Die forging equipment hydraulic system energy consumption prediction method with self-learning function

Abstract

The embodiment of the invention provides a die forging equipment hydraulic system energy consumption prediction method with a self-learning function, and belongs to the technical field of data processing, and the method specifically comprises the steps: analyzing the energy consumption characteristics of an energy conversion unit of a die forging equipment hydraulic system, and obtaining the key energy consumption characteristics of the die forging equipment hydraulic system; an energy consumption data set is extracted and preprocessed, and an energy consumption feature set corresponding to the data information is extracted in combination with the key energy consumption features; based on the key energy consumption characteristics and the energy consumption characteristic set, using different energy consumption prediction fusion algorithms to construct an energy consumption prediction sub-model of each energy conversion unit; optimizing each energy consumption prediction sub-model through an incremental learning and transfer learning algorithm; evaluating each optimized energy consumption prediction sub-model through cross validation and a preset evaluation index; and performing energy consumption prediction on the energy conversion unit of the hydraulic system of the die forging equipment by utilizing each optimized energy consumption prediction sub-model. Through the scheme disclosed by the invention, the prediction efficiency, accuracy and adaptability are improved.

Description

Technical Field

[0001] The disclosed embodiments relate to the field of data processing technology, and in particular to a method for predicting energy consumption of a hydraulic system of die forging equipment with a self-learning function. Background Art

[0002] At present, die forging equipment is the core equipment necessary for the forming of important components in the fields of aerospace, shipbuilding and transportation. However, there is a huge energy loss in its operation process. With the improvement of digital technology and industrial supply level, driven by scientific and technological innovation, the manufacturing industry is gradually developing towards high-end, intelligent and green. The current die forging equipment design criteria are mainly based on ensuring the reliability and service capability of the forming process, and do not take into account the energy dissipation during the operation of the equipment, which also leads to the current low energy utilization rate of die forging equipment. The die forging equipment system has many components and a complex energy conversion process. In order to effectively manage and control its energy consumption, the first problem that needs to be solved is to achieve accurate prediction of the energy loss of each core energy consumption unit of the die forging equipment, reveal the factors that cause its high energy consumption in different working stages of the service cycle through the energy consumption prediction model, and realize real-time monitoring of the energy consumption status of the die forging equipment during the service process, thereby avoiding the occurrence of high energy consumption operating conditions and achieving high energy efficiency operation of the equipment.

[0003] The existing energy consumption prediction methods focus on predicting the total energy consumption of the equipment, but do not take into account the different energy consumption mechanisms and data distribution characteristics of different components, which makes the prediction results often lack physical interpretability. In addition, the prediction model does not take into account the problem of model prediction performance attenuation caused by future changes in the equipment's operating status and working environment, resulting in the prediction accuracy being unable to meet the prediction requirements of the entire service life of the die forging equipment.

[0004] It can be seen that there is an urgent need for a method for predicting energy consumption of the hydraulic system of die forging equipment with high prediction efficiency, accuracy and adaptability and self-learning function. Summary of the invention

[0005] In view of this, an embodiment of the present disclosure provides a method for predicting energy consumption of a hydraulic system of die forging equipment with a self-learning function, which at least partially solves the problems of poor prediction efficiency, accuracy and adaptability in the prior art.

[0006] The embodiment of the present disclosure provides a method for predicting energy consumption of a hydraulic system of a die forging equipment with a self-learning function, comprising:

[0007] Step 1: Analyze the energy consumption characteristics of the energy conversion unit of the die forging equipment hydraulic system, build an energy consumption mechanism model, analyze the flow process of the energy die forging equipment hydraulic system and extract nonlinear complex links, and obtain the key energy consumption characteristics of the die forging equipment hydraulic system;

[0008] Step 2: extract the energy consumption data set of each energy conversion unit according to the historical operation data of the hydraulic system of the die forging equipment and preprocess it to obtain a data information set, and extract the energy consumption feature set corresponding to the data information in combination with the key energy consumption characteristics;

[0009] Step 3: Based on the key energy consumption characteristics and energy consumption feature set, different energy consumption prediction fusion algorithms are used to construct energy consumption prediction sub-models for each energy conversion unit according to different energy consumption characteristics of different energy consumption units;

[0010] Step 4: collect the real-time operation data of the hydraulic system of the die forging equipment, use a sliding window to store the data in the recent period, and optimize each energy consumption prediction sub-model through incremental learning and transfer learning algorithms;

[0011] Step 5, evaluate each optimized energy consumption prediction sub-model through cross-validation and preset evaluation indicators. If the evaluation result does not meet the preset requirements, return to step 4 until the evaluation result meets the preset requirements;

[0012] Step 6: Use the optimized energy consumption prediction sub-models to predict the energy consumption of the energy conversion unit of the hydraulic system of the die forging equipment.

[0013] According to a specific implementation of the embodiment of the present disclosure, the energy conversion unit includes a servo motor, an electronically controlled plunger pump and a control valve group;

[0014] The key energy consumption characteristics include the energy consumption characteristics of the servo motor, the energy consumption characteristics of the electronically controlled plunger pump and the energy consumption characteristics of the control valve group.

[0015] According to a specific implementation of the embodiment of the present disclosure, the steps of analyzing the flow process of the hydraulic system of the energy die forging equipment and extracting nonlinear complex links to obtain the key energy consumption characteristics of the hydraulic system of the die forging equipment specifically include:

[0016] Step 1.1, calculate the energy consumption characteristics of the servo motor based on the servo motor's drive power loss, motor iron loss and copper loss

[0017] P Mloss =P SD +P Fe +P Cu

[0018]

[0019] E Mloss =∫P Min (1-η M )dt

[0020] Among them, P Mloss is the power loss of the motor, η M is the efficiency of the motor, EMloss is the energy consumption of the motor, P SD is the driving power loss of the motor, P Fe and P Cu They are the iron loss and copper loss of the motor, P Min is the power input to the motor, t is the time;

[0021] Step 1.2: Calculate the energy consumption characteristics of the electronically controlled piston pump based on mechanical efficiency and volumetric efficiency

[0022]

