WO2023103652A1 - 基站方位角纠正方法、装置和系统、存储介质 - Google Patents

基站方位角纠正方法、装置和系统、存储介质 Download PDF

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Publication number
WO2023103652A1
WO2023103652A1 PCT/CN2022/128947 CN2022128947W WO2023103652A1 WO 2023103652 A1 WO2023103652 A1 WO 2023103652A1 CN 2022128947 W CN2022128947 W CN 2022128947W WO 2023103652 A1 WO2023103652 A1 WO 2023103652A1
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Prior art keywords
base station
azimuth
measurement report
report data
cell
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PCT/CN2022/128947
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English (en)
French (fr)
Inventor
王秋森
许盛宏
郑三强
宫云平
马泽雄
王谦
罗伟华
原思平
王金波
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中国电信股份有限公司
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Publication of WO2023103652A1 publication Critical patent/WO2023103652A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present disclosure relates to the technical field of communications, and in particular to a base station azimuth correction method, device and system, and a storage medium.
  • the transmission and reception of space wireless signals are realized by antennas. Therefore, antennas play a pivotal role in mobile communication networks.
  • the antenna azimuth is a very important part.
  • the azimuth angle of the antenna sometimes shifts due to natural disasters such as strong winds and earthquakes, which will lead to a decrease in the quality of the mobile communication network.
  • a base station azimuth correction method including:
  • the screening condition is that the received reference signal power is greater than a predetermined received reference signal power.
  • the base station azimuth correction method further includes:
  • the revised predetermined reference signal received power repeatedly perform the screening of the measurement report data satisfying the screening conditions, the screening of the measurement report data of the accurate time advance value, and the calculation of the base station azimuth angle according to the measurement report data of the accurate time advance value A step of.
  • the base station azimuth correction method further includes:
  • the determining the cell with a problematic base station azimuth angle according to the predicted azimuth angle includes:
  • the difference between the predicted azimuth of the first cell and the recorded azimuth of the second cell is less than the first predetermined angle, and the second cell If the difference between the predicted azimuth angle of the first cell and the recorded azimuth angle of the first cell is less than the second predetermined angle, it is determined that the azimuth angles of the base station of the first cell and the second cell are reversed.
  • the predetermined range is greater than 130 degrees and less than 176 degrees; the first predetermined angle is 10 degrees; and the second predetermined angle is 65 degrees.
  • the determining the cell with a problematic base station azimuth angle according to the predicted azimuth angle includes:
  • the error between the predicted azimuth and the filing azimuth is within a predetermined range, if the ratio of the number of measurement report data within the third predetermined angle range on both sides of the filing azimuth of the cell to the total number of measurement report data is less than the predetermined ratio value , it is determined that the deviation of the azimuth angle of the cell is greater than a predetermined threshold.
  • the third predetermined angle range is half of the beam width; the predetermined ratio is 20%.
  • the screening out the measurement report data meeting the screening conditions includes:
  • the measurement report data satisfying the filter condition is filtered out.
  • the screening out the measurement report data of the accurate time advance value includes:
  • the area ranges of multiple concentric ring areas are determined, and the area ranges are valid time advance value areas.
  • the determining the predicted azimuth according to the measurement report data of the accurate time advance value includes:
  • the predicted azimuth is determined.
  • the determining the predicted azimuth according to the measurement report data of the accurate time advance value includes:
  • the predicted azimuth is calculated according to the prediction model of the azimuth of the base station.
  • a base station azimuth correction device including:
  • the first screening module is used to filter out the measurement report data satisfying the screening conditions
  • the second screening module is used to filter out the measurement report data with accurate time advance value from the measurement report data meeting the screening conditions;
  • the azimuth correction module is used to determine the predicted azimuth according to the measurement report data of the accurate time advance value.
  • the screening condition is that the received reference signal power is greater than a predetermined received reference signal power.
  • the apparatus for correcting the azimuth angle of the base station is configured to implement operations for implementing the method for correcting the azimuth angle of the base station as described in any of the foregoing embodiments.
  • a base station azimuth correction device including:
  • the processor is configured to execute the instructions, so that the apparatus for correcting the azimuth angle of the base station performs operations for implementing the method for correcting the azimuth angle of the base station as described in any one of the above embodiments.
  • a system for correcting a base station azimuth angle including the apparatus for correcting a base station azimuth angle as described in any one of the above embodiments.
  • a non-transitory computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, any of the above-mentioned The base station azimuth correction method described in the embodiment.
  • a computer program including: instructions, which, when executed by a processor, cause the processor to execute the base station azimuth correction method according to any one of the above embodiments.
  • Fig. 1 is a schematic diagram of some embodiments of a method for correcting azimuth angle of a base station according to the present disclosure.
  • Fig. 2 is a schematic diagram of other embodiments of the method for correcting the azimuth angle of the base station according to the present disclosure.
  • Fig. 3 is an analysis diagram of the TA value range of the base station error in some embodiments of the present disclosure.
  • Fig. 4 is a schematic diagram of a value range of TA with a base station as a reference point in some embodiments of the present disclosure.
  • Fig. 5 shows the actual distribution range of TA points in the map in some embodiments of the present disclosure.
  • Fig. 6 is a schematic diagram of some embodiments of a base station azimuth correction device of the present disclosure.
  • Fig. 7 is a schematic structural diagram of some other embodiments of the device for correcting the azimuth angle of the base station according to the present disclosure.
  • the relevant technical verification method is based on road test data, which requires vehicles, personnel, and tools.
  • the test cost is high, and the daily inspection of the azimuth angle of the community requires frequent field tests and frequent on-site collections, threatening Personal safety of maintenance personnel, manual verification is inefficient, and automatic verification cannot be realized.
  • the present disclosure provides a base station azimuth correction method, device and system, and a storage medium, which can correct the base station azimuth based on MR (Measurement Report, measurement report) data, and can realize azimuth intelligence. Reduce the number of manual high-risk collections.
  • MR Measurement Report, measurement report
  • Fig. 1 is a schematic diagram of some embodiments of a method for correcting azimuth angle of a base station according to the present disclosure.
  • this embodiment can be implemented by the disclosed base station azimuth correction device or the disclosed base station azimuth correction system.
  • the method may include at least one of step 11-step 13, wherein:
  • Step 11 filter out the measurement report data that meets the filtering condition, wherein the filtering condition is that (Reference Signal Receiving Power, reference signal receiving power) is greater than a predetermined reference signal receiving power.
  • step 11 may include: counting the MR data of a predetermined number of days, classifying the base stations, and for a certain cell corresponding to a base station, collecting MR data points with high RSRP and good signal and whose proportion is greater than the total MR
  • the MR data corresponding to x% of the number (for example, the first 80%) is taken out, and the RSRP lower limit value of these MR data is found, and the MR data can be screened out by using the lower limit value.
  • the predetermined number of days may be 15 days.
  • the measurement report MR periodically reported by the 4G user terminal of the mobile network includes GPS latitude and longitude and network coverage quality data, which can lay the foundation for automatic collection of massive coverage data and automatic correction of base station azimuth angle .
  • Step 12 from the measurement report data satisfying the filtering condition, filter out the measurement report data with accurate TA (Timing Advance, timing advance) value.
