Networking node data processing method and system based on prediction algorithm

Abstract

The invention discloses a networking node data processing method and system based on a prediction algorithm, and the method comprises the steps: determining node roles and inter-node path information corresponding to a plurality of equipment nodes in a target home scene when to-be-transmitted communication data is received; predicting multiple pieces of optimal transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm; determining node transmission paths corresponding to the at least two equipment nodes according to the information of the plurality of optimal transmission paths and the information of the node roles and the paths between the nodes; and according to the node transmission path, transmitting the communication data to a target receiving device through at least two device nodes in the target home scene. Therefore, the optimal node transmission path can be determined by fully combining the node role and the data characteristics, so that the data in the home scene can be stably and efficiently transmitted.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for processing networking node data based on a prediction algorithm. Background Art

[0002] As home appliances become more and more intelligent, the communication requirements for devices in home scenes are also increasing, and higher requirements are placed on the communication efficiency and stability of devices in scenes. Therefore, more and more device nodes are set up in home scenes to maintain communication within the scenes. When dealing with the communication of nodes in home scenes, the existing technology generally only forwards data based on preset rules by fixed nodes, without fully considering the node role and data characteristics to predict the communication operation between nodes. Therefore, its communication stability and communication effect are both lacking. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a networking node data processing method and system based on a prediction algorithm, which can fully combine the node role and data characteristics to determine the optimal node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for processing networking node data based on a prediction algorithm, the method comprising: When receiving the communication data to be transmitted, determining the node roles and inter-node path information corresponding to the plurality of device nodes in the target home scene; Predicting multiple preferred transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm; Determine node transmission paths corresponding to at least two of the device nodes according to the plurality of preferred transmission path information and the node roles and inter-node path information; According to the node transmission path, the communication data is transmitted to the target receiving device via at least two of the device nodes in the target home scene.

[0005] As an optional implementation, in the first aspect of the present invention, the node role is a leader node, a follower node or other nodes.

[0006] As an optional implementation, in the first aspect of the present invention, the inter-node path information includes optional communication paths, established communication paths, and closed communication paths between any two of the device nodes.

[0007] As an optional implementation, in the first aspect of the present invention, determining the node roles and inter-node path information corresponding to the plurality of device nodes in the target home scene includes: Based on the device historical data, determining at least one first device node whose node role is a leader node in the target home scene; The first device node communicates data with any one of the target home devices to determine at least one second device node whose node role is a follower node; Determine each of the first device nodes and the second device nodes determined by the first device node as a device network; In each of the device networks, inter-node path information between device nodes in the device network is determined.

[0008] As an optional implementation, in the first aspect of the present invention, the method of determining at least one first device node whose node role is a leader node in the target home scene based on the device history data includes: For each home device in the target home scene, obtain device history data of the home device; Counting the first frequency of the leader node in the historical data of the device; Counting the second frequency of the device serving as a follower node in the historical data of the device; Counting the proportion of communication records of the device communicating with the node whose node role is a leader in the historical data; Calculate the product of the ratio of the first frequency to the second frequency and the communication weight to obtain a device leadership parameter corresponding to the home device; the communication weight is proportional to the proportion of the communication records; The home device whose device leadership parameter is greater than the first parameter threshold is determined as the first device node whose node role is a leader node.

[0009] As an optional implementation, in the first aspect of the present invention, predicting multiple preferred transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm includes: Determine the communication purpose and endpoint device information corresponding to the communication data; the communication purpose is full data synchronization or incremental data synchronization; the endpoint device information includes device status, device attributes and device events; The communication data and the terminal device information are input into a trained path information prediction neural network corresponding to the communication purpose to obtain a plurality of preferred transmission path information corresponding to the communication data.

[0010] As an optional embodiment, in the first aspect of the present invention, the path information prediction neural network is trained by a training data set including multiple training communication data and corresponding endpoint device information annotations and transmission path information annotations; the transmission path information annotations or the preferred transmission path information include the node role type, the number of nodes, the transmission efficiency requirements and the transmission path complexity requirements in the path.

