An optimization method, system and storage medium for a 5G antenna feeder system

Abstract

An optimization method, system and storage medium for a 5G antenna feeder system, belonging to the field of wireless communication technology, including: Step S1: Obtain the overlapping area of the first coverage area and the second coverage area, and obtain the roads located within the overlapping area; Step S2: Set the positions of the intersection points of the overlapping area boundary and each road as the first positioning points, and obtain the shortest driving path between each first positioning point; Step S3: Test and obtain the cumulative time when the signal state of the test device is in an abnormal state between two first positioning points; Step S4: Calculate the adjustment value based on the cumulative time. If the adjustment value exceeds the preset critical threshold, adjust the device parameters of the first base station and the second base station based on the adjustment value; Step S5: Repeat Step S2 to Step S5 until the calculation between all first positioning points is completed; Through the present invention, defective areas within the coverage range of the base station can be discovered, and it can be determined whether the base station parameters need to be adjusted.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and particularly relates to an optimization method, system and storage medium for a 5G antenna feeder system. Background Art

[0002] The antenna feeder system refers to the antenna and feeder system, which is an essential part of communication base station equipment. When there are defective areas in the coverage area composed of multiple base stations, such as coverage holes, weak coverage areas or over-coverage areas, by adjusting parameters such as the downtilt angle and azimuth angle in the antenna feeder system, the signal quality of the above-mentioned defective areas can be improved, and even the defective areas can be eliminated, thereby improving the communication quality of users within the coverage of the base station.

[0003] Based on the above analysis, in order to improve and eliminate the signal quality in the defective area, the following technical solutions have been proposed in the prior art. For example, Chinese Patent Application "CN113438658B" discloses a method and device for determining the coverage area of a base station. First, the maximum allowable path loss of the target signal is determined based on a first formula through a base station coverage area determination device. The first formula is related to the antenna gain of the target signal, and the target signal is any radiation signal in the first sector. After that, the base station coverage area determination device determines the propagation distance of the target signal according to the maximum allowable path loss and a second formula. The second formula is related to the propagation model of the base station, and the propagation model is related to the scenario where the base station is located. Finally, the base station coverage area determination device determines the coverage area of the base station according to the propagation distances of multiple target signals. Another example is Chinese Patent Application "CN107682864B" which discloses a base station construction method based on coverage rate evaluation. This method obtains several base station combination schemes according to the coverage rate situation, and selects the base station combination scheme that meets the base station planning objective function as the base station planning scheme, corrects the coverage rate, and constructs the base station according to the base station planning scheme that meets the coverage requirements. By continuously correcting the coverage rate, this method not only ensures that the selected base station belongs to the preferred scheme within the planning scheme, but also ensures that the actual coverage rate meets the actual requirements of this area.

[0004] However, the above two methods are both applied in the planning stage. By predicting the future coverage area of the base station, the defective areas can be discovered in time and the design plan can be adjusted. For the base stations that have been planned, there is no corresponding technical solution in the prior art on how to obtain the defective areas that were not discovered in the planning stage, and whether it is necessary to adjust the parameters of the antenna feeder system for this area to eliminate and improve the defective areas. Summary of the Invention

[0005] To solve the above problems, the present invention provides an optimization method, system and storage medium for a 5G antenna feeder system, so as to provide a method capable of discovering defective areas within the coverage of a base station and determining whether it is necessary to improve the signal quality of the defective areas.

[0006] To achieve the above invention purpose, the present invention proposes an optimization method for a 5G antenna feeder system, including:

[0007] Step S1: Obtain the first coverage area and the second coverage area of the first base station and the second base station, as well as the overlapping area of the first coverage area and the second coverage area, and obtain the traffic information located in the overlapping area, where the traffic information includes road directions, traffic flow data and driving speed distribution data;

[0008] Step S2: Set the positions of the intersections of the overlapping area boundary and each road as the first positioning points, and set the intersections between different roads as the second positioning points. Number the first positioning points as O1, O2, …, O i , number the roads between adjacent first positioning points or second positioning points as L1, L2, …, L j , obtain the shortest driving paths from the first positioning point O1 to the remaining first positioning points O2, O3, …, O i , obtain the road numbers based on the shortest driving paths, and encode each of the shortest driving paths based on the road numbers to obtain an encoded sequence;