  • TA Timing Advance, timing advance
  • Step 13 according to the measurement report data of the accurate time advance value, determine the predicted azimuth angle.
  • step 13 may include: taking the base station as the center of the circle, dividing into 360 small sectors of 1 degree, calculating the proportion of MR data in each sector, sorting in descending order, and removing the first 5 proportions The largest angle; calculate the ratio of the MR numbers of the small sector of each (horizontal beam width/2) degree in 360 angles to the total MR number, sort them in descending order, and obtain the angle corresponding to the highest ratio, which is the best azimuth angle.
  • step 13 may include: according to scenarios such as urban areas and rural areas, combined with base station coverage, base station physical identification, base station latitude and longitude, user longitude and latitude, RSRP, TA, cell physical identification and other multi-dimensional user terminal reporting measurement Report (MR) data to establish a prediction model of the base station azimuth angle; calculate the base station azimuth angle (that is, the prediction angle) according to the prediction model of the base station azimuth angle.
  • scenarios such as urban areas and rural areas, combined with base station coverage, base station physical identification, base station latitude and longitude, user longitude and latitude, RSRP, TA, cell physical identification and other multi-dimensional user terminal reporting measurement Report (MR) data to establish a prediction model of the base station azimuth angle; calculate the base station azimuth angle (that is, the prediction angle) according to the prediction model of the base station azimuth angle.
  • scenarios such as urban areas and rural areas, combined with base station coverage, base station physical identification, base station latitude and longitude, user longitude and latitude,
  • the foregoing embodiments of the present disclosure propose a method for correcting the azimuth angle of a base station based on MR data.
  • the above-mentioned embodiments of the present disclosure establish a big data analysis algorithm model to periodically check the cells of the entire network, which improves the accuracy of the basic data. Therefore, the above-mentioned embodiments of the present disclosure can Quickly judge whether the azimuth angle of the base station antenna of the source cell is deviated, and reduce costs and increase efficiency for enterprises.
  • Fig. 2 is a schematic diagram of other embodiments of the method for correcting the azimuth angle of the base station according to the present disclosure.
  • this embodiment can be implemented by the disclosed base station azimuth correction device or the disclosed base station azimuth correction system.
  • the method may include at least one of steps 21-24, wherein:
  • Step 21 screen out MR data satisfying the RSRP value.
  • step 21 may include at least one of step 211 and step 212, wherein:
  • Step 211 associate the latitude and longitude, RSRP, base station identification (base station id), cell identification (community id), and TA data in the MR with the base station identification (base station id), and base station longitude and latitude data, and the association condition is that the two base station ids are equal.
  • 250 communities in a certain province may be selected to collect MR data for 15 days for a pilot project.
  • Step 212 classify the associated MR data according to the coverage of the base station, take the MR data of one type of base station, and set the preset RSRP value to -85dBm (assuming that the corresponding RSRP lower limit of 90% is -85dBm), the The MR data of all communities are screened, and the screening condition is RSRP>-85dBm, and the MR data meeting the screening conditions are obtained.
  • step 22 MR data with accurate TA values are screened out.
  • step 22 may include: for each time advance value in a single base station, determine that the acquisition range of the corresponding position point is a circular area; for multiple time advance values in a single base station, determine The multi-ring area range of multiple concentric ring areas, the area range is the effective time advance value area.
  • Fig. 3 is an analysis diagram of the TA value range of the base station error in some embodiments of the present disclosure.
  • Fig. 4 is a schematic diagram of a value range of TA with a base station as a reference point in some embodiments of the present disclosure.
  • Fig. 5 shows the actual distribution range of TA points in the map in some embodiments of the present disclosure.
  • the collection range of a point corresponding to a single TA value in a single base station is a ring area
  • the rings are concentric rings
  • the center position of the ring is determined by the base station
  • the latitude and longitude of the ring is determined
  • the outer radius of the ring is about TA value * 78.12
  • the width of the ring area in the radial direction is equal to the suspension height of the base station antenna
  • the area where the accurate TA data is obtained by screening the MR data in the ring for With multiple TA values for a single base station, multiple concentric circular ring areas can be obtained.
  • This area range is the effective TA area
  • the MR data in this range is the MR data of the effective TA area for calculating the azimuth.
  • the range of the intersection area between the effective RSRP area and the effective TA area is the final effective area range used to calculate the azimuth angle, and the MR data with more than 30 MR data in this range is the base station azimuth prediction The data to use for the model.
  • step 23 may include: determining the predicted azimuth according to the measurement report data of the accurate time advance value.
  • step 23 may include: determining the predicted azimuth angle according to the longitude and latitude of the two AGPS (Assisted Global Positioning System, Assisted Global Positioning System) and the accurate time advance value.
  • AGPS Assisted Global Positioning System, Assisted Global Positioning System
  • Formula (1) is two AGPS longitude and latitude distance formulas:
  • Formula (2) is the conversion formula between TA and actual distance:
  • Equation (3) is the azimuth calculation formula:
  • step 23 may include: taking the base station as the center, dividing the circular area with a radius within the range of [80m, 800m] into 360 small sectors of 1 degree In the circular area, calculate the proportion of the number of MR data of each sector ring, and sort them in descending order, and remove the first 5 angles with the largest proportion; the 360 angles of the small sector rings with 33 (horizontal beam width/2) degrees each The ratio of the MR number to the total MR number is calculated in descending order, and the angle corresponding to the highest ratio is obtained, which is the best azimuth angle.
  • Step 24 calculating the iterative RSRP value of the comparison between the azimuth and the recorded azimuth.
  • step 24 may include: taking the recorded azimuth angles of the base stations of 30 cells for verification as training data, comparing the loss error with the calculated azimuth angles of the corresponding cells obtained in step 23, and iterating through gradient descent Obtain the proportion corresponding to the RSRP lower limit value, and then deduce and correct the k value in the screening condition RSRP>k, and then repeat steps 21, 22, and 23 with the RSRP value.
  • the base station azimuth correction method of the present disclosure may further include: combining base station coverage, base station physical identifier, base station longitude and latitude, user longitude and latitude, RSRP, TA, cell physical identifier, etc.
  • the multi-dimensional MR data establishes a prediction model for the azimuth angle of the base station; the prediction model will automatically generate the initial parameters (RSRP value) used to screen the MR data, and iteratively update and optimize the initial parameter values according to the effect after solving the model to obtain different
  • the RSRP value corresponding to the class base station locate the area where the MR data whose reference signal received power is greater than the RSRP value is located, this area is the effective RSRP area, and the MR data screened out according to the RSRP value in this range is used to calculate the azimuth angle MR data in the valid RSRP area.
  • the method for correcting the azimuth angle of the base station may further include: step 25, obtaining a cell with a problematic azimuth angle of the base station and verifying the rectification.
  • step 25 may include: determining a cell with a problematic base station azimuth according to the predicted azimuth; sending the cell with a problematic base station azimuth to the user for verification and rectification.
