Multidimensional data feature extraction method, device, equipment and storage medium

Abstract

The embodiments of the present application provide a multidimensional data feature extraction method, apparatus, device and storage medium, and relate to the field of data processing technology. The method obtains discrete protein residue data and continuous protein structure data, then determines discrete network parameters and continuous network parameters, inputs the protein residue data and protein structure data as initial input data into a continuous discrete convolutional neural network model, uses a continuous discrete convolution layer to update the convolution kernel parameters according to the discrete network parameters and the continuous network parameters, and extracts features of the initial input data based on the updated convolution kernel parameters to obtain multidimensional representation output data. Utilize a continuous discrete convolution layer to fuse the feature extraction processes of continuous convolution and discrete convolution according to the discrete network parameters and the continuous network parameters, use the updated convolution kernel parameters to extract features of the multidimensional input data, and improve the accuracy of the feature extraction results of the multidimensional data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, apparatus, device and storage medium for extracting multidimensional data features. Background Art

[0002] Through multi-layer convolution and pooling operations, convolutional neural networks (CNNs) can effectively extract local features from data and gradually construct high-level feature representations. However, in real-world applications, many datasets contain information from multiple dimensions rather than just a single dimension. These dimensions can include both discrete and continuous features. Therefore, feature extraction from multidimensional data is crucial when representing data using CNNs.

[0003] In related technologies, discrete data typically has clear category boundaries, while continuous data may exhibit more complex distribution characteristics. When processing this type of multidimensional mixed data, convolutional neural networks often struggle to fully exploit the combined characteristics of discrete and continuous data. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a multidimensional data feature extraction method, device, equipment and storage medium to improve the accuracy of feature extraction of multidimensional data by convolutional neural networks.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application proposes a multidimensional data feature extraction method, which is applied to a continuous discrete convolutional neural network model, wherein the continuous discrete convolutional neural network model includes at least a continuous discrete convolutional layer, and the method includes:

[0006] Obtain discrete protein residue data and continuous protein structure data;

[0007] determining discrete network parameters from at least one discrete convolutional network based on the protein residue data, and determining continuous network parameters based on the protein structure data;

[0008] The protein residue data and the protein structure data are input into the continuous discrete convolutional neural network model as initial input data, the convolution kernel parameters are updated according to the discrete network parameters and the continuous network parameters using the continuous discrete convolution layer, and the initial input data is feature extracted based on the updated convolution kernel parameters to obtain multidimensional representation output data.

[0009] In one embodiment, determining discrete network parameters from at least one discrete convolutional network based on the protein residue data, and determining continuous network parameters based on the protein structure data, comprises:

[0010] Selecting a one-dimensional convolutional network from at least one discrete convolutional network according to the protein residue data, and obtaining a convolution kernel size parameter of the one-dimensional convolutional network as the discrete network parameter;

[0011] The convolution kernel space radius parameter of the continuous convolution network is selected as the continuous network parameter according to the protein structure data.

[0012] In one embodiment, obtaining discrete protein residue data and continuous protein structure data includes:

[0013] Acquire initial protein data corresponding to at least one protein, each of the initial protein data including a protein identifier;

[0014] Obtaining initial residue information and / or initial structure information from the initial protein data;

[0015] Using the protein identifier, pairing the residue initial information and the structure initial information belonging to the same initial protein data to obtain at least one data pair;

[0016] In each data pair, the residue initial information is encoded to obtain the protein residue data, and the structure initial information is encoded to obtain the protein structure data.

[0017] In one embodiment, encoding the initial residue information to obtain the protein residue data includes:

[0018] Obtaining the residue type of each protein main chain atom in the initial residue information;

[0019] Performing integer encoding on the residue type to obtain a residue type code corresponding to each atom of the protein main chain;

[0020] The protein residue data is obtained based on the residue type coding, and the protein residue data is discrete one-dimensional data.

[0021] In one embodiment, encoding the initial structure information to obtain the protein structure data includes:

[0022] Obtaining the three-dimensional coordinates of each protein main chain atom from the initial structural information;

[0023] The protein structure data is obtained according to the three-dimensional coordinates, and the protein structure data is continuous three-dimensional data.

[0024] In one embodiment, the updating of convolution kernel parameters using the continuous discrete convolution layer according to the discrete network parameters and the continuous network parameters includes:

[0025] Determining at least one three-dimensional offset according to the convolution kernel spatial radius parameter, and determining at least one one-dimensional offset according to the convolution kernel size parameter;

[0026] Calculating the sum of the three-dimensional offset and the convolution kernel space radius parameter to obtain a first intermediate value, and calculating the reciprocal of the first intermediate value as an offset parameter;

[0027] Obtaining model parameters of the continuous discrete convolutional layer corresponding to the one-dimensional offset;

[0028] Obtaining an updated convolution kernel according to the product of the offset parameter and the model parameter;

[0029] The values ​​of the one-dimensional offset and / or the three-dimensional offset are changed one by one to obtain multiple offset combinations, and the convolution kernel parameters corresponding to each offset combination are calculated.

[0030] In one embodiment, the feature extraction of the initial input data based on the updated convolution kernel to obtain multi-dimensional representation output data includes:

[0031] Determining the three-dimensional coordinates of the main chain atoms of the protein one by one from the initial input data as current coordinates;

[0032] Obtaining the three-dimensional offsets one by one as current three-dimensional offsets, obtaining neighboring coordinates according to the current coordinates and the current three-dimensional offsets, and obtaining the residue types corresponding to the neighboring coordinates as current residue types;

[0033] Obtaining at least one convolution kernel parameter corresponding to the current three-dimensional offset, multiplying the corresponding current residue type and the convolution kernel parameter to obtain a second intermediate value, and accumulating the second intermediate values ​​corresponding to all three-dimensional offsets to obtain representation data corresponding to the current coordinate;

[0034] The multi-dimensional representation output data is obtained according to all the representation data.

