 3 shows a flowchart of a method for predicting back pressure of a direct air cooling system according to an embodiment of the present invention;
fig. 4 shows a block diagram of structural connection of a back pressure prediction device of a direct air cooling system according to an embodiment of the present invention.
Reference numerals:
1-a first acquisition module; 2-a pretreatment module; 3-building a module; 4-a training module; 5-a calculation module; 6-updating the module; 10-a second acquisition module; 20-prediction module.
Detailed Description
The embodiment of the invention provides a method for constructing a back pressure prediction model of a direct air cooling system, a prediction method and a device thereof, which are used for solving the technical problem that the back pressure prediction model of the existing direct air cooling system is easy to have larger errors under the influence of the operation working condition and environmental change.
In order to make the objects, features and advantages of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a back pressure prediction model construction method of a direct air cooling system.
Referring to fig. 1, fig. 1 shows a flowchart of a method for constructing a back pressure prediction model of a direct air cooling system according to an embodiment of the present invention.
The back pressure prediction model construction method of the direct air cooling system provided by the embodiment of the invention comprises the steps S1-S6.
Step S1, acquiring historical operation data of target parameters of a target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure.
The unit load, the main steam flow, the main steam pressure and the reheating temperature indirectly reflect the heat release amount of the steam, the fan rotating speed, the ambient wind speed and the wind direction reflect the heat release amount of the direct air cooling system, the ambient temperature is a key parameter affecting the heat exchange effect of the direct air cooling system, and the atmospheric pressure reflects the influence of air humidity, air temperature change and other air physical parameters. In the embodiment, the fan rotation speed, the ambient temperature, the ambient wind speed, the ambient wind direction, the unit load, the main steam flow, the main steam pressure, the reheating temperature and the atmospheric pressure are used as target parameters of the target direct air cooling system, and historical operation data of each target parameter are acquired for the subsequent model modeling step, so that the accuracy and the authenticity of the constructed model are guaranteed.
And step S2, preprocessing the historical operation data to obtain preprocessed data.
In one implementation, the preprocessing the historical operating data includes:
Removing outliers in the historical operation data;
removing unsteady state data in the historical operation data;
and/or, carrying out data standardization processing on the historical operation data.
The purpose of outlier detection is to find outliers that deviate significantly from most of the process data, and then reject or supplement the outliers. Common causes of outliers in industrial process data are electrical disturbances or short-time failures of sensors, which are often rejected as noise. The outlier detection method is many, and common methods include outlier detection based on statistical methods, distance, density and cluster analysis. In one implementation, the removing outliers in the historical operating data includes:
calculating the local density value and the dispersion of each data point in the historical operation data; the dispersion is a distance value from a corresponding data point to a nearest data point with higher local density;
calculating the product of the local density value and the dispersion of the data points as a global representative index of the corresponding data points;
and when the global representative index is lower than a preset outlier screening threshold, removing the corresponding data point as an outlier.
As a specific embodiment, givenmPersonal (S)nDimension sampleThen calculate +.>In the neighborhood radius->The local density in is:
；
in the method, in the process of the invention,is data point->To->European distance,/, of->Is a mapping function.
Wherein, the liquid crystal display device comprises a liquid crystal display device,the method meets the following conditions: />。
In the embodiment, the outlier screening method is a clustering method based on density, and has the advantages that two indexes of local density and dispersion are adopted to represent local characteristics and global characteristics of data respectively, so that the problem that selection of a neighborhood radius in the outlier detection method based on density is difficult is solved, and the number of clustering clusters is not required to be specified.
The DCS historical database of the thermal power plant contains all data during the running period of the unit, wherein the data of the excessive states such as start-stop and variable working conditions of the unit and the like are also data in stable running. The application aims to establish a data driving model of a direct air cooling system under a steady-state working condition so as to further improve the system operation efficiency, so that steady-state working condition screening is required for operation data. In actual operation, the state variable is maintained for a period of time and can be regarded as a stable state. In the thermal power plant, when the fluctuation of important variables such as unit load, main steam temperature or main steam flow is smaller than a certain range, the thermal power plant can be considered to be in steady-state operation, and unnecessary unsteady-state operation data can be removed through a sliding window method.