[0009] Step S3: Define the device for obtaining the signal strength sent by the base station as the test device. Based on the signal strength, divide the signal state of the test device into a normal state and an abnormal state. Determine two first positioning points that need to be tested, obtain the encoded sequence based on the numbers of the first positioning points, and test and obtain the cumulative time during which the signal state of the test device is in the abnormal state between the two first positioning points according to the encoded sequence and the driving speed distribution data;

[0010] Step S4: Calculate the adjustment value based on the cumulative time. If the adjustment value exceeds a preset critical threshold, adjust the device parameters of the first base station and the second base station based on the adjustment value, and change the sizes of the first coverage area and the second coverage area to reduce the adjustment value;

[0011] Step S5: Continue to obtain the shortest driving paths from the first positioning point O2 to the remaining first positioning points O1, O3, …, O i , and repeat Step S2 to Step S5 until all calculations between the first positioning points are completed.

[0012] Further, in step S3, the steps of classifying the signal state of the test device into the abnormal state include the following steps:

[0013] Step S31: On the shortest driving path, set test points at preset sampling distances, and obtain the first signal strength and the second signal strength received by the test device at the test points. The first signal strength and the second signal strength respectively correspond to the first base station and the second base station, as well as the first distance and the second distance between the test point and the first base station and the second base station;

[0014] Step S32: Classify the test points into first abnormal points and second abnormal points. Among them, if the first distance of the test point is greater than the second distance, and the first signal strength is less than the second signal strength, or the first distance is less than the second distance, and the first signal strength is greater than the second signal strength, then the test point is defined as the first abnormal point. If the test device is connected to the first base station and the second base station at two of the test points respectively, then the two test points are defined as the second abnormal points;

[0015] Step S33: When the test device is located between the first abnormal points or between two of the second abnormal points, classify the signal state of the test device into the abnormal state.

[0016] Further, the steps of obtaining the cumulative time when the test device is located include the following steps:

[0017] Based on the driving speed distribution data, divide each road into multiple sub-sections, and each sub-section corresponds to an average driving speed. Based on the sampling distance and the average driving speed, obtain the time intervals between adjacent first abnormal points and the time intervals between adjacent second abnormal points. Calculate the first time ε1 and the second time ε2 respectively based on the first formula and the second formula. The first formula is: The second formula is where t n is the time interval between the nth group of adjacent first abnormal points, and ΔT n is the time interval between the mth group of adjacent first abnormal points. Add the first time and the second time to obtain the cumulative time.

[0018] Further, the steps of calculating the adjustment value based on the cumulative time include the following steps:

[0019] Set multiple traffic flow intervals, and assign corresponding weights to each of the traffic flow intervals. Obtain the traffic flow data between adjacent first abnormal points and between adjacent second abnormal points. Match the corresponding traffic flow intervals based on the traffic flow data, and calculate the first value Score1 and the second value Score2 respectively through the third formula and the fourth formula. The third formula is: The fourth formula is: where ω n is the weight corresponding to the nth traffic flow interval. Add the first value and the second value to obtain the adjusted value.

[0020] Further, changing the sizes of the first coverage area and the second coverage area includes the following steps:

[0021] If the first value is greater than the second value, correspondingly reduce the first coverage area or the second coverage area. If the first value is greater than the second value, correspondingly enlarge the first coverage area or the second coverage area.

[0022] The present invention also provides an optimization system for a 5G antenna feeder system. This system is used to implement the above-mentioned optimization method for a 5G antenna feeder system. This system mainly includes:

[0023] A preprocessing module, which is used to obtain the first coverage area and the second coverage area of the first base station and the second base station, as well as the overlapping area between the first coverage area and the second coverage area, and obtain the traffic information located in the overlapping area. The traffic information includes road directions, traffic flow data, and driving speed distribution data;

[0024] An encoding module, which sets the positions of the intersections of the overlapping area boundary and each road as the first positioning points, and sets the intersections between different roads as the second positioning points. Number the first positioning points as O1, O2, …, O i , and number the roads between adjacent first positioning points or second positioning points as L1, L2, …, L j , obtain the shortest driving paths between each of the first positioning points, obtain the road numbers based on the shortest driving paths, and encode each of the shortest driving paths based on the road numbers to obtain an encoded sequence;

[0025] A defect detection module, which divides the signal state of the test device into a normal state and an abnormal state, determines two first positioning points to be tested, obtains the encoded sequence based on the numbers of the first positioning points, and tests and obtains the cumulative time during which the signal state of the test device is in the abnormal state between the two first positioning points according to the encoded sequence and the driving speed distribution data;

[0026] A decision-making module calculates an adjustment value based on the cumulative time. If the adjustment value exceeds a preset critical threshold, the device parameters of the first base station and the second base station are adjusted based on the adjustment value, and the sizes of the first coverage area and the second coverage area are changed to reduce the adjustment value.

