Network node data processing method and system based on prediction algorithm

Abstract

The present invention discloses a method and system for processing networking node data based on a prediction algorithm. The method includes: upon receiving communication data to be transmitted, determining node roles and inter-node path information corresponding to multiple device nodes in a target home scene; predicting multiple preferred transmission path information corresponding to the communication data based on the communication data and a preset prediction algorithm; determining node transmission paths corresponding to at least two of the device nodes based on the multiple preferred transmission path information and the node roles and inter-node path information; and transmitting the communication data to a target receiving device via at least two of the device nodes in the target home scene based on the node transmission paths. It can be seen that the present invention can fully combine node roles and data characteristics to determine a preferred node transmission path, so that data in the home scene can maintain stable and efficient transmission.

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 increasingly intelligent, the demand for device communication within home scenarios is also increasing, placing higher demands on the efficiency and stability of device communication within these scenarios. Consequently, an increasing number of device nodes are being deployed within home scenarios to maintain communication within these scenarios. Existing technologies for handling node communication within home scenarios generally rely solely on fixed nodes forwarding data based on preset rules. These technologies fail to fully consider node roles and data characteristics to predict inter-node communication operations, resulting in limited communication stability and effectiveness. This demonstrates that existing technologies have shortcomings that urgently need to be addressed. 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 data in the home scene can be transmitted stably and efficiently.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for processing network node data based on a prediction algorithm, the method comprising:

[0005] Upon receiving communication data to be transmitted, determining node roles and inter-node path information corresponding to multiple device nodes in the target home scene;

[0006] Predicting information of multiple preferred transmission paths corresponding to the communication data based on the communication data and a preset prediction algorithm;

[0007] Determining 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;

[0008] According to the node transmission path, the communication data is transmitted to a target receiving device via at least two of the device nodes in the target home scene.

[0009] 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.

[0010] 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.

[0011] As an optional embodiment, 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:

[0012] Based on the device historical data, determining at least one first device node with a node role as a leader node in the target home scene;

[0013] 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;

[0014] Determine each first device node and the second device node determined by it as a device network;

[0015] In each of the device networks, inter-node path information between device nodes in the device network is determined.

[0016] As an optional embodiment, in the first aspect of the present invention, determining at least one first device node with a node role as a leader node in the target home scene based on the device historical data includes:

[0017] For each home device in the target home scene, obtain device history data of the home device;

[0018] Counting the first frequency of the leader node in the historical data of the device;

[0019] Counting a second frequency of the device serving as a follower node in historical data;

[0020] Counting the proportion of communication records with a leader node in the device's historical data;

[0021] 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;

[0022] The 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.

[0023] As an optional embodiment, in the first aspect of the present invention, predicting multiple preferred transmission path information corresponding to the communication data based on the communication data and a preset prediction algorithm includes:

[0024] 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;

[0025] 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.

[0026] 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.

[0027] As an optional embodiment, in the first aspect of the present invention, determining the node transmission paths corresponding to at least two of the device nodes based on the multiple preferred transmission path information and the node roles and inter-node path information includes:

[0028] Determine multiple possible transmission paths corresponding to all the device nodes based on a spatial search algorithm and the inter-node path information;

[0029] 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;

[0030] Calculating the path similarity between the possible transmission path and the corresponding path parameter and each piece of the preferred transmission path information;

[0031] Calculating an average value of all the path similarities corresponding to the possible transmission path to obtain a path optimization parameter corresponding to the possible transmission path;

[0032] The possible transmission path with the highest path preference parameter is determined as the node transmission path.

[0033] 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:

[0034] A first determination module is configured to determine, upon receiving communication data to be transmitted, node roles and inter-node path information corresponding to a plurality of device nodes in a target home scene;

[0035] A prediction module, configured to predict information of multiple preferred transmission paths corresponding to the communication data based on the communication data and a preset prediction algorithm;

[0036] A second determining module is configured to determine node transmission paths corresponding to at least two of the device nodes based on the plurality of preferred transmission path information and the node roles and inter-node path information;

[0037] 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.

[0038] 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.

[0039] 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.

[0040] As an optional embodiment, 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:

[0041] Based on the device historical data, determining at least one first device node with a node role as a leader node in the target home scene;

[0042] 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;

[0043] Determine each first device node and the second device node determined by it as a device network;

[0044] In each of the device networks, inter-node path information between device nodes in the device network is determined.