[0035] To achieve the above objectives, a second aspect of an embodiment of the present application provides a multidimensional data feature extraction device, which is applied to a continuous discrete convolutional neural network model, wherein the continuous discrete convolutional neural network model includes at least a continuous discrete convolution layer, and the device includes:

[0036] Data acquisition module: used to obtain discrete protein residue data and continuous protein structure data;

[0037] a model parameter acquisition module configured to determine discrete network parameters from at least one discrete convolutional network based on the protein residue data, and to determine continuous network parameters based on the protein structure data;

[0038] Continuous discrete convolution module: used to input the protein residue data and the protein structure data as initial input data into the continuous discrete convolutional neural network model, use the continuous discrete convolution layer to update the convolution kernel parameters according to the discrete network parameters and the continuous network parameters, and extract features of the initial input data based on the updated convolution kernel parameters to obtain multi-dimensional representation output data.

[0039] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0040] To achieve the above-mentioned purpose, the fourth aspect of the embodiment of the present application proposes a storage medium, which is a storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0041] The multidimensional data feature extraction method, apparatus, device and storage medium proposed in the embodiments of the present application obtain discrete protein residue data and continuous protein structure data, determine discrete network parameters from at least one discrete convolutional network based on the protein residue data, and determine continuous network parameters based on the protein structure data, input the protein residue data and protein structure data as initial input data into the continuous discrete convolutional neural network model, use the continuous discrete convolution layer to update the convolution kernel parameters based on the discrete network parameters and the continuous network parameters, and extract features from the initial input data based on the updated convolution kernel parameters to obtain multidimensional representation output data. After obtaining the discrete network parameters and the continuous network parameters in the embodiments of the present application, the continuous discrete convolution layer is used to fuse the feature extraction processes of continuous convolution and discrete convolution based on the two network parameters. By updating the convolution kernel parameters and using the updated convolution kernel parameters to extract features from the multidimensional input data, it is possible to more comprehensively understand and characterize the complex features in the multidimensional data. This processing process converts the data into an efficient and information-rich representation form, which can not only improve the accuracy of the feature data obtained by feature extraction, but also improve the performance of the feature data in various application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a structural diagram of the continuous discrete convolutional neural network model provided in the embodiment of the present application.

[0043] Figure 2 This is a flowchart of the multidimensional data feature extraction method provided in an embodiment of the present application.

[0044] Figure 3This is a flowchart for obtaining discrete protein residue data and continuous protein structure data provided in an embodiment of the present application.

[0045] Figure 4 This is a flowchart of encoding initial residue information to obtain protein residue data provided in an embodiment of the present application.

[0046] Figure 5 This is a flowchart of encoding initial structural information to obtain protein structure data provided by an embodiment of the present application.

[0047] Figure 6 This is a schematic diagram of obtaining discrete protein residue data and continuous protein structure data provided in an embodiment of the present application.

[0048] Figure 7 This is a schematic diagram of network parameter determination provided in an embodiment of the present application.

[0049] Figure 8 This is a flowchart provided by an embodiment of the present application for determining discrete network parameters from at least one discrete convolutional network based on protein residue data and determining continuous network parameters based on protein structure data.

[0050] Figure 9 This is a schematic diagram of the principle of a one-dimensional discrete convolutional network provided in an embodiment of the present application.

[0051] Figure 10 This is a schematic diagram of the principle of the continuous convolutional network provided in an embodiment of the present application.

[0052] Figure 11 This is a flowchart of an embodiment of the present application for updating convolution kernel parameters using a continuous discrete convolution layer according to discrete network parameters and continuous network parameters.

[0053] Figure 12 This is a flowchart of an embodiment of the present application for extracting features from initial input data based on an updated convolution kernel to obtain multi-dimensional representation output data.

[0054] Figure 13 This is a schematic diagram of multi-dimensional characterization provided in an embodiment of the present application.

[0055] Figure 14 This is a structural block diagram of a multidimensional data feature extraction device provided by another embodiment of the present application.

[0056] Figure 15 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0058] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flowchart.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0060] First, let’s analyze some of the terms used in this application:

[0061] Artificial intelligence (AI) is a new technical discipline that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. A branch of computer science, AI seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thinking. It also encompasses the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0062] Convolutional neural networks (CNNs) are algorithms that automatically extract useful features from data. They are widely used in fields such as image processing and speech recognition, extracting important information from data through a process called "convolution." Through multiple layers of convolution and pooling operations, CNNs effectively extract local features from data and gradually construct high-level feature representations. However, in real-world applications, many datasets contain information from multiple dimensions, including both discrete and continuous features. Therefore, feature extraction from multidimensional data is crucial for data representation using CNNs.

[0063] In related technologies, discrete data typically has clear category boundaries, while continuous data may exhibit more complex distribution characteristics. When processing this type of multidimensional mixed data, convolutional neural networks often struggle to fully exploit the combined characteristics of discrete and continuous data.

[0064] Based on this, the embodiments of the present application provide a method, apparatus, device and storage medium for extracting features from multidimensional data. After obtaining discrete network parameters and continuous network parameters, the feature extraction processes of continuous convolution and discrete convolution are integrated using a continuous discrete convolution layer according to the two network parameters. By updating the convolution kernel parameters and using the updated convolution kernel parameters to extract features from multidimensional input data, it is possible to more comprehensively understand and characterize the complex features in the multidimensional data. This processing converts the data into an efficient and information-rich representation, which can not only improve the accuracy of the feature data obtained by feature extraction, but also improve the performance of the feature data in various application scenarios.