[0027] The present invention also provides a computer storage medium storing program instructions, wherein when the program instructions run, the device where the computer storage medium is located is controlled to execute the above-mentioned optimization method for a 5G antenna feeder system.

[0028] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0029] The present invention first obtains the overlapping area of the coverage ranges of the first base station and the second base station, as well as the traffic information of the overlapping area. Then, according to the road direction and the coverage area of the overlapping area in the traffic information, all driving paths entering and leaving the coverage area are obtained. Then, based on the test device, the signal quality status when driving on each path is obtained, and the duration of the poor signal quality status is obtained. If the duration is too long and the path is an important road with a large traffic flow, the parameters of the first base station or the second base station are adjusted to improve the signal coverage quality within the path. Therefore, through the present invention, the signal coverage conditions of each road within the overlapping coverage area of the two base stations can be obtained, so as to discover the signal quality defect areas and adjust them in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of the steps of an optimization method for a 5G antenna feeder system of the present invention;

[0031] Figure 2 It is a schematic diagram of the principle of the base station coverage area of the present invention;

[0032] Figure 3 It is a structure diagram of an optimization system for a 5G antenna feeder system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0034] It can be understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, the first xx script may be referred to as the second xx script, and similarly, the second xx script may be referred to as the first xx script.

[0035] As Figure 1 shown, an optimization method for a 5G antenna feeder system includes:

[0036] Step S1: Obtain the first coverage area and the second coverage area of the first base station and the second base station, as well as the overlapping area between the first coverage area and the second coverage area, and obtain the traffic information located in the overlapping area. The traffic information includes road directions, traffic flow data, and driving speed distribution data;

[0037] Step S2: Set the positions of the intersections of the overlapping area boundary with each road as the first positioning points, and set the intersections between different roads as the second positioning points. Number the first positioning points as O1, O2,..., O i , number the roads between adjacent first positioning points or second positioning points as L1, L2,..., L j , obtain the shortest driving paths from the first positioning point O1 to the remaining first positioning points O2, O3,..., O i , obtain the road numbers based on the shortest driving paths, and encode each of the shortest driving paths based on the road numbers to obtain an encoded sequence;

[0038] Since defective areas often appear in the overlapping area of the coverage ranges of two base stations, because the overlapping area is generally located at the boundary of the base station coverage range, the base station coverage signal in this area is weak. In addition, when the mobile terminal moves in this area, it needs to switch the base station it is connected to, and it is easy to have a call drop situation. Therefore, the present invention detects the defective areas in this area; referring to Figure 2 , the first base station P1 and the second base station P2 respectively have their own first coverage area E1 and second coverage area E2, and there is an overlapping coverage area E3 between the two coverage areas; the traffic information in the coverage area E3 includes multiple roads passing through this area, and the present invention also obtains the traffic flow data and driving speed distribution data of each road. For example, for the road 1 running vertically through the overlapping area, at 700 km to 750 km, the number of vehicles passing through between 10:00 and 11:00 is its traffic flow data. Similarly, for the road 1, at 700 km to 750 km, the driving speed of each vehicle in the section between 10:00 and 11:00 is the driving speed distribution data.

[0039] At Figure 2Among them, the first positioning point is represented by a solid circle, and the second positioning point is represented by a solid triangle. Then, according to step S2, the road between the first positioning point O1 and the second positioning point R1 is numbered as L1. Similarly, the roads Figure 2 within the coverage area in are numbered as L2, L3, L4, L5, L6, L7, and L8 in sequence; after numbering the roads, continue to obtain the coding sequences of each shortest driving path. The shortest driving path refers to the shortest path from a certain first positioning point to another first positioning point. For example, from the first positioning point O1 to the first positioning point O2, first walk on road L1, then on road L8, and then on road L7 to arrive. This path is the shortest driving path between the two, so the coding sequence corresponding to this path is L1-L8-L7; by encoding each shortest path in the above manner, when the numbers of two first positioning points are determined, the corresponding road coding sequence can be obtained according to the numbers, and then the corresponding traffic information can be quickly obtained according to the road coding sequence.

