A server data processing method based on deep learning model

Abstract

The present invention belongs to the field of server data processing technology, and specifically relates to a server data processing method based on a deep learning model. First, a data set is divided into multiple data blocks, and the multiple data blocks are respectively transmitted to each primary processing node of the primary processing node network for primary processing; then the data processed by each group of primary processing nodes are respectively transmitted to the corresponding secondary processing nodes for secondary processing; then the data processed by each group of secondary processing nodes are respectively transmitted to the corresponding heavy processing nodes for heavy processing; finally, the data processed by the heavy processing node is transmitted to the cloud server for ultimate processing, and all data processing is completed. By establishing a pyramid-shaped server processing node network, the step-by-step processing of data is realized, the transmission amount in the data processing process and the data storage amount of each level of processing nodes are effectively reduced, and the burden of each node in the entire server processing node network is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of server data processing, and in particular, relates to a server data processing method based on a deep learning model. Background Art

[0002] With the development of global big data, the data sets involved in the data processing of servers are becoming larger and larger, and the size and complexity of the data sets are also growing exponentially. The computing power of a single server cannot keep up with the development speed of big data. On the one hand, the data processing capabilities of existing servers are unable to handle the large and complex data sets in the existing big data environment; on the other hand, due to insufficient data processing capabilities, it has a certain impact on the communication security between servers under big data.

[0003] In the prior art, a large model with a huge number of parameters and a complex hierarchical structure is created to process a large and complex data set. However, even when processing data under a large model, a large data sample may still exceed the processing capacity of a single node. In order to solve the above technical difficulties, distributed data processing came into being. In the distributed data processing mode, the huge data set is transmitted to different node processing nodes for processing, and communication and interaction are performed on each processing node.

[0004] In the current distributed data processing process, different data blocks are calculated by each processing node, and then the data processed by each processing node is stacked step by step, and finally sent to each processing node separately. However, this distributed data processing method requires a huge amount of data transmission. With the increase of processing nodes, large data transmission volume and large data storage volume have become new technical problems.

[0005] Therefore, there is an urgent need to improve the existing distributed data processing process to solve the technical problems of large data transmission volume and large data storage volume in the above distributed processing process. Summary of the invention

[0006] The purpose of the present invention is to provide a server data processing method based on a deep learning model to solve the technical problems of large data transmission volume and large data storage volume in the above-mentioned distributed processing process.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0008] A server data processing method based on a deep learning model, wherein the server processing node network creation process is as follows: first, a primary processing node network is created, and then primary processing nodes in the primary processing node network are divided into a plurality of groups, and a secondary processing node network is created according to the number of groups of the primary processing nodes, and secondary processing nodes in the secondary processing node network are divided into a plurality of groups, and a heavy processing node network is created according to the number of groups of the secondary processing nodes, and communication between the heavy processing node network and a cloud server is established;

[0009] The process of data processing through the server processing node network includes the following steps:

[0010] S1: dividing the data set into multiple data blocks, and transmitting the multiple data blocks to each primary processing node of the primary processing node network for primary processing respectively;

[0011] S2: transmitting the data processed by each group of primary processing nodes to the corresponding secondary processing nodes for secondary processing;

[0012] S3: Transmitting the data processed by each group of secondary processing nodes to the corresponding heavy-level processing nodes for heavy-level processing;

[0013] S4: The data processed by the heavy-level processing node is transmitted to the cloud server for final processing to complete all data processing.

[0014] Preferably, step S1 includes the following specific process:

[0015] S11: After each primary processing node of the primary processing node network performs primary processing on a plurality of data blocks, the primary processed data is divided into primary important data and primary non-important data;

[0016] S12: transmitting primary non-important data directly to the edge server that generates the data set;

[0017] S13: Transmitting primary important data to secondary processing nodes according to the server processing node network grouping.

[0018] Preferably, step S2 includes the following specific process:

[0019] S21: each secondary processing node of the secondary processing node network performs secondary processing on the data processed by the primary processing node, and divides the secondary processed data into secondary important data and secondary non-important data;

[0020] S22: directly transmitting the secondary non-important data to each primary processing node;

[0021] S23: Transmitting the secondary important data to the heavy-level processing node according to the server processing node network grouping.

