CN114866460A

CN114866460A - Path planning method based on artificial intelligence and related equipment

Info

Publication number: CN114866460A
Application number: CN202210459440.8A
Authority: CN
Inventors: 温业逵; 周健
Original assignee: Jitter Technology Shenzhen Co ltd
Current assignee: Jitter Technology Shenzhen Co ltd
Priority date: 2022-04-27
Filing date: 2022-04-27
Publication date: 2022-08-05
Anticipated expiration: 2042-04-27
Also published as: CN114866460B

Abstract

The application provides a path planning method and device based on artificial intelligence, an electronic device and a storage medium, wherein the path planning method based on artificial intelligence comprises the following steps: constructing a server cluster, wherein the server cluster comprises a plurality of server nodes; calculating the receiving cost of each server node; calculating the sending cost of each server node; calculating the communication priority of the server node according to the receiving cost and the sending cost; combining server nodes to construct a path set, wherein the path set comprises a plurality of communication paths, and calculating a priority index of each communication path according to the communication priority; and screening a target path and an alternative path from the path set according to the priority index to complete path planning. The method can screen the target path and the alternative paths according to the priority of the server nodes and the number of the server nodes in the communication path, so that the cost performance and the fault tolerance of path planning can be improved.

Description

Path planning method based on artificial intelligence and related equipment

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for path planning based on artificial intelligence, an electronic device, and a storage medium.

Background

With the development of information technology, the demand of each industry for stable and fast data transmission is increasing day by day, and an enterprise generally needs to use a plurality of server nodes in a server cluster to transfer data, and the quality of a data transmission path directly affects the efficiency of data transmission.

At present, an enterprise usually plans a data transmission path with a relatively low communication cost from a server cluster to serve as a target path for data transmission, and this method does not find an alternative path with a relatively high anisotropy with the target path from the server cluster, so that it is difficult to ensure the fault-tolerant capability of the data transmission process, and this method does not consider the cost of the target path in combination with the number of nodes in the target path, so that the cost performance of the target path may be relatively low.

Disclosure of Invention

In view of the foregoing, there is a need to provide a path planning method based on artificial intelligence and related apparatus, so as to solve the technical problem of how to improve the cost performance and fault tolerance of path planning, where the related apparatus includes a path planning apparatus based on artificial intelligence, an electronic device and a storage medium.

The embodiment of the application provides a path planning method based on artificial intelligence, which comprises the following steps:

the method comprises the steps that a server cluster is built, the server cluster comprises a plurality of server nodes, each server node is used for transmitting data, and the plurality of server nodes comprise a sending node and a receiving node;

calculating the receiving cost of each server node, wherein the receiving cost is used for representing the difficulty of the server node for receiving the data sent by the sending node;

calculating the sending cost of each server node, wherein the sending cost is used for representing the difficulty of sending data to the receiving node by the server node;

calculating a communication priority of the server node according to the receiving cost and the sending cost, wherein the communication priority is used for representing the degree of preferential calling of the server node when data is transmitted;

combining the server nodes to construct a path set, wherein the path set comprises a plurality of communication paths, each communication path comprises a plurality of server nodes, and the priority index of each communication path is calculated according to the communication priority;

and screening a target path and an alternative path from the path set according to the priority index to complete path planning.

According to the path planning method based on artificial intelligence, a server cluster is constructed to obtain a plurality of server nodes, the plurality of server nodes comprise a sending node and a last receiving node, the receiving cost and the sending cost of each server node are further calculated, the communication priority of the server nodes is calculated according to the receiving cost and the sending cost, the server nodes are combined randomly to obtain a plurality of communication paths, the priority indexes of the communication paths are calculated according to the communication priority, and finally the target paths and the alternative paths are screened out according to the priority indexes and the number of the nodes in the communication paths, so that the cost performance and the accuracy of path planning can be improved, and the fault tolerance of the path planning can be improved.

In some embodiments, the calculating the receiving cost of each server node comprises:

querying a physical distance between the server node and the sending node to calculate a first receiving cost;

inquiring hardware information of each server node to calculate a performance index of the server node, and taking the reciprocal of the performance index as a second receiving cost, wherein the hardware information at least comprises a disk writing speed, a cache frequency, and the core number and frequency of a central processing unit;

and calculating the receiving cost of the server node according to the first receiving cost and the second receiving cost.

Therefore, the first receiving cost of the server node is calculated by inquiring the physical distance between the server node and the target node, the second receiving cost of each server node is calculated by inquiring the hardware information of each server node, the receiving cost of the server node is calculated according to the first receiving cost and the second receiving cost, and data support is provided for subsequently calculating the priority of the server node and planning a communication path, so that the rationality of communication path planning is improved.

In some embodiments, the calculating the sending cost of each server node comprises:

inquiring the maximum node number of the server node jumping to the receiving node as a jumping node number;

normalizing the number of the jumping nodes to obtain the normalized number of the jumping nodes;

inputting the normalized hop node number into a custom mapping model to obtain a mapping result and taking the mapping result as the sending cost of the server node, wherein the higher the sending cost is, the harder the receiving node receives the data sent by the server node, and the custom mapping model satisfies the following relational expression:

wherein x represents the server node; h (x) represents the sending cost of the server node represented by x, wherein the higher the sending cost is, the higher the cost is when the data is sent from the server node to the receiving node is; n is a radical of ^x Represents the normalized number of hop nodes of the server node x.

