Method, device, equipment and storage medium for generating consistent data sets

Abstract

The present application discloses a method, apparatus, device, and storage medium for generating a consistent data set, which relates to the field of artificial intelligence technology. By using the same problem data set through at least one neural network to generate a consistent result data set, the risk of tampering caused by direct transmission of the result data set can be avoided, thereby ensuring the security of generating a consistent data set. The method includes: selecting any one neural network from at least two neural networks as an initial neural network, so that the initial neural network generates a first result data set based on the problem data set; selecting at least one node in the system and / or network, so that the neural network deployed on at least one node generates at least one second result data set based on the problem data set; and performing target operations on the first result data set and the at least one second result data set as consistent data sets.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, device, and storage medium for generating a consistent data set. Background Art

[0002] A consistent dataset refers to a collection of data that maintains uniformity and reliability across multiple dimensions, ensuring that the data meets predetermined constraints across different scenarios or tasks. This is typically applied within systems and / or networks. As application scenarios expand and technological requirements deepen, consistent datasets are becoming a crucial feature of database systems, directly impacting data accuracy, completeness, and reliability.

[0003] In related technologies, consistent datasets are generated differently in different fields. In the medical field, consistent datasets can be obtained through sharing, for example, different hospitals share patient diagnostic data for joint analysis. In the financial field, consistent datasets can be obtained through synchronization, for example, multiple data centers synchronize transaction information in real time to ensure ledger consistency. However, datasets obtained through sharing or synchronization require direct data transfer, which increases the risk of data tampering and makes it difficult to ensure data security. Summary of the Invention

[0004] In view of this, the present application provides a method, apparatus, device and storage medium for generating a consistent data set, the main purpose of which is to solve the problem in the prior art that directly transmitting data increases the risk of data tampering and makes it difficult to ensure data security.

[0005] According to a first aspect of the present application, a method for generating a consistent data set is provided, comprising:

[0006] Selecting any one of at least two neural networks as an initial neural network, so that the initial neural network generates a first result data set based on the problem data set, wherein the at least two neural networks have the same structural parameters and are respectively deployed on different nodes in a system and / or network, wherein there is at least one system and / or network;

[0007] selecting at least one node in the system and / or network so that a neural network deployed on the at least one node generates at least one second result data set based on the problem data set;

[0008] The first result data set and the at least one second result data set are used as consistent data sets to perform a target operation.

[0009] Furthermore, each neural network has a preset local data set, and selecting at least one node in the system and / or network so that the neural network deployed on the at least one node generates at least one second result data set based on the problem data set includes:

[0010] If the problem dataset is not initially stored in the local dataset set of the neural network, the problem dataset is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset; or a first storage identifier of the problem dataset in the remote dataset set is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node requests a verification value of the problem dataset and the result dataset from the remote dataset set through the first storage identifier, and generates at least one second result dataset based on the problem dataset;

[0011] If the problem dataset is initially stored in a local dataset set of a neural network, a second storage identifier of the problem dataset in the local dataset set is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node obtains the problem dataset in the local dataset set through the second storage identifier, and generates at least one second result dataset based on the problem dataset. The second storage identifier is used to point to the same problem dataset in the local dataset sets of different neural networks.

[0012] Furthermore, before performing a target operation on the first result data set and the at least one second result data set as consistent data sets, the method further includes:

[0013] transmitting a checksum of the question dataset and the first result dataset to at least one node in the system and / or network, so that a neural network deployed on the at least one node generates at least one second result dataset based on the question dataset;

[0014] comparing the check value of the first result data set with the check value of the at least one second result data set;

[0015] Correspondingly, if the comparison results are consistent, the first result data set and the at least one second result data set are used as consistent data sets to perform target operations.

[0016] Furthermore, if the comparison results are inconsistent, the first result data set and the at least one second result data set are treated as inconsistent data sets for troubleshooting.

[0017] Furthermore, before comparing the check value of the first result data set with the check value of the at least one second result data set, the method further includes:

[0018] Calculating a check value of the first result data set to uniquely identify a feature of the first result data set through the check value; and

[0019] A check value of the second result data set is calculated to uniquely identify a feature of the second result data set through the check value.

[0020] Furthermore, calculating the check value of the first result data set includes:

[0021] Converting the first result data set into a string of fixed length using a hash function to obtain a check value of the first result data set; or

[0022] Performing feature extraction on the first result data set to obtain a check value of the first result data set;

[0023] Accordingly, calculating the check value of the second result data set includes:

[0024] converting the second result data set into a string of fixed length using a hash function to obtain a check value of the second result data set; or

[0025] Feature extraction is performed on the second result data set to obtain a verification value of the second result data set.

[0026] Furthermore, the verification values ​​of the question data set and the first result data set are transmitted via a network or a physical carrier.

[0027] Furthermore, before transmitting the question dataset to at least one node in the system and / or network so that the neural network deployed on the at least one node generates at least one second result dataset based on the question dataset, the method further includes:

[0028] Presetting a transfer order of the data set between different nodes in the system and / or network, wherein the transfer order is a one-way transfer order from the initial neural network to the neural network deployed on at least one node in the system and / or network;

[0029] Accordingly, the problem data set is unidirectionally transmitted to at least one node in the system and / or network in the transmission order, so that the neural network deployed on the at least one node generates at least one second result data set based on the problem data set.

[0030] Furthermore, during the one-way transmission process, the neural network deployed at the next node in the system and / or network is determined according to the transmission order, and one-way transmission is performed from the initial neural network to the neural network of the next node. The neural network deployed at the next node repeats the above one-way transmission process until the neural network deployed at the last node.

[0031] Furthermore, before transmitting the question dataset to at least one node in the system and / or network so that the neural network deployed on the at least one node generates at least one second result dataset based on the question dataset, the method further includes:

[0032] Encrypting the problem data set to obtain a data set ciphertext;

[0033] Accordingly, the ciphertext of the data set is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node decrypts the ciphertext of the data set to obtain the problem data set, and generates at least one second result data set based on the problem data set.

[0034] According to a second aspect of the present application, a device for generating a consistent data set is provided, comprising:

[0035] a selection unit, configured to select any one of at least two neural networks as an initial neural network, so that the initial neural network generates a first result data set based on the problem data set, wherein the at least two neural networks have the same structural parameters and are respectively deployed on different nodes in a system and / or network, wherein the system and / or network is at least one;

[0036] a selecting unit, configured to select at least one node in the system and / or network, so that a neural network deployed on the at least one node generates at least one second result data set based on the problem data set;

[0037] A generating unit is configured to perform a target operation on the first result data set and the at least one second result data set as consistent data sets.