In one implementation, the removing unsteady state data from the historical operating data includes:
intercepting window data corresponding to preset important parameters serving as steady-state screening representation from the historical operation data by adopting a first preset sliding window, and calculating standard deviation of the intercepted window data; the preset important parameters comprise unit load and main steam flow;
and if the standard deviation is smaller than a preset steady-state working condition screening threshold value, removing the corresponding window data as unsteady-state data.
As a specific embodiment, the standard deviation is calculated using the following formula:
；
in the method, in the process of the invention,represents standard deviation->Corresponding to the preset important parameter as steady state screening representation +.>Window data of time,>to average the window data taken out,N for the length of the first preset sliding window, +.>Indicating the current time.
By reasonably setting parametersN Andsteady state data in the operational data may be obtained.
The purpose of data normalization is to convert sampled data into dimensionless pure values, because by machine learning modeling, large-valued variables can impair the effect of small-valued variables, and the use of data with larger dimensional differences for modeling can reduce the accuracy of the model. As an embodiment, Z-score normalization may be used, with the data being normalized using the sample mean and standard deviation. The normalized data conforms to a standard normal distribution:
；
In the method, in the process of the invention,mean value of samples>The standard deviation of the samples is shown.
S3, constructing a sparse LS-SVR model by adopting a sparse LS-SVR algorithm based on dictionary learning; the sparse LS-SVR model takes the target parameter as an input variable and takes the back pressure of the target direct air cooling system as an output variable.
In the embodiment, in combination with the actual conditions of the direct air cooling system model and the field measuring point, the following 9 variables are selected as input variables of the sparse LS-SVR model: fan rotation speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheat temperature, atmospheric pressure, and the output variable is back pressure. Thus, the backpressure of the direct air cooling system can be expressed as a function of:
；
in the method, in the process of the invention,indicating back pressure->Represents the rotation speed (r/min) of the fan and is +.>Is the ambient temperature (in c),is the ambient wind speed (in m/s),>is the ambient wind direction (in degrees)>For the unit load (unit is MW),>is the main steam flow (unit is t/h),>is the main steam pressure (unit is MPa), +.>Is reheat air temperature (in degrees Celsius), the temperature is determined to be greater than the temperature>Is atmospheric pressure (in kPa).
The least square support vector machine is an improved supervised learning algorithm based on the support vector machine, and can be applied to the problems of data classification, regression and the like. LS-SVR converts the convex quadratic programming problem of SVR into solving linear equation set by equation constraint, and is widely applied to nonlinear system modeling in the engineering field due to the characteristics of good generalization performance, low computational complexity and the like. However, in contrast to the SVR algorithm, LS-SVR is a support vector for each data, and almost all Lagrangian multipliers are non-zero, losing the sparseness of SVR. The non-sparsity of the LS-SVR model results in increased computational complexity, limiting its further application.
In the research of sparse LS-SVR, the main idea is to form a sparse support vector set based on an iterative process, and the method is divided into two methods of deletion and increment. Sparse LS-SVR can be seen as a process of extracting data points and computing an approximate regression function. In the embodiment of the invention, a sparse LS-SVR algorithm based on dictionary learning is adopted to establish an approximate regression function model.
To better illustrate the modeling process of the present application, the LS-SVR algorithm is described in detail below.
Given data setThe regression model of the LS-SVR algorithm can be expressed as:
；
In the method, in the process of the invention,for the parameter vector +.>To map the original data to a non-linear function of the high-dimensional space +.>Is biased.
Based on structural risk minimization criteria, the LS-SVR algorithm can be transformed to solve the following optimization problem:
；
in the method, in the process of the invention,indicating error, & lt>Namely +.>Error of individual data->Representing a penalty factor.
