Industrial Internet Platform Data Processing Method, Device, Medium and Electronic Device

Abstract

Embodiments of the present application provide an industrial Internet platform data processing method, apparatus, computer-readable medium, and electronic device. Embodiments of the present application first obtain industrial sensor data to be subjected to data filtering processing; then configure the sequence corresponding to the industrial sensor data into a number of nodes, enabling verification between the nodes, and according to a preset verification method, taking the nodes with failed verification as invalid nodes and the nodes with successful verification as valid nodes; forming a new sequence with the valid nodes, inputting the new sequence into a trained equivalent determination neural network model, performing equivalent grouping on the valid nodes in the new sequence, and selecting one valid node in each group as a representative node; finally, forming a storage sequence with the representative nodes and forwarding the storage sequence to a cloud server for storage, which improves the data processing efficiency and also reduces the storage space pressure on the cloud server.

Description

[0001] This application is a divisional application of the invention application with the application date of February 21, 2022, the Chinese application number of 202210155523.8, and the invention name of "Industrial Internet Platform Data Processing Method, Device and Electronic Equipment". Technical Field

[0002] This application relates to the field of computer technology, and in particular, to an industrial Internet platform data processing method, device, computer-readable medium and electronic equipment. Background Art

[0003] Industrial real-time data is an important data source in the industrial Internet, usually generated by sensors in industrial systems. Industrial real-time data is generally transmitted to a cloud server through edge devices for unified storage.

[0004] In traditional industrial Internet, when performing data filtering processing, a device on the office network obtains all industrial real-time data from the real-time database and performs data filtering processing based on all industrial real-time data. However, taking an industrial system of a certain scale as an example, there are approximately 100,000 sensors, and the industrial real-time data generated every day can reach hundreds of GB. If a device on the office network performs data filtering processing based on all industrial real-time data, it will result in low data processing efficiency. Summary of the Invention

[0005] Embodiments of this application provide an industrial Internet platform data processing method, device, computer-readable medium and electronic equipment, thereby solving the problem of low data processing efficiency.

[0006] Other features and advantages of this application will become apparent through the following detailed description, or will be partially learned through the practice of this application.

[0007] According to one aspect of the embodiments of this application, there is provided an industrial Internet platform data processing method, which is applied to an edge device in the industrial Internet. The method includes:

[0008] Obtain industrial sensor data to be subjected to data filtering processing;

[0009] Configure the sequence corresponding to the industrial sensor into several nodes, so that the nodes verify each other. According to a preset verification method, the nodes that fail the verification are used as invalid nodes, and the nodes that pass the verification are used as valid nodes;

[0010] Determine representative nodes from the valid nodes through a trained equivalent determination neural network model;

[0011] Form a storage sequence with the representative nodes and forward the storage sequence to a cloud server for storage.

[0012] In some embodiments of the present application, based on the foregoing solution, determining a representative node from the valid nodes by means of the trained equivalent determination neural network model includes: forming a new sequence from the valid nodes, inputting the new sequence into the trained equivalent determination neural network model, performing equivalent grouping on the valid nodes in the new sequence, and selecting one valid node from each group as the representative node.

[0013] In some embodiments of the present application, based on the foregoing solution, forming a new sequence from the valid nodes, inputting the new sequence into the trained equivalent determination neural network model, performing equivalent grouping on the valid nodes in the new sequence, and selecting at least one valid node from each group as the representative node includes:

[0014] Inputting the new sequence formed by the valid nodes into the trained equivalent determination neural network model;

[0015] The equivalent determination neural network model makes a judgment on the valid nodes based on a preset dimension, and takes the valid nodes that can be represented by one or two representative nodes as valid node groups; the preset dimension includes the value of the sensor data, the deviation from the previous sensor, the absolute value of the non-adjacent sensor deviation, and the probability of occurrence of this value;

[0016] Grouping the valid nodes based on the valid node groups to obtain multiple equivalent groupings; and determining the representative node of each equivalent grouping.

[0017] In some embodiments of the present application, based on the foregoing solution, configuring the sequence corresponding to the industrial sensor into a plurality of nodes, enabling the nodes to perform verification, and according to a preset verification method, taking the nodes with failed verification as invalid nodes and the nodes with successful verification as valid nodes includes:

[0018] Calculating the probability that the current node is a valid node through the following formula;

[0019]

[0020] where P is the probability that the current node is a valid node, P xln is the probability that the adjacent node of the nth node is an invalid node, P fxl_n is the probability that the non-adjacent node of the nth node is an invalid node, W xln and W fxl_n are preset weights respectively, and W xl_n +W fxl_n =1, 1≤n≤N, and N is the total number of sensor nodes;

[0021] If the probability that the current node is a valid node is greater than a preset threshold, it is determined that the verification of the current node is established; if the probability that the current node is a valid node is less than or equal to the preset threshold, it is determined that the verification of the current node is not established;

[0022] The nodes with unverified establishment are regarded as invalid nodes, and the nodes with verified establishment are regarded as valid nodes.