[0011] As an optional implementation, in the first aspect of the present invention, determining the node transmission paths corresponding to at least two of the device nodes according to the multiple preferred transmission path information and the node roles and inter-node path information includes: Based on the spatial search algorithm and the inter-node path information, determining multiple possible transmission paths corresponding to all the device nodes; For each of the possible transmission paths, the possible transmission path is input into a trained path parameter prediction neural network to obtain path parameters corresponding to the possible transmission path; the path parameters include path transmission efficiency and path transmission complexity; Calculating the path similarity between the possible transmission path and the corresponding path parameter and each of the preferred transmission path information; Calculate the average value of all the path similarities corresponding to the possible transmission path to obtain the path optimization parameter corresponding to the possible transmission path; The possible transmission path with the highest path preference parameter is determined as the node transmission path.

[0012] A second aspect of an embodiment of the present invention discloses a network node data processing system based on a prediction algorithm, the system comprising: A first determination module is used to determine the node roles and inter-node path information corresponding to multiple device nodes in the target home scene when receiving the communication data to be transmitted; A prediction module, used to predict multiple preferred transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm; A second determination module, configured to determine node transmission paths corresponding to at least two of the device nodes according to the plurality of preferred transmission path information and the node roles and inter-node path information; A transmission module is used to transmit the communication data to a target receiving device via at least two of the device nodes in the target home scene according to the node transmission path.

[0013] As an optional implementation, in the second aspect of the present invention, the node role is a leader node, a follower node or other nodes.

[0014] As an optional implementation, in the second aspect of the present invention, the inter-node path information includes optional communication paths, established communication paths, and closed communication paths between any two of the device nodes.

[0015] As an optional implementation, in the second aspect of the present invention, the specific manner in which the first determination module determines the node roles and inter-node path information corresponding to the multiple device nodes in the target home scene includes: Based on the device historical data, determining at least one first device node whose node role is a leader node in the target home scene; The first device node communicates data with any one of the target home devices to determine at least one second device node whose node role is a follower node; Determine each of the first device nodes and the second device nodes determined by the first device node as a device network; In each of the device networks, inter-node path information between device nodes in the device network is determined.

[0016] As an optional implementation, in the second aspect of the present invention, the first determination module determines, based on the device history data, a specific manner in which at least one first device node whose node role is a leader node in the target home scene comprises: For each home device in the target home scene, obtain device history data of the home device; Counting the first frequency of the leader node in the historical data of the device; Counting the second frequency of the device serving as a follower node in the historical data of the device; Counting the proportion of communication records of the device communicating with the node whose node role is a leader in the historical data; Calculate the product of the ratio of the first frequency to the second frequency and the communication weight to obtain a device leadership parameter corresponding to the home device; the communication weight is proportional to the proportion of the communication records; The home device whose device leadership parameter is greater than the first parameter threshold is determined as the first device node whose node role is a leader node.

[0017] As an optional implementation, in the second aspect of the present invention, the specific manner in which the prediction module predicts the multiple preferred transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm includes: Determine the communication purpose and endpoint device information corresponding to the communication data; the communication purpose is full data synchronization or incremental data synchronization; the endpoint device information includes device status, device attributes and device events; The communication data and the terminal device information are input into a trained path information prediction neural network corresponding to the communication purpose to obtain a plurality of preferred transmission path information corresponding to the communication data.

[0018] As an optional embodiment, in the second aspect of the present invention, the path information prediction neural network is trained by a training data set including multiple training communication data and corresponding endpoint device information annotations and transmission path information annotations; the transmission path information annotations or the preferred transmission path information include the node role type, the number of nodes, the transmission efficiency requirements and the transmission path complexity requirements in the path.

[0019] As an optional implementation, in the second aspect of the present invention, the second determination module determines the specific manner of the node transmission path corresponding to at least two of the device nodes according to the multiple preferred transmission path information and the node roles and inter-node path information, including: Based on the spatial search algorithm and the inter-node path information, determining multiple possible transmission paths corresponding to all the device nodes; For each of the possible transmission paths, the possible transmission path is input into a trained path parameter prediction neural network to obtain path parameters corresponding to the possible transmission path; the path parameters include path transmission efficiency and path transmission complexity; Calculating the path similarity between the possible transmission path and the corresponding path parameter and each of the preferred transmission path information; Calculate the average value of all the path similarities corresponding to the possible transmission path to obtain the path optimization parameter corresponding to the possible transmission path; The possible transmission path with the highest path preference parameter is determined as the node transmission path.

