Dynamic configuration switching system in serialized annotation form and control method

Abstract

The invention discloses a dynamic configuration switching system in a serialized annotation form and a control method. The method comprises the following steps: acquiring a real-time running state of industrial equipment; inputting the real-time operation state into a preset parameter configuration model, and outputting a dynamic configuration parameter set; loading a plurality of configuration parameters according to the dynamic configuration parameter set; wherein the dynamic configuration parameter set comprises a plurality of configuration parameters which are output by a parameter configuration model and are expressed in a serialized annotation format; according to the method, the dynamic configuration parameter set is automatically output through the multi-task learning model according to the real-time operation state of the industrial equipment, and is represented in a serialized annotation format, so that the real-time dynamic configuration switching of the equipment is realized. Particularly, input of multiple parameters is simplified through principal component analysis, meanwhile, the most core data structure is reserved, and the dynamic adjustment function of configuration parameters is improved.

Description

Technical Field

[0001] The present invention relates to a configuration switching system, in particular to a dynamic configuration switching system and a control method in a serialized annotation form. Background Art

[0002] During the operation of industrial equipment, the configuration parameters of the equipment (such as temperature setting, current threshold, working mode, etc.) have an important impact on the performance and efficiency of the equipment. In modern industrial equipment, there are already some methods to automatically change the configuration parameters based on the operating status, such as machine learning adjustment based on multiple parameters; however, multi-parameter input increases the complexity of data processing, requires processing high-dimensional data, and is easily affected by noise. In addition, the correlation between multiple parameters causes data redundancy, affecting the training efficiency and prediction accuracy of machine learning. Patent document CN118467212A discloses a method and system for processing industrial equipment data, which can adjust the resource configuration of each industrial equipment.

[0003] However, the above documents and prior art are difficult to achieve the problem of retaining the core data structure when multiple parameters are input to reduce the data redundancy of the processing process. To this end, the present invention provides a dynamic configuration switching system and control method in the form of serialized annotations. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a dynamic configuration switching system and control method in the form of serialized annotations, which solve the technical problems raised in the background technology through multi-parameter data dimensionality reduction.

[0005] This embodiment provides a dynamic configuration switching system and control method in the form of serialized annotations, exploring how to solve the problem of retaining the core data structure when multiple parameters are input to reduce data redundancy in the processing process.

[0006] In a first aspect, the present invention provides a dynamic configuration switching control method in the form of serialized annotations, which is used in an industrial equipment server, and the method comprises: Obtaining the real-time operating status of the industrial equipment; Inputting the real-time operating status into a preset parameter configuration model and outputting a dynamic configuration parameter set; According to the dynamic configuration parameter set, a plurality of configuration parameters are loaded; wherein the dynamic configuration parameter set is output by a parameter configuration model and is a plurality of configuration parameters represented in a serialized annotation format.

[0007] In some of the embodiments, the modeling method of the parameter configuration model includes: S1. Obtain multi-parameter trend characteristics of n industrial equipment in the same time period and their corresponding historical configuration parameter sets; S2, using the multi-parameter trend feature as an input feature and the corresponding historical configuration parameter set as a target label to construct a model training sample; S3, collect n model training samples to obtain parameter training set; S4. Input the model training samples of the parameter training set into the multi-task learning model to train the parameter configuration model.

[0008] In some embodiments, obtaining multi-parameter trend characteristics of n industrial devices in the same time period includes: S1-1, collecting n operating parameter variables of the industrial equipment within a reference time length, the n operating parameter variables including: temperature change rate, current fluctuation amplitude and other selected operating parameter variables; S1-2. Perform principal component vector analysis on the n operating parameter variables to obtain the multi-parameter trend characteristics.

[0009] In some embodiments, n operating parameter variables of the industrial equipment are collected within a reference time length, and the n operating parameter variables include: temperature change rate, current fluctuation amplitude and other selected operating parameter variables, including: S1-1-1. Define a reference time length, where the reference time length includes a start time point and an end time point; S1-1-2. Collect the device temperature, current and other selected operating parameters at the start time point and the end time point respectively to calculate the operating parameter variables within the reference time length.

