Distributed self-redundancy intelligent regulation and control equipment networking method

Abstract

The invention provides a distributed self-redundancy intelligent regulation and control equipment networking method, relates to the technical field of networking, and solves the problems that in the prior art, a traditional 5G base station point selection planning and equipment networking regulation and control method is low in flexibility, cannot provide a stable network environment and cannot be quickly applied in various complex emergency scenes. The method comprises the following steps: calculating link loss between networking base stations, calculating the coverage rate of the networking base stations and the coordinated planning cost of the 5G base stations, and determining an initial node arrangement model; optimizing the initial node arrangement model by taking the link loss, the networking base station coverage rate and the 5G base station coordination planning cost as constraint conditions to obtain an optimized node arrangement model; optimizing the optimized node arrangement model again by using a genetic algorithm to obtain a node arrangement model; performing link establishment on the node arrangement model to obtain a node network; according to the method, the purposes of reducing the construction cost and intelligently regulating and controlling equipment networking are achieved.

Description

Technical Field

[0001] The present invention relates to the field of networking technology, and in particular to a distributed self-redundant intelligent control equipment networking method. Background Art

[0002] With the increase in the number of terminal accesses, the degree of intelligence, and the number of services, the existing 4G wireless private network for power grids is unable to meet the needs of power grid services. In contrast, 5G can effectively improve the communication level of power grid services, but it also faces the problems of high construction costs and high operating power consumption. The traditional 5G base station site selection planning and equipment networking control methods are inflexible and cannot provide a stable network environment, and cannot be quickly applied in various complex emergency scenarios. Summary of the invention

[0003] The present invention provides a distributed self-redundant intelligent control equipment networking method to solve the problems of low flexibility, inability to provide a stable network environment, and inability to achieve rapid application in various complex emergency scenarios in the conventional 5G base station site selection planning and equipment networking control methods in the prior art, thereby achieving the purpose of reducing construction costs and intelligent control equipment networking.

[0004] The present invention provides a distributed self-redundant intelligent control device networking method, the method comprising:

[0005] S101, calculating the space loss and propagation loss between each 5G base station in the networking base station, and obtaining the link loss between each 5G base station in the networking base station according to the space loss and the propagation loss;

[0006] S102, calculating the coverage radius of each 5G base station in the networking base station according to the link loss between each 5G base station, and calculating the initial coverage rate of the networking base station according to the coverage radius of each 5G base station;

[0007] S103, calculating the operation cost and construction cost of each 5G base station of the networking base station, and then calculating the coordination planning cost of the networking base station according to the operation cost and construction cost of each 5G base station;

[0008] S104, calculating and obtaining first output information according to the initial coverage rate, the coordination planning cost and the genetic algorithm;

[0009] S105, arranging each 5G base station in the networking base stations according to the first output information to obtain a first networking base station;

[0010] S106, calculating the coverage rate of the first group of network base stations, determining the size of the coverage rate of the first group of network base stations and the preset coverage rate, if the coverage rate of the first group of network base stations is greater than or equal to the preset coverage rate, outputting the first group of network base stations; if the coverage rate of the first group of network base stations is less than the preset coverage rate, replacing the initial coverage rate with the coverage rate of the first group of network base stations, looping S104 to S106, until the updated coverage rate corresponding to the first group of network base stations is greater than or equal to the preset coverage rate, and outputting the updated first group of network base stations;

[0011] S107, establishing a link with the first networking base station to obtain a node arrangement model after the node networking.

[0012] In a possible implementation, the space loss formula is expressed as:

[0013] P Lmax =P Tx -L f +G Tx -M f -M1+G Rx -S Rx ;

[0014] Among them, P Tx Indicates the transmitting end power; L f Indicates equipment loss; G Tx Indicates antenna gain; M f represents the fading margin; M1 represents the interference margin; G Rx Represents the receiving antenna gain; S Rx Indicates receiver sensitivity.

[0015] In a possible implementation, the propagation loss is expressed as:

[0016] P LUMa-NLOS =13.54+39.08log 10 (d 3D )+20log 10 (f c )-0.6(h UT -1.5);

[0017] Among them, P LUMa-NLOS represents the propagation loss; h UT represents the height of the service terminal; f represents the 5G construction frequency; d 3D Indicates the spatial distance between the 5G base station antenna and the terminal.

[0018] In a possible implementation, the calculating the coverage radius of each 5G base station according to the link loss between each 5G base station in the networking base station includes:

[0019] The coverage radius is calculated based on the link loss and Uma-NLOS loss model.