[0038] Furthermore, each neural network has a preset local data set set, and the selected unit includes:

[0039] a first transmitting module, configured to transmit the problem dataset to at least one node in the system and / or network, if the problem dataset is not initially stored in the local dataset set of the neural network, so that the neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset; or transmit a first storage identifier of the problem dataset in the remote dataset set to at least one node in the system and / or network, so that the neural network deployed on the at least one node requests a verification value of the problem dataset and the result dataset from the remote dataset set through the first storage identifier, and generates at least one second result dataset based on the problem dataset;

[0040] A second transmission module is used to transmit the second storage identifier of the problem dataset in the local dataset set to at least one node in the system and / or network if the problem dataset is initially stored in the local dataset set of the neural network, so that the neural network deployed on the at least one node obtains the problem dataset in the local dataset set through the second storage identifier, and generates at least one second result dataset based on the problem dataset, and the second storage identifier is used to point to the same problem dataset in the local dataset sets of different neural networks.

[0041] Furthermore, the selection unit further includes:

[0042] a third transmitting module, configured to transmit the check values ​​of the problem dataset and the first result dataset to at least one node in the system and / or network before performing a target operation on the first result dataset and the at least one second result dataset as consistent datasets, so that a neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset;

[0043] a comparing unit, configured to compare the check value of the first result data set with the check value of the at least one second result data set;

[0044] Correspondingly, the generating unit is specifically configured to perform a target operation on the first result data set and the at least one second result data set as consistent data sets if the comparison results are consistent.

[0045] Furthermore, the device further comprises:

[0046] The problem troubleshooting unit is configured to treat the first result data set and the at least one second result data set as inconsistent data sets for problem troubleshooting if the comparison result is inconsistent.

[0047] Furthermore, the device further comprises:

[0048] a first calculation unit, configured to calculate a check value of the first result data set before comparing the check value of the first result data set with the check value of the at least one second result data set, so as to uniquely identify a feature of the first result data set by the check value; and

[0049] The second calculation unit is configured to calculate a check value of the second result data set, so as to uniquely identify a feature of the second result data set through the check value.

[0050] Furthermore, the first computing unit is specifically configured to:

[0051] Converting the first result data set into a string of fixed length using a hash function to obtain a check value of the first result data set; or

[0052] Performing feature extraction on the first result data set to obtain a check value of the first result data set;

[0053] Accordingly, the second computing unit is specifically configured to:

[0054] converting the second result data set into a string of fixed length using a hash function to obtain a check value of the second result data set; or

[0055] Feature extraction is performed on the second result data set to obtain a verification value of the second result data set.

[0056] Furthermore, the verification values ​​of the question data set and the first result data set are transmitted via a network or a physical carrier.

[0057] Furthermore, the selection unit further includes:

[0058] a setting module configured to pre-set a transfer order of the datasets between different nodes in the system and / or network before transferring the problem dataset to at least one node in the system and / or network so that the neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset, wherein the transfer order is an order of unidirectional transfer from the initial neural network to the neural network deployed on at least one node in the system and / or network;

[0059] Correspondingly, the first transmission module is specifically used to unidirectionally transmit the problem data set to at least one node in the system and / or network in accordance with the transmission order, so that the neural network deployed on the at least one node generates at least one second result data set based on the problem data set.

[0060] Furthermore, during the one-way transmission process, the neural network deployed at the next node in the system and / or network is determined according to the transmission order, and one-way transmission is performed from the initial neural network to the neural network of the next node. The neural network deployed at the next node repeats the above one-way transmission process until the neural network deployed at the last node.

[0061] Furthermore, the selection unit further includes:

[0062] an encryption module, configured to encrypt the problem dataset to obtain a dataset ciphertext before transmitting the problem dataset to at least one node in the system and / or network so that a neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset;

[0063] Correspondingly, the first transmission module is further specifically used to transmit the ciphertext of the data set to at least one node in the system and / or network, so that the neural network deployed on the at least one node decrypts the ciphertext of the data set to obtain the problem data set, and generates at least one second result data set based on the problem data set.

[0064] According to a third aspect of the present application, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in the first aspect when executing the computer program.

[0065] According to a fourth aspect of the present application, a readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0066] By means of the above technical solution, the present application provides a method, apparatus, device and storage medium for generating a consistent data set. Compared with the existing method of generating a consistent data set by sharing, synchronizing or exchanging, the present application selects any one of at least two neural networks as an initial neural network so that the initial neural network generates a first result data set based on a pre-constructed problem data set. The at least two neural networks have the same structural parameters and are respectively deployed on different nodes in the system and / or network, with at least one system and / or network; selects at least one node in the system and / or network so that the neural network deployed on at least one node generates at least one second result data set based on the problem data set; and uses the first result data set and the at least one second result data set as the consistent data set for target operation. The entire process does not obtain a consistent data set by sharing or synchronizing, but rather achieves the transmission of the same problem data set by deploying at least two neural networks in the system and / or network. Since the at least two neural networks have the same structural parameters, at least one neural network can use the same problem data set to generate a consistent result data set, which can avoid the risk of tampering caused by direct transmission of the result data set and ensure the security of generating a consistent data set.

[0067] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0069] Figure 1 1 is a flow chart of a method for generating a consistent data set in one embodiment of the present application;

[0070] Figure 2 is a flowchart of a method for generating a consistent data set in another embodiment of the present application;

[0071] Figure 3 is a flowchart of a method for generating a consistent data set in another embodiment of the present application;

[0072] Figure 4 is a flowchart of a method for generating a consistent data set in another embodiment of the present application;

[0073] Figure 5is a flowchart of a method for generating a consistent data set in another embodiment of the present application;

[0074] Figure 6 FIG. 1 is a network architecture diagram of a consistent data set generation process in an embodiment of the present application;

[0075] Figure 7 Schematic diagram of the structure of a device for generating a consistent data set in one embodiment of the present application;

[0076] Figure 8 The figure is a schematic diagram of the device structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] The present invention will now be discussed with reference to several exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the present invention, rather than to imply any limitation on the scope of the present invention.