[0065] The embodiments of the present application provide a multidimensional data feature extraction method, apparatus, device, and storage medium, which are specifically illustrated by the following embodiments. First, the multidimensional data feature extraction method in the embodiments of the present application is described.

[0066] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0067] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0068] The multidimensional data feature extraction method provided in the embodiment of the present application relates to the field of data processing technology. The multidimensional data feature extraction method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be a computer program running in a terminal or a server side. For example, a computer program can be a native program or software module in an operating system; it can be a local (Native) application (Application, APP), that is, a program that needs to be installed in the operating system to run, such as a client that supports multidimensional data feature extraction, that is, a program that can be run only by downloading it to a browser environment; it can also be a small program that can be embedded in any APP. In short, the above-mentioned computer program can be an application, module or plug-in in any form. Wherein, the terminal communicates with the server via a network. The multidimensional data feature extraction method can be executed by a terminal or a server, or performed in collaboration by a terminal and a server.

[0069] In some embodiments, the terminal can be a smartphone, tablet computer, laptop computer, desktop computer, or smartwatch. The server can be an independent server, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. It can also be a service node in a blockchain system, where each service node in the blockchain system forms a peer-to-peer (P2P) network. The P2P protocol is an application layer protocol that runs on top of the Transmission Control Protocol (TCP). The terminal and the server can be connected via a communication connection method such as Bluetooth, Universal Serial Bus (USB), or a network, which is not limited in this embodiment.

[0070] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0071] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0072] First, the continuous discrete convolutional neural network model in the embodiment of the present application is described. Figure 1 , Figure 1 It is a structural diagram of the continuous discrete convolutional neural network model provided in the embodiment of the present application.

[0073] Reference Figure 1In a continuous-discrete convolutional neural network model, input data is sequentially fed into a batch normalization layer and a first linear layer. After processing with an activation function, it is fed into a continuous-discrete convolutional layer for multidimensional representation, resulting in multidimensional representation output data. This multidimensional representation output data is then fed into a second linear layer for the final output. The batch normalization layer standardizes the mean and variance of the input data by batch to reduce internal covariate bias. The batch normalized data is then fed into the first linear layer, which can be a fully connected layer. In this layer, each input node is connected to the output node via a weight, forming a linear combination to capture linear relationships in the data. The output data from the first linear layer is passed through an activation function, such as ReLU, Sigmoid, or Tanh, to introduce nonlinearity, enabling the continuous-discrete convolutional neural network model to learn and represent complex nonlinear relationships. The continuous-discrete convolutional layer then performs multidimensional representation to produce multidimensional representation output data, which contains feature information of the input data across different dimensions. The multi-dimensional representation output data is then fed into the second linear layer for further feature integration and transformation to obtain the final output data. This output can be a classification result, regression value, or other form of prediction result, depending on the design goals and tasks of the continuous-discrete convolutional neural network model.

[0074] In one embodiment, a batch normalization layer, a first linear layer, and a continuous discrete convolution layer are used as a feature extraction structure. Multiple feature extraction structures are cascaded to further extract multi-dimensional features of the input data. For example, the number of cascades can be eight, which is not specifically limited in this embodiment.

[0075] The following combination Figure 1 The multidimensional data feature extraction method in the embodiment of the present application is described.

[0076] Figure 2 This is an optional flowchart of the multidimensional data feature extraction method provided in the embodiment of the present application. Figure 2 The method may include but is not limited to steps 110 to 130. It is also understood that this embodiment is for Figure 2 The order of step 110 to step 130 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0077] Step 110: Obtain discrete protein residue data and continuous protein structure data.

[0078] In one embodiment, the multidimensional data feature extraction method of the embodiment of the present application can be used for data in different fields, and characterizing multidimensional data can analyze and process multidimensional complex data, and convert it into a more useful form so that it can be applied to tasks such as classification, prediction or optimization. The specific application scenarios are wide-ranging. For example, in the field of image processing, the spatial structure (discrete) of the image and the continuous motion of the object can be analyzed simultaneously; in time series analysis, the changing trends of stock prices or meteorological data can be processed; in the field of biological data processing, such as the analysis of protein sequences and structures, accuracy and efficiency can also be improved. Through this method, complex data from different dimensions can be efficiently processed and converted into useful information, thereby improving the accuracy and efficiency of various tasks. The analysis process of protein sequences and structures is used as an example for explanation below, which does not limit specific data types and application scenarios.

[0079] In one embodiment, referring to Figure 3 , Figure 3 This is a flowchart of obtaining discrete protein residue data and continuous protein structure data provided in an embodiment of the present application, which specifically includes the following steps:

[0080] Step 310: Obtain initial protein data corresponding to at least one protein.

[0081] In one embodiment, protein-related data may be obtained from a database, where the protein-related data includes initial protein data corresponding to a plurality of proteins. To distinguish the data, a unique protein identifier is generated for each initial protein data.

[0082] Step 320: Obtain initial residue information and / or initial structure information from the initial protein data.

[0083] In one embodiment, a residue refers to a portion of an amino acid molecule that constitutes a protein. When amino acids are linked to form a polypeptide chain through peptide bonds, the amino group (-NH2) and carboxyl group (-COOH) on the α-carbon atom of each amino acid participate in the formation of the peptide bond, and the remaining portion (including the side chain R group) is called a residue. Residues are the basis of protein function because different amino acid residues have different chemical properties and spatial structures, which directly affect the folding, stability, and interaction of proteins with other molecules. The type of residue is determined by its side chain R group. There are 20 standard amino acid residues, each with a unique single-letter or three-letter code. The structure of a protein usually refers to its three-dimensional spatial conformation, that is, the relative position and arrangement of all atoms in the protein molecule.