A lagrangian function was introduced:
；
in the method, in the process of the invention,represents the Lagrangian multiplier, +.>Indicate->Lagrangian multipliers, and +.>。
Depending on the KKT condition (karlo-coul-tak condition), the optimization problem can be converted into the following dual problem:
；
in the method, in the process of the invention,indicate->Lagrangian multiplier>Represents a kernel function, and->。
The gaussian radial basis function is one of the most commonly used kernel functions, and its expression is:
；
in the method, in the process of the invention,representing the core parameters.
Thus, for dataThe predicted value +.can be calculated by the LS-SVR function as follows>：
；
As a specific embodiment, parametersAnd->Can be obtained by solving the dual problem, and the nuclear parameter and the penalty factor are combined by a grid search methodkAnd (5) determining by a fold cross validation method. The grid search method is an enumeration method by being in a given parameter spaceA given combination of parameters is traversed to optimize the model. In order to improve the searching efficiency, a power of 2 is often adopted as a coordinate sequence to search. kThe fold-cross validation method divides training data into layers by hierarchical samplingkMutually exclusive subsets, each time employing thereink-1 subset as training set and the remaining 1 subset as test set. Execution ofkAfter the time, the time point is that,kthe average of the individual test results was used as a performance evaluation index. The set of parameters with the lowest average error of model training is the optimal parameters of the LS-SVR model.
The sparse LS-SVR algorithm (i.e., S-LS-SVR) is described in detail below.
Dictionary learning is one of the ways data is sparse. Dictionary learning finds a proper dictionary by learning data expressed densely, and converts the data into a proper sparse representation form, so that the complexity of a model is reduced, and the learning difficulty is reduced. S-LS-SVR is a greedy algorithm whose main idea is to add one basis function at a time from a set of kernel functions according to a certain rule to form a dictionary and to iteratively solve the approximate regression function.
Given a set of kernel functionsFrom a empty set->And complete->Initially, the S-LS-SVR selects one kernel at a time from the set of kernels and subscribes it from the setPMove to collectionQIn solving the setPAn approximate objective function is formed for the kernel function of the subscript until the stop condition is satisfied.
The above dual problem can be rewritten in the following matrix form:
；
in the method, in the process of the invention,，/>is a unitary matrix->，/>，。
Assuming that it has passednObtaining Lagrangian multiplier by multiple iterationsAnd bias->S-LS-SVR algorithm No.nThe approximate objective function for +1 iterations is:
；
；
in the method, in the process of the invention,，/>，/>representing a penalty factor.
The above can be simplified as:
；
in the method, in the process of the invention,the kernel function sequence number corresponding to the minimum value of the objective function can be calculated as follows:
；
order theThe following formula is applied to calculate +.>：
；
In the method, in the process of the invention,，/>。
nth (n)+1 iterations to obtain Lagrangian multiplierAnd->The method can be calculated by the following formula: />
；
The stopping conditions for the iteration are:
；
in the method, in the process of the invention,is the iteration termination threshold, ++>。
And S4, randomly extracting part of data from the preprocessed data as a training sample to train the sparse LS-SVR model, so as to obtain a backpressure prediction model of the target direct air cooling system.
As an implementation manner, the extraction ratio may be set so that corresponding data is randomly extracted from the preprocessed data as training samples based on a preset extraction ratio. As an example, the preset extraction ratio is 90%, and from the data obtained after preprocessing, 90% of the data is randomly selected as a training sample of the sparse LS-SVR model, and the remaining 10% of the data can be used as test values to construct a test sample.
In this embodiment, model training is an iterative process, and each step randomly extracts a subset from the preprocessed dataset to iterate until a given threshold is reached, and model training is completed.
In one implementation, during the training of the sparse LS-SVR model, the optimal kernel function index of the sparse LS-SVR model is searched in a random subset of a given set of kernel function indices;
the number of samples of the random subset is calculated according to the following equation:
；
in the method, in the process of the invention,sample number representing random subset, +.>For the relative error of the model, +.>For the maximum number of samples of said random subset, < >>The value of (2) is the number of samples of the kernel index set,/I>A minimum value of the number of samples that is the random subset;
wherein the minimum value of the number of samples of the random subset is based on a given probability valueSum function estimate +.>Performing calculation if the value is +.>The probability obtained value of (2) is +.>Function estimation of>。
One computational bottleneck of the sparse LS-SVR algorithm isIs updated according to the update of the update program. In order to reduce the amount of calculation, in the present embodiment, in the aggregationQRandom subset of (a)MInstead of searching through the whole set, a relatively good kernel index is obtained QSearching for the optimal kernel function index. The expected model effect is to search in a random subset with a larger sample number at the beginning of the algorithm, reduce the sample number of the random subset as the relative error of the model is reduced, and set a lowest random subset sample number according to probability so as to reduce the calculation amount of the model under the condition of ensuring accuracy.