[0045] As an optional embodiment, in the second aspect of the present invention, the first determination module determines, based on the device historical data, a specific manner in which at least one first device node with a node role as a leader node in the target home scene includes:

[0046] For each home device in the target home scene, obtain device history data of the home device;

[0047] Counting the first frequency of the leader node in the historical data of the device;

[0048] Counting a second frequency of the device serving as a follower node in historical data;

[0049] Counting the proportion of communication records with a leader node in the device's historical data;

[0050] 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;

[0051] The 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.

[0052] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the prediction module predicts the information of multiple preferred transmission paths corresponding to the communication data based on the communication data and a preset prediction algorithm includes:

[0053] 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;

[0054] 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.

[0055] 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.

[0056] As an optional embodiment, in the second aspect of the present invention, the second determination module determines the specific manner of the node transmission paths corresponding to at least two of the device nodes based on the multiple preferred transmission path information and the node roles and inter-node path information, including:

[0057] Determine multiple possible transmission paths corresponding to all the device nodes based on a spatial search algorithm and the inter-node path information;

[0058] 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;

[0059] Calculating the path similarity between the possible transmission path and the corresponding path parameter and each piece of the preferred transmission path information;

[0060] Calculating an average value of all the path similarities corresponding to the possible transmission path to obtain a path optimization parameter corresponding to the possible transmission path;

[0061] The possible transmission path with the highest path preference parameter is determined as the node transmission path.

[0062] A third aspect of the present invention discloses another network node data processing system based on a prediction algorithm, the system comprising:

[0063] a memory storing executable program code;

[0064] a processor coupled to the memory;

[0065] 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.

[0066] 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.

[0067] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0068] The present invention can predict multiple preferred transmission path information corresponding to communication data based on communication data and a preset prediction algorithm, and then determine at least two node transmission paths based on multiple preferred transmission path information and node roles and inter-node path information to perform more reasonable and efficient node data transmission, thereby being able to fully combine node roles and data characteristics to determine the preferred node transmission path, so that data in home scenarios can maintain stable and efficient transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] 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.

[0070] 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.

[0071] Figure 2 This is a structural diagram of a network node data processing system based on a prediction algorithm disclosed in an embodiment of the present invention.

[0072] 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

[0073] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0074] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0075] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0076] The present invention discloses a network node data processing method and system based on a prediction algorithm. The method can predict multiple preferred transmission path information corresponding to the communication data based on the communication data and a preset prediction algorithm. Then, based on the multiple preferred transmission path information and the node roles and inter-node path information, at least two node transmission paths are determined to perform more reasonable and efficient node data transmission. This method can fully combine the node roles and data characteristics to determine the preferred node transmission path, ensuring stable and efficient data transmission in home scenarios. The following details each of these.

[0077] Example 1

[0078] See also Figure 1 , Figure 1 This 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:

[0079] 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.

[0080] 102. Predict multiple preferred transmission path information corresponding to the communication data based on the communication data and a preset prediction algorithm.

[0081] 103. Determine node transmission paths corresponding to at least two device nodes based on the multiple preferred transmission path information, node roles, and inter-node path information.

[0082] 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.

[0083] 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 based on the multiple preferred transmission path information and the node roles and inter-node path information to perform more reasonable and efficient node data transmission, so as 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.

[0084] As an optional embodiment, in the above steps, the node role is a leader node, a follower node or other nodes.

[0085] It can be seen that through the above optional embodiments, the content of the node role is limited to comprehensively characterize the role characteristics of the device node, 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.

[0086] 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.

[0087] 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 assist in fully combining the node roles and data characteristics to determine the preferred node transmission path, so that data in the home scene can maintain stable and efficient transmission.

[0088] As an optional embodiment, in the above step, determining the node roles and inter-node path information corresponding to multiple device nodes in the target home scene includes:

[0089] Based on the device historical data, determining at least one first device node with a node role as a leader node in the target home scene;

[0090] The first device node communicates data with any one of the target home devices to determine a second device node having at least one node role as a follower node;

[0091] Determine each first device node and the second device node determined by it as a device network;

[0092] In each device network, inter-node path information between device nodes in the device network is determined.

[0093] It can be seen that through the above 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 path information determination 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.

[0094] As an optional embodiment, in the above step, determining at least one first device node with a node role as a leader node in the target home scene based on the device historical data includes:

[0095] For each home device in the target home scene, obtain device history data of the home device;

[0096] The first frequency of the leader node in the historical data of the statistical device;

[0097] Counting the second frequency of the device serving as a follower node in historical data;

[0098] Count the percentage of communication records with leader nodes in the device's historical data.

[0099] 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;

[0100] 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.