[0084] In one embodiment, when performing data separation from the initial protein data, since the initial protein data may be incomplete, for example, some initial protein data can only obtain one of the residue initial information and the structure initial information, data separation is performed from the initial protein data to obtain the residue initial information, the structure initial information, or the residue initial information + the structure initial information.

[0085] Step 330: Use the protein identifier to pair the residue initial information and the structure initial information belonging to the same initial protein data to obtain at least one data pair.

[0086] In one embodiment, therefore, in the present embodiment, both the initial residue information and the initial structure information are labeled accordingly according to the protein identifier corresponding to the initial protein data from which they originate. In other words, the initial residue information or initial structure information from the same initial protein data all have the same protein identifier. Therefore, the present embodiment uses the protein identifier to pair the initial residue information and initial structure information belonging to the same initial protein data, obtaining at least one data pair, each of which contains the initial residue information and initial structure information from the same initial protein data.

[0087] Step 340: In each data pair, the residue initial information is encoded to obtain protein residue data, and the structure initial information is encoded to obtain protein structure data.

[0088] In one embodiment, after the pairing is successful, it is necessary to encode the initial residue information to obtain protein residue data and encode the initial structure information to obtain protein structure data. Figure 4 , Figure 4 This is a flow chart of encoding initial residue information to obtain protein residue data provided in an embodiment of the present application, which specifically includes the following steps:

[0089] Step 410: Obtain the residue type of each protein main chain atom in the initial residue information.

[0090] In one embodiment, residue type refers to the type of amino acid residue that makes up the protein backbone. Each amino acid has a specific side chain (R group) that determines the amino acid's chemical properties and function. Therefore, when referring to residue type, we are referring to these different amino acid types, which are linked together by peptide bonds to form polypeptide chains, which in turn fold into proteins with specific functions.

[0091] Step 420: integer-code the residue type to obtain the residue type code corresponding to each protein main chain atom.

[0092] In one embodiment, the amino acid at each position in the protein sequence is represented by its corresponding single-letter code or three-letter code. For example, alanine can be represented as "A" or "Ala". These codes are standardized and are used to represent the 20 common amino acids and an additional symbol "X" to represent unknown or unspecified amino acid residues. Therefore, the present embodiment represents the residue type as p i (1≤i≤20,i∈N * ), N * Indicates an integer and uses p 21 Indicates unknown residue types. The residue types of each protein main chain atom in the initial residue information are encoded as integers to obtain the residue type code corresponding to each protein main chain atom. For example, the residue type of a protein main chain atom is p i , then it is based on p i →i is mapped and represented as an integer i between 1 and 21, which is the corresponding residue type code.

[0093] Step 430: Obtain protein residue data based on the residue type code.

[0094] In one embodiment, all the residue types obtained are coded as a sequence, that is, the protein residue data P is obtained. seq , denoted as P seq =[q 1 ,q 2 ,…,q l ], where q∈{i|1≤i≤21,i∈N *}, l is the length of the residue sequence.

[0095] As can be seen from the above, protein residue data is discrete one-dimensional data, which describes the order of amino acids corresponding to the main chain atoms of the protein. Each residue type code in the protein residue data can be regarded as an independent unit, so the protein residue data is one-dimensional discrete data.

[0096] Next, refer to Figure 5 , Figure 5 This is a flowchart of encoding initial structural information to obtain protein structure data provided by an embodiment of the present application, which specifically includes the following steps:

[0097] Step 510: Obtain the three-dimensional coordinates of each protein main chain atom from the initial structure information.

[0098] In one embodiment, the three-dimensional coordinates of the i-th protein backbone atom are represented as s i=(x,y,z)∈R3, the three-dimensional coordinates of the protein main chain atoms provide the conformational information of the protein in space. These coordinates are continuous because they are used to describe the specific positions of the protein main chain atoms in three-dimensional space. These positions can be any real numbers, not just integers or discrete values.

[0099] Step 520: Obtain protein structure data according to the three-dimensional coordinates.

[0100] In one embodiment, all three-dimensional coordinates are aggregated to obtain protein structure data P str , denoted as P str =[s 1 ,s 2 ,…,s l ], where l is the protein main chain atom C α The corresponding number of atoms is equivalent to the length of the residue sequence mentioned above. It can be seen that protein structure data is continuous three-dimensional data.

[0101] In one embodiment, referring to Figure 6 , Figure 6 This is a schematic diagram of obtaining discrete protein residue data and continuous protein structure data provided by the embodiment of this application. Figure 6 Initial protein data corresponding to at least one protein is obtained from a protein database. Whether initial structural information exists in the initial protein data is determined. If not, the corresponding data is discarded. If so, the initial protein data is decomposed to obtain initial residue information and initial structural information. The initial residue information is then encoded to obtain discrete protein residue data, and the initial structural information is encoded to obtain continuous protein structure data.

[0102] Step 120: Determine discrete network parameters from at least one discrete convolutional network based on the protein residue data, and determine continuous network parameters based on the protein structure data.

[0103] In one embodiment, referring to Figure 7 , Figure 7 This is a schematic diagram of network parameter determination provided by an embodiment of the present application. Corresponding convolutional networks are selected for discrete data and continuous data respectively. For example, for discrete data, a corresponding network is selected from three discrete convolutional networks, such as a one-dimensional discrete convolutional network, a two-dimensional discrete convolutional network, or a three-dimensional discrete convolutional network, according to the corresponding data dimension. For continuous data, a continuous convolutional network is determined regardless of the data dimension.

