A mobile communication network transmission planning method and system based on the Internet of Things

Abstract

The present invention relates to the technical field of network transmission planning, and discloses a method and system for mobile communication network transmission planning based on the Internet of Things. First, in a two-dimensional grid area, a mobile communication network coverage function is established based on the mobile communication network coverage rate, and then a mobile communication network node power function is established based on the mobile communication cost. The mobile communication network transmission function is obtained by combining the mobile communication network coverage function and the mobile communication network node power function; then, a multi-strategy fusion Arctic Puffin optimization algorithm is used to solve the mobile communication network transmission function to obtain a global optimal solution; finally, based on the global optimal solution, a mobile communication network transmission route is generated, network interference is calculated, and the mobile communication network transmission route is adjusted in real time to obtain the final mobile communication network transmission route. The present invention achieves the purpose of mobile communication network transmission planning by analyzing and processing the mobile communication network, and the method is objective and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of network transmission planning, and in particular to a method and system for mobile communication network transmission planning based on the Internet of Things. Background Art

[0002] Chinese patent CN108574594B discloses a method and system for network service transmission. The method specifically includes obtaining network topology information and network service information; obtaining a set of other nodes based on the source node and destination node in the network topology information and network service information; determining the node centripetality of the set of other nodes; selecting a segment node from the set of other nodes, wherein the segment node is an intermediate node in the shortest transmission path for network service transmission; obtaining the transmission path of the network service based on the source node, destination node, and segment node; and calculating the traffic volume of the network service transmitted along the transmission path based on the transmission path and traffic information of the network service. Although this invention reduces the complexity of network service transmission path planning, it does not plan the shortest network transmission path in detail.

[0003] Traditional network transmission planning methods are usually affected by the layout of network transmission nodes and the mobile communication environment, resulting in the inability to provide efficient and stable mobile communication services. At the same time, they also do not use technologies such as artificial intelligence and the Internet of Things. The cost of mobile communication network transmission is high, and it cannot effectively improve the coverage of network nodes, which is prone to problems such as mobile communication network transmission failure. Summary of the Invention

[0004] In response to the problems in the related art, the present invention provides a mobile communication network transmission planning method and system based on the Internet of Things to overcome the above-mentioned technical problems existing in the existing related art.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a method for planning mobile communication network transmission based on the Internet of Things, comprising the following steps:

[0007] S1. Acquire data to be transmitted, where the data to be transmitted is transmitted within a mobile communication network area, convert the mobile communication network area into a two-dimensional grid area, and establish a mobile communication network coverage function based on the mobile communication network coverage rate;

[0008] S2. Selecting mobile communication network nodes on the two-dimensional gridded area, establishing a mobile communication network node power function based on mobile communication costs, and combining the mobile communication network coverage function to obtain a mobile communication network transmission function;

[0009] S3. Improve the Arctic Puffin optimization algorithm by fusing the spiral search strategy and the Osprey strategy to obtain a multi-strategy fusion Arctic Puffin optimization algorithm, and use the multi-strategy fusion Arctic Puffin optimization algorithm to solve the mobile communication network transmission function to obtain a global optimal solution;

[0010] S4. Obtain a global optimal solution for the mobile communication network transmission function, generate a mobile communication network transmission route, calculate network interference on the mobile communication network transmission route, adjust the mobile communication network transmission route in real time, and obtain a final mobile communication network transmission route.

[0011] The invention converts the mobile communication network area where data is to be transmitted into a two-dimensional grid area, calculates the mobile communication network coverage of the mobile communication network nodes in the two-dimensional grid area, and establishes a mobile communication network coverage function; this method quantifies the mobile communication network area through gridding, which is convenient for simulating the distribution of network nodes, reducing environmental factor errors, and improving the problem of weak network coverage; secondly, the mobile communication cost of the mobile communication network nodes in the two-dimensional grid area is calculated, and combined with the mobile communication network coverage function, the mobile communication network transmission function is obtained; this method uses cost as the objective function, effectively reduces the resource consumption of mobile communication network transmission, and also ensures the quality of user mobile communication; and then uses multiple strategies to integrate the Arctic puffin optimization. The algorithm solves the mobile communication network transmission function and obtains the global optimal solution. By integrating the spiral search strategy and the osprey strategy into the Arctic Puffin optimization algorithm, the algorithm is improved and applicable to complex engineering optimization problems. Compared with the traditional algorithm, the algorithm not only ensures the convergence speed of the algorithm, but also increases the diversity of individuals and improves the ability to escape the local optimal solution, ensuring that the algorithm can effectively utilize the current optimal solution. Finally, the mobile communication network transmission route is generated through the global optimal solution, and the mobile communication network transmission route is adjusted in real time by calculating the network interference. The method calculates the weight threshold of the route and obtains a network transmission route with less network interference, avoiding the problem of heavy network transmission load and improving the route planning effect of the optimization algorithm and the success rate of network transmission.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11. Acquire data to be transmitted, where the data to be transmitted is transmitted within a mobile communication network area, consider the mobile communication network area as a two-dimensional plane, collect locations of mobile communication base stations within the mobile communication network area, and record the locations of the mobile communication base stations as mobile communication network nodes; place a grid of size α1×α1 within the mobile communication network area, ensuring that there is at most one mobile communication network node in each grid, and divide the mobile communication network area into α2×α3 grid areas to obtain a two-dimensional gridded area;