[0078] As used herein, the term "including" and variations thereof are to be interpreted as open-ended terms meaning "including, but not limited to." The term "based on" is to be interpreted as "based, at least in part, on." The terms "one embodiment" and "an embodiment" are to be interpreted as meaning "at least one embodiment." The term "another embodiment" is to be interpreted as meaning "at least one other embodiment."

[0079] In related technologies, consistent datasets are generated differently in different fields. In healthcare, consistent datasets can be obtained through sharing, for example, different hospitals share patient diagnostic data for joint analysis. In finance, consistent datasets can be obtained through synchronization, for example, multiple data centers synchronize transaction information in real time to ensure ledger consistency. However, datasets obtained through sharing or synchronization require direct data transfer, which increases the risk of data tampering and makes it difficult to ensure data security.

[0080] In order to solve this problem, this embodiment provides a method for generating a consistent data set, such as Figure 1 As shown, the following steps are included:

[0081] 101. Select any one of at least two neural networks as an initial neural network, so that the initial neural network generates a first result data set according to the problem data set.

[0082] In today's era of rapid digitalization and intelligent development, system and / or network computing and neural network technologies have become important driving forces for scientific and technological progress. With the widespread application of human-functional intelligence, particularly in fields such as medical diagnosis, financial analysis, and autonomous driving, distributed neural networks formed by systems and / or networks are becoming core tools for solving large-scale data processing and complex tasks. Generally speaking, distributed neural network application scenarios, from cloud computing platforms to edge computing devices to IoT terminals, require ensuring data consistency and accuracy across multiple nodes during collaboration. For example, in the medical field, different hospitals need to share patient diagnostic data for joint analysis; in the financial field, multiple data centers need to synchronize transaction information in real time to ensure ledger consistency; and in the field of autonomous driving, data exchange between vehicles and cloud servers must maintain high consistency to ensure driving safety. These application scenarios place extremely high demands on the data consistency of distributed neural networks.

[0083] At least two neural networks have identical structural parameters and are deployed on different nodes in a system and / or network, where there is at least one system and / or network. The execution entity of the embodiments of the present invention may be the system or server where the at least two neural networks reside. The at least two neural networks having the same network structure can output the same result dataset based on the same problem dataset.

[0084] A twin neural network can be used here. The core structure of the twin neural network is composed of at least two neural networks. The neural network in the twin neural network is deployed to at least one network and / or system. That is to say, the twin neural network can exist in different networks and / or systems. For example, the twin neural network includes neural network A and neural network B. The neural network A and neural network B in the twin neural network can be deployed together on different nodes in the same network, or on different nodes in the same system. The neural network A and neural network B in the twin neural network can also be deployed separately on any node in different networks, or on any node in different systems. The neural network A in the neural network can also be deployed on any node in the network, and the neural network B can be deployed on any node in the system.

[0085] It should be noted that each neural network in a twin neural network shares the same weights and parameters, meaning they have the same structural parameters. This ensures that each neural network in the twin neural network outputs the same result dataset when fed the same problem dataset. For example, if the same image is fed into different neural networks in the twin neural network for feature extraction, theoretically each neural network will output the same features.

[0086] In this embodiment, the pre-constructed question dataset can be randomly generated by the initial neural network, i.e., after generating the question dataset, the initial neural network generates a first result dataset based on the question dataset. The question dataset can also be imported from an external source, i.e., after the initial neural network receives the external input question dataset, it generates the first result dataset based on the question dataset. The specific question dataset can be in various forms, such as images, vectors, or any other data that can serve as neural network input values.

[0087] 102. Select at least one node in the system and / or network so that a neural network deployed on the at least one node generates at least one second result data set based on the problem data set;

[0088] In this embodiment, at least one node in the selected system and / or network may be designated as a node, and a neural network deployed on the node may generate at least one second result dataset based on the question dataset. The question dataset may be obtained from a local dataset collection, a remote dataset collection, or transmitted from the initial neural network, and this embodiment is not limited thereto.

[0089] That is to say, at least one node in the selected system and / or network has the ability to obtain the problem data set, and a neural network is deployed on the node. After receiving the problem data set, the neural network deployed on the node can generate a second result data set according to the problem data set.

[0090] 103. Perform a target operation on the first result dataset and the at least one second result dataset as consistent datasets.

[0091] It can be understood that since at least two neural networks have the same structural parameters, theoretically, at least two neural networks can generate consistent result data sets through the same problem data set, that is, the first result data and at least one second result data set are consistent, and the first result data set and the second result data set can be used as consistent data sets for target operations, for example, merging data of consistent data sets, triggering a business process, or starting a distributed transaction, etc.

[0092] Specifically, in the scenario of order data synchronization on an e-commerce platform, the initial neural network can generate order data based on the source data, and transmit the source data to at least one node in the network and / or system. The neural network deployed on at least one node generates at least one second order data based on the source data, and the first order data and the at least one second order data are treated as consistent order data to achieve order data synchronization.

[0093] The method for generating a consistent data set provided in an embodiment of the present application is compared with the method of generating a consistent data set by sharing, synchronizing or exchanging in the current prior art. The present application selects any one neural network from at least two neural networks as the initial neural network so that the initial neural network generates a first result data set based on a pre-constructed problem data set. The at least two neural networks have the same structural parameters and are respectively deployed on different nodes in the system and / or network, and there is at least one system and / or network; at least one node in the system and / or network is selected so that the neural network deployed on at least one node generates at least one second result data set based on the problem data set; and the first result data set and the at least one second result data set are used as consistent data sets for target operation. The whole process does not obtain a consistent data set by sharing or synchronizing, but rather realizes the transmission of the same problem data set by deploying at least two neural networks in the system and / or network. Since the at least two neural networks have the same structural parameters, this allows at least one neural network to use the same problem data set to generate a consistent result data set, which can avoid the risk of tampering caused by direct transmission of the result data set and ensure the security of generating a consistent data set.

[0094] In actual application scenarios, each neural network has a preset local data set set. In the process of selecting at least one node in the system and / or network so that the neural network deployed on at least one node generates at least one second result data set based on the problem data set, the problem data set may be initially stored in the local data set set or may not be initially stored in the local data set set.