[0023] E Ploss =∫P Pin (1-η pv ·η pm )dt

[0024] Among them, η pm is the mechanical efficiency of the electric piston pump, η pv is the volumetric efficiency of the electric piston pump, E Ploss is the energy consumption of the electric piston pump, μ is the dynamic viscosity of the fluid, C v is the laminar drag coefficient, C f is the mechanical resistance coefficient, T s is the torque loss, Δp p is the pressure difference between the inlet and outlet of the electric piston pump, V max is the maximum displacement of the electric piston pump, C s is the laminar leakage coefficient, β is the ratio of the current working displacement of the electric control plunger pump to the maximum displacement, P Pin is the power input to the electric controlled plunger pump;

[0025] Step 1.3, calculate the energy consumption characteristics of the control valve group based on the pressure loss of each valve in the control valve group

[0026]

[0027] Among them, Δp v is the pressure difference between the inlet and outlet of the valve, E Vloss is the total energy consumption of the control valve group, C q is the valve port flow coefficient, ρ is the density of the fluid, q v Flow rate at the valve port, A v is the flow area of ​​the valve port, j is the valve serial number running during the forging process, and z is the number of valves used in the control valve group.

[0028] According to a specific implementation of the embodiment of the present disclosure, the step of extracting energy consumption data of each energy conversion unit according to historical operation data of the hydraulic system of the die forging equipment and preprocessing to obtain data information includes:

[0029] Step 2.1, perform stationarity analysis on the energy consumption data set through the autocorrelation function:

[0030]

[0031] Where k is the time lag delay order, ACF(k) represents the value of ACF after k time intervals lag, yt is the energy consumption data value at time t, and y y+k is the energy consumption data value lagged by k time intervals, Cov(y t ,y y+k ) is the covariance of the two, σ z (t) is the standard deviation of energy consumption data at time t, σ z (t+k) is the standard deviation of the energy consumption data lagged by k time intervals, μ is the overall mean, and E represents the expectation;

[0032] Step 2.2, using the Z-score outlier detection method to calculate the Z-score value of each energy consumption data in the energy consumption data set, and removing the energy consumption data with a Z-score value greater than the threshold as an outlier, where the expression of the Z-score value is:

[0033]

[0034] Where X is the value of the energy consumption data set, μ is the mean value of the data set, and σ is the standard deviation of the data set;

[0035] Step 2.3, use Sigmoid normalization to normalize the energy consumption data set after removing outliers to eliminate the dimension effect:

[0036]

[0037] Among them, a and b are constant parameters.

[0038] According to a specific implementation of the embodiment of the present disclosure, step 3 specifically includes:

[0039] Step 3.1, use the autoencoder to extract features from the data of the servo motor and the electronically controlled piston pump, map the energy consumption feature set to a low-dimensional hidden representation through the encoder, and then reconstruct the hidden representation into an energy consumption feature set through the decoder, where the calculation relationship between the encoder and the decoder is as follows:

[0040] h=f(Wx+b)

[0041]

[0042] Among them, x is the energy consumption feature set, f and f' are nonlinear functions used for encoding and decoding, W and W' are weight matrices, b and b' are bias vectors, and the hidden layer neurons are decoded by the decoder to output the reconstructed energy consumption feature set. The energy consumption feature set obtained after feature extraction The feature sequence in is used as the input layer to the informer algorithm to obtain the energy consumption prediction sub-model of the servo motor and the electronically controlled plunger pump;

[0043] Step 3.2, based on the key energy consumption characteristics and energy consumption feature set, the residual fusion mechanism data fusion modeling method is used to characterize the energy consumption generated by the control valve group, and the residual data based on the mechanism prediction and the actual energy consumption is obtained.

[0044] P′ res =f CGA (p v ,q v ,U v ,T o )

[0045] Among them, p v is the valve inlet pressure, U v is the electric control signal of the valve, T o is the oil temperature, f CGA It is a data-driven residual prediction model;

[0046] Step 3.3: Use the energy consumption mechanism model to output the preliminary energy consumption forecast value. After calculating the residual with the actual energy consumption value, use the data-driven residual prediction model to predict the energy consumption residual value and compensate it to the energy consumption mechanism model to build the energy consumption forecast sub-model corresponding to the control valve group:

[0047] ΔP′ pred =ΔP pred +P′ res

[0048] Among them, ΔP pred is the predicted value of the energy consumption mechanism model, ΔP′ pred It is the predicted value of the fusion model of mechanism and data.

[0049] According to a specific implementation of the embodiment of the present disclosure, step 4 specifically includes:

[0050] Step 4.1, collect the real-time operation data of the hydraulic system of the die forging equipment and use the offset detection algorithm to determine the data distribution change of the real-time operation data. If the data distribution change is a small gradual change and the model error gradually increases, then execute step 4.2; if the data distribution change is a sudden change and the model performance drops sharply, then execute step 4.3;

[0051] Step 4.2, define the loss function through the EWC incremental learning algorithm and optimize each energy consumption prediction sub-model accordingly. When the loss function is minimized, the optimized energy consumption prediction sub-model is obtained, where the expression of the loss function is:

[0052]

[0053] Among them, L B (θ) is the loss function under the new training data, λ is the regularization coefficient, F i is the Fisher information matrix, θ i are network parameters, is the optimal parameter obtained from the old data, and N is the number of samples;

[0054] Step 4.3: Use the transfer learning algorithm domain adversarial neural network to transfer each energy consumption prediction sub-model in the source domain to the target domain for energy consumption prediction. The source domain and target domain inputs are mapped to θ through the feature extraction layer. f Parameterize the new space, and the prediction layer maps this new space representation to a weighted y Parameterized output, then the prediction loss under the i-th task is converted into an optimization problem and each energy consumption prediction sub-model is updated accordingly, where the expression of the optimization problem is

[0055]

[0056] Among them, θ d is a parameterized domain classifier, is a logistic regressor that is used to determine whether a given input comes from the source domain or the target domain.

[0057] According to a specific implementation of the embodiment of the present disclosure, step 6 specifically includes:

[0058] Deploy optimized energy consumption prediction sub-models on edge devices, build data storage and analysis modules on the cloud, realize online update of energy consumption models through cloud-edge collaboration, output energy consumption prediction results of energy conversion units of hydraulic systems of die forging equipment, and display them on a visual interface.