[0014] S12: Calculate the mobile communication network coverage of the mobile communication network nodes in the two-dimensional grid area, and establish a mobile communication network coverage function based on the mobile communication network coverage. The specific steps are as follows:

[0015] S121, obtain the coordinates of the mobile communication network nodes in the two-dimensional grid area, set the coordinates of the ath mobile communication network node to (x a ,y a ), the coordinates of the bth mobile communication network node are (x b ,y b ), calculate the distance of the mobile communication network node, the calculation formula is as follows:

[0016]

[0017] Wherein, l(a,b) represents the distance between the a-th mobile communication network node and the b-th mobile communication network node;

[0018] Calculate the distances of all mobile communication network nodes in sequence, set a coverage radius threshold, and when the distance of the mobile communication network node is less than or equal to the coverage radius threshold, record the mobile communication network coverage rate corresponding to the mobile communication network node as 1; otherwise, record the mobile communication network coverage rate corresponding to the mobile communication network node as 0, thereby obtaining the mobile communication network coverage rate;

[0019] S122: In the two-dimensional grid area, assume that the ath mobile communication network node is covered by c mobile communication network nodes, and the c mobile communication network nodes constitute a covering mobile communication network node set A = {b1, b2, b3, ..., b c}, where b c represents the cth covered mobile communication network node, then the calculation formula for the mobile communication network comprehensive coverage rate of the ath mobile communication network node is as follows:

[0020]

[0021] Among them, B a represents the comprehensive coverage rate of the mobile communication network of the ath mobile communication network node, b c′ represents the c′th covered mobile communication network node, B(a,b c′ ) represents the mobile communication network coverage rate of the a-th mobile communication network node and the c′-th covered mobile communication network node;

[0022] S123. Calculate the comprehensive mobile communication network coverage of all mobile communication network nodes in sequence. Calculate the ratio of the set of covered mobile communication network nodes to all mobile communication network nodes based on the size of the mobile communication network area to obtain a coverage function of the mobile communication network nodes, as follows:

[0023]

[0024] Wherein, F1 represents the coverage function of the mobile communication network node;

[0025] The maximum value of the coverage function of the mobile communication network node is selected to obtain the mobile communication network coverage function.

[0026] This invention converts the mobile communication network area where data is to be transmitted into a two-dimensional grid area, quantifies the mobile communication network area through gridding, facilitates the simulation of network node distribution, and then calculates the mobile communication network coverage rate of the mobile communication network nodes in the two-dimensional grid area, establishes a mobile communication network coverage function, reduces environmental factor errors, and improves the problem of weak network coverage.

[0027] Preferably, said S2 comprises the following steps:

[0028] S21, select a mobile communication network node on the two-dimensional grid area, denoted as mobile communication network node d, when the data to be transmitted selects mobile communication network node d for mobile communication network transmission, denoted as b d ′=1, otherwise it is recorded as b d ′=0, obtaining the first constraint condition; when the mobile communication network node d satisfies the mobile communication network transmission, the number of mobile communication network nodes transmitted is less than or equal to the total number of mobile communication network nodes in the two-dimensional grid area, obtaining the second constraint condition, combining the first constraint condition and the second constraint condition to obtain the mobile communication network transmission constraint condition;

[0029] S22. When the mobile communication network node d is communicating, the power coefficient of the mobile communication network node d is set to β, the distance from the mobile communication network node d to the location where the data is to be transmitted is calculated, and the average value of the transmission data demand of the mobile communication network node d within the time period δ is calculated. The power calculation formula of the mobile communication network node d is as follows:

[0030]

[0031] Where l′ represents the distance from the mobile communication network node d to the location where the data is to be transmitted. represents the average value of the transmission data demand of node d in the mobile communication network within the time period δ;

[0032] The power of all mobile communication network nodes is summed in the two-dimensional grid area to obtain the total power of the mobile communication network nodes. The minimum value of the total power of the mobile communication network nodes is selected, and combined with the mobile communication network transmission constraints, a mobile communication network node power function F2 is established; the mobile communication network coverage function and the mobile communication network node power function are combined to obtain the mobile communication network transmission function.

[0033] This invention calculates the mobile communication cost of mobile communication network nodes in a two-dimensional grid area, takes the cost as the objective function, effectively reduces the resource consumption of mobile communication network transmission, and combines it with the mobile communication network coverage function to obtain the mobile communication network transmission function, thereby ensuring the quality of users' mobile communications.