[0022] Preferably, step S3 includes the following specific process:

[0023] S31: each heavy-level processing node of the heavy-level processing node network performs heavy-level processing on the data processed by the secondary processing node, and reclassifies the reclassified data into important data and non-important data;

[0024] S32: directly transmitting the heavy non-important data to the secondary processing node;

[0025] S33: Transmit important data to the cloud server for final data processing.

[0026] Preferably, data processing is performed based on a deep learning model in the corresponding data processing processes performed by processing nodes at each level in the server processing node network.

[0027] Preferably, the process of processing nodes at all levels in the server processing node network performing data processing based on the deep learning model includes the following specific steps:

[0028] Step 1: Obtain historical processing data samples at each data processing node, and perform data preprocessing on the historical processing data samples;

[0029] Step 2: Divide the preprocessed historical data samples into training set, test set and validation set;

[0030] Step 3: Create a corresponding data processing model at each data processing node, input the training set, test set and validation set into the data processing model in sequence, and train, test and validate the data processing model;

[0031] Step 4: Obtain the data set to be processed currently, input the data set to be processed currently into the data processing model, perform data processing on the data set to be processed currently, and output corresponding data processing results.

[0032] Preferably, step 3 includes the following specific processes:

[0033] Step 31: inputting the training set into the data processing model to train the data processing model;

[0034] Step 32: Input the test set into the data processing model, test the trained data processing model, and determine whether the test result of the data processing model reaches the specified test performance. If so, execute step 33; if not, re-input a new training set and execute step S1;

[0035] Step 33: Input the verification set into the tested data processing model, verify the test results of the tested data processing model, and determine whether the verification results of the data processing model meet the specified requirements; if yes, execute step 4; if not, re-input the new training set and execute step S1.

[0036] Preferably, the data preprocessing in step 1 includes data cleaning processing, data conversion processing, data integration processing and data normalization processing.

[0037] After the data processed by each group of primary processing nodes are transmitted to the corresponding secondary processing nodes for secondary processing, the following data processing process is also included:

[0038] The primary processing node uses the private key to sign the processed data, generates signature data, and performs noise processing on the signature data; the secondary processing node encrypts the signature data after noise processing, transmits the encrypted data to the secondary processing node for decryption, and extracts the data to be processed;

[0039] The data processing process in which the data processed by the secondary processing nodes are transmitted to the corresponding heavy processing nodes for heavy processing, or the data processed by the heavy processing nodes are transmitted to the cloud server for final processing is the same as the process in which the data processed by the primary processing nodes are transmitted to the corresponding secondary processing nodes for secondary processing.

[0040] Preferably, data processing is performed through the following process:

[0041] S1': Divide the data set into multi-level data blocks, wherein the multi-level data blocks include primary data blocks, secondary data blocks, re-level data blocks and ultimate data blocks, and add corresponding level labels to all data blocks, wherein the primary data blocks, the secondary data blocks, the re-level data blocks and the ultimate data blocks each include a number of data blocks, and randomly transmit the multiple data blocks to each primary processing node of the primary processing node network, wherein each primary processing node only performs primary processing on the primary data blocks therein, and transmits the primary processed data as well as the secondary data blocks, the re-level data blocks and the ultimate data blocks to the secondary processing nodes;

[0042] S2': Randomly transmit the data processed by each group of primary processing nodes and the secondary data blocks, re-data blocks and ultimate data blocks to the corresponding secondary processing nodes. The secondary processing nodes perform secondary processing on the data processed by the primary processing and the secondary data, and transmit the data processed by the secondary processing and the re-data blocks and ultimate data blocks to the re-processing nodes.

[0043] S3': randomly transmitting the data processed by the secondary processing node, the heavy data block and the ultimate data block to the corresponding heavy processing node, the heavy processing node performs heavy processing on the secondary processed data and the heavy data block, and transmits the heavy processed data and the ultimate data block to the ultimate processing node;

[0044] S4': The cloud server performs final processing on the data processed by the heavy-level processing nodes and the final data to complete all data processing.

[0045] The beneficial effects of the present invention include:

[0046] The server data processing method based on the deep learning model provided by the present invention first divides the data set into multiple data blocks, and transmits the multiple data blocks to each primary processing node of the primary processing node network for primary processing; then transmits the data processed by each group of primary processing nodes to the corresponding secondary processing nodes for secondary processing; then transmits the data processed by each group of secondary processing nodes to the corresponding heavy processing nodes for heavy processing; finally, transmits the data processed by the heavy processing node to the cloud server for ultimate processing to complete all data processing.