[0010] In some embodiments, performing principal component vector analysis on the n operating parameter variables includes: S1-2-1, standardize n operating parameter variables to obtain standardized parameters, and combine them into a standardized matrix; S1-2-2. Calculate the covariance matrix based on the standardized matrix; S1-2-3, perform singular value decomposition on the auxiliary defense difference moment to obtain the eigenvalue and its corresponding eigenvector; S1-2-4, arranging the eigenvalues ​​in descending order, determining the principal component vector according to a preset variance explanation rate; and constructing a principal component vector matrix based on the principal component vector; Furthermore, the principal component matrix is ​​constructed by arranging each selected principal component vector as a column vector in sequence to form a principal component vector matrix.

[0011] S1-2-5. Project the standardized matrix onto the principal component vector matrix to obtain a reduced-dimensional matrix, wherein the elements of the reduced-dimensional matrix are the multi-parameter trend features.

[0012] The expression of the matrix after dimension reduction is: ; in, represents the matrix after dimension reduction, Represents the principal component vector matrix, with dimension , each element of which is expressed as , represents the projection value of the i-th parameter sample on the j-th principal component vector, and the projection value is the numerical representation of the multi-parameter trend feature.

[0013] In some embodiments, n operating parameter variables are standardized to obtain standardized parameters, which are combined into a standardized matrix, including: S1-2-1-1, collect n operating parameter variables as parameter samples, and construct an independent data set for each operating parameter; Wherein, the expression of the independent data set of each operating parameter is: , represents the kth running parameter variable of the i-th parameter sample; S1-2-1-2. Calculate the mean and standard deviation of each independent data set of operating parameters to obtain the mean and standard deviation of the operating parameter variables; The mean expression of the operating parameter variable is: ; represents the mean value of the kth running parameter variable, represents the kth running parameter variable of the i-th parameter sample; The standard deviation expression of the operating parameter variable is: ; represents the standard deviation of the kth running parameter variable; S1-2-1-3. Standardize each operating parameter variable using its mean and standard deviation to obtain n standardized operating parameter variables; The expression of the standardized operating parameter variable is: ; represents the kth standardized operating parameter variable of the i-th parameter sample; S1-2-1-4, combining n standardized operating parameter variables into a standardized matrix ; ; Indicates the index of the running parameter. represents the index of the parameter sample, m represents the total number of running parameter variables, and n represents the number of parameter samples. represents the kth standardized running parameter variable of the ith parameter sample.

[0014] In some of the embodiments, calculating the covariance matrix according to the standardized matrix includes: S1-2-2-1, the standardized matrix Transpose to get the transposed matrix ; The transposed matrix The expression is: ; S1-2-2-2, transpose the matrix and the standardized matrix Multiply them to get the product matrix ; The product matrix The expression is: ; Wherein, the elements of the product matrix are represented as unnormalized values ​​of the elements in the covariance matrix; S1-2-2-3. Product Matrix The unnormalized value of is unbiasedly estimated to obtain the covariance matrix ; The covariance matrix The expression is: ; Among them, the covariance matrix The elements of are defined as follows: ; ; represents the covariance between the kth standardized operating parameter variable and the jth standardized operating parameter variable.

[0015] In some of the embodiments, performing singular value decomposition on the defense difference moment to obtain eigenvalues ​​and their corresponding eigenvectors includes: S1-2-3-1. Perform singular value decomposition on the covariance matrix to obtain a decomposed matrix; The expression of the decomposed matrix is: ; in, represents the first orthogonal matrix, D represents a diagonal matrix whose diagonal elements are singular values, is the second orthogonal matrix; S1-2-3-2, selecting a column vector of the first orthogonal matrix as an eigenvector of the covariance matrix, wherein the eigenvector is used to characterize the principal component vector; S1-2-3-3. Select the square of the singular value of the diagonal matrix as the eigenvalue of the covariance matrix, and the eigenvalue is used to characterize the variance of the principal component vector.

[0016] In some embodiments, arranging the eigenvalues ​​in descending order and determining the principal component vector according to a preset variance explanation rate includes: S1-2-4-1. Arrange the eigenvalues ​​obtained after singular value decomposition in descending order to obtain a sorted eigenvalue sequence; S1-2-4-2, calculating the cumulative variance explanation rate of the eigenvalues ​​in descending order in the eigenvalue sequence until the cumulative variance explanation rate reaches the preset variance explanation rate; S1-2-4-3. Select the eigenvector corresponding to the eigenvalue that satisfies the cumulative variance explanation ratio and use it as the principal component vector.