The selection basis is from the quotients: set independent random variable ∈ ->Is +.>Then->Is +.>. For uniform distribution +.>，/>Is +.>. If it is desired to obtain the best 2.5% estimate with a probability of 97.5%, only one size of +.>Is a random subset of (a). The flow of the sparse LS-SVR algorithm (i.e., PS-LS-SVR algorithm) modified based on this example is shown in Table 1.
In the embodiment, the sparse LS-SVR algorithm is improved, and the sparse LS-SVR model is constructed and trained based on the improved sparse LS-SVR algorithm, so that the calculation amount of the model is reduced under the condition that the accuracy is ensured.
And S5, determining a predicted value of the training sample based on the backpressure prediction model, and calculating an average relative error between the predicted value and the actual value of the training sample.
Wherein, the calculation formula of the average relative error is:
；
In the method, in the process of the invention,mean relative error +.>Representing the actual value +.>The predicted value is represented by a value of the prediction,Nrepresenting the width of the second preset sliding window.
And S6, updating a training sample when the average relative error is larger than a preset model updating threshold value, and re-performing model training based on the updated training sample so as to update the back pressure prediction model.
When the unit operation condition or environmental factors change, the accuracy of LS-SVR model prediction can be greatly affected. In order to avoid failure of a trained data model due to working condition changes, in the embodiment of the invention, a model updating strategy is provided on the basis of a PS-LS-SVR algorithm, so that the capability of adapting to different working conditions of the data model is enhanced, and the accuracy of a prediction result is ensured.
In this embodiment, the moving average relative error is used as an index of model update, and when the average relative error in the sliding window is greater than the model update threshold, the data model is updated offline. Because the PS-LS-SVR is an iterative data model, only training samples are added and iteration is carried out on the basis of the original model until the model meets the iteration termination threshold. And then testing the model by adopting new measurement data until the model update index is smaller than the update threshold value, thereby completing model update. The key to the model update strategy is the method of selecting the newly added training samples. There are two methods of selecting the new training samples, one is to add the current new sample to the training samples, and the other is to screen from the history samples.
The first method is to directly add new samples within the second preset sliding window to the model training samples when the sliding average relative error is greater than the model update threshold. This method has the advantage of simplicity and as the system operates, new operating conditions may be included in the new sample. The method has the defects that the model updating speed is low, the data volume in one sliding window is small, the model updating index is not enough to be quickly reduced, and the model is required to be iterated continuously when a plurality of sliding windows are accumulated, so that the calculation performance of the system is high.
The second method is to screen the newly added training samples by the distance between the history sample and the new sample. When the distance between the history sample and the new sample is smaller than the distance screening threshold value, the history sample is selected as the training sample for the new addition.
In combination with the above two methods, in one implementation manner, the updating the training samples when the average relative error is greater than a preset model update threshold includes:
when the average relative error is larger than a preset model updating threshold value, sample data reflecting a new operation condition is intercepted from the training sample based on a second preset sliding window to serve as first new sample data;
Screening historical sample data corresponding to preset main parameters from historical training samples; the preset main parameters comprise ambient temperature and ambient wind speed;
calculating the distance between each screened historical sample data and the first new sample data of the corresponding parameter type, and taking the historical sample data meeting the preset distance condition as second new sample data;
and constructing a new training sample based on the first new sample data and the second new sample data, thereby completing updating of the training sample.
In one implementation, the preset distance condition is:
；
in the method, in the process of the invention,indicating the selected->Historical sample data->Is->First new sample data of corresponding parameter type, < ->And screening a threshold value for the preset distance.