[0040] Step S3: Define the device for obtaining the signal strength sent by the base station as the test device. Based on the signal strength, divide the signal state of the test device into a normal state and an abnormal state. Determine two first positioning points to be tested, obtain the coding sequence based on the numbers of the first positioning points, and test and obtain the cumulative time when the signal state of the test device is in the abnormal state between the two first positioning points according to the coding sequence and the driving speed distribution data;

[0041] Step S4: Calculate the adjustment value based on the cumulative time. If the adjustment value exceeds the preset critical threshold, then adjust the device parameters of the first base station and the second base station based on the adjustment value, and change the sizes of the first coverage area and the second coverage area to reduce the adjustment value;

[0042] The test equipment in the present invention is a dedicated device for measuring the signal strength transmitted by a base station. The test equipment can automatically or manually select a base station and connect to it. As for how to divide the signals received by the test equipment into normal and abnormal states, it will be described later. Next, step S3 will be further explained. For example, when testing the signal conditions between the first positioning point O1 and the first positioning point O2, the vehicle carrying the test equipment travels successively on roads L1, L8, and L7, and records the time when the signals received by the test equipment are in normal and abnormal states. When in an abnormal state, it indicates that there is a signal defect area in this area, and the longer the cumulative time lasts, the larger the range of this defect area, and then the corresponding adjustment value will be greater. If the adjustment value exceeds the preset critical threshold, it indicates that there will be a situation with poor signal quality for a relatively long time during the driving on this path. At this time, targeted adjustments are required. The specific adjustment method can be to adjust parameters such as the downtilt angle and azimuth angle of the antenna feeder system to change the coverage range of the first base station or the second base station, so as to improve the signal quality within this path.

[0043] Step S5: Continue to obtain the shortest driving paths from the first positioning point O2 to the remaining first positioning points O1, O3, …, O i and repeat steps S2 to S5 until the calculations between all the first positioning points are completed.

[0044] After processing the shortest driving path including the first positioning point O1 and starting from it, obtain the shortest driving path starting from the first positioning point O2 again. In particular, when starting from the first positioning point O2, still obtain the shortest driving path from the first positioning point O2 to the first positioning point O1. This is because roads generally have the characteristic of two-way driving, and the traffic flow and average driving speed of the two lanes in different directions will be different, and the traffic flow will affect the adjustment value in subsequent calculations. Therefore, here it is still necessary to evaluate and process the shortest driving path from the first positioning point O2 to the first positioning point O1.

[0045] It should be particularly noted that through the above method, the present invention provides a method capable of discovering the defect areas within the coverage of the base station and judging whether it is necessary to improve the signal quality of these defect areas.

[0046] The present invention first obtains the overlapping area of the coverage ranges of the first base station and the second base station, as well as the traffic information of the overlapping area. Then, based on the road direction in the traffic information and the coverage area of the overlapping area, all driving paths entering and leaving the coverage area are obtained. After that, based on the test equipment, the signal quality status during driving on each path is obtained, and the duration of the poor signal quality status is obtained. If the duration is too long and the path is an important road with a large traffic flow, the parameters of the first base station or the second base station are adjusted to improve the signal coverage quality within the path. Therefore, through the present invention, the signal coverage conditions of each road within the overlapping coverage area of the two base stations can be obtained, so as to discover the signal quality defect areas and adjust them in a timely manner.

[0047] In step S3, dividing the signal status of the test equipment into the abnormal status includes the following steps:

[0048] Step S31: On the shortest driving path, test points are set at preset sampling distances, and the first signal strength and the second signal strength received by the test equipment at the test points are obtained. The first signal strength and the second signal strength respectively correspond to the first base station and the second base station, as well as the first distance and the second distance between the test point and the first base station and the second base station.

[0049] Step S32: The test points are divided into the first abnormal points and the second abnormal points. Among them, if the first distance of the test point is greater than the second distance and the first signal strength is less than the second signal strength, or the first distance is less than the second distance and the first signal strength is greater than the second signal strength, the test point is defined as the first abnormal point. If the test equipment is connected to the first base station and the second base station at two test points respectively, the two test points are defined as the second abnormal points.

[0050] Step S33: When the test equipment is located between the first abnormal points or between the two second abnormal points, the signal status of the test equipment is divided into the abnormal status.