[0059] According to a specific implementation of the embodiment of the present disclosure, the preset evaluation indicators include root mean square error, mean absolute percentage error and R 2 coefficient;

[0060] The expression of the root mean square error is:

[0061]

[0062] Among them, y i is the actual energy consumption value of the ith data point, is the predicted energy consumption value of the i-th data point, and n is the total amount of data;

[0063] The expression of the mean absolute percentage error is:

[0064]

[0065] The R 2 The expression of the coefficient is

[0066]

[0067] Among them, SSR means regression sum of squares, and SST means total sum of squares.

[0068] The energy consumption prediction scheme of the die forging equipment hydraulic system with self-learning function in the embodiment of the present disclosure includes: step 1, analyzing the energy consumption characteristics of the energy conversion unit of the die forging equipment hydraulic system, building an energy consumption mechanism model, analyzing the flow process of the energy die forging equipment hydraulic system and extracting nonlinear complex links, and obtaining the key energy consumption characteristics of the die forging equipment hydraulic system; step 2, extracting the energy consumption data set of each energy conversion unit according to the historical operation data of the die forging equipment hydraulic system and preprocessing it to obtain a data information set, and extracting the energy consumption feature set corresponding to the data information in combination with the key energy consumption characteristics; step 3, based on the key energy consumption characteristics and the energy consumption feature set, for different energy consumption units Different energy consumption characteristics are used, and different energy consumption prediction fusion algorithms are used to construct energy consumption prediction sub-models of various energy conversion units; Step 4, collect real-time operation data of the hydraulic system of the die forging equipment, use a sliding window to store the data in the recent period, and optimize each energy consumption prediction sub-model based on this through incremental learning and transfer learning algorithms; Step 5, evaluate each optimized energy consumption prediction sub-model through cross-validation and preset evaluation indicators, if the evaluation result does not meet the preset requirements, return to step 4 until the evaluation result meets the preset requirements; Step 6, use each optimized energy consumption prediction sub-model to predict the energy consumption of the energy conversion unit of the hydraulic system of the die forging equipment.

[0069] The beneficial effects of the embodiments of the present disclosure are as follows: through the scheme of the present disclosure, the energy consumption mechanism of different energy consumption units of the hydraulic system of the die forging equipment is combined with the distribution characteristics of its historical operating data, and different mechanisms and data fusion modeling methods are adopted to ensure the model prediction accuracy while enhancing the physical interpretability of the model. At the same time, the introduction of incremental learning and transfer learning optimization algorithms allows the model to continuously realize self-optimization of the model according to the data flow of the equipment service cycle, eliminating the problem of reduced prediction performance caused by fluctuations in the operating environment and attenuation of component performance, meeting the needs of real-time energy consumption prediction of the die forging equipment throughout its service cycle, and improving prediction efficiency, accuracy and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 A schematic flow chart of a method for predicting energy consumption of a hydraulic system of a die forging equipment with a self-learning function provided in an embodiment of the present disclosure;

[0072] Figure 2 A schematic diagram of a method for predicting energy consumption of a hydraulic system of a die forging equipment with a self-learning function provided in an embodiment of the present disclosure;

[0073] Figure 3 A schematic diagram of energy consumption mechanism analysis of a die forging equipment system provided in an embodiment of the present disclosure;

[0074] Figure 4 An energy consumption prediction algorithm framework based on mechanism and data dual drive is provided for the embodiment of the present disclosure;

[0075] Figure 5 A flow chart of a model self-optimization algorithm based on incremental learning and transfer learning provided in an embodiment of the present disclosure;

[0076] Figure 6 A system architecture for real-time monitoring of energy consumption of die forging equipment is provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0077] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0078] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0079] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0080] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0081] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0082] An embodiment of the present disclosure provides a method for predicting energy consumption of a hydraulic system of die forging equipment with a self-learning function, and the method can be applied to the process of predicting energy consumption of die forging equipment in industrial scenarios.

[0083] See also Figure 1 , is a flow chart of a method for predicting energy consumption of a hydraulic system of a die forging equipment with a self-learning function provided by an embodiment of the present disclosure. Figure 1 and Figure 2 As shown, the method mainly comprises the following steps:

[0084] Step 1: Analyze the energy consumption characteristics of the energy conversion unit of the die forging equipment hydraulic system, build an energy consumption mechanism model, analyze the flow process of the energy die forging equipment hydraulic system and extract nonlinear complex links, and obtain the key energy consumption characteristics of the die forging equipment hydraulic system;

[0085] Optionally, the energy conversion unit includes a servo motor, an electronically controlled plunger pump and a control valve group;

[0086] The key energy consumption characteristics include the energy consumption characteristics of the servo motor, the energy consumption characteristics of the electronically controlled plunger pump and the energy consumption characteristics of the control valve group.

[0087] Further, the steps of analyzing the flow process of the hydraulic system of the energy die forging equipment and extracting nonlinear complex links to obtain the key energy consumption characteristics of the hydraulic system of the die forging equipment specifically include:

[0088] Step 1.1, calculate the energy consumption characteristics of the servo motor based on the servo motor's drive power loss, motor iron loss and copper loss

[0089] P Mloss =P SD +P Fe +P Cu

[0090]

[0091] E Mloss =∫P Min (1-η M )dt

[0092] Among them, P Mloss is the power loss of the motor, η M is the efficiency of the motor, E Mloss is the energy consumption of the motor, P SD is the driving power loss of the motor, P Fe and P Cu They are the iron loss and copper loss of the motor, P Min is the power input to the motor, t is the time;

[0093] Step 1.2: Calculate the energy consumption characteristics of the electronically controlled piston pump based on mechanical efficiency and volumetric efficiency

[0094]

[0095]

[0096] E Ploss =∫P Pin (1-η pv ·η pm )dt

[0097] Among them, η pm is the mechanical efficiency of the electric piston pump, η pv is the volumetric efficiency of the electric piston pump, E Ploss is the energy consumption of the electric piston pump, μ is the dynamic viscosity of the fluid, C v is the laminar drag coefficient, C f is the mechanical resistance coefficient, T s is the torque loss, Δp p is the pressure difference between the inlet and outlet of the electric piston pump, V max is the maximum displacement of the electric piston pump, C sis the laminar leakage coefficient, β is the ratio of the current working displacement of the electric control plunger pump to the maximum displacement, P Pin is the power input to the electric controlled plunger pump;

[0098] Step 1.3, calculate the energy consumption characteristics of the control valve group based on the pressure loss of each valve in the control valve group

[0099]

[0100] Among them, Δp v is the pressure difference between the inlet and outlet of the valve, E Vloss is the total energy consumption of the control valve group, C q is the valve port flow coefficient, ρ is the density of the fluid, q v Flow rate at the valve port, A v is the flow area of ​​the valve port, j is the valve serial number running during the forging process, and z is the number of valves used in the control valve group.