[0034] Preferably, the step S3 includes the following steps:

[0035] S31. Using the mobile communication network transmission function as the fitness function, and under the conditions that the mobile communication network transmission constraints are satisfied, when the mobile communication network coverage function and the mobile communication network node power function are simultaneously satisfied, obtaining the optimal fitness function value; simultaneously using the Arctic Puffin optimization algorithm to solve the mobile communication network transmission function, and introducing the spiral search strategy and the Osprey strategy to improve the Arctic Puffin optimization algorithm, thereby obtaining a multi-strategy fusion Arctic Puffin optimization algorithm;

[0036] S32. Solve the mobile communication network transmission function using the multi-strategy fusion Arctic Puffin optimization algorithm to obtain a global optimal solution. The specific steps are as follows:

[0037] S321. Consider the two-dimensional gridded area as a search space, wherein there is an Arctic puffin population in the search space, and the individual positions of Arctic puffins in the Arctic puffin population represent the solution of the mobile communication network transmission function. Set the number of Arctic puffin populations to m, the dimension of Arctic puffin individuals to n, initialize the Arctic puffin population using a chaotic map, and obtain the initialization positions of Arctic puffin individuals. During the flight phase of the Arctic puffin population, the Arctic puffin population selects an aerial search strategy, and replaces the aerial search strategy with a spiral search strategy. Set the current number of iterations to e, and the position of the Arctic puffin individual searching for prey at the e-th iteration to C′(e), and the position of the i-th Arctic puffin individual at the e-th iteration to C i (e), then the distance between the prey and the individual position of the Arctic puffin is D = |C′(e)-C i (e) |; Set the logarithmic spiral parameter to ε, d1 represents a random number between the interval [-1, 1], and the position C of the i-th Arctic puffin individual at the e+1th iteration is i The calculation formula for (e+1) is as follows:

[0038]

[0039] The Arctic puffin population selects a surprise predation strategy, introduces a speed coefficient φ to adjust the individual position of the Arctic puffin, sets d2 to represent a random number between the interval [0, 1], and the speed coefficient φ = tan((d2-0.5)π). i (e+1) is adjusted to obtain the updated position C′ of the Arctic puffin individual i (e+1)=C i (e+1)·φ; calculate the updated position C′ of the Arctic puffin individual i (e+1) and the individual position C of the Arctic puffin i The union of (e+1) gives the new position C″ of the Arctic puffin individual i (e+1), at this time, the fitness function value corresponding to the Arctic puffin population is calculated to obtain the current optimal fitness function value, and the current optimal fitness function value is used as the current solution of the mobile communication network transmission function;

[0040] S322, the Arctic puffin population is in the underwater foraging stage. The Arctic puffin population chooses a collective foraging strategy. The predation strategy of the osprey optimization algorithm is used to replace the collective foraging strategy. The prey position selected by the Arctic puffin population is set to d3 represents a constant of 1 or 2, which updates the new position of the Arctic puffin individual to obtain the individual position of the Arctic puffin The Arctic puffin population selects an enhanced search strategy and sets the adaptive factor to For the individual position D of the Arctic puffin i ′(e+1) is updated to obtain the individual position of the Arctic puffin The Arctic puffin population chooses a predator avoidance strategy, which again affects the individual position of the Arctic puffin D i ″(e+1) is updated to obtain the individual position D of the Arctic puffin i ″′(e+1); at this time calculate the individual position D of the Arctic puffin i ′(e+1), D i ″(e+1) and D i The final position of the ith Arctic puffin individual at the e+1th iteration is obtained by taking the union of ″′(e+1), and the final position of the Arctic puffin population at the e+1th iteration is calculated in sequence. The Arctic puffin population is screened to generate the next generation of Arctic puffin population. The maximum number of iterations is set. When the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the final Arctic puffin population. The individual position of the Arctic puffin corresponding to the global best fitness function value is calculated to obtain the global optimal solution.

[0041] This invention solves the mobile communication network transmission function by using a multi-strategy fusion Arctic Puffin optimization algorithm, and improves the Arctic Puffin optimization algorithm by integrating the spiral search strategy and the Osprey strategy. It is applicable to complex engineering optimization problems and obtains the global optimal solution. Compared with traditional algorithms, it not only ensures the convergence speed of the algorithm, but also increases the diversity of individuals, improves the ability to jump out of the local optimal solution, and ensures that the algorithm can effectively utilize the current optimal solution.

[0042] Preferably, the S4 comprises the following steps:

[0043] S41. Based on the global optimal solution, the mobile communication network transmission function obtains a final result, calculates the final position of the Arctic puffin individual in the Arctic puffin population, regards the final position of the Arctic puffin individual in the Arctic puffin population as an intermediate node of the mobile communication network, obtains a set of intermediate nodes of the mobile communication network, sets a final transmission position of the data to be transmitted, connects the position of the data to be transmitted, the set of intermediate nodes of the mobile communication network, and the final transmission position of the data to be transmitted, generates a mobile communication network transmission route, and completes route planning.