[0047] First, by establishing a pyramid-shaped server processing node network, we can achieve step-by-step processing of data, effectively reducing the transmission volume during data processing and the data storage volume of processing nodes at all levels, and reducing the burden on each node in the entire server processing node network.

[0048] Secondly, the primary processing node uses the private key to sign the processed data, generates signature data, and adds noise to the signature data; the secondary processing node encrypts the noisy signature data, transmits the encrypted data to the heavy processing node for decryption, and extracts the data to be processed. Through multi-level data processing, data security is effectively guaranteed during data transmission.

[0049] Finally, by dividing the data set into multi-level data blocks, the multi-level data blocks include primary data blocks, secondary data blocks, heavy data blocks and ultimate data blocks, and adding corresponding level labels to all data blocks, the primary data blocks, secondary data blocks, heavy data blocks and ultimate data blocks each include several data blocks, and each level of processing nodes processes the data blocks of the corresponding level. The more important data is processed by the corresponding server processing node, which effectively ensures the data processing efficiency and processing effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart of the server data processing method based on the deep learning model of the present invention.

[0051] Figure 2This is a schematic diagram of data processing of two adjacent levels of processing nodes in the server data processing method based on the deep learning model of the present invention.

[0052] Figure 3 It is a schematic diagram of data processing flow in another embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following is combined with Figure 1-Figure 3 The present invention is further described in detail:

[0054] Example 1

[0055] See attached Figure 1 As shown, a server data processing method based on a deep learning model, the server processing node network creation process is: first create a primary processing node network, then divide the primary processing nodes in the primary processing node network into multiple groups, and create a secondary processing node network according to the number of groups of the primary processing nodes, and divide the secondary processing nodes in the secondary processing node network into multiple groups, and create a heavy processing node network according to the number of groups of the secondary processing nodes, and establish communication between the heavy processing node network and the cloud server;

[0056] The process of data processing through the server processing node network includes the following steps:

[0057] S1: dividing the data set into multiple data blocks, and transmitting the multiple data blocks to each primary processing node of the primary processing node network for primary processing respectively;

[0058] S2: transmitting the data processed by each group of primary processing nodes to the corresponding secondary processing nodes for secondary processing;

[0059] S3: Transmitting the data processed by each group of secondary processing nodes to the corresponding heavy-level processing nodes for heavy-level processing;

[0060] S4: The data processed by the heavy-level processing node is transmitted to the cloud server for final processing to complete all data processing.

[0061] After creating the server processing node network, the data set is first divided into multiple data blocks according to the number of primary processing nodes, and the multiple data blocks are transmitted to each primary processing node of the primary processing node network for primary processing; then the data processed by each group of primary processing nodes are transmitted to the corresponding secondary processing nodes for secondary processing; then the data processed by each group of secondary processing nodes are transmitted to the corresponding heavy processing nodes for heavy processing; finally, the data processed by the heavy processing nodes are transmitted to the cloud server for final processing to complete all data processing.

[0062] By establishing a pyramid-like server processing node network, the cloud server is at the top of the pyramid and processes the most important data. The pyramid-like processing of data is achieved through the primary processing node network, the secondary processing node network, and the heavy processing node network. The higher you go in the pyramid, the more important the data you process. In this way, the amount of transmission during data processing and the amount of data storage at each level of processing nodes are effectively reduced, reducing the burden on each node in the entire server processing node network.

[0063] By dividing the data set into multi-level data blocks, the multi-level data blocks include primary data blocks, secondary data blocks, heavy data blocks and ultimate data blocks, and adding corresponding level labels to all data blocks, the primary data blocks, secondary data blocks, heavy data blocks and ultimate data blocks each include several data blocks, and each level of processing node processes the data blocks of the corresponding level. The more important data is processed by the corresponding server processing node, which effectively ensures the data processing efficiency and processing effect.

[0064] Example 2

[0065] On the basis of Example 1, the corresponding data processing process performed by the processing nodes at all levels in the server processing node network is based on the deep learning model for data processing, and step S1 includes the following specific process:

[0066] S11: After each primary processing node of the primary processing node network performs primary processing on a plurality of data blocks, the primary processed data is divided into primary important data and primary non-important data;

[0067] S12: transmitting primary non-important data directly to the edge server that generates the data set;

[0068] S13: Transmitting primary important data to secondary processing nodes according to the server processing node network grouping.