[0101] In the specific implementation, based on the energy dissipation principle of each core energy conversion unit of the die forging equipment hydraulic system, the system energy consumption mechanism model is constructed, and its energy flow process is analyzed according to the mechanism model. Figure 3 For example, the energy dissipation principles of the mechanical, electronic control and hydraulic systems of die forging equipment were analyzed. The components that generate energy loss during the system energy flow include motors, hydraulic pumps, control valve groups, hydraulic cylinders and mechanical structure friction losses. The hydraulic cylinder and structure friction losses are very small and can often be ignored. Therefore, the energy consumption unit is mainly composed of motors, hydraulic pumps and control valve groups. The energy consumption of the motor mainly includes drive power loss, motor iron loss and copper loss. The energy consumption can be calculated from these losses to obtain the working efficiency of the motor. The energy consumption expression is:

[0102] P Mloss =P SD +P Fe +P Cu

[0103]

[0104] E Mloss =∫P Min (1-η M )dt

[0105] Among them, P Mloss is the power loss of the motor, η M is the efficiency of the motor, E Mloss is the energy consumption of the motor, P SD is the driving power loss of the motor, P Fe and P Cu They are the iron loss and copper loss of the motor, P Minis the power input to the motor and t is the time.

[0106] The energy consumption of the plunger pump comes from the mechanical loss caused by the friction caused by the movement of the internal structural components of the pump body. On the other hand, it comes from the volume loss caused by the leakage of the high-pressure chamber of the pump body, the compressibility of the hydraulic oil and the oil pumping resistance. The above energy consumption can generally be expressed by mechanical efficiency and volumetric efficiency. The energy consumption characteristics of the pump can be expressed as:

[0107]

[0108] E Ploss =∫P Pin (1-η pv ·η pm )dt

[0109] Among them, η pm is the mechanical efficiency of the pump, η pv is the volumetric efficiency of the pump, E Ploss is the energy consumption of the pump, μ is the dynamic viscosity of the fluid, C v is the laminar drag coefficient, C f is the mechanical resistance coefficient, T s is the torque loss, Δp p is the pressure difference between the pump inlet and outlet, V max is the maximum displacement of the pump, C s is the laminar leakage coefficient, β is the ratio of the current working displacement of the pump to the maximum displacement, P Pin is the power input to the pump.

[0110] There are many types and quantities of control valve groups in the die forging equipment system. For control valves such as cartridge valves, check valves, reversing valves, servo valves, and solenoid switching valves, the energy loss is mainly reflected in the pressure loss at both ends of the valve. Therefore, during the operation of the press hydraulic system, the energy consumption generated by the entire control valve group can be expressed as:

[0111]

[0112] Among them, Δp v is the pressure difference between the inlet and outlet of the valve, E Vloss is the total energy consumption of the control valve group, C q is the valve port flow coefficient, ρ is the density of the fluid, q v Flow rate at the valve port, A v is the flow area of ​​the valve port, i is the valve number running during the forging process, and z is the number of valves used in the control valve group.

[0113] Step 2: extract the energy consumption data set of each energy conversion unit according to the historical operation data of the hydraulic system of the die forging equipment and preprocess it to obtain a data information set, and extract the energy consumption feature set corresponding to the data information in combination with the key energy consumption characteristics;

[0114] On the basis of the above embodiment, the step of extracting the energy consumption data of each energy conversion unit according to the historical operation data of the hydraulic system of the die forging equipment and preprocessing to obtain data information includes:

[0115] Step 2.1, perform stationarity analysis on the energy consumption data set through the autocorrelation function:

[0116]

[0117] Where k is the time lag delay order, ACF(k) represents the value of ACF after k time intervals lag, yt is the energy consumption data value at time t, and y y+k is the energy consumption data value lagged by k time intervals, Cov(y t ,y y+k ) is the covariance of the two, σ z (t) is the standard deviation of energy consumption data at time t, σ z (t+k) is the standard deviation of the energy consumption data lagged by k time intervals, k is the time lag delay order, μ is the overall mean, and E represents the expectation;

[0118] Step 2.2, using the Z-score outlier detection method to calculate the Z-score value of each energy consumption data in the energy consumption data set, and removing the energy consumption data with a Z-score value greater than the threshold as an outlier, where the expression of the Z-score value is:

[0119]

[0120] Among them, X is the value of the energy consumption data set, μ is the mean value of the data set, and σ is the standard deviation of the data set;

[0121] Step 2.3, use Sigmoid normalization to normalize the energy consumption data set after removing outliers to eliminate the dimension effect:

[0122]

[0123] Among them, a and b are constant parameters.

[0124] In the specific implementation, the operating data of the hydraulic system of the die forging equipment is processed and features are extracted. After the operating data of the equipment is collected, the data often has problems such as missing values, outliers and duplicate values. Therefore, it is necessary to pre-process the data. The data processing methods used include stationarity analysis, outlier processing and data normalization. Stationarity analysis is used to test whether the data has stable distribution characteristics and avoid the deviation of prediction results caused by non-stationary data. The stationarity analysis of time series data is performed through the autocorrelation function. The expression is as follows:

[0125]

[0126] Where k is the time lag delay order, μ is the overall mean, and E is the expectation. The stationarity of the sequence is judged by observing how fast the autocorrelation coefficient decays as k increases. The autocorrelation coefficient of a stationary sequence decays quickly to near zero and fluctuates randomly around zero.