[0023] According to one aspect of the embodiments of the present application, there is provided an industrial Internet platform data processing device, including:

[0024] An acquisition module, configured to acquire industrial sensor data to be subjected to data filtering processing;

[0025] A verification module, configured to configure the sequence corresponding to the industrial sensor data into a plurality of nodes, so that the nodes perform verification, and according to a preset verification method, regard the nodes with unverified establishment as invalid nodes and the nodes with verified establishment as valid nodes;

[0026] A representative node determination module, configured to determine a representative node from the valid nodes through a trained equivalent determination neural network model;

[0027] A storage module, configured to form a storage sequence with the representative nodes and forward the storage sequence to a cloud server for storage.

[0028] According to one aspect of the embodiments of the present application, there is provided a computer-readable medium, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the following method is implemented:

[0029] Acquire industrial sensor data to be subjected to data filtering processing;

[0030] Configure the sequence corresponding to the industrial sensor data into a plurality of nodes, so that the nodes perform verification, and according to a preset verification method, regard the nodes with unverified establishment as invalid nodes and the nodes with verified establishment as valid nodes;

[0031] Determine a representative node from the valid nodes through a trained equivalent determination neural network model;

[0032] Form a storage sequence with the representative nodes and forward the storage sequence to a cloud server for storage.

[0033] According to one aspect of the embodiments of the present application, there is provided an electronic device, characterized in that it includes:

[0034] One or more processors;

[0035] A storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the following method:

[0036] Obtain industrial sensor data to be subjected to data filtering processing;

[0037] Configure the sequence corresponding to the industrial sensor data into a number of nodes, such that the nodes perform verification, and according to a preset verification method, regard the nodes for which the verification fails as invalid nodes and the nodes for which the verification succeeds as valid nodes;

[0038] Determine representative nodes from the valid nodes through a trained equivalent determination neural network model;

[0039] Form a storage sequence from the representative nodes and forward the storage sequence to a cloud server for storage.

[0040] In the technical solutions provided in some embodiments of the present application, by first obtaining industrial sensor data to be subjected to data filtering processing; then configuring the sequence corresponding to the industrial sensor data into a number of nodes, such that the nodes perform verification, and according to a preset verification method, regarding the nodes for which the verification fails as invalid nodes and the nodes for which the verification succeeds as valid nodes; determining representative nodes from the valid nodes through a trained equivalent determination neural network model; and finally, forming a storage sequence from the representative nodes and forwarding the storage sequence to a cloud server for storage, the data processing efficiency is improved, and at the same time, the storage space pressure on the cloud server is reduced.

[0041] It should be understood that the above general description and the following detailed description are only exemplary and explanatory and should not limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0043] Figure 1 A flowchart showing the process of a data processing method for an industrial Internet platform to which the technical solutions of the embodiments of the present application can be applied;

[0044] Figure 2 A schematic diagram schematically showing a data processing device for an industrial Internet platform according to an embodiment of the present application;

[0045] Figure 3Schematically shows a schematic diagram of a verification module according to an embodiment of the present application;

[0046] Figure 4 Schematically shows another schematic diagram of a verification module according to an embodiment of the present application;

[0047] Figure 5 Schematically shows yet another schematic diagram of a verification module according to an embodiment of the present application;

[0048] Figure 6 Shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. Detailed implementation manners

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0050] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0051] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0052] The flowcharts shown in the drawings are only illustrative and do not necessarily include all the contents and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0053] As some embodiments of the present invention, Figure 1 Shows a schematic flowchart of a data processing method for an industrial Internet platform. The data processing method for the industrial Internet platform is applied to edge devices in the industrial Internet. The method includes:

[0054] S101: Obtain industrial sensor data to be subjected to data filtering processing.

[0055] Among them, the industrial sensor data includes data from various sensors, and the types of sensors include, but are not limited to, temperature sensors, humidity sensors, voltage sensors, current sensors, pressure sensors, light sensors, acceleration sensors, and angular velocity sensors. In order to achieve unified management of various sensors, "sensor channels" can be defined. Specifically, one sensor channel is used to complete the acquisition of one physical signal, and the system assigns a unique ID to each sensor channel. When it is necessary to obtain sensor data, just call the function interface for obtaining sensor data in the application program.

