Industrial AIGC basic models, training methods, devices, equipment, storage media and program products embedded in physical mechanisms

Abstract

The embodiment of the present application provides an industrial AIGC basic model, training method, device, equipment, storage medium and program product with embedded physical mechanism. It belongs to the field of machine learning technology. It includes: adding original noise to training data to obtain noise data; inputting noise data and training data labels into the model to be trained to obtain predicted noise; determining constraint strength according to the number of training steps and a preset step threshold; determining physical loss according to the predicted noise and physical constraints; determining comprehensive physical loss according to the constraint strength and physical loss; determining an error value according to the predicted noise, original noise and comprehensive physical loss; optimizing the model to be trained using the error value to obtain a new model to be trained, and repeating the process of adding original noise to determine the error value until the error value is less than the preset error threshold or the new number of training steps reaches the step threshold to obtain a data generation model. It solves the problem of high energy consumption in training models that meet physical constraints.

Description

Technical Field

[0001] The present application relates to the field of machine learning technology, and in particular to an industrial AIGC basic model, training method, device, equipment, storage medium and program product with embedded physical mechanism. Background Art

[0002] Data generation has been applied to data expansion and enhancement of industrial equipment operation data. Currently, data generation models such as the AIGC (Artificial Intelligence Generated Content) model can be used for data generation.

[0003] At present, the AIGC model in related technologies is usually trained using pre-collected data, and only the patterns of the data are learned during the training process.

[0004] However, device operation data naturally follows specific physical constraints. If a model that can generate device operation data that complies with physical constraints is needed during current model training, a large amount of training data is required. The energy consumption generated by the computer during model training is high, and it is difficult to generate data that complies with physical constraints from the data obtained through training. Summary of the Invention

[0005] The embodiments of the present application provide an industrial AIGC basic model, training method, device, equipment, storage medium and program product with embedded physical mechanism to solve the problem that the energy consumption generated by the computer during the model training process is high and the data generation model obtained by training is difficult to generate data that complies with physical constraints.

[0006] In a first aspect, an embodiment of the present application provides an industrial AIGC basic model training method with embedded physical mechanisms, which is applied to a computer, including: obtaining multiple sets of training data and training data labels corresponding to the training data; adding original noise to the training data to obtain noise data; inputting the noise data and the training data labels into the model to be trained so that the model to be trained outputs predicted noise; adding 1 to the number of training steps to obtain a new number of training steps; determining the constraint strength based on the new number of training steps and a preset step threshold; determining the physical loss based on the predicted noise and the preset physical constraint; determining the comprehensive physical loss based on the constraint strength and the physical loss; determining the error value based on the predicted noise, the original noise and the comprehensive physical loss; optimizing the model to be trained using the error value to obtain a new model to be trained, and again executing the process of adding original noise to the training data to determine the error value, until the error value is less than the preset error threshold or the new number of training steps reaches the step threshold, and the new model to be trained is determined as the industrial AIGC basic model.

[0007] In one possible implementation, the constraint strength is determined based on the new number of training steps and a preset step threshold, including: dividing the new number of training steps by the step threshold to obtain a training progress; and calculating the constraint strength using the training progress.

[0008] In one possible implementation, the training progress is used to calculate the constraint strength, including: determining the difference between a preset value and the training progress as the incomplete ratio; dividing the training progress by the incomplete ratio to obtain a progress ratio; taking the logarithm of the progress ratio with a preset constant as the base to obtain a logarithmic value; multiplying the logarithmic value by a preset coefficient and inputting the result into a Sigmoid function to obtain the constraint strength.

[0009] In one possible implementation, physical loss is determined based on predicted noise and preset physical constraints, including: inputting the predicted noise into the constraint equations corresponding to each physical constraint to obtain the output results of each constraint equation; and adding the output results of each constraint equation to obtain the physical loss.

[0010] In one possible implementation, the physical constraints include change trend constraints; the predicted noise is input into the constraint equations corresponding to each physical constraint to obtain the output results of each constraint equation, including: the predicted noise is input into the constraint equations corresponding to the change trend constraints to obtain the output results of the constraint equations corresponding to the change trend constraints.

[0011] In one possible implementation, each training step includes at least two time steps; the training data includes at least two data types, and the data types include a baseline type and at least one associated type associated with the baseline type; the predicted noise is input into the constraint equation corresponding to the change trend constraint to obtain the output result of the constraint equation corresponding to the change trend constraint, including: subtracting the predicted noise of the previous time step from the predicted noise of the current time step of the baseline type to obtain a baseline predicted noise difference; dividing the baseline predicted noise difference by the sum of the predicted noise of the previous time step of the baseline type and a preset value to obtain a characteristic slope corresponding to the baseline type; subtracting the predicted noise of the previous time step from the predicted noise of the current time step of the associated type to obtain an associated predicted noise difference; dividing the associated predicted noise difference by the sum of the predicted noise of the previous training step of the associated type and a preset value to obtain a characteristic slope corresponding to the associated type; calculating the mean square error between the characteristic slope corresponding to each associated type and the characteristic slope corresponding to the baseline type; summing the mean square errors and dividing the sum by the total number of time steps to obtain the output result of the constraint equation corresponding to the change trend constraint.

[0012] In one possible implementation, an error value is determined based on the predicted noise, the original noise, and the comprehensive physical loss, including: calculating a noise mean square error between the predicted noise and the original noise; and adding the noise mean square error to the comprehensive physical loss to obtain the error value.

[0013] In one possible implementation, after optimizing the model to be trained using the error value to obtain a new model to be trained, and again performing the process of adding original noise to the training data to determine the error value until the error value is less than a preset error threshold or the new number of training steps reaches the step threshold, and the new model to be trained is determined as the industrial AIGC basic model, it also includes: in response to detecting a data label input by a user, inputting the data label into the industrial AIGC basic model so that the industrial AIGC basic model outputs device operating parameters; and outputting the device operating parameters.