[0020] In a possible implementation, the initial coverage is expressed as:

[0021]

[0022] Among them, n 5G Indicates the number of 5G base stations in the networking base station; g j Indicates the coverage rate of 5G base stations, g j Specifically expressed as: R xi Indicates the coverage radius of 5G base station; d ij Indicates the distance between the service point and the 5G base station; x i Indicates the probability of the presence or absence of 5G base stations.

[0023] In a possible implementation, the operating cost is expressed as:

[0024]

[0025] in, express; represents the static power consumption of 5G base station i at time t; α represents the energy efficiency coefficient; represents the dynamic power consumption of 5G base station i at time t; t represents time.

[0026] In a possible implementation, the construction cost is expressed as:

[0027]

[0028] Among them, x i represents the probability of having or not having a 5G base station; C represents the construction cost of each 5G base station; n 5G Indicates the number of 5G base stations in the networking base stations.

[0029] In a possible implementation manner, the coordination planning cost of the networking base stations is expressed as:

[0030]

[0031] Where D represents the construction cost of the networking base station; represents the operating cost of 5G base station i; C represents the construction cost of each 5G base station; x i Indicates the probability of the presence or absence of a 5G base station; n 5G Indicates the number of 5G base stations in the networking base stations.

[0032] In a possible implementation, establishing a link with the first networking base station to obtain a node arrangement model after node networking includes: determining a node connection method using node self-healing, intelligent search, node channel avoidance, and roaming switching.

[0033] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0034] (1) The present invention reduces the individual coding dimension of the population, narrows the search space, and improves the search efficiency by analyzing and preprocessing the location information of 5G test points and candidate 5G base stations;

[0035] (2) In order to reduce randomness, the initial population formation method proposed in this paper is based on the power business attributes and communication requirements. Some individuals in the initial population are assigned 5G base stations to generate a high-quality initial population;

[0036] (3) The crossover and mutation probabilities of the present invention vary with the number of iterations. The mutation probability is high in the early stage of the algorithm to increase diversity and help escape from the local optimum. The mutation probability decreases in the later stage to promote algorithm convergence. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flow chart of steps of a distributed self-redundant intelligent control device networking method provided by an embodiment of the present invention;

[0038] Figure 2 Iteration curve of the optimized genetic algorithm provided by the embodiment of the present invention;

[0039] Figure 3 A simplified schematic diagram of a distributed self-redundant device network provided by an embodiment of the present invention;

[0040] Figure 4 A schematic diagram of a genetic algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The present invention provides a distributed self-redundant intelligent control device networking method, such as Figure 1 As shown, the method includes the following steps S101 to S107.

[0043] S101, calculate the space loss and propagation loss between each 5G base station in the networking base station, and obtain the link loss between each 5G base station in the networking base station based on the space loss and propagation loss.

[0044] Specifically, in step S101, the space loss formula is expressed as:

[0045] P Lmax =P Tx -L f +G Tx -M f -M1+G Rx -S Rx ;

[0046] Among them, P Tx Indicates the transmitting end power; L f Indicates equipment loss, mainly feeder and connector loss, in dBm; G Tx Indicates antenna gain in dBm; M f represents the fading margin in dBm; M1 represents the interference margin in dBm; G Rx Indicates the receiving antenna gain, in dBm; S Rx Indicates receiver sensitivity in dBm.

[0047] The propagation loss is expressed as:

[0048] P LUMa-NLOS =13.54+39.08log 10 (d 3D )+20log 10 (f c )-0.6(h UT -1.5);

[0049] Among them, P LUMa-NLOS represents the propagation loss; h UT represents the height of the service terminal; f represents the 5G construction frequency, in GHz; d 3D Indicates the spatial distance between the 5G base station antenna and the terminal, in meters.

[0050] Here, the link propagation loss adopts the Uma-NLOS model of non-line-of-sight propagation.

[0051] S102, calculate the coverage radius of each 5G base station in the networking base station according to the link loss between each 5G base station, and calculate the initial coverage rate of the networking base station according to the coverage radius of each 5G base station.

[0052] Specifically, in step S102, the coverage radius of each 5G base station is calculated according to the link loss between each 5G base station in the networking base station, including: calculating the coverage radius according to the link loss and Uma-NLOS loss model. Here, according to the Uma-NLOS loss model, P Lmax =P LUMa-NLOS The obtained d 3D As a single 5G base station x i Cover radius

[0053] Coverage, expressed as:

[0054]

[0055] Among them, n 5G Indicates the number of 5G base stations in the networking base station; g j Indicates the coverage rate of 5G base stations, g j Specifically expressed as:

[0056]

[0057] Among them, R xi Indicates the coverage radius of 5G base station; d ij Indicates the distance between the service point and the 5G base station; x i Indicates the probability of the presence or absence of 5G base stations.

