Intelligent Radio Frequency Optimization Method, Device, Electronic Device and Storage Medium

Abstract

An embodiment of the present invention discloses an intelligent radio frequency optimization method, device, electronic device and storage medium. The intelligent radio frequency optimization method includes: obtaining the test simulation data results of the intelligent radio frequency coverage effect, and establishing an initial decision model library based on the test simulation data results; obtaining the coverage problem analysis point information, and based on the coverage problem analysis point information, obtaining the area to be optimized; matching the initial adjustment target decision of the area based on the initial decision model library; performing refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtaining the final intelligent radio frequency adjustment plan based on the optimization results. The embodiments of the present invention can efficiently and accurately intelligentize the artificial analysis process, and can also accurately analyze and process independent problem points according to the specific conditions of the area, greatly reducing the data processing level, improving the analysis efficiency, reducing the input cost, and achieving the purpose of reducing costs and increasing efficiency in network operation and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless intelligent operation and maintenance, and particularly to an intelligent radio frequency optimization method, device, electronic device and storage medium. Background Art

[0002] At present, the 5G coverage optimization work mainly relies on manual analysis. Based on the familiarity of the optimization personnel with the geographical environment of the area and the construction of wireless base stations, according to the coverage optimization experience, the optimal radio frequency adjustment scheme is manually analyzed. There are two types of intelligent means as follows:

[0003] 1. Automatic test analysis tool: The test data is reported by manual or vehicle-mounted test terminals to perform automated demarcation analysis on wireless network quality problems. The analysis mainly focuses on event-based problems such as connection, disconnection, and interference quality degradation. For coverage problems, it mainly focuses on the analysis of the reasons for weak coverage quality degradation, such as whether the handover is reasonable and whether the signal strength of the main coverage cell is abnormal, rather than providing effective adjustment schemes and suggestions for existing basic coverage problems. 2. Simulation software ACP function: The simulation analysis software can achieve coverage prediction based on information such as engineering parameters, geographical environment, and radio frequency configuration parameters, and then iteratively calculate the optimal engineering and configuration parameters in a reverse cycle. However, this function is based on the simulation ray model kernel of the simulation software manufacturer, with fixed internal algorithms and weights. It only provides a cleaning scheme for large areas and is only applicable to the initial stage of 5G large-scale construction. It is even more impossible to perform customized iteration in combination with on-site conditions. At the same time, relying entirely on the capabilities of the manufacturer incurs high costs. Summary of the Invention

[0004] Based on the problems existing in the prior art, embodiments of the present invention provide an intelligent radio frequency optimization method, device, electronic device and storage medium.

[0005] In a first aspect, an embodiment of the present invention provides an intelligent radio frequency optimization method, including:

[0006] Obtaining the test simulation data results of the intelligent radio frequency coverage effect, and establishing an initial decision model library based on the test simulation data results;

[0007] Obtaining the information of the coverage problem analysis points, and obtaining the area to be optimized based on the information of the coverage problem analysis points;

[0008] Matching the initial adjustment target decision of the area based on the initial decision model library;

[0009] Performing refined coverage calculation and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtaining the final intelligent radio frequency adjustment scheme based on the optimization results.

[0010] In some examples, obtaining the test simulation data results of the intelligent radio frequency coverage effect and establishing an initial decision model library based on the test simulation data results includes:

[0011] Obtaining a plurality of test data, testing the intelligent radio frequency coverage effect based on the plurality of test data to obtain the test simulation data results, and establishing the initial decision model library according to the test simulation data results, wherein the test data includes some or all of the distance between the 5G site and the coverage analysis point at different frequency bands, the azimuth difference of a single beam in the 5G service area, the vertical beam angle difference, and the power margin conversion gain.

[0012] In some examples, obtaining the coverage problem analysis point information and obtaining the area to be optimized based on the coverage problem analysis point information includes:

[0013] Obtaining the coverage problem analysis point information;

[0014] Calculating a plurality of cells corresponding to the coverage temperature analysis point based on the coverage problem analysis point information;

[0015] Sorting the plurality of cells to use the preset number of cells with the top rankings as the area to be optimized.