[0095] Specifically, if the problem dataset is not initially stored in the local dataset set of the neural network, the problem dataset is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset; or the first storage identifier of the problem dataset in the remote dataset set is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node requests the verification value of the problem dataset and the result dataset from the remote dataset set through the first storage identifier, and generates at least one second result dataset based on the problem dataset;

[0096] In the scenario where the problem dataset or the first storage identifier of the problem dataset in the remote dataset set is transmitted, the problem dataset or the first storage identifier may be transmitted to a selected node in the system and / or network, for example, to node A1 in the distributed system. The problem dataset or the first storage identifier may also be transmitted to multiple nodes in the system and / or network, for example, to node A1 in the distributed system and node B1 in the distributed network.

[0097] It should be noted that in the scenario where the problem dataset is first stored in the remote dataset collection, the remote dataset collection can also generate a corresponding result dataset using a standard neural network in advance, along with the problem dataset. Here, the standard neural network and the at least two neural networks have the same structural parameters, and the result dataset generated by the standard neural network is theoretically consistent with the result datasets generated by the at least two neural networks. A check value of the result dataset is calculated accordingly. Upon receiving a data acquisition request, the check values ​​of the problem dataset and the result dataset are fed back to the neural network, so that after the neural network generates at least one second result dataset based on the problem dataset, it performs consistency verification on the at least one second result dataset based on the check value of the result dataset.

[0098] In this scenario, the problem dataset is not initially stored in the local dataset set of the neural network, indicating that the problem dataset is not initially stored in the local dataset set of the transferred neural network. Then the problem dataset needs to be transferred to at least one node in the system and / or network, or the problem dataset needs to be requested from the remote dataset set. At this time, the first storage identifier of the problem dataset in the remote dataset set needs to be transferred. Through the first storage identifier, a data acquisition request can be initiated to the remote dataset set, and the same problem dataset can be obtained accordingly.

[0099] Specifically, if the problem dataset is initially stored in the local dataset set of the neural network, the second storage identifier of the problem dataset in the local dataset set is passed to at least one node in the system and / or network, so that the neural network deployed on the at least one node obtains the problem dataset in the local dataset set through the second storage identifier, and generates at least one second result dataset based on the problem dataset. The second storage identifier is used to point to the same problem dataset in the local dataset sets of different neural networks.

[0100] In this scenario, the problem dataset is initially stored in the local dataset set of the neural network. In this way, there is no need to transfer the problem dataset between neural networks. Only the second storage identifier of the problem dataset in the local dataset set needs to be transferred. Since the second storage identifier is used to point to the same problem dataset in the local dataset sets of different neural networks, the neural network at the receiving end can obtain the same problem dataset from the local dataset set and generate a consistent result dataset.

[0101] In this embodiment, the local dataset set is a dataset set stored locally by each neural network in the system and / or network. That is, the local dataset set of each neural network contains at least one identical problem dataset. By passing the second storage identifier of the problem dataset in the local dataset set, the receiving neural network can obtain the same problem dataset from the local dataset set according to the second storage identifier, and then generate a second result dataset based on the same problem dataset.

[0102] Furthermore, in order to verify the second result data set, the second storage identifier of the problem data set in the local data set set and the verification value of the first result data set can also be transmitted to at least one node in the system and / or network, so that the neural network deployed on at least one node obtains the problem data through the storage identifier, generates at least one second result data set based on the problem data set, and compares the verification value of the first result data set with the verification value of at least one second result data set to verify the consistency of the generated result data set.

[0103] It should be noted that to reduce the amount of data transferred, the local dataset collection can also, while storing the problem dataset, pre-generate a corresponding result dataset using a standard neural network. Here, the standard neural network and the at least two neural networks share the same structural parameters, and the result dataset generated by the standard neural network is theoretically consistent with the result datasets generated by the at least two neural networks. Accordingly, the receiving neural network can directly verify the consistency of the generated second result dataset using the result dataset stored in the local dataset collection.

[0104] In actual application scenarios, in order to verify the consistency of at least two neural network generated result data sets, in the scenario where the problem data set is not initially stored in the local data set set of the neural network, that is, before the problem data set is transferred, the check value of the result data set can be pre-calculated, and the consistency verification can be performed based on the check value of the result data set. Further, Figure 2 As shown, before step 103, the method further includes:

[0105] 201. Transmitting the verification values ​​of the problem dataset and the first result dataset to at least one node in the system and / or network, so that a neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset.

[0106] 202. Compare the check value of the first result data set with the check value of the at least one second result data set.

[0107] In this embodiment, the checksum of the first result dataset is used to verify the integrity of the first result dataset to ensure that the first result dataset has not been tampered with or damaged. Specifically, the checksum of the first result dataset can be generated by calculating the contents of the first result dataset using a specific algorithm. The checksum is typically a unique string of fixed length that identifies the contents of the second result dataset. This string will be different if any portion of the first result dataset is changed.

[0108] It should be noted that the check value of the second result data set is generated in the same way as the check value of the first result data set, that is, a specific algorithm is used to calculate and generate the content of the second result data set to obtain a unique fixed-length string for identifying the content of the second result data set.

[0109] In this embodiment, the process of calculating the check value of the first result data set is performed after the initial neural network generates the first result data set. Similarly, the process of calculating the check value of the second result data set is performed after the receiving neural network generates at least one second result data set. Theoretically, if the structural parameters of at least one neural network are identical to those of the initial neural network and the input problem data set is the same, the corresponding output first result data set and at least one second result data set should also be identical. Here, by comparing the check value of the first result data set with the check value of the second result data set, the consistency of the data sets can be determined without having to compare the data sets byte by byte.

[0110] However, if the problem dataset is damaged or tampered with during the transfer process, the problem dataset received by at least one of the transferred nodes will differ from the problem dataset used by the initial neural network. As a result, the second result dataset generated by the transferred neural network will be different from the first result dataset. If the comparison results are inconsistent, the first result dataset and at least one second result dataset are treated as inconsistent datasets for troubleshooting.

[0111] Correspondingly, in step 103 , if the comparison results are consistent, the first result data set and the at least one second result data set are used as consistent data sets to perform a target operation.

[0112] In actual application scenarios, directly transmitting the result data set often increases the risk of data being tampered with during the transmission process. Due to the large amount of data in the result data set, it is difficult to perform detailed security authentication on each data fragment. Once the data is maliciously tampered with during transmission, it is difficult for the receiving node to detect it, resulting in subsequent processing being based on incorrect data, affecting the correctness of the entire system and / or network. Figure 3 As shown, before step 202, the method further includes:

[0113] 301. Calculate a check value of the first result data set to uniquely identify a feature of the first result data set through the check value; and

[0114] 302. Calculate a check value of the second result dataset to uniquely identify a feature of the second result dataset using the check value.