[0014] In one possible implementation, in response to detecting a data label input by a user, the data label is input into the industrial AIGC basic model so that the industrial AIGC basic model outputs the equipment operating parameters, and the method further includes: inputting the data label into a pre-trained denoising model so that the denoising model denoises the preset noise data for multiple time steps to obtain the equipment operating data corresponding to the data label; obtaining the various intermediate data generated by the denoising model in the process of generating the equipment operating data; determining the intermediate physical loss based on the various intermediate data and preset physical constraints; for any time step, taking the partial derivative of the intermediate physical loss based on the intermediate data to obtain the error gradient; determining the operating data to be output based on the equipment operating data and the error gradient corresponding to each time step; and outputting the operating data to be output.

[0015] In the second aspect, an embodiment of the present application provides an industrial AIGC basic model with embedded physical mechanism, which is trained using the first aspect and / or various possible implementation methods of the first aspect above; the industrial AIGC basic model with embedded physical mechanism includes: a label detection module, a data generation module and a data output module; a label acquisition module for obtaining data labels input by the user; a data generation module for using data labels to perform denoising on preset noise data to obtain equipment operating parameters that meet preset physical constraints; and a data output module for outputting equipment operating parameters.

[0016] In a third aspect, an embodiment of the present application provides an industrial AIGC basic model training device with embedded physical mechanisms, which is applied to a computer and includes: a data acquisition module for acquiring multiple sets of training data and training data labels corresponding to the training data; a noise addition module for adding original noise to the training data to obtain noise data; a model output module for inputting the noise data and the training data labels into the model to be trained so that the model to be trained outputs predicted noise; a step increase module for adding 1 to the training step number to obtain a new training step number; a strength determination module for determining the constraint strength based on the new training step number and a preset step threshold; a physical loss module for determining the physical loss based on the predicted noise and the preset physical constraint; a comprehensive loss module for determining the comprehensive physical loss based on the constraint strength and the physical loss; an error determination module for determining the error value based on the predicted noise, the original noise and the comprehensive physical loss; a model optimization module for optimizing the model to be trained using the error value to obtain a new model to be trained, and again executing the process of adding the original noise to the training data to determine the error value until the error value is less than the preset error threshold or the new number of training steps reaches the step threshold, and the new model to be trained is determined as the industrial AIGC basic model.

[0017] In a fourth aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0018] Memory stores computer-executable instructions;

[0019] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0020] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0021] In a sixth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the first aspect and / or various possible implementation methods of the first aspect.

[0022] The embodiments of the present application provide an industrial AIGC basic model, training method, apparatus, equipment, storage medium, and program product with embedded physical mechanisms. In the process of training the model, the constraint strength is determined according to the number of training steps and the step threshold, and the physical loss is obtained by using physical constraints. The comprehensive physical loss is determined by combining the constraint strength and the physical loss, and added to the error value. The model is optimized using the error value with the comprehensive physical loss added, so that the model learns the physical constraints during the training process, thereby making the data generated by the trained industrial AIGC basic model conform to the physical constraints. During the training process, less training data is required and the training time is shorter than when no physical constraints are added. Moreover, since the constraint strength varies with the number of training steps, the data pattern can be learned first in the early stage of training, and the influence of the physical constraints on the model training process can be gradually increased in the later stage of training to increase the stability of the training. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0024] Figure 1 Schematic diagram of the scenario of the industrial AIGC basic model training method with embedded physical mechanism provided in this application;

[0025] Figure 2 A flow chart of the method for training an industrial AIGC basic model with embedded physical mechanisms provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of the structure of the industrial AIGC basic model training device with embedded physical mechanism provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0028] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0029] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0030] Data generation technology is now widely used in the industrial field, playing an important role in expanding and enhancing equipment operation data.

[0031] In existing technologies, industrial AIGC basic models are generally trained based on pre-collected data. However, this training method only enables the model to learn the surface patterns of the data.

[0032] It's important to note that equipment operating data inherently has distinct physical constraints. Generating equipment operating data that adheres to these constraints through model training currently presents numerous challenges. On the one hand, a vast amount of training data is required to ensure the model fully learns the physical constraints. On the other hand, the model training process consumes significant computer energy. More importantly, even with this massive investment of data and energy, generating data that strictly adheres to these constraints remains a significant challenge for trained industrial AIGC foundational models.

[0033] This application is used in the scenario of training the basic model of industrial AIGC. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0034] Figure 1 This is a schematic diagram of the scenario of the industrial AIGC basic model training method embedded with the physical mechanism provided in this application. Figure 1 , this scenario includes: computer 101 and database 102.

[0035] In a specific implementation process, the terminal device 101 may include a computer, a server, a tablet, a mobile phone, a personal digital assistant (PDA), a notebook, etc., which can input data.

[0036] The database 102 may include a single database or a combination of multiple databases. For example, one or more of an Oracle database, a MySQL database, a DRDS database, an Elasticsearch database, and the like are not particularly limited in this application.

[0037] The connection between computer 101 and database 102 can be wired or wireless. Computer 101 is used to read the training data and the training data labels corresponding to the training data stored in database 102, perform model training using the training data and the training data labels corresponding to the training data, and generate data after the training is completed.

[0038] It is understandable that the scenarios illustrated in the embodiments of this application do not constitute a specific limitation on the training method of the industrial AIGC basic model embedded in the physical mechanism. In other feasible implementations of this application, the above scenarios may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The specific details can be determined according to the actual application scenario and are not limited here. Figure 1 The scenarios shown can be implemented by hardware, software, or a combination of software and hardware.

[0039] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0040] Figure 2 A flow chart of the method for training an industrial AIGC basic model with embedded physical mechanism provided in the embodiment of the present application. The execution subject of the embodiment of the present application may be Figure 1 Computer 101 in. Figure 2 As shown, the method includes:

[0041] S201: Acquire multiple sets of training data and training data labels corresponding to the training data.