[0069] Step S2 includes the following specific processes:

[0070] S21: each secondary processing node of the secondary processing node network performs secondary processing on the data processed by the primary processing node, and divides the secondary processed data into secondary important data and secondary non-important data;

[0071] S22: directly transmitting the secondary non-important data to each primary processing node;

[0072] S23: Transmitting the secondary important data to the heavy-level processing node according to the server processing node network grouping.

[0073] Step S3 includes the following specific processes:

[0074] S31: each heavy-level processing node of the heavy-level processing node network performs heavy-level processing on the data processed by the secondary processing node, and reclassifies the reclassified data into important data and non-important data;

[0075] S32: directly transmitting the heavy non-important data to the secondary processing node;

[0076] S33: Transmit important data to the cloud server for final data processing.

[0077] In another embodiment, the process of performing data processing based on the deep learning model by processing nodes at each level in the server processing node network includes the following specific steps:

[0078] Step 1: Obtain historical processing data samples at each data processing node, and perform data preprocessing on the historical processing data samples. The data preprocessing includes data cleaning processing, data conversion processing, data integration processing, and data normalization processing;

[0079] Step 2: Divide the preprocessed historical data samples into training set, test set and validation set;

[0080] Step 3: Create a corresponding data processing model at each data processing node, input the training set, test set and validation set into the data processing model in sequence, and train, test and validate the data processing model;

[0081] Step 4: Obtain the data set to be processed currently, input the data set to be processed currently into the data processing model, perform data processing on the data set to be processed currently, and output corresponding data processing results.

[0082] Among them, step 3 includes the following specific processes:

[0083] Step 31: inputting the training set into the data processing model to train the data processing model;

[0084] Step 32: Input the test set into the data processing model, test the trained data processing model, and determine whether the test result of the data processing model reaches the specified test performance. If so, execute step 33; if not, re-input a new training set and execute step S1;

[0085] Step 33: Input the verification set into the tested data processing model, verify the test results of the tested data processing model, and determine whether the verification results of the data processing model meet the specified requirements; if yes, execute step 4; if not, re-input the new training set and execute step S1.

[0086] See also Figure 2, after transmitting the data processed by each group of primary processing nodes to the corresponding secondary processing nodes for secondary processing, it also includes the following data processing process: the primary processing node uses the private key to sign the data processed by it, generates signature data, and performs noise processing on the signature data; the secondary processing node encrypts the noisy signature data, and transmits the encrypted data to the heavy-level processing node for decryption, and extracts the data to be processed; the data processed by the secondary processing nodes are transmitted to the corresponding heavy-level processing nodes for heavy processing, or the data processed by the heavy-level processing nodes are transmitted to the cloud server for final processing. The data processing process is the same as the process of transmitting the data processed by the primary processing nodes to the corresponding secondary processing nodes for secondary processing.

[0087] In the above processing, the process of data signing is a digital string that can only be generated by the sender of the information and cannot be forged by others. This digital string is also an effective proof of the authenticity of the information sent by the sender of the information, which is equivalent to adding specified data to the data to be processed or performing a password transformation. This additional specified data or transformation allows the receiver of the data unit to confirm the source and integrity of the data unit and protect the data from being forged. The noise processing process is used to further protect the data and protect the data sender or data receiver from interference. The subsequent encryption processing process further improves the security of the data transmission process. In other words, through the multi-level data processing process, the security of the data transmission process at all levels is effectively guaranteed to avoid abnormal situations such as data loss or data theft.

[0088] Example 3

[0089] See also Figure 3 In this embodiment, data processing can also be performed by the following method:

[0090] S1': Divide the data set into multi-level data blocks, wherein the multi-level data blocks include primary data blocks, secondary data blocks, re-level data blocks and ultimate data blocks, and add corresponding level labels to all data blocks, wherein the primary data blocks, the secondary data blocks, the re-level data blocks and the ultimate data blocks each include a number of data blocks, and randomly transmit the multiple data blocks to each primary processing node of the primary processing node network, wherein each primary processing node only performs primary processing on the primary data blocks therein, and transmits the primary processed data as well as the secondary data blocks, the re-level data blocks and the ultimate data blocks to the secondary processing nodes;

[0091] S2': Randomly transmit the data processed by each group of primary processing nodes and the secondary data blocks, re-data blocks and ultimate data blocks to the corresponding secondary processing nodes. The secondary processing nodes perform secondary processing on the data processed by the primary processing and the secondary data, and transmit the data processed by the secondary processing and the re-data blocks and ultimate data blocks to the re-processing nodes.