[0115] It can be understood that the calculation of the verification value of the first result data set can be triggered after the initial neural network generates the first result data set based on the pre-constructed problem data set, and correspondingly, the calculation of the verification value of the second result data set can be triggered after the neural network deployed on at least one node generates at least one second result data set based on the problem data set.

[0116] Specifically, in the process of calculating the check value of the first result data set, the first result data set can be converted into a string of fixed length through a hash function to obtain the check value of the first result data set; or the first result data set can be subjected to feature extraction to obtain the check value of the first result data set. Here, the hash function is a common method for generating check values, including SHA-256, MD5, etc. The hash function can convert data of arbitrary length into a string of fixed length, has collision resistance and determinism, and is suitable for verifying the integrity and consistency of data. The feature extraction process here does not rely on the byte-by-byte integrity of the data, but identifies the core content of the data set through semantic or structural features, and is suitable for verifying the content consistency of the data set. The specific feature extraction process can select a feature extraction method according to the type of the first result data set, convert the extracted features into a vector of fixed dimension or a structured summary, and obtain the check value of the first result data set.

[0117] Accordingly, when calculating the check value for the second result dataset, the second result dataset can be converted into a fixed-length string using a hash function to obtain the check value for the second result dataset; or feature extraction can be performed on the second result dataset to obtain the check value for the second result dataset. This process is the same as the process for calculating the check value for the first result dataset and is not further described here.

[0118] It should be noted that the same hash function should be used to calculate the check value of the second result data set and the check value of the first result data set, so as to ensure the accuracy of the data verification result.

[0119] In actual application scenarios, in order to effectively manage and transmit data, in scenarios where the problem dataset is not initially stored in the local dataset set of the neural network, that is, in the process of transmitting the verification values ​​of the problem dataset and the result dataset, the verification values ​​of the problem dataset and the first result dataset are transmitted through a network carrier or a physical carrier.

[0120] Here, network transmission can be achieved by using data encapsulation or other methods, where the problem dataset and the checksum of the first result dataset are encapsulated into a data packet and then transmitted. Here, physical carrier transmission can be achieved by using a USB flash drive or other methods, where the checksum of the problem dataset and the first result dataset are transmitted via the USB flash drive.

[0121] Specifically, in the process of using data encapsulation to achieve transmission, the problem data set and the verification value of the first result data set can be combined together and encapsulated according to a certain format and protocol to form a data packet. Here, other metadata can also be added to the data packet during the encapsulation process, such as the data set identifier corresponding to the verification value, the generation time, etc.

[0122] Correspondingly, after receiving the data packet, the neural network at the receiving end will parse it according to the previously encapsulated format and protocol. The parsing process is to extract the various parts of the data packet, including the problem data set, the result data set check value, and related metadata.

[0123] In practical application scenarios, considering the order of data set transfer between nodes in the system and / or network, in scenarios where the problem data set is not initially stored in the local data set set of the neural network, the transfer order can be predefined before the problem data set is transferred. Further, Figure 4 As shown, before step 102, the method further includes the following steps:

[0124] 401. Preset a transmission order of a data set between different nodes in the system and / or network.

[0125] Correspondingly, in step 102, the problem data set is unidirectionally transmitted to at least one node in the system and / or network in the transmission order, so that the neural network deployed on the at least one node generates at least one second result data set based on the problem data set.

[0126] In this embodiment, the transmission order is the order of unidirectional transmission from the initial neural network to the neural network deployed on at least one node in the system and / or network. Unidirectional transmission here refers to transmission in one direction only, not in the reverse direction. That is, the neural network receiving the transmission can only receive the dataset and cannot transmit the dataset to the initial neural network. For example, if the system includes nodes A1 and A2, and the initial neural network is deployed on node A1, the unidirectional transmission order is that the neural network deployed on node A1 transmits the dataset to the neural network deployed on node A2, while the neural network deployed on node A2 cannot transmit the dataset to the neural network deployed on node A1. Specifically, the unidirectional transmission process can be achieved through data diodes, which ensure that the dataset is only transmitted from the initial neural network to the neural network deployed on at least one node in the system and / or network, while completely preventing reverse communication.

[0127] Specifically, during the one-way transmission process, the neural network deployed at the next node in the system and / or network is determined according to the transmission order, and the initial neural network is transmitted one-way to the neural network of the next node. The neural network deployed at the next node is used as the initial neural network, and the above one-way transmission process is repeated until the neural network deployed at the last node is reached.

[0128] In one application scenario, the transmission order can be transmission within a system or network, and the specific transmission method can be traversal transmission. In this case, the data set is traversed and transmitted from the initial neural network in the system or network to the neural networks deployed on all nodes. Taking the transmission of data sets in a distributed system as an example, there are three nodes A1, A2, and A3 deployed in the distributed system. The neural network deployed on node A2 is used as the initial neural network. The transmission order is node A2-node A1-node A3. In this way, the check value of the data set is transmitted from the initial neural network deployed on node A2 to the neural network deployed on node A1. After the neural network deployed on node A1 generates a consistent data set, the neural network deployed on node A1 is used as the initial neural network. The check value of the corresponding data set is transmitted from the initial neural network deployed on node A1 to the neural network deployed on node A3. After the neural network deployed on node A3 generates a consistent data set, the neural networks deployed on all nodes in the distributed system have generated consistent data sets. The specific transmission method can also be decentralized transmission. In this case, the data set is decentralized and transmitted from the initial neural network in the system or network to the neural networks deployed on multiple or all nodes. Continuing with the above example, the neural network deployed on node A2 is used as the initial neural network, and the transmission order is node A2-node A1, node A2-node A3. In this way, the check value of the data set is transmitted from the initial neural network deployed on node A2 to the neural network deployed on node A1, and the neural network deployed on node A1 generates a consistent data set. At the same time, the check value of the data set is transmitted from the initial neural network deployed on node A2 to the neural network deployed on node A3, and the neural network deployed on node A3 generates a consistent data set. In this way, the neural networks deployed on all nodes in the distributed system generate consistent data sets. The specific transmission method can also be a combination of traversal transmission and decentralized transmission. Taking the transmission of a data set in a distributed system as an example, there are four nodes A1, A2, A3, and A4 deployed in the distributed system. The neural network deployed on node A2 is used as the initial neural network, and the transmission order is node A2-node A1-node A3, node A2-node A4.