[0042] In this step, the training data and the associated new linked data tags may be read from a preset location in a storage unit in the computer, or the training data and the associated new linked data tags may be read from a database.

[0043] S202: Add original noise to the training data to obtain noise data.

[0044] In this step, Gaussian noise may be gradually added to the training data to obtain noise data.

[0045] Specifically, this step can be expressed as a Markov chain:

[0046]

[0047] in, The time step is Noise data at time The time step is Noise data at time is a preset parameter that controls the amount of noise added at each step. Denotes the normal distribution and I denotes the identity matrix. At the end of the forward diffusion process, the data is severely corrupted by noise and transformed into a distribution that is closer to a standard Gaussian distribution.

[0048] S203: Input the noise data and the training data label into the model to be trained, so that the model to be trained outputs the predicted noise.

[0049] In this step, the model to be trained can be any neural network model. The model to be trained attempts to gradually restore the noisy data to the original data to obtain the predicted noise. The reverse denoising process aims to train the neural network to gradually restore the Gaussian noise to the original data distribution. This process is defined as:

[0050]

[0051] in, Indicated by the time step The noise data at time t is restored to the time step Noise data at time The time step is Noise data at time The time step is Noise data at time represents a normal distribution, represents the mean of the prediction noise at time step t, represents the variance of the prediction noise at time step t.

[0052] S204: Add 1 to the number of training steps to obtain a new number of training steps.

[0053] For example, if the initial number of training steps is 0, then add 1 to the number of training steps, and the new number of training steps is 1. For another example, if the initial number of training steps is 5, then add 1 to the number of training steps, and the new number of training steps is 6.

[0054] S205: Determine the constraint strength according to the new number of training steps and the preset step threshold.

[0055] In this step, the training progress may be determined according to the number of training steps and the step threshold, and the corresponding relationship between the preset training progress and the constraint strength may be found according to the training progress to obtain the constraint strength.

[0056] The correspondence between the training progress and the constraint strength can be preset by the staff and stored in a table, key-value pair or other format.

[0057] S206: Determine physical loss based on the predicted noise and preset physical constraints.

[0058] In this step, the predicted noise is input into the constraint equation corresponding to the preset physical constraint, and the physical loss is calculated.

[0059] S207: Determine the comprehensive physical loss based on the constraint strength and the physical loss.

[0060] In this step, the constraint strength is multiplied by the physical loss to obtain the comprehensive physical loss.

[0061] S208: Determine an error value based on the predicted noise, the original noise, and the comprehensive physical loss.

[0062] In this step, the noise loss is determined based on the predicted noise and the original noise, and the noise loss and the comprehensive physical loss are weighted and summed to obtain an error value.

[0063] The noise loss is determined according to the predicted noise and the original noise, including inputting the predicted noise and the original noise into a preset loss function to calculate the noise loss.

[0064] S209: Use the error value to optimize the model to be trained to obtain a new model to be trained, and again perform the process of adding original noise to the training data to determine the error value until the error value is less than the preset error threshold or the new number of training steps reaches the step threshold, and the new model to be trained is determined as the industrial AIGC basic model.

[0065] In this step, the loss function is minimized through optimization algorithms such as stochastic gradient descent to optimize the model to be trained.

[0066] From the description of the above embodiments, it can be seen that the embodiments of the present disclosure determine the constraint strength according to the number of training steps and the step threshold during the model training process, and use physical constraints to obtain physical loss. The comprehensive physical loss is determined by combining the constraint strength and physical loss, and added to the error value. The model is optimized using the error value with the comprehensive physical loss added, so that the model learns the physical constraints during the training process, so that the data generated by the trained industrial AIGC basic model conforms to the physical constraints. During the training process, less training data is required and the training time is shorter than when no physical constraints are added. Moreover, since the constraint strength changes with the number of training steps, the data pattern can be learned first in the early stage of training, and the influence of physical constraints on the model training process can be gradually increased in the later stage of training to increase the stability of the training.

[0067] In a possible implementation, the above step S205 determines the constraint strength according to the new number of training steps and a preset step threshold, including: step S2051 and step S2052.

[0068] S2051: Divide the new number of training steps by the step threshold to obtain the training progress.

[0069] In this step, for example, if the new number of training steps is 10 and the step threshold is 50, the training progress is 0.2. For another example, if the new number of training steps is 10 and the step threshold is 100, the training progress is 0.1 or 10%.

[0070] S2052: Calculate constraint strength using training progress.

[0071] In this step, the training progress may be multiplied by a preset value to obtain the constraint strength, or the training progress may be directly used as the constraint strength.

[0072] From the description of the above embodiments, it can be seen that the embodiments of the present disclosure calculate the training progress and use the training progress to calculate the constraint strength, so that the constraint strength changes with the training progress, thereby giving priority to learning data patterns in the early stage of training, and gradually increasing the influence of physical constraints on model training during training, thereby increasing the stability of the model training process and reducing the possibility of training errors.

[0073] In a possible implementation, in the above step S2052 , the constraint strength is calculated using the training progress, including steps S521 to S524 .

[0074] S521: Determine the difference between the preset value and the training progress as the incomplete ratio.

[0075] In this step, the preset values are, for example, 1, 2, 3, etc.

[0076] S522: Divide the training progress by the incomplete ratio to obtain a progress ratio.

[0077] In this step, for example, if the training progress is 0.4 and the incomplete ratio is 0.6, the progress ratio is 2:3. For another example, if the training progress is 0.2 and the incomplete ratio is 0.8, the progress ratio is 0.25.

[0078] S523: Calculate the logarithm of the progress ratio with a preset constant as the base to obtain a logarithmic value.

[0079] In this step, the preset constants, such as the natural constants e, 10, etc., can also be pre-calibrated by the staff.

[0080] S524: Multiply the logarithmic value by the preset coefficient and input the result into the Sigmoid function to obtain the constraint strength.

[0081] In this step, the preset coefficient can be pre-set by the staff.