[0104] The following is an explanation of the application scenarios of protein sequences and structures in the embodiments of the present application, specifically determining discrete network parameters from at least one discrete convolutional network based on protein residue data, and determining continuous network parameters based on protein structure data. In one embodiment, referring to Figure 8 , Figure 8 This is a flowchart of determining discrete network parameters from at least one discrete convolutional network based on protein residue data and determining continuous network parameters based on protein structure data provided by an embodiment of the present application, specifically comprising the following steps:

[0105] Step 810: Select a one-dimensional convolutional network from at least one discrete convolutional network based on the protein residue data, and obtain a convolution kernel size parameter of the one-dimensional convolutional network as a discrete network parameter.

[0106] In one embodiment, since protein residue data is one-dimensional discrete data, Figure 7 A one-dimensional discrete convolutional network is selected from the three discrete convolutional networks shown. At this time, the convolution kernel size parameters of the one-dimensional convolutional network are obtained as discrete network parameters.

[0107] Reference Figure 9 , Figure 9 This is a schematic diagram of the principle of a one-dimensional discrete convolutional network provided in an embodiment of the present application. For a one-dimensional discrete convolutional network, its convolution operation slides the input data through the convolution kernel and calculates the local features of each position. The formula is as follows:

[0108]

[0109] Among them, x(t) is the input data, w1(Δ) is the convolution kernel with a convolution kernel size parameter of k, and y(t) is the output data, which represents the convolution result of the input and the convolution kernel at time t. During the convolution calculation process, the convolution kernel w1 slides on the input data x, and the sliding range is determined by the convolution kernel size parameter k. In the figure, k = 3 is shown, and the dot product is calculated during the sliding process.

[0110] Taking the protein residue data in the embodiment of this application as an example, if it is represented by a one-dimensional discrete convolutional network, it can be expressed as:

[0111]

[0112] Where q(t) is the residue type at position t of the input data.

[0113] In one embodiment, the convolution kernel size parameter k in the above formula is used as a discrete network parameter.

[0114] Step 820: Select the convolution kernel space radius parameter of the continuous convolution network as the continuous network parameter according to the protein structure data.

[0115] In one embodiment, the protein structure data is three-dimensional continuous data, so a continuous convolutional network is selected, and the convolution kernel space radius parameter of the continuous convolutional network is obtained as the continuous network parameter.

[0116] Reference Figure 10 , Figure 10 This is a schematic diagram of the principle of the continuous convolutional network provided in the embodiment of the present application. For the continuous convolutional network, the kernel function w2 is parameterized using the displacement weight θ, and the formula is as follows:

[0117]

[0118] Among them, x(s) is the input data, w2(δ;θ) is The parameterized convolution kernel in the range, θ represents the model parameters of the continuous convolution network, y(s) is the output data, which represents the convolution result of the input data and the parameterized convolution kernel at the three-dimensional coordinate s, and δ represents the three-dimensional offset from the point to the three-dimensional coordinate s.

[0119] For the protein structure data in the examples of this application, δ is a three-dimensional vector. The continuous convolution process is expressed as:

[0120]

[0121] Among them, q(s) is the residue type of the input data at the three-dimensional coordinate s, and r represents the convolution kernel space radius parameter, which is used to limit the convolution kernel space. the specific scope of The range is the spherical space shown in the figure, which contains multiple protein main chain atoms, and ||δ||≤r means The offset corresponding to the point in the range.

[0122] From the above, we can see that the convolution kernel size parameter k is selected for discrete network parameters, and the convolution kernel space radius parameter r is selected for continuous network parameters. These two parameters can reflect the computational nature of discrete convolution and continuous convolution.

[0123] Step 130: Input the protein residue data and protein structure data as initial input data into the continuous discrete convolutional neural network model, use the continuous discrete convolution layer to update the convolution kernel parameters according to the discrete network parameters and the continuous network parameters, and perform feature extraction on the initial input data based on the updated convolution kernel parameters to obtain multi-dimensional representation output data.

[0124] In one embodiment, referring to Figure 11 , Figure 11This is a flowchart of updating convolution kernel parameters according to discrete network parameters and continuous network parameters using a continuous discrete convolution layer provided by an embodiment of the present application, specifically including the following steps:

[0125] Step 1110: Determine at least one three-dimensional offset based on the convolution kernel spatial radius parameter, and determine at least one one-dimensional offset based on the convolution kernel size parameter.

[0126] In one embodiment, a three-dimensional offset δ is set to represent parameter changes of continuous convolution during continuous discrete convolution. Under the restriction of the convolution kernel space radius parameter r, the numerical range of the three-dimensional offset is expressed as: ||δ||≤r.

[0127] In addition, a one-dimensional offset Δ is set to represent the parameter change of discrete convolution during continuous discrete convolution. Under the restriction of the convolution kernel size parameter k, the numerical range of the one-dimensional offset is expressed as: 0≤Δ≤k-1.

[0128] Step 1120: Calculate the sum of the three-dimensional offset and the convolution kernel space radius parameter to obtain a first intermediate value, and calculate the reciprocal of the first intermediate value as the offset parameter.

[0129] In one embodiment, the first intermediate value is expressed as:

[0130] ||δ||+r

[0131] The offset parameter is expressed as:

[0132]

[0133] Step 1130: Obtain model parameters of the continuous discrete convolution layer corresponding to the one-dimensional offset.

[0134] In one embodiment, the model parameters of the continuous discrete convolution layer corresponding to the one-dimensional offset are expressed as θ Δ , that is, the discrete convolution process slides on the sequence corresponding to the protein residue data, and the one-dimensional offset Δ can determine the range of sliding. The continuous discrete convolution layer completed by training will have corresponding model parameters θ under different one-dimensional offset Δ conditions. Δ , the model parameters mainly refer to the weights of the convolution kernel.