In this way, the hop number corresponding to the server node is obtained by inquiring the maximum node number required by the server node to hop to the receiving node, and the normalized hop number is obtained by performing normalization processing on the hop number and is used as the sending cost of the server node, and the higher the sending cost is, the more difficult the server node sends data to the receiving node.

In some embodiments, said calculating the communication priority of the server node according to the receiving cost and the sending cost comprises:

inquiring a physical distance between the server node and the sending node to be used as a receiving distance, and calculating a receiving weight according to the receiving distance;

inquiring the physical distance between the server node and the receiving node to be used as a sending distance, and calculating a sending weight according to the sending distance;

calculating a communication priority of the server node based on the reception weight, the transmission weight, the reception cost, and the transmission cost.

Therefore, the receiving weight is calculated by inquiring the physical distance between the server node and the sending node, the sending weight is calculated by inquiring the physical distance between the server node and the receiving node, the communication priority of the server node is further calculated based on the receiving weight, the sending weight, the receiving cost and the sending cost, data support is provided for subsequent path planning, and the accuracy of path planning can be improved.

In some embodiments, the method of calculating the communication priority satisfies the following relationship:

wherein x represents the server node; w is a ₁ Representing the receive weight; g (x) represents the receiving cost; w is a ₁ Representing the transmit weight; h (x) represents the transmission cost; f (x) represents the communication priority of the server node represented by the x, and the higher the value of the communication priority, the more the server node should be called to transmit data.

Therefore, the importance of the receiving cost and the importance of the sending cost are adjusted through the receiving weight and the sending weight, the importance of the receiving cost and the sending cost of the server node can be dynamically adjusted in the path planning process, data can rapidly reach the receiving node, and therefore the path planning efficiency is improved.

In some embodiments, said calculating a priority indicator for each communication path in dependence on said communication priority comprises:

randomly combining the server nodes to construct a path set, wherein the path set comprises a plurality of communication paths, and each communication path comprises a plurality of server nodes;

respectively calculating the sum of the communication priorities of all the server nodes in each communication path to serve as the communication priority of each communication path;

counting the number of server nodes in the communication path, and taking the reciprocal of the number of the server nodes as the cost priority of the communication path, wherein the higher the cost priority is, the more the communication path is used for transmitting data;

respectively normalizing the communication priority and the cost priority of the communication path to obtain a normalized communication priority and a normalized cost priority;

and calculating the sum of the normalized communication priority and the normalized cost priority of each communication path to serve as the priority index of the corresponding path.

Therefore, a plurality of communication paths are obtained by randomly combining the server nodes, the number of the server nodes in the communication paths is counted to obtain the cost priority of the communication paths, the communication priority and the cost priority are respectively subjected to normalization processing to obtain a normalized communication priority and a normalized cost priority, and finally the priority index of the communication paths is obtained by calculating the sum of the normalized communication priority and the normalized cost priority, so that data support is provided for the subsequent path screening step, and the accuracy of path planning is improved.

In some embodiments, the screening target paths and alternative paths from the path set according to the priority index to complete path planning includes:

taking the communication path with the highest priority index as a target path, and taking the other communication paths as alternative paths;

calculating the intersection of the alternative path and the target path, and calculating the second-order rate of the alternative path according to the intersection length and the priority index of the alternative path;

and when a certain server node in the target path fails, sequentially starting the alternative paths according to the sequence from the highest to the lowest of the suboptimum rate.

Therefore, a target path is screened out from the path set through the priority indexes, the rest paths are used as alternative paths, the intersection of the alternative paths and the target path is calculated, the sub-optimal rate of each alternative path is calculated according to the length of the intersection and the priority indexes, when the target path fails, the alternative paths can be started in sequence according to the sequence of the sub-optimal rates from large to small, data guidance is provided for path planning, and the fault tolerance of data transmission can be improved.

The embodiment of the present application further provides a path planning device based on artificial intelligence, the device includes:

the system comprises a construction unit, a receiving unit and a sending unit, wherein the construction unit is used for constructing a server cluster, the server cluster comprises a plurality of server nodes, each server node is used for transmitting data, and the plurality of server nodes comprise a sending node and a receiving node;

the first calculation unit is used for calculating the receiving cost of each server node, wherein the receiving cost is used for representing the difficulty of the server node for receiving the data sent by the sending node;

the second calculating unit is used for calculating the sending cost of each server node, and the sending cost is used for representing the difficulty of sending data to the receiving node by the server node;

a third calculating unit, configured to calculate a communication priority of the server node according to the receiving cost and the sending cost, where the communication priority is used to represent a degree to which the server node is preferentially invoked when transmitting data;

a combining unit, configured to combine the server nodes to construct a path set, where the path set includes a plurality of communication paths, each communication path includes a plurality of server nodes, and calculate a priority index of each communication path according to the communication priority;

and the screening unit is used for screening the target path and the alternative paths from the path set according to the priority indexes so as to complete path planning.

