Base station site selection method, computer storage medium and terminal

Abstract

The invention discloses a base station site selection method, a computer storage medium and a terminal, aiming at the condition that indexes are different in unit and order of magnitude and do not have comparability, dimension differences are eliminated through normalization processing, and all quality indexes are ensured to be on a unified scale; the measurement of the uncertainty of the quality index is realized by determining the entropy value of the quality index; the weight coefficient of each quality index is determined according to the entropy value, so that the weight coefficients for measuring different quality indexes are determined in the environments of different to-be-determined areas; through the normalized quality indexes and the weight coefficient corresponding to each quality index, the data characteristics of the quality indexes in different environments are objectively reflected, the problem that the influence of different quality indexes in different environments on base station erection judgment is inconsistent is solved, whether the to-be-determined area is suitable for base station erection or not is judged, and the base station erection judgment efficiency is improved. And data support is provided for implementation of base station erection.

Description

Technical Field

[0001] This document relates to differential positioning technology, especially a base station site selection method, a computer storage medium, and a terminal. Background Art

[0002] With the advent of technological innovation and the Internet of Things era, people's demand for high-precision positioning has become increasingly strong. The positioning accuracy has been improved from the original meter level to the current decimeter level, or even centimeter level. The application fields of high-precision positioning have also become more and more diverse, ranging from the initial surveying receivers to surveying and mapping integrated machines, and then to current fields such as vehicles and intelligent robots.

[0003] The improvement and expansion of positioning accuracy and application fields are inseparable from the wide application of differential positioning systems. When obtaining differential data by building a self-built reference station, it is required that the base station be erected in an open area without obstruction by buildings or trees. With the expansion of high-precision application fields, the site selection of the reference station cannot meet the requirement of being open and unobstructed. It may be necessary to erect the base station in complex scenarios such as under the shade of trees or in urban canyons. However, not all areas in complex scenarios meet the requirements for erecting a base station. How to determine whether a complex scenario area is suitable for erecting a base station has become a problem to be solved. Summary of the Invention

[0004] An embodiment of this application provides a base station site selection method, including:

[0005] Obtaining receiver observation values within a to-be-determined area where a base station is erected, and performing normalization processing on the quality indicators of the receiver observation values, where the quality indicators include: visible satellite availability rate, satellite occlusion coefficient, CN0 observation ratio, and multipath interference flag;

[0006] For the quality indicators after normalization processing, determining the entropy value corresponding to each quality indicator;

[0007] Determining the weight coefficient corresponding to each quality indicator according to the determined entropy value;

[0008] Determining whether the to-be-determined area is suitable for erecting a base station according to the quality indicators after normalization processing and the weight coefficient corresponding to each quality indicator.

[0009] On the other hand, an embodiment of this application also provides a computer storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the above base station site selection method is implemented.

[0010] On yet another aspect, an embodiment of this application also provides a terminal, including: a memory and a processor, where a computer program is stored in the memory; among them,

[0011] The processor is configured to execute the computer program in the memory;

[0012] When the computer program is executed by the processor, it implements the base station site selection method as described above.

[0013] In the embodiments of the present disclosure, since the various indicators have different units and orders of magnitude and are not comparable, the dimensional difference is eliminated through normalization processing to ensure that all quality indicators are on the same scale; by determining the entropy value of the quality indicators, the measurement of the uncertainty of the quality indicators is realized; according to the entropy value, the weight coefficient of each quality indicator is determined, and the weight coefficients for measuring different quality indicators are determined in the environments of different regions to be determined; through the quality indicators after normalization processing and the corresponding weight coefficient of each quality indicator, the data characteristics of the quality indicators in different environments are objectively reflected, the problem that the influence of different quality indicators on the determination of base station erection in different environments is inconsistent is solved, the determination of whether the region to be determined is suitable for base station erection is realized, and data support is provided for the implementation of base station erection.

[0014] Other features and advantages of the present application will be described in the subsequent description, and, in part, will be obvious from the description, or will be understood by implementing the present application. Other advantages of the present application can be realized and obtained through the solutions described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings are used to provide an understanding of the technical solutions of the present application and constitute a part of the description, and are used together with the embodiments of the present application to explain the technical solutions of the present application, and do not constitute a limitation to the technical solutions of the present application.