[0129] In another application scenario, the transfer order can be interactive transfer within the system and network. Similarly, the specific transfer method can be traversal transfer, in which case the dataset is traversed and transferred from the initial neural network in the system or network to the neural networks deployed in other networks or systems. The dataset is transferred between the neural networks deployed on nodes in distributed systems 1 and 2. For example, distributed system 1 includes nodes A1 and A2, and distributed system 2 includes nodes B1 and B2. The initial neural network is the neural network deployed on node A1, and the transfer order is node A1-node B1-node B2-node A2. The corresponding process for generating consistent data is the same as the traversal transfer process described above and is not further described here. The specific transfer method can also be decentralized transfer, in which case the dataset is decentralized and transferred from the initial neural network in the system or network to the neural networks deployed in other networks or systems. Continuing with the above example, the initial neural network is the neural network deployed on node A1, and the transfer order is node A1-node B1, node A1-node A2, and node A1-node B2. The corresponding process for generating consistent data is the same as the decentralized transfer process described above and is not further described here. Another specific method of transmission can be a combination of traversal transmission and decentralized transmission. Continuing with the example above, the initial neural network is the neural network deployed on node A1. The transmission order is node A1-node B1, node A1-node A2-node B2. The corresponding process of generating consistent data is the same as the above process of combining traversal transmission and decentralized transmission, so it will not be repeated here.

[0130] In actual application scenarios, considering the privacy security of the problem dataset during transmission, the problem dataset can be encrypted before it is transmitted, for example, in scenarios where the problem dataset is not initially stored in the local dataset set of the neural network. Figure 5 As shown, further, before step 102, the method further includes the following steps:

[0131] 501. Encrypt the problem data set to obtain a ciphertext of the data set.

[0132] Correspondingly, in step 102, the ciphertext of the data set is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node decrypts the ciphertext of the data set to obtain the problem data set, and generates at least one second result data set based on the problem data set.

[0133] In this embodiment, the problem dataset can be transmitted in either plaintext or ciphertext. To allow for flexibility in data transmission, the problem dataset can also be encrypted before transmission. If the network and / or system has an encryption / decryption system, the problem dataset can be encrypted so that the neural network receiving the problem dataset can decrypt the dataset ciphertext according to the encryption / decryption system. This protects data privacy and integrity and prevents the dataset from being stolen or tampered with during transmission. Specific encryption methods include, but are not limited to, symmetric encryption algorithms, asymmetric algorithms, and hash algorithms.

[0134] Similarly, in a scenario where the problem dataset is not initially stored in the local dataset set of the neural network, the problem dataset and the first result dataset can also be encrypted before being transmitted. In this case, the problem dataset and the first result dataset are encrypted before the verification values ​​of the problem dataset and the first result dataset are transmitted to at least one node in the system and / or network. Accordingly, the dataset ciphertext obtained by encrypting the dataset and the first result dataset is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node decrypts the dataset ciphertext to obtain the verification values ​​of the problem dataset and the first result dataset, generates at least one second result dataset based on the problem dataset, and performs consistency verification on the at least one second result dataset based on the verification value of the first result dataset.

[0135] In actual application scenarios, the network architecture of the consistent dataset generation process is as follows: Figure 6 As shown in Figure 2, a large financial institution needs to conduct real-time risk assessments on its customers' transactions and implement targeted operations across multiple data centers based on the assessment results. To improve computational efficiency and security, the institution adopted a distributed neural network architecture, distributing the risk assessment tasks across multiple data centers (nodes). One data center (neural network A) is responsible for generating the problem set and result dataset, transmitting this information via a one-way transmission channel. The other data center (neural network B) uses the same result dataset to complete specific business operations.

[0136] Specifically, during the generation of the consistency dataset, neural network A receives an external input question dataset, for example, a set of customer transaction behavior characteristics, including "transaction amount range," "transaction frequency," "transaction time distribution," and so on. Then, neural network A generates a first result dataset based on the question dataset, which is the real-time risk score for each customer, for example, low, medium, or high risk levels. However, these scores are highly sensitive privacy data and should be encrypted to calculate the verification values ​​of the question dataset and the first result dataset. For example, the SHA-256 hash function is used to generate unique summary information for the question dataset and the first result dataset, respectively, as the verification value. Furthermore, neural network A encapsulates the verification values ​​of the question dataset and the result dataset into a data packet and transmits it to neural network B via a one-way transmission channel. Accordingly, after receiving the data packet, neural network B parses the question dataset and generates a second result dataset based on the same question set, which is the real-time risk score for each customer. Since neural networks A and B have identical structural parameters, the resulting datasets they generate should theoretically be identical. However, there is still a risk of inconsistency. Neural network B then uses the same processing method to calculate a checksum for its second resulting dataset and compares this checksum with the checksum of the first resulting dataset received from neural network A. If the two match, the resulting datasets generated by neural networks A and B are identical, and consistency verification is successful. This means that neural network B can use the generated second resulting dataset to perform specific business operations. For example, based on the customer's risk score, it can restrict certain transaction permissions (such as large transfers or nighttime trading) for high-risk customers; provide personalized financial product recommendations or promotions for low- and medium-risk customers; and automatically trigger an internal alarm system when high-risk behavior is detected, alerting relevant departments for manual verification. If the two do not match, it indicates that the dataset may contain data transmission errors or other anomalies, requiring further investigation. Accordingly, neural network B can trigger error handling mechanisms, such as notifying administrators for intervention.

[0137] In the above-mentioned consistency data generation process, firstly, by transmitting only the problem set and the check value instead of the complete result data set, the communication bandwidth occupancy and delay are greatly reduced, thereby improving the real-time and scalability of the system. Secondly, the use of the check value avoids the direct exposure of sensitive data, meets the needs of privacy protection, and is particularly suitable for application scenarios involving user privacy or commercial secrets. Finally, the consistency verification mechanism based on the check value ensures that even in the case of network delays or partitions, each node can still accurately judge the data consistency, thereby improving the reliability and stability of the system. Through the above-mentioned consistency data generation process, not only the problems of high communication overhead and high privacy risks in the existing technology are solved, but also a more efficient and secure solution is provided for the data consistency of distributed neural networks.