[0092] S3': randomly transmitting the data processed by the secondary processing node, the heavy data block and the ultimate data block to the corresponding heavy processing node, the heavy processing node performs heavy processing on the secondary processed data and the heavy data block, and transmits the heavy processed data and the ultimate data block to the ultimate processing node;

[0093] S4': The cloud server performs final processing on the data processed by the heavy-level processing nodes and the final data to complete all data processing.

[0094] By dividing the data set into multi-level data blocks, the multi-level data blocks include primary data blocks, secondary data blocks, heavy data blocks and ultimate data blocks, and adding corresponding level labels to all data blocks, the primary data blocks, secondary data blocks, heavy data blocks and ultimate data blocks each include several data blocks, and each level of processing node processes the data blocks of the corresponding level. The more important data is processed by the corresponding server processing node, which effectively ensures the data processing efficiency and processing effect.

[0095] In summary, the server data processing method based on the deep learning model provided by the present invention is that the server processing node network first divides the data set to be processed into multiple data blocks, and transmits the multiple data blocks to each primary processing node of the primary processing node network for primary processing respectively; then the data processed by each group of primary processing nodes are transmitted to the corresponding secondary processing nodes for secondary processing; then the data processed by each group of secondary processing nodes are transmitted to the corresponding heavy processing nodes for heavy processing; finally, the data processed by the heavy processing node is transmitted to the cloud server for ultimate processing to complete all data processing. By establishing a pyramid-shaped server processing node network, the step-by-step processing of data is realized, the transmission volume in the data processing process and the data storage volume of each level of processing nodes are effectively reduced, and the burden of each node in the entire server processing node network is reduced.

[0096] By establishing a pyramid-shaped server processing node network, data can be processed step by step, effectively reducing the transmission volume in the data processing process and the data storage volume of each level of processing nodes, and reducing the burden of each node in the entire server processing node network. The primary processing node uses the private key to sign the processed data, generate signature data, and perform noise processing on the signature data; the secondary processing node encrypts the signature data after noise processing, and transmits the encrypted data to the heavy processing node for decryption, and extracts the data to be processed. Through multi-level data processing, data security during data transmission is effectively guaranteed. By dividing the data set into multi-level data blocks, the multi-level data blocks include primary data blocks, secondary data blocks, heavy data blocks and ultimate data blocks, and adding corresponding level labels to all data blocks, the primary data blocks, secondary data blocks, heavy data blocks and ultimate data blocks all include several data blocks, and the processing nodes at each level process the corresponding level of data blocks. The more important data is processed by the corresponding server processing node, which effectively guarantees the data processing efficiency and processing effect.

Claims

1. A server data processing method based on a deep learning model, characterized in that: The server processing node network creation process is as follows: first, a primary processing node network is created, then the primary processing nodes in the primary processing node network are divided into a plurality of groups, and a secondary processing node network is created according to the number of groups of the primary processing nodes, and the secondary processing nodes in the secondary processing node network are divided into a plurality of groups, and a heavy processing node network is created according to the number of groups of the secondary processing nodes, and communication between the heavy processing node network and the cloud server is established; The process of data processing through the server processing node network includes the following steps: S1: dividing the data set into multiple data blocks, and transmitting the multiple data blocks to each primary processing node of the primary processing node network for primary processing respectively; S2: transmitting the data processed by each group of primary processing nodes to the corresponding secondary processing nodes for secondary processing; S3: Transmitting the data processed by each group of secondary processing nodes to the corresponding heavy-level processing nodes for heavy-level processing; S4: The data processed by the heavy processing node is transmitted to the cloud server for final processing to complete all data processing; The corresponding data processing process performed by each level of processing nodes in the server processing node network is based on the corresponding deep learning model created at each data processing node for data processing; Data processing is done through the following process: S1': Divide the data set into multi-level data blocks, wherein the multi-level data blocks include primary data blocks, secondary data blocks, re-level data blocks and ultimate data blocks, and add corresponding level labels to all data blocks, wherein the primary data blocks, the secondary data blocks, the re-level data blocks and the ultimate data blocks each include a number of data blocks, and randomly transmit the multiple data blocks to each primary processing node of the primary processing node network, wherein each primary processing node only performs primary processing on the primary data blocks therein, and transmits the primary processed data as well as the secondary data blocks, the re-level data blocks and the ultimate data blocks to the secondary processing nodes; S2': Randomly transmit the data processed by each group of primary processing nodes and the secondary data blocks, re-data blocks and ultimate data blocks to the corresponding secondary processing nodes. The secondary processing nodes perform secondary processing on the data processed by the primary processing and the secondary data, and transmit the data processed by the secondary processing and the re-data blocks and ultimate data blocks to the re-processing nodes. S3': randomly transmitting the data processed by the secondary processing node, the heavy data block and the ultimate data block to the corresponding heavy processing node, the heavy processing node performs heavy processing on the secondary processed data and the heavy data block, and transmits the heavy processed data and the ultimate data block to the ultimate processing node; S4': The cloud server performs final processing on the data processed by the heavy-level processing nodes and the final data to complete all data processing.