An embodiment of the present application further provides an electronic device, where the electronic device includes:

a memory storing computer readable instructions; and

a processor executing computer readable instructions stored in the memory to implement the artificial intelligence based path planning method.

Embodiments of the present application further provide a computer-readable storage medium, in which computer-readable instructions are stored, and the computer-readable instructions are executed by a processor in an electronic device to implement the artificial intelligence based path planning method.

Drawings

Fig. 1 is a flow chart of a preferred embodiment of an artificial intelligence based path planning method according to the present application.

Fig. 2 is a functional block diagram of a preferred embodiment of an artificial intelligence based path planning apparatus according to the present application.

Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the artificial intelligence based path planning method according to the present application.

Fig. 4 is a schematic structural diagram of a server cluster according to the present application.

Fig. 5 is a schematic diagram illustrating how to query the maximum hop count of a server node jumping to a receiving node by using a shell script according to the present application.

Fig. 6 is a schematic diagram of the structure of an evaluation matrix according to the present application.

Detailed Description

For a clearer understanding of the objects, features and advantages of the present application, reference is made to the following detailed description of the present application along with the accompanying drawings and specific examples. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict. In the following description, numerous specific details are set forth to provide a thorough understanding of the present application, and the described embodiments are merely a subset of the embodiments of the present application and are not intended to be a complete embodiment.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The embodiment of the present application provides a path planning method based on artificial intelligence, which can be applied to one or more electronic devices, where the electronic device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and hardware of the electronic device includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a programmable gate array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive web television (IPTV), an intelligent wearable device, and the like.

The electronic device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a cloud computing (cloud computing) based cloud consisting of a large number of hosts or network servers.

The network where the electronic device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a Virtual Private Network (VPN), and the like.

Fig. 1 is a flowchart of a preferred embodiment of the artificial intelligence based path planning method according to the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.

S10, a server cluster is constructed, the server cluster comprises a plurality of server nodes, each server node is used for transmitting data, and the plurality of server nodes comprise a sending node and a receiving node.

In this optional embodiment, the server cluster is composed of a plurality of server nodes, each server node has a function of computing and transmitting data, the server node may be a computer or a router or other device with a data transmission function, the server cluster may use a plurality of computers to perform parallel computing to obtain a higher computing speed, and may also use a plurality of computers as backups, so as to ensure that the server cluster can operate normally when any one server node in the cluster fails.

In this alternative embodiment, the communication mode between each server node is optical fiber communication, and the longer the length of the optical fiber is, the longer the time required for transmitting data between two server nodes is.

In this optional embodiment, the server cluster includes a sending node and a receiving node, where the sending node is configured to send data to the receiving node, and the receiving node is configured to receive data transmitted by the sending node.

In this alternative embodiment, the function of the plurality of server nodes is to transmit data sent by the sending node to the receiving node.

Exemplarily, as shown in fig. 4, a schematic structural diagram of a server cluster according to the present solution is shown.

Therefore, data are transmitted through the server cluster, so that the reliability of data transmission is ensured, and the timeliness of data transmission can be improved.

And S11, calculating the receiving cost of each server node, wherein the receiving cost is used for representing the difficulty of the server node for receiving the data sent by the sending node.

In an alternative embodiment, calculating the receive cost for each server node comprises:

In this optional embodiment, the physical distance refers to a total length of a communication optical fiber between two server nodes, and since the longer the physical distance is, the longer the time taken for the server node to receive data is, the physical distance between the server node and the sending node may be queried to calculate the first receiving cost of the server node, and in this scheme, the physical distance between the server node and the target node may be recorded as Dis.

In this alternative embodiment, the physical distance may be normalized by using a maximization algorithm to obtain a normalized physical distance as a first receiving cost, where the first receiving cost may be denoted as C1 in this embodiment, and the first receiving cost C1 is calculated by satisfying the following relation:

wherein C1 represents the first receiving cost, Dis ^max Represents the maximum of the normalized physical distances; the DIS represents the normalized physical distance.

For example, when the fiber distance between the server node a and the target node is 10 km and the maximum value of the physical distance is 100 km, the first receiving cost of the server node B is calculated by:

the receiving cost of the server node a takes a value of 0.1.

In this alternative embodiment, the hardware information of the server node may be queried to calculate the performance index of the server node, and the reciprocal of the performance index may be used as the second receiving cost. The hardware information at least comprises the core number and frequency, cache frequency and disk writing speed of a central processing unit of the server node, the higher the core number and frequency of the central processing unit are, the faster the data processing speed of the server node is, the higher the cache frequency is, the faster the data processing speed of the server node is, and the higher the disk writing speed is, the faster the data receiving speed of the server node is.

In this optional embodiment, the core number and frequency of the central processing unit, the cache frequency, and the disk write speed may be normalized respectively according to a maximization algorithm to obtain normalized hardware information, where taking the core number of the central processing unit as an example, the maximization algorithm satisfies the following relation:

wherein Xn ⁱ Representing the core number of the normalized central processing unit of the ith server node; x ⁱ Representing the core number of the central processing unit of the ith server; x ^max Representing the maximum number of central processing unit cores of all server nodes.