[0016] Figure 1 It is a flowchart of the base station site selection method according to the embodiments of the present disclosure;

[0017] Figure 2 It is a structural block diagram of the base station site selection device according to the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those of ordinary skill in the art that there can be more embodiments and implementation solutions within the scope of the embodiments described in the present application. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically restricted, any feature or element of any embodiment can be combined with any other feature or element in any other embodiment, or can replace any other feature or element in any other embodiment.

[0019] This application includes and contemplates combinations with features and elements known to those of ordinary skill in the art. The disclosed embodiments, features, and elements of this application can also be combined with any conventional features or elements to form a unique inventive solution. Any feature or element of any embodiment can also be combined with features or elements from other inventive solutions to form another unique inventive solution. Therefore, it should be understood that any feature shown and / or discussed in this application can be implemented alone or in any suitable combination. Accordingly, the embodiments are not subject to other limitations except those imposed by the appended claims and their equivalents. In addition, various modifications and changes can be made within the scope of the appended claims.

[0020] In addition, when describing representative embodiments, the specification may have presented a method and / or process as a particular sequence of steps. However, to the extent that the method or process does not depend on the particular order of the steps described herein, the method or process should not be limited to the described particular order of steps. As will be understood by those of ordinary skill in the art, other step orders are possible. Therefore, the particular order of steps set forth in the specification should not be construed as a limitation on the claims. In addition, the claims directed to the method and / or process should not be limited to performing their steps in the order written, as those skilled in the art can readily understand that these orders can vary and still remain within the spirit and scope of the embodiments of this application.

[0021] Figure 1 The flowchart of the base station site selection method for the embodiments of this disclosure is as Figure 1 shown, and includes:

[0022] Step 101: Obtain the receiver observations within the area to be determined where the base station is installed, and perform normalization processing on the quality indicators of the receiver observations. Among them, the quality indicators include: the effective rate of visible satellites, the satellite occlusion coefficient, the CN0 observation ratio, and the multipath interference identifier;

[0023] Step 102: For the quality indicators after normalization processing, determine the entropy value corresponding to each quality indicator;

[0024] Step 103: Determine the weight coefficient corresponding to each quality indicator according to the determined entropy value;

[0025] Step 104: Determine whether the area to be determined is suitable for installing the base station according to the quality indicators after normalization processing and the weight coefficient corresponding to each quality indicator.

[0026] In the embodiments of the present disclosure, since various indicators have different units and orders of magnitude and are not comparable, the dimensional differences are eliminated through normalization processing to ensure that all quality indicators are on a unified scale; the entropy value method is used to determine the entropy value of the quality indicator to achieve the measurement of the uncertainty of the quality indicator; by determining the weight coefficient of each quality indicator, the weight coefficients for measuring different quality indicators are determined in the environment of different pending areas; the normalized quality indicators and the corresponding weight coefficients of each quality indicator objectively reflect the data characteristics of the quality indicators in different environments, solve the problem of inconsistent effects of different quality indicators in different environments on the determination of base station installation, realize the determination of whether the pending area is suitable for installing base stations, and provide data support for the implementation of base station installation.

[0027] In an exemplary embodiment, the quality indicator in the embodiment of the present disclosure may also include at least one of the following:

[0028] Calculate satellite obstruction rate, electromagnetic interference intensity, estimated carrier phase standard deviation (ADRSTD) and pseudorange observation standard deviation (PSRSTD).

[0029] In an exemplary embodiment, the quality indicator in the embodiment of the present disclosure can be calculated or obtained in the following manner:

[0030] Visible satellite efficiency Where N AdrVld Indicates the number of satellites with valid ADR in the visible satellites, N SatVis Indicates the number of visible satellites;

[0031] Satellite occlusion factor Where N SatEph Indicates the number of satellites for which the ephemeris is valid;

[0032] Calculating satellite occlusion rate Where N SatSln Indicates the number of satellites involved in positioning solution;

[0033] CN0 observation ratio is P CN0 , Where, CN0 real Represents the actual observed CN0 value; CN0 the The CN0 theoretical value of the current signal is determined by the elevation angle between the receiver antenna and the current satellite. The approximate CN0 theoretical value can be obtained in an open, unobstructed and interference-free environment.

[0034] Electromagnetic interference intensity P CW : Electromagnetic interference intensity mainly refers to single-tone, narrowband, broadband interference, and deception interference, which are identified and calculated by the receiver baseband chip to give the electromagnetic interference intensity P of the current environment. CW .

[0035] Multipath interference identifier P MP It refers to the multipath intensity of each satellite tracked by the receiver, which is identified and calculated by the receiver baseband chip.

