Working method and system for base station positioning based on complex environmental factors of GIS (Geographic Information System) and medium

Abstract

The invention provides a system and a method for positioning a base station based on complex environmental factors of a GIS (Geographic Information System). According to the system, through integration of multi-source data and application of a deep learning model, an intelligent and automatic base station site selection and network optimization scheme is provided. The method comprises the following steps: firstly, collecting multi-source data including geographic information, environment characteristics and signal propagation characteristics, and constructing a characteristic matrix; through data preprocessing, the feature matrix is normalized. And then, constructing a deep learning model by using a convolutional neural network, automatically extracting spatial features, and predicting suitability of base station site selection and a signal coverage range. And then visual analysis is carried out on base station site selection and signal coverage in combination with a GIS tool. Through the method, intelligent site selection of the base station position, effective prediction and optimization of the signal coverage range and reasonable scheduling of network resources can be realized, and the method is especially suitable for network planning and base station deployment of areas with relatively large influence of complex terrains and environmental factors.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method, system and medium for performing base station positioning based on complex environmental factors of GIS. Background Art

[0002] The mountain environment collected through GIS needs to ensure that the propagation model can transmit signals in the 5G environment. In order to build more valuable and meet the evaluation accuracy of signal coverage in complex scenarios, it is necessary to expand the point-grid discontinuous site selection problem in previous base station planning to a three-dimensional-continuous site selection problem that is more in line with actual construction conditions, taking into account various complex scenes in mountains and hills, optimizing the early construction plan, and providing a scientific basis and guidelines for the future construction of 5G base stations.

[0003] The point-grid 2D site selection algorithm mainly deals with 2D plane maps. In this algorithm, the study area is regarded as a 2D plane divided by a regular grid. Each grid point represents a potential site selection point in the study area, and the algorithm determines the best site selection location by evaluating specific conditions or indicators at these points.

[0004] The two-dimensional plan view processing method is suitable for most ground projects and some basic spatial analysis tasks, such as urban planning, facility layout, and service area optimization. It simplifies complex three-dimensional space problems, making the implementation and calculation of the algorithm more intuitive and efficient. However, this simplification also means that it is difficult for the algorithm to directly deal with problems related to height or depth, such as floor selection of high-rise buildings and layout of underground facilities, unless these problems are projected or converted into two-dimensional plane problems in some way. This type of method cannot take into account the propagation mode of the signal in the three-dimensional geographic space (x, y, z) and the impact of actual buildings, terrain, vegetation, and other real surface environments on the signal. It is not enough to truly simulate and emulate the actual coverage of base station signals. At the same time, the lack of three-dimensional data also makes it difficult to simulate and predict signal coverage.

[0005] For site selection problems that require consideration of height or depth information, it is necessary to use a three-dimensional spatial analysis method or incorporate height information as an additional parameter into a two-dimensional analysis to provide a more comprehensive spatial solution. This urgently requires those skilled in the art to solve the corresponding technical problems. Summary of the invention

[0006] The present invention aims to at least solve the technical problems existing in the prior art, and in particular innovatively proposes a base station positioning system and method based on complex environmental factors of GIS.

[0007] In order to achieve the above-mentioned object of the present invention, the present invention provides a system and method for performing base station positioning based on complex environmental factors of GIS, including:

[0008] Information acquisition module: obtain MR data and CQT data;

[0009] Signal propagation modeling: combining the ray tracing model with the WINNER II model to build a comprehensive model using the advantages of both models;

[0010] Feature engineering module: introduces signal features and environmental features and combines them into a total feature matrix;

[0011] Model training module: input the feature matrix into the convolutional neural network for deep learning training;

[0012] Base station optimization module: uses the training results of convolutional neural networks to optimize base station operations.

[0013] In a preferred embodiment of the present invention, the information collection and processing module includes:

[0014] Use periodic test data to perform MR data acquisition and CQT data acquisition operations,

[0015] MR data include reference signal received power, reference signal received quality, and signal-to-interference-plus-noise ratio;

[0016] CQT data needs to be tested at a specific location for fixed-point call quality testing to evaluate network performance by simulating user communication processes;

[0017] Preprocess the collected MR and CQT data, first perform data cleaning:

[0018] Missing value processing: Check whether there are missing values ​​in the data set, and use mean filling, interpolation or discarding missing samples to handle them;

[0019] Outlier detection and processing: Identify obviously unreasonable outliers in the data and filter them using standard deviation analysis;

[0020] The reference signal received power, reference signal received quality, and signal interference plus noise ratio data are normalized or standardized so that subsequent model analysis can be more effective. The normalization uses the Z-score method, and the normalization scales the data values ​​to the range of [0,1].