[0082] The above steps S521 to S524 can be expressed as the following formula:

[0083] Φ

[0084] Where Φ represents the constraint strength, Sigmoid represents the Sigmoid function, α represents the preset coefficient, ln represents the logarithmic function, and x represents the training progress.

[0085] From the description of the above embodiments, it can be seen that the embodiments of the present disclosure use the Sigmoid function to smoothly map the input to the range of "[0,1]". The growth is slow when the input is small, and the growth is accelerated when the input is large, so as to reduce the impact of physical constraints on model training in the early stage of training and increase the impact of physical constraints on model training in the later stage. The preset coefficient α controls the rate and amplitude of the change of constraint strength. A larger value will lead to steeper strength changes, thereby increasing the effect of constraint strength.

[0086] In a possible implementation, in the above step S206 , determining the physical loss according to the predicted noise and the preset physical constraints includes: step S2061 and step S2062 .

[0087] S2061: Input the predicted noise into the constraint equation corresponding to each physical constraint to obtain the output result of each constraint equation.

[0088] In this step, there may be multiple types of prediction noises, and different types of prediction noises may correspond to different constraint equations. The prediction noises are input into the corresponding constraint equations to obtain output results of the constraint equations.

[0089] For example, the predicted noise includes the intake air pressure and the exhaust air pressure of the aircraft engine. The intake air pressure and the exhaust air pressure correspond to a preset constraint equation. Then, the intake air pressure and the exhaust air pressure are input into the corresponding constraint equation to obtain the output result.

[0090] S2062: Add the output results of each constraint equation to obtain the physical loss.

[0091] In this step, for example, if there are currently five constraint equations and the corresponding output results are 0.2, 0.5, 1, 0.3, and 0.5, the physical loss is 2.5. For another example, if there are currently three constraint equations and the corresponding output results are 2, 5, and 10, the physical loss is 17.

[0092] From the description of the above embodiments, it can be seen that the embodiments of the present disclosure input the predicted noise into the constraint equation to obtain the output results of each constraint equation, add up the output results to obtain the physical loss, and realize the simultaneous consideration of multiple physical constraints, so that the trained model conforms to various physical constraints. The trained industrial AIGC basic model can generate a model that conforms to multiple physical constraints.

[0093] In a possible implementation, the physical constraint includes a change trend constraint.

[0094] The change trend constraint is used to determine whether the change trend between the associated physical quantities meets the preset change trend requirements.

[0095] In the above step S2061, the predicted noise is input into the constraint equation corresponding to each physical constraint to obtain the output results of each constraint equation, including:

[0096] S20611: Input the predicted noise into the constraint equation corresponding to the change trend constraint to obtain an output result of the constraint equation corresponding to the change trend constraint.

[0097] In this step, the prediction noise of adjacent time steps may be input into a constraint equation corresponding to the change trend constraint to obtain an output result of the constraint equation corresponding to the change trend constraint.

[0098] It can be seen from the description of the above embodiments that the embodiments of the present disclosure use a change trend constraint to determine whether the changes between different predicted noises (device operation data) comply with preset physical constraints.

[0099] In a possible implementation, each training step includes at least two time steps. The training data includes at least two data categories, where the data categories include a reference category and at least one associated category associated with the reference category.

[0100] The data change trend of the associated category should be related to the change trend of the benchmark category, showing a positive or negative correlation.

[0101] The above step S20611 predicts the constraint equation corresponding to the change trend constraint of the noise input to obtain the output result of the constraint equation corresponding to the change trend constraint, including: steps S6111 to S6116.

[0102] S6111: Subtract the prediction noise of the previous time step from the prediction noise of the current time step of the benchmark type to obtain a benchmark prediction noise difference.

[0103] In this step, for example, if the predicted noise of the benchmark category at the current time step is 100 and the predicted noise at the previous time step is 90, then the benchmark predicted noise difference is 10. For another example, if the predicted noise of the benchmark category at the current time step is 20 and the predicted noise at the previous time step is 8, then the benchmark predicted noise difference is 12.

[0104] S6112: Using the reference prediction noise difference, divide it by the sum of the prediction noise of the previous time step of the reference type and the preset value to obtain the characteristic slope corresponding to the reference type.

[0105] In this step, the preset value is a minimum value to avoid the denominator being zero.

[0106] In a possible implementation, the above steps S6111 and S6112 can be expressed as follows:

[0107]

[0108] Where, represents the characteristic slope, represents the prediction noise at the current time step, represents the prediction noise of the previous time step, Indicates the preset value.

[0109] S6113: Subtract the prediction noise of the previous time step from the prediction noise of the current time step of the associated type to obtain the associated prediction noise difference.

[0110] This step is similar to the above step S6111 and will not be repeated here.

[0111] S6114: Using the associated prediction noise difference, divide it by the sum of the prediction noise of the previous training step of the associated category and a preset value to obtain a characteristic slope corresponding to the associated category.

[0112] This step is similar to the above step S6112 and will not be repeated here.

[0113] S6115: Calculate the mean square error between the characteristic slope corresponding to each associated category and the characteristic slope corresponding to the reference category.

[0114] In this step, the characteristic slope corresponding to the target association category is subtracted from the characteristic slope corresponding to the reference category to obtain the slope difference. The square of the slope difference is calculated to obtain the square of the difference corresponding to the target association category. The square of the difference corresponding to each association category is added together and then divided by the number of association categories to obtain the mean square error.

[0115] S6116: Sum the mean square errors and divide the sum by the total number of time steps to obtain the output result of the constraint equation corresponding to the change trend constraint.

[0116] In this step, the total number of time steps can be pre-calibrated by the staff, or can be equal to the number of data groups in each set of training data.

[0117] In a possible implementation, the above steps S6115 and S6116 can be expressed as follows:

[0118]

[0119] Where, Represents the data results, i represents the i-th time step, n represents the total number of time steps, represents the characteristic slope corresponding to the associated type, It represents the characteristic slope corresponding to the benchmark category, and MSN represents the mean square error function.