[0020] The third aspect of the present invention discloses another network node data processing system based on a prediction algorithm, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the networking node data processing method based on the prediction algorithm disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the networking node data processing method based on the prediction algorithm disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can predict multiple preferred transmission path information corresponding to the communication data based on the communication data and a preset prediction algorithm, and then determine at least two node transmission paths according to the multiple preferred transmission path information and the node roles and the path information between the nodes, so as to perform more reasonable and efficient node data transmission, thereby being able to fully combine the node roles and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a method for processing networking node data based on a prediction algorithm disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural diagram of a network node data processing system based on a prediction algorithm disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural diagram of another network node data processing system based on a prediction algorithm disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a network node data processing method and system based on a prediction algorithm, which can predict multiple preferred transmission path information corresponding to the communication data based on the communication data and a preset prediction algorithm, and then determine at least two node transmission paths according to the multiple preferred transmission path information and the node role and the path information between nodes, so as to perform more reasonable and efficient node data transmission, thereby being able to fully combine the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can be transmitted stably and efficiently. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 1 is a flow chart of a method for processing network node data based on a prediction algorithm disclosed in an embodiment of the present invention. Figure 1 The described network node data processing method based on the prediction algorithm can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the network node data processing method based on the prediction algorithm may include the following operations: 101. When receiving communication data to be transmitted, determine the node roles and inter-node path information corresponding to multiple device nodes in the target home scene.

[0032] 102. Predict multiple preferred transmission path information corresponding to the communication data based on the communication data and a preset prediction algorithm. 103. Determine node transmission paths corresponding to at least two device nodes according to multiple preferred transmission path information, node roles, and inter-node path information. 104. According to the node transmission path, the communication data is transmitted to the target receiving device via at least two device nodes in the target home scene.

[0033] It can be seen that the above-mentioned embodiments of the invention can predict multiple preferred transmission path information corresponding to the communication data based on the communication data and the preset prediction algorithm, and then determine at least two node transmission paths according to the multiple preferred transmission path information and the node roles and the path information between the nodes, so as to perform more reasonable and efficient node data transmission, thereby being able to fully combine the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0034] As an optional embodiment, in the above steps, the node role is a leader node, a follower node or other nodes.

[0035] It can be seen that through the above optional embodiments, the content of the node role is defined to comprehensively characterize the role characteristics of the device node, and to assist in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0036] As an optional embodiment, in the above steps, the inter-node path information includes optional communication paths, established communication paths, and closed communication paths between any two device nodes.

[0037] It can be seen that through the above optional embodiments, the content of the path information between nodes is limited to comprehensively characterize the communication path information between nodes, and to assist in fully combining the node roles and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0038] As an optional embodiment, in the above step, determining the node roles and inter-node path information corresponding to the multiple device nodes in the target home scene includes: Based on the device historical data, determining at least one first device node whose node role is a leader node in the target home scene; The first device node communicates data with any one of the target home devices to determine at least one second device node whose node role is a follower node; Determine each first device node and the second device node determined by it as a device network; In each device network, inter-node path information between device nodes in the device network is determined.

[0039] It can be seen that through the above-mentioned optional embodiments, the leader node can be first determined in the home scene through the device historical data, and then the leader node can determine the follower node to realize device networking and determination of path information between nodes, so as to facilitate the subsequent accurate determination of the node transmission path, and assist in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0040] As an optional embodiment, in the above steps, based on the device history data, determining at least one first device node whose node role is a leader node in the target home scene includes: For each home device in the target home scene, obtain device history data of the home device; The first frequency of the leader node in the historical data of the statistical device; Count the second frequency of the device serving as a follower node in historical data; The percentage of communication records with the leader node in the device's historical data is counted; Calculate the product of the ratio of the first frequency to the second frequency and the communication weight to obtain the device leadership parameter corresponding to the home device; optionally, the communication weight is proportional to the proportion of communication records; A home device whose device leadership parameter is greater than a first parameter threshold is determined as a first device node whose node role is a leader node.

[0041] It can be seen that through the above-mentioned optional embodiments, the leadership parameters of the device can be accurately calculated based on the role information and communication records in the device historical data to determine the leader node, which is convenient for subsequent device networking and accurate determination of the node transmission path, and assists in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0042] As an optional embodiment, in the above step, predicting multiple preferred transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm includes: Determine the communication purpose and endpoint device information corresponding to the communication data; optionally, the communication purpose is full data synchronization or incremental data synchronization; the endpoint device information includes device status, device attributes and device events; The communication data and the terminal device information are input into the trained path information prediction neural network corresponding to the communication purpose to obtain a plurality of preferred transmission path information corresponding to the communication data.