For example, when the number of cores of the central processing unit of the 1 st server node is 4 and the maximum number of cores of the central processing unit is 32, the core number of the central processing unit normalized by the 1 st server node is calculated by:

the normalized core number of the central processing unit of the 1 st server node is 0.125.

In this optional embodiment, the sum of the normalized hardware information may be calculated to serve as the performance index of the server node, and a reciprocal of the performance index is taken as a second receiving cost of the server node, where the faster the speed at which the server receives and processes data, the smaller the second receiving cost of the server node is, in this scheme, the second receiving cost may be denoted as C2, and a calculation manner of the second receiving cost C2 satisfies the following relation:

wherein C2 represents the second reception cost, a higher value of C2 indicates that it is more difficult for the server node to receive data from the sending node; xn represents the core number of the normalized central processing unit; yn represents the frequency of the normalized central processing unit; zn represents the normalized buffer frequency; wn represents the normalized disk write speed.

For example, if the core number of the normalized central processing unit of a server node is 0.125, the core frequency of the normalized central processing unit is 0.5, the normalized cache frequency is 0.8, and the normalized disk write speed is1, the second receiving cost of the server node is calculated as follows:

the second receive cost of the server node is 0.41.

In this optional embodiment, the receiving cost of each server node may be calculated based on the first receiving cost and the second receiving cost, and the higher the receiving cost is, the harder the server node receives data from the sending node, in this scheme, the receiving cost may be denoted by g (x), where x represents a server node x, and the receiving cost g (x) is calculated in a manner that:

wherein x represents the server node; g (x) represents the receiving cost of the server node represented by x, wherein the higher the receiving cost is, the higher the cost is when the server node receives data from the sending node is; c1 ^x A first receiving cost representing the server node x; c2 ^x A second receive cost on behalf of the server node x; e represents a natural constant.

For example, when the first receiving cost of the server node x is 10 and the second receiving cost is 0.41, the receiving cost of the server node is calculated as follows:

the receiving cost of the server node takes a value of 0.964. Therefore, the first receiving cost of the server node is calculated by inquiring the physical distance between the server node and the target node, the second receiving cost of each server node is calculated by inquiring the hardware information of each server node, the receiving cost of the server node is calculated according to the first receiving cost and the second receiving cost, and data support is provided for subsequently calculating the priority of the server node and planning a communication path, so that the rationality of communication path planning is improved.

And S12, calculating the sending cost of each server node, wherein the sending cost is used for representing the difficulty of sending data to the receiving node by the server node.

In an optional embodiment, the calculating the sending cost of each server node includes:

inquiring the maximum node number of the server node jumping to the receiving node as a jumping node number, wherein the higher the jumping node number is, the harder the receiving node receives the data sent by the server node;

normalizing the number of the jump nodes to obtain a normalized number of the jump nodes;

and inputting the normalized hop-node number into a preset mapping model to obtain a mapping result and using the mapping result as the sending cost of the server node, wherein the higher the sending cost is, the more difficult the receiving node receives the data sent by the server node.

In this optional embodiment, the maximum number of nodes that the server node jumps to the middle of the receiving node may be queried according to a preset program to serve as the number of jumping nodes, and the higher the number of jumping nodes is, the higher the sending cost of the server node is.

For example, the preset program may be a shell script, and the shell script may be in the form of a "tracert node ip", where tracert is a built-in function of the sehll script and is a function of querying a number of hops required by the server node to jump to a target node, and a node ip represents an ip address of the receiving node, and fig. 5 is a schematic diagram illustrating a maximum number of hops for querying the server node to jump to the receiving node by using the shell script.

In this optional embodiment, the number of the jumping nodes may be normalized to obtain a normalized number of jumping nodes, for example, when the number of the jumping nodes of a certain server node is 90 and the number of the maximum jumping nodes is 100, the normalized number of the jumping nodes of the server may be calculated in the following manner:

in this optional embodiment, the normalized hop node number may be input into a preset mapping model to obtain a mapping result and be used as a sending cost of the server node, in this scheme, the sending cost may be recorded as h (x), where x represents a certain server node, and a calculation manner of the sending cost h (x) satisfies the following relation:

For example, when the normalized hop node number is 0.9, the sending cost h (x) of the server node x is calculated as follows:

the sending cost of the server node takes a value of 0.978.

And S13, calculating a communication priority of the server node according to the receiving cost and the sending cost, wherein the communication priority is used for representing the degree of preferential calling of the server node when transmitting data.

In an optional embodiment, calculating the communication priority of the server node according to the receiving cost and the sending cost includes:

In this optional embodiment, the physical distance between the server node and the sending node may be queried as a receiving distance, and the receiving distance may be normalized according to the maximization algorithm to obtain a normalized receiving distance, in this scheme, the normalized receiving distance is recorded as Dis1, and further, a receiving weight w may be calculated according to the normalized receiving distance ₁ The calculation method of the receiving weight is as follows:

w ₁ ＝1+Dis1

wherein, w ₁ Representing receive weights for adjusting the receive path in the process of path selectionThe importance of the receiving cost is that the higher the value of the receiving weight is, the higher the influence degree of the receiving cost on the communication priority of the server node is; dis1 represents the normalized reception distance.