[0120] From the description of the above embodiments, it can be seen that the embodiments of the present disclosure calculate the characteristic slopes of the baseline category and the characteristic slopes of the associated category at different time steps, and calculate the mean square error of the characteristic slopes of the baseline category and the associated category, sum the mean square errors, and then divide them by the total number of time steps to obtain the output result corresponding to the change trend constraint. The output result corresponding to the change trend constraint can be used to determine the correlation between the changes in the associated category and the baseline category in the predicted noise, so that the change trend of the related data tends to be consistent.

[0121] Application scenarios of the above trend constraints, such as the total pressure at the fan inlet of an aircraft engine ( )、Fan outlet total pressure( ), low pressure compressor outlet pressure ( ), and physical fan speed ( ) are physical constraints on the operating parameters between the two. The Bernoulli equation describes the relationship between gas flow rate and pressure. This relationship can be expressed as:

[0122]

[0123] in is the static pressure of the gas, and represent the gas density and gas flow rate respectively, is the total pressure of the gas. The total pressure of the gas is the static pressure of the gas and gas dynamic pressure At the air intake of an aircraft engine, the total pressure at the fan inlet is Indicates the total pressure of the gas , fan intake speed Indicates gas flow rate .therefore, and The relationship between can be expressed by the above Bernoulli equation:

[0124]

[0125] At the same time, the fan speed It is a key factor affecting the efficiency of air compression. The increase in intake speed Increases the fan's efficiency and increases the total pressure at the fan inlet. .

[0126] In all turbulence models of compressors, the inlet and outlet pressures and Following the isentropic compression relationship:

[0127]

[0128] in is the ratio of specific heats, R is the gas constant, , In aircraft engines, the total pressure at the fan outlet and the total pressure at the fan inlet can be considered as the outlet and inlet pressures. and .therefore, and The relationship between can be expressed by the above isentropic compression equation:

[0129]

[0130] in exist middle, is the inlet temperature. Absolute speed V is related to fan speed This equation shows that increasing Can improve air compression efficiency and increase Mach number and outlet pressure .along with The increase in the low-pressure compressor will result in a higher pressure, which will lead to a greater compression ratio and increase the outlet pressure of the low-pressure compressor. Increase accordingly.

[0131] In summary, the pressures at each stage of compression are interdependent. Specifically, the total pressure at the fan inlet ( )、Fan outlet total pressure( ), low pressure compressor outlet pressure ( ), and physical fan speed ( ) show synchronous changes and are applicable to the above-mentioned change trend constraints.

[0132] In a possible implementation, in step S208, determining the error value based on the predicted noise, the original noise, and the comprehensive physical loss includes:

[0133] S2081: Calculate the noise mean square error between the predicted noise and the original noise.

[0134] In this step, the prediction noise can be the prediction noise of all time steps. The prediction noise of each time step can include data of one or more data types. The difference between the data of each data type in the prediction noise and the original noise is calculated, the square of the difference is calculated, and the square of the difference of each data type is added and divided by the total number of data types to obtain the noise mean square error.

[0135] S2082: Add the noise mean square error and the comprehensive physical loss to obtain an error value.

[0136] In this step, for example, if the noise mean square error is 2 and the comprehensive physical loss is 5, the error value is 7. For another example, if the noise mean square error is 10 and the comprehensive physical loss is 7, the error value is 17.

[0137] In this step, the above steps S2081 and S2082 can be expressed as:

[0138]

[0139] Where, Indicates the error value, Represents the expected symbol, specifically the training data is collected from the data distribution D, Represents the original noise is sampled from a Gaussian distribution of the identity matrix, The overall representation is the noise mean square error, Overall represents the comprehensive physical loss.

[0140] From the description of the above embodiments, it can be seen that the embodiments of the present disclosure obtain error values by combining the error of noise and the comprehensive physical loss, and thus optimize the model by combining the loss of noise prediction and the loss of physical constraints, so that the model not only learns data laws during the training process, but also learns physical laws, thereby increasing the speed at which the model learns physical laws, reducing the energy consumption generated by the computer during training, and ensuring that the data generated by the trained model conforms to physical laws.

[0141] In a possible implementation, without going to S209 above to optimize the model to be trained using the error value to obtain a new model to be trained, and again executing the process of adding original noise to the training data to determine the error value until the error value is less than a preset error threshold or the new number of training steps reaches the step threshold, and the new model to be trained is determined as the industrial AIGC basic model, it also includes: step S210 and step S211.

[0142] S210: In response to detecting a data tag input by a user, inputting the data tag into an industrial AIGC basic model so that the industrial AIGC basic model outputs equipment operating parameters.

[0143] This step may include detecting a data tag input by a user through a keyboard, or detecting a data tag when the method of the embodiment of the present application is running.

[0144] S211: Output device operating parameters.

[0145] This step may include displaying the device operating parameters, or writing the device operating parameters into a database.

[0146] From the description of the above embodiments, it can be seen that the embodiments of the present disclosure use the industrial AIGC basic model to generate device operating parameters of the data label correspondence table after detecting the data label input by the user, thereby achieving the generation of device operating parameters that meet physical constraints.

[0147] In one possible implementation, after the above step S210 responds to detecting the data label input by the user and inputs the data label into the pre-trained industrial AIGC basic model so that the industrial AIGC basic model outputs the equipment operating parameters, it also includes: steps S220 to S225.

[0148] S220: Inputting the data label into a pre-trained denoising model, so that the denoising model denoises the preset noise data in multiple time steps to obtain the equipment operation data corresponding to the data label.

[0149] The denoising model used in this step is a model that is not trained with physical constraints compared to the above step S210.

[0150] Among them, the reverse sampling process without physical information can be defined as:

[0151]

[0152] in Indicates that at time step The data input to the denoising model is represents the noise schedule, , .also, represents the noise estimate of the denoising model predictions.

[0153] S221: Acquire various intermediate data generated by the denoising model in the process of generating equipment operation data.