[0056] S102: Configure the sequence corresponding to the industrial sensor data into several nodes, so that the nodes verify each other. According to the preset verification method, the nodes with failed verification are regarded as invalid nodes, and the nodes with successful verification are regarded as valid nodes.

[0057] S103: Form a new sequence with the valid nodes, input the new sequence into the trained equivalent determination neural network model, group the valid nodes in the new sequence equivalently, and select one valid node in each group as the representative node.

[0058] For S102 and S103, the purpose is to find out the representative nodes, that is, the valid and representative sensor data.

[0059] S104: Form a storage sequence with the representative nodes, and forward the storage sequence to the cloud server for storage.

[0060] In this step, the cloud server can centrally process the sensor data in the storage sequence. Due to the processing in S01 and S103, the magnitude of the sensor data is greatly reduced, effectively improving the data processing efficiency.

[0061] In the technical solutions provided in some embodiments of the present application, by first obtaining industrial sensor data to be subjected to data filtering processing; then configuring the sequence corresponding to the industrial sensor data into several nodes, so that the nodes verify each other. According to the preset verification method, the nodes with failed verification are regarded as invalid nodes, and the nodes with successful verification are regarded as valid nodes; forming a new sequence with the valid nodes, inputting the new sequence into the trained equivalent determination neural network model, grouping the valid nodes in the new sequence equivalently, and selecting one valid node in each group as the representative node; finally, forming a storage sequence with the representative nodes and forwarding the storage sequence to the cloud server for storage, the data processing efficiency is improved, and at the same time, the storage space pressure on the cloud server is reduced.

[0062] In some embodiments of the present application, based on the foregoing solution, S102 specifically includes:

[0063] S201: The first node receives a first request from the second node. The first request is used to request verification of whether to allow uploading of the sensor data of the second node. Both the first node and the second node are industrial sensor data. The first node determines a first verification result according to the identification information of the sensor data corresponding to the second node. The first verification result is used to indicate that uploading of the sensor data corresponding to the second node is allowed, or the first verification result is used to indicate that uploading of the sensor data corresponding to the second node is not allowed. The identification information of the sensor data corresponding to the second node comes from a node information identification neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the first verification result to the second node. Among them, the first node and the second node are adjacent nodes;

[0064] S202: If the first verification result is that uploading of the sensor data corresponding to the second node is allowed, it is determined that the second node is a valid node; if the first verification result is that uploading of the sensor data corresponding to the second node is not allowed, it is determined that the second node is an invalid node;

[0065] S203: The first node receives a first request from the third node. The first request is used to request verification of whether to allow uploading of the sensor data of the third node. Both the first node and the third node are industrial sensor data. The first node determines a second verification result according to the identification information of the sensor data corresponding to the third node. The second verification result is used to indicate that uploading of the sensor data corresponding to the third node is allowed, or the second verification result is used to indicate that uploading of the sensor data corresponding to the third node is not allowed. The identification information of the sensor data corresponding to the third node comes from a node information identification neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the second verification result to the third node. Among them, the first node and the third node are non - adjacent nodes;

[0066] S204: If the second verification result is that uploading of the sensor data corresponding to the third node is allowed, it is determined that the third node is a valid node; if the second verification result is that uploading of the sensor data corresponding to the third node is not allowed, it is determined that the third node is an invalid node.

[0067] In some embodiments of the present application, based on the foregoing solution, S102 specifically includes:

[0068] S301: The second node receives a first request from the first node. The first request is used to request verification of whether uploading the sensor data of the first node is allowed. Both the first node and the second node are industrial sensor data. The first node determines a first verification result based on the identification information of the corresponding sensor data of the second node. The first verification result is used to indicate that uploading the sensor data corresponding to the first node is allowed, or the first verification result is used to indicate that uploading the sensor data corresponding to the first node is not allowed. The identification information of the sensor data corresponding to the first node comes from a node information identification neural network model that is trained on the cloud server side and then sent to the edge device. The second node sends the first verification result to the first node. Among them, the first node and the second node are adjacent nodes.

[0069] S302: If the first verification result is that uploading the sensor data corresponding to the second node is not allowed, then it is determined that the second node is an invalid node. If the first verification result is that uploading the sensor data corresponding to the first node is allowed, then the first node sends a request to the third node.