[0043] It can be seen that through the above-mentioned optional embodiments, the trained path information prediction neural network can be based on the communication data and the endpoint device information to predict multiple preferred transmission path information, which is convenient for the subsequent precise determination of the node transmission path, and assists in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0044] As an optional embodiment, in the above steps, the path information prediction neural network is trained by a training data set including multiple training communication data and corresponding endpoint device information annotations and transmission path information annotations; the transmission path information annotations or preferred transmission path information include the node role type, the number of nodes, the transmission efficiency requirements and the transmission path complexity requirements in the path.

[0045] It can be seen that through the above-mentioned optional embodiments, the details of the path information prediction neural network and the content of the predicted path information are limited, which facilitates the subsequent precise determination of the node transmission path, and assists in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene maintains stable and efficient transmission.

[0046] As an optional embodiment, in the above step, determining the node transmission paths corresponding to at least two device nodes according to the plurality of preferred transmission path information and the node roles and the inter-node path information includes: Based on the spatial search algorithm and the path information between nodes, multiple possible transmission paths corresponding to all device nodes are determined; For each possible transmission path, the possible transmission path is input into a trained path parameter prediction neural network to obtain a path parameter corresponding to the possible transmission path; optionally, the path parameter includes a path transmission efficiency and a path transmission complexity; Calculating the path similarity between the possible transmission path and the corresponding path parameters and each preferred transmission path information; Calculate the average value of all path similarities corresponding to the possible transmission path to obtain the path optimization parameter corresponding to the possible transmission path; The possible transmission path with the highest path optimization parameter is determined as the node transmission path.

[0047] It can be seen that through the above optional embodiments, multiple possible transmission paths corresponding to all device nodes can be determined based on the spatial search algorithm and the path information between nodes, and then the most reasonable and efficient node transmission path can be determined based on the similarity calculation after the path parameters are predicted based on the neural network, which helps to fully combine the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0048] Embodiment 2 See also Figure 2 , Figure 2 Schematic diagram of a network node data processing system based on a prediction algorithm disclosed in an embodiment of the present invention. Figure 2 The described network node data processing system based on the prediction algorithm can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the network node data processing system based on the prediction algorithm may include: The first determination module 201 is used to determine the node roles and inter-node path information corresponding to multiple device nodes in the target home scene when receiving the communication data to be transmitted.

[0049] The prediction module 202 is used to predict multiple preferred transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm. The second determination module 203 is used to determine the node transmission paths corresponding to at least two device nodes according to the multiple preferred transmission path information and the node roles and the inter-node path information. The transmission module 204 is used to transmit the communication data to the target receiving device via at least two device nodes in the target home scene according to the node transmission path.

[0050] It can be seen that the above-mentioned embodiments of the invention can predict multiple preferred transmission path information corresponding to the communication data based on the communication data and the preset prediction algorithm, and then determine at least two node transmission paths according to the multiple preferred transmission path information and the node roles and the path information between the nodes, so as to perform more reasonable and efficient node data transmission, thereby being able to fully combine the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0051] As an optional embodiment, the node role is a leader node, a follower node or other nodes.

[0052] It can be seen that through the above optional embodiments, the content of the node role is defined to comprehensively characterize the role characteristics of the device node, and to assist in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0053] As an optional embodiment, the inter-node path information includes optional communication paths, established communication paths, and closed communication paths between any two device nodes.

[0054] It can be seen that through the above optional embodiments, the content of the path information between nodes is limited to comprehensively characterize the communication path information between nodes, and to assist in fully combining the node roles and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0055] As an optional embodiment, the specific manner in which the first determination module determines the node roles and inter-node path information corresponding to the multiple device nodes in the target home scene includes: Based on the device historical data, determining at least one first device node whose node role is a leader node in the target home scene; The first device node communicates data with any one of the target home devices to determine at least one second device node whose node role is a follower node; Determine each first device node and the second device node determined by it as a device network; In each device network, inter-node path information between device nodes in the device network is determined.