[0044] S42: When the data to be transmitted is transmitted on the mobile communication network transmission route, network interference on the mobile communication network transmission route is calculated, and the mobile communication network transmission route is adjusted in real time to obtain a final mobile communication network transmission route. The specific steps are as follows:

[0045] S421. Randomly select a mobile communication network intermediate node k in the mobile communication network transmission route, record the mobile communication network intermediate nodes connected to the mobile communication network intermediate node k as a mobile communication network neighbor node set, count the number of mobile communication network neighbor nodes in the mobile communication network neighbor node set, and then calculate the allocation weight of the mobile communication network neighbor node set in the mobile communication network transmission route and the interference level of the mobile communication network intermediate node k. In this case, the average interference level calculation formula of the mobile communication network transmission route is as follows:

[0046]

[0047] Where η represents the average interference level of the mobile communication network transmission route, q represents the number of mobile communication network neighbor nodes in the mobile communication network neighbor node set, γ represents the distribution weight of the mobile communication network neighbor node set in the mobile communication network transmission route, λ represents the network interference quantization factor, and p k represents the interference level of the intermediate node k in the mobile communication network, and E represents the transmission route of the mobile communication network;

[0048] S422: Assume that the length of the mobile communication network transmission route is r, the interference factor is μ, and calculate the network interference degree of the mobile communication network transmission route based on the average interference level of the mobile communication network transmission route. The formula is as follows:

[0049]

[0050] Where G represents the degree of network interference in the transmission route of the mobile communication network;

[0051] A network interference threshold is set. When the network interference degree of the mobile communication network transmission route is greater than or equal to the network interference threshold, the network interference of the mobile communication network transmission route is large, and the route is replanned. Otherwise, the route is not planned. The mobile communication network transmission route is adjusted to generate a new mobile communication network transmission route until the network interference degree of the mobile communication network transmission route is less than the network interference threshold, thereby obtaining a final mobile communication network transmission route.

[0052] This invention generates a mobile communication network transmission route through a global optimal solution, calculates the weight threshold of the route, obtains a network transmission route with less network interference, and adjusts the mobile communication network transmission route in real time, avoiding the problem of heavy network transmission load and improving the route planning effect of the optimization algorithm and the success rate of network transmission.

[0053] The present invention also discloses a system for a mobile communication network transmission planning method based on the Internet of Things, which specifically includes: a mobile communication network coverage calculation module, a mobile communication network transmission function establishment module, an optimization algorithm solution module, and a mobile communication network transmission route adjustment module;

[0054] The mobile communication network coverage calculation module is used to convert the mobile communication network area into a two-dimensional grid area and then calculate the mobile communication network coverage;

[0055] The mobile communication network transmission function establishment module is used to establish a mobile communication network transmission function based on the mobile communication network coverage and the mobile communication cost;

[0056] The optimization algorithm solving module is used to solve the mobile communication network transmission function by using a multi-strategy fusion Arctic Puffin optimization algorithm;

[0057] The mobile communication network transmission route adjustment module is used to calculate network interference and adjust the mobile communication network transmission route in real time.

[0058] The present invention has the following beneficial effects:

[0059] 1. This invention converts the mobile communication network area where data is to be transmitted into a two-dimensional grid area, and quantifies the mobile communication network area through gridding, so as to facilitate the simulation of network node distribution, establish a mobile communication network transmission function, reduce environmental factor errors, improve the problem of weak network coverage, ensure the quality of users' mobile communications, and reduce transmission costs.

[0060] 2. This invention solves the mobile communication network transmission function by using a multi-strategy fusion Arctic Puffin optimization algorithm, and improves the Arctic Puffin optimization algorithm by integrating the spiral search strategy and the Osprey strategy. It is applicable to complex engineering optimization problems and obtains the global optimal solution. Compared with traditional algorithms, it not only ensures the convergence speed of the algorithm, but also increases the diversity of individuals, improves the ability to jump out of the local optimal solution, and ensures that the algorithm can effectively utilize the current optimal solution.

[0061] 3. This invention generates a mobile communication network transmission route through a global optimal solution, calculates the weight threshold of the route, obtains a network transmission route with less network interference, and adjusts the mobile communication network transmission route in real time, thereby avoiding the problem of heavy network transmission load and improving the route planning effect of the optimization algorithm and the success rate of network transmission.

[0062] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying any creative effort.

[0064] Figure 1 A schematic diagram of the process of mobile communication network transmission planning by a mobile communication network transmission planning system based on the Internet of Things provided by the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative work are within the scope of protection of the present invention.

[0066] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0067] Example 1

[0068] Please refer to Figure 1 The present invention provides a mobile communication network transmission planning method based on the Internet of Things, comprising the following steps:

[0069] S1. Acquire data to be transmitted, where the data to be transmitted is transmitted within a mobile communication network area, convert the mobile communication network area into a two-dimensional grid area, and establish a mobile communication network coverage function based on the mobile communication network coverage rate;

[0070] Said S1 comprises the following steps:

[0071] S11. Acquire data to be transmitted, where the data to be transmitted is transmitted within a mobile communication network area, consider the mobile communication network area as a two-dimensional plane, collect locations of mobile communication base stations within the mobile communication network area, and record the locations of the mobile communication base stations as mobile communication network nodes; place a grid of size α1×α1 within the mobile communication network area, ensuring that there is at most one mobile communication network node in each grid, and divide the mobile communication network area into α2×α3 grid areas to obtain a two-dimensional gridded area;