[0070] S303: The third node receives a first request from the first node. The first request is used to request verification of whether uploading the sensor data of the first node is allowed. Both the first node and the third node are industrial sensor data. The third node determines a second verification result based on the identification information of the corresponding sensor data of the first node. The second verification result is used to indicate that uploading the sensor data corresponding to the first node is allowed, or the second verification result is used to indicate that uploading the sensor data corresponding to the first node is not allowed. The identification information of the sensor data corresponding to the first node comes from a node information identification neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the second verification result to the first node. Among them, the first node and the third node are non - adjacent nodes.

[0071] S304: If the second verification result is that uploading the sensor data corresponding to the third node is allowed, then it is determined that the third node is a valid node. If the second verification result is that uploading the sensor data corresponding to the third node is not allowed, then it is determined that the third node is an invalid node.

[0072] In some embodiments of the present application, based on the foregoing solution, S102 specifically includes:

[0073] S401: The first node receives a first request from the second node. The first request is used to request verification of whether to allow uploading of the sensor data of the second node. Both the first node and the second node are industrial sensor data. The first node determines a first verification result based on the authentication information of the sensor data corresponding to the second node. The first verification result is used to indicate that uploading of the sensor data corresponding to the second node is allowed, or the first verification result is used to indicate that uploading of the sensor data corresponding to the second node is not allowed. The authentication information of the sensor data corresponding to the second node comes from the node information authentication neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the first verification result to the second node. Among them, the first node and the second node are adjacent nodes.

[0074] S402: If the first verification result is that uploading of the sensor data corresponding to the second node is allowed, then determine that the second node is a valid node; if the first verification result is that uploading of the sensor data corresponding to the second node is not allowed, then determine that the second node is an invalid node.

[0075] S403: The first node receives a first request from the third node. The first request is used to request verification of whether to allow uploading of the sensor data of the third node. Both the first node and the third node are industrial sensor data. The first node determines a second verification result based on the authentication information of the sensor data corresponding to the third node. The second verification result is used to indicate that uploading of the sensor data corresponding to the third node is allowed, or the second verification result is used to indicate that uploading of the sensor data corresponding to the third node is not allowed. The authentication information of the sensor data corresponding to the third node comes from the node information authentication neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the second verification result to the third node. Among them, the first node and the third node are non - adjacent nodes.

[0076] S404: If the second verification result is that uploading of the sensor data corresponding to the third node is allowed, then determine that the third node is a valid node; if the second verification result is that uploading of the sensor data corresponding to the third node is not allowed, then determine that the third node is an invalid node.

[0077] S405: The second node receives a first request from the first node. The first request is used to request verification of whether uploading the sensor data of the first node is allowed. Both the first node and the second node are industrial sensor data. The first node determines a first verification result based on the authentication information of the corresponding sensor data of the second node. The first verification result is used to indicate that uploading the sensor data corresponding to the first node is allowed, or the first verification result is used to indicate that uploading the sensor data corresponding to the first node is not allowed. The authentication information of the sensor data corresponding to the first node comes from the node information authentication neural network model that is trained on the cloud server side and then sent to the edge device. The second node sends the first verification result to the first node. Among them, the first node and the second node are adjacent nodes.

[0078] S406: If the first verification result is that uploading the sensor data corresponding to the second node is not allowed, then it is determined that the second node is an invalid node. If the first verification result is that uploading the sensor data corresponding to the first node is allowed, then the first node sends a request to the third node.

[0079] S407: The third node receives a first request from the first node. The first request is used to request verification of whether uploading the sensor data of the first node is allowed. Both the first node and the third node are industrial sensor data. The third node determines a second verification result based on the authentication information of the corresponding sensor data of the first node. The second verification result is used to indicate that uploading the sensor data corresponding to the first node is allowed, or the second verification result is used to indicate that uploading the sensor data corresponding to the first node is not allowed. The authentication information of the sensor data corresponding to the first node comes from the node information authentication neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the second verification result to the first node. Among them, the first node and the third node are non - adjacent nodes.

[0080] S408: If the second verification result is that uploading the sensor data corresponding to the third node is allowed, then it is determined that the third node is a valid node. If the second verification result is that uploading the sensor data corresponding to the third node is not allowed, then it is determined that the third node is an invalid node.

[0081] In this embodiment, steps S401 - S404 and S405 - S408 are processed in parallel. In other words, the first node and the second node perform cross - verification, which further ensures the accuracy of the verification. At the same time, since it is parallel verification and calculation, the calculation efficiency is not reduced.