[0016] In some examples, the coverage problem analysis point information includes some or all of the central longitude, central latitude, range radius, RSRP, and expected RSRP.

[0017] In some examples, matching the initial adjustment target decision for the area based on the initial decision model library includes:

[0018] Calculating whether the theoretical gain of the area matches the expected enhanced signal reception power based on the initial decision model library;

[0019] If so, the initial adjustment target decision for the area is matched.

[0020] In some examples, performing refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtaining the final intelligent radio frequency adjustment scheme based on the optimization results includes:

[0021] Based on the high-precision map ray tracing simulation technology, performing refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtaining the final intelligent radio frequency adjustment scheme based on the optimization results.

[0022] In some examples, obtaining the final intelligent radio frequency adjustment scheme based on the optimization results includes:

[0023] Adjust the intelligent radio frequency of the area according to the optimization result, and obtain the coverage performance index of the area;

[0024] If the coverage performance index meets the predetermined requirements, use the intelligent radio frequency adjustment scheme corresponding to the optimization result as the final intelligent radio frequency adjustment scheme.

[0025] In a second aspect, an embodiment of the present invention provides an intelligent radio frequency optimization device, including:

[0026] A model library acquisition module, configured to obtain the test simulation data results of the intelligent radio frequency coverage effect, and establish an initial decision model library based on the test simulation data results;

[0027] An area determination module, configured to obtain coverage problem analysis point information, and obtain the area to be optimized based on the coverage problem analysis point information;

[0028] An initial decision acquisition module, configured to match the initial adjustment target decision of the area based on the initial decision model library;

[0029] An optimization module, configured to perform refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtain the final intelligent radio frequency adjustment scheme based on the optimization result.

[0030] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the intelligent radio frequency optimization method described in the first aspect is implemented.

[0031] In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the intelligent radio frequency optimization method described in the first aspect is implemented.

[0032] As can be seen from the above technical solutions, the intelligent radio frequency optimization method, device, electronic device, and storage medium provided by the embodiments of the present invention establish a coverage optimization decision model library to complete the optimization target selection, and based on the multi-model simulation path loss strength judgment, use intelligent algorithms to implement the best radio frequency scheme decision and simulation verification. It can not only efficiently and accurately intelligentize the artificial analysis process on the premise of ensuring network stability, but also accurately analyze and process independent problem points according to the specific conditions of the area. Thus, it can greatly reduce the data processing level, improve the analysis efficiency, reduce the input cost, and further achieve the purpose of reducing costs and increasing efficiency in network operation and maintenance. Description of the Drawings

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0034] Figure 1 is a flowchart of an intelligent radio frequency optimization method provided by an embodiment of the present invention;

[0035] Figure 2 is a schematic diagram of the detailed implementation process of an intelligent RF optimization method based on 5G coverage modeling and calculation in the intelligent radio frequency optimization method provided by an embodiment of the present invention;

[0036] Figure 3 is a schematic diagram of the description of coverage-related variable elements in the intelligent radio frequency optimization method provided by an embodiment of the present invention;

[0037] Figure 4 is a schematic diagram of the change in signal reception power under the configuration model in the intelligent radio frequency optimization method provided by an embodiment of the present invention;

[0038] Figure 5 is a schematic diagram of the problem point test or accurate position correlation northbound MR optimization target cell sequence allocation algorithm in the intelligent radio frequency optimization method provided by an embodiment of the present invention;

[0039] Figure 6 is a schematic diagram of the calibration of the initial decision model matching optimization target cell sequence in the intelligent radio frequency optimization method provided by an embodiment of the present invention;

[0040] Figure 7 is a flowchart of the optimization strategy for the radio frequency optimization configuration scheme in the intelligent radio frequency optimization method provided by an embodiment of the present invention;