[0154] In this step, the intermediate data may include the prediction noise generated by the denoising model at different time steps during the denoising process.

[0155] S222: Determine the intermediate physical loss according to the intermediate data and the preset physical constraints.

[0156] In this step, the intermediate data is input into a physical constraint function corresponding to a preset physical constraint to obtain an intermediate physical loss.

[0157] S223: For any time step, the partial derivative of the intermediate physical loss is calculated based on the intermediate data to obtain the error gradient.

[0158] In this step, the intermediate data of each time step is used as the variable, the intermediate physical loss is used as the objective function, and the derivative function is called to obtain the partial derivative of the intermediate data with respect to the intermediate physical loss, that is, the error gradient.

[0159] S224: Determine the operation data to be output based on the device operation data and the error gradient corresponding to each time step.

[0160] This step involves summing the error gradients at each time step and multiplying them by a preset weight value to obtain a weighted total error gradient. This weighted total error gradient is then subtracted from the device operating data to obtain the output operating data. Alternatively, the step involves subtracting the error gradients of each practice unit from the device operating data to obtain the output operating data.

[0161] In a possible implementation, the above steps S222 to S224 can be expressed as the following formula:

[0162]

[0163] Where, Indicates equipment operation data. Indicates the preset weight value, represents the sum of the error gradients at each time step.

[0164] S225: Output the operation data to be output.

[0165] This step is similar to the above step S211 and will not be repeated here.

[0166] From the description of the above embodiments, it can be seen that the embodiments of the present disclosure use a pre-trained denoising model to predict the equipment operation data, and use the intermediate data generated in the prediction process to calculate the error gradient, remove the error gradient data from the equipment operation data, and obtain the operation data to be output, thereby achieving the goal of not adding physical constraints during the model training process, so that the final data conforms to the physical constraints while reducing the model training time, thereby reducing the energy consumption of the computer during the model training process.

[0167] An embodiment of the present application also provides an industrial AIGC basic model with embedded physical mechanism. The industrial AIGC basic model is trained using the training method for the industrial AIGC basic model with embedded physical mechanism provided in any of the above embodiments. The industrial AIGC basic model with embedded physical mechanism includes: a label acquisition module, a data generation module and a data output module. The label acquisition module is used to obtain the data label input by the user; the data generation module is used to use the data label to perform noise reduction processing on the preset noise data to obtain the equipment operating parameters that meet the preset physical constraints; the data output module is used to output the equipment operating parameters.

[0168] The tag acquisition module can obtain the user input content by reading the user input content, reading the content stored in the memory, or reading the value of a preset variable. The data output module can output data to a file, output to the memory, etc.

[0169] The industrial AIGC basic model with embedded physical mechanism provided in the embodiment of the present application is trained using the industrial AIGC basic model with embedded physical mechanism training method in the above embodiment, and can output equipment operating parameters that comply with physical constraints.

[0170] Figure 3 This is a schematic diagram of the structure of the industrial AIGC basic model training device with embedded physical mechanism provided in the embodiment of this application. Figure 3 As shown, the industrial AIGC basic model training device 300 with embedded physical mechanism includes: a data acquisition module 301, a noise addition module 302, a model output module 303, and a step increase module 304.

[0171] The data acquisition module 301 is used to obtain multiple sets of training data and training data labels corresponding to the training data;

[0172] A noise adding module 302 is used to add original noise to the training data to obtain noise data;

[0173] The model output module 303 is used to input the noise data and the training data label into the model to be trained so that the model to be trained outputs the predicted noise;

[0174] The step number increasing module 304 is used to increase the number of training steps by 1 to obtain a new number of training steps;

[0175] Strength determination module 305, for determining the constraint strength according to the new training step number and the preset step number threshold;

[0176] A physical loss module 306 is configured to determine physical loss based on the predicted noise and preset physical constraints;

[0177] Comprehensive loss module 307, for determining comprehensive physical loss based on the restraint strength and physical loss;

[0178] An error determination module 308 is configured to determine an error value based on the predicted noise, the original noise, and the comprehensive physical loss;

[0179] The model optimization module 309 is used to optimize the model to be trained using the error value to obtain a new model to be trained, and again perform the process of adding original noise to the training data to determine the error value until the error value is less than the preset error threshold or the new number of training steps reaches the step threshold, and the new model to be trained is determined as the industrial AIGC basic model.

[0180] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0181] In a possible implementation, the strength determination module 305 is specifically configured to divide the new number of training steps by the step threshold to obtain a training progress; and calculate the constraint strength using the training progress.

[0182] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0183] In one possible implementation, the strength determination module 305 is specifically configured to determine the difference between a preset value and the training progress as the incomplete ratio; divide the training progress by the incomplete ratio to obtain a progress ratio; obtain a logarithm of the progress ratio with a preset constant as the base; and multiply the logarithm by a preset coefficient and input the result into a Sigmoid function to obtain the constraint strength.

[0184] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0185] In a possible implementation, the physical loss module 306 is configured to input the predicted noise into the constraint equation corresponding to each physical constraint to obtain the output result of each constraint equation; and add the output results of each constraint equation to obtain the physical loss.

[0186] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0187] In one possible implementation, the physical constraint includes a change trend constraint; in one possible implementation, the physical loss module 306 is used to input the predicted noise into the constraint equation corresponding to the change trend constraint to obtain the output result of the constraint equation corresponding to the change trend constraint.

[0188] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0189] In one possible implementation, each training step includes at least two time steps; the training data includes at least two data types, and the data types include a baseline type and at least one associated type associated with the baseline type; the physical loss module 306 is specifically used to subtract the predicted noise of the previous time step from the predicted noise of the current time step of the baseline type to obtain a baseline predicted noise difference; the baseline predicted noise difference is divided by the sum of the predicted noise of the previous time step of the baseline type and a preset value to obtain a characteristic slope corresponding to the baseline type; the predicted noise of the current time step of the associated type is subtracted from the predicted noise of the previous time step to obtain an associated predicted noise difference; the associated predicted noise difference is divided by the sum of the predicted noise of the previous training step of the associated type and a preset value to obtain a characteristic slope corresponding to the associated type; the mean square error between the characteristic slope corresponding to each associated type and the characteristic slope corresponding to the baseline type is calculated; the mean square errors are summed and then divided by the total number of time steps to obtain the output result of the constraint equation corresponding to the change trend constraint.