[0036] ADRSTD and PSRSTD can be directly obtained from the satellite observation data.

[0037] In an exemplary instance, the normalization process of the quality indicators of the receiver observations in the embodiments of the present disclosure may include:

[0038] Based on the receiver observations obtained within a preset duration, obtain the quality indicators of the receiver observations; wherein, each quality indicator includes m groups of data;

[0039] Obtain a decision matrix based on the m groups of data of each quality indicator;

[0040] Through the obtained decision matrix, perform normalization processing on the quality indicators.

[0041] The embodiments of the present disclosure define the decision matrix as X, X = [x ij mn , where m represents the total number of groups of each quality indicator collected, n represents the number of the four or more quality indicators, and X ij represents the i-th group of data of the j-th quality indicator before the normalization processing.

[0042] It should be noted that the embodiments of the present disclosure may use other methods to normalize two or more quality indicators, as long as it can eliminate the dimension difference and ensure that all quality indicators are on the same scale.

[0043] In an exemplary instance, the preset duration in the embodiments of the present disclosure may be 30 - 60 seconds; the number of acquisitions per second is 1 - 10 times; when the preset duration in the embodiments of the present disclosure is longer, the number of acquisitions can be set smaller. By setting the preset duration and the number of acquisitions, it is ensured that the amount of data collected meets the requirements.

[0044] In an exemplary instance, the embodiments of the present disclosure determine the entropy value corresponding to each quality indicator, including:

[0045] Based on each group of data of each quality indicator before the normalization processing, calculate each group of data of each quality indicator after the normalization processing;

[0046] Based on each group of data of each quality indicator after the normalization processing, calculate the proportion of each quality indicator in each group of data;

[0047] Determine the entropy value corresponding to each quality indicator according to the proportion.

[0048] ​In an exemplary instance, the entropy value of the j-th metric in the embodiments of the present disclosure is E j , E j can be calculated based on the following formula:

[0049]

[0050] where m represents the number of the j-th metric collected; k represents the normalization factor, P ij represents the proportion of the j-th quality metric in the i-th group of data, X′ ij represents the j-th metric of the i-th group of data after normalization processing, X ij represents the j-th metric of the i-th group of data before normalization processing.

[0051] In the embodiments of the present disclosure, P ij converts the performance of each sample under this metric into a probability value, reflecting the contribution of the sample to the overall distribution; through the entropy value, a probability value P ij is converted into the degree of uniformity of the distribution. The smaller the entropy value, the more uneven the distribution and the more information is provided.

[0052] In an exemplary instance, the embodiments of the present disclosure can determine the weight coefficient corresponding to each quality metric according to the determined entropy value, including:

[0053] Determine the difference coefficient of each quality metric according to the entropy value of each quality metric;

[0054] Determine the weight coefficient corresponding to each quality metric according to the difference coefficient.

[0055] In an exemplary instance, the weight coefficient of the j-th quality metric in the embodiments of the present disclosure is w j , w j The expression can be:

[0056]

[0057] where n represents the number of quality metrics, g j represents the difference coefficient of the j-th metric, g j = 1 - E j , E j is the entropy value of the j-th quality metric.

[0058] In the embodiments of the present disclosure, g j can identify the difference in the effective information reflected by each quality metric, which is essentially a supplement to the entropy value and converts the entropy value into information content. The smaller E j , the larger g jThe larger it is, that is, the smaller the entropy value is, the more information is provided; g j The larger it is, it means that this quality index provides more effective information, and its weight should be relatively large in the proportion used to evaluate whether it is suitable to install a base station.

[0059] In an exemplary example, the embodiments of the present disclosure determine whether a to-be-determined area is suitable for installing a base station according to the normalized quality index and the corresponding weight coefficient of each quality index, including:

[0060] According to the normalized quality index and the corresponding weight coefficient of each quality index, calculate the score value Val for whether the to-be-determined area is suitable for installing a base station. The expression of the score value Val is: Where m represents the total number of groups of the jth quality index collected; w j represents the weight coefficient of the jth quality index; X′ ij represents the ith group of data of the jth quality index after normalization processing, X ij represents the ith group of data of the jth quality index before normalization processing;

[0061] When the calculated score value Val is greater than the preset score threshold, determine whether the to-be-determined area is suitable for installing a base station.