  • the step of determining the cell with a problematic base station azimuth angle according to the predicted azimuth angle may include: combining the coverage area of the base station, the physical identifier of the base station, the longitude and latitude of the base station, and the longitude and latitude of the user according to the urban area, rural areas and other scenarios , RSRP, TA, cell physical identity and other multi-dimensional MR data to establish a prediction model for the base station azimuth angle; calculate the base station azimuth angle (that is, the predicted angle) according to the prediction model and compare the error with the recorded base station azimuth angle (that is, the filed angle), Identify cells with problematic base station azimuths.
  • the step of calculating the azimuth angle of the base station (that is, the predicted angle) based on the prediction model and comparing the error with the azimuth angle of the base station (that is, the angle for the record) for the record, and determining the cell with the problematic azimuth angle of the base station may be Including: for the first cell and the second cell where the error between the predicted azimuth and the recorded azimuth is within a predetermined range, if the difference between the predicted azimuth of the first cell and the recorded azimuth of the second cell is less than the first predetermined angle, and the second If the difference between the predicted azimuth angle of the second cell and the recorded azimuth angle of the first cell is less than the second predetermined angle, it is determined that the azimuth angles of the base stations of the first cell and the second cell are reversed.
  • the predetermined range is greater than 130 degrees and less than 176 degrees; the first predetermined angle is 10 degrees; and the second predetermined angle is 65 degrees.
  • the base station azimuth angle (that is, the predicted angle) calculated according to the prediction model is compared with the recorded base station azimuth angle (that is, the filed angle) for error comparison, and the location of the cell with a problematic base station azimuth angle is determined.
  • the step may include: for a cell whose error between the predicted azimuth and the filing azimuth is within a predetermined range, if the ratio of the number of measurement report data within the third predetermined angle range on both sides of the filing azimuth of the cell to the total number of measurement report data If it is smaller than the predetermined ratio value, it is determined that the deviation of the azimuth angle of the cell is greater than the predetermined threshold.
  • the third predetermined angle range may be 33 degrees (lobe width/2).
  • the predetermined ratio may be 20%.
  • the MR data of 250 residential areas in a certain province were selected for 15 days as a pilot, and the azimuth angles of 212 residential areas were finally calculated.
  • the result analysis table is shown in Table 1:
  • the rule for judging the azimuth angle of the problematic base station is as follows: first, take two cells (a cell and b cell) whose error between the predicted angle and the recorded angle is greater than 130 degrees and less than 176 degrees, when the error of the a cell The difference between the predicted angle and the filing angle of cell b is less than 10 degrees, and the difference between the predicted angle of cell b and the filing angle of cell a is less than 65 degrees, that is, it is judged that the azimuth angles of the base stations of the two cells are reversed.
  • the above-mentioned embodiments of the present disclosure propose an automatic base station azimuth angle correction based on big data. corrective method.
  • the above embodiments of the present disclosure perform systematic data analysis on the closed-loop process of "RSRP initial parameter determination” - "azimuth calculation program operation” - “feedback azimuth data training to optimize RSRP parameters”.
  • the parameter determination method is open-loop and separated from big data analysis, and the output parameters are more reliable.
  • the above-mentioned embodiments of the present disclosure do not rely on the carrier-to-interference ratio, get rid of the dependence on the base station of the neighboring cell, can judge more base station azimuth angle deviations than the related technology, and solve the pain point of the related technology.
  • the above embodiments of the present disclosure use geometric knowledge to obtain accurate MR data collection ranges (as shown in Figure 3, Figure 4, and Figure 5).
  • the multi-sector ring area value of one sector ring area can effectively extract the characteristics of big data, thereby reducing the loss of cluster computing power, and can reduce costs and increase efficiency for enterprises.
  • Fig. 6 is a schematic diagram of some embodiments of a base station azimuth correction device of the present disclosure.
  • the base station azimuth correction device of the present disclosure may include a first screening module 61, a second screening module 62, and an azimuth correction module 63, wherein:
  • the first screening module 61 is configured to filter out the measurement report data satisfying the screening condition, wherein the screening condition is that the received reference signal power is greater than the predetermined reference signal received power.
  • the first screening module 61 can be used to associate the longitude and latitude, reference signal received power, base station identifier, cell identifier, and timing advance data in the measurement report data with the base station identifier and base station latitude and longitude ; Among the associated measurement report data, filter out the measurement report data that meets the filtering conditions.
  • the second screening module 62 is configured to screen the measurement report data with accurate time advance value from the measurement report data satisfying the screening condition.
  • the second screening module 62 can be used to determine, for each time advance value in a single base station, that the acquisition range of the corresponding position point is a circular area; for multiple time advances in a single base station
  • the magnitude value is to determine the region range of multiple concentric ring regions, and the region range is an effective time advance magnitude value region.
  • the azimuth correction module 63 is configured to determine the predicted azimuth according to the measurement report data of the accurate time advance value.
  • the azimuth correction module 63 may be used to determine the predicted azimuth according to the longitude and latitude of the two assisted global satellite positioning systems and the accurate time advance value.
  • the device for correcting the azimuth angle of the base station in the present disclosure may also include 2364, where:
  • the comparison and correction module 64 is used to compare the predicted azimuth and the record azimuth, and iteratively correct the predetermined reference signal received power; according to the revised predetermined reference signal received power, then instruct the first screening module 61 and the second screening module 62 and the azimuth correction module 63 repeatedly perform the operations of filtering out the measurement report data satisfying the filtering conditions, filtering out the measurement report data with accurate time advance values, and calculating the base station azimuth angle according to the measurement report data with accurate time advance values.
  • the device for correcting the azimuth angle of the base station in the present disclosure may also include a problem cell determination module 65, wherein:
  • the problematic cell determination module 65 is used to determine the cell with a problematic base station azimuth angle according to the predicted azimuth angle; and send the cell with a problematic base station azimuth angle to the user for verification and rectification.
  • the problem cell determination module 65 can be used for the first cell and the second cell whose error between the predicted azimuth angle and the record azimuth angle is within a predetermined range, if the predicted azimuth angle of the first cell is different from that of the second cell The difference between the recorded azimuth angles of the two cells is less than the first predetermined angle, and the difference between the predicted azimuth angle of the second cell and the recorded azimuth angle of the first cell is less than the second predetermined angle, then determine the base station azimuths of the first cell and the second cell The corners are reversed.
  • the problematic cell determination module 65 can be used for a cell whose error between the predicted azimuth angle and the filing azimuth angle is within a predetermined range, if the cell is within the third predetermined angle range on both sides of the filing azimuth angle If the ratio of the number of measurement report data to the total number of measurement report data is less than a predetermined ratio value, it is determined that the deviation of the azimuth angle of the cell is greater than a predetermined threshold.
  • the apparatus for correcting the azimuth angle of the base station is configured to implement operations for implementing the method for correcting the azimuth angle of the base station as described in any of the above embodiments (for example, any of the embodiments in FIGS. 1-5 ).
  • the foregoing embodiments of the present disclosure propose a device for correcting the azimuth angle of a base station based on MR data.
  • the above-mentioned embodiments of the present disclosure establish a big data analysis algorithm model to periodically check the cells of the entire network, which improves the accuracy of the basic data. Therefore, the above-mentioned embodiments of the present disclosure can Quickly judge whether the azimuth angle of the base station antenna of the source cell is deviated, and reduce costs and increase efficiency for enterprises.