[0072] S12: Calculate the mobile communication network coverage of the mobile communication network nodes in the two-dimensional grid area, and establish a mobile communication network coverage function based on the mobile communication network coverage. The specific steps are as follows:

[0073] S121, obtain the coordinates of the mobile communication network nodes in the two-dimensional grid area, set the coordinates of the ath mobile communication network node to (x a ,y a ), the coordinates of the bth mobile communication network node are (x b ,y b ), calculate the distance of the mobile communication network node, the calculation formula is as follows:

[0074]

[0075] Wherein, l(a,b) represents the distance between the a-th mobile communication network node and the b-th mobile communication network node;

[0076] Calculate the distances of all mobile communication network nodes in sequence, set a coverage radius threshold, and when the distance of the mobile communication network node is less than or equal to the coverage radius threshold, record the mobile communication network coverage rate corresponding to the mobile communication network node as 1; otherwise, record the mobile communication network coverage rate corresponding to the mobile communication network node as 0, thereby obtaining the mobile communication network coverage rate;

[0077] S122: In the two-dimensional grid area, assume that the ath mobile communication network node is covered by c mobile communication network nodes, and the c mobile communication network nodes constitute a covering mobile communication network node set A = {b1, b2, b3, ..., b c}, where b c represents the cth covered mobile communication network node, then the calculation formula for the mobile communication network comprehensive coverage rate of the ath mobile communication network node is as follows:

[0078]

[0079] Among them, B a represents the comprehensive coverage rate of the mobile communication network of the ath mobile communication network node, b c′ represents the c′th covered mobile communication network node, B(a,b c′ ) represents the mobile communication network coverage rate of the a-th mobile communication network node and the c′-th covered mobile communication network node;

[0080] S123. Calculate the comprehensive mobile communication network coverage of all mobile communication network nodes in sequence. Calculate the ratio of the set of covered mobile communication network nodes to all mobile communication network nodes based on the size of the mobile communication network area to obtain a coverage function of the mobile communication network nodes, as follows:

[0081]

[0082] Wherein, F1 represents the coverage function of the mobile communication network node;

[0083] Selecting the maximum value of the coverage function of the mobile communication network node to obtain a mobile communication network coverage function;

[0084] S2. Selecting mobile communication network nodes on the two-dimensional gridded area, establishing a mobile communication network node power function based on mobile communication costs, and combining the mobile communication network coverage function to obtain a mobile communication network transmission function;

[0085] The S2 comprises the following steps:

[0086] S21, select a mobile communication network node on the two-dimensional grid area, denoted as mobile communication network node d, when the data to be transmitted selects mobile communication network node d for mobile communication network transmission, denoted as b d ′=1, otherwise it is recorded as b d ′=0, obtaining the first constraint condition; when the mobile communication network node d satisfies the mobile communication network transmission, the number of mobile communication network nodes transmitted is less than or equal to the total number of mobile communication network nodes in the two-dimensional grid area, obtaining the second constraint condition, combining the first constraint condition and the second constraint condition to obtain the mobile communication network transmission constraint condition;

[0087] S22. When the mobile communication network node d is communicating, the power coefficient of the mobile communication network node d is set to β, the distance from the mobile communication network node d to the location where the data is to be transmitted is calculated, and the average value of the transmission data demand of the mobile communication network node d within the time period δ is calculated. The power calculation formula of the mobile communication network node d is as follows:

[0088]

[0089] Where l′ represents the distance from the mobile communication network node d to the location where the data is to be transmitted. represents the average value of the transmission data demand of node d in the mobile communication network within the time period δ;

[0090] Summing the power of all mobile communication network nodes in the two-dimensional grid area to obtain the total power of the mobile communication network nodes, selecting the minimum value of the total power of the mobile communication network nodes, and establishing a mobile communication network node power function F2 in combination with the mobile communication network transmission constraint condition; and jointly combining the mobile communication network coverage function and the mobile communication network node power function to obtain a mobile communication network transmission function;

[0091] S3. Improve the Arctic Puffin optimization algorithm by fusing the spiral search strategy and the Osprey strategy to obtain a multi-strategy fusion Arctic Puffin optimization algorithm, and use the multi-strategy fusion Arctic Puffin optimization algorithm to solve the mobile communication network transmission function to obtain a global optimal solution;

[0092] The S3 includes the following steps:

[0093] S31. Using the mobile communication network transmission function as the fitness function, and under the conditions that the mobile communication network transmission constraints are satisfied, when the mobile communication network coverage function and the mobile communication network node power function are simultaneously satisfied, obtaining the optimal fitness function value; simultaneously using the Arctic Puffin optimization algorithm to solve the mobile communication network transmission function, and introducing the spiral search strategy and the Osprey strategy to improve the Arctic Puffin optimization algorithm, thereby obtaining a multi-strategy fusion Arctic Puffin optimization algorithm;

[0094] S32. Solve the mobile communication network transmission function using the multi-strategy fusion Arctic Puffin optimization algorithm to obtain a global optimal solution. The specific steps are as follows:

[0095] S321. Consider the two-dimensional gridded area as a search space, wherein there is an Arctic puffin population in the search space, and the individual positions of Arctic puffins in the Arctic puffin population represent the solution of the mobile communication network transmission function. Set the number of Arctic puffin populations to m, the dimension of Arctic puffin individuals to n, initialize the Arctic puffin population using a chaotic map, and obtain the initialization positions of Arctic puffin individuals. During the flight phase of the Arctic puffin population, the Arctic puffin population selects an aerial search strategy, and replaces the aerial search strategy with a spiral search strategy. Set the current number of iterations to e, and the position of the Arctic puffin individual searching for prey at the e-th iteration to C′(e), and the position of the i-th Arctic puffin individual at the e-th iteration to C i (e), then the distance between the prey and the individual position of the Arctic puffin is D = |C′(e)-C i (e) |; Set the logarithmic spiral parameter to ε, d1 represents a random number between the interval [-1, 1], and the position C of the i-th Arctic puffin individual at the e+1th iteration is i The calculation formula for (e+1) is as follows:

[0096]

[0097] The Arctic puffin population selects a surprise predation strategy, introduces a speed coefficient φ to adjust the individual position of the Arctic puffin, sets d2 to represent a random number between the interval [0, 1], and the speed coefficient φ = tan((d2-0.5)π). i (e+1) is adjusted to obtain the updated position C′ of the Arctic puffin individual i (e+1)=C i (e+1)·φ; calculate the updated position C′ of the Arctic puffin individual i (e+1) and the individual position C of the Arctic puffin i The union of (e+1) gives the new position C of the Arctic puffin individual. i ″(e+1), at this time, the fitness function value corresponding to the Arctic puffin population is calculated to obtain the current optimal fitness function value, and the current optimal fitness function value is used as the current solution of the mobile communication network transmission function;

[0098] S322, the Arctic puffin population is in the underwater foraging stage. The Arctic puffin population chooses a collective foraging strategy. The predation strategy of the osprey optimization algorithm is used to replace the collective foraging strategy. The prey position selected by the Arctic puffin population is set to d3 represents a constant of 1 or 2, which updates the new position of the Arctic puffin individual to obtain the individual position of the Arctic puffin The Arctic puffin population selects an enhanced search strategy and sets the adaptive factor to For the individual position D of the Arctic puffin i ′(e+1) is updated to obtain the individual position of the Arctic puffin The Arctic puffin population chooses a predator avoidance strategy, which again affects the individual position of the Arctic puffin D i ″(e+1) is updated to obtain the individual position D of the Arctic puffin i ″′(e+1); at this time calculate the individual position D of the Arctic puffin i ′(e+1), D i ″(e+1) and D i The final position of the ith Arctic puffin individual at the e+1th iteration is obtained by combining the values of ″′(e+1). The final position of the Arctic puffin population at the e+1th iteration is calculated in sequence, and the Arctic puffin population is screened to generate the next generation of Arctic puffin population. The maximum number of iterations is set. When the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the final Arctic puffin population. The individual position of the Arctic puffin corresponding to the global optimal fitness function value is calculated to obtain the global optimal solution.

[0099] S4. Obtaining a global optimal solution for the mobile communication network transmission function, generating a mobile communication network transmission route, calculating network interference on the mobile communication network transmission route, and adjusting the mobile communication network transmission route in real time to obtain a final mobile communication network transmission route;

[0100] The S4 comprises the following steps:

[0101] S41. Based on the global optimal solution, the mobile communication network transmission function obtains a final result, calculates the final position of the Arctic puffin individual in the Arctic puffin population, regards the final position of the Arctic puffin individual in the Arctic puffin population as an intermediate node of the mobile communication network, obtains a set of intermediate nodes of the mobile communication network, sets a final transmission position of the data to be transmitted, connects the position of the data to be transmitted, the set of intermediate nodes of the mobile communication network, and the final transmission position of the data to be transmitted, generates a mobile communication network transmission route, and completes route planning.

[0102] S42: When the data to be transmitted is transmitted on the mobile communication network transmission route, network interference on the mobile communication network transmission route is calculated, and the mobile communication network transmission route is adjusted in real time to obtain a final mobile communication network transmission route. The specific steps are as follows:

[0103] S421. Randomly select a mobile communication network intermediate node k in the mobile communication network transmission route, record the mobile communication network intermediate nodes connected to the mobile communication network intermediate node k as a mobile communication network neighbor node set, count the number of mobile communication network neighbor nodes in the mobile communication network neighbor node set, and then calculate the allocation weight of the mobile communication network neighbor node set in the mobile communication network transmission route and the interference level of the mobile communication network intermediate node k. In this case, the average interference level calculation formula of the mobile communication network transmission route is as follows:

[0104]

[0105] Where η represents the average interference level of the mobile communication network transmission route, q represents the number of mobile communication network neighbor nodes in the mobile communication network neighbor node set, γ represents the distribution weight of the mobile communication network neighbor node set in the mobile communication network transmission route, λ represents the network interference quantization factor, and p k represents the interference level of the intermediate node k in the mobile communication network, and E represents the transmission route of the mobile communication network;

[0106] S422: Assume that the length of the mobile communication network transmission route is r, the interference factor is μ, and calculate the network interference degree of the mobile communication network transmission route based on the average interference level of the mobile communication network transmission route. The formula is as follows:

[0107]

[0108] Where G represents the degree of network interference in the transmission route of the mobile communication network;

[0109] A network interference threshold is set. When the network interference degree of the mobile communication network transmission route is greater than or equal to the network interference threshold, the network interference of the mobile communication network transmission route is large, and the route is replanned. Otherwise, the route is not planned. The mobile communication network transmission route is adjusted to generate a new mobile communication network transmission route until the network interference degree of the mobile communication network transmission route is less than the network interference threshold, thereby obtaining a final mobile communication network transmission route.