[0021] In a preferred embodiment of the present invention, the signal propagation modeling includes:

[0022] Path tracing: Use the ray tracing model to calculate all paths from the launch point to the receiving point, including direct paths, reflection paths, and diffraction paths;

[0023] For each path i, calculate the path loss due to reflection, refraction and diffraction:

[0024]

[0025] is the total path loss of the ith path, is the free space loss of the ith path, and are the path losses caused by reflection, diffraction and refraction of the i-th path respectively;

[0026] Use the WINNER II model to generate the multipath components, fast fading, and shadow fading characteristics of each path;

[0027] For each path i, calculate the multipath component loss in the WINNER II model:

[0028]

[0029] in, is the calculation result of the superposition of the multipath statistical characteristics of the i-th path, A i is the intercept associated with path i, e i is the path loss exponent of the ith path, which depends on the environment through which the path passes, d i is the distance of the ith path, is the shadow fading factor of the ith path;

[0030] Combining the path loss calculated by ray tracing with the statistical properties of the WINNER II model:

[0031]

[0032] is the comprehensive loss of the ith path, including the exact physical loss of ray tracing and the statistical loss of WINNER II;

[0033] Considering the arrival delays of different paths, the losses of all paths are combined, and the received signal power is obtained by multipath superposition:

[0034]

[0035] P r is the received signal power, N is the total number of signal measurement points P t is the transmit power, G t is the gain of the transmitting antenna, G r is the gain of the receiving antenna, λ is the signal wavelength, is the attenuation of the signal on the ith path due to the comprehensive loss;

[0036] In order to simulate the fast fading effect, the multipath interference and fast fading characteristics in WINNER II are used, and the Rice distribution is used to calculate the superposition effect of different paths at the receiving point;

[0037] The fast fading part of the received signal is expressed as:

[0038]

[0039] h(t) represents the fast fading value of the signal, a i represents the amplitude of the i-th path, which follows the Rice distribution, φ i is the phase of the ith path.

[0040] In a preferred embodiment of the present invention, the feature engineering module includes:

[0041] Introducing the joint features of signal strength and location information: Combine signal strength with geographic location information to create more spatial features:

[0042] F location =[P r ,x,y];

[0043] F location is a joint location feature, used to represent the combination of signal strength and location, P r is the received signal power, x and y are the geographical coordinates of the signal measurement point;

[0044] Introducing time-varying features: Introducing the time dimension, calculating the signal strength variation trend within a window at regular intervals to represent network performance in different time periods;

[0045]

[0046] Where Δt is the time interval, Indicates the rate of change of signal strength over time, P r (t) is the received signal power at time t, P r (t+1) is the received signal power at time t+1;

[0047] Introduce spatial clustering of signal coverage areas: cluster different measurement points to find areas with similar signal strengths, which represent similar signal coverage conditions;

[0048] C = kmeans([x,y,P r ]);

[0049] C is the clustering result, indicating similar signal coverage areas. kmeans(*) is a clustering algorithm used to divide data points into several clusters.

[0050] Introducing environmental context information: Introducing more environmental information as features, including road types, building density, and user density, obtained through GIS datasets or field surveys;

[0051] F environment =[P r ,B d ,user];

[0052] F environment is the environmental characteristic, indicating the signal strength and its influence by the surrounding environment, B d is the building density, user is the user density,

[0053] Signal stability and fluctuation characteristics: Calculate the standard deviation of the signal strength in the measurement points to indicate the signal's fluctuation and stability;

[0054]

[0055] is the signal strength fluctuation, n represents the number of measurement points, represents the received signal power at the i-th measurement point, is the mean signal power at the measurement point;

[0056] Frequency domain characteristics of the signal: Perform Fourier transform on the time series data of the signal to extract frequency domain features. These features are used to describe the changing frequency of the signal and its periodic fluctuations.

[0057] F frequency =FFT ( P r ′(t));

[0058] F frequency It is the frequency characteristic of the signal, which is used to describe the characteristics of the signal in the frequency domain. r ′(t) is a time series signal, which represents the time domain characteristics of the received signal power. FFT(*) is a fast Fourier transform algorithm used to convert the signal from the time domain to the frequency domain;

[0059] Fusion of signal features into a feature matrix:

[0060]

[0061] The matrix X is the fusion feature matrix.