[0135] Step 1140: Obtain an updated convolution kernel according to the product of the offset parameter and the model parameter.

[0136] In one embodiment, for a continuous convolution process, the updated convolution kernel is expressed as:

[0137]

[0138] Step 1150: Change the values ​​of the one-dimensional offset and / or the three-dimensional offset one by one to obtain multiple offset combinations, and calculate the convolution kernel parameters corresponding to each offset combination.

[0139] In one embodiment, within the range of ||δ||≤r, 0≤Δ≤k-1, the values ​​of the one-dimensional offset and / or the three-dimensional offset are varied one by one to obtain different (δ, Δ) combinations as offset combinations. The convolution kernel parameters w(δ, Δ) corresponding to each offset combination are then calculated.

[0140] Next, refer to Figure 12 , Figure 12 This is a flowchart of extracting features from initial input data based on an updated convolution kernel to obtain multi-dimensional representation output data, provided by an embodiment of the present application, specifically including the following steps:

[0141] Step 1210: Determine the three-dimensional coordinates of the protein main chain atoms one by one from the initial input data as the current coordinates.

[0142] In one embodiment, referring to Figure 13 , Figure 13 This is a schematic diagram of multi-dimensional characterization provided in the embodiment of the present application. Assuming that the i-th protein main chain atom is selected in the figure, its three-dimensional coordinate s i as the current coordinates.

[0143] Step 1220: Obtain the three-dimensional offsets one by one as the current three-dimensional offset, obtain the adjacent coordinates according to the current coordinates and the current three-dimensional offset, and obtain the residue type corresponding to the adjacent coordinates as the current residue type.

[0144] In one embodiment, referring to Figure 13 , according to the current coordinate and the 3D offset, multiple adjacent coordinates can be determined within the range of the convolution kernel space radius parameter r, as shown in the filled circle in the figure. Each 3D offset corresponds to a neighboring coordinate. At this time, assuming that the jth 3D offset is selected, the current 3D offset is δ j , get the residue type of the protein main chain atom corresponding to the neighboring coordinates as the current residue type.

[0145] Step 1230: Obtain at least one convolution kernel parameter corresponding to the current three-dimensional offset, multiply the corresponding current residue type and the convolution kernel parameter to obtain a second intermediate value, and accumulate the second intermediate values ​​corresponding to all three-dimensional offsets to obtain the characterization data corresponding to the current three-dimensional offset.

[0146] In one embodiment, Figure 13The black dotted line is used to connect the one-dimensional offset. It can be seen that the three-dimensional offset will correspond to multiple one-dimensional offsets. For example, in the figure, k = 6, and the protein main chain atom at the current coordinate is taken as the center. Three protein main chain atoms are obtained before and after it, corresponding to Δ = 0, Δ = 1, Δ = 2, Δ = 3, Δ = 4, and Δ = 5 respectively. At this time, the convolution kernel parameters corresponding to each corresponding one-dimensional offset are obtained, that is, when ||δ|| = δ j ,0≤Δ≤k-1 range (δ j ,Δ) combination, then the corresponding current residue type and convolution kernel parameter are multiplied to obtain the second intermediate value.

[0147] For example, the mth one-dimensional offset Δ m , the corresponding second intermediate value is expressed as:

[0148] q(s i +δ j )·w(δ j ,Δ m )

[0149] Next, the second intermediate value corresponding to the current three-dimensional offset is accumulated to obtain the intermediate representation data y(s) corresponding to the current three-dimensional offset. i , δ j ), expressed as:

[0150]

[0151] By accumulating the intermediate representation data corresponding to all three-dimensional offsets, we can obtain the representation data corresponding to the protein main chain atoms at the current coordinates, which is expressed as:

[0152]

[0153] From the above, we can see that the calculation process of the characterization data corresponding to each protein main chain atom combines continuous convolution and discrete convolution, so the obtained characterization data is more accurate.

[0154] Step 1240: Obtain multi-dimensional representation output data based on all the representation data.

[0155] In one embodiment, the above process is performed on each protein main chain atom to obtain corresponding characterization data. Finally, the characterization data corresponding to all protein main chain atoms are output in the form of a sequence to obtain multidimensional characterization output data corresponding to the initial input data, which is expressed as: [y(s 1 ),y(s 2 ),…,y(s l )].

[0156] In one embodiment, a training process for a continuous-discrete convolutional neural network model is described.

[0157] Different loss functions are used depending on the training task, such as cross-entropy loss (Cross-Entropy Loss) for classification tasks and mean squared error (MSE) for regression tasks. The following describes these two loss functions.

[0158] If the network's task is to classify proteins, such as predicting the functional category of proteins, then the cross-entropy loss function is a common choice. For classification tasks, the cross-entropy loss measures the gap between the predicted probability distribution of the network output and the probability distribution of the true category. The formula is as follows:

[0159]

[0160] Where C is the number of categories, y i is the true label, which is 1 for the correct class and 0 for other classes. is the probability of the i-th class predicted by the model. The goal of cross-entropy loss is to maximize the probability output corresponding to the true class and minimize the output probabilities of other classes. For multi-class classification tasks, backpropagation guides the continuous discrete convolutional neural network model to update weights, thereby gradually improving the prediction of the correct class.

[0161] In the prediction of continuous variables (such as predicting the physical properties or activity of proteins), the mean square error is usually used. The mean square error calculates the average of the squared differences between the predicted value and the true value. The formula is as follows:

[0162]

[0163] Where N is the number of samples, y i is the true value of the i-th sample, is the predicted value of the i-th sample. The MSE penalizes the square of the difference between the predicted value and the true value; the larger the difference, the greater the loss. Through backpropagation, the continuous-discrete convolutional neural network model attempts to minimize this gap, making the predicted value closer to the true value.