[0051] The above steps will be explained below. For example, on the shortest driving paths L1, L8, and L7, test points are set at every preset sampling distance. Here, the sampling distance is set to 10 meters. Taking the sampling distance as the setting interval can ensure the uniformity of the coverage of the test points. If the fixed time is used as the interval, due to the change of the driving speed, the distribution of the test points may be uneven. When the test device reaches a test point, the first signal strength and the second signal strength received from the first base station and the second base station at this test point are obtained, as well as the first distance and the second distance between this test point and the first base station and the second base station. Under normal circumstances, the closer a test point is to a certain base station, the stronger the signal strength it receives. For example, if the measuring device is closer to the first base station and farther from the second base station, then the first signal strength should be greater than the second signal strength. If the first signal strength is less than the second signal strength, it indicates that there is an over-coverage situation of the second base station, that is, through this test point, it shows that the signal strength setting of the second base station is unreasonable and needs to be adjusted. Therefore, this test point is set as the first abnormal point.

[0052] In addition, the present invention also sets a second abnormal point. Specifically, if there are test points Q1, Q2, and Q3, when at the test point Q1, the test device automatically connects to the first base station. When at the test point Q2, the test device is in a switching state. When at the test point Q3, the test device connects to the third base station. Then the test points Q1 and Q3 are set as the second abnormal points. The reason for this division is that when the test device moves in the overlapping area, the mobile terminal will automatically switch the base station it is connected to according to the signal strength. During the switching period, the communication quality may deteriorate. Therefore, the test points where the base station switching occurs are determined by the above method, that is, the second abnormal points. Finally, when the position of the test device is between the first abnormal points or between the second abnormal points, its state is set as the abnormal state. It should be noted that when there is an over-coverage of the base station, the first abnormal points usually appear continuously in multiple numbers.

[0053] Obtaining the duration of the test device within the cumulative time includes the following steps:

[0054] Based on the driving speed distribution data, each road is divided into multiple sub-sections, and each sub-section corresponds to an average driving speed. Based on the sampling distance and the average driving speed, the time intervals between adjacent first abnormal points and the time intervals between adjacent second abnormal points are obtained. The first time ε1 and the second time ε2 are calculated respectively based on the first formula and the second formula. The first formula is: The second formula is where, t n is the time interval between the nth group of adjacent first abnormal points, and ΔT n is the time interval between the mth group of adjacent first abnormal points. The first time and the second time are added together to obtain the cumulative time.

[0055] Specifically, after obtaining the distribution data, the K-means clustering algorithm is used to aggregate the data to obtain multiple average speeds. For example, three clusters are obtained through the clustering algorithm, and the center points of each cluster are 30, 60, and 90. Then, three average speeds of 30 km / h, 60 km / h, and 90 km / h are obtained. Similarly, based on the above embodiments, the shortest driving path is divided into three sub-paths, L1, L8, and L7. This is just for convenience of description. If there are four average speeds, the above short driving path can also be divided into 4. After obtaining the average speed of each sub-section, the time interval between adjacent abnormal points can be calculated by way of example. For example, there are the first abnormal points a1, a2, a3, and the second abnormal points a8, a9, a10. Then, a1 and a2 are divided into the first group of the first abnormal points, and a2 and a3 are divided into the second group of the first abnormal points. Similarly, the same division method is performed on the above second abnormal points. Then, t1 represents the time interval between a1 and a2, and ΔT m represents the time interval between a8 and a9. Particularly, if the test point a1 is in a normal state and the test point a2 is in an abnormal state, the time interval between a1 and a2 is not counted. Additionally, if the second abnormal point coincides with the first abnormal point, the coincident second abnormal point is deleted. For example, if the second abnormal points are a3, a4, a5, then the second abnormal point a3 is deleted, and the corresponding ΔT is also deleted. m After obtaining the above basic data and substituting it into the first formula and the second formula and adding them together, the cumulative time of the shortest driving path can be obtained.

[0056] In this embodiment, calculating the adjustment value based on the cumulative time includes the following steps:

[0057] Set multiple traffic flow intervals, assign corresponding weights to each traffic flow interval, obtain the traffic flow data between each adjacent first abnormal point and between each adjacent second abnormal point, match the corresponding traffic flow interval based on the traffic flow data, and calculate the first value Score1 and the second value Score2 through the third formula and the fourth formula respectively. The third formula is: The fourth formula is: where ω n is the weight corresponding to the nth traffic flow interval. Add the first value and the second value to obtain the adjustment value.