  • Fig. 7 is a schematic structural diagram of some other embodiments of the device for correcting the azimuth angle of the base station according to the present disclosure.
  • the base station azimuth correction device includes a memory 71 and a processor 72 .
  • the memory 71 is used to store instructions, and the processor 72 is coupled to the memory 71.
  • the processor 72 is configured to implement the base station azimuth angle as described in any of the above-mentioned embodiments (such as the embodiment in FIG. 1 or FIG. 4 ) based on the instructions stored in the memory. corrective method.
  • the apparatus for correcting the azimuth angle of the base station also includes a communication interface 73 for exchanging information with other devices.
  • the base station azimuth correction device further includes a bus 74 , and the processor 72 , the communication interface 73 , and the memory 71 communicate with each other through the bus 74 .
  • the memory 71 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
  • the memory 71 may also be a memory array.
  • the storage 71 may also be divided into blocks, and the blocks can be combined into virtual volumes according to certain rules.
  • processor 72 may be a central processing unit CPU, or may be an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement embodiments of the present disclosure.
  • the above-mentioned embodiments of the present disclosure do not rely on the carrier-to-interference ratio, get rid of the dependence on the base station of the neighboring cell, can judge more base station azimuth angle deviations than the related technology, and solve the pain point of the related technology.
  • the above embodiments of the present disclosure use geometric knowledge to obtain accurate MR data collection ranges (as shown in Figure 3, Figure 4, and Figure 5).
  • the multi-sector ring area value of one sector ring area can effectively extract the characteristics of big data, thereby reducing the loss of cluster computing power, and can reduce costs and increase efficiency for enterprises.
  • a base station azimuth correction system including the base station azimuth correction device described in any one of the above embodiments (for example, the embodiment in FIG. 6 or FIG. 7 ).
  • a non-transitory computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, any of the above-mentioned The method for correcting the azimuth angle of the base station described in the embodiment (such as any embodiment in FIG. 1-FIG. 5).
  • the present disclosure can correct base station azimuth based on MR data.
  • the disclosure can quickly determine whether the azimuth angle of the base station antenna of the source cell deviates, thereby reducing costs and increasing efficiency.
  • the embodiments of the present disclosure may be provided as methods, apparatuses, or computer program products. Accordingly, the present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein. .
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
  • the device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.
  • the base station azimuth correction device described above can be realized as including a general-purpose processor, a programmable logic controller (PLC), a digital signal processor (DSP), an application-specific integrated circuit (ASIC) for performing the functions described in this application , a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or any suitable combination thereof.
  • PLC programmable logic controller
  • DSP digital signal processor