[0164] In addition, during the training of the continuous discrete convolutional neural network model, the RAdam optimizer was used to adjust the network parameters, and the initial learning rate was 1.0×10 -4 , with a total training cycle of 150 epochs. Furthermore, a learning rate adjustment strategy was introduced. When training loss stagnated, the learning rate was appropriately reduced to ensure that the continuous-discrete convolutional neural network model could continue to learn effectively. Through these steps, the training process of the entire continuous-discrete convolutional neural network model improved learning efficiency and accelerated convergence, achieving ideal performance more quickly.

[0165] The embodiment of the present application utilizes a continuous discrete convolution layer to integrate discrete and continuous data to perform a unified feature extraction process. By performing a joint convolution operation on discrete data and continuous data, the feature representation of the data is extracted from multiple dimensions, such as one-dimensional sequences and three-dimensional coordinates, to form a unified high-dimensional feature representation, thereby improving the accuracy of multi-dimensional representation.

[0166] The technical solution provided in the embodiment of the present application obtains discrete protein residue data and continuous protein structure data, determines discrete network parameters from at least one discrete convolutional network based on the protein residue data, and determines continuous network parameters based on the protein structure data, inputs the protein residue data and protein structure data as initial input data into the continuous discrete convolutional neural network model, uses the continuous discrete convolution layer to update the convolution kernel parameters based on the discrete network parameters and the continuous network parameters, and extracts features from the initial input data based on the updated convolution kernel parameters to obtain multidimensional representation output data. After obtaining the discrete network parameters and the continuous network parameters in the embodiment of the present application, the continuous discrete convolution layer is used to fuse the feature extraction processes of continuous convolution and discrete convolution based on the two network parameters. By updating the convolution kernel parameters and extracting features from the multidimensional input data using the updated convolution kernel parameters, the complex features in the multidimensional data can be understood and characterized more comprehensively. This processing process converts the data into an efficient and information-rich representation form, which can not only improve the accuracy of the feature data obtained by feature extraction, but also improve the performance of the feature data in various application scenarios.

[0167] The present application also provides a multidimensional data feature extraction device, which can implement the multidimensional data feature extraction method. Figure 14 , the device comprises:

[0168] Data acquisition module 1410: used to acquire discrete protein residue data and continuous protein structure data.

[0169] Model parameter acquisition module 1420: used to determine discrete network parameters from at least one discrete convolutional network based on protein residue data, and to determine continuous network parameters based on protein structure data.

[0170] Continuous discrete convolution module 1430: used to input protein residue data and protein structure data as initial input data into the continuous discrete convolutional neural network model, use the continuous discrete convolution layer to update the convolution kernel parameters according to the discrete network parameters and the continuous network parameters, and extract features of the initial input data based on the updated convolution kernel parameters to obtain multi-dimensional representation output data.

[0171] The specific implementation of the multidimensional data feature extraction device of this embodiment is basically the same as the specific implementation of the multidimensional data feature extraction method described above, and will not be repeated here.

[0172] An embodiment of the present application further provides an electronic device, including:

[0173] at least one memory;

[0174] at least one processor;

[0175] at least one program;

[0176] The program is stored in the memory, and the processor executes the at least one program to implement the multidimensional data feature extraction method implemented in this application. The electronic device can be any smart terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), an in-vehicle computer, etc.

[0177] See also Figure 15 , Figure 15 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0178] The processor 1501 may be implemented as a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0179] The memory 1502 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1502 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1502 and is called by the processor 1501 to execute the multidimensional data feature extraction method of the embodiments of this application.

[0180] Input / output interface 1503, used to implement information input and output;

[0181] Communication interface 1504, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); and

[0182] Bus 1505 , which transmits information between various components of the device (e.g., processor 1501 , memory 1502 , input / output interface 1503 , and communication interface 1504 );

[0183] The processor 1501 , the memory 1502 , the input / output interface 1503 and the communication interface 1504 are connected to each other in communication within the device via the bus 1505 .

[0184] An embodiment of the present application further provides a storage medium, which is a storage medium storing a computer program. When the computer program is executed by a processor, the above-mentioned multidimensional data feature extraction method is implemented.

[0185] The memory, as a non-transient storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0186] The multidimensional data feature extraction method, apparatus, device and storage medium proposed in the embodiments of the present application obtain discrete protein residue data and continuous protein structure data, determine discrete network parameters from at least one discrete convolutional network based on the protein residue data, and determine continuous network parameters based on the protein structure data, input the protein residue data and protein structure data as initial input data into the continuous discrete convolutional neural network model, use the continuous discrete convolution layer to update the convolution kernel parameters based on the discrete network parameters and the continuous network parameters, and extract features from the initial input data based on the updated convolution kernel parameters to obtain multidimensional representation output data. After obtaining the discrete network parameters and the continuous network parameters in the embodiments of the present application, the continuous discrete convolution layer is used to fuse the feature extraction processes of continuous convolution and discrete convolution based on the two network parameters. By updating the convolution kernel parameters and using the updated convolution kernel parameters to extract features from the multidimensional input data, it is possible to more comprehensively understand and characterize the complex features in the multidimensional data. This processing process converts the data into an efficient and information-rich representation form, which can not only improve the accuracy of the feature data obtained by feature extraction, but also improve the performance of the feature data in various application scenarios.

[0187] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0188] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0189] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0190] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0191] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0192] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0193] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as 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.