[0058] Specifically, the traffic flow is calculated in hours. For example, in this embodiment, the traffic flow is divided into 4 intervals, namely 501 - 800 vehicles per hour, 801 - 1000 vehicles per hour, 1001 - 1300 vehicles per hour, and 1301 - 1500 vehicles per hour. Then their corresponding weights are ω1, ω2, ω3, ω4, and ω1 < ω2 < ω3 < ω4. That is, the larger the traffic flow of a road, the greater its proportion. This is because the larger the traffic flow, the more vehicles pass through this road. If the communication quality of this road is poor, it will have a greater impact. Then, according to the positions of the first abnormal point and the second abnormal point in the shortest driving path, the corresponding weights are obtained. For example, the first abnormal points a1, a2 are in the first traffic flow interval, and a2, a3 are in the second traffic flow interval. Then the weight corresponding to t1 is ω1, and the weight corresponding to t2 is ω2. By multiplying the time by the corresponding weight, the adjustment value can be obtained. The reason for setting the above formula is that if the obtained adjustment value is large, it means that there may be the following two situations within this road path: one is that although the road traffic flow is small, the defective area is large, which will affect the communication quality for a long time; the other is that the defective area is small, but the traffic flow in this area is large. Even if the influence time is short, the defective area will affect the communication quality of a large number of people. Therefore, through this step, the above situations can be discovered in time, providing a reference for the optimization and adjustment of the antenna feeder system.

[0059] In this embodiment, changing the sizes of the first coverage area and the second coverage area includes the following steps:

[0060] If the first value is greater than the second value, correspondingly reduce the first coverage area or the second coverage area. If the first value is greater than the second value, correspondingly enlarge the first coverage area or the second coverage area.

[0061] The following explains the above steps. If the first value is greater than the second value, it indicates that the area of the first coverage area or the second coverage area is too large, resulting in over - coverage. For example, the first coverage area of the first base station is too large, causing a large amount of signals from the first coverage area to appear in the second coverage area. Although this is allowed, when the test point is far from the first base station and close to the second base station, the signal of the first base station is greater than the signal of the second base station, which will affect the signal of the second base station. And the fact that the first value is greater than the second value indicates that this influence accounts for a large proportion and needs to be adjusted first, that is, reduce the coverage area of the first base station.

[0062] If the first value is less than the second value, it indicates that a relatively long handover time occurs in the mobile terminal. The reason for this phenomenon may be, for example, that the signal strength of the second base station is inconsistent at the boundary of its coverage area. When the mobile terminal moves at this boundary, due to the continuous change of the signal strength of the second base station, the mobile terminal continuously switches the base station it is connected to, and repeated handovers will extend the handover time, thus easily resulting in dropped calls. In this case, the coverage area of the second base station can be increased so that this road is no longer located at the boundary, thereby avoiding this situation.

[0063] In step S1, obtaining the first coverage area includes the following steps:

[0064] Step S11: Obtain historical data, where the historical data includes the device parameters of the first base station, the theoretical coverage area calculated based on the device parameters and the signal quality propagation model, the actual coverage area obtained based on the measurement device, and the geographic information data within the theoretical coverage area;

[0065] Step S22: Establish a coverage area correction model based on a neural network, train the coverage area correction model based on the historical data, input the device parameters, theoretical coverage area, and geographic information data of the first base station to be predicted into the coverage area correction model to obtain the actual coverage area, and set the actual coverage area as the first coverage area of the first base station.

[0066] Specifically, the historical data includes the speculated theoretical coverage area, the actually measured coverage area, and the geographic information of the coverage area. Among the above data, the theoretical coverage area and the geographic information of the coverage area are input features, and the actually measured coverage area is the output feature; use the above data to train the neural network model. Here, a BP neural network model is adopted, and the ratio of the training set to the validation set is divided into 7:3; in addition, the signal quality propagation model is an existing model, such as the Okumura-Hata model. Through this model, a preliminary theoretical speculation of the coverage area of the first base station can be made. After obtaining the theoretical coverage area, further collect the geographic information of the theoretical coverage area, where the geographic information includes building information and tree information. Input the above data into the coverage area correction model, and the coverage area correction model outputs the corrected coverage area, thereby obtaining a relatively accurate coverage area. Through this method, both the problem of low prediction accuracy of the coverage area by the traditional model is avoided, and there is no need to measure the coverage area manually on-site, saving manpower and time resources.