[0127] The environmental factors in the operation of die forging equipment are complex. Due to factors such as vibration and impact, the data collected by the sensor often contains a lot of noise and abnormal data. The Z-score outlier detection method is used for unified outlier detection. The calculation formula is as follows:

[0128]

[0129] Among them, Z is the Z-score value, X is the value of the data set, μ is the mean value of the data set, and σ is the standard deviation of the data set. If the absolute value of the Z-score is greater than a certain threshold, then the data point is usually considered an outlier. Generally, the threshold is 2 or 3. All outliers contained in the collected data are removed, and the adjacent data are used for linear interpolation to obtain and complete the data.

[0130] Due to the difference in dimensions in the data, there will be a large difference in the values ​​of different data. In order to avoid the difference in feature scales caused by this phenomenon, Sigmoid normalization is used to normalize the data to eliminate the influence of dimensions. The calculation formula is:

[0131]

[0132] Among them, a and b are parameters that need to be adjusted according to the specific problem.

[0133] Step 3: Based on the key energy consumption characteristics and energy consumption feature set, different energy consumption prediction fusion algorithms are used to construct energy consumption prediction sub-models for each energy conversion unit according to different energy consumption characteristics of different energy consumption units;

[0134] Based on the above embodiment, step 3 specifically includes:

[0135] Step 3.1, use the autoencoder to extract features from the data of the servo motor and the electronically controlled piston pump, map the energy consumption feature set to a low-dimensional hidden representation through the encoder, and then reconstruct the hidden representation into an energy consumption feature set through the decoder, where the calculation relationship between the encoder and the decoder is as follows:

[0136] h=f(Wx+b)

[0137]

[0138] Among them, x is the energy consumption feature set, f and f' are nonlinear functions used for encoding and decoding, W and W' are weight matrices, b and b' are bias vectors, and the hidden layer neurons are decoded by the decoder to output the reconstructed energy consumption feature set. The energy consumption feature set obtained after feature extraction The feature sequence in is used as the input layer to the informer algorithm to obtain the energy consumption prediction sub-model of the servo motor and the electronically controlled plunger pump;

[0139] Step 3.2, based on the key energy consumption characteristics and energy consumption feature set, the residual fusion mechanism data fusion modeling method is used to characterize the energy consumption generated by the control valve group, and the residual data based on the mechanism prediction and the actual energy consumption is obtained.

[0140] P′ res =f CGA (p v ,q v ,U v ,T o )

[0141] Among them, p v is the valve inlet pressure, U v is the electric control signal of the valve, T o is the oil temperature, f CGA It is a data-driven residual prediction model;

[0142] Step 3.3: Use the energy consumption mechanism model to output the preliminary energy consumption forecast value. After calculating the residual with the actual energy consumption value, use the data-driven residual prediction model to predict the energy consumption residual value and compensate it to the energy consumption mechanism model to build the energy consumption forecast sub-model corresponding to the control valve group:

[0143] ΔP′ pred =ΔP pred +P′ res

[0144] Among them, ΔP pred is the predicted value of the energy consumption mechanism model, ΔP′ pred It is the predicted value of the fusion model of mechanism and data.

[0145] In the specific implementation, an energy consumption prediction sub-model is constructed for each energy transfer unit by integrating the mechanism and data. Figure 4 Taking the fusion model construction process in as an example for analysis, the two types of energy units, motors and hydraulic pumps, have fewer input variables, complex internal energy dissipation mechanisms, and many nonlinear parameters in the energy consumption model. It is often impossible to predict energy consumption by accurately setting their mechanism parameters. The feature fusion mechanism data fusion modeling method is more suitable for the energy consumption prediction of motors and hydraulic pumps. First, the autoencoder is used to extract features from the data of the motor and hydraulic pump, and the input data is mapped to a low-dimensional hidden representation through the encoder (Encoder), and then the hidden representation is reconstructed into the original data through the decoder (Decoder). The hidden representation is considered to be a valuable feature of the data. The calculation relationship between the encoder and the decoder is as follows:

[0146] h=f(Wx+b)

[0147]

[0148] Among them, x is the original input data, f and f' are nonlinear functions used for encoding and decoding, W and W' are weight matrices, b and b' are bias vectors, and the hidden layer neurons are decoded and output by the decoder to reconstruct The feature sequence obtained after feature extraction is input into the informer algorithm as the input layer to predict the energy consumption of the motor and hydraulic pump.

[0149] As for the valve components of die forging equipment, the energy consumption mechanism model of each valve component is relatively simple, but there are many valve components in the system, and it is difficult to obtain the energy consumption data before and after each valve component during the data acquisition process. Therefore, by integrating each valve component into a control valve group, based on the energy consumption characteristic data before and after the valve group, the residual fusion mechanism data fusion modeling method can more accurately characterize the energy consumption generated by the control valve group. According to the data structure analysis of the control valve group, its input feature categories are relatively small. A layer of convolutional neural network (CNN) is introduced to increase the dimension of the input features, blending low-dimensional features into high-dimensional features, thereby enhancing the expression ability of existing features; at the same time, the noise and irrelevant information in the training data are also converted into useful features. After that, the obtained features are input into the GRU module for training, and the attention mechanism is introduced to automatically learn and calculate the contribution of the input data to the output data. That is, when the model is doing the energy consumption prediction task, it allows the model to pay attention to the energy consumption data that is more critical to the prediction results among a large number of input features, and reduce the emphasis on other non-critical feature data. The final output is the residual data based on the mechanism prediction and the actual energy consumption. The prediction result expression is:

[0150] P′ res =f CGA (pv ,q v ,U v ,T o )

[0151] Among them, p v is the valve inlet pressure, U v is the electric control signal of the valve, T o is the oil temperature, P r ' es is the prediction target, i.e., the residual data between the mechanism prediction result and the actual energy consumption, f CGA Data-driven residual prediction model. The mechanism model is used to output the preliminary energy consumption prediction value. After the residual calculation with the actual energy consumption value, the data-driven residual prediction model is used to predict the energy consumption residual value and compensate it into the mechanism model to build a comprehensive energy consumption prediction model. The expression is as follows:

[0152] ΔP′ pred =ΔP pred +P′ res

[0153] Among them, ΔP pred is the predicted value of the energy consumption mechanism model, ΔP′ pred It is the predicted value of the fusion model of mechanism and data. In this way, the error caused by nonlinearity and uncertainty factors in the control valve group is corrected to ensure the accuracy of the energy consumption of the entire control valve group.