For example, when the normalized receiving distance between the server node and the sending node is 0.1, the receiving weight of the server node is calculated in the following manner:

w ₁ ＝1+0.1＝1.1

the value of the receiving weight of the server node is 1.1.

In this optional embodiment, the physical distance between the server node and the receiving node may be queried as a sending distance, and the sending distance may be normalized according to the maximization algorithm to obtain a normalized sending distance, in this scheme, the normalized sending distance is recorded as Dis2, and further, a sending weight w may be calculated according to the normalized sending distance ₂ The method for calculating the transmission weight satisfies the following relation:

w ₂ ＝1+Dis2

wherein, w ₂ Representing a sending weight used for adjusting the importance of the sending cost in the process of path selection, wherein the higher the value of the sending weight is, the higher the influence degree of the sending cost on the communication priority of the server node is; the Dis2 represents the normalized transmission distance.

For example, when the Dis2 is 0.5, the sending weight of the server node is calculated as follows:

w ₂ ＝1+0.5＝1.5

the value of the sending weight of the server node is 1.5.

In this optional embodiment, the communication priority of the server node may be calculated based on the weight, the receiving cost, and the sending cost, and the specific calculation manner of the communication priority satisfies the following relation:

wherein x represents the server node; f (x) represents the communication priority of the server node represented by x; w is a ₁ Representing the receive weight; g (x) represents the reception cost; w is a ₁ Representing the transmission weight; h (x) represents the transmission cost.

For example, when the receiving weight of a certain server node is 11, the receiving cost is 0.964, the sending weight is 11, and the sending cost is 0.978, the communication priority corresponding to the server node is calculated in the following manner:

the communication priority corresponding to the server takes a value of 2.67.

In another alternative embodiment, may be according to A ^* Calculating the communication priority of the server node by an algorithm, A ^* The algorithm is a direct search method which is more effective for solving the shortest path in the static road network. In this alternative embodiment, the reciprocal of the receiving cost may be regarded as a ^* The cost value in the algorithm and the reciprocal of the sending cost can be taken as A ^* And calculating the communication priority of the server node according to the cost value and the heuristic value, wherein the calculation mode of the communication priority satisfies the following relational expression:

wherein x represents the server node; f (x) represents the communication priority of the server node x; g (x) represents the receiving cost of the server node x; h (x) represents the sending cost of the server node x.

Illustratively, when the receiving cost of the server node x is 0.964 and the sending cost of the server node x is 0.978, the communication priority f (x) of the server node x is calculated as follows:

the communication priority of the server node x takes a value of 2.05.

Therefore, the receiving weight is calculated by inquiring the physical distance between the server node and the sending node, the sending weight is calculated by inquiring the physical distance between the server node and the receiving node, and the communication priority of the server node is further calculated based on the receiving weight, the sending weight, the receiving cost and the sending cost, so that data support is provided for subsequent path planning, and the accuracy of path planning can be improved.

S14, combining the server nodes to construct a path set, wherein the path set comprises a plurality of communication paths, each communication path comprises a plurality of server nodes, and the priority index of each communication path is calculated according to the communication priority;

in an optional embodiment, said calculating a priority indicator for each communication path according to said communication priority comprises:

and respectively calculating the sum of the communication priorities of all the server nodes in each communication path as the communication priority of each communication path.

In this alternative embodiment, the server nodes may be randomly combined to construct a path set, where the path set includes a plurality of communication paths, each communication path includes a plurality of server nodes, and the function of the path is to transmit data sent by the sending node to the receiving node through the server nodes.

In this alternative embodiment, the sum of the communication priorities of all the server nodes in the communication path may be calculated as the communication priority of the communication path, and the reciprocal of the number of the server nodes in the communication path may be used as the cost priority, and the higher the cost priority is, the more the priority path should be selected as the target path.

In this optional embodiment, the communication priority and the cost priority may be normalized according to a preset normalization algorithm to obtain a normalized communication priority and a normalized cost priority, where the preset normalization algorithm may be a maximization algorithm, and the maximization minimization algorithm satisfies the following relation:

wherein T represents the normalized communication priority or normalized cost priority; i represents the category of the priority and the value of i is { communication, path }; x represents a communication priority or a cost priority of a certain path;

representing the maximum of the communication priorities or the maximum of the cost priorities.

For example, when the communication priority of a certain path is 10 and the maximum value of the communication priorities is 100, the normalized communication priority corresponding to the path is calculated in the following manner:

the normalized communication priority corresponding to the path takes a value of 0.1.

In this alternative embodiment, the sum of the normalized communication priority and the normalized cost priority of each path may be used as a priority index corresponding to each path, and the greater the priority index is, the more the path is to be used as the preferred communication scheme.

In this optional embodiment, the calculation method of the priority index is as follows:

T＝T ^{communication} +T ^{Route of travel}

Wherein T represents the priority index, and the larger the priority index is, the more the path is taken as a preferred communication scheme; t is ^{Communication} Representing a normalized communication priority index corresponding to the path; t is ^{Route of travel} Representing the normalized cost priority index corresponding to the path.