The method for screening the new samples through the historical samples has the advantages that a large number of training samples similar to the working condition of the new samples can be selected for random search of the PS-LS-SVR data model, and the method has the disadvantages of a certain calculated amount and difficult selection of a distance screening threshold value. In this embodiment, a part of parameters of the backpressure prediction model are selected to calculate the distance between samples, so that the calculated amount can be effectively reduced. In this embodiment, the selected preset main parameters include the ambient temperature and the ambient wind speed, because these two parameters are main external parameters affecting the direct air cooling system, and the numerical distribution thereof has a certain rule.
The selection basis of the distance screening threshold value is that the capacity of the newly added sample is enough for the model to quickly reach the iteration stop condition, so that the distance screening threshold value can be set according to the sample capacity and the actual condition of the iteration termination threshold value.
In the embodiment of the invention, the adopted model newly added sample selection strategy combines the two methods, and when the average relative error is larger than the model updating threshold value, a new sample in a second preset sliding window and an approximate sample screened from the historical samples are added to be used as training samples for model iteration, so that the calculated amount can be reduced, and the model updating speed can be effectively improved.
According to the embodiment of the invention, the back pressure prediction model of the direct air cooling system which can be updated in real time is established aiming at the problem that the mechanism model of the direct air cooling system has deviation under the influence of equipment aging, environmental parameters and the like, so that the back pressure of the turbine under different environmental temperatures and operating conditions is predicted, the timeliness of the data model is considered, the capability of the back pressure prediction model for adapting to different operating conditions is enhanced, the accuracy of a prediction result is ensured, and the technical problem that the back pressure prediction model of the traditional direct air cooling system is easy to have larger error under the influence of the operating conditions and environmental changes is solved; the method has the advantages of good sparsity, high accuracy, high calculation speed and real-time updating, can provide accurate operation guidance for operators, provides effective real-time information for backpressure optimization control strategies, and effectively improves the economy of the cold end system of the thermal power unit.
The invention also provides a back pressure prediction model construction device of the direct air cooling system, which can be used for executing the back pressure prediction model construction method of the direct air cooling system.
Referring to fig. 2, fig. 2 is a block diagram illustrating structural connection of a back pressure prediction model building device of a direct air cooling system according to an embodiment of the present invention.
The back pressure prediction model construction device of the direct air cooling system provided by the embodiment of the invention comprises:
the first acquisition module 1 is used for acquiring historical operation data of target parameters of the target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
the preprocessing module 2 is used for preprocessing the historical operation data to obtain preprocessed data;
the construction module 3 is used for constructing a sparse LS-SVR model by adopting a sparse LS-SVR algorithm based on dictionary learning; the sparse LS-SVR model takes the target parameter as an input variable and takes the back pressure of the target direct air cooling system as an output variable;
The training module 4 is used for randomly extracting part of data from the preprocessed data as a training sample to train the sparse LS-SVR model, so as to obtain a backpressure prediction model of the target direct air cooling system;
a calculation module 5, configured to determine a predicted value of the training sample based on the backpressure prediction model, and calculate an average relative error between the predicted value and an actual value of the training sample;
and the updating module 6 is used for updating the training sample when the average relative error is larger than a preset model updating threshold value, and re-performing model training based on the updated training sample so as to update the backpressure prediction model.
In one possible implementation, the preprocessing module 2 includes:
the first preprocessing unit is used for removing outliers in the historical operation data;
the second preprocessing unit is used for removing unsteady state data in the historical operation data;
and/or a third preprocessing unit, configured to perform data normalization processing on the historical operation data.
In one implementation manner, the first preprocessing unit is specifically configured to:
calculating the local density value and the dispersion of each data point in the historical operation data; the dispersion is a distance value from a corresponding data point to a nearest data point with higher local density;
Calculating the product of the local density value and the dispersion of the data points as a global representative index of the corresponding data points;
and when the global representative index is lower than a preset outlier screening threshold, removing the corresponding data point as an outlier.