[0062] The score threshold in the embodiments of the present disclosure can be set by those skilled in the art based on empirical analysis; for example, the score threshold can be analyzed and determined according to the score values of the communication quality of the already installed base stations.

[0063] The embodiments of the present disclosure need to install the base station in complex scenarios such as under the shade of trees or in urban canyons, but not all complex scenarios meet the requirements for installing a base station; the embodiments of the present disclosure quantify the complexity of the environment through a receiver, calculate the score value for reference through the above method, and realize the judgment of whether the to-be-determined area is suitable for installing a base station based on the score value.

[0064] The following briefly illustrates the embodiments of the present disclosure through examples. In the examples, the quality indicators of the receiver include: visible satellite efficiency, satellite occlusion coefficient, CN0 observation ratio, and multipath interference identifier. Three groups of receiver observation values are obtained, and the corresponding three groups of quality indicators are shown in Table 1;

[0065]

[0066] Table 1

[0067] Obtain the decision matrix according to the three groups of quality indicators in Table 1. The decision matrix X = [x ij mn , m represents the total number of groups of each quality index collected, n represents the number of the four or more quality indicators, X​ij represents the i-th group of data of the j-th quality index before the normalization process;

[0068] Based on the decision matrix for the quality index, according to perform normalization processing. The normalized quality index is shown in Table 2:

[0069]

[0070] Table 2

[0071] For the normalized quality index, according to calculate the proportion of the j-th index in the i-th group of quality data. Taking the 4 quality indexes of the first group of data as an example, the obtained proportions are respectively:

[0072]

[0073] Similarly, calculate the proportion of each quality index in all groups of data shown in Table 3:

[0074]

[0075] According to calculate the entropy value of each quality index:

[0076] For example, when the quality index is the visual satellite availability rate, its entropy value is:

[0077]

[0078] Similarly, calculate the entropy values of other quality indexes to be: E2 = 0.95; E3 = 0.94; E4 = 0.85.

[0079] According to calculate the weight coefficient of each quality, for example, the weight coefficient of the first quality index is:

[0080]

[0081] Similarly, calculate the weight coefficients of other quality indexes to be: w2 = 0.17; w3 = 0.2; w1 = 0.5.

[0082] According to calculate the scoring value Val for whether the to-be-determined area is suitable for erecting a base station, which are respectively:

[0083] Val1 = [(0.13 * 0.26) + (0.17 * 0.26) + (0.2 * 0.25) + (0.5 * 0.23)] * 100 = 24;

[0084] Val2 = [(0.13 * 0.28) + (0.17 * 0.29) + (0.2 * 0.33) + (0.5 * 0.10)] * 100 = 17;

[0085] Val3 = [(0.13 * 0.24) + (0.17 * 0.22) + (0.2 * 0.30) + (0.5 * 0.34)] * 100 = 21;

[0086] Val1 represents the formula when i takes 1, that is, the score obtained based on the first set of quality calculations. Val2 represents the formula when i takes 2, that is, the score obtained based on the second set of quality calculations. Val3 represents the formula when i takes 3, that is, the score obtained based on the third set of quality calculations;

[0087] Based on the above results, the present disclosure embodiment calculates

[0088] Based on the calculated score value, it is determined that the currently undetermined area is not suitable for erecting a base station.

[0089] Through experimental verification, the present disclosure embodiment determines the relationship between the score value shown in Table 4 and whether it is suitable to erect a base station; when the value of the score value Val is less than 80, it is considered that the undetermined area is not suitable for erecting a base station; when the score value is greater than or equal to 80, it is considered that a reference station can be erected.

[0090] . Base station application judgment criteria 90—100 Excellent 85—90 Relatively good 80—85 Average 1—80 Unsuitable for building a station below 80

[0091] Table 4

[0092] The present disclosure embodiment selects 7 undetermined areas, and the point numbers of the undetermined areas are 1 to 7 respectively. Among them, the environments of point 1 and point 2 are that the sky view is open, there are no tall buildings, almost no obstruction, the satellite reception signal is good, the signal strength is high, the multipath effect is slight, and the positioning accuracy is high; the environment of point 3 is that the tree branches and leaves partially block the sky, the satellite signal is blocked to varying degrees, the signal strength begins to decay, the multipath effect appears, and the positioning accuracy begins to decline. The environments of point 4 and point 5 are surrounded by high-rise buildings on all sides, the sky view is narrow, very few satellites can be seen, the signal strength decays severely, the signal is frequently lost, the multipath effect is obvious, the positioning accuracy is low, and positioning is difficult. The environments of point 6 and point 7 are that the sky view is restricted, high-rise buildings are dense, the number of visible satellites decreases, the signal is severely blocked, the signal strength decays significantly, the signal may frequently lose lock, and the positioning is unstable; the score values calculated according to the method of the present disclosure embodiment are shown in Table 5, and referring to the size of the score value can determine whether it is suitable to erect a base station.