For example, when the normalized communication priority corresponding to a certain path is 0.1 and the normalized cost priority corresponding to the certain path is 0.1, the calculation method of the priority index corresponding to the path is as follows:

T＝0.1+0.1＝0.2

the priority index corresponding to the path is 0.2.

And S15, screening a target path and an alternative path from the path set according to the priority index to complete path planning.

In an optional embodiment, screening the target path and the alternative paths from the path set according to the priority index to complete path planning includes:

calculating the intersection of the alternative path and the target path, and calculating the second-order rate of the alternative path according to the intersection length and the priority index of the communication path;

In an alternative embodiment, the communication path with the highest priority index may be used as the target path, and the remaining communication paths may be used as the alternative paths.

In another alternative embodiment, a dynamic planning algorithm may be utilized to screen the target path from the set of paths. In this optional embodiment, the dynamic programming algorithm is specifically implemented by the following steps:

a1: marking each server node by using a natural number from 1, wherein for example, if the server cluster comprises 4 server nodes, the marks of the server nodes are 1, 2, 3 and 4 respectively, and a marking sequence is constructed according to the sequence of the marks of the server nodes from small to large, and for example, if the marks of the server nodes are 1, 2, 3 and 4 respectively, the marking sequence is [1, 2, 3 and 4 ];

a2: constructing an evaluation matrix according to the tag sequence and the communication priority of the server node, wherein the row names of the evaluation matrix from top to bottom are sequentially each element in the tag sequence, the column names of the evaluation matrix from left to right are sequentially each element in the tag sequence, the value of each element in the evaluation matrix is the server node corresponding to the name of the column in which the element is located, and exemplarily, if the column name of one element in the evaluation matrix is2, the value of the element is the communication priority corresponding to the server node marked as 2;

a3: traversing elements of each column in the row from the first row of the evaluation matrix according to the sequence from left to right, taking the column of the maximum value in the elements of the row as a target node corresponding to the row, and setting the values of all the elements in the column of the maximum value to be minus infinity;

a4: and sequentially arranging the target nodes corresponding to each row in the evaluation matrix according to the sequence from top to bottom to obtain the target path.

In this alternative embodiment, fig. 6 is a schematic structural diagram of the evaluation matrix.

In an alternative embodiment, the intersection of the alternative path and the target path may be calculated, and the second best rate of the alternative path may be calculated according to the length of the intersection and the priority index of the communication path.

In this alternative embodiment, the target path may be recorded as P ^R And can use said target path P ^R Characterised by the form of a collection, i.e. said

Where n represents the number of server nodes in the target path,

representing a first server node in the target path.

In this alternative embodiment, the intersection of each of the remaining paths and the set may be determined according to a preset program, where the preset program may be a Python program, and the preset program may be in the form of a "print (len (P)) ^R .intersection(P ^e ) ) ", wherein, P ^R Representing said target path, P ^e Representing the e-th candidate path, wherein the interjection represents a function for solving intersection in Python language, the result output by the Python program is the length of the intersection, the longer the length of the intersection is, the more nodes shared by nodes in the rest paths and the target path are indicated, and if the target path has a problem, the higher the probability that the alternative path with the longer intersection has a fault is.

In this optional embodiment, the length of the intersection may be normalized according to the maximization algorithm to obtain a normalized intersection length, and the maximization method may be calculated in a manner that:

wherein L is ^k Representing the normalized intersection length of the kth candidate path; k represents an index of the alternative path; l ^max Represents the maximum value of the length of the intersection.

For example, when the intersection length of the 1 st candidate path and the target path is 10 and the maximum value of the intersection length is 100, the normalized intersection length of the first candidate path is calculated in the following manner:

the normalized intersection length of the 1 st alternative path is 0.1.

In this optional embodiment, the second goodness of the alternative path may be calculated according to the intersection length and the priority index, where the calculation method of the second goodness is as follows:

wherein S is ^k Representing the suboptimal rate of the kth alternative path, wherein the higher the suboptimal rate is, the more the alternative path is selected for communication when the target path fails; l is ^k Representing the normalized intersection length of the kth candidate path and the target path; t is ^k Representing the priority index of the kth candidate path.

For example, when the normalized intersection length of the 1 st candidate path is 0.1 and the corresponding priority index is1, the calculation method of the second goodness of the 1 st candidate path is as follows:

the value of the second goodness of the 1 st alternative path is 11.

In this optional embodiment, when a server node in the target path fails, the alternative paths may be sequentially enabled to perform data transmission according to a sequence from the highest to the lowest of the suboptimal rates.

Fig. 2 is a functional block diagram of a preferred embodiment of an artificial intelligence based path planning apparatus according to an embodiment of the present application. The artificial intelligence based path planning device 11 includes a construction unit 110, a first calculation unit 111, a second calculation unit 112, a third calculation unit 113, a combination unit 114, and a screening unit 115. The module/unit referred to in this application refers to a series of computer program segments that can be executed by the processor 13 and that can perform a fixed function, and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.