[0110] Example 2

[0111] The present invention also discloses a system for a mobile communication network transmission planning method based on the Internet of Things, which specifically includes: a mobile communication network coverage calculation module, a mobile communication network transmission function establishment module, an optimization algorithm solution module, and a mobile communication network transmission route adjustment module;

[0112] The mobile communication network coverage calculation module is used to convert the mobile communication network area into a two-dimensional grid area and then calculate the mobile communication network coverage;

[0113] The mobile communication network transmission function establishment module is used to establish a mobile communication network transmission function based on the mobile communication network coverage and the mobile communication cost;

[0114] The optimization algorithm solving module is used to solve the mobile communication network transmission function by using a multi-strategy fusion Arctic Puffin optimization algorithm;

[0115] The mobile communication network transmission route adjustment module is used to calculate network interference and adjust the mobile communication network transmission route in real time.

[0116] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0117] The preferred embodiments of the invention disclosed above are intended only to help illustrate the invention. These preferred embodiments do not exhaust all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A mobile communication network transmission planning method based on the Internet of Things, characterized in that: The steps include: S1. Acquire data to be transmitted, where the data to be transmitted is transmitted within a mobile communication network area, convert the mobile communication network area into a two-dimensional grid area, and establish a mobile communication network coverage function based on the mobile communication network coverage rate; S2. Selecting mobile communication network nodes on the two-dimensional gridded area, establishing a mobile communication network node power function based on mobile communication costs, and combining the mobile communication network coverage function to obtain a mobile communication network transmission function; S3, fusing the spiral search strategy and the osprey strategy to improve the optimization algorithm to obtain a multi-strategy fusion optimization algorithm, and using the multi-strategy fusion optimization algorithm to solve the mobile communication network transmission function to obtain a global optimal solution; The S3 comprises the following steps: S31. Using the mobile communication network transmission function as the fitness function, and under the conditions that the mobile communication network transmission constraints are satisfied, when the mobile communication network coverage function and the mobile communication network node power function are simultaneously satisfied, obtaining the optimal fitness function value; simultaneously using the Arctic Puffin optimization algorithm to solve the mobile communication network transmission function, and introducing the spiral search strategy and the Osprey strategy to improve the Arctic Puffin optimization algorithm, thereby obtaining a multi-strategy fusion Arctic Puffin optimization algorithm; S32, using the multi-strategy fusion Arctic Puffin optimization algorithm to solve the mobile communication network transmission function to obtain a global optimal solution; The S32 includes the following steps: The two-dimensional gridded area is regarded as a search space, a population of Arctic puffins exists in the search space, and the positions of individual Arctic puffins in the population represent the solution of the mobile communication network transmission function. The number of Arctic puffins in the population is set to m, and the dimension of individual Arctic puffins is set to n. The population of Arctic puffins is initialized using a chaotic map to obtain the initialization positions of individual Arctic puffins. During the aerial flight phase of the Arctic puffins, the population of Arctic puffins selects an aerial search strategy and replaces the aerial search strategy with a spiral search strategy. During the underwater foraging phase, the Arctic puffin population chooses a collective foraging strategy and uses the Osprey optimization algorithm's predation strategy to replace the collective foraging strategy; S4. Obtain a global optimal solution for the mobile communication network transmission function, generate a mobile communication network transmission route, calculate network interference on the mobile communication network transmission route, adjust the mobile communication network transmission route in real time, and obtain a final mobile communication network transmission route.

2. The method for mobile communication network transmission planning based on the Internet of Things according to claim 1, characterized in that: The S1 comprises the following steps: S11. Acquire data to be transmitted, where the data to be transmitted is transmitted within a mobile communication network area, and treat the mobile communication network area as a two-dimensional plane and perform gridding processing to obtain a two-dimensional gridded area; S12: Calculate the mobile communication network coverage of the mobile communication network nodes in the two-dimensional gridded area, and establish a mobile communication network coverage function based on the mobile communication network coverage.

3. The method for mobile communication network transmission planning based on the Internet of Things according to claim 2, characterized in that: The S12 includes the following steps: S121. Obtain coordinates of mobile communication network nodes in the two-dimensional grid area, and calculate mobile communication network coverage based on distances between the mobile communication network nodes; S122. Calculate the comprehensive coverage rate of the mobile communication network based on the mobile communication network coverage rate; S123. Obtain a coverage function of a mobile communication network node according to the comprehensive coverage rate of the mobile communication network, select a maximum value of the coverage function of the mobile communication network node, and obtain a mobile communication network coverage function.