[0093] Point number 1 2 3 4 5 6 7 Score value 95 96 81 54 57 61 71

[0094] Table 5

[0095] The embodiments of the present disclosure further provide a computer storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the above base station site selection method is implemented.

[0096] The embodiments of the present disclosure further provide a terminal, including: a memory and a processor, and a computer program is stored in the memory;

[0097] Among them,

[0098] the processor is configured to execute the computer program in the memory;

[0099] when the computer program is executed by the processor, the above base station site selection method is implemented.

[0100] Figure 2 is a structural block diagram of the base station site selection device according to the embodiments of the present disclosure, as Figure 2 shown, including: a normalization unit, an entropy value unit, a weight determination unit, and a processing unit; among them,

[0101] The normalization unit is configured to: obtain the receiver observation values in the to-be-determined area where the base station is installed, and perform normalization processing on the quality indicators of the receiver observation values, where the quality indicators include: visible satellite efficiency, satellite occlusion coefficient, CN0 observation ratio, and multipath interference identifier;

[0102] The entropy value unit is configured to: determine the entropy value corresponding to each quality indicator for the quality indicators after the normalization processing;

[0103] The weight determination unit is configured to: determine the weight coefficient corresponding to each quality indicator according to the determined entropy value;

[0104] The processing unit is configured to: determine whether the to-be-determined area is suitable for installing a base station according to the quality indicators after the normalization processing and the weight coefficient corresponding to each quality indicator.

[0105] In an exemplary example, the normalization unit of the embodiments of the present disclosure is configured to:

[0106] According to the obtained receiver observation values within a preset time period, obtain the quality indicators of the receiver observation values; where each quality indicator includes m groups of data;

[0107] Obtain a decision matrix according to the m groups of data of each quality indicator;

[0108] Perform normalization processing on the quality indicators through the obtained decision matrix.

[0109] In an exemplary example, the entropy value unit of the embodiments of the present disclosure is configured to:

[0110] For each set of data of each quality indicator before normalization processing, calculate each set of data of each quality indicator after normalization processing;

[0111] For each set of data of each quality indicator after normalization processing, calculate the proportion of each quality indicator in each set of data;

[0112] Determine the entropy value corresponding to each quality indicator according to the proportion.

[0113] In an exemplary instance, the entropy value unit of the embodiment of the present disclosure is set to calculate the entropy value E of the jth indicator based on the following formula j :

[0114]

[0115] In the formula, m represents the number of the jth indicator collected; k represents the normalization factor, P ij represents the proportion of the jth indicator in the ith set of data, X′ ij represents the jth indicator of the ith set of data after normalization processing, X ij represents the jth indicator of the ith set of data before normalization processing.

[0116] In an exemplary instance, the weight determination unit of the embodiment of the present disclosure is set to determine the weight coefficient corresponding to each quality indicator according to the determined entropy value, including:

[0117] Determine the difference coefficient of each quality indicator according to the entropy value of each quality indicator;

[0118] Determine the weight coefficient corresponding to each quality indicator according to the difference coefficient.

[0119] In an exemplary instance, the weight determination unit of the embodiment of the present disclosure is set to calculate the weight coefficient w of the jth quality indicator through the following formula j :

[0120]

[0121] In the formula, n represents the total number of quality indicators, g j represents the difference coefficient of the jth indicator, g j =1 - E j E j is the entropy value of the jth quality indicator.

[0122] In an exemplary instance, the quality indicators in the embodiment of the present disclosure may further include at least one of the following:

[0123] Solve for the satellite occlusion rate, electromagnetic interference intensity, estimated carrier phase standard deviation (ADRSTD), and pseudorange observation standard deviation (PSRSTD).

