Data management method based on artificial intelligence

Abstract

The invention discloses a data management method based on artificial intelligence, and belongs to the technical field of data management. An artificial intelligence model is adopted to construct a data classification model, and sample data corresponding to each data type is adopted to train the data classification model; and identifying the target data by adopting the trained data classification model to obtain a data type corresponding to the target data, and finally executing a data operation method set by a worker according to the data type corresponding to the target data, so that intelligent data management can be realized, the data management efficiency can be effectively improved, and the user experience is improved. And the data management accuracy can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data management, and in particular relates to a data management method based on artificial intelligence. Background Art

[0002] Data management refers to a series of activities to effectively organize, store, maintain, retrieve and use data resources to ensure the quality, availability, security and compliance of data. With the advent of the big data era, the amount of data faced by enterprises is exploding. In the existing technology, the collected data is usually stored directly or analyzed by staff before storage, that is, the data is not managed or managed manually. Such disorganized data storage or management leads to problems such as low efficiency and poor accuracy when processing massive data. In addition, the existing data management methods lack intelligence and cannot provide users with personalized data services. Summary of the invention

[0003] The present invention provides a data management method based on artificial intelligence to solve the problems of low data management efficiency, low accuracy and inability to provide personalized services in the prior art.

[0004] A data management method based on artificial intelligence, comprising:

[0005] Obtain the data operation method corresponding to each data type input by the staff, and obtain the sample data corresponding to each data type;

[0006] An artificial intelligence model is used to build a data classification model, and the data classification model is trained using sample data corresponding to each data type to obtain a trained data classification model;

[0007] Collect target data generated in real time, and use the trained data classification model to identify the target data to obtain the data type corresponding to the target data;

[0008] Based on the data operation method corresponding to the data type corresponding to the target data, the target data is managed to complete the data management of artificial intelligence.

[0009] Furthermore, the data operation method corresponding to each data type input by the staff is obtained, including: discarding the data, storing the data in a designated area, or issuing an early warning for the data.

[0010] Furthermore, an artificial intelligence model is used to construct a data classification model, including: using a convolutional neural network model to construct a data classification model.

[0011] Furthermore, the data classification model is trained using sample data corresponding to each data type to obtain a trained data classification model, including:

[0012] For each data type, assign different outputs of the data classification model to the data type, determine the output category of each data type, and set the expected label of the sample data corresponding to each data type to the corresponding output category;

[0013] Based on the parameters of the data classification model, multiple different parameter vectors are generated to complete the training initialization;

[0014] Take the sample data as the actual input, take the output category corresponding to the sample data as the expected output, and obtain the loss function value corresponding to each parameter vector;

[0015] According to the loss function value corresponding to each parameter vector, the optimal vector and the historical optimal value corresponding to each parameter vector are determined;

[0016] Perform a sinusoidal local search on the parameter vector to obtain a parameter vector after the sinusoidal local search;

[0017] Perform boundary information fusion search on the parameter vector after the sinusoidal local search to obtain the parameter vector after the boundary information fusion search;

[0018] According to the optimal vector and the historical optimal value corresponding to each parameter vector, an adaptive speed change information fusion search is performed on the parameter vector after the boundary information fusion search to obtain the parameter vector after the adaptive speed change information fusion search;

[0019] Performing a global random flight search on the parameter vector after the adaptive speed change information fusion search, and obtaining the parameter vector after the global random flight search;

[0020] The sinusoidal local search, boundary information fusion search, adaptive speed change information fusion search and global random flight search are repeatedly performed until the maximum number of training times is reached, the optimal vector is obtained again, and the optimal vector is decoded into the final parameters of the data classification model to obtain the trained data classification model.

[0021] Furthermore, a sinusoidal local search is performed on the parameter vector to obtain a parameter vector after the sinusoidal local search, including:

[0022] Generate a first random number between (0, 1), and obtain a search factor using a sine function based on the first random number;

[0023] For any parameter vector, randomly match another parameter vector to the parameter vector to obtain the first target parameter vector corresponding to each parameter vector;

[0024] Obtaining a first difference vector between the parameter vector and a first target parameter vector corresponding thereto, and processing the first difference vector using a search factor to obtain a processed first difference vector;

[0025] The parameter vector is updated according to the processed first difference vector to obtain the parameter vector after the sinusoidal local search.