In an optional embodiment, the constructing unit 110 is configured to construct a server cluster, where the server cluster includes a plurality of server nodes, each server node is configured to transmit data, and the plurality of server nodes includes a sending node and a receiving node.

In an optional embodiment, the first calculating unit 111 is configured to calculate a receiving cost of each server node, where the receiving cost is used to characterize difficulty of the server node receiving the data sent by the sending node.

the receiving cost of the server node a takes a value of 0.1.

the second receive cost of the server node is 0.41.

wherein x represents the server node; g (x) represents the receiving cost of the server node represented by x, wherein the higher the receiving cost is, the higher the cost is when the server node receives data from the sending node is; c1 ^x A first receiving cost on behalf of the server node x; c2 ^x A second receive cost on behalf of the server node x; e represents a natural constant.

the receiving cost of the server node takes a value of 0.964.

In an alternative embodiment, the second calculating unit 112 is configured to calculate a sending cost of each server node, where the sending cost is used to characterize difficulty of sending data to the receiving node by the server node.

In this optional embodiment, the maximum number of nodes that the server node jumps to the middle of the receiving nodes may be queried according to a preset program to serve as the number of jumping nodes, and the higher the number of jumping nodes is, the higher the sending cost of the server node is.

In this optional embodiment, normalization processing may be performed on the number of jumping nodes to obtain a normalized number of jumping nodes, and for example, when the number of jumping nodes of a certain server node is 90 and the number of largest jumping nodes is 100, the normalized number of jumping nodes of the server is calculated in the following manner:

wherein x represents the serverA node; h (x) represents the sending cost of the server node represented by x, wherein the higher the sending cost is, the higher the cost is when the data is sent from the server node to the receiving node is; n is a radical of ^x Represents the normalized number of hop nodes of the server node x.

For example, when the normalized hop node number is 0.9, the sending cost h (x) of the server node x is calculated by:

the sending cost of the server node takes a value of 0.978.

In an optional embodiment, the third calculating unit 113 is configured to calculate a communication priority of the server node according to the receiving cost and the sending cost, where the communication priority is used to characterize a degree to which the server node is preferentially invoked when transmitting data.

In this optional embodiment, the physical distance between the server node and the sending node may be queried as a receiving distance, and the receiving distance may be normalized according to the maximization algorithm to obtain a normalized receiving distance, in this scheme, the normalized receiving distance is recorded as Dis1, and further, a receiving weight w may be calculated according to the normalized receiving distance ₁ Said is connected toThe weighting calculation method comprises the following steps:

w ₁ ＝1+Dis1

wherein, w ₁ Representing a receiving weight used for adjusting the importance of the receiving cost in the process of path selection, wherein the higher the value of the receiving weight is, the higher the influence degree of the receiving cost on the communication priority of the server node is; dis1 represents the normalized reception distance.

w ₁ ＝1+0.1＝1.1

the value of the receiving weight of the server node is 1.1.

w ₂ ＝1+Dis2

w ₂ ＝1+0.5＝1.5

the value of the sending weight of the server node is 1.5.

wherein x represents the server node; f (x) represents the communication priority of the server node represented by x; w is a ₁ Representing the receive weight; g (x) represents the receiving cost; w is a ₁ Representing the transmit weight; h (x) represents the transmission cost.

the communication priority corresponding to the server takes a value of 2.67.

In another alternative embodiment, may be according to A ^* Calculating the communication priority of the server node by an algorithm, A ^* The algorithm is a direct search method which is more effective for solving the shortest path in the static road network. In this alternative embodiment, the reciprocal of the receiving cost may be regarded as a ^* The cost value in the algorithm, and the reciprocal of the sending cost can be taken as A ^* And calculating the communication priority of the server node according to the cost value and the heuristic value, wherein the calculation mode of the communication priority satisfies the following relational expression:

the communication priority of the server node x takes a value of 2.05.

In an optional embodiment, the combining unit 114 is configured to combine the server nodes to construct a path set, where the path set includes a plurality of communication paths, each communication path includes a plurality of server nodes, and calculate a priority index of each communication path according to the communication priority.

T＝T ^{communication} +T ^{Route of travel}

T＝0.1+0.1＝0.2

the priority index corresponding to the path is 0.2.

In an optional embodiment, the screening unit 115 is configured to screen a target path and an alternative path from the path set according to the priority index to complete path planning.

Where n represents the number of server nodes in the target path,

representing a first server node in the target path.

the normalized intersection length of the 1 st alternative path is 0.1.

the value of the second goodness of the 1 st alternative path is 11.

Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 1 comprises a memory 12 and a processor 13. The memory 12 is used for storing computer readable instructions, and the processor 13 is used for executing the computer readable instructions stored in the memory to implement the artificial intelligence based path planning method according to any of the above embodiments.

In an alternative embodiment, the electronic device 1 further comprises a bus, a computer program, such as an artificial intelligence based path planning program, stored in the memory 12 and executable on the processor 13.

Fig. 3 only shows the electronic device 1 with components 12-13, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.