[0058] Here, according to the coverage radius of the 5G base station in the networking base station The distance d between the service point and the 5G base station ij , calculate the 5G base station coverage g corresponding to each 5G base station j . N 5G ={1,2,3,…,n 5G} is the 5G service set, and the coverage radius of the power 5G base station is The distance from 5G service point j to power 5G base station i is d ij .

[0059] S103, calculate the operating cost and construction cost of each 5G base station in the networking base station, and then calculate the coordination planning cost of the networking base station based on the operating cost and construction cost of each 5G base station.

[0060] Specifically, in step S103, the operating cost is expressed as:

[0061]

[0062] in, represents the operating cost of 5G base station i; represents the static power consumption of 5G base station i at time t; α represents the energy efficiency coefficient; represents the dynamic power consumption of 5G base station i at time t; t represents time.

[0063] The construction costs of 5G base stations are as follows:

[0064] Let C be the construction cost of a 5G base station; M = {1, 2, 3, ..., m} is a set of self-owned resources such as photovoltaic panels, indicating the optional locations for 5G base station construction; thus, the following function is defined:

[0065]

[0066] The construction cost is expressed as:

[0067]

[0068] Among them, x i represents the probability of having or not having a 5G base station; C represents the construction cost of each 5G base station; n 5G Indicates the number of 5G base stations in the networking base stations.

[0069] In summary, the 5G base station coordination planning cost is expressed as:

[0070]

[0071] Where D represents the construction cost of the networking base station; represents the operating cost of 5G base station i; C represents the construction cost of each 5G base station; x i Indicates the probability of the presence or absence of a 5G base station; n 5G Indicates the number of 5G base stations in the networking base stations.

[0072] S104, calculating and obtaining first output information according to the initial coverage rate, the coordination planning cost and the genetic algorithm.

[0073] Exemplarily, this genetic algorithm uses binary coding, "1" indicates that the candidate location is selected to build a 5G base station, and "0" indicates that the candidate location is not selected.

[0074] The initial population N in the genetic algorithm 1 Select according to the following formula:

[0075]

[0076] N 1 is the number of 5G candidate 5G base stations selected in the population individuals; S is the approximate distribution area of ​​5G services; R is the coverage area of ​​5G base stations; K is the redundancy coefficient, which is 1.5 to 2;

[0077] In order to achieve the lowest possible cost for distributed 5G base station construction, the fitness function in the genetic algorithm is as follows:

[0078]

[0079] Among them, F is the coordination planning cost, which is the coordination operation cost of the aforementioned 5G base station; σ is the penalty function, which is inversely proportional to the coverage rate. The higher the coverage rate, the lower the penalty value. When the coverage is complete, the penalty function is 0; Among them,

[0080] The selection strategy in the genetic algorithm adopts a selection strategy that considers both the current and previous generations, merges the population after crossover and mutation with the previous generation population, and removes duplicate individuals; if the number of individuals in the population is less than the population size n, it is supplemented; if it exceeds, the first n individuals with the highest fitness are selected to form a new generation of population; Figure 2 Shown is the iteration curve of the genetic algorithm.

[0081] The crossover operation in the genetic algorithm uses single-point crossover. First, the random number and the crossover probability are compared to determine whether the population individuals cross. Then the crossover position is determined by random numbers and the 5G base station distribution information is exchanged. The crossover probability changes dynamically with the number of iterations i and the total number of iterations T. The specific formula is as follows:

[0082]

[0083] Where i is the number of iterations, and T is the total number of iterations;

[0084] The mutation operation in the genetic algorithm uses gene position mutation, that is, when the random number is less than the mutation probability, a random integer is used to determine which parts of the individual need to mutate, and the binary code "1" becomes "0", indicating that a 5G base station is built at the candidate location in the current generation, and no 5G base station is built at the location in the next iteration; "0" becomes "1", which is the opposite;

[0085] The mutation probabilities are as follows:

[0086]

[0087] In the embodiment, the simulation experiment parameters are set as follows:

[0088] Population size P opSize =80; encoding dimension L = 20, i.e., the distribution information of 5G base stations; number of iterations G m =300; 5G coverage threshold δ 5G =100%.

[0089] The specific process of genetic algorithm is as follows Figure 4 shown.

[0090] In the embodiment, the improved genetic algorithm adopted has the following advantages:

[0091] (1) By analyzing and preprocessing the location information of 5G test points and candidate 5G base stations, the individual coding dimensions of the population are reduced, the search space is narrowed, and the search efficiency is improved.