[0026] Furthermore, a boundary information fusion search is performed on the parameter vector after the sinusoidal local search to obtain a parameter vector after the boundary information fusion search, including:

[0027] Obtain a random information vector according to the upper bound vector and the lower bound vector corresponding to the parameter vector;

[0028] For any parameter vector after the sinusoidal local search, based on the random information vector, and using the parameter vector and the reference vector in the previous training process, obtain the reference vector in the current training process;

[0029] For any parameter vector after the sinusoidal local search, a second difference vector between the parameter vector and the optimal vector is obtained, and the second difference vector is processed using a second random number between (0, 1) to obtain a processed second difference vector;

[0030] Obtaining a third difference vector between the parameter vector and the reference vector, and processing the third difference vector using a third random number between (0, 1) to obtain a processed third difference vector;

[0031] According to the processed second difference vector and the processed third difference vector, the parameter vector after the sinusoidal local search is updated to obtain the parameter vector after the boundary information fusion search.

[0032] Further, according to the optimal vector and the historical optimal value corresponding to each parameter vector, an adaptive speed change information fusion search is performed on the parameter vector after the boundary information fusion search to obtain the parameter vector after the adaptive speed change information fusion search, including:

[0033] Based on the current number of training times, the adaptive speed change weight is obtained;

[0034] For the parameter vector after the boundary information fusion search, the adaptive speed change weight is used to weight the historical search speed of the adaptive speed change information fusion search in the previous training process to obtain the weighted historical search speed;

[0035] Obtaining a social learning information vector between the parameter vector and the optimal vector, and processing the social learning information vector using a social learning factor to obtain a processed social learning information vector;

[0036] The individual learning information between the parameter vector and its historical optimal value is obtained, and the individual learning information is processed by using the individual learning factor to obtain the processed individual learning information;

[0037] According to the weighted historical search speed, the processed social learning information vector and the processed individual learning information volume, the real-time search speed in the current training process is obtained;

[0038] According to the real-time search speed in the current training process, the parameter vector after the boundary information fusion search is updated to obtain the parameter vector after the adaptive speed change information fusion search.

[0039] Furthermore, a global random flight search is performed on the parameter vector after the adaptive speed change information fusion search to obtain the parameter vector after the global random flight search, including:

[0040] Determine a global search vector for the parameter vector after the adaptive speed change information fusion search;

[0041] Use Levy flight to generate random Levy flight step length;

[0042] For the vector to be globally searched, randomly match another parameter vector to obtain a second target parameter vector corresponding to each vector to be globally searched;

[0043] Obtaining a fourth difference vector between the global search vector and the second target parameter vector corresponding to the global search vector, and processing the fourth difference vector using the Levy flight step length and a preset search control factor to obtain a processed fourth difference vector;

[0044] For the vector to be globally searched, a fourth random number between (0, 1) is used to process the vector to be globally searched, so as to obtain the processed vector to be globally searched;

[0045] The optimal vector is processed using a fifth random number between (0, 1) to obtain the optimal vector after processing;

[0046] According to the processed fourth difference vector, the processed vector to be globally searched and the processed optimal vector, the vector to be globally searched is updated to obtain a parameter vector after global random flight search.

[0047] Furthermore, the target data generated in real time is collected, and the trained data classification model is used to identify the target data to obtain the data type corresponding to the target data, including:

[0048] Collecting target data generated in real time, and converting the target data into data in the same format as the sample data, to obtain the converted target data;

[0049] The converted target data is constructed as the input data of the trained data classification model, and is input into the trained data classification model to obtain the data type corresponding to the target data.

[0050] Furthermore, the target data is managed based on the data operation method corresponding to the data type corresponding to the target data, including:

[0051] According to the data type corresponding to the target data, determine the data operation method corresponding to the target data from the data operation methods corresponding to each data type input by the staff;

[0052] Execute the data operation method corresponding to the target data to manage the target data.

[0053] The present invention provides an artificial intelligence-based data management method, which constructs a data classification model by adopting an artificial intelligence model, and trains the data classification model by adopting sample data corresponding to each data type, and uses the trained data classification model to identify the target data to obtain the data type corresponding to the target data, and finally executes the data operation method set by the staff according to the data type corresponding to the target data, thereby realizing intelligent data management, which can not only effectively improve the data management efficiency, but also improve the data management accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0055] Figure 1 A flowchart of an artificial intelligence-based data management method provided in an embodiment of the present invention.