[0138] Further, as Figure 1-5 The specific implementation of the method, the embodiment of the present application provides a device for generating a consistent data set, such as Figure 7 As shown, the device includes: a selection unit 61, a selection unit 62 and a generation unit 63.

[0139] a selection unit 61 configured to select any one of at least two neural networks as an initial neural network, so that the initial neural network generates a first result dataset based on the problem dataset, wherein the at least two neural networks have the same structural parameters and are respectively deployed on different nodes in the system and / or network, and each neural network has a preset local dataset set;

[0140] a selecting unit 62, configured to select at least one node in the system and / or network, so that a neural network deployed on the at least one node generates at least one second result data set based on the problem data set;

[0141] The generating unit 63 is configured to perform a target operation on the first result data set and the at least one second result data set as consistent data sets.

[0142] The embodiment of the present application provides a device for generating a consistent data set. The present application selects any one neural network as an initial neural network from at least two neural networks, so that the initial neural network generates a first result data set based on a pre-constructed problem data set. The at least two neural networks have the same structural parameters and are respectively deployed on different nodes in the system and / or network, and there is at least one system and / or network; selects at least one node in the system and / or network, so that the neural network deployed on at least one node generates at least one second result data set based on the problem data set; and uses the first result data set and the at least one second result data set as consistent data sets for target operation. The entire process does not obtain a consistent data set through sharing or synchronization, but rather realizes the transmission of the same problem data set by deploying at least two neural networks in the system and / or network. Since the at least two neural networks have the same structural parameters, this allows at least one neural network to use the same problem data set to generate a consistent result data set, which can avoid the risk of tampering caused by the direct transmission of the result data set and ensure the security of generating a consistent data set.

[0143] In actual application scenarios, each neural network has a preset local data set set, and the selected unit includes:

[0144] a first transmitting module, configured to transmit the problem dataset to at least one node in the system and / or network, if the problem dataset is not initially stored in the local dataset set of the neural network, so that the neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset; or transmit a first storage identifier of the problem dataset in the remote dataset set to at least one node in the system and / or network, so that the neural network deployed on the at least one node requests a verification value of the problem dataset and the result dataset from the remote dataset set through the first storage identifier, and generates at least one second result dataset based on the problem dataset;

[0145] A second transmission module is used to transmit the second storage identifier of the problem dataset in the local dataset set to at least one node in the system and / or network if the problem dataset is initially stored in the local dataset set of the neural network, so that the neural network deployed on the at least one node obtains the problem dataset in the local dataset set through the second storage identifier, and generates at least one second result dataset based on the problem dataset, and the second storage identifier is used to point to the same problem dataset in the local dataset sets of different neural networks.

[0146] In an actual application scenario, the selection unit further includes:

[0147] a third transmitting module, configured to transmit the check values ​​of the problem dataset and the first result dataset to at least one node in the system and / or network before performing a target operation on the first result dataset and the at least one second result dataset as consistent datasets, so that a neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset;

[0148] a comparing unit, configured to compare the check value of the first result data set with the check value of the at least one second result data set;

[0149] Correspondingly, the generating unit is specifically configured to perform a target operation on the first result data set and the at least one second result data set as consistent data sets if the comparison results are consistent.

[0150] In actual application scenarios, the device further includes:

[0151] The problem troubleshooting unit is configured to treat the first result data set and the at least one second result data set as inconsistent data sets for problem troubleshooting if the comparison result is inconsistent.

[0152] In actual application scenarios, the device further includes:

[0153] a first calculation unit, configured to calculate a check value of the first result data set before comparing the check value of the first result data set with the check value of the at least one second result data set, so as to uniquely identify a feature of the first result data set by the check value; and

[0154] The second calculation unit is configured to calculate a check value of the second result data set, so as to uniquely identify a feature of the second result data set through the check value.

[0155] In an actual application scenario, the first computing unit is specifically configured to:

[0156] Converting the first result data set into a string of fixed length using a hash function to obtain a check value of the first result data set; or

[0157] Performing feature extraction on the first result data set to obtain a check value of the first result data set;

[0158] Accordingly, the second computing unit is specifically configured to:

[0159] converting the second result data set into a string of fixed length using a hash function to obtain a check value of the second result data set; or

[0160] Feature extraction is performed on the second result data set to obtain a verification value of the second result data set.

[0161] In an actual application scenario, the verification values ​​of the problem data set and the first result data set are transmitted via a network or a physical carrier.

[0162] In an actual application scenario, the selection unit further includes:

[0163] a setting module configured to pre-set a transfer order of the datasets between different nodes in the system and / or network before transferring the problem dataset to at least one node in the system and / or network so that the neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset, wherein the transfer order is an order of unidirectional transfer from the initial neural network to the neural network deployed on at least one node in the system and / or network;

[0164] Correspondingly, the first transmission module is specifically used to unidirectionally transmit the problem data set to at least one node in the system and / or network in accordance with the transmission order, so that the neural network deployed on the at least one node generates at least one second result data set based on the problem data set.

[0165] In actual application scenarios, during the one-way transmission process, the neural network deployed at the next node in the system and / or network is determined according to the transmission order, and one-way transmission is performed from the initial neural network to the neural network of the next node. The neural network deployed at the next node repeats the above one-way transmission process until the neural network deployed at the last node.

[0166] In an actual application scenario, the selection unit further includes:

[0167] an encryption module, configured to encrypt the problem dataset to obtain a dataset ciphertext before transmitting the problem dataset to at least one node in the system and / or network so that a neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset;

[0168] Correspondingly, the first transmission module is further specifically used to transmit the ciphertext of the data set to at least one node in the system and / or network, so that the neural network deployed on the at least one node decrypts the ciphertext of the data set to obtain the problem data set, and generates at least one second result data set based on the problem data set.

[0169] It should be noted that for other corresponding descriptions of the functional units involved in the apparatus for generating a consistent data set provided in this embodiment, please refer to Figure 1-Figure 5 The corresponding description in will not be repeated here.

[0170] Based on the above Figure 1-Figure 5 The method shown in FIG. 1 is a method for performing the above-mentioned operation. Accordingly, the embodiment of the present application further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned operation is performed. Figure 1-Figure 5 The method for generating the consistency dataset shown.