[0092] (2) In order to reduce randomness, the initialization population formation method proposed in this paper is based on the power business attributes and communication requirements. Some individuals in the initial population are assigned 5G base stations to generate a high-quality initial population.

[0093] (3) The crossover and mutation probabilities change with the number of iterations. In the early stage of the algorithm, the mutation probability is high to increase diversity and help escape the local optimum. In the later stage, the mutation probability gradually decreases to promote algorithm convergence.

[0094] S105: Arrange each 5G base station in the networking base station according to the first output information to obtain a first networking base station.

[0095] S106, S106, calculate the coverage rate of the first group of network base stations, determine the size of the coverage rate of the first group of network base stations and the preset coverage rate, if the coverage rate of the first group of network base stations is greater than or equal to the preset coverage rate, output the first group of network base stations; if the coverage rate of the first group of network base stations is less than the preset coverage rate, replace the initial coverage rate with the coverage rate of the first group of network base stations, loop S104 to S106, until the updated coverage rate corresponding to the first group of network base stations is greater than or equal to the preset coverage rate, output the updated first group of network base stations.

[0096] S107, establish a link with the first networking base station to obtain a node arrangement model after the node networking. Figure 3 shown.

[0097] Specifically, in step S107, a link is established for the first networking base station to obtain a node network, including: determining a node connection method by using node self-healing, intelligent search, node channel avoidance and roaming switching.

[0098] For example, using the node intelligent search mechanism, the 5G wireless client in the node device searches for other wireless access points with available channel nodes in the surrounding area by actively scanning by sending broadcast detection request frames. If no such wireless access points exist, the search continues. If such such wireless access points exist, the search is performed according to the formula:

[0099] R v =10lgP;

[0100] Wherein, Rv represents the signal strength judgment value, and P represents the searched signal power.

[0101] The signal strength is judged and classified, the channel information of all searched accessible nodes is stored in sequence, and the signal source node with the best signal strength is selected for connection.

[0102] When there are multiple accessible signal sources with the same strength, the earliest recorded node channel is selected for connection in the order of recording. If the strongest signal source node fails to connect successfully, a new link target node is selected in the order of recording or the second signal strength until the connection is successful or the wireless access point channel information is searched again; otherwise, after the connection is successful, the execution of the network ring avoidance processing mechanism is triggered.

[0103] The node design incorporates multi-channel automatic frequency hopping channel avoidance technology to constantly explore the output power intensity and noise conditions of each frequency band. If the current transmission channel is suddenly interfered by high-power noise data, causing the transmission data arrival rate to be less than 75%, it will actively search for surrounding channels to find a channel that satisfies the transmission stability of more than 80% and synchronize the receiving end of the self-grouping node to the new frequency band to avoid electromagnetic interference targeting a specific frequency band.

[0104] Optimize the roaming strategy of network terminals and use the network layer2 exchange protocol to forward, so that the switching time is less than 50ms. Use the underlying driver code in the device to quickly connect to nearby hotspots. Use the discrete and weighted algorithms to solve the link establishment misjudgment problem caused by signal fluctuations through the own mathematical model. Notify the entire network layer of the change result of the node link in a broadcast manner, and judge whether the signal strength of the connection point is stable through a loop. If stable, maintain the connection and periodically judge the stability of the signal; if unstable, abandon the access point, re-search nearby accessible points and judge the stability of the signal, abandon or determine the access point and notify the entire network, and repeat the detection and judgment.

[0105] In the embodiment, the device is tested for delay under no load in a real environment, and for delay and uplink and downlink rates under extreme full load conditions in emergency scenarios. The number of test hops increases from 1 to 35.

[0106] According to the test results, in the real environment, the throughput loss of the backhaul network data after 10 hops is 20%, and after 20 hops, the backhaul throughput loss does not exceed 30% and the bandwidth remains above 200Mb / s. The delay results show that within 20 hops, the uplink backhaul delay is controlled within 100ms, and even after 35 hops, the delay does not exceed 150ms. The performance of the designed self-organizing network node is outstanding, and the delay of no more than 100ms meets the requirements of VoIP and video applications. It can better meet the multi-hop high-bandwidth and low-latency network networking requirements in various emergency networking communication scenarios. The simplified schematic diagram of the self-redundant networking of the equipment implemented by this method in the emergency scenario is shown as follows: Figure 3 shown.