[0056] The above drawings have shown clear embodiments of the present invention, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present invention in any way, but to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0057] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0058] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0059] like Figure 1 As shown, an embodiment of the present invention provides a data management method based on artificial intelligence, including:

[0060] S101, obtaining a data operation method corresponding to each data type input by a staff member, and obtaining sample data corresponding to each data type;

[0061] Data operation methods can be: data modification, data deletion, data storage, data warning and other data-related operations, which are set by the staff. For example, when processing motor operation data, when the motor is operating normally, its data has no other use except monitoring, so the motor data can be deleted or stored. When the motor is not operating normally, the data can be warned and stored.

[0062] In the embodiment of the present invention, the staff can customize the data operation method, which can effectively realize the customized management of data and realize the intelligentization of data management.

[0063] S102, using an artificial intelligence model to build a data classification model, and using sample data corresponding to each data type to train the data classification model to obtain a trained data classification model;

[0064] Artificial intelligence models such as convolutional neural network models can be used to build data classification models to achieve rapid data recognition. By training the data classification model with sample data corresponding to each data type, the data classification model can identify the data relationship between data, thereby having data classification capabilities and ultimately completing data intelligent management.

[0065] S103, collecting target data generated in real time, and using the trained data classification model to identify the target data, to obtain a data type corresponding to the target data;

[0066] It is worth mentioning that in order to ensure that the data classification model after training can accurately identify the data, the data type and data structure between the target data and the sample data should be the same, and the length of data of the same type should be the same. If they are different, zeros should be added to the front of the data to ensure normal data recognition.

[0067] S104. Manage the target data based on the data operation method corresponding to the data type corresponding to the target data, and complete the data management of artificial intelligence.

[0068] The embodiment of the present invention not only realizes intelligent data management but also improves the standardization and accuracy of data management by executing the data operation method set by the staff.

[0069] In an embodiment of the present invention, the data operation method corresponding to each data type input by the staff is obtained, including: discarding data, storing data in a designated area, or issuing an early warning for the data. It is worth noting that the above data operation method is only a preferred method of the embodiment of the present invention, and other data operation methods can also be used to achieve more comprehensive data management.

[0070] In an embodiment of the present invention, an artificial intelligence model is used to construct a data classification model, including: using a convolutional neural network model to construct a data classification model. It is worth noting that the convolutional neural network model is only a preferred method of the embodiment of the present invention, and an artificial intelligence model can also be used to construct a data classification model to achieve a data classification effect.

[0071] In an embodiment of the present invention, the data classification model is trained using sample data corresponding to each data type to obtain a trained data classification model, including:

[0072] For each data type, assign different outputs of the data classification model to the data type, determine the output category of each data type, and set the expected label of the sample data corresponding to each data type to the corresponding output category;

[0073] Based on the parameters of the data classification model, multiple different parameter vectors are generated to complete the training initialization; for example, initialization can be performed by randomly initializing between an upper limit and a lower limit.

[0074] For example, based on processing the operating parameters of the motor, assuming that the operating parameters of the motor are current, voltage, speed, vibration frequency, etc., then the data type can be two types: normal type and abnormal type. At this time, the data classification model has two outputs, and the first output category can be determined as the output category of the normal type, and the other output category can be determined as the output category of the abnormal type. Then, for any sample data, the expected label can be constructed as [1,0] or [0,1]. [1,0] indicates that the sample data is a normal type of data, and [0,1] indicates that the sample data is an abnormal type of data. It is worth noting that the above label setting process is only an example of an embodiment of the present invention. The number of abnormal types can be set according to actual needs, so as to achieve more refined data classification and data operations and improve data management capabilities.

[0075] Take the sample data as the actual input, take the output category corresponding to the sample data as the expected output, and obtain the loss function value corresponding to each parameter vector;

[0076] According to the loss function value corresponding to each parameter vector, the optimal vector and the historical optimal value corresponding to each parameter vector are determined;

[0077] Perform a sinusoidal local search on the parameter vector to obtain a parameter vector after the sinusoidal local search;

[0078] Perform boundary information fusion search on the parameter vector after the sinusoidal local search to obtain the parameter vector after the boundary information fusion search;

[0079] According to the optimal vector and the historical optimal value corresponding to each parameter vector, an adaptive speed change information fusion search is performed on the parameter vector after the boundary information fusion search to obtain the parameter vector after the adaptive speed change information fusion search;

[0080] Performing a global random flight search on the parameter vector after the adaptive speed change information fusion search, and obtaining the parameter vector after the global random flight search;

[0081] The sinusoidal local search, boundary information fusion search, adaptive speed change information fusion search and global random flight search are repeatedly performed until the maximum number of training times is reached, the optimal vector is obtained again, and the optimal vector is decoded into the final parameters of the data classification model to obtain the trained data classification model.