[0171] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0172] Based on the above Figure 1-Figure 5 The method shown, and Figure 7In order to achieve the above-mentioned purpose, the embodiment of the present application also provides a physical device for generating a consistent data set, which can be a computer, a smart phone, a tablet computer, a smart watch, a server, or a network device, etc. The physical device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1-Figure 5 The method for generating the consistency dataset shown.

[0173] Optionally, the physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, etc. The user interface may include a display, an input unit such as a keyboard, etc. Optional user interfaces may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Wi-Fi interface), etc.

[0174] In an exemplary embodiment, see Figure 8 The physical device includes a communication bus, a processor, a memory, and a communication interface. It may also include an input / output interface and a display device. The various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor is configured to execute the program stored in the memory and perform the method for generating a consistent data set in the above embodiment.

[0175] Those skilled in the art will understand that the physical device structure for generating a consistent data set provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0176] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical device that generates the consistent data set, supporting the operation of the information processing program and other software and / or programs. The network communication module is used to enable communication between components within the storage medium and with other hardware and software in the physical information processing device.

[0177] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the technical solution of the present application, compared with the current existing methods, the present application implements the transmission of the same problem data set by deploying at least two neural networks in the system and / or network. Since at least two neural networks have the same structural parameters, this allows at least one neural network to use the same problem data set to generate a consistent result data set, which can avoid the risk of tampering caused by the direct transmission of the result data set and ensure the security of generating a consistent data set.

[0178] Those skilled in the art will understand that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required to implement the present application. Those skilled in the art will understand that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0179] The serial numbers of the above application are for descriptive purposes only and do not represent the advantages or disadvantages of the implementation scenarios. The above disclosure only discloses several specific implementation scenarios of the present application, but the present application is not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present application.

Claims

1. A method for generating a consistent data set, characterized in that: include: Selecting any one of at least two neural networks as an initial neural network, so that the initial neural network generates a first result data set based on the problem data set, wherein the at least two neural networks have the same structural parameters and are respectively deployed on different nodes in a system and / or network, wherein there is at least one system and / or network; Presetting a transmission order of the problem data set between different nodes in the system and / or network, wherein the transmission order is a one-way transmission order from the initial neural network to the neural network deployed on at least one node in the system and / or network; selecting at least one node in the system and / or network, and unidirectionally transmitting the problem data set to the at least one node in the system and / or network in the transmission order, so that the neural network deployed on the at least one node generates at least one second result data set based on the problem data set; The first result data set and the at least one second result data set are used as consistent data sets to perform a target operation.

2. The method according to claim 1, characterized in that Each neural network has a preset local data set, and the problem data set is unidirectionally transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node generates at least one second result data set based on the problem data set, including: If the problem dataset is not initially stored in the local dataset set of the neural network, the problem dataset is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset; or a first storage identifier of the problem dataset in the remote dataset set is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node requests a verification value of the problem dataset and the result dataset from the remote dataset set through the first storage identifier, and generates at least one second result dataset based on the problem dataset; If the problem dataset is initially stored in a local dataset set of a neural network, a second storage identifier of the problem dataset in the local dataset set is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node obtains the problem dataset in the local dataset set through the second storage identifier, and generates at least one second result dataset based on the problem dataset. The second storage identifier is used to point to the same problem dataset in the local dataset sets of different neural networks.

3. The method according to claim 2, characterized in that Before performing a target operation on the first result data set and the at least one second result data set as consistent data sets, the method further includes: transmitting a checksum of the question dataset and the first result dataset to at least one node in the system and / or network, so that a neural network deployed on the at least one node generates at least one second result dataset based on the question dataset; comparing the check value of the first result data set with the check value of the at least one second result data set; Correspondingly, if the comparison results are consistent, the first result data set and the at least one second result data set are used as consistent data sets to perform target operations.

4. The method according to claim 3, characterized in that If the comparison results are inconsistent, the first result data set and the at least one second result data set are treated as inconsistent data sets for troubleshooting.

5. The method according to claim 3, characterized in that Before comparing the check value of the first result data set with the check value of the at least one second result data set, the method further includes: Calculating a check value of the first result data set to uniquely identify a feature of the first result data set through the check value; and A check value of the second result data set is calculated to uniquely identify a feature of the second result data set through the check value.

6. The method according to claim 5, characterized in that The calculating the check value of the first result data set includes: Converting the first result data set into a string of fixed length using a hash function to obtain a check value of the first result data set; or Performing feature extraction on the first result data set to obtain a check value of the first result data set; Accordingly, calculating the check value of the second result data set includes: converting the second result data set into a string of fixed length using a hash function to obtain a check value of the second result data set; or Feature extraction is performed on the second result data set to obtain a verification value of the second result data set.

7. The method according to claim 3, characterized in that The verification values ​​of the question data set and the first result data set are transmitted via a network or a physical carrier.

8. The method according to claim 1, characterized in that During the one-way transmission process, the neural network deployed at the next node in the system and / or network is determined according to the transmission order, and one-way transmission is performed from the initial neural network to the neural network of the next node. The neural network deployed at the next node repeats the one-way transmission process until the neural network deployed at the last node.

9. The method according to claim 2, characterized in that Before transmitting the question dataset to at least one node in the system and / or network so that the neural network deployed on the at least one node generates at least one second result dataset based on the question dataset, the method further includes: Encrypting the problem data set to obtain a data set ciphertext; Accordingly, the ciphertext of the data set is transmitted to at least one node in the system and / or network, so that the neural network deployed on the at least one node decrypts the ciphertext of the data set to obtain the problem data set, and generates at least one second result data set based on the problem data set.

10. A device for generating a consistent data set, characterized in that: include: a selection unit, configured to select any one of at least two neural networks as an initial neural network, so that the initial neural network generates a first result data set based on the problem data set, wherein the at least two neural networks have the same structural parameters and are respectively deployed on different nodes in a system and / or network, wherein the system and / or network is at least one; a selecting unit configured to pre-set a transfer order of the problem dataset between different nodes in the system and / or network, wherein the transfer order is an order of unidirectional transfer from the initial neural network to the neural network deployed on at least one node in the system and / or network; select at least one node in the system and / or network, and unidirectionally transfer the problem dataset to the at least one node in the system and / or network according to the transfer order, so that the neural network deployed on the at least one node generates at least one second result dataset based on the problem dataset; A generating unit is configured to perform a target operation on the first result data set and the at least one second result data set as consistent data sets.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for generating a consistent data set according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for generating a consistent data set according to any one of claims 1 to 9 are implemented.

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