[0082] In some embodiments of the present application, based on the foregoing solution, S103 specifically includes:

[0083] S501: Input the new sequence composed of valid nodes into the trained equivalent determination neural network model.

[0084] S502: The equivalent determination neural network model judges the valid nodes based on a preset dimension, and takes the valid nodes that can be represented by one or two representative nodes as valid node groups; the preset dimension includes the value of sensor data, the deviation from the previous sensor, the absolute value of the deviation of non-adjacent sensors, and the probability of the occurrence of this value.

[0085] S503: Group the valid nodes based on the valid node groups to obtain multiple equivalent groups; and determine the representative nodes of each equivalent group.

[0086] In some embodiments of the present application, based on the foregoing solution, S102 specifically includes:

[0087] S601: Calculate the probability that the current node is a valid node through the following formula;

[0088]

[0089] where P is the probability that the current node is a valid node, P xln is the probability that the adjacent node of the nth node is an invalid node, P fxl_n is the probability that the non-adjacent node of the nth node is an invalid node, W xln and W fxl_n are preset weights respectively, and W xl_n +W fxl_n = 1, 1 ≤ n ≤ N, and N is the total number of sensor nodes.

[0090] S602: If the probability that the current node is a valid node is greater than the preset threshold, determine that the current node verification is established; if the probability that the current node is a valid node is less than or equal to the preset threshold, determine that the current node verification is not established.

[0091] S603: Take the nodes with unverified establishment as invalid nodes, and the nodes with verified establishment as valid nodes.

[0092] As some embodiments of the present application, as Figure 2 shown, an industrial Internet platform data processing device 10 is provided. The industrial Internet platform data processing device 10 includes an acquisition module 11, a verification module 12, a representative node determination module 13, and a storage module 14.

[0093] Among them, an acquisition module 11 is configured to acquire industrial sensor data to be subjected to data filtering processing; a verification module 12 is configured to configure the sequence corresponding to the industrial sensor data into a plurality of nodes, so that the nodes perform verification, and according to a preset verification method, the nodes with failed verification are used as invalid nodes, and the nodes with successful verification are used as valid nodes; a representative node determination module 13 is configured to form a new sequence with the valid nodes, input the new sequence into a trained equivalent determination neural network model, perform equivalent grouping on the valid nodes in the new sequence, and select one valid node in each group as a representative node; a storage module 14 is configured to form a storage sequence with the representative nodes and forward the storage sequence to a cloud server for storage.

[0094] As some embodiments of the present application, as Figure 3 shown, a schematic diagram of the verification module 12 is provided. The verification module 12 includes a first verification module 121, a first judgment module 122, a second verification module 123, and a second judgment module 124.

[0095] Among them, the first verification module 121 is configured to receive, by a first node, a first request from a second node, where the first request is used to request verification of whether to allow uploading of sensor data of the second node, and both the first node and the second node are industrial sensor data; the first node determines a first verification result according to the identification information of the sensor data corresponding to the second node, where the first verification result is used to indicate allowing uploading of the sensor data corresponding to the second node, or the first verification result is used to indicate not allowing uploading of the sensor data corresponding to the second node, and the identification information of the sensor data corresponding to the second node comes from a node information identification neural network model that is trained at the cloud server side and then sent to the edge device; the first node sends the first verification result to the second node; among them, the first node and the second node are adjacent nodes.

[0096] The first judgment module 122 is configured to determine that the second node is a valid node if the first verification result is allowing uploading of the sensor data corresponding to the second node; and determine that the second node is an invalid node if the first verification result is not allowing uploading of the sensor data corresponding to the second node.

[0097] The second verification module 123 is used for the first node to receive a first request from the third node. The first request is used to request verification of whether to allow uploading the sensor data of the third node. Both the first node and the third node are industrial sensor data. The first node determines a second verification result according to the identification information of the sensor data corresponding to the third node. The second verification result is used to indicate that the sensor data corresponding to the third node is allowed to be uploaded, or the second verification result is used to indicate that the sensor data corresponding to the third node is not allowed to be uploaded. The identification information of the sensor data corresponding to the third node comes from the node information identification neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the second verification result to the third node. Among them, the first node and the third node are non-adjacent nodes.

[0098] The second judgment module 124 is used to determine that the third node is a valid node if the second verification result is that the sensor data corresponding to the third node is allowed to be uploaded; and determine that the third node is an invalid node if the second verification result is that the sensor data corresponding to the third node is not allowed to be uploaded.