[0107] A distributed self-redundant intelligent control equipment networking method provided by the present invention is conducive to reducing the communication delay between devices, can take into account the power's own business, realize the effective planning of the layout of power communication base stations, improve the coordination and optimization efficiency of power 5G base stations, and solve the problem of multi-hop, high-bandwidth, and low-latency network requirements of wireless local area networking in various complex scenarios, which is conducive to realizing the intelligence of the power system.

[0108] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the present invention.

Claims

1. A distributed self-redundant intelligent control equipment networking method, characterized in that: include: S101, calculating the space loss and propagation loss between each 5G base station in the networking base station, and obtaining the link loss between each 5G base station in the networking base station according to the space loss and the propagation loss; S102, calculating the coverage radius of each 5G base station in the networking base station according to the link loss between each 5G base station, and calculating the initial coverage rate of the networking base station according to the coverage radius of each 5G base station; S103, calculating the operation cost and construction cost of each 5G base station of the networking base station, and then calculating the coordination planning cost of the networking base station according to the operation cost and construction cost of each 5G base station; S104, calculating and obtaining first output information according to the initial coverage rate, the coordination planning cost and the genetic algorithm; S105, arranging each 5G base station in the networking base stations according to the first output information to obtain a first networking base station; S106, calculating the coverage rate of the first network base station, determining the difference between the coverage rate of the first network base station and a preset coverage rate, and outputting the first network base station if the coverage rate of the first network base station is greater than or equal to the preset coverage rate; If the coverage rate of the first network base station is less than the preset coverage rate, the initial coverage rate is replaced by the coverage rate of the first network base station, and S104 to S106 are repeated until the updated coverage rate corresponding to the first network base station is greater than or equal to the preset coverage rate, and the updated first network base station is output; S107, establishing a link with the first networking base station to obtain a node arrangement model after the node networking.

2. The distributed self-redundant intelligent control device networking method according to claim 1 is characterized in that: The space loss formula is expressed as: P Lmax =P Tx -L f +G Tx -M f -M1+G Rx -S Rx ; Among them, P Tx Indicates the transmitting end power; L f Indicates equipment loss; G Tx Indicates antenna gain; M f represents the fading margin; M1 represents the interference margin; G Rx Represents the receiving antenna gain; S Rx Indicates receiver sensitivity; P Lmax Indicates space loss.

3. The distributed self-redundant intelligent control device networking method according to claim 1 is characterized in that: The propagation loss is expressed as: P LUMa-NLOS =13.54+39.08log 10 (d 3D )+20log 10 (f c )-0.6(h UT -1.5); Among them, P LUMa-NLOS represents the propagation loss; h UT represents the height of the service terminal; f represents the 5G construction frequency; d 3D Indicates the spatial distance between the 5G base station antenna and the terminal.

4. The distributed self-redundant intelligent control device networking method according to claim 1 is characterized in that: The calculating the coverage radius of each 5G base station according to the link loss between each 5G base station in the networking base station includes: The coverage radius is calculated based on the link loss and Uma-NLOS loss model.

5. The distributed self-redundant intelligent control device networking method according to claim 1 is characterized in that: The coverage is expressed as: Among them, n 5G Indicates the number of 5G base stations in the networking base station; g j Indicates the coverage rate of 5G base stations, g j Specifically expressed as: Indicates the coverage radius of 5G base station; d ij Indicates the distance between the service point and the 5G base station; x i Indicates the probability of the presence or absence of a 5G base station; f 5G Indicates the coverage rate of the networking base station.

6. The distributed self-redundant intelligent control device networking method according to claim 1 is characterized in that: The operating cost is expressed as: in, represents the operating cost of 5G base station i; represents the static power consumption of 5G base station i at time t; α represents the energy efficiency coefficient; represents the dynamic power consumption of 5G base station i at time t; t represents time.

7. The distributed self-redundant intelligent control device networking method according to claim 1 is characterized in that: The construction cost is expressed as: Among them, x i represents the probability of having or not having a 5G base station; C represents the construction cost of each 5G base station; n 5G Indicates the number of 5G base stations in the networking base stations.

8. The distributed self-redundant intelligent control device networking method according to claim 1 is characterized in that: The coordination planning cost of the networking base station is expressed as: Where D represents the construction cost of the networking base station; represents the operating cost of 5G base station i; C represents the construction cost of each 5G base station; x i Indicates the probability of the presence or absence of a 5G base station; n 5G Indicates the number of 5G base stations in the networking base stations.

9. The distributed self-redundant intelligent control device networking method according to claim 1 is characterized in that: The link establishment for the first networking base station to obtain a node arrangement model after the node networking includes: using node self-healing, intelligent search, node channel avoidance and roaming switching to determine the node connection method.

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