[0124] In an exemplary instance, the processing unit of the embodiments of the present disclosure is configured to:

[0125] According to the normalized quality indicators and the corresponding weight coefficients of each quality indicator, calculate the score value Val for whether the area to be determined is suitable for installing a base station. The expression for the score value Val is: where m represents the total number of groups of the j-th quality indicator collected; w j represents the weight coefficient of the j-th quality indicator; X′ ij represents the i-th group of data of the j-th quality indicator after normalization processing, X ij represents the i-th group of data of the j-th quality indicator before the normalization processing;

[0126] When the calculated score value Val is greater than a preset score threshold, determine whether the area to be determined is suitable for installing a base station;

[0127] Those of ordinary skill in the art will understand that all or some of the steps in the methods disclosed above, and the functional modules / units in systems and devices, can be implemented as software, firmware, hardware, and appropriate combinations thereof. In the hardware implementation, the division of functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be executed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term "computer storage medium" includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

Claims

1. A base station site selection method, characterized in that: include: Obtaining receiver observation values ​​in a pending area where a base station is set up, and normalizing the quality indicators of the receiver observation values, wherein the quality indicators include: visible satellite efficiency, satellite shielding coefficient, CN0 observation ratio and multipath interference flag; For the normalized quality indicators, determine the entropy value corresponding to each quality indicator; Determine the corresponding weight coefficient of each quality indicator according to the determined entropy value; Whether the to-be-determined area is suitable for setting up a base station is determined based on the normalized quality indicators and the corresponding weight coefficient of each quality indicator.

2. The base station site selection method according to claim 1, characterized in that: The normalizing of the quality indicator of the receiver observation value includes: Obtaining a quality indicator of the receiver observation value according to the obtained receiver observation value within a preset time length; wherein each quality indicator includes m groups of data; Obtain a decision matrix based on m groups of data for each quality indicator; The quality index is normalized by using the obtained decision matrix.

3. The base station site selection method according to claim 1, characterized in that: Determining the entropy value corresponding to each quality indicator includes: According to each set of data of each quality indicator before normalization, each set of data of each quality indicator after normalization is calculated; According to each group of data of each quality indicator after the normalization process, calculating the ratio of each quality indicator in each group of data; The entropy value corresponding to each quality indicator is determined according to the ratio.

4. The base station site selection method according to claim 3, characterized in that: The entropy value of the jth quality index is E j , E j Calculated based on the following formula: In the formula, m represents the total number of groups of the jth quality indicator collected, i represents the specific number of groups in the m groups of quality indicator data; k represents the normalization factor, P ij It represents the proportion of the jth indicator in the i-th group of data. X′ ij represents the i-th group of data of the j-th quality indicator after the normalization process, X ij Represents the i-th group of data of the j-th quality indicator before the normalization process.

5. The base station site selection method according to claim 1, characterized in that: Determining the corresponding weight coefficient of each quality indicator according to the determined entropy value includes: According to the entropy value of each quality indicator, the coefficient of difference of each quality indicator is determined; According to the difference coefficient, a corresponding weight coefficient of each quality indicator is determined.

6. The base station site selection method according to claim 5, characterized in that: The weight coefficient of the jth quality index is w j , w j The expression is: Where n represents the number of quality indicators, g j represents the coefficient of difference of the jth indicator, g j =1-E j , E j is the entropy value of the j-th quality indicator.

7. The base station site selection method according to claim 1, characterized in that: The quality indicator also includes at least one of the following: Calculate satellite obstruction rate, electromagnetic interference intensity, estimate carrier phase standard deviation ADRSTD and pseudorange observation standard deviation PSRSTD.

8. The base station site selection method according to any one of claims 1 to 7, characterized in that: The determining whether the to-be-determined area is suitable for setting up a base station according to the normalized quality index and the corresponding weight coefficient of each quality index includes: According to the normalized quality index and the corresponding weight coefficient of each quality index, a score value of whether the undetermined area is suitable for setting up a base station is calculated; the score value Where m represents the total number of groups of the jth quality index collected; w j represents the weight coefficient of the jth quality index; X′ ij represents the i-th group of data of the j-th quality indicator after normalization, X ij represents the i-th group of data of the j-th quality indicator before the normalization process; Whether the undetermined area is suitable for setting up a base station is determined based on the calculated score value.

9. A computer storage medium, wherein a computer program is stored in the computer storage medium, and when the computer program is executed by a processor, the base station site selection method according to any one of claims 1 to 8 is implemented.

10. A terminal, comprising: A memory and a processor, wherein the memory stores a computer program; wherein, The processor is configured to execute the computer program in the memory; When the computer program is executed by the processor, the base station site selection method according to any one of claims 1 to 8 is implemented.

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