[0101] It can be seen that through the above 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, facilitate subsequent device networking and accurately determine the node transmission path, and assist in fully combining the node role and data characteristics to determine the preferred node transmission path, so that data in the home scene maintains stable and efficient transmission.

[0102] As an optional embodiment, in the above step, predicting multiple preferred transmission path information corresponding to the communication data based on the communication data and a preset prediction algorithm includes:

[0103] 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;

[0104] 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 multiple preferred transmission path information corresponding to the communication data.

[0105] It can be seen that through the above optional embodiments, the trained path information prediction neural network corresponding to the communication purpose can be used to predict multiple preferred transmission path information based on the communication data and terminal device information, which is convenient for the subsequent 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.

[0106] 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, number of nodes, transmission efficiency requirements and transmission path complexity requirements in the path.

[0107] It can be seen that through the above 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.

[0108] As an optional embodiment, in the above step, determining the node transmission paths corresponding to at least two device nodes based on the plurality of preferred transmission path information, node roles, and inter-node path information includes:

[0109] Based on the spatial search algorithm and inter-node path information, multiple possible transmission paths corresponding to all device nodes are determined;

[0110] For each possible transmission path, the possible transmission path is input into a trained path parameter prediction neural network to obtain path parameters corresponding to the possible transmission path; optionally, the path parameters include path transmission efficiency and path transmission complexity;

[0111] Calculating the path similarity between the possible transmission path and the corresponding path parameters and each preferred transmission path information;

[0112] 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;

[0113] The possible transmission path with the highest path optimization parameter is determined as the node transmission path.

[0114] 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 path parameters can be predicted based on the neural network, and the most reasonable and efficient node transmission path can be determined based on the similarity calculation, 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.

[0115] Example 2

[0116] See also Figure 2 , Figure 2 This is a schematic diagram of the structure 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:

[0117] 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.

[0118] The prediction module 202 is configured to predict information of multiple preferred transmission paths corresponding to the communication data based on the communication data and a preset prediction algorithm.

[0119] The second determining module 203 is configured to determine node transmission paths corresponding to at least two device nodes according to the plurality of preferred transmission path information, node roles, and inter-node path information.

[0120] 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.

[0121] 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 based on the multiple preferred transmission path information and the node roles and inter-node path information to perform more reasonable and efficient node data transmission, so as 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.

[0122] As an optional embodiment, the node role is a leader node, a follower node or other nodes.

[0123] It can be seen that through the above optional embodiments, the content of the node role is limited to comprehensively characterize the role characteristics of the device node, 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.

[0124] 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.

[0125] 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 assist in fully combining the node roles and data characteristics to determine the preferred node transmission path, so that data in the home scene can maintain stable and efficient transmission.

[0126] As an optional embodiment, the first determination module determines the node roles and inter-node path information corresponding to the multiple device nodes in the target home scene in a specific manner, including:

[0127] Based on the device historical data, determining at least one first device node with a node role as a leader node in the target home scene;

[0128] The first device node communicates data with any one of the target home devices to determine a second device node having at least one node role as a follower node;

[0129] Determine each first device node and the second device node determined by it as a device network;

[0130] In each device network, inter-node path information between device nodes in the device network is determined.

[0131] It can be seen that through the above 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 path information determination 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.

[0132] 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 with a node role as a leader node in the target home scene includes:

[0133] For each home device in the target home scene, obtain device history data of the home device;

[0134] The first frequency of the leader node in the historical data of the statistical device;

[0135] Counting the second frequency of the device serving as a follower node in historical data;

[0136] Count the percentage of communication records with leader nodes in the device's historical data.

[0137] 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;

[0138] 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.

[0139] It can be seen that through the above 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, facilitate subsequent device networking and accurately determine the node transmission path, and assist in fully combining the node role and data characteristics to determine the preferred node transmission path, so that data in the home scene maintains stable and efficient transmission.

[0140] As an optional embodiment, the specific manner in which the prediction module predicts multiple preferred transmission path information corresponding to the communication data based on the communication data and a preset prediction algorithm includes:

[0141] 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;

[0142] 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 multiple preferred transmission path information corresponding to the communication data.

[0143] It can be seen that through the above optional embodiments, the trained path information prediction neural network corresponding to the communication purpose can be used to predict multiple preferred transmission path information based on the communication data and terminal device information, which is convenient for the subsequent 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.

[0144] 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, number of nodes, transmission efficiency requirements and transmission path complexity requirements in the path.

[0145] It can be seen that through the above 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.