[0017] Compared with the prior art, the present invention provides a dynamic configuration switching control method in the form of serialized annotations. Through a multi-task learning model, the dynamic configuration parameter set is automatically output according to the real-time operating status of the industrial equipment, and is expressed in a serialized annotation format to achieve real-time dynamic configuration switching of the equipment. In particular, the present invention simplifies the input of multiple parameters through principal component analysis, while retaining the most core data structure, thereby improving the training efficiency and prediction accuracy of the model. It solves the problems of complex multi-parameter input, insufficient generalization ability of single-task models, and limited ability of real-time monitoring systems to handle complex relationships in the prior art, and improves the dynamic adjustment function of configuration parameters.

[0018] In a second aspect, the present invention provides a dynamic configuration switching system in the form of serialized annotations, comprising: A real-time operation status acquisition module, used to acquire the real-time operation status of the industrial equipment; A configuration parameter set output module, used to input the real-time operating status into a preset parameter configuration model and output a dynamic configuration parameter set; The configuration parameter loading module is used to load a number of configuration parameters according to the dynamic configuration parameter set; wherein the dynamic configuration parameter set is a number of configuration parameters output by the parameter configuration model and represented in a serialized annotation format.

[0019] Compared with the prior art, the beneficial effects of the dynamic configuration switching system in the form of serialized annotations of the present invention are the same as the beneficial effects of the dynamic configuration switching control method in the form of serialized annotations described above, so they will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a dynamic configuration switching control method in the form of serialized annotations of the present invention; Figure 2 A modeling flow chart of the parameter configuration model of the present invention; Figure 3It is a structural block diagram of a dynamic configuration switching system in the form of serialized annotations of the present invention; DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1: Please refer to Figure 1 The present invention provides a dynamic configuration switching control method in the form of serialized annotations, which is used in an industrial equipment server. The method includes: Obtain the real-time operating status of industrial equipment; Input the real-time operation status into the preset parameter configuration model and output the dynamic configuration parameter set; According to the dynamic configuration parameter set, several configuration parameters are loaded; wherein the dynamic configuration parameter set is output by the parameter configuration model and is several configuration parameters represented in a serialized annotation format.

[0023] Specifically, the real-time operating status of the industrial equipment is input into the parameter configuration model to obtain a dynamic configuration parameter set based on the real-time operating status, so as to realize the real-time dynamic configuration switching of the industrial equipment. The dynamic configuration parameter set includes several configuration parameters that meet the current industrial equipment.

[0024] Among them, the several configuration parameters represented by the serialized annotation format refer to the configuration parameters and their related information being serialized in a structured manner and accompanied by annotation information, thereby forming a dynamic configuration parameter set; in the industrial equipment server, the dynamic configuration switching control method can output a set of predicted dynamic configuration parameter sets according to the real-time operating status of the industrial equipment through a preset parameter configuration model. The configuration parameters are represented in a serialized annotation format and can be easily loaded into the device to achieve real-time adjustment and optimization of the operating status of the device. For example, the configuration parameters may include temperature settings, current thresholds, operating modes, etc. These parameters are represented in JSON format and accompanied by type, unit and description information.

[0025] Embodiment 2: The technical solution of Embodiment 2 is different from that of Embodiment 1 in that a modeling method of a parameter configuration model is disclosed. Figure 2 , methods include: S1. Obtain multi-parameter trend characteristics of n industrial equipment in the same time period and their corresponding historical configuration parameter sets; S2, taking multi-parameter trend features as input features and the corresponding historical configuration parameter sets as target labels, constructing model training samples; S3, collect n model training samples to obtain parameter training set; S4. Input the model training samples of the parameter training set into the multi-task learning model to train the parameter configuration model.

[0026] Specifically, by using multi-parameter trend features as input features and historical configuration parameter sets as target labels, combined with multi-task learning models for training, it is possible to accurately construct parameter configuration models, thereby capturing the configuration parameter requirements of industrial equipment under different operating conditions. Among them, the historical configuration parameter set is actually a collection of optimal parameters of the industrial equipment under different operating conditions in historical time. Of course, the optimal parameters are actually determined based on the performance of the industrial equipment. After obtaining feedback on the performance, the configuration parameters corresponding to the best performance are stored as historical data, and the historical configuration parameter set can be obtained by summarizing the configuration parameters in multiple historical data.