In conjunction with fig. 1, the memory 12 in the electronic device 1 stores a plurality of computer-readable instructions to implement an artificial intelligence based path planning method, and the processor 13 may execute the plurality of instructions to implement:

Specifically, the specific implementation method of the instruction by the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, which is not described herein again.

It will be understood by those skilled in the art that the schematic diagram is merely an example of the electronic device 1, and does not constitute a limitation to the electronic device 1, the electronic device 1 may have a bus-type structure or a star-type structure, and the electronic device 1 may further include more or less hardware or software than those shown in the figures, or different component arrangements, for example, the electronic device 1 may further include an input and output device, a network access device, etc.

It should be noted that the electronic device 1 is only an example, and other existing or future electronic products, such as those that may be adapted to the present application, should also be included in the scope of protection of the present application, and are included by reference.

Memory 12 includes at least one type of readable storage medium, which may be non-volatile or volatile. The readable storage medium includes flash memory, removable hard disks, multimedia cards, card type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 1, for example a removable hard disk of the electronic device 1. The memory 12 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card (FlashCard), and the like, provided on the electronic device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 12 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of an artificial intelligence-based path planning program, etc., but also to temporarily store data that has been output or is to be output.

The processor 13 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is a control unit (control unit) of the electronic device 1, connects various components of the electronic device 1 by various interfaces and lines, and executes various functions of the electronic device 1 and processes data by running or executing programs or modules stored in the memory 12 (for example, executing a path planning program based on artificial intelligence, etc.), and calling data stored in the memory 12.

The processor 13 executes an operating system of the electronic device 1 and various types of application programs installed. The processor 13 executes the application program to implement the steps in the various artificial intelligence based path planning method embodiments described above, such as the steps shown in fig. 1.

Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to accomplish the present application. The one or more modules/units may be a series of computer-readable instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the electronic device 1. For example, the computer program may be divided into a building unit 110, a first computing unit 111, a second computing unit 112, a third computing unit 113, a combining unit 114, a screening unit 115.

The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute parts of the artificial intelligence based path planning method according to the embodiments of the present application.

The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the processes in the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer-readable storage medium and executed by a processor, to implement the steps of the embodiments of the methods described above.

Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, Read-only memory (ROM), random access memory and other memory, etc.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus. The bus is arranged to enable connected communication between the memory 12 and at least one processor 13 or the like.

Although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 13 through a power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.

Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (organic light-emitting diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.

An embodiment of the present application further provides a computer-readable storage medium (not shown), in which computer-readable instructions are stored, and the computer-readable instructions are executed by a processor in an electronic device to implement the artificial intelligence based path planning method according to any of the above embodiments.

It is to be understood that the embodiments described are illustrative only and are not to be construed as limiting the scope of the claims.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the specification may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A path planning method based on artificial intelligence is characterized by comprising the following steps:

2. The artificial intelligence based path planning method of claim 1, wherein the calculating the receive cost for each server node comprises:

3. The artificial intelligence based path planning method of claim 1, wherein the calculating the sending cost of each server node comprises:

inputting the normalized number of the skip nodes into a self-defined mapping model to obtain a mapping result and taking the mapping result as the sending cost of the server node, wherein the higher the sending cost is, the harder the receiving node receives the data sent by the server node, and the self-defined mapping model satisfies the following relational expression:

wherein x represents the server node; h (x) represents a transmission cost of the server node represented by x, and the higher the transmission cost is, the higher the cost is when data is transmitted from the server node to the receiving node is; n is a radical of ^x A normalized number of hop nodes representing the server node x; e represents a natural constant.

4. The artificial intelligence based path planning method of claim 1, wherein said calculating a communication priority of the server node in accordance with the reception cost and the transmission cost comprises:

5. The artificial intelligence based path planning method of claim 4, wherein the calculation method of the communication priority satisfies the following relation:

wherein x represents the server node; w is a ₁ Representing the receive weight; g (x) represents the receiving cost of the server node x; w is a ₁ Representing the transmit weight; h (x) represents the sending cost of the server node x; f (x) represents the communication priority of the server node x, the higher the value of the communication priority, the more the server node should be invoked to transmit data.

6. The artificial intelligence based path planning method of claim 5, wherein said calculating a priority index for each communication path as a function of the communication priority comprises:

randomly combining the server nodes to construct a path set, the path set comprising a plurality of communication paths, each communication path comprising a plurality of server nodes;

7. The artificial intelligence based path planning method of claim 1, wherein the screening target paths and alternative paths from the path set according to the priority index to complete path planning comprises:

8. An artificial intelligence based path planning apparatus, the apparatus comprising:

the first calculation unit is used for calculating the receiving cost of each server node, and the receiving cost is used for representing the difficulty of the server node for receiving the data sent by the sending node;

9. An electronic device, characterized in that the electronic device comprises:

a memory storing computer readable instructions; and

a processor executing computer readable instructions stored in the memory to implement the artificial intelligence based path planning method of any of claims 1 to 7.

10. A computer-readable storage medium characterized by: the computer-readable storage medium has stored therein computer-readable instructions that are executed by a processor in an electronic device to implement the artificial intelligence based path planning method of any of claims 1-7.