[0099] As some embodiments of the present application, as Figure 4 shown, a schematic diagram of the verification module 12 is provided. The verification module 12 includes a third verification module 125, a third judgment module 126, a fourth verification module 127, and a fourth judgment module 128.

[0100] The third verification module 125 is used for the second node to receive a first request from the first node. The first request is used to request verification of whether to allow uploading the sensor data of the first node. Both the first node and the second node are industrial sensor data. The first node determines a first verification result according to the identification information of the sensor data corresponding to the second node. The first verification result is used to indicate that the sensor data corresponding to the first node is allowed to be uploaded, or the first verification result is used to indicate that the sensor data corresponding to the first node is not allowed to be uploaded. The identification information of the sensor data corresponding to the first node comes from the node information identification neural network model that is trained on the cloud server side and then sent to the edge device. The second node sends the first verification result to the first node. Among them, the first node and the second node are adjacent nodes.

[0101] The third judgment module 126 is used to determine that the second node is an invalid node if the first verification result is that the sensor data corresponding to the second node is not allowed to be uploaded; and if the first verification result is that the sensor data corresponding to the first node is allowed to be uploaded, the first node sends a request to the third node.

[0102] The fourth verification module 127 is configured for the third node to receive a first request from the first node. The first request is used to request verification of whether to allow uploading of the sensor data of the first node. Both the first node and the third node are industrial sensor data. The third node determines a second verification result according to the authentication information of the sensor data corresponding to the first node. The second verification result is used to indicate that uploading of the sensor data corresponding to the first node is allowed, or the second verification result is used to indicate that uploading of the sensor data corresponding to the first node is not allowed. The authentication information of the sensor data corresponding to the first node comes from a node information authentication neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the second verification result to the first node. Among them, the first node and the third node are non-adjacent nodes.

[0103] The fourth determination module 128 is configured to determine that the third node is a valid node if the second verification result is that uploading of the sensor data corresponding to the third node is allowed; and determine that the third node is an invalid node if the second verification result is that uploading of the sensor data corresponding to the third node is not allowed.

[0104] As some embodiments of the present application, as Figure 5 shown, a schematic diagram of the verification module 12 is provided. The verification module 12 includes a first verification module 121, a first determination module 122, a second verification module 123, a second determination module 124, a third verification module 125, a third determination module 126, a fourth verification module 127, and a fourth determination module 128.

[0105] Among them, the first verification module 121 is configured for the first node to receive a first request from the second node. The first request is used to request verification of whether to allow uploading of the sensor data of the second node. Both the first node and the second node are industrial sensor data. The first node determines a first verification result according to the authentication information of the sensor data corresponding to the second node. The first verification result is used to indicate that uploading of the sensor data corresponding to the second node is allowed, or the first verification result is used to indicate that uploading of the sensor data corresponding to the second node is not allowed. The authentication information of the sensor data corresponding to the second node comes from a node information authentication neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the first verification result to the second node. Among them, the first node and the second node are adjacent nodes;

[0106] The first determination module 122 is configured to determine that the second node is a valid node if the first verification result allows uploading of the sensor data corresponding to the second node; and determine that the second node is an invalid node if the first verification result does not allow uploading of the sensor data corresponding to the second node.

[0107] The second verification module 123 is configured for the first node to receive a first request from the third node, where the first request is used to request verification of whether to allow uploading of the sensor data of the third node, and both the first node and the third node are industrial sensor data; the first node determines a second verification result according to the identification information of the sensor data corresponding to the third node, where the second verification result is used to indicate allowing uploading of the sensor data corresponding to the third node, or the second verification result is used to indicate not allowing uploading of the sensor data corresponding to the third node, and the identification information of the sensor data corresponding to the third node comes from a node information identification neural network model that is trained on the cloud server side and then sent to the edge device; the first node sends the second verification result to the third node; where the first node and the third node are non-adjacent nodes.

[0108] The second determination module 124 is configured to determine that the third node is a valid node if the second verification result allows uploading of the sensor data corresponding to the third node; and determine that the third node is an invalid node if the second verification result does not allow uploading of the sensor data corresponding to the third node.

[0109] The third verification module 125 is configured for the second node to receive a first request from the first node, where the first request is used to request verification of whether to allow uploading of the sensor data of the first node, and both the first node and the second node are industrial sensor data; the first node determines a first verification result according to the identification information of the sensor data corresponding to the second node, where the first verification result is used to indicate allowing uploading of the sensor data corresponding to the first node, or the first verification result is used to indicate not allowing uploading of the sensor data corresponding to the first node, and the identification information of the sensor data corresponding to the first node comes from a node information identification neural network model that is trained on the cloud server side and then sent to the edge device; the second node sends the first verification result to the first node; where the first node and the second node are adjacent nodes.