[0041] Figure 8 is a schematic diagram of the simulation system docking multi-path ray tracing signal strength change measurement in the intelligent radio frequency optimization method provided by an embodiment of the present invention;

[0042] Figure 9 is a schematic diagram of the antenna weight adjustment parameter calculation and matching method in the intelligent radio frequency optimization method provided by an embodiment of the present invention;

[0043] Figure 10 is a structural block diagram of an intelligent radio frequency optimization device provided by an embodiment of the present invention;

[0044] Figure 11 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0045] The following further describes the specific implementation manners of the present invention in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0046] The following describes an intelligent radio frequency optimization method, device, electronic device, and storage medium according to an embodiment of the present invention in conjunction with the accompanying drawings.

[0047] Figure 1 The flowchart of the intelligent radio frequency optimization method provided by an embodiment of the present invention is shown. As Figure 1 shown, the intelligent radio frequency optimization method provided by an embodiment of the present invention includes the following:

[0048] S101: Obtain the test simulation data results of the intelligent radio frequency coverage effect, and establish an initial decision model library based on the test simulation data results.

[0049] Combined with Figure 2 and Figure 3 shown, obtaining the test simulation data results of the intelligent radio frequency coverage effect and establishing an initial decision model library based on the test simulation data results includes: obtaining a plurality of test data, and testing the intelligent radio frequency coverage effect based on the plurality of test data to obtain the test simulation data results, and establishing the initial decision model library according to the test simulation data results, where the test data includes some or all of the distance between the 5G site and the coverage analysis point at different frequency bands, the single-beam azimuth difference in the 5G service area, the vertical beam angle difference, and the power margin conversion gain.

[0050] That is: establish an initial decision library for adjustment targets, and based on the on-site test and simulation tool data results, test the effects of key coverage elements such as the distance between the 5G site and the coverage analysis point at different frequency bands, the single-beam azimuth difference in the 5G service cell, the vertical beam angle difference, and the power margin conversion gain, and screen the sampling results of different configuration scenarios. Among them, the description of the coverage-related variable elements is as Figure 3 shown.

[0051] Take values and perform field strength change statistics on the configuration constants and variables of the 5G cell frequency band, AAU / antenna type, weight, site height, etc. - the distance S between the base station and the analysis point, the beam horizontal azimuth angle difference α, the beam vertical direction angle difference β, and the power margin conversion gain value W:

[0052] In the existing network under the following constant configuration conditions, such as freq band is N41, AAUtype is AAU5639, heightLEVEL is 30 - 35. The weight configuration is a single-beam 65° shaping for synchronous 4G coverage, and the distance between the antenna and the problem analysis point is 400m.

[0053] Test the RSRP difference results of the configuration scenarios with different beam horizontal azimuth angle differences α and beam vertical direction angle differences β. As Figure 4 shown, it shows the signal reception power change under the configuration model. The transmit power configuration is adjusted and calculated, and the configuration parameters are as follows: MaxTransmitPower: the adjustment step is 0.1 dBm. The reference power ReferencePwr = MaxTransmitPower - 10 * log 10 (RBcell * 12) (Note: each RB contains 12 REs). Calculate the power parameter adjustment gain value W according to the current configuration value and the maximum configuration value. Finally, fuse the relevant constant parameters and variable configurations to establish an initial decision model library, as shown in Table 1:

[0054] Table 1

[0055]

[0056]

[0057] S102: Obtain the coverage problem analysis point information, and based on the coverage problem analysis point information, obtain the area to be optimized.

[0058] Specifically, to obtain the coverage problem analysis point information and, based on the coverage problem analysis point information, obtain the area to be optimized, including: obtaining the coverage problem analysis point information; calculating multiple cells corresponding to the coverage temperature analysis point based on the coverage problem analysis point information; sorting the multiple cells to use the top-ranked preset number of cells as the area to be optimized. In this example, the coverage problem analysis point information includes some or all of the central longitude, central latitude, range radius, RSRP, and expected RSRP.