[0067] As Figure 3 shown, the present invention also provides an optimization system for a 5G antenna feeder system. This system is used to implement the above-mentioned optimization method for a 5G antenna feeder system. This system mainly includes:

[0068] A preprocessing module for obtaining the first coverage area and the second coverage area of the first base station and the second base station from within the server, as well as the overlapping area between the first coverage area and the second coverage area, and obtaining traffic information located within the overlapping area, where the traffic information includes road directions, traffic flow data, and driving speed distribution data;

[0069] An encoding module that sets the positions of the intersections between the overlapping area boundary and each road as the first positioning points, and sets the intersections between different roads as the second positioning points. The first positioning points are numbered as O1, O2, …, O i and numbers the roads between adjacent first positioning points or second positioning points as L1, L2, …, L j obtains the shortest driving paths between each pair of first positioning points, obtains the road numbers based on the shortest driving paths, and encodes each shortest driving path respectively based on the road numbers to obtain an encoding sequence;

[0070] A defect detection module that obtains detection data detected by multiple test devices. The detection data includes signal strength. Based on the signal strength, the signal status of the test devices is classified into a normal status and an abnormal status. Determines two first positioning points that need to be tested, obtains the encoding sequence based on the numbers of the first positioning points, and tests and obtains the cumulative time during which the signal status of the test devices is in the abnormal status between the two first positioning points according to the encoding sequence and the driving speed distribution data;

[0071] A decision-making module that calculates an adjustment value based on the cumulative time. If the adjustment value exceeds a preset critical threshold, it adjusts the device parameters of the first base station and the second base station based on the adjustment value, and changes the sizes of the first coverage area and the second coverage area to reduce the adjustment value.

[0072] The present invention also provides a computer storage medium storing program instructions, where when the program instructions run, they control the device where the computer storage medium is located to execute the above-mentioned optimization method for a 5G antenna feeder system.

[0073] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and the execution order of these sub-steps or stages is not necessarily sequential either, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0074] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The above program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0075] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0076] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be subject to the appended claims.

[0077] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimization method for a 5G antenna feeder system, characterized in that, Including: Step S1: Obtain the first coverage area and the second coverage area of the first base station and the second base station, as well as the overlapping area of the first coverage area and the second coverage area, and obtain the traffic information located in the overlapping area. The traffic information includes road directions, traffic flow data, and driving speed distribution data; Step S2: Set the positions of the intersections of the boundary of the overlapping area with each road as the first positioning points, and set the intersections between different roads as the second positioning points. Number the first positioning points as O1, O2, …, O i , and number the roads between adjacent first positioning points or second positioning points as L1, L2, …, L j , obtain the shortest driving paths from the first positioning point O1 to the remaining first positioning points O2, O3, ..., O i , obtain the road numbers based on the shortest driving paths, and encode each of the shortest driving paths based on the road numbers to obtain an encoded sequence; Step S3: Define the device for obtaining the signal strength sent by the base station as the test device. Based on the signal strength, divide the signal state of the test device into a normal state and an abnormal state, determine the two first positioning points to be tested, obtain the coding sequence based on the numbers of the first positioning points, and test and obtain the cumulative time when the signal state of the test device is in the abnormal state between the two first positioning points according to the coding sequence and the driving speed distribution data; Step S4: Calculate the adjustment value based on the cumulative time. If the adjustment value exceeds the preset critical threshold, adjust the device parameters of the first base station and the second base station based on the adjustment value, and change the sizes of the first coverage area and the second coverage area to reduce the adjustment value; Step S5: Continuously obtain the shortest driving path from the first positioning point O2 to the remaining first positioning points O1, O3,..., O i and repeat steps S2 to S5 until the calculation between all the first positioning points is completed.

2. The optimization method of a 5G antenna feeder system according to claim 1, characterized in that In the said step S3, dividing the signal state of the test device into the abnormal state includes the following steps: Step S31: Set test points at preset sampling distances on the shortest driving path, and obtain the first signal strength and the second signal strength received by the test device at the test points. The first signal strength and the second signal strength correspond to the first base station and the second base station respectively, as well as the first distance and the second distance between the test point and the first base station and the second base station; Step S32: Divide the test points into first abnormal points and second abnormal points. Among them, if the first distance of the test point is greater than the second distance and the first signal strength is less than the second signal strength, or the first distance is less than the second distance and the first signal strength is greater than the second signal strength, then define the test point as the first abnormal point. If the test device is connected to the first base station and the second base station at two test points respectively, then define the two test points as the second abnormal points; Step S33: When the test device is between the first abnormal points or between the two second abnormal points, divide the signal state of the test device into the abnormal state.