  • ASIC application-specific integrated circuit
  • FPGA field programmable gate array
  • the steps for realizing the above embodiments can be completed by hardware, and can also be used to instruct related hardware to complete by a program, and the program can be stored in a non-transitory computer-readable storage medium
  • the storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, and the like.

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Abstract

本公开涉及一种基站方位角纠正方法、装置和系统、存储介质。该基站方位角纠正方法包括：筛选出满足筛选条件的测量报告数据，其中，所述筛选条件为参考信号接收功率大于预定参考信号接收功率；在满足筛选条件的测量报告数据中，筛选出准确时间提前量值的测量报告数据；根据准确时间提前量值的测量报告数据，确定预测方位角。本公开可以基于MR数据纠正基站方位角。本公开能快速判断源小区的基站天线方位角是否发生偏差，由此可以实现降本增效。

Description

基站方位角纠正方法、装置和系统、存储介质
相关申请的交叉引用
本申请是以CN申请号为202111491398.X，申请日为2021年12月8日的申请为基础，并主张其优先权，该CN申请的公开内容在此作为整体引入本申请中。
技术领域
本公开涉及通信技术领域，特别涉及一种基站方位角纠正方法、装置和系统、存储介质。
背景技术
在相关技术移动通信系统中，空间无线信号的发射和接收都是依靠天线来实现的。因此天线对于移动通信网络来说，起着举足轻重的作用。在配置基站天线的各项参数中，天线方位角是非常重要的一环。但在实际的移动通信网络中，天线方位角有时会因为强风、地震等自然灾害而发生偏移这将会导致移动通信网络质量下降。
发明内容
根据本公开的一个方面，提供一种基站方位角纠正方法，包括：
筛选出满足筛选条件的测量报告数据；
在满足筛选条件的测量报告数据中，筛选出准确时间提前量值的测量报告数据；
根据准确时间提前量值的测量报告数据，确定预测方位角。
在本公开的一些实施例中，所述筛选条件为参考信号接收功率大于预定参考信号接收功率。
在本公开的一些实施例中，所述基站方位角纠正方法还包括：
将预测方位角和备案方位角进行比对，迭代修正预定参考信号接收功率；
按照修正后的预定参考信号接收功率，重复执行所述筛选出满足筛选条件的测量报告数据、筛选出准确时间提前量值的测量报告数据和根据准确时间提前量值的测量报告数据计算基站方位角的步骤。
在本公开的一些实施例中，所述基站方位角纠正方法还包括：
根据预测方位角，确定基站方位角有问题的小区；
将基站方位角有问题的小区发送给用户进行核实整改。
在本公开的一些实施例中，所述根据预测方位角，确定基站方位角有问题的小区包括：
对于预测方位角和备案方位角的误差处于预定范围的第一小区和第二小区，若第一小区的预测方位角与第二小区的备案方位角之差小于第一预定角度，且第二小区的预测方位角与第一小区的备案方位角之差小于第二预定角度，则判定第一小区和第二小区的基站方位角接反。
在本公开的一些实施例中，预定范围为大于130度且小于176度；第一预定角度为10度；第二预定角度为65度。
在本公开的一些实施例中，所述根据预测方位角，确定基站方位角有问题的小区包括：
对于预测方位角和备案方位角的误差处于预定范围的小区，若该小区备案方位角两侧各第三预定角度范围内的测量报告数据个数占测量报告数据总个数的比例小于预定比例值，则判定该小区的方位角的偏差大于预定阈值。
在本公开的一些实施例中，第三预定角度范围为波瓣宽的一半；预定比例值为20％。
在本公开的一些实施例中，所述筛选出满足筛选条件的测量报告数据包括：
将测量报告数据中的经纬度、参考信号接收功率、基站标识、小区标识、时间提前量数据，与基站标识和基站经纬度进行关联；
在关联后的测量报告数据中，筛选出满足筛选条件的测量报告数据。
在本公开的一些实施例中，所述筛选出准确时间提前量值的测量报告数据包括：
对于单个基站中每个时间提前量值，确定对应位置点的采集范围为一个圆环区域；
对于单个基站中多个时间提前量值，确定多个同心圆环区域的多圆环的区域范围，所述区域范围为有效时间提前量值区域。
在本公开的一些实施例中，所述根据准确时间提前量值的测量报告数据，确定预测方位角包括：
根据两个辅助全球卫星定位系统的经度和纬度、准确时间提前量值，确定预测方位角。
在本公开的一些实施例中，所述根据准确时间提前量值的测量报告数据，确定预测方位角包括：
根据多维度用户终端上报的测量报告数据建立基站方位角的预测模型；
根据基站方位角的预测模型计算出预测方位角。
根据本公开的另一方面，提供一种基站方位角纠正装置，包括：
第一筛选模块，用于筛选出满足筛选条件的测量报告数据；
第二筛选模块，用于在满足筛选条件的测量报告数据中，筛选出准确时间提前量值的测量报告数据；
方位角纠正模块，用于根据准确时间提前量值的测量报告数据，确定预测方位角。
在本公开的一些实施例中，所述筛选条件为参考信号接收功率大于预定参考信号接收功率。
在本公开的一些实施例中，所述基站方位角纠正装置用于执行实现如上述任一实施例所述的基站方位角纠正方法的操作。
根据本公开的另一方面，提供一种基站方位角纠正装置，包括：
存储器，用于存储指令；
处理器，用于执行所述指令，使得所述基站方位角纠正装置执行实现如上述任一实施例所述的基站方位角纠正方法的操作。
根据本公开的另一方面，提供一种基站方位角纠正系统，包括如上述任一实施例所述的基站方位角纠正装置。
根据本公开的另一方面，提供一种非瞬时性计算机可读存储介质，其中，所述非瞬时性计算机可读存储介质存储有计算机指令，所述指令被处理器执行时实现如上述任一实施例所述的基站方位角纠正方法。
根据本公开的一些实施例，还提供了一种计算机程序，包括：指令，所述指令当由处理器执行时使所述处理器执行根据上述任一个实施例所述的基站方位角纠正方法。
附图说明
为了更清楚地说明本公开实施例或相关技术中的技术方案，下面将对实施例或相关技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本公开基站方位角纠正方法一些实施例的示意图。
图2为本公开基站方位角纠正方法另一些实施例的示意图。
图3为本公开一些实施例中基站误差的TA值范围分析图。
图4为本公开一些实施例中以基站为参考点的TA取值范围的示意图。
图5为本公开一些实施例中地图中实际TA点分布范围。
图6为本公开基站方位角纠正装置一些实施例的示意图。
图7为本公开基站方位角纠正装置又一些实施例的结构示意图。
具体实施方式
下面将结合本公开实施例中的附图，对本公开实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本公开一部分实施例，而不是全部的实施例。以下对至少一个示例性实施例的描述实际上仅仅是说明性的，决不作为对本公开及其应用或使用的任何限制。基于本公开中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本公开保护的范围。
除非另外具体说明，否则在这些实施例中阐述的部件和步骤的相对布置、数字表达式和数值不限制本公开的范围。
同时，应当明白，为了便于描述，附图中所示出的各个部分的尺寸并不是按照实际的比例关系绘制的。
对于相关领域普通技术人员已知的技术、方法和设备可能不作详细讨论，但在适当情况下，所述技术、方法和设备应当被视为授权说明书的一部分。
在这里示出和讨论的所有示例中，任何具体值应被解释为仅仅是示例性的，而不是作为限制。因此，示例性实施例的其它示例可以具有不同的值。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步讨论。
发明人注意到：相关技术的核查方式在方位角更改后，不能及时确认该更改是否有效、准确，实现闭环管理。以某省为例，34万小区中共有4312(1.27％)例方位角接反或异常情况。