[0154] Step 4: collect the real-time operation data of the hydraulic system of the die forging equipment, use a sliding window to store the data in the recent period, and optimize each energy consumption prediction sub-model through incremental learning and transfer learning algorithms;

[0155] Based on the above embodiment, step 4 specifically includes:

[0156] Step 4.1, collect the real-time operation data of the hydraulic system of the die forging equipment and use the offset detection algorithm to determine the data distribution change of the real-time operation data. If the data distribution change is a small gradual change and the model error gradually increases, then execute step 4.2; if the data distribution change is a sudden change and the model performance drops sharply, then execute step 4.3;

[0157] Step 4.2, define the loss function through the EWC incremental learning algorithm and optimize each energy consumption prediction sub-model accordingly. When the loss function is minimized, the optimized energy consumption prediction sub-model is obtained, where the expression of the loss function is:

[0158]

[0159] Among them, L B(θ) is the loss function under the new training data, λ is the regularization coefficient, F i is the Fisher information matrix, θ i are network parameters, is the optimal parameter obtained from the old data, and N is the number of samples;

[0160] Step 4.3: Use the transfer learning algorithm domain adversarial neural network to transfer each energy consumption prediction sub-model in the source domain to the target domain for energy consumption prediction. The source domain and target domain inputs are mapped to θ through the feature extraction layer. f Parameterize the new space, and the prediction layer maps this new space representation to a weighted y Parameterized output, then the prediction loss under the i-th task is converted into an optimization problem and each energy consumption prediction sub-model is updated accordingly, where the expression of the optimization problem is

[0161]

[0162] Among them, θ d is a parameterized domain classifier, is a logistic regressor that is used to determine whether a given input comes from the source domain or the target domain.

[0163] In the specific implementation, the energy consumption prediction model is optimized through incremental learning and transfer learning algorithms. Figure 5 The algorithm flow in the model is used to self-optimize the model. First, the offset detection algorithm is used to determine the changes in the data distribution of the real-time running data stream of the device. If there is a small gradual change and the model error gradually increases, incremental learning is used to adjust and optimize the model parameters to adapt to the slowly changing operating environment and keep the model updated in real time. The model is optimized using the EWC incremental learning algorithm. A weight value is assigned to each parameter to indicate the importance of the parameter to the old task. For each task, the penalty intensity of each parameter depends on the importance of the parameter to the task. For more important parameters, the penalty is higher. During the training process of the new task, the impact on the old task is controlled by modifying the weight. The loss function can be expressed as:

[0164]

[0165] Among them, L B (θ) is the loss function under the new training data, λ is the regularization coefficient, F i is the Fisher information matrix, θ i are network parameters, The optimal parameters for the old data.

[0166] The Fisher information matrix used to measure the importance of network parameters is expressed as follows:

[0167]

[0168] Among them, N is the number of samples. The larger it is, the smaller the uncertainty of the network parameters is, and the more important the parameters are. The network parameters of the new task need to be closer to the optimal parameters obtained from the old task, so that the important parameters learned by the model in the old task can be retained. The EWC incremental algorithm can find the place where the loss function of the model is very small in both the new and old data, so that the model can still maintain the important model parameters of the old data after learning the new data, thereby ensuring the prediction accuracy of the model under continuous tasks.

[0169] If the offset detection algorithm results show that the data distribution has changed suddenly and the model performance has dropped sharply, consider using a transfer learning algorithm to migrate the model of the source domain to the target domain for energy consumption prediction to meet the prediction needs in the new scenario. The transfer learning algorithm Domain Adversarial Neural Network (DANN) can combine domain adaptation and feature learning in a training process. After the training is completed, the different domain features obtained have both discriminative and domain-invariant characteristics, so that the learned domain-invariant features can be directly applied to the target domain. DANN mainly measures the distance between the distributions of two domains by calculating the H-divergence. The calculation method is as follows:

[0170]

[0171] Among them, S and T are two different domain distributions, and I represents the discriminant function. The principle of DANN is to control the H-divergence by finding indistinguishable representations between the source domain and the target domain, and its loss function is also designed as a regularization factor that approximates the H-divergence. The source domain and target domain inputs are mapped to θ through the feature extraction layer. f Parameterize the new space, and the prediction layer maps this new space representation to a weighted y The parameterized output can therefore transform the prediction loss under the i-th task into the following optimization problem:

[0172]

[0173]

[0174] Among them, θ d is a parameterized domain classifier, The logistic regressor is used to determine whether the given input comes from the source domain or the target domain. Through domain generation adversarial, domain-invariant features between different data sets can be obtained, thereby ensuring the prediction effect of the prediction model in new scenarios.

[0175] Step 5, evaluate each optimized energy consumption prediction sub-model through cross-validation and preset evaluation indicators. If the evaluation result does not meet the preset requirements, return to step 4 until the evaluation result meets the preset requirements;

[0176] Furthermore, the preset evaluation indicators include root mean square error, mean absolute percentage error and R 2 coefficient;

[0177] The expression of the root mean square error is:

[0178]

[0179] Among them, y i is the actual energy consumption value of the ith data point, is the predicted energy consumption value of the i-th data point, and n is the total amount of data;

[0180] The expression of the mean absolute percentage error is:

[0181]

[0182] The R 2 The expression of the coefficient is

[0183]

[0184] Among them, SSR means regression sum of squares, and SST means total sum of squares.

[0185] In the specific implementation, the prediction performance of the model is evaluated, and the effectiveness of the model is evaluated from two aspects: the accuracy of the prediction results and the stability of the prediction model. The evaluation indicators include root mean square error (RMSE), mean absolute percentage error (MAPE), R 2 Coefficient. RMSE is a commonly used prediction error metric that measures the square root of the average of the squares of the differences between the predicted value and the actual value. The smaller the RMSE value, the better the prediction performance of the model. The specific calculation formula is as follows:

[0186]

[0187] where y i is the actual energy consumption value of the ith data point, is the predicted energy consumption value of the ith data point, and n is the total amount of data.