[0190] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0191] In a possible implementation, the error determination module 308 is configured to calculate a noise mean square error between the predicted noise and the original noise; and add the noise mean square error to the comprehensive physical loss to obtain an error value.

[0192] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0193] In one possible implementation, the model optimization module 309 is configured to, in response to detecting a data tag input by a user, input the data tag into the industrial AIGC basic model so that the industrial AIGC basic model outputs the device operating parameters; and output the device operating parameters.

[0194] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0195] In a possible implementation, the industrial AIGC basic model training device 300 with embedded physical mechanism further includes: a physical correction module 310.

[0196] The physical correction module 310 is used to input the data label into a pre-trained denoising model so that the denoising model can denoise the preset noise data for multiple time steps to obtain the equipment operation data corresponding to the data label; obtain the intermediate data generated by the denoising model in the process of generating the equipment operation data; determine the intermediate physical loss based on the intermediate data and the preset physical constraints; for any time step, calculate the partial derivative of the intermediate physical loss based on the intermediate data to obtain the error gradient; determine the operation data to be output based on the equipment operation data and the error gradient corresponding to each time step; and output the operation data to be output.

[0197] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0198] In order to implement the above embodiment, the embodiment of the present application also provides an electronic device.

[0199] refer to Figure 4 , which shows a schematic structural diagram of an electronic device 400 suitable for implementing an embodiment of the present application. The electronic device 400 may be a terminal device or a server. The terminal device may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, personal digital assistants (PDAs), tablet computers (Portable Android Devices, PADs), portable multimedia players (PMPs), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0200] like Figure 4As shown, electronic device 400 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 401 and a memory 402 communicatively connected to the processor. The processor 402 may perform various appropriate actions and processes based on programs stored in memory 402, computer-executable instructions, or programs loaded from storage device 408 into random access memory (RAM) 403, thereby implementing the physical mechanism-embedded industrial AIGC basic model training method described in any of the above-described embodiments. The memory may be a read-only memory (ROM). RAM 403 also stores various programs and data required for the operation of electronic device 400. Processing device 401, memory 402, and RAM 403 are interconnected via bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0201] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0202] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 409, or installed from the storage device 408, or installed from the memory 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present application are performed.

[0203] It should be noted that the computer-readable storage medium referred to in this application may be a computer-readable signal medium or a computer storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0204] The computer-readable storage medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0205] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0206] The computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0207] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0208] The modules described in the embodiments of the present application may be implemented in software or hardware. In some cases, the name of a unit does not necessarily limit the module itself. For example, a noise addition module may also be described as a "primary noise addition module."

[0209] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0210] The present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the processor executes the computer-executable instructions, the technical solution of the industrial AIGC basic model training method with embedded physical mechanism in any of the above embodiments is implemented. Its implementation principle and beneficial effects are similar to the implementation principle and beneficial effects of the industrial AIGC basic model training method with embedded physical mechanism. Please refer to the implementation principle and beneficial effects of the industrial AIGC basic model training method with embedded physical mechanism, which will not be repeated here.

[0211] In the context of this application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0212] The present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the technical solution of the industrial AIGC basic model training method with embedded physical mechanism in any of the above embodiments. Its implementation principle and beneficial effects are similar to the implementation principle and beneficial effects of the industrial AIGC basic model training method with embedded physical mechanism. Please refer to the implementation principle and beneficial effects of the industrial AIGC basic model training method with embedded physical mechanism, and no further details will be given here.

[0213] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the disclosure herein is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned disclosure. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this application.

[0214] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0215] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A physical mechanism embedded industrial AIGC basic model training method, characterized by: Applications to computers include: Acquire multiple sets of training data and training data labels corresponding to the training data, wherein the training data includes training data of a fan inlet total pressure and training data of a fan outlet total pressure of an aircraft engine; Adding original noise to the training data to obtain noise data; Inputting the noise data and the training data labels into a to-be-trained model so that the to-be-trained model outputs predicted noise, wherein the predicted noise includes a predicted fan inlet total pressure and a predicted fan outlet total pressure; Add 1 to the number of training steps to get the new number of training steps; Determining the constraint strength according to the new number of training steps and a preset step threshold; Determining physical losses based on the predicted noise and preset physical constraints, wherein the physical constraints include constraint equations for fan inlet total pressure and fan outlet total pressure; determining a comprehensive physical loss based on the restraint strength and the physical loss; determining an error value according to the predicted noise, the original noise, and the comprehensive physical loss; The error value is used to optimize the model to be trained to obtain a new model to be trained, and the process of adding original noise to the training data to determine the error value is performed again until the error value is less than a preset error threshold or the new number of training steps reaches the step threshold, and the new model to be trained is determined as the industrial AIGC basic model; The constraint equations for the fan inlet total pressure and the fan outlet total pressure are as follows: Where, P 21 represents the total pressure at the fan outlet, P2 represents the total pressure at the fan inlet, γ represents the specific heat ratio, and Ma represents the Mach number; wherein determining the comprehensive physical loss according to the constraint strength and the physical loss comprises: multiplying the constraint strength by the physical loss to obtain the comprehensive physical loss; The step of determining the physical loss based on the predicted noise and the preset physical constraints includes: inputting the predicted noise into the constraint equations corresponding to the physical constraints to obtain the output results of the constraint equations; and adding the output results of the constraint equations to obtain the physical loss.

2. The method according to claim 1, characterized in that The determining of the constraint strength according to the new number of training steps and a preset step threshold comprises: Divide the new number of training steps by the step threshold to obtain training progress; Using the training schedule, constraint strength is calculated.