[0082] In the prior art, the gradient descent optimization algorithm is often used to train the hyperparameters (such as weights) of the data classification model, which is prone to fall into the local optimum, causing the algorithm to converge prematurely, resulting in poor data classification effect. Therefore, the embodiment of the present invention provides a new intelligent optimization algorithm to improve the global search capability and ultimately improve the accuracy of data management.

[0083] Optionally, after each search, the parameter vector may be processed for out-of-bounds errors, thereby ensuring that the parameter is always within a valid range.

[0084] In the embodiment of the present invention, a sinusoidal local search is performed on the parameter vector to obtain a parameter vector after the sinusoidal local search, including:

[0085] Generate a first random number r1 between (0, 1), and based on the first random number r1, use the sine function to obtain a search factor: sin(2πr1)*(r6+1); wherein sin represents the sine function, π represents pi, and r6 represents the sixth random number between (0, 1);

[0086] For any parameter vector, randomly match another parameter vector to the parameter vector to obtain the first target parameter vector corresponding to each parameter vector;

[0087] A first difference vector between the parameter vector and the first target parameter vector corresponding thereto is obtained, and the first difference vector is processed using a search factor, and the processed first difference vector is obtained as follows: Among them, Δ1 represents the first difference vector, represents the i-th parameter vector in the t-th training process, i=1,2,…,I, I represents the total number of parameter vectors, Represents the parameter vector The corresponding first target parameter vector, r6 represents the sixth random number between (0,1);

[0088] According to the first difference vector after processing, the parameter vector is updated, and the parameter vector after the sine local search is obtained as follows: in, represents the parameter vector after the sinusoidal local search

[0089] The sinusoidal local search provided in the embodiment of the present invention can perform local search on parameter vectors with a sinusoidal waveform, which can improve the local development efficiency. In the process of local development, it is also possible to search for the global optimal area.

[0090] In the embodiment of the present invention, a boundary information fusion search is performed on the parameter vector after the sinusoidal local search to obtain the parameter vector after the boundary information fusion search, including:

[0091] According to the upper bound vector and lower bound vector corresponding to the parameter vector, the random information vector is obtained as follows: α*cos(θ)*((ub-lb)*r7+lb; where ub represents the upper bound vector, i.e., the vector composed of the upper bounds of the parameters of each dimension; lb represents the lower bound vector, i.e., the vector composed of the lower bounds of the parameters of each dimension; cos represents the cosine function, θ represents the random degree between (0,2π), and r7 represents the seventh random number between (0,1); α represents the adaptive adjustment parameter, and α=(1-t / T) 2t / T , T represents the preset maximum number of training times;

[0092] For any parameter vector after a sinusoidal local search, the reference vector in the current training process is obtained based on the random information vector and the parameter vector and reference vector in the previous training process: in, represents the reference vector corresponding to the mth parameter vector in the last training process, Represents the reference vector corresponding to the mth parameter vector in the current training process, m = 1, 2, ..., I;

[0093] For any parameter vector after the sinusoidal local search, the second difference vector between the parameter vector and the optimal vector is obtained, and the second difference vector is processed using a second random number r2 between (0,1), and the second difference vector after processing is obtained as follows: in, represents the optimal vector, Δ2 represents the second difference vector;

[0094] A third difference vector between the parameter vector and the reference vector is obtained, and the third difference vector is processed using a third random number r3 between (0,1), and the processed third difference vector is obtained as follows: Wherein, Δ3 represents the third difference vector;

[0095] According to the processed second difference vector and the processed third difference vector, the parameter vector after the sine local search is updated, and the parameter vector after the boundary information fusion search is obtained as follows: in, Represents the parameter vector after boundary information fusion search represents the parameter vector after the mth sinusoidal local search during the tth training process.

[0096] The boundary information fusion search provided in the embodiment of the present invention can perform random search with boundary information, and combine with reference vectors to realize memory search with random search, and further integrate the search of optimal vectors, which not only increases the diversity of the algorithm and improves the global search capability, but also effectively guarantees the search speed of the algorithm.