[0059] That is: input the coverage problem analysis point information, including: central longitude, central latitude, range radius, RSRP, and expected RSRP. As shown in Table 2:

[0060] Table 2

[0061] Longitude Latitude Dis O_rsrp T_rsrp 120.3**** 32.8**** 50 -105 -100

[0062] Currently, the 5G network is mainly built with the same site. Generally, the 5G cell inherits the coverage direction or range of the corresponding LTE cell. While realizing the hierarchical simulation of the coverage cell, based on the MR data and test data fitting of the 45G co-coverage OTT / MDT accurate location backfill, a more accurate coverage cell sequence calculation is realized, such as Figure 5 shown, which is a schematic of the problem point test or the northbound MR optimization target cell sequence allocation algorithm for accurate location association, and the arrangement rules and algorithms:

[0063] 1. Since the outdoor gNB cell has the same frequency band, site, height, etc., only one target cell needs to be selected by screening and optimization.

[0064] 2. Calculate the sampling weights of the received signal power within a certain threshold D of the expected target received power when the calculation area is used as the serving cell or neighboring cell, and sort the priority sequence according to the weights. When the weight of the TOP cell: When the weight T is greater than 0.8, the single-cell optimization scheme is preferentially executed in this area; when it is less than 0.8, the multi-cell joint optimization scheme is executed.

[0065] 3. Synchronously output the average difference Rsrp Diff between the cell and the expected signal received power target T_RSRP.

[0066] 4. If the blacklist cell cannot be adjusted, the sequence of cells to be optimized is excluded, as shown in Table 3:

[0067] Table 3

[0068] Sequence CellID T Rsrp Diff 1 A 0.82 0 2 B 0.1 3 3 C 0.05 4

[0069] S103: Match the initial adjustment target decision of the area based on the initial decision model library. Among them, matching the initial adjustment target decision of the area based on the initial decision model library includes: calculating whether the theoretical gain of the area matches the expected enhanced signal received power based on the initial decision model library; if so, the initial adjustment target decision of the area is matched.

[0070] Specifically, the cells to be optimized at the problem analysis points are matched with the initial decision model library according to the TOPN sequence, including the frequency band of the cells to be optimized, AAU / antenna type, weight, station height, distance S between the cell antenna and the problem point, horizontal azimuth difference α, vertical angle difference β, adjustable power margin W, and calculate whether its theoretical gain meets or is close to the expected enhanced signal received power in combination with the sequence Rsrp Diff (Rsrp difference of non-primary coverage cells in step 3):

[0071] Among them, the expected enhanced signal received power = expected target RSRP - current RSRP;

[0072] The theoretical enhanced signal received power is obtained by matching the decision model library, and the satisfaction degree P of the expected enhanced signal received power is calculated (theoretical enhanced signal received power / expected enhanced signal received power). When P is greater than the set value, the cell is listed as the target optimization cell, and the optimization target priority is sorted according to the P value.

[0073] Among them, the minimum value of the expected gain satisfaction degree is set to 0.6, and the optimization target priority sorting result. As Figure 6As shown in the figure, it is a schematic diagram of calibrating the optimized target cell sequence for the initial decision-making model.

[0074] S104: Conduct refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtain the final intelligent radio frequency adjustment plan based on the optimization results.

[0075] In an embodiment of the present invention, conducting refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtaining the final intelligent radio frequency adjustment plan based on the optimization results includes: based on the high-precision map ray tracing simulation technology, conducting refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtaining the final intelligent radio frequency adjustment plan based on the optimization results.

[0076] In this example, obtaining the final intelligent radio frequency adjustment plan based on the optimization results includes: adjusting the intelligent radio frequency of the area according to the optimization results and obtaining the coverage performance index of the area; if the coverage performance index meets the predetermined requirements, then use the intelligent radio frequency adjustment plan corresponding to the optimization results as the final intelligent radio frequency adjustment plan.