4. The method for mobile communication network transmission planning based on the Internet of Things according to claim 1, characterized in that: The S2 comprises the following steps: S21. Selecting mobile communication network nodes on the two-dimensional gridded area, setting constraints on the mobile communication network nodes, and obtaining mobile communication network transmission constraints; S22. Calculate the total power of the mobile communication network nodes, select the minimum value of the total power of the mobile communication network nodes, and establish a mobile communication network node power function in combination with the mobile communication network transmission constraints; and jointly combine the mobile communication network coverage function and the mobile communication network node power function to obtain a mobile communication network transmission function.

5. The method for mobile communication network transmission planning based on the Internet of Things according to claim 1, characterized in that: The S32 further comprises the following steps: Assume that the current number of iterations is e, the position of the prey searched by the Arctic puffin individual at the e-th iteration is C′(e), and the position of the i-th Arctic puffin individual at the e-th iteration is C i (e), then the distance between the prey and the individual position of the Arctic puffin is D = |C′(e)-C i (e) |; Set the logarithmic spiral parameter to ε, d1 represents a random number between the interval [-1, 1], and the position C of the i-th Arctic puffin individual at the e+1th iteration is i The formula for calculating (e+1) is as follows: The Arctic puffin population selects a surprise predation strategy, introduces a speed coefficient φ to adjust the individual position of the Arctic puffin, sets d2 to represent a random number between the interval [0, 1], and the speed coefficient φ = tan((d2-0.5)π). i (e+1) is adjusted to obtain the updated position C′ of the Arctic puffin individual i (e+1)=C i (e+1)·φ; Calculate the updated position C′ of the Arctic puffin individual i (e+1) and the individual position C of the Arctic puffin i The union of (e+1) gives the new position C″ of the Arctic puffin individual i (e+1), at this time, the fitness function value corresponding to the Arctic puffin population is calculated to obtain the current optimal fitness function value, and the current optimal fitness function value is used as the current solution of the mobile communication network transmission function; Assume that the prey location selected by the Arctic puffin population is d3 represents a constant of 1 or 2, which updates the new position of the Arctic puffin individual to obtain the individual position of the Arctic puffin The Arctic puffin population selects an enhanced search strategy and sets the adaptive factor to The individual position D′ of the Arctic puffin i (e+1) is updated to obtain the individual position of the Arctic puffin The Arctic puffin population chooses a predator avoidance strategy, which again affects the individual position D″ of the Arctic puffin. i (e+1) is updated to obtain the individual position D″′ of the Arctic puffin i (e+1); at this time calculate the individual position D′ of the Arctic puffin i (e+1), D″ i (e+1) and D″′ i The final position of the ith Arctic puffin individual at the e+1th iteration is obtained by taking the union of (e+1), and the final position of the Arctic puffin population at the e+1th iteration is calculated in sequence, and the Arctic puffin population is screened to generate the next generation of Arctic puffin population; Set the maximum number of iterations. When the current number of iterations reaches the maximum number of iterations, stop the iteration and obtain the final Arctic puffin population. Calculate the individual positions of the Arctic puffin corresponding to the global optimal fitness function value to obtain the global optimal solution.

6. The method for mobile communication network transmission planning based on the Internet of Things according to claim 1, characterized in that: The S4 comprises the following steps: S41. Obtain a set of intermediate nodes in the mobile communication network according to the global optimal solution, generate a mobile communication network transmission route, and complete route planning; S42. When the data to be transmitted is transmitted on the mobile communication network transmission route, network interference on the mobile communication network transmission route is calculated, and the mobile communication network transmission route is adjusted in real time to obtain a final mobile communication network transmission route.

7. The method for mobile communication network transmission planning based on the Internet of Things according to claim 6, characterized in that: The S42 includes the following steps: S421. Calculate the average interference level of the mobile communication network transmission route; S422. Calculate the network interference level of the mobile communication network transmission route based on the average interference level of the mobile communication network transmission route; A network interference threshold is set, and by comparing the network interference degree of the mobile communication network transmission route with the network interference threshold, the mobile communication network transmission route is adjusted in real time to obtain a final mobile communication network transmission route.

8. A system for implementing the method for mobile communication network transmission planning based on the Internet of Things according to any one of claims 1 to 7, characterized in that: Specifically include: Mobile communication network coverage calculation module, mobile communication network transmission function establishment module, optimization algorithm solution module and mobile communication network transmission route adjustment module; The mobile communication network coverage calculation module is used to convert the mobile communication network area into a two-dimensional grid area and then calculate the mobile communication network coverage; The mobile communication network transmission function establishment module is used to establish a mobile communication network transmission function based on the mobile communication network coverage and the mobile communication cost; The optimization algorithm solving module is used to solve the mobile communication network transmission function by using a multi-strategy fusion Arctic Puffin optimization algorithm; The mobile communication network transmission route adjustment module is used to calculate network interference and adjust the mobile communication network transmission route in real time.

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