[0146] As an optional embodiment, the second determining module determines the specific manner of the node transmission paths corresponding to at least two device nodes based on the multiple preferred transmission path information, the node roles, and the inter-node path information, including:

[0147] Based on the spatial search algorithm and inter-node path information, multiple possible transmission paths corresponding to all device nodes are determined;

[0148] For each possible transmission path, the possible transmission path is input into a trained path parameter prediction neural network to obtain path parameters corresponding to the possible transmission path; optionally, the path parameters include path transmission efficiency and path transmission complexity;

[0149] Calculating the path similarity between the possible transmission path and the corresponding path parameters and each preferred transmission path information;

[0150] 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;

[0151] The possible transmission path with the highest path optimization parameter is determined as the node transmission path.

[0152] 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 path parameters can be predicted based on the neural network, and the most reasonable and efficient node transmission path can be determined based on the similarity calculation, 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.

[0153] Example 3

[0154] See also Figure 3 , Figure 3 This is another network node data processing system based on a 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:

[0155] A memory 301 storing executable program code;

[0156] a processor 302 coupled to the memory 301;

[0157] 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.

[0158] Example 4

[0159] 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.

[0160] Example 5

[0161] 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 network node data processing method based on the prediction algorithm described in Example 1.

[0162] The foregoing description of specific embodiments of the present disclosure is intended to illustrate a method for performing a multi-tasking process. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0163] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having 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 smartphone, 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.

[0164] For the convenience of description, the above devices are described as being divided into 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.

[0165] 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 take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] 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 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.

[0167] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] 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 the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0169] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0170] 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. Memory is an example of a computer-readable medium.

[0171] 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. The 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 disc read-only memory (CD-ROM), digital versatile disc (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 transitory media such as modulated data signals and carrier waves.

[0172] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0173] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like 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 communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0174] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0175] 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 is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution 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 solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions 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: Upon receiving communication data to be transmitted, determining node roles and inter-node path information corresponding to multiple device nodes in the target home scene; Predicting, based on the communication data and a preset prediction algorithm, information of a plurality of preferred transmission paths corresponding to the communication data, including: 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; Inputting the communication data and the endpoint device information into a trained path information prediction neural network corresponding to the communication destination to obtain a plurality of preferred transmission path information corresponding to the communication data; the path information prediction neural network is trained using a training data set including a plurality of 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 node role types, number of nodes, transmission efficiency requirements, and transmission path complexity requirements in the path; Determining 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 includes: Determine multiple possible transmission paths corresponding to all the device nodes based on a spatial search algorithm and the inter-node path information; 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 piece of the preferred transmission path information; Calculating an average value of all the path similarities corresponding to the possible transmission path to obtain a path optimization parameter corresponding to the possible transmission path; Determine the possible transmission path with the highest path optimization parameter as the node transmission path; According to the node transmission path, the communication data is transmitted to a 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 a 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 a 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 a prediction algorithm according to claim 2, characterized in that: The determining of node roles and inter-node path information corresponding to multiple device nodes in the target home scene includes: Based on the device historical data, determining at least one first device node with a node role as 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 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 a prediction algorithm according to claim 4, characterized in that: The step of determining, based on the device historical data, 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; Counting the first frequency of the leader node in the historical data of the device; Counting a second frequency of the device serving as a follower node in historical data; Counting the proportion of communication records with a leader node in the device's 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 a first parameter threshold is determined as a first device node whose node role is a leader node.

6. A network node data processing system based on a prediction algorithm, characterized in that: The system comprises: A first determination module is configured to determine, upon receiving communication data to be transmitted, node roles and inter-node path information corresponding to a plurality of device nodes in a target home scene; A prediction module, configured to predict, based on the communication data and a preset prediction algorithm, information of multiple preferred transmission paths corresponding to the communication data, including: 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; Inputting the communication data and the endpoint device information into a trained path information prediction neural network corresponding to the communication destination to obtain a plurality of preferred transmission path information corresponding to the communication data; the path information prediction neural network is trained using a training data set including a plurality of 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 node role types, number of nodes, transmission efficiency requirements, and transmission path complexity requirements in the path; The second determining module is configured to determine, based on the plurality of preferred transmission path information and the node roles and inter-node path information, node transmission paths corresponding to at least two of the device nodes, including: Determine multiple possible transmission paths corresponding to all the device nodes based on a spatial search algorithm and the inter-node path information; 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 piece of the preferred transmission path information; Calculating an average value of all the path similarities corresponding to the possible transmission path to obtain a path optimization parameter corresponding to the possible transmission path; Determine the possible transmission path with the highest path optimization parameter as the node transmission path; 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.

7. 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 to 5.

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