[0097] In the embodiment of the present invention, according to the optimal vector and the historical optimal value corresponding to each parameter vector, an adaptive speed change information fusion search is performed on the parameter vector after the boundary information fusion search to obtain the parameter vector after the adaptive speed change information fusion search, including:

[0098] Based on the current number of training times, the adaptive speed change weight is obtained as: Where ω represents the adaptive speed change weight, ω max Represents the maximum value of the adaptive speed change weight, ω min represents the minimum value of the adaptive speed change weight, r8 represents the eighth random number between (0,1), b represents the intermediate parameter, and b=ln(ω max / ω min );

[0099] In the early stage of the search, the probability of the adaptive speed change weight taking a larger value can be increased, the parameter vector searches the entire space at a larger flight speed, and determines the range of the global optimal solution, ensuring the global search capability of the population; in the later stage of the search, the probability of the adaptive speed change weight taking a smaller value can be increased, the parameter vector obtains a smaller flight speed, and then the neighborhood of the optimal solution position can be searched more accurately, ensuring the local development capability of the population, which can balance the search performance of the algorithm. At the same time, in the process of searching, the adaptive speed change weight value is dynamically adjusted with the number of iterations, which can continuously correct the speed and position of the parameter vector, strengthen the interaction of the parameter vector, and help the parameter vector quickly find the optimal solution, avoiding the algorithm from falling into the local optimum, and the dynamic change of the adaptive speed change weight meets the nonlinear and complex characteristics of the actual search process.

[0100] For the parameter vector after the boundary information fusion search, the adaptive speed change weight is used to weight the historical search speed of the adaptive speed change information fusion search in the previous training process, and the weighted historical search speed is obtained as follows: in, Indicates the speed corresponding to the parameter vector after the nth boundary information fusion search during the tth training process;

[0101] The social learning information vector between the parameter vector and the optimal vector is obtained, and the social learning information vector is processed using the social learning factor to obtain the processed social learning information vector: Among them, c1 represents the social learning factor, r9 represents the ninth random number between (0,1), represents the optimal parameter vector, represents the parameter vector after the nth boundary information fusion search in the tth training process, n = 1, 2, ..., I;

[0102] The amount of individual learning information between the parameter vector and its historical optimal value is obtained, and the amount of individual learning information is processed using the individual learning factor. The amount of individual learning information after processing is obtained as follows: Among them, c2 represents the individual learning factor, r 10 represents the tenth random number between (0,1), Represents the historical optimal value corresponding to the parameter vector after the nth boundary information fusion search in the tth training process;

[0103] According to the weighted historical search speed, the processed social learning information vector, and the processed individual learning information volume, the real-time search speed in the current training process is obtained as follows: in, Indicates the real-time search speed corresponding to the parameter vector after the nth boundary information fusion search in the current training process;

[0104] According to the real-time search speed in the current training process, the parameter vector after the boundary information fusion search is updated, and the parameter vector after the adaptive speed change information fusion search is obtained as follows: in, Represents the parameter vector after adaptive speed change information fusion search

[0105] The adaptive speed-changing information fusion search provided by the embodiment of the present invention can effectively improve the search speed and search quality of the algorithm and provide higher precision in the later stage of the algorithm.

[0106] In the embodiment of the present invention, a global random flight search is performed on the parameter vector after the adaptive speed change information fusion search to obtain the parameter vector after the global random flight search, including:

[0107] For the parameter vector after the adaptive speed change information fusion search, determine the vector to be globally searched; for example: determine the K parameter vectors with the largest loss function value as the vector to be globally searched;

[0108] Use Levy flight to generate random Levy flight step length levy;

[0109] For the vector to be globally searched, randomly match another parameter vector to obtain a second target parameter vector corresponding to each vector to be globally searched;

[0110] The fourth difference vector between the global search vector and its corresponding second target parameter vector is obtained as: Wherein, Δ4 represents the fourth difference vector, represents the kth vector to be globally searched during the tth training process, k = 1, 2, ..., K, Represents the vector to be searched globally The corresponding second target parameter vector;

[0111] The fourth difference vector is processed using the Levy flight step length and the preset search control factor, and the processed fourth difference vector is: Δ4*levy*γ; wherein γ represents the preset search control factor;

[0112] For the vector to be globally searched, a fourth random number r4 between (0, 1) is used to process the vector to be globally searched, and the processed vector to be globally searched is:

[0113] The optimal vector is processed using the fifth random number r5 between (0,1), and the optimal vector after processing is obtained as follows:

[0114] According to the processed fourth difference vector, the processed global search vector and the processed optimal vector, the global search vector is updated to obtain the parameter vector after the global random flight search: in, represents the parameter vector after global random flight search

[0115] The global random flight search provided by the embodiment of the present invention can not only effectively assist the algorithm to jump out of the local optimum, but also will not affect the algorithm search speed.