[0110] The third determination module 126 is configured to determine that the second node is an invalid node if the first verification result does not allow uploading of the sensor data corresponding to the second node; and if the first verification result allows uploading of the sensor data corresponding to the first node, the first node sends a request to the third node.

[0111] The fourth verification module 127 is used for the third node to receive a first request from the first node. The first request is used to request verification of whether to allow uploading of the sensor data of the first node. Both the first node and the third node are industrial sensor data. The third node determines a second verification result according to the authentication information of the sensor data corresponding to the first node. The second verification result is used to indicate that uploading of the sensor data corresponding to the first node is allowed, or the second verification result is used to indicate that uploading of the sensor data corresponding to the first node is not allowed. The authentication information of the sensor data corresponding to the first node comes from a node information authentication neural network model that is trained on the cloud server side and then sent to the edge device. The first node sends the second verification result to the first node. Among them, the first node and the third node are non-adjacent nodes.

[0112] The fourth judgment module 128 is used to determine that the third node is a valid node if the second verification result is to allow uploading of the sensor data corresponding to the third node; and determine that the third node is an invalid node if the second verification result is not to allow uploading of the sensor data corresponding to the third node.

[0113] According to one aspect of the embodiments of the present application, there is provided a computer-readable medium, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the following method is implemented:

[0114] Obtain industrial sensor data to be subjected to data filtering processing;

[0115] Configure the sequence corresponding to the industrial sensor data into a number of nodes, so that verification is performed between the nodes. According to a preset verification method, the nodes where the verification fails are used as invalid nodes, and the nodes where the verification succeeds are used as valid nodes;

[0116] Form a new sequence with the valid nodes, and input the new sequence into a trained equivalent determination neural network model. Group the valid nodes in the new sequence equivalently, and select one valid node in each group as a representative node;

[0117] Form a storage sequence with the representative nodes, and forward the storage sequence to the cloud server for storage.

[0118] According to one aspect of the embodiments of the present application, there is provided an electronic device, characterized in that it includes:

[0119] One or more processors;

[0120] A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the following method:

[0121] Obtain industrial sensor data to be subjected to data filtering processing;

[0122] Configure the sequence corresponding to the industrial sensor data into a number of nodes, so that verification is performed between the nodes. According to a preset verification method, the nodes with failed verification are regarded as invalid nodes, and the nodes with successful verification are regarded as valid nodes;

[0123] Form a new sequence with the valid nodes, and input the new sequence into a trained equivalent determination neural network model. Group the valid nodes in the new sequence into equivalent groups, and select one valid node in each group as a representative node;

[0124] Form a storage sequence with the representative nodes, and forward the storage sequence to a cloud server for storage.

[0125] Figure 6 The figure shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application.

[0126] It should be noted that Figure 6 The computer system 600 of the electronic device shown is only an example, and should not bring any restrictions to the functions and usage scopes of the embodiments of the present application.

[0127] As Figure 6 shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage section 608 into the random access memory (RAM) 603, such as executing the method described in the above embodiments. In the RAM 603, various programs and data required for system operation are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 606. The input / output (I / O) interface 606 is also connected to the bus 606.

[0128] The following components are connected to the I / O interface 606: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 606 as required. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as required so that a computer program read therefrom is installed into the storage section 608 as required.

[0129] Specifically, according to an embodiment of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by a central processing unit (CPU) 601, various functions defined in the system of the present application are executed.

[0130] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The computer program contained on a computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0132] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the unit itself.

[0133] According to one aspect of the present application, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above various alternative implementation manners.

[0134] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist alone without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the one or more programs are executed by an electronic device, the electronic device implements the methods described in the above embodiments.

[0135] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0136] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the methods according to the embodiments of the present application.

[0137] After considering the specification and practicing the embodiments disclosed herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application.