[0062] In a preferred embodiment of the present invention, the model training module includes:

[0063] Input features include signal propagation characteristics, geographical characteristics, user density, signal fluctuation degree, and frequency characteristics;

[0064] Normalize the feature matrix to ensure that all features are in the numerical range of [0,1];

[0065] Model construction:

[0066] Convolutional layer: The core of the convolutional neural network is the convolutional layer, which uses different convolution kernels to scan the input data and automatically learn features. These convolution kernels can extract local spatial features of the data;

[0067] Activation layer: Add an activation layer after the convolution layer, use the ReLU (Rectified Linear Unit) activation function, truncate negative values ​​to zero, introduce nonlinear characteristics, and enable the model to learn more complex features;

[0068] Pooling layer: In order to reduce the amount of calculation and extract the main features in the data, a pooling layer is added to downsample the convolution output to make the feature map smaller but retain the main information;

[0069] Fully connected layer: After the convolutional layer and pooling layer, the features are flattened and connected to the fully connected layer for final output prediction. The fully connected layer can integrate the features extracted by the convolutional layer for classification or regression;

[0070] Calculate the gradient of the loss function with respect to the network parameters, use the back-propagation algorithm to pass the gradient back to each neuron, update the weights in the network to minimize the loss function, and use the gradient descent method to update the parameters;

[0071] Finally, the training result of the convolutional neural network f model (X) is used for subsequent base station optimization.

[0072] In a preferred embodiment of the present invention, the base station optimization module includes:

[0073] Deep learning model output: Base station location suitability score S j and signal strength coverage prediction P r (x,y);

[0074] Base station site optimization uses the data trained by the CNN convolutional neural network to optimize the optimal base station location:

[0075] S=[S1,S2,.....,S j ,.....,S J ];

[0076] S j =f model (X j );

[0077] Where S is the set of base station locations in a given area, J is the total number of base stations in a given area, and f model (X j ) is the base station location suitability score calculated by the CNN model, X j Feature matrix at the candidate base station location;

[0078] User resource scheduling:

[0079] Use deep learning models to analyze user mobility patterns and needs and output user density hotspot distribution;

[0080] U m =f model (X user );

[0081] U m is the user density at the mth position, X user It is the user data feature;

[0082] Visualization tool combined with:

[0083] Combine the prediction results of the deep learning model with GIS tools to display the predicted base station site selection and signal coverage on the map;

[0084] Using signal intensity distribution to predict P c , generate a contour map of the coverage to visualize the coverage of each base station:

[0085] P c =f model (X coverage );

[0086] Among them, X coverage are the characteristics of different locations in the region;

[0087] Check in GIS whether the location of each base station is affected by terrain and buildings, and use 3D visualization tools to analyze the propagation effect of base station signals in complex terrain;

[0088] Using signal strength coverage to predict P r (x,y) generates three-dimensional graphics:

[0089] Contour(P r (x,y)=P threshold );

[0090] Contour(P r (x,y)=P threshold ) represents the generation of a three-dimensional graph based on signal strength, P threshold It is the signal strength threshold used to distinguish between effective coverage and unavailable coverage areas.

[0091] The present invention also discloses a computer system, comprising:

[0092] a memory for storing processor-executable instructions;

[0093] Wherein, the processor is configured to implement a base station positioning system based on complex environmental factors of GIS when executing the executable instructions.

[0094] The present invention also discloses a computer-readable storage medium, comprising:

[0095] a memory having a computer program stored thereon;

[0096] The processor is used to execute the program in the memory to realize a base station positioning system based on complex environmental factors of GIS.

[0097] The present invention also discloses a method for positioning a base station based on complex environmental factors based on GIS, comprising:

[0098] S1, information collection: obtaining MR data and CQT data;

[0099] S2, signal propagation modeling: combining the ray tracing model with the WINNER II model to build a comprehensive model using the advantages of both models;

[0100] S3, feature engineering: introduce signal features and environmental features and combine them into a total feature matrix;

[0101] S4, model training: input the feature matrix into the convolutional neural network for deep learning training;

[0102] S5, base station optimization: use the training results of convolutional neural network to optimize the base station operation.

[0103] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0104] Collecting this data can help researchers and network engineers accurately analyze the coverage and quality of existing networks and determine the best