发明人通过研究发现：相关技术核查方式是基于路测数据，需配车、配人、配工具，测试成本高昂，而且小区方位角日常检查需要实地测试以及上站等手段进行频繁现场采集，威胁维护人员人身安全，人工核查效率低，无法实现自动化核查。相关技术存在以下一些问题：
1)将日常维护人员高危维护采集工作量降低，解决无法自动化核查，优化成本高昂的问题。
2)解决方位角更改后，不能及时确认该更改是否有效、准确，实现闭环管理的问题。
3)需要结合不同多个系统兜转人工分析，分析问题片面、不准确且工作效率低下。
鉴于以上技术问题中的至少一项，本公开提供了一种基站方位角纠正方法、装置和系统、存储介质，可以基于MR(Measurement Report，测量报告)数据纠正基站方位角，可以实现方位角智能化核查，降低人工高危采集次数。下面通过具体实施例对本公开进行说明。
图1为本公开基站方位角纠正方法一些实施例的示意图。优选的，本实施例可由本公开基站方位角纠正装置或本公开基站方位角纠正系统执行。该方法可以包括步骤11-步骤13中的至少一个步骤，其中：
步骤11，筛选出满足筛选条件的测量报告数据，其中，所述筛选条件为(Reference Signal Receiving Power，参考信号接收功率)大于预定参考信号接收功率。
在本公开的一些实施例中，步骤11可以包括：统计预定天数的MR数据，将基站进行分类，针对一个基站对应的某小区，将RSRP高、信号好的MR数据点且占比大于总MR数的x％(例如先设前80％)对应的MR数据取出来，找到这些MR数据的RSRP下限值，用下限值就可以筛选出MR数据。
在本公开的一些实施例中，所述预定天数可以为15天。
在本公开的一些实施例中，移动网络的4G用户终端周期性上报的测量报告MR包含了GPS经纬度和网络覆盖质量数据，由此可为自动采集海量覆盖数据而实现基站方位角自动纠偏奠定基础。
步骤12，在满足筛选条件的测量报告数据中，筛选出准确TA(Timing Advance，时间提前量)值的测量报告数据。
步骤13，根据准确时间提前量值的测量报告数据，确定预测方位角。
在本公开的一些实施例中，步骤13可以包括：以基站为圆心，分成360个1度的小扇形，求每个扇形的MR数据个数占比，并降序排序，剔除前5个占比最大的角度；把360个角度左右各(水平波束宽/2)度的小扇形的MR数统计出来占总MR数比值，降序排序，获得比值最高对应的角度，即为最佳方位角。
在本公开的一些实施例中，步骤13可以包括：根据市区、农村等场景结合基站覆盖范围、基站物理标识、基站经纬度、用户经纬度、RSRP、TA、小区物理标识等多维度用户终端上报测量报告(MR)数据建立基站方位角的预测模型；根据基站方位角的预测模型计算出基站方位角(即预测角度)。
本公开上述实施例提出了一种基于MR数据纠正基站方位角的方法。针对相关技术基站方位角偏差无法及时发现的问题，本公开上述实施例通过建立大数据分析算法模型，对 全网小区进行周期性核查，提升了基础数据准确率，由此本公开上述实施例能快速判断源小区的基站天线方位角是否发生偏差，为企业实现降本增效。
图2为本公开基站方位角纠正方法另一些实施例的示意图。优选的，本实施例可由本公开基站方位角纠正装置或本公开基站方位角纠正系统执行。该方法可以包括步骤21-步骤24中的至少一个步骤，其中：
步骤21，筛选出满足RSRP值的MR数据。
在本公开的一些实施例中，步骤21可以包括步骤211和步骤212中的至少一项，其中：
步骤211，将MR中的经纬度、RSRP、基站标识(基站id)、小区标识(小区id)、TA数据与基站标识(基站id)、基站经纬度数据进行关联，关联条件是二者基站id相等。
在本公开的一些实施例中，可以选取某省250个小区统计15天的MR数据进行试点。
步骤212，将关联后的MR数据按基站的覆盖范围分类，取其中一类基站的MR数据，预设RSRP值为-85dBm(假设占比90％对应的RSRP下限值是-85dBm)，将所有小区的MR数据进行筛选，筛选条件是RSRP>-85dBm，获得满足筛选条件的MR数据。
步骤22，筛选出准确TA值的MR数据。
在本公开的一些实施例中，步骤22可以包括：对于单个基站中每个时间提前量值，确定对应位置点的采集范围为一个圆环区域；对于单个基站中多个时间提前量值，确定多个同心圆环区域的多圆环的区域范围，所述区域范围为有效时间提前量值区域。
在本公开的一些实施例中，步骤22可以包括：将步骤21中筛选出来的MR数据取以基站为中心，继续筛选半径在[80m,800m]范围内的圆环区域的数据；然后，针对TA＝2的数据，筛选得到半径在[78.12*2-40,78.12*2](单位米)范围内的圆环区域的准确的TA＝2对应的数据；针对TA＝3的数据，筛选得到半径在[78.12*3-40,78.12*3](单位米)范围内的圆环区域的准确的TA＝3对应的数据；以此类推，获得准确的TA＝4,5，...，10对应的数据。
图3为本公开一些实施例中基站误差的TA值范围分析图。图4为本公开一些实施例中以基站为参考点的TA取值范围的示意图。图5为本公开一些实施例中地图中实际TA点分布范围。
在本公开的一些实施例中，如图3和图4所示，单个基站中单个TA值对应位置点的采集范围是一个圆环区域，圆环是同心圆环，圆环的圆心位置由基站的经纬度确定，圆环 外圆半径约为TA值*78.12，圆环区域在半径方向的宽度等于基站天线的悬挂高度，通过筛选圆环内的MR数据获得准确的TA数据所在的区域范围，针对单个基站多个TA值，可以获得多个同心圆环区域的多圆环的区域范围，该区域范围就是有效TA区域，该范围内的MR数据就是计算方位角的有效TA区域的MR数据。
在本公开的一些实施例中，有效RSRP区域与有效TA区域的相交区域范围就是用于计算方位角的最终有效区域范围，该范围内的MR数据个数大于30的MR数据就是基站方位角预测模型要用到的数据。
步骤23，方位角计算。
在本公开的一些实施例中，步骤23可以包括：根据准确时间提前量值的测量报告数据，确定预测方位角。
在本公开的一些实施例中，步骤23可以包括：根据两个AGPS(Assisted Global Positioning System，辅助全球卫星定位系统)的经度和纬度、准确时间提前量值，确定预测方位角。
将上文筛选出的数据按公式(1-3)进行计算获得基站与MR数据位置点的距离和方位角，计算公式如下：
公式(1)为两个AGPS经纬度距离公式：
Figure PCTCN2022128947-appb-000001
公式(1)中，A 2,B 2是第一个点的AGPS的经度、纬度；C 2,D 2是第二个点的AGPS的经度、纬度。
公式(2)为TA与实际距离的换算公式：
meter＝TA×78.12    (2)
公式(3)为方位角计算公式：
angle＝{{arctan2[sin(A 2×π/180-C 2×π/180)×cos(B 2×π/180),
cos(D 2×π/180)×sin(B 2×π/180)-sin(D 2×π/180)×cos(B 2×π/180)
×cos(A 2×π/180-C 2×π/180)]×180/π}+360.0}％360.0    (3)
公式(3)中，A 2,B 2是第一个点的AGPS的经度、纬度；C 2,D 2是用户的AGPS的经度、纬度。
在本公开的一些实施例中，如图3和图4所示，步骤23可以包括：以基站为圆心，将半径在[80m,800m]范围内的圆环区域分成360个1度的小扇环形区域，求每个扇环的 MR数据个数占比，并降序排序，剔除前5个占比最大的角度；把360个角度左右各33(水平波束宽/2)度的小扇环的MR数统计出来占总MR数比值，降序排序，获得比值最高对应的角度，即为最佳方位角。
步骤24，计算方位角与备案方位角比对迭代RSRP值。
在本公开的一些实施例中，步骤24可以包括：取核查准确30个小区的基站备案方位角作为训练数据，与步骤23获得的对应的小区的计算方位角比对损失误差，通过梯度下降迭代获得RSRP下限值对应的占比，进而反推修正筛选条件RSRP>k中的k值，再用该RSRP值重复步骤21、步骤22、步骤23。