In one possible implementation, the second preprocessing unit is specifically configured to:
intercepting window data corresponding to preset important parameters serving as steady-state screening representation from the historical operation data by adopting a first preset sliding window, and calculating standard deviation of the intercepted window data; the preset important parameters comprise unit load and main steam flow;
and if the standard deviation is smaller than a preset steady-state working condition screening threshold value, removing the corresponding window data as unsteady-state data.
In one implementation, during the training of the sparse LS-SVR model, the optimal kernel function index of the sparse LS-SVR model is searched in a random subset of a given set of kernel function indices;
the number of samples of the random subset is calculated according to the following equation:
；
in the method, in the process of the invention,sample number representing random subset, +.>For the relative error of the model, +.>For the maximum number of samples of said random subset, < > >The value of (2) is the number of samples of the kernel index set,/I>A minimum value of the number of samples that is the random subset;
wherein the minimum value of the number of samples of the random subset is based on a given probability valueSum function estimate +.>Performing calculation if the value is +.>The probability obtained value of (2) is +.>Function estimation of>。
In one possible implementation, the updating module 6 comprises:
the first screening unit is used for intercepting sample data reflecting new operation conditions from the training samples based on a second preset sliding window to serve as first new sample data when the average relative error is larger than a preset model updating threshold value;
the second screening unit is used for screening historical sample data corresponding to preset main parameters from historical training samples; the preset main parameters comprise ambient temperature and ambient wind speed;
the calculation unit is used for calculating the distance between each screened historical sample data and the first new sample data of the corresponding parameter type, and taking the historical sample data meeting the preset distance condition as the second new sample data;
and the construction unit is used for constructing a new training sample based on the first new sample data and the second new sample data so as to finish updating the training sample.
In one implementation, the preset distance condition is:
；
in the method, in the process of the invention,indicating the selected->Historical sample data->Is->First new sample data of corresponding parameter type, < ->And screening a threshold value for the preset distance.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working processes of the above-described apparatus, modules and units may refer to corresponding processes in the foregoing method embodiments, and specific beneficial effects of the above-described apparatus, modules and units may refer to corresponding beneficial effects in the foregoing method embodiments, which are not repeated herein.
The invention also provides a back pressure prediction method of the direct air cooling system.
Referring to fig. 3, fig. 3 shows a flowchart of a back pressure prediction method of a direct air cooling system according to an embodiment of the present invention.
The back pressure prediction method of the direct air cooling system provided by the embodiment of the invention comprises the following steps:
step S10, acquiring real-time operation data of target parameters of a target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
Step S20, inputting the real-time operation data into a current back pressure prediction model to obtain a predicted back pressure value; the current back pressure prediction model is constructed according to the back pressure prediction model construction method of the direct air cooling system in any mode.
The invention also provides a back pressure prediction device of the direct air cooling system, which can be used for executing the back pressure prediction method of the direct air cooling system.
Referring to fig. 4, fig. 4 is a block diagram illustrating structural connection of a back pressure prediction apparatus of a direct air cooling system according to an embodiment of the present invention.
The back pressure prediction device of the direct air cooling system provided by the embodiment of the invention comprises:
a second obtaining module 10, configured to obtain real-time operation data of a target parameter of the target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
the prediction module 20 is configured to input the real-time operation data into a current backpressure prediction model to obtain a predicted backpressure value; the current back pressure prediction model is constructed according to the back pressure prediction model construction method of the direct air cooling system in any mode.