[0188] MAPE is a commonly used forecast accuracy evaluation indicator, which measures the average value of the percentage error between the predicted value and the actual value. It helps to understand the degree to which the predicted value deviates from the actual value. The smaller the value of MAPE, the smaller the deviation of the model's predicted value. The specific calculation formula is as follows:

[0189]

[0190] R 2 The coefficient, also known as the coefficient of determination or the square of the correlation coefficient, is an indicator used to assess the goodness of fit of a regression model and indicates the proportion of data variance that the model can explain. 2 The value of the coefficient is between 0 and 1. The closer it is to 1, the better the regression model fits the data. Conversely, the worse the model's ability to explain the data is. The specific calculation is mainly to calculate the regression square (SSR) and the total sum of squares (SST) to calculate R 2 coefficient:

[0191]

[0192] Step 6: Use the optimized energy consumption prediction sub-models to predict the energy consumption of the energy conversion unit of the hydraulic system of the die forging equipment.

[0193] Based on the above embodiment, step 6 specifically includes:

[0194] Deploy optimized energy consumption prediction sub-models on edge devices, build data storage and analysis modules on the cloud, realize online update of energy consumption models through cloud-edge collaboration, output energy consumption prediction results of energy conversion units of hydraulic systems of die forging equipment, and display them on a visual interface.

[0195] During the specific implementation, the development of the real-time energy consumption prediction system for die forging equipment was completed. Figure 6The system development architecture shown in the figure is used to develop a real-time energy consumption prediction system, establish communication between the sensor devices at the die forging equipment layer and the data layer data acquisition equipment, collect the running speed and beam displacement of the mechanical structure of the die forging equipment, the input current, input voltage, output torque and output speed of the servo motor, the input torque, input speed, output flow, output pressure and oil temperature of the hydraulic pump, the valve pre-valve pressure, valve pre-valve flow, valve post-valve pressure and valve post-valve flow of the control valve group, build a cloud data storage platform, upload the data stored in the data acquisition equipment, and realize data storage and processing in the cloud data management platform. The established energy consumption prediction model is deployed at the model layer, and the real-time data collected in the data platform is called to predict the energy consumption prediction results of the equipment, and the energy consumption related prediction values ​​are uploaded to the cloud platform for data storage. At the same time, the energy consumption results of the equipment operation process are displayed on the computer side with a visual interface, which is convenient for decision-making managers to monitor the equipment energy consumption in real time and formulate system operation and maintenance strategies. Finally, the decision results are applied to the equipment layer to realize effective management of equipment energy consumption.

[0196] The energy consumption prediction method of the die forging equipment hydraulic system with self-learning function provided in this embodiment combines the energy consumption mechanism of different energy consumption units of the die forging equipment hydraulic system with the distribution characteristics of its historical operation data, adopts different mechanism and data fusion modeling methods, ensures the model prediction accuracy and enhances the physical interpretability of the model. At the same time, the introduction of incremental learning and transfer learning optimization algorithms enables the model to continuously realize self-optimization of the model according to the data flow of the equipment service cycle, eliminates the problem of prediction performance degradation caused by operating environment fluctuations and component performance attenuation, meets the needs of real-time energy consumption prediction of the die forging equipment throughout its service cycle, and improves prediction efficiency, accuracy and adaptability. Real-time prediction of the energy consumption of die forging equipment can effectively improve the energy management efficiency of the equipment, achieve energy saving and consumption reduction and intelligent production. Through real-time monitoring and data analysis, energy consumption trends can be predicted, which helps to detect anomalies in advance, optimize equipment operating parameters, and avoid energy waste. It not only reduces production costs, but also improves the stability and service life of equipment operation, and promotes the transformation of the die forging manufacturing field to green manufacturing and intelligent manufacturing.

[0197] It should be understood that various parts of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

[0198] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for predicting energy consumption of a hydraulic system of die forging equipment with a self-learning function, characterized in that: include: Step 1: Analyze the energy consumption characteristics of the energy conversion unit of the die forging equipment hydraulic system, build an energy consumption mechanism model, analyze the flow process of the energy die forging equipment hydraulic system and extract nonlinear complex links, and obtain the key energy consumption characteristics of the die forging equipment hydraulic system; Step 2: extract the energy consumption data set of each energy conversion unit according to the historical operation data of the hydraulic system of the die forging equipment and preprocess it to obtain a data information set, and extract the energy consumption feature set corresponding to the data information in combination with the key energy consumption characteristics; Step 3: Based on the key energy consumption characteristics and energy consumption feature set, different energy consumption prediction fusion algorithms are used to construct energy consumption prediction sub-models for each energy conversion unit according to different energy consumption characteristics of different energy consumption units; Step 4: collect the real-time operation data of the hydraulic system of the die forging equipment, use a sliding window to store the data in the recent period, and optimize each energy consumption prediction sub-model through incremental learning and transfer learning algorithms; Step 5, evaluate each optimized energy consumption prediction sub-model through cross-validation and preset evaluation indicators. If the evaluation result does not meet the preset requirements, return to step 4 until the evaluation result meets the preset requirements; Step 6: Use the optimized energy consumption prediction sub-models to predict the energy consumption of the energy conversion unit of the hydraulic system of the die forging equipment.

2. The method according to claim 1, characterized in that: The energy conversion unit includes a servo motor, an electric-controlled plunger pump and a control valve group; The key energy consumption characteristics include the energy consumption characteristics of the servo motor, the energy consumption characteristics of the electronically controlled plunger pump and the energy consumption characteristics of the control valve group.