[0077] Specifically, based on the high-precision map ray tracing simulation technology, the signal received at the problem analysis point is decomposed into three propagation types: direct wave δ1, reflected wave δ2, and diffracted wave δ3, and calculations are performed separately according to different types of ray propagation and then superimposed to obtain the final result. Among them, the measurement logic and steps (i.e., supporting priority logic modification for specific scenarios) are as Figure 7 shown, which shows the schematic of the optimization strategy for the radio frequency optimization configuration plan.

[0078] Calculate whether the adjustable power margin meets the expectation, output the power parameter adjustment configuration plan, and list the part that does not reach the gain into the antenna weight / adjustment configuration simulation.

[0079] According to the horizontal azimuth angle difference α and vertical angle difference β of the beam of the target optimized cell, set the measurement interval step size for the horizontal azimuth and vertical angles (in the present invention, the measurement interval step size for the horizontal azimuth is 3°, and the maximum adjustment is 60°; the vertical azimuth is 2°, and the maximum adjustment is 20°). List the configuration parameters obtained by deflecting the horizontal or vertical direction of the beam of the target optimized cell towards the position of the problem analysis point by the corresponding step size into the simulation operation as new cell configuration parameters, as Figure 8 shown, which is a schematic diagram of the simulation system for docking the measurement of the signal intensity change of multi-path ray tracing.

[0080] Calculate the Rsrp difference between the target optimized cell and the problem analysis point at different azimuths and vertical angles under the ray tracing model; during the process, if the result meets the expected target, the simulation calculation process ends and the optimization configuration plan calculation starts; if the minimum expected target still cannot be achieved after maximizing the parameter adjustment, the calculation of the next sequence priority target cell starts. If all target cells cannot meet the minimum expected value, mark that the problem area cannot be optimized and solved, and include it in other measures such as planning.

[0081] Antenna configuration optimization strategy: According to the influence of the existing network coverage model and the implementation difficulty, set the priority as weight configuration -> downtilt control adjustment -> horizontal azimuth adjustment.

[0082] Horizontal weight configuration strategy:

[0083] Judge whether α and β in the above default single-beam coverage measurement results meet the adjustable coverage width range of weight configuration (the vertical azimuth can be adjusted in combination with the antenna electrical downtilt, so the weight configuration mainly examines the horizontal azimuth measurement): Currently, the maximum coverage width of ordinary outdoor cells is 110°. Within this range, the number of sub-beams and the coverage azimuth can be customized. Therefore, the maximum adjustable range of the horizontal azimuth is 55 degrees. When the single-beam ray tracing coverage measurement scheme recommends an angle within 55 degrees, it can be directly implemented based on the weight configuration. As Figure 9 shown, it shows a schematic diagram of the calculation and matching method of antenna weight adjustment parameters. The original weight configuration of the cell is the default scenario 2 configuration, the coverage problem area is outside the main lobe gain area of the cell, and the vertical shaping is inherited. The scheme output is adjusted to the weight scenario 4 to achieve enhanced horizontal coverage.

[0084] Downtilt adjustment configuration plan:

[0085] Based on the adjusted weight scenario, update the antenna shaping configuration, and then perform the power measurement of different vertical angle differences β, and output the adjustment configuration in ascending order of value;

[0086] (3) Supplementary adjustment:

[0087] If the single-beam expected effect still cannot be achieved after the weight scenario adjustment and the downtilt configuration, perform the remaining azimuth supplementary adjustment. The final output includes a full set of configuration plans including power, weight, and physical parameters.

[0088] For the cell with the final configuration plan that meets the expectation, perform the coverage index statistics of the original main coverage area, and calculate the coverage performance index of the original main coverage area after the updated configuration plan adjustment. Automatically compare the coverage performance of the affected area before and after the measurement scheme adjustment, evaluate the security of the scheme, and output the final feasible configuration plan.