在本公开的一些实施例中，本公开基站方位角纠正方法还可以包括：根据市区、农村等场景结合基站覆盖范围、基站物理标识、基站经纬度、用户经纬度、RSRP、TA、小区物理标识等多维度MR数据建立基站方位角的预测模型；该预测模型会自动生成用于筛选MR数据的初始参数(RSRP值)，根据求解模型后的效果对初始参数值进行迭代式的更新优化，获得不同类基站对应的RSRP值，定位出来参考信号接收功率大于该RSRP值的MR数据所在的区域范围，该区域范围就是有效RSRP区域，该范围内的根据RSRP值筛选出来的MR数据就是计算方位角的有效RSRP区域的MR数据。
在本公开的一些实施例中，所述基站方位角纠正方法还可以包括：步骤25，获得基站方位角有问题的小区并核实整改。
在本公开的一些实施例中，步骤25可以包括：根据预测方位角，确定基站方位角有问题的小区；将基站方位角有问题的小区发送给用户进行核实整改。
在本公开的一些实施例中，本公开根据预测方位角，确定基站方位角有问题的小区的步骤可以包括：根据市区、农村等场景结合基站覆盖范围、基站物理标识、基站经纬度、用户经纬度、RSRP、TA、小区物理标识等多维度MR数据建立基站方位角的预测模型；根据预测模型计算出基站方位角(即预测角度)与备案的基站方位角(即备案角度)进行误差比对，确定基站方位角有问题的小区。
本公开的一些实施例中，所述根据预测模型计算出基站方位角(即预测角度)与备案的基站方位角(即备案角度)进行误差比对，确定基站方位角有问题的小区的步骤可以包括：对于预测方位角和备案方位角的误差处于预定范围的第一小区和第二小区，若第一小区的预测方位角与第二小区的备案方位角之差小于第一预定角度，且第二小区的预测方位角与第一小区的备案方位角之差小于第二预定角度，则判定第一小区和第二小区的基站方位角接反。
本公开的一些实施例中，预定范围为大于130度且小于176度；第一预定角度为10度；第二预定角度为65度。
在本公开的一些实施例中，所述根据预测模型计算出基站方位角(即预测角度)与备案的基站方位角(即备案角度)进行误差比对，确定基站方位角有问题的小区的的步骤可以包括：对于预测方位角和备案方位角的误差处于预定范围的小区，若该小区备案方位角两侧各第三预定角度范围内的测量报告数据个数占测量报告数据总个数的比例小于预定比例值，则判定该小区的方位角的偏差大于预定阈值。
本公开的一些实施例中，第三预定角度范围可以为33度(波瓣宽/2)。
本公开的一些实施例中，预定比例值可以为20％。
在本公开的一些实施例中，选取了某省250个小区统计15天的MR数据进行试点，最终计算出来212个小区的方位角，结果分析表如表1所示：
表1
误差范围(度) 0-5 6-10 11-15 16-20 20-30 31-65 65-130 130以上
占比 9％ 7.08％ 8.02％ 5.66％ 16.50％ 30.66％ 14.62％ 8.00％
小区数(个) 20 15 17 12 35 65 31 17
累计小区数(个) 20 35 52 64 99 164 195 212
累计占比 9％ 16.51％ 24.53％ 30.19％ 46.70％ 77.36％ 92％ 100％
在本公开的一些实施例中，判断问题基站方位角规则为：第一、取预测角度和备案角度误差大于130度且小于176度的两个小区(a小区和b小区)，当a小区的预测角度与b小区的备案角度之差小于10度，且b小区的预测角度与a小区的备案角度之差小于65度，即判断两个小区的基站方位角接反。
第二、取预测角度和备案角度误差大于130度且小于176度的小区，且计算该小区备案方位角左右各33度(波瓣宽/2)的MR数据个数占总个数的占比小于20％，即认为是该小区的方位角有较大偏差。
最终，根据上述规则挑选出了9个问题小区，并交给分公司核实整改，整改情况如表2的基站方位角问题小区核实情况表所示，可以发现挑出来的问题小区确实存在问题，说明该纠正基站方位角的方法是可行的。
表2
Figure PCTCN2022128947-appb-000002
针对相关技术目前的基站方位角纠偏需要消耗大量人力和物力、工作量很大且效率低下、分析问题片面且准确性不高等技术问题，本公开上述实施例提出了基于大数据的基站方位角自动纠正的方法。
如图2所示，本公开上述实施例对“RSRP初始参数确定”-“方位角计算程序运行”-“反馈方位角数据训练优化RSRP参数”的闭环流程进行系统的数据分析，相比相关技术开环的且脱离大数据分析的参数确定方法，输出的参数更可信。
本公开上述实施例不依赖载干比，摆脱了对邻区基站的依赖，可以判断比相关技术更多的基站方位角偏差，解决了相关技术的痛点。
本公开上述实施例利用几何知识获得了准确MR数据的采集范围(如图3、图4、图5)，相比于现有技术的单扇形区域取值，本公开上述实施例可以实现一个TA值一个扇环区域的多扇环区域取值，高效地提取了大数据的特征，从而降低了对集群算力的损耗，可以为企业实现降本增效。
图6为本公开基站方位角纠正装置一些实施例的示意图。如图6所示，本公开基站方位角纠正装置可以包括第一筛选模块61、第二筛选模块62和方位角纠正模块63，其中：
第一筛选模块61，用于筛选出满足筛选条件的测量报告数据，其中，所述筛选条件为 参考信号接收功率大于预定参考信号接收功率。
在本公开的一些实施例中，第一筛选模块61，可以用于将测量报告数据中的经纬度、参考信号接收功率、基站标识、小区标识、时间提前量数据，与基站标识和基站经纬度进行关联；在关联后的测量报告数据中，筛选出满足筛选条件的测量报告数据。
第二筛选模块62，用于在满足筛选条件的测量报告数据中，筛选出准确时间提前量值的测量报告数据。
在本公开的一些实施例中，第二筛选模块62，可以用于对于单个基站中每个时间提前量值，确定对应位置点的采集范围为一个圆环区域；对于单个基站中多个时间提前量值，确定多个同心圆环区域的多圆环的区域范围，所述区域范围为有效时间提前量值区域。
方位角纠正模块63，用于根据准确时间提前量值的测量报告数据，确定预测方位角。
在本公开的一些实施例中，方位角纠正模块63，可以用于根据两个辅助全球卫星定位系统的经度和纬度、准确时间提前量值，确定预测方位角。
在本公开的一些实施例中，如图6所示，本公开基站方位角纠正装置还可以包括2364，其中：
比对修正模块64，用于将预测方位角和备案方位角进行比对，迭代修正预定参考信号接收功率；按照修正后的预定参考信号接收功率，之后指示第一筛选模块61、第二筛选模块62和方位角纠正模块63重复执行所述筛选出满足筛选条件的测量报告数据、筛选出准确时间提前量值的测量报告数据和根据准确时间提前量值的测量报告数据计算基站方位角的操作。
在本公开的一些实施例中，如图6所示，本公开基站方位角纠正装置还可以包括问题小区确定模块65，其中：
问题小区确定模块65，用于根据预测方位角，确定基站方位角有问题的小区；将基站方位角有问题的小区发送给用户进行核实整改。
在本公开的一些实施例中，问题小区确定模块65，可以用于对于预测方位角和备案方位角的误差处于预定范围的第一小区和第二小区，若第一小区的预测方位角与第二小区的备案方位角之差小于第一预定角度，且第二小区的预测方位角与第一小区的备案方位角之差小于第二预定角度，则判定第一小区和第二小区的基站方位角接反。
在本公开的一些实施例中，问题小区确定模块65，可以用于对于预测方位角和备案方位角的误差处于预定范围的小区，若该小区备案方位角两侧各第三预定角度范围内的测量报告数据个数占测量报告数据总个数的比例小于预定比例值，则判定该小区的方位角的偏 差大于预定阈值。
在本公开的一些实施例中，所述基站方位角纠正装置用于执行实现如上述任一实施例(例如图1-图5任一实施例)所述的基站方位角纠正方法的操作。
本公开上述实施例提出了一种基于MR数据纠正基站方位角的装置。针对相关技术基站方位角偏差无法及时发现的问题，本公开上述实施例通过建立大数据分析算法模型，对全网小区进行周期性核查，提升了基础数据准确率，由此本公开上述实施例能快速判断源小区的基站天线方位角是否发生偏差，为企业实现降本增效。
图7为本公开基站方位角纠正装置又一些实施例的结构示意图。如图7所示，基站方位角纠正装置包括存储器71和处理器72。
存储器71用于存储指令，处理器72耦合到存储器71，处理器72被配置为基于存储器存储的指令执行实现如上述任一实施例(例如图1或图4实施例)所述的基站方位角纠正方法。
如图7所示，该基站方位角纠正装置还包括通信接口73，用于与其它设备进行信息交互。同时，该基站方位角纠正装置还包括总线74，处理器72、通信接口73、以及存储器71通过总线74完成相互间的通信。