[0138] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for processing data of an industrial Internet platform, characterized in that, it is applied to edge devices in the industrial Internet, and the method includes: Obtain industrial sensor data to be processed by data filtering; Configure the sequence corresponding to the industrial sensor data into several nodes, so that the nodes verify each other. According to the preset verification method, the nodes with unverified results are regarded as invalid nodes, and the nodes with verified results are regarded as valid nodes; Form a new sequence with the valid nodes, and input the new sequence into a trained equivalent determination neural network model. Group the valid nodes in the new sequence equivalently, and select one valid node in each group as a representative node; Form a storage sequence with the representative nodes, and forward the storage sequence to the cloud server for storage; wherein, the cloud server can centrally process the sensor data in the storage sequence; Wherein, the step of forming a new sequence with the valid nodes, inputting the new sequence into a trained equivalent determination neural network model, equivalently grouping the valid nodes in the new sequence, and selecting at least one valid node in each group as a representative node includes: Input the new sequence composed of valid nodes into a trained equivalent determination neural network model; The equivalent determination neural network model judges the valid nodes based on a preset dimension, and regards the valid nodes that can be represented by one or two representative nodes as valid node groups; the preset dimension includes the value of sensor data, the deviation from the previous sensor, the absolute value of the deviation from non-adjacent sensors, and the probability of the occurrence of this value; Group the valid nodes based on the valid node groups to obtain multiple equivalent groups; and determine the representative nodes of each equivalent group; Wherein, the step of configuring the sequence corresponding to the industrial sensor data into several nodes, so that the nodes verify each other. According to the preset verification method, the nodes with unverified results are regarded as invalid nodes, and the nodes with verified results are regarded as valid nodes includes: Calculate the probability that the current node is a valid node through the following formula; Among them, P is the probability that the current node is a valid node, P xln is the probability that the adjacent node of the nth node is an invalid node, P fxl_n is the probability that the non - adjacent node of the nth node is an invalid node, W xln and W fxl_n are preset weights respectively, and W xln +W fxl_n = 1, 1 ≤ n ≤ N, where N is the total number of sensor nodes; If the probability that the current node is a valid node is greater than a preset threshold, it is determined that the current node is verified; if the probability that the current node is a valid node is less than or equal to the preset threshold, it is determined that the current node is not verified; Regard the nodes with unverified results as invalid nodes, and the nodes with verified results as valid nodes.

2. An industrial Internet platform data processing device, characterized in that, the device includes: An acquisition module for acquiring industrial sensor data to be processed by data filtering; A verification module for configuring the sequence corresponding to the industrial sensor data into several nodes, so that the nodes verify each other. According to the preset verification method, the nodes with unverified results are regarded as invalid nodes, and the nodes with verified results are regarded as valid nodes; A representative node determination module for determining representative nodes from the valid nodes through a trained equivalent determination neural network model; Form a new sequence from the valid nodes, input the new sequence into the trained equivalent determination neural network model, group the valid nodes in the new sequence into equivalent groups, and select one valid node in each group as a representative node; Among them, the step of forming a new sequence from the valid nodes, inputting the new sequence into the trained equivalent determination neural network model, grouping the valid nodes in the new sequence into equivalent groups, and selecting at least one valid node in each group as a representative node includes: Input the new sequence formed by the valid nodes into the trained equivalent determination neural network model; The equivalent determination neural network model makes a judgment on the valid nodes based on a preset dimension, and regards the valid nodes that can be represented by one or two representative nodes as valid node groups; the preset dimension includes the value of the sensor data, the deviation from the previous sensor, the absolute value of the deviation of non-adjacent sensors, and the probability of the occurrence of this value; Group the valid nodes based on the valid node groups to obtain multiple equivalent groups; and determine the representative nodes of each equivalent group; A storage module for forming a storage sequence from the representative nodes and forwarding the storage sequence to a cloud server for storage; wherein, the cloud server can centrally process the sensor data in the storage sequence; The verification module is further configured to: Calculate the probability that the current node is a valid node through the following formula; Where P is the probability that the current node is a valid node, P xln is the probability that the adjacent node of the nth node is an invalid node, P fxl_n is the probability that the non - adjacent node of the nth node is an invalid node, W xln and W fxl_n are preset weights respectively, and W xln +W fxl_n = 1, 1 ≤ n ≤ N, where N is the total number of sensor nodes; If the probability that the current node is a valid node is greater than a preset threshold, it is determined that the verification of the current node is established; if the probability that the current node is a valid node is less than or equal to the preset threshold, it is determined that the verification of the current node is not established; Regard the nodes with failed verification as invalid nodes and the nodes with successful verification as valid nodes.

3. A computer-readable medium, on which a computer program is stored, Characterized in that, When the computer program is executed by a processor, it implements the industrial Internet platform data processing method as described in claim 1.

4. An electronic device, Characterized in that, Comprising: One or more processors; A storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the industrial Internet platform data processing method as described in claim 1.

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