[0089] The intelligent radio frequency optimization method according to an embodiment of the present invention establishes a coverage optimization decision model library to complete the selection of the optimization target, and based on the judgment of the strength of the path loss of multi-model simulation, realizes the decision-making and simulation verification of the best radio frequency scheme through an intelligent algorithm. It can not only efficiently and accurately intelligentize the manual analysis process on the premise of ensuring network stability, but also accurately analyze and process independent problem points according to specific regional conditions. Therefore, it can greatly reduce the data processing level, improve the analysis efficiency, reduce the input cost, and further achieve the purpose of cost reduction and efficiency improvement in network operation and maintenance.

[0090] Figure 10 The structural schematic diagram of the intelligent radio frequency optimization device provided by an embodiment of the present invention is shown as Figure 10 As shown, the intelligent radio frequency optimization device provided by an embodiment of the present invention includes: a model library acquisition module 110, a region determination module 120, an initial decision acquisition module 130, and an optimization module 140, where:

[0091] The model library acquisition module 110 is used to obtain the test simulation data results of the intelligent radio frequency coverage effect, and establish an initial decision model library based on the test simulation data results;

[0092] The region determination module 120 is used to obtain the information of the coverage problem analysis points, and obtain the region to be optimized based on the information of the coverage problem analysis points;

[0093] The initial decision acquisition module 130 is used to match the initial adjustment target decision of the region based on the initial decision model library;

[0094] The optimization module 140 is used to perform refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtain the final intelligent radio frequency adjustment scheme based on the optimization results.

[0095] The intelligent radio frequency optimization device according to an embodiment of the present invention establishes a coverage optimization decision model library to complete the selection of the optimization target, and based on the judgment of the strength of the path loss of multi-model simulation, realizes the decision-making and simulation verification of the best radio frequency scheme through an intelligent algorithm. It can not only efficiently and accurately intelligentize the manual analysis process on the premise of ensuring network stability, but also accurately analyze and process independent problem points according to specific regional conditions. Therefore, it can greatly reduce the data processing level, improve the analysis efficiency, reduce the input cost, and further achieve the purpose of cost reduction and efficiency improvement in network operation and maintenance.

[0096] It should be noted that the specific implementation manner of the intelligent radio frequency optimization device in the embodiment of the present invention is similar to the specific implementation manner of the intelligent radio frequency optimization method in the embodiment of the present invention. For details, please refer to the description in the method part. To reduce redundancy, it will not be elaborated here.

[0097] Based on the same inventive concept, another embodiment of the present invention provides an electronic device. Refer to Figure 11 , the electronic device specifically includes the following components: a processor 401, a memory 402, a communication interface 403, and a communication bus 404;

[0098] Among them, the processor 401, the memory 402, and the communication interface 403 complete mutual communication through the communication bus 404; the communication interface 403 is used to implement information transmission between devices;

[0099] The processor 401 is used to call the computer program in the memory 402. When the processor executes the computer program, all steps of the above-mentioned intelligent radio frequency optimization method are implemented. For example, when the processor executes the computer program, the following steps are implemented: obtaining the test simulation data result of the intelligent radio frequency coverage effect, and establishing an initial decision model library based on the test simulation data result; obtaining the information of the coverage problem analysis point, and obtaining the area to be optimized based on the information of the coverage problem analysis point; matching the initial adjustment target decision of the area based on the initial decision model library; performing refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtaining the final intelligent radio frequency adjustment plan based on the optimization result.

[0100] Based on the same inventive concept, another embodiment of the present invention provides a non-transitory computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, all steps of the above-mentioned intelligent radio frequency optimization method are implemented. For example, when the processor executes the computer program, the following steps are implemented: obtaining the test simulation data result of the intelligent radio frequency coverage effect, and establishing an initial decision model library based on the test simulation data result; obtaining the information of the coverage problem analysis point, and obtaining the area to be optimized based on the information of the coverage problem analysis point; matching the initial adjustment target decision of the area based on the initial decision model library; performing refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtaining the final intelligent radio frequency adjustment plan based on the optimization result.