2. A server data processing method based on a deep learning model according to claim 1, characterized in that: Step S1 is replaced by the following process: S11: After each primary processing node of the primary processing node network performs primary processing on the primary data block, the primary processed data is divided into primary important data and primary non-important data; S12: transmitting primary non-important data directly to the edge server that generates the data set; S13: Transmitting primary important data to secondary processing nodes according to the server processing node network grouping.

3. The server data processing method based on the deep learning model according to claim 1, characterized in that: Step S2 includes the following specific processes: S21: each secondary processing node of the secondary processing node network performs secondary processing on the data processed by the primary processing node, and divides the secondary processed data into secondary important data and secondary non-important data; S22: directly transmitting the secondary non-important data to each primary processing node; S23: Transmitting the secondary important data to the heavy-level processing node according to the server processing node network grouping.

4. The server data processing method based on the deep learning model according to claim 1, characterized in that: Step S3 includes the following specific processes: S31: each heavy-level processing node of the heavy-level processing node network performs heavy-level processing on the data processed by the secondary processing node, and divides the heavy-level processed data into heavy-level important data and heavy-level non-important data; S32: directly transmitting the heavy non-important data to the secondary processing node; S33: Transmit important data to the cloud server for final data processing.

5. The server data processing method based on the deep learning model according to claim 1, characterized in that: The process of processing nodes at all levels in the server processing node network performing data processing based on the deep learning model includes the following specific steps: Step 1: Obtain historical processing data samples at each data processing node, and perform data preprocessing on the historical processing data samples; Step 2: Divide the preprocessed historical data samples into training set, test set and validation set; Step 3: Create a corresponding data processing model at each data processing node, input the training set, test set and validation set into the data processing model in sequence, and train, test and validate the data processing model; Step 4: Obtain the data set to be processed currently, input the data set to be processed currently into the data processing model, perform data processing on the data set to be processed currently, and output corresponding data processing results.

6. A server data processing method based on a deep learning model according to claim 5, characterized in that: Step 3 includes the following specific processes: Step 31: inputting the training set into the data processing model to train the data processing model; Step 32: Input the test set into the data processing model, test the trained data processing model, and determine whether the test result of the data processing model reaches the specified test performance. If so, execute step 33; if not, re-input a new training set and execute step S1; Step 33: Input the verification set into the tested data processing model, verify the test results of the tested data processing model, and determine whether the verification results of the data processing model meet the specified requirements. If yes, execute step 4; if not, re-input the new training set and execute step S1.

7. A server data processing method based on a deep learning model according to claim 6, characterized in that: The data preprocessing in step 1 includes data cleaning, data conversion, data integration and data normalization.

8. The server data processing method based on the deep learning model according to claim 1, characterized in that: After the data processed by each group of primary processing nodes are transmitted to the corresponding secondary processing nodes for secondary processing, the following data processing process is also included: The primary processing node signs the processed data using the private key, generates signature data, and performs noise processing on the signature data; The secondary processing node encrypts the noisy signature data, transmits the encrypted data to the heavy processing node for decryption, and extracts the data to be processed; The data processing process in which the data processed by the secondary processing nodes are transmitted to the corresponding heavy processing nodes for heavy processing, or the data processed by the heavy processing nodes are transmitted to the cloud server for final processing is the same as the process in which the data processed by the primary processing nodes are transmitted to the corresponding secondary processing nodes for secondary processing.

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