[0027] S1 specifically includes: S1-1. Collect n operating parameter variables of industrial equipment within a reference time length, where the n operating parameter variables include: temperature change rate, current fluctuation amplitude and other selected operating parameter variables; S1-2. Perform principal component vector analysis on n operating parameter variables to obtain multi-parameter trend characteristics.

[0028] Specifically, collecting multi-parameter trend characteristics of industrial equipment in the same time period can fully reflect the multi-dimensional changes of industrial equipment in a specific operating time, and provide richer input features for parameter configuration models. For example, parameters such as temperature change rate and current fluctuation amplitude can reflect the operating status and performance changes of industrial equipment.

[0029] S1-1 specifically includes: S1-1-1. Define the reference time length, which includes the starting time point and the ending time point; S1-1-2. Collect the device temperature, current and other selected operating parameters at the start time point and the end time point respectively to calculate the operating parameter variables within the reference time length.

[0030] Specifically, by defining a reference time length and collecting multiple operating parameters within the time length, the consistency of the operating parameters is ensured.

[0031] S1-2 specifically includes: S1-2-1, standardize n operating parameter variables to obtain standardized parameters, and combine them into a standardized matrix; S1-2-2. Calculate the covariance matrix based on the standardized matrix; S1-2-3, perform singular value decomposition on the auxiliary defense difference moment to obtain the eigenvalue and its corresponding eigenvector; S1-2-4, arranging the eigenvalues ​​in descending order, determining the principal component vector according to the preset variance explanation rate; and constructing the principal component vector matrix based on the principal component vector; Furthermore, the principal component matrix is ​​constructed by arranging each selected principal component vector as a column vector in sequence to form a principal component vector matrix.

[0032] S1-2-5. Project the standardized matrix onto the principal component vector matrix to obtain a reduced-dimensional matrix, wherein the elements of the reduced-dimensional matrix are multi-parameter trend features.

[0033] The expression of the matrix after dimensionality reduction is: ; in, represents the matrix after dimension reduction, Represents the principal component vector matrix, with dimension , each element of which is expressed as , which represents the projection value of the i-th parameter sample on the j-th principal component vector. The projection value is the numerical representation of the multi-parameter trend feature.

[0034] Specifically, principal component vector analysis is used to process multiple operating parameter variables. The multi-parameter trend characteristics obtained after dimensionality reduction simplify the model input data structure while retaining the main change trends of the data.

[0035] S1-2-1 specifically includes: S1-2-1-1, collect n operating parameter variables as parameter samples, and construct an independent data set for each operating parameter; The expression of the independent data set for each operating parameter is: , represents the kth running parameter variable of the i-th parameter sample; For example: The expression for the first data set is: represents the i-th temperature change rate; The expression for the second data set is: , represents the i-th current fluctuation amplitude; S1-2-1-2. Calculate the mean and standard deviation of each independent data set of operating parameters to obtain the mean and standard deviation of the operating parameter variables; The mean expression of the running parameter variable is: ; represents the mean value of the kth running parameter variable, represents the kth running parameter variable of the i-th parameter sample; The standard deviation expression of the running parameter variable is: ; represents the standard deviation of the kth running parameter variable; S1-2-1-3. Standardize each operating parameter variable using its mean and standard deviation to obtain n standardized operating parameter variables; The expression for the standardized running parameter variable is: ; represents the kth standardized operating parameter variable of the i-th parameter sample; S1-2-1-4. Combine n standardized operating parameter variables into a standardized matrix ; ; Indicates the index of the running parameter. represents the index of the parameter sample, m represents the total number of running parameter variables, and n represents the number of parameter samples. represents the kth standardized running parameter variable of the ith parameter sample.

[0036] Specifically, the construction and standardization of independent data sets for each operating parameter effectively eliminated the dimensional differences between different variables and enabled the data to be analyzed under the same standard.