[0056] It can be seen that through the above-mentioned optional embodiments, the leader node can be first determined in the home scene through the device historical data, and then the leader node can determine the follower node to realize device networking and determination of path information between nodes, so as to facilitate the subsequent accurate determination of the node transmission path, and assist in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0057] As an optional embodiment, the first determination module determines, based on the device history data, a specific manner in which at least one first device node whose node role is a leader node in the target home scene includes: For each home device in the target home scene, obtain device history data of the home device; The first frequency of the leader node in the historical data of the statistical device; Count the second frequency of the device serving as a follower node in historical data; The percentage of communication records with the leader node in the device's historical data is counted; Calculate the product of the ratio of the first frequency to the second frequency and the communication weight to obtain the device leadership parameter corresponding to the home device; optionally, the communication weight is proportional to the proportion of communication records; A home device whose device leadership parameter is greater than a first parameter threshold is determined as a first device node whose node role is a leader node.

[0058] It can be seen that through the above-mentioned optional embodiments, the leadership parameters of the device can be accurately calculated based on the role information and communication records in the device historical data to determine the leader node, which is convenient for subsequent device networking and accurate determination of the node transmission path, and assists in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0059] As an optional embodiment, the specific manner in which the prediction module predicts the multiple preferred transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm includes: Determine the communication purpose and endpoint device information corresponding to the communication data; optionally, the communication purpose is full data synchronization or incremental data synchronization; the endpoint device information includes device status, device attributes and device events; The communication data and the terminal device information are input into the trained path information prediction neural network corresponding to the communication purpose to obtain a plurality of preferred transmission path information corresponding to the communication data.

[0060] It can be seen that through the above-mentioned optional embodiments, the trained path information prediction neural network can be based on the communication data and the endpoint device information to predict multiple preferred transmission path information, which is convenient for the subsequent precise determination of the node transmission path, and assists in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0061] As an optional embodiment, the path information prediction neural network is trained by a training data set including multiple training communication data and corresponding endpoint device information annotations and transmission path information annotations; the transmission path information annotations or preferred transmission path information include the node role type, the number of nodes, the transmission efficiency requirements and the transmission path complexity requirements in the path.

[0062] It can be seen that through the above-mentioned optional embodiments, the details of the path information prediction neural network and the content of the predicted path information are limited, which facilitates the subsequent precise determination of the node transmission path, and assists in fully combining the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene maintains stable and efficient transmission.

[0063] As an optional embodiment, the second determination module determines the specific manner of the node transmission paths corresponding to at least two device nodes according to the multiple preferred transmission path information and the node roles and the inter-node path information, including: Based on the spatial search algorithm and the path information between nodes, multiple possible transmission paths corresponding to all device nodes are determined; For each possible transmission path, the possible transmission path is input into a trained path parameter prediction neural network to obtain a path parameter corresponding to the possible transmission path; optionally, the path parameter includes a path transmission efficiency and a path transmission complexity; Calculating the path similarity between the possible transmission path and the corresponding path parameters and each preferred transmission path information; Calculate the average value of all path similarities corresponding to the possible transmission path to obtain the path optimization parameter corresponding to the possible transmission path; The possible transmission path with the highest path optimization parameter is determined as the node transmission path.

[0064] It can be seen that through the above optional embodiments, multiple possible transmission paths corresponding to all device nodes can be determined based on the spatial search algorithm and the path information between nodes, and then the most reasonable and efficient node transmission path can be determined based on the similarity calculation after the path parameters are predicted based on the neural network, which helps to fully combine the node role and data characteristics to determine the preferred node transmission path, so that the data in the home scene can maintain stable and efficient transmission.

[0065] Embodiment 3 See also Figure 3 , Figure 3 It is another network node data processing system based on prediction algorithm disclosed in an embodiment of the present invention. Figure 3 The described network node data processing system based on the prediction algorithm is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the network node data processing system based on the prediction algorithm may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the method for processing networking node data based on the prediction algorithm described in the first embodiment.

[0066] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the method for processing networking node data based on a prediction algorithm described in the first embodiment.

[0067] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the networking node data processing method based on the prediction algorithm described in Example 1.

[0068] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0070] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0079] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0081] Finally, it should be noted that the networking node data processing method and system based on the prediction algorithm disclosed in the embodiment of the present invention discloses only the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical schemes recorded in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the various embodiments of the present invention.