3. The method according to claim 2, characterized in that The steps of analyzing the flow process of the hydraulic system of the energy die forging equipment and extracting nonlinear complex links to obtain the key energy consumption characteristics of the hydraulic system of the die forging equipment specifically include: Step 1.1, calculate the energy consumption characteristics of the servo motor based on the servo motor's drive power loss, motor iron loss and copper loss P Mloss =P SD +P Fe +P Cu E Mloss =∫P Min (1-η M )dt Among them, P Mloss is the power loss of the motor, η M is the efficiency of the motor, E Mloss is the energy consumption of the motor, P SD is the driving power loss of the motor, P Fe and P Cu They are the iron loss and copper loss of the motor, P Min is the power input to the motor, t is the time; Step 1.2: Calculate the energy consumption characteristics of the electric piston pump based on mechanical efficiency and volumetric efficiency E Ploss =∫P Pin (1-η pv ·η pm )dt Among them, η pm is the mechanical efficiency of the electric piston pump, η pv is the volumetric efficiency of the electric piston pump, E Ploss is the energy consumption of the electric piston pump, μ is the dynamic viscosity of the fluid, C v is the laminar drag coefficient, C f is the mechanical resistance coefficient, T s is the torque loss, Δp p is the pressure difference between the inlet and outlet of the electric piston pump, V max is the maximum displacement of the electric piston pump, C s is the laminar leakage coefficient, β is the ratio of the current working displacement of the electric control plunger pump to the maximum displacement, P Pin is the power input to the electric controlled plunger pump; Step 1.3, calculate the energy consumption characteristics of the control valve group based on the pressure loss of each valve in the control valve group Among them, Δp v is the pressure difference between the inlet and outlet of the valve, E Vloss is the total energy consumption of the control valve group, C q is the valve port flow coefficient, ρ is the density of the fluid, q v Flow rate at the valve port, A v is the flow area of ​​the valve port, j is the valve number running during the forging process, and z is the number of valves used in the control valve group.

4. The method according to claim 3, characterized in that The step of extracting energy consumption data of each energy conversion unit according to historical operation data of the hydraulic system of the die forging equipment and preprocessing to obtain data information includes: Step 2.1, perform stationarity analysis on the energy consumption data set through the autocorrelation function: Where k is the time lag delay order, ACF(k) represents the value of ACF after k time intervals lag, yt is the energy consumption data value at time t, and y y+k is the energy consumption data value lagged by k time intervals, Cov(y t ,y y+k ) is the covariance of the two, σ z (t) is the standard deviation of energy consumption data at time t, σ z (t+k) is the standard deviation of the energy consumption data lagged by k time intervals, μ is the overall mean, and E represents the expectation; Step 2.2, using the Z-score outlier detection method to calculate the Z-score value of each energy consumption data in the energy consumption data set, and removing the energy consumption data with a Z-score value greater than the threshold as an outlier, where the expression of the Z-score value is: Where X is the value of the energy consumption data set, μ is the mean value of the data set, and σ is the standard deviation of the data set; Step 2.3, use Sigmoid normalization to normalize the energy consumption data set after removing outliers to eliminate the dimension effect: Among them, a and b are constant parameters.

5. The method according to claim 4, characterized in that The step 3 specifically includes: Step 3.1, use the autoencoder to extract features from the data of the servo motor and the electronically controlled piston pump, map the energy consumption feature set to a low-dimensional hidden representation through the encoder, and then reconstruct the hidden representation into an energy consumption feature set through the decoder, where the calculation relationship between the encoder and the decoder is as follows: h=f(Wx+b) Among them, x is the energy consumption feature set, f and f' are nonlinear functions used for encoding and decoding, W and W' are weight matrices, b and b' are bias vectors, and the hidden layer neurons are decoded by the decoder to output the reconstructed energy consumption feature set. The energy consumption feature set obtained after feature extraction The feature sequence in is used as the input layer to the informer algorithm to obtain the energy consumption prediction sub-model of the servo motor and the electronically controlled plunger pump; Step 3.2, based on the key energy consumption characteristics and energy consumption feature set, the residual fusion mechanism data fusion modeling method is used to characterize the energy consumption generated by the control valve group, and the residual data based on the mechanism prediction and the actual energy consumption is obtained. P′ res =f CGA (p v ,q v ,U v ,T o ) Among them, p v is the valve inlet pressure, U v is the electric control signal of the valve, T o is the oil temperature, f CGA It is a data-driven residual prediction model; Step 3.3: Use the energy consumption mechanism model to output the preliminary energy consumption forecast value. After calculating the residual with the actual energy consumption value, use the data-driven residual prediction model to predict the energy consumption residual value and compensate it to the energy consumption mechanism model to build the energy consumption forecast sub-model corresponding to the control valve group: ΔP′ pred =ΔP pred +P′ res Among them, ΔP pred is the predicted value of the energy consumption mechanism model, ΔP′ pred It is the predicted value of the fusion model of mechanism and data.

6. The method according to claim 5, characterized in that The step 4 specifically includes: Step 4.1, collect the real-time operation data of the hydraulic system of the die forging equipment and use the offset detection algorithm to determine the data distribution change of the real-time operation data. If the data distribution change is a small gradual change and the model error gradually increases, then execute step 4.2; if the data distribution change is a sudden change and the model performance drops sharply, then execute step 4.3; Step 4.2, define the loss function through the EWC incremental learning algorithm and optimize each energy consumption prediction sub-model accordingly. When the loss function is minimized, the optimized energy consumption prediction sub-model is obtained, where the expression of the loss function is: Among them, L B (θ) is the loss function under the new training data, λ is the regularization coefficient, F i is the Fisher information matrix, θ i are network parameters, is the optimal parameter obtained from the old data, and N is the number of samples; Step 4.3: Use the transfer learning algorithm domain adversarial neural network to transfer each energy consumption prediction sub-model in the source domain to the target domain for energy consumption prediction. The source domain and target domain inputs are mapped to θ through the feature extraction layer. f Parameterize the new space, and the prediction layer maps this new space representation to a weighted y Parameterized output, then the prediction loss under the i-th task is converted into an optimization problem and each energy consumption prediction sub-model is updated accordingly, where the expression of the optimization problem is Among them, θ d is a parameterized domain classifier, is a logistic regressor that is used to determine whether a given input comes from the source domain or the target domain.

7. The method according to claim 6, characterized in that The step 6 specifically includes: Deploy optimized energy consumption prediction sub-models on edge devices, build data storage and analysis modules on the cloud, realize online update of energy consumption models through cloud-edge collaboration, output energy consumption prediction results of energy conversion units of hydraulic systems of die forging equipment, and display them on a visual interface.

8. The method according to claim 7, characterized in that The preset evaluation indicators include root mean square error, mean absolute percentage error and R 2 coefficient; The expression of the root mean square error is: Among them, y i is the actual energy consumption value of the ith data point, is the predicted energy consumption value of the i-th data point, and n is the total amount of data; The expression of the mean absolute percentage error is: The R 2 The expression of the coefficient is Among them, SSR means regression sum of squares, and SST means total sum of squares.

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