3. The optimization method of a 5G antenna feeder system according to claim 2, wherein Obtaining the duration of the test device during the cumulative time includes the following steps: Based on the driving speed distribution data, each road is divided into multiple sub-sections, and each of the sub-sections corresponds to an average driving speed. Based on the sampling distance and the average driving speed, the time intervals between adjacent first abnormal points and the time intervals between adjacent second abnormal points are obtained. Based on the first formula and the second formula, the first time ε1 and the second time ε2 are calculated respectively. The first formula is: The second formula is where t n is the time interval between the nth group of adjacent first abnormal points, and ΔT n is the time interval between the mth group of adjacent first abnormal points. The first time and the second time are added together to obtain the cumulative time.

4. The optimization method of a 5G antenna feeder system according to claim 3, characterized in that, Calculating the adjustment value based on the cumulative time includes the following steps: Set multiple traffic flow intervals, assign corresponding weights to each of the traffic flow intervals, obtain the traffic flow data between adjacent ones of the first abnormal points and between adjacent ones of the second abnormal points, match the corresponding traffic flow intervals based on the traffic flow data, calculate a first value Score1 and a second value Score2 respectively through a third formula and a fourth formula, and the third formula is: The fourth formula is: where ω n is the weight corresponding to the nth traffic flow interval, add the first value and the second value to obtain the adjusted value.

5. The optimization method of a 5G antenna feeder system according to claim 4, wherein Changing the sizes of the first coverage area and the second coverage area includes the following steps: If the first value is greater than the second value, correspondingly reduce the first coverage area or the second coverage area. If the first value is greater than the second value, correspondingly enlarge the first coverage area or the second coverage area.

6. The optimization method of a 5G antenna feeder system according to claim 1, characterized in that, In the said step S1, obtaining the first coverage area includes the following steps: Step S11: Obtain historical data, where the historical data includes the device parameters of the first base station, the theoretical coverage area calculated based on the device parameters and the signal quality propagation model, the actual coverage area obtained based on the measurement device, and the geographical information data within the theoretical coverage area; Step S22: Establish a coverage area correction model based on a neural network, train the coverage area correction model based on the historical data, input the device parameters, the theoretical coverage area, and the geographical information data of the first base station to be predicted into the coverage area correction model to obtain the actual coverage area, and set the actual coverage area as the first coverage area of the first base station.

7. An optimization system for a 5G antenna feeder system, which is used to implement the optimization method for a 5G antenna feeder system according to any one of claims 1-6, characterized in that, It includes: A preprocessing module, configured to obtain the first coverage area and the second coverage area of the first base station and the second base station, as well as the overlapping area between the first coverage area and the second coverage area, and obtain the traffic information located within the overlapping area, where the traffic information includes the road direction, traffic flow data, and driving speed distribution data; The encoding module sets the positions of the boundaries of the overlapping regions and the intersections of each road as the first positioning points, and sets the intersections between different roads as the second positioning points. The first positioning points are numbered as O1, O2, …, O i , and numbers the roads between adjacent first positioning points or second positioning points as L1, L2, …, L j , obtains the shortest driving paths between each of the first positioning points, obtains the road numbers based on the shortest driving paths, and encodes each of the shortest driving paths respectively based on the road numbers to obtain an encoding sequence; A defect detection module, which classifies the signal status of the test device into a normal status and an abnormal status, determines two first positioning points to be tested, obtains the coding sequence based on the numbers of the first positioning points, and tests and obtains the cumulative time during which the signal status of the test device is in the abnormal status between the two first positioning points according to the coding sequence and the driving speed distribution data; A decision-making module, which calculates an adjustment value based on the cumulative time. If the adjustment value exceeds a preset critical threshold, it adjusts the device parameters of the first base station and the second base station based on the adjustment value, and changes the sizes of the first coverage area and the second coverage area to reduce the adjustment value.

8. A computer storage medium, characterized in that, The computer storage medium stores program instructions, where, when the program instructions run, they control the device where the computer storage medium is located to execute an optimization method for a 5G antenna feeder system according to any one of claims 1-6.

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