Claims

1. A method for processing network node data based on a prediction algorithm, characterized in that: The method comprises: When receiving the communication data to be transmitted, determining the node roles and inter-node path information corresponding to the plurality of device nodes in the target home scene; Predicting multiple preferred transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm; Determine node transmission paths corresponding to at least two of the device nodes according to the plurality of preferred transmission path information and the node roles and inter-node path information; According to the node transmission path, the communication data is transmitted to the target receiving device via at least two of the device nodes in the target home scene.

2. The method for processing network node data based on prediction algorithm according to claim 1, characterized in that: The node role is a leader node, a follower node, or other nodes.

3. The method for processing network node data based on prediction algorithm according to claim 1, characterized in that: The inter-node path information includes optional communication paths, established communication paths, and closed communication paths between any two of the device nodes.

4. The method for processing network node data based on prediction algorithm according to claim 2, characterized in that: The determining of node roles and inter-node path information corresponding to a plurality of device nodes in the target home scene includes: Based on the device historical data, determining at least one first device node whose node role is a leader node in the target home scene; The first device node communicates data with any one of the target home devices to determine at least one second device node whose node role is a follower node; Determine each of the first device nodes and the second device nodes determined by the first device node as a device network; In each of the device networks, inter-node path information between device nodes in the device network is determined.

5. The method for processing network node data based on prediction algorithm according to claim 4, characterized in that: The method of determining at least one first device node whose node role is a leader node in the target home scene based on the device historical data includes: For each home device in the target home scene, obtain device history data of the home device; Counting the first frequency of the leader node in the historical data of the device; Counting the second frequency of the device serving as a follower node in the historical data; Counting the proportion of communication records of the device communicating with the node whose node role is a leader in the historical data; Calculate the product of the ratio of the first frequency to the second frequency and the communication weight to obtain a device leadership parameter corresponding to the home device; the communication weight is proportional to the proportion of the communication records; The home device whose device leadership parameter is greater than the first parameter threshold is determined as the first device node whose node role is a leader node.

6. The method for processing network node data based on prediction algorithm according to claim 1, characterized in that: The predicting, based on the communication data and a preset prediction algorithm, a plurality of preferred transmission path information corresponding to the communication data includes: Determine the communication purpose and endpoint device information corresponding to the communication data; the communication purpose is full data synchronization or incremental data synchronization; the endpoint device information includes device status, device attributes and device events; The communication data and the terminal device information are input into a trained path information prediction neural network corresponding to the communication purpose to obtain a plurality of preferred transmission path information corresponding to the communication data.

7. The method for processing network node data based on prediction algorithm according to claim 6, characterized in that: The path information prediction neural network is trained by a training data set including multiple training communication data and corresponding endpoint device information annotations and transmission path information annotations; the transmission path information annotations or the preferred transmission path information include the node role type, the number of nodes, the transmission efficiency requirements and the transmission path complexity requirements in the path.

8. The method for processing network node data based on prediction algorithm according to claim 7, characterized in that: The determining, according to the plurality of preferred transmission path information and the node roles and the inter-node path information, the node transmission paths corresponding to at least two of the device nodes comprises: Based on the spatial search algorithm and the inter-node path information, determining multiple possible transmission paths corresponding to all the device nodes; For each of the possible transmission paths, the possible transmission path is input into a trained path parameter prediction neural network to obtain path parameters corresponding to the possible transmission path; the path parameters include path transmission efficiency and path transmission complexity; Calculating the path similarity between the possible transmission path and the corresponding path parameter and each of the preferred transmission path information; Calculate the average value of all the path similarities corresponding to the possible transmission path to obtain the path optimization parameter corresponding to the possible transmission path; The possible transmission path with the highest path preference parameter is determined as the node transmission path.

9. A network node data processing system based on a prediction algorithm, characterized in that: The system comprises: A first determination module is used to determine the node roles and inter-node path information corresponding to multiple device nodes in the target home scene when receiving the communication data to be transmitted; A prediction module, used to predict multiple preferred transmission path information corresponding to the communication data according to the communication data and a preset prediction algorithm; A second determination module, configured to determine node transmission paths corresponding to at least two of the device nodes according to the plurality of preferred transmission path information and the node roles and inter-node path information; A transmission module is used to transmit the communication data to a target receiving device via at least two of the device nodes in the target home scene according to the node transmission path.

10. A network node data processing system based on a prediction algorithm, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the networking node data processing method based on the prediction algorithm as described in any one of claims 1-8.

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