[0037] S1-2-2 specifically includes: S1-2-2-1, standardize the matrix Transpose to get the transposed matrix ; Transpose a matrix The expression is: ; S1-2-2-2, transpose the matrix and the standardized matrix Multiply them to get the product matrix ; Product Matrix The expression is: ; Among them, the elements of the product matrix are represented by the unnormalized values ​​of the elements in the covariance matrix; S1-2-2-3. Product Matrix The unnormalized value of is used for unbiased estimation, and the covariance matrix is ​​obtained ; Covariance matrix The expression is: ; Among them, the covariance matrix The elements of are defined as follows: ; ; represents the covariance between the kth standardized operating parameter variable and the jth standardized operating parameter variable.

[0038] Specifically, the calculation of the covariance matrix can reveal the correlation between the parameter variables, so as to better extract features, achieve effective data compression and dimensionality reduction, and improve the prediction accuracy of the model.

[0039] S1-2-3 specifically includes: S1-2-3-1. Perform singular value decomposition on the covariance matrix to obtain a decomposed matrix; The expression of the decomposed matrix is: ; in, represents the first orthogonal matrix, D represents a diagonal matrix whose diagonal elements are singular values, is the second orthogonal matrix; S1-2-3-2, select the column vector of the first orthogonal matrix as the eigenvector of the covariance matrix, the eigenvector is used to characterize the principal component vector; S1-2-3-3. Select the square of the singular value of the diagonal matrix as the eigenvalue of the covariance matrix. The eigenvalue is used to characterize the variance of the principal component vector.

[0040] Specifically, the covariance matrix is ​​subjected to singular value decomposition to obtain eigenvalues ​​and corresponding eigenvectors, which provides a basis for subsequent principal component analysis. Singular value decomposition can extract the main features from high-dimensional data, simplify the data structure, and retain the core information of the data.

[0041] S1-2-4 specifically includes: S1-2-4-1. Arrange the eigenvalues ​​obtained after singular value decomposition in descending order to obtain a sorted eigenvalue sequence; S1-2-4-2, calculating the cumulative variance explanation rate of the eigenvalues ​​in descending order in the eigenvalue sequence until the cumulative variance explanation rate reaches the preset variance explanation rate; S1-2-4-3. Select the eigenvector corresponding to the eigenvalue that satisfies the cumulative variance explanation rate and use it as the principal component vector.

[0042] Specifically, the principal component vector is selected according to the variance explanation rate of the eigenvalue, so that the features after dimensionality reduction can fully express the main information of the original data.

[0043] S4 specifically includes: S4-1, receiving multi-parameter trend features in model training samples as input to the multi-task learning model, and outputting a configuration parameter prediction set through forward propagation; S4-2, using a multi-task loss function to calculate the loss between the configuration parameter prediction set and the historical configuration parameter set; S4-3, perform back propagation in the multi-task learning model and iterate to obtain the model update parameters; S4-4. Use the model to update the parameters for the next forward propagation, output the next configuration parameter prediction set, and repeat forward propagation and backward propagation until the convergence conditions are met.

[0044] S4-5. Export the multi-task learning model that meets the convergence conditions as a parameter configuration model.

[0045] Specifically, through the forward propagation and back propagation of the multi-task learning model, efficient model training and accurate prediction of dynamic configuration parameters are achieved. Among them, the convergence condition can be reaching a preset number of iterations, or the multi-task loss reaches a preset loss threshold, etc.

[0046] In this embodiment, the multi-task loss function is: ; in, represents the multi-task loss, n is the number of model training samples, is the historical configuration parameter set in the i-th model training sample, The configuration parameter prediction set of the i-th model training sample; C represents the total number of categories of the historical configuration parameter set, represents the one-hot encoding of the cth category in the i-th model training sample, represents the predicted probability of the cth category in the i-th model training sample; represents the weight of the mean squared error loss, is the weight of the cross entropy loss.

[0047] Specifically, a multi-task loss function is used to integrate different types of losses, which improves the prediction accuracy of the model on mixed configuration parameters of continuous values ​​and discrete categories. This solution further enhances the model's generalization ability and performance in different tasks by adjusting the weights of different loss functions, making dynamic configuration more accurate and reliable.