The present invention also provides an electronic device including:
a memory for storing instructions; the instruction is used for realizing the method for constructing the back pressure prediction model of the direct air cooling system in the mode capable of being realized in any one of the above modes, or the instruction is used for realizing the method for predicting the back pressure of the direct air cooling system in the mode;
and the processor is used for executing the instructions in the memory.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, the computer program implementing the method for constructing a back pressure prediction model of a direct air-cooling system according to any one of the modes described above when executed by a processor, or implementing the method for predicting back pressure of a direct air-cooling system as described above when executed by a processor.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described back pressure prediction method, device and electronic equipment may refer to the corresponding process in the foregoing embodiment of the back pressure prediction model building method, and the specific beneficial effect of the above-described back pressure prediction method, device and electronic equipment may refer to the corresponding beneficial effect in the foregoing embodiment of the back pressure prediction model building method, which will not be repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the backpressure prediction model building apparatus embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or components may be combined or integrated into another apparatus, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (12)
1. The back pressure prediction model construction method of the direct air cooling system is characterized by comprising the following steps of:
acquiring historical operation data of target parameters of a target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
preprocessing the historical operation data to obtain preprocessed data;
constructing a sparse LS-SVR model by adopting a sparse LS-SVR algorithm based on dictionary learning; the sparse LS-SVR model takes the target parameter as an input variable and takes the back pressure of the target direct air cooling system as an output variable;
randomly extracting part of data from the preprocessed data as a training sample to train the sparse LS-SVR model, so as to obtain a backpressure prediction model of the target direct air cooling system;
determining a predicted value of the training sample based on the backpressure prediction model, and calculating an average relative error between the predicted value and an actual value of the training sample;
updating a training sample when the average relative error is greater than a preset model updating threshold value, and re-performing model training based on the updated training sample to update the backpressure prediction model;
The sparse LS-SVR algorithm based on dictionary learning adds one basic function at a time from a group of kernel functions to form a dictionary, and iteratively solves an approximate regression function, and comprises the following steps:
given a set of kernel functionsFrom a empty set->And complete->Initially, one kernel function is selected from the set of kernel functions at a time and its subscript is removed from the setPMove to collectionQIn solving the setPAn approximate objective function formed for the kernel function of the subscript until a stop condition is satisfied;
the optimization problem can be translated into the following dual problem:
；
in the method, in the process of the invention,，/>is a unitary matrix->，/>，，/>Representing a kernel function->Represents penalty factors->Represents the Lagrangian multiplier, +.>For biasing (I)>Is output.
2. The method for constructing a back pressure prediction model of a direct air cooling system according to claim 1, wherein the preprocessing the historical operation data includes:
removing outliers in the historical operation data;
removing unsteady state data in the historical operation data;
and/or, carrying out data standardization processing on the historical operation data.
3. The method for constructing a back pressure prediction model of a direct air cooling system according to claim 2, wherein the removing outliers in the historical operating data comprises:
Calculating the local density value and the dispersion of each data point in the historical operation data; the dispersion is a distance value from a corresponding data point to a nearest data point with higher local density;
calculating the product of the local density value and the dispersion of the data points as a global representative index of the corresponding data points;
and when the global representative index is lower than a preset outlier screening threshold, removing the corresponding data point as an outlier.
4. The method for constructing a back pressure prediction model of a direct air cooling system according to claim 2, wherein the removing unsteady state data from the historical operating data comprises:
intercepting window data corresponding to preset important parameters serving as steady-state screening representation from the historical operation data by adopting a first preset sliding window, and calculating standard deviation of the intercepted window data; the preset important parameters comprise unit load and main steam flow;
and if the standard deviation is smaller than a preset steady-state working condition screening threshold value, removing the corresponding window data as unsteady-state data.
5. The method for constructing a back pressure prediction model of a direct air cooling system according to claim 1, wherein in the training process of the sparse LS-SVR model, the optimal kernel function subscript of the sparse LS-SVR model is searched in a random subset of a given set of kernel function subscripts;
The number of samples of the random subset is calculated according to the following equation:
；
in the method, in the process of the invention,sample number representing random subset, +.>For the relative error of the model, +.>For the maximum number of samples of said random subset, < >>The value of (2) is the number of samples of the kernel index set,/I>A minimum value of the number of samples that is the random subset;
wherein the minimum value of the number of samples of the random subset is based on a given probability valueSum function estimate +.>Performing calculation if the value is +.>The probability obtained value of (2) is +.>Function estimation of>，The selection basis is from the quotients: set independent random variable ∈ ->Is +.>ThenIs +.>The method comprises the steps of carrying out a first treatment on the surface of the For uniform distribution +.>，/>Is of the distribution of (a)。
6. The method for constructing a back pressure prediction model of a direct air cooling system according to claim 1, wherein updating the training samples when the average relative error is greater than a preset model update threshold value comprises:
when the average relative error is larger than a preset model updating threshold value, sample data reflecting a new operation condition is intercepted from the training sample based on a second preset sliding window to serve as first new sample data;
screening historical sample data corresponding to preset main parameters from historical training samples; the preset main parameters comprise ambient temperature and ambient wind speed;
Calculating the distance between each screened historical sample data and the first new sample data of the corresponding parameter type, and taking the historical sample data meeting the preset distance condition as second new sample data;
and constructing a new training sample based on the first new sample data and the second new sample data, thereby completing updating of the training sample.