[0194] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0195] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0196] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0197] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for extracting features from multidimensional data, characterized in that: Applied to a continuous discrete convolutional neural network model, the continuous discrete convolutional neural network model includes at least a continuous discrete convolution layer, and the method includes: Obtain discrete protein residue data and continuous protein structure data; Selecting a one-dimensional convolutional network from at least one discrete convolutional network based on the protein residue data, and obtaining a convolution kernel size parameter of the one-dimensional convolutional network as a discrete network parameter; selecting a convolution kernel space radius parameter of a continuous convolutional network as a continuous network parameter based on the protein structure data; Inputting the protein residue data and the protein structure data as initial input data into the continuous discrete convolutional neural network model, using the continuous discrete convolution layer to update convolution kernel parameters according to the discrete network parameters and the continuous network parameters, and performing feature extraction on the initial input data based on the updated convolution kernel parameters to obtain multidimensional representation output data; The method of using the continuous discrete convolution layer to update the convolution kernel parameters according to the discrete network parameters and the continuous network parameters includes: determining at least one three-dimensional offset according to the convolution kernel space radius parameter, and determining at least one one-dimensional offset according to the convolution kernel size parameter; calculating the sum of the three-dimensional offset and the convolution kernel space radius parameter to obtain a first intermediate value, and calculating the inverse of the first intermediate value as an offset parameter; obtaining the model parameters of the continuous discrete convolution layer corresponding to the one-dimensional offset; obtaining an updated convolution kernel according to the product of the offset parameter and the model parameter; changing the values ​​of the one-dimensional offset and / or the three-dimensional offset one by one to obtain multiple offset combinations, and calculating the convolution kernel parameters corresponding to each offset combination.

2. The multidimensional data feature extraction method according to claim 1, characterized in that: The obtaining of discrete protein residue data and continuous protein structure data comprises: Acquire initial protein data corresponding to at least one protein, each of the initial protein data including a protein identifier; Obtaining initial residue information and / or initial structure information from the initial protein data; Using the protein identifier, pairing the residue initial information and the structure initial information belonging to the same initial protein data to obtain at least one data pair; In each data pair, the residue initial information is encoded to obtain the protein residue data, and the structure initial information is encoded to obtain the protein structure data.

3. The multidimensional data feature extraction method according to claim 2, characterized in that: The step of encoding the initial residue information to obtain the protein residue data comprises: Obtaining the residue type of each protein main chain atom in the initial residue information; Performing integer encoding on the residue type to obtain a residue type code corresponding to each atom of the protein main chain; The protein residue data is obtained based on the residue type coding, and the protein residue data is discrete one-dimensional data.

4. The multidimensional data feature extraction method according to claim 3, characterized in that: The step of encoding the initial structural information to obtain the protein structural data comprises: Obtaining the three-dimensional coordinates of each protein main chain atom from the initial structural information; The protein structure data is obtained according to the three-dimensional coordinates, and the protein structure data is continuous three-dimensional data.

5. The multidimensional data feature extraction method according to claim 1, characterized in that: The feature extraction of the initial input data based on the updated convolution kernel to obtain multi-dimensional representation output data includes: Determining the three-dimensional coordinates of the main chain atoms of the protein one by one from the initial input data as current coordinates; Obtaining the three-dimensional offsets one by one as current three-dimensional offsets, obtaining neighboring coordinates according to the current coordinates and the current three-dimensional offsets, and obtaining the residue types corresponding to the neighboring coordinates as current residue types; Obtaining at least one convolution kernel parameter corresponding to the current three-dimensional offset, multiplying the corresponding current residue type and the convolution kernel parameter to obtain a second intermediate value, and accumulating the second intermediate values ​​corresponding to all the three-dimensional offsets to obtain representation data corresponding to the current coordinate; The multi-dimensional representation output data is obtained according to all the representation data.

6. A multidimensional data feature extraction device, characterized in that: Applied to a continuous discrete convolutional neural network model, the continuous discrete convolutional neural network model includes at least a continuous discrete convolution layer, and the device includes: Data acquisition module: used to obtain discrete protein residue data and continuous protein structure data; A model parameter acquisition module is configured to select a one-dimensional convolutional network from at least one discrete convolutional network based on the protein residue data, and obtain a convolution kernel size parameter of the one-dimensional convolutional network as a discrete network parameter; and select a convolution kernel space radius parameter of a continuous convolutional network based on the protein structure data as a continuous network parameter; A continuous discrete convolution module is configured to input the protein residue data and the protein structure data as initial input data into the continuous discrete convolutional neural network model, update convolution kernel parameters using the continuous discrete convolution layer according to the discrete network parameters and the continuous network parameters, perform feature extraction on the initial input data based on the updated convolution kernel parameters, and obtain multi-dimensional representation output data; The method of using the continuous discrete convolution layer to update the convolution kernel parameters according to the discrete network parameters and the continuous network parameters includes: determining at least one three-dimensional offset according to the convolution kernel space radius parameter, and determining at least one one-dimensional offset according to the convolution kernel size parameter; calculating the sum of the three-dimensional offset and the convolution kernel space radius parameter to obtain a first intermediate value, and calculating the inverse of the first intermediate value as an offset parameter; obtaining the model parameters of the continuous discrete convolution layer corresponding to the one-dimensional offset; obtaining an updated convolution kernel according to the product of the offset parameter and the model parameter; changing the values ​​of the one-dimensional offset and / or the three-dimensional offset one by one to obtain multiple offset combinations, and calculating the convolution kernel parameters corresponding to each offset combination.

7. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the multidimensional data feature extraction method according to any one of claims 1 to 5 when executing the computer program.

8. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the multidimensional data feature extraction method according to any one of claims 1 to 5 is implemented.

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