[0048] Example 3: See Figure 3 The technical solution of this embodiment 3 is different from that of embodiments 1 and 2 in that a dynamic configuration switching system in the form of serialized annotations is disclosed, which is used to implement a dynamic configuration switching control method in the form of serialized annotations; the system includes: A real-time operation status acquisition module is used to obtain the real-time operation status of industrial equipment; A configuration parameter set output module is used to input the real-time operation status into a preset parameter configuration model and output a dynamic configuration parameter set; The configuration parameter loading module is used to load a number of configuration parameters according to a dynamic configuration parameter set; wherein the dynamic configuration parameter set is a number of configuration parameters output by a parameter configuration model and represented in a serialized annotation format.

[0049] In summary, the present invention uses a multi-task learning model to automatically output a dynamic configuration parameter set according to the real-time operating status of industrial equipment, and expresses it in a serialized annotation format to achieve real-time dynamic configuration switching of the equipment. In particular, the present invention simplifies the input of multiple parameters through principal component analysis, while retaining the most core data structure, improving the training efficiency and prediction accuracy of the model. It solves the problems of complex multi-parameter input, insufficient generalization ability of single-task models, and limited ability of real-time monitoring systems to handle complex relationships in the prior art, and improves the dynamic adjustment function of configuration parameters.

[0050] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means.

[0051] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., DVD ), or semiconductor media. The semiconductor media may be a solid state drive.

[0052] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division of a waterway underwater terrain change analysis system and method. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0053] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A dynamic configuration switching control method in the form of serialized annotations, used in an industrial equipment server, characterized in that: The method comprises: Obtaining the real-time operating status of the industrial equipment; Inputting the real-time operating status into a preset parameter configuration model and outputting a dynamic configuration parameter set; According to the dynamic configuration parameter set, a plurality of configuration parameters are loaded; wherein the dynamic configuration parameter set is output by a parameter configuration model and is a plurality of configuration parameters represented in a serialized annotation format.

2. According to a method for dynamic configuration switching control in the form of serialized annotations as described in claim 1, it is characterized in that: The modeling method of the parameter configuration model includes: S1. Obtaining multi-parameter trend characteristics of n industrial equipment in the same time period and their corresponding historical configuration parameter sets; S2, using the multi-parameter trend feature as an input feature and the corresponding historical configuration parameter set as a target label to construct a model training sample; S3, collect n model training samples to obtain parameter training set; S4. Input the model training samples of the parameter training set into the multi-task learning model to train the parameter configuration model.

3. According to a method for dynamic configuration switching control in the form of serialized annotations as described in claim 1, it is characterized in that: The obtaining of multi-parameter trend characteristics of n industrial equipment in the same time period includes: S1-1, collecting n operating parameter variables of the industrial equipment within a reference time length, the n operating parameter variables including: temperature change rate, current fluctuation amplitude and other selected operating parameter variables; S1-2. Perform principal component vector analysis on the n operating parameter variables to obtain the multi-parameter trend characteristics.

4. According to a method for dynamic configuration switching control in the form of serialized annotations as described in claim 3, it is characterized in that: The collecting of n operating parameter variables of the industrial equipment within a reference time length includes: S1-1-1. Define a reference time length, where the reference time length includes a start time point and an end time point; S1-1-2. Collect the device temperature, current and other selected operating parameters at the start time point and the end time point respectively to calculate the operating parameter variables within the reference time length.

5. According to a method for dynamic configuration switching control in the form of serialized annotations as described in claim 3, it is characterized in that: The performing principal component vector analysis on the n operating parameter variables comprises: S1-2-1, standardize n operating parameter variables to obtain standardized parameters, and combine them into a standardized matrix; S1-2-2. Calculate the covariance matrix based on the standardized matrix; S1-2-3, perform singular value decomposition on the auxiliary defense difference moment to obtain the eigenvalue and its corresponding eigenvector; S1-2-4, arranging the eigenvalues ​​in descending order, determining the principal component vector according to a preset variance explanation rate; and constructing a principal component vector matrix based on the principal component vector; Furthermore, the principal component matrix is ​​constructed by: arranging each selected principal component vector as a column vector in sequence to form a principal component vector matrix; S1-2-5, projecting the standardized matrix onto the principal component vector matrix to obtain a dimension-reduced matrix, wherein the elements of the dimension-reduced matrix are the multi-parameter trend features; The expression of the matrix after dimension reduction is: ; in, represents the matrix after dimension reduction, Represents the principal component vector matrix, with dimension , each element of which is expressed as , represents the projection value of the i-th parameter sample on the j-th principal component vector, and the projection value is the numerical representation of the multi-parameter trend feature.