[0116] Optionally, an annealing simulation algorithm may be used to control the global random flight search, or a greedy algorithm may be used to control the global random flight search, thereby further improving the algorithm search speed.

[0117] In an embodiment of the present invention, target data generated in real time is collected, and the target data is identified using a trained data classification model to obtain a data type corresponding to the target data, including:

[0118] Collecting target data generated in real time, and converting the target data into data in the same format as the sample data, to obtain the converted target data;

[0119] The converted target data is constructed as the input data of the trained data classification model, and is input into the trained data classification model to obtain the data type corresponding to the target data.

[0120] In an embodiment of the present invention, the target data is managed based on the data operation method corresponding to the data type corresponding to the target data, including:

[0121] According to the data type corresponding to the target data, determine the data operation method corresponding to the target data from the data operation methods corresponding to each data type input by the staff;

[0122] Execute the data operation method corresponding to the target data to manage the target data.

[0123] The present invention provides an artificial intelligence-based data management method, which constructs a data classification model by adopting an artificial intelligence model, and trains the data classification model by adopting sample data corresponding to each data type, and uses the trained data classification model to identify the target data to obtain the data type corresponding to the target data, and finally executes the data operation method set by the staff according to the data type corresponding to the target data, thereby realizing intelligent data management, which can not only effectively improve the data management efficiency, but also improve the data management accuracy.

[0124] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A data management method based on artificial intelligence, characterized in that: include: Obtain the data operation method corresponding to each data type input by the staff, and obtain the sample data corresponding to each data type; An artificial intelligence model is used to build a data classification model, and the data classification model is trained using sample data corresponding to each data type to obtain a trained data classification model; Collect target data generated in real time, and use the trained data classification model to identify the target data to obtain the data type corresponding to the target data; Based on the data operation method corresponding to the data type corresponding to the target data, the target data is managed to complete the data management of artificial intelligence.

2. The data management method based on artificial intelligence according to claim 1, characterized in that: Get the data operation method corresponding to each data type entered by the staff, including: discarding data, storing data in a specified area, or issuing an early warning for the data.

3. The data management method based on artificial intelligence according to claim 1, characterized in that: Adopting artificial intelligence models to build data classification models, including: adopting convolutional neural network models to build data classification models.

4. The data management method based on artificial intelligence according to claim 1, characterized in that: The data classification model is trained using sample data corresponding to each data type to obtain a trained data classification model, including: For each data type, assign different outputs of the data classification model to the data type, determine the output category of each data type, and set the expected label of the sample data corresponding to each data type to the corresponding output category; Based on the parameters of the data classification model, multiple different parameter vectors are generated to complete the training initialization; Take the sample data as the actual input, take the output category corresponding to the sample data as the expected output, and obtain the loss function value corresponding to each parameter vector; According to the loss function value corresponding to each parameter vector, the optimal vector and the historical optimal value corresponding to each parameter vector are determined; Perform a sinusoidal local search on the parameter vector to obtain a parameter vector after the sinusoidal local search; Perform boundary information fusion search on the parameter vector after the sinusoidal local search to obtain the parameter vector after the boundary information fusion search; According to the optimal vector and the historical optimal value corresponding to each parameter vector, an adaptive speed change information fusion search is performed on the parameter vector after the boundary information fusion search to obtain the parameter vector after the adaptive speed change information fusion search; Performing a global random flight search on the parameter vector after the adaptive speed change information fusion search, and obtaining the parameter vector after the global random flight search; The sinusoidal local search, boundary information fusion search, adaptive speed change information fusion search and global random flight search are repeatedly performed until the maximum number of training times is reached, the optimal vector is obtained again, and the optimal vector is decoded into the final parameters of the data classification model to obtain the trained data classification model.