存储器71可以包含高速RAM存储器，也可还包括非易失性存储器(non-volatile memory)，例如至少一个磁盘存储器。存储器71也可以是存储器阵列。存储器71还可能被分块，并且块可按一定的规则组合成虚拟卷。
此外，处理器72可以是一个中央处理器CPU，或者可以是专用集成电路ASIC，或是被配置成实施本公开实施例的一个或多个集成电路。
本公开上述实施例对“RSRP初始参数确定”-“方位角计算程序运行”-“反馈方位角数据训练优化RSRP参数”的闭环流程进行系统的数据分析，相比相关技术开环的且脱离大数据分析的参数确定方法，输出的参数更可信。
本公开上述实施例不依赖载干比，摆脱了对邻区基站的依赖，可以判断比相关技术更多的基站方位角偏差，解决了相关技术的痛点。
本公开上述实施例利用几何知识获得了准确MR数据的采集范围(如图3、图4、图5)，相比于现有技术的单扇形区域取值，本公开上述实施例可以实现一个TA值一个扇环区域的多扇环区域取值，高效地提取了大数据的特征，从而降低了对集群算力的损耗，可以为企业实现降本增效。
根据本公开的另一方面，提供一种基站方位角纠正系统，包括如上述任一实施例(例如图6或图7实施例)所述的基站方位角纠正装置。
根据本公开的另一方面，提供一种非瞬时性计算机可读存储介质，其中，所述非瞬时性计算机可读存储介质存储有计算机指令，所述指令被处理器执行时实现如上述任一实施例(例如图1-图5任一实施例)所述的基站方位角纠正方法。
本公开可以基于MR数据纠正基站方位角。本公开能快速判断源小区的基站天线方位角是否发生偏差，由此可以实现降本增效。
本领域内的技术人员应明白，本公开的实施例可提供为方法、装置、或计算机程序产品。因此，本公开可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本公开可采用在一个或多个其中包含有计算机可用程序代码的计算机可用非瞬时性存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本公开是参照根据本公开实施例的方法、设备(系统)和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在上面所描述的基站方位角纠正装置可以实现为包括用于执行本申请所描述功能的通用处理器、可编程逻辑控制器(PLC)、数字信号处理器(DSP)、专用集成电路(ASIC)、现场可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分 立硬件组件或者其任意适当组合。
至此，已经详细描述了本公开。为了避免遮蔽本公开的构思，没有描述本领域所公知的一些细节。本领域技术人员根据上面的描述，完全可以明白如何实施这里公开的技术方案。
本领域普通技术人员可以理解实现上述实施例的全部或部分步骤可以通过硬件来完成，也可以通过程序来指示相关的硬件完成，所述的程序可以存储于一种非瞬时性计算机可读存储介质中，上述提到的存储介质可以是只读存储器，磁盘或光盘等。
本公开的描述是为了示例和描述起见而给出的，而并不是无遗漏的或者将本公开限于所公开的形式。很多修改和变化对于本领域的普通技术人员而言是显然的。选择和描述实施例是为了更好说明本公开的原理和实际应用，并且使本领域的普通技术人员能够理解本公开从而设计适于特定用途的带有各种修改的各种实施例。

Claims (19)

  1. 一种基站方位角纠正方法，包括：
    筛选出满足筛选条件的测量报告数据；
    在满足筛选条件的测量报告数据中，筛选出准确时间提前量值的测量报告数据；
    根据准确时间提前量值的测量报告数据，确定预测方位角。
  2. 根据权利要求1所述的基站方位角纠正方法，其中，所述筛选条件为参考信号接收功率大于预定参考信号接收功率。
  3. 根据权利要求1所述的基站方位角纠正方法，还包括：
    将预测方位角和备案方位角进行比对，迭代修正预定参考信号接收功率；
    按照修正后的预定参考信号接收功率，重复执行所述筛选出满足筛选条件的测量报告数据、筛选出准确时间提前量值的测量报告数据和根据准确时间提前量值的测量报告数据计算基站方位角的步骤。
  4. 根据权利要求1-3中任一项所述的基站方位角纠正方法，还包括：
    根据预测方位角，确定基站方位角有问题的小区；
    将基站方位角有问题的小区发送给用户进行核实整改。
  5. 根据权利要求4所述的基站方位角纠正方法，其中，所述根据预测方位角，确定基站方位角有问题的小区包括：
    对于预测方位角和备案方位角的误差处于预定范围的第一小区和第二小区，若第一小区的预测方位角与第二小区的备案方位角之差小于第一预定角度，且第二小区的预测方位角与第一小区的备案方位角之差小于第二预定角度，则判定第一小区和第二小区的基站方位角接反。
  6. 根据权利要求5所述的基站方位角纠正方法，其中，预定范围为大于130度且小于176度；第一预定角度为10度；第二预定角度为65度。
  7. 根据权利要求4所述的基站方位角纠正方法，其中，所述根据预测方位角，确定基站方位角有问题的小区包括：
    对于预测方位角和备案方位角的误差处于预定范围的小区，若该小区备案方位角两侧各第三预定角度范围内的测量报告数据个数占测量报告数据总个数的比例小于预定比例值，则判定该小区的方位角的偏差大于预定阈值。
  8. 根据权利要求7所述的基站方位角纠正方法，其中，第三预定角度范围为波瓣宽的一半；预定比例值为20％。
  9. 根据权利要求1-3中任一项所述的基站方位角纠正方法，其中，所述筛选出满足筛选条件的测量报告数据包括：
    将测量报告数据中的经纬度、参考信号接收功率、基站标识、小区标识、时间提前量数据，与基站标识和基站经纬度进行关联；
    在关联后的测量报告数据中，筛选出满足筛选条件的测量报告数据。
  10. 根据权利要求1-3中任一项所述的基站方位角纠正方法，其中，所述筛选出准确时间提前量值的测量报告数据包括：
    对于单个基站中每个时间提前量值，确定对应位置点的采集范围为一个圆环区域；
    对于单个基站中多个时间提前量值，确定多个同心圆环区域的多圆环的区域范围，所述区域范围为有效时间提前量值区域。
  11. 根据权利要求1-3中任一项所述的基站方位角纠正方法，其中，所述根据准确时间提前量值的测量报告数据，确定预测方位角包括：
    根据两个辅助全球卫星定位系统的经度和纬度、准确时间提前量值，确定预测方位角。
  12. 根据权利要求1-3中任一项所述的基站方位角纠正方法，其中，所述根据准确时间提前量值的测量报告数据，确定预测方位角包括：
    根据多维度用户终端上报的测量报告数据建立基站方位角的预测模型；
    根据基站方位角的预测模型计算出预测方位角。
  13. 一种基站方位角纠正装置，包括：
    第一筛选模块，用于筛选出满足筛选条件的测量报告数据；
    第二筛选模块，用于在满足筛选条件的测量报告数据中，筛选出准确时间提前量值的测量报告数据；
    方位角纠正模块，用于根据准确时间提前量值的测量报告数据，确定预测方位角。
  14. 根据权利要求13所述的基站方位角纠正装置，其中，所述筛选条件为参考信号接收功率大于预定参考信号接收功率。
  15. 根据权利要求13或14所述的基站方位角纠正装置，其中，所述基站方位角纠正装置用于执行实现如权利要求1-12中任一项所述的基站方位角纠正方法的操作。
  16. 一种基站方位角纠正装置，包括：
    存储器，用于存储指令；
    处理器，用于执行所述指令，使得所述基站方位角纠正装置执行实现如权利要求1-12中任一项所述的基站方位角纠正方法的操作。
  17. 一种基站方位角纠正系统，包括如权利要求13-16中任一项所述的基站方位角纠正装置。
  18. 一种非瞬时性计算机可读存储介质，其中，所述非瞬时性计算机可读存储介质存储有计算机指令，所述指令被处理器执行时实现如权利要求1-12中任一项所述的基站方位角纠正方法。
  19. 一种计算机程序，包括：
    指令，所述指令当由处理器执行时使所述处理器执行根据权利要求1-12中任一项所述的基站方位角纠正方法。
PCT/CN2022/128947 2021-12-08 2022-11-01 基站方位角纠正方法、装置和系统、存储介质 WO2023103652A1 (zh)

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