[0101] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0102] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention. A person of ordinary skill in the art can understand and implement it without creative labor.

[0103] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disks, optical discs, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the index monitoring methods described in various embodiments or some parts of the embodiments.

[0104] In addition, in the present invention, terms such as "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0105] In addition, in the present invention, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0106] In addition, in the description of this specification, descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent radio frequency optimization method, characterized in that, it includes: Obtain the test simulation data results of the intelligent radio frequency coverage effect, and establish an initial decision model library based on the test simulation data results; Obtain the information of the coverage problem analysis points, and based on the information of the coverage problem analysis points, obtain the area to be optimized; Match the initial adjustment target decision of the area based on the initial decision model library; Perform refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtain the final intelligent radio frequency adjustment scheme based on the optimization results; The matching of the initial adjustment target decision of the area based on the initial decision model library includes: Based on the initial decision model library, calculate whether the theoretical gain of the area matches the expected enhanced signal reception power; If so, match the initial adjustment target decision of the area.

2. The intelligent radio frequency optimization method according to claim 1, characterized in that, the obtaining of the test simulation data results of the intelligent radio frequency coverage effect and the establishment of the initial decision model library based on the test simulation data results include: Obtain multiple test data, and test the intelligent radio frequency coverage effect based on the multiple test data to obtain the test simulation data results, and establish the initial decision model library according to the test simulation data results, wherein the test data includes part or all of the distance between the 5G site and the coverage analysis point at different frequency bands, the single-beam azimuth difference in the 5G service area, the vertical beam angle difference, and the power margin conversion gain.

3. The intelligent radio frequency optimization method according to claim 1, characterized in that, the obtaining of the information of the coverage problem analysis points and the obtaining of the area to be optimized based on the information of the coverage problem analysis points include: Obtain the information of the coverage problem analysis points; Based on the information of the coverage problem analysis points, calculate multiple cells corresponding to the coverage temperature analysis point; Sort the multiple cells to use the top-ranked preset number of cells as the area to be optimized.

4. The intelligent radio frequency optimization method according to claim 3, characterized in that, the information of the coverage problem analysis points includes part or all of the central longitude, central latitude, range radius, RSRP, and expected RSRP.

5. The intelligent radio frequency optimization method according to claim 1, characterized in that, performing refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtaining the final intelligent radio frequency adjustment scheme based on the optimization results includes: Based on the high-precision map ray tracing simulation technology, perform refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtain the final intelligent radio frequency adjustment scheme based on the optimization results.

6. The intelligent radio frequency optimization method according to claim 5, characterized in that, the obtaining of the final intelligent radio frequency adjustment scheme based on the optimization results includes: Adjust the intelligent radio frequency of the area according to the optimization results, and obtain the coverage performance index of the area; If the coverage performance index meets the predetermined requirements, the intelligent radio frequency adjustment scheme corresponding to the optimization result is used as the final intelligent radio frequency adjustment scheme.

7. An intelligent radio frequency optimization device, characterized in that, it includes: a model library acquisition module, configured to obtain the test simulation data results of the intelligent radio frequency coverage effect, and establish an initial decision model library based on the test simulation data results; a region determination module, configured to obtain the coverage problem analysis point information, and obtain the region to be optimized based on the coverage problem analysis point information; an initial decision acquisition module, configured to match the initial adjustment target decision of the region based on the initial decision model library; an optimization module, configured to perform refined coverage measurement and hierarchical optimization on different parameter configurations of the initial adjustment target decision, and obtain the final intelligent radio frequency adjustment scheme based on the optimization result; the model library acquisition module, the region determination module, the initial decision acquisition module, and the optimization module are used to implement the intelligent radio frequency optimization method according to any one of claims 1 to 6.

8. An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the intelligent radio frequency optimization method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, it implements the intelligent radio frequency optimization method according to any one of claims 1 to 6.

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