6. A method for dynamic configuration switching control in the form of serialized annotations according to claim 5, characterized in that: The step of standardizing the n operating parameter variables includes: S1-2-1-1, collect n operating parameter variables as parameter samples, and construct an independent data set for each operating parameter; Wherein, the expression of the independent data set of each operating parameter is: , represents the kth running parameter variable of the i-th parameter sample; S1-2-1-2. Calculate the mean and standard deviation of each independent data set of operating parameters to obtain the mean and standard deviation of the operating parameter variables; The mean expression of the operating parameter variable is: ; represents the mean value of the kth running parameter variable, represents the kth running parameter variable of the i-th parameter sample; The standard deviation expression of the operating parameter variable is: ; represents the standard deviation of the kth running parameter variable; S1-2-1-3, using the mean and standard deviation of each operating parameter variable to standardize each operating parameter variable to obtain n standardized operating parameter variables; The expression of the standardized operating parameter variable is: ; represents the kth standardized operating parameter variable of the i-th parameter sample; S1-2-1-4, combining n standardized operating parameter variables into a standardized matrix ; ； Indicates the index of the running parameter. represents the index of the parameter sample, m represents the total number of running parameter variables, and n represents the number of parameter samples. represents the kth standardized running parameter variable of the ith parameter sample.

7. A method for dynamic configuration switching control in the form of serialized annotations according to claim 5, characterized in that: The step of calculating the covariance matrix according to the standardized matrix includes: S1-2-2-1, the standardized matrix Transpose to get the transposed matrix ; The transposed matrix The expression is: ； S1-2-2-2, transpose the matrix and the standardized matrix Multiply them to get the product matrix ; The product matrix The expression is: ； Wherein, the elements of the product matrix are represented as unnormalized values ​​of the elements in the covariance matrix; S1-2-2-3. Product Matrix The unnormalized value of is unbiasedly estimated to obtain the covariance matrix ; The covariance matrix The expression is: ; Among them, the covariance matrix The elements of are defined as follows: ； ； represents the covariance between the kth standardized operating parameter variable and the jth standardized operating parameter variable.

8. A method for dynamic configuration switching control in the form of serialized annotations according to claim 5, characterized in that: The performing singular value decomposition on the auxiliary defense difference moment includes: S1-2-3-1. Perform singular value decomposition on the covariance matrix to obtain a decomposed matrix; The expression of the decomposed matrix is: ; in, represents the first orthogonal matrix, D represents a diagonal matrix whose diagonal elements are singular values, is the second orthogonal matrix; S1-2-3-2, selecting a column vector of the first orthogonal matrix as an eigenvector of the covariance matrix, wherein the eigenvector is used to characterize the principal component vector; S1-2-3-3. Select the square of the singular value of the diagonal matrix as the eigenvalue of the covariance matrix, and the eigenvalue is used to characterize the variance of the principal component vector.

9. A method for controlling dynamic configuration switching in the form of serialized annotations according to claim 5, characterized in that: The step of arranging the eigenvalues ​​in descending order and determining the principal component vector according to a preset variance explanation rate comprises: S1-2-4-1. Arrange the eigenvalues ​​obtained after singular value decomposition in descending order to obtain a sorted eigenvalue sequence; S1-2-4-2, calculating the cumulative variance explanation rate of the eigenvalues ​​in descending order in the eigenvalue sequence until the cumulative variance explanation rate reaches the preset variance explanation rate; S1-2-4-3. Select the eigenvector corresponding to the eigenvalue that satisfies the cumulative variance explanation rate and use it as the principal component vector.

10. A dynamic configuration switching system in the form of serialized annotations, characterized in that: include: A real-time operation status acquisition module, used to acquire the real-time operation status of the industrial equipment; A configuration parameter set output module, used for inputting the real-time operation status into a preset parameter configuration model and outputting a dynamic configuration parameter set; The configuration parameter loading module is used to load a number of configuration parameters according to the dynamic configuration parameter set; wherein the dynamic configuration parameter set is a number of configuration parameters output by the parameter configuration model and represented in a serialized annotation format.

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