5. The artificial intelligence-based data management method according to claim 4, characterized in that: Perform a sinusoidal local search on the parameter vector to obtain a parameter vector after the sinusoidal local search, including: Generate a first random number between (0, 1), and obtain a search factor using a sine function based on the first random number; For any parameter vector, randomly match another parameter vector to the parameter vector to obtain the first target parameter vector corresponding to each parameter vector; Obtaining a first difference vector between the parameter vector and a first target parameter vector corresponding thereto, and processing the first difference vector using a search factor to obtain a processed first difference vector; The parameter vector is updated according to the processed first difference vector to obtain the parameter vector after the sinusoidal local search.

6. The artificial intelligence-based data management method according to claim 5, characterized in that: Perform boundary information fusion search on the parameter vector after the sinusoidal local search to obtain the parameter vector after the boundary information fusion search, including: Obtain a random information vector according to the upper bound vector and the lower bound vector corresponding to the parameter vector; For any parameter vector after the sinusoidal local search, based on the random information vector, and using the parameter vector and the reference vector in the previous training process, obtain the reference vector in the current training process; For any parameter vector after the sinusoidal local search, a second difference vector between the parameter vector and the optimal vector is obtained, and the second difference vector is processed using a second random number between (0, 1) to obtain a processed second difference vector; Obtaining a third difference vector between the parameter vector and the reference vector, and processing the third difference vector using a third random number between (0, 1) to obtain a processed third difference vector; According to the processed second difference vector and the processed third difference vector, the parameter vector after the sinusoidal local search is updated to obtain the parameter vector after the boundary information fusion search.

7. The artificial intelligence-based data management method according to claim 6, characterized in that: According to the optimal vector and the historical optimal value corresponding to each parameter vector, the parameter vector after the boundary information fusion search is subjected to an adaptive speed change information fusion search to obtain the parameter vector after the adaptive speed change information fusion search, including: Based on the current number of training times, the adaptive speed change weight is obtained; For the parameter vector after the boundary information fusion search, the adaptive speed change weight is used to weight the historical search speed of the adaptive speed change information fusion search in the previous training process to obtain the weighted historical search speed; Obtaining a social learning information vector between the parameter vector and the optimal vector, and processing the social learning information vector using a social learning factor to obtain a processed social learning information vector; The individual learning information between the parameter vector and its historical optimal value is obtained, and the individual learning information is processed by using the individual learning factor to obtain the processed individual learning information; According to the weighted historical search speed, the processed social learning information vector and the processed individual learning information volume, the real-time search speed in the current training process is obtained; According to the real-time search speed in the current training process, the parameter vector after the boundary information fusion search is updated to obtain the parameter vector after the adaptive speed change information fusion search.

8. The artificial intelligence-based data management method according to claim 7, characterized in that: Performing a global random flight search on the parameter vector after the adaptive speed change information fusion search, obtaining the parameter vector after the global random flight search, including: Determine a global search vector for the parameter vector after the adaptive speed change information fusion search; Use Levy flight to generate random Levy flight step length; For the vector to be globally searched, randomly match another parameter vector to obtain a second target parameter vector corresponding to each vector to be globally searched; Obtaining a fourth difference vector between the global search vector and the second target parameter vector corresponding to the global search vector, and processing the fourth difference vector using the Levy flight step length and a preset search control factor to obtain a processed fourth difference vector; For the vector to be globally searched, a fourth random number between (0, 1) is used to process the vector to be globally searched, so as to obtain the processed vector to be globally searched; The optimal vector is processed using a fifth random number between (0, 1) to obtain the optimal vector after processing; According to the processed fourth difference vector, the processed vector to be globally searched and the processed optimal vector, the vector to be globally searched is updated to obtain a parameter vector after global random flight search.

9. The data management method based on artificial intelligence according to claim 1, characterized in that: Collect the target data generated in real time, and use the trained data classification model to identify the target data to obtain the data type corresponding to the target data, including: Collecting target data generated in real time, and converting the target data into data in the same format as the sample data, to obtain the converted target data; The converted target data is constructed as the input data of the trained data classification model, and is input into the trained data classification model to obtain the data type corresponding to the target data.

10. The artificial intelligence-based data management method according to claim 9, characterized in that: The target data is managed based on the data operation method corresponding to the data type corresponding to the target data, including: According to the data type corresponding to the target data, determine the data operation method corresponding to the target data from the data operation methods corresponding to each data type input by the staff; Execute the data operation method corresponding to the target data to manage the target data.

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