7. The method for constructing a back pressure prediction model of a direct air cooling system according to claim 6, wherein the preset distance condition is:
；
in the method, in the process of the invention,indicating the selected->Historical sample data->Is->First new sample data of corresponding parameter type, < ->And screening a threshold value for the preset distance.
8. The back pressure prediction model construction device of the direct air cooling system is characterized by comprising the following components:
the first acquisition module is used for acquiring historical operation data of target parameters of the target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
the preprocessing module is used for preprocessing the historical operation data to obtain preprocessed data;
The construction module is used for constructing a sparse LS-SVR model by adopting a sparse LS-SVR algorithm based on dictionary learning; the sparse LS-SVR model takes the target parameter as an input variable and takes the back pressure of the target direct air cooling system as an output variable; the sparse LS-SVR algorithm based on dictionary learning adds one basic function at a time from a group of kernel functions to form a dictionary, and iteratively solves an approximate regression function, and comprises the following steps:
given a set of kernel functionsFrom a empty set->And complete->Initially, one kernel function is selected from the set of kernel functions at a time and its subscript is removed from the setPMove to collectionQIn solving the setPAn approximate objective function formed for the kernel function of the subscript until a stop condition is satisfied;
the optimization problem can be translated into the following dual problem:
；
in the method, in the process of the invention,，/>is a unitary matrix->，/>，，/>Representing a kernel function->Represents penalty factors->Represents the Lagrangian multiplier, +.>For biasing (I)>Is output;
the training module is used for randomly extracting part of data from the preprocessed data as a training sample to train the sparse LS-SVR model, so as to obtain a backpressure prediction model of the target direct air cooling system;
the calculation module is used for determining a predicted value of the training sample based on the backpressure prediction model and calculating an average relative error between the predicted value and an actual value of the training sample;
And the updating module is used for updating the training sample when the average relative error is larger than a preset model updating threshold value, and re-carrying out model training based on the updated training sample so as to update the backpressure prediction model.
9. The back pressure prediction method of the direct air cooling system is characterized by comprising the following steps of:
acquiring real-time operation data of target parameters of a target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
inputting the real-time operation data into a current back pressure prediction model to obtain a predicted back pressure value; the current back pressure prediction model is constructed according to the back pressure prediction model construction method of the direct air cooling system according to any one of claims 1-7.
10. A backpressure predicting device of a direct air cooling system, comprising:
the second acquisition module is used for acquiring real-time operation data of target parameters of the target direct air cooling system; the target parameters comprise fan rotating speed, ambient temperature, ambient wind speed, ambient wind direction, unit load, main steam flow, main steam pressure, reheating temperature and atmospheric pressure;
The prediction module is used for inputting the real-time operation data into a current back pressure prediction model to obtain a predicted back pressure value; the current back pressure prediction model is constructed according to the back pressure prediction model construction method of the direct air cooling system according to any one of claims 1-7.
11. An electronic device, comprising:
a memory for storing instructions; wherein the instruction is used for realizing the back pressure prediction model construction method of the direct air cooling system according to any one of claims 1 to 7, or the instruction is used for realizing the back pressure prediction method of the direct air cooling system according to claim 9;
and the processor is used for executing the instructions in the memory.
12. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program when executed by a processor implements the back pressure prediction model construction method of the direct air cooling system according to any one of claims 1 to 7, or the computer program when executed by a processor implements the back pressure prediction method of the direct air cooling system according to claim 9.
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