3. The method according to claim 2, characterized in that The calculating the constraint strength by using the training progress includes: Determine the difference between the preset value and the training progress as the incomplete ratio; Divide the training progress by the incomplete ratio to obtain a progress ratio; Taking a preset constant as the base of the logarithm of the progress ratio, a logarithm value is obtained; The logarithmic value is multiplied by a preset coefficient and then input into a Sigmoid function to obtain the constraint strength.

4. The method according to claim 1, wherein The physical constraints include change trend constraints; Inputting the predicted noise into the constraint equations corresponding to the physical constraints to obtain output results of the constraint equations includes: The predicted noise is input into a constraint equation corresponding to the change trend constraint to obtain an output result of the constraint equation corresponding to the change trend constraint.

5. The method according to claim 4, characterized in that Each training step includes at least two time steps; the training data includes at least two data types, the data types including a reference type and at least one associated type associated with the reference type; The step of inputting the predicted noise into a constraint equation corresponding to a change trend constraint to obtain an output result of the constraint equation corresponding to the change trend constraint comprises: Subtracting the prediction noise of the previous time step from the prediction noise of the current time step of the benchmark type to obtain a benchmark prediction noise difference; The reference prediction noise difference is divided by the sum of the prediction noise of the previous time step of the reference type and a preset value to obtain a characteristic slope corresponding to the reference type; Subtracting the prediction noise of the previous time step from the prediction noise of the current time step of the association type to obtain an association prediction noise difference; The association prediction noise difference is divided by the sum of the prediction noise of the previous training step of the association type and the preset value to obtain a characteristic slope corresponding to the association type; Calculating the mean square error between the characteristic slope corresponding to each associated category and the characteristic slope corresponding to the reference category; The mean square errors are summed and then divided by the total number of time steps to obtain the output result of the constraint equation corresponding to the change trend constraint.

6. The method according to claim 1, wherein The determining of the error value according to the predicted noise, the original noise and the comprehensive physical loss includes: Calculating a noise mean square error between the predicted noise and the original noise; The noise mean square error is added to the comprehensive physical loss to obtain the error value.

7. The method according to any one of claims 1 to 3, characterized in that After optimizing the model to be trained by using the error value to obtain a new model to be trained, and again performing the process of adding original noise to the training data to determine the error value until the error value is less than a preset error threshold or the new number of training steps reaches the step threshold, and determining the new model to be trained as the industrial AIGC basic model, the method further includes: In response to detecting a data tag input by a user, inputting the data tag into the industrial AIGC base model so that the industrial AIGC base model outputs a device operating parameter; Output the device operating parameters.

8. The method according to claim 7, characterized in that After inputting the data tag into the industrial AIGC basic model in response to detecting the data tag input by the user so that the industrial AIGC basic model outputs the equipment operating parameters, the method further includes: Inputting the data label into a pre-trained denoising model so that the denoising model denoises the preset noise data at multiple time steps to obtain the device operation data corresponding to the data label; Acquire various intermediate data generated by the denoising model in the process of generating the device operation data; Determine the intermediate physical loss based on the intermediate data and the preset physical constraints; For any time step, the partial derivative of the intermediate physical loss is calculated based on the intermediate data to obtain the error gradient; Determining the operation data to be output according to the device operation data and the error gradient corresponding to each time step; The to-be-outputted operating data is outputted.

9. An industrial AIGC basic model with embedded physical mechanism, characterized by: The industrial AIGC basic model is trained using the method according to any one of claims 1 to 8; The industrial AIGC basic model embedded in the physical mechanism includes: a label acquisition module, a data generation module and a data output module; The label acquisition module is used to obtain the data label input by the user; The data generation module is configured to perform noise reduction processing on the preset noise data using the data labels to obtain equipment operating parameters that meet the preset physical constraints; The data output module is used to output the equipment operating parameters.

10. A physical mechanism embedded industrial AIGC basic model training device, characterized by: Applications to computers include: a data acquisition module, configured to acquire multiple sets of training data and training data labels corresponding to the training data, wherein the training data includes training data on the total fan inlet pressure and the total fan outlet pressure of the aircraft engine; A noise adding module, used for adding original noise to the training data to obtain noise data; a model output module, configured to input the noise data and the training data label into a to-be-trained model, so that the to-be-trained model outputs predicted noise, wherein the predicted noise includes a predicted fan inlet total pressure and a predicted fan outlet total pressure; The step increase module is used to add 1 to the training step number to obtain a new training step number; a strength determination module, configured to determine the constraint strength according to the new number of training steps and a preset step threshold; a physical loss module, configured to determine physical loss based on the predicted noise and preset physical constraints, wherein the physical constraints include constraint equations for fan inlet total pressure and fan outlet total pressure; a comprehensive loss module, configured to determine a comprehensive physical loss based on the constraint strength and the physical loss; an error determination module, configured to determine an error value based on the predicted noise, the original noise, and the comprehensive physical loss; a model optimization module, configured to optimize the model to be trained using the error value to obtain a new model to be trained, and again perform the process of adding original noise to the training data to determine the error value until the error value is less than a preset error threshold or the new number of training steps reaches the step threshold, and determine the new model to be trained as the industrial AIGC basic model; The constraint equations for the fan inlet total pressure and the fan outlet total pressure are as follows: Where, P 21 represents the total pressure at the fan outlet, P2 represents the total pressure at the fan inlet, γ represents the specific heat ratio, and Ma represents the Mach number; wherein determining the comprehensive physical loss according to the constraint strength and the physical loss comprises: multiplying the constraint strength by the physical loss to obtain the comprehensive physical loss; The step of determining the physical loss based on the predicted noise and the preset physical constraints includes: inputting the predicted noise into the constraint equations corresponding to the physical constraints to obtain the output results of the constraint equations; and adding the output results of the constraint equations to obtain the physical loss.

11. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 8.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 8 when executed by a processor.

13. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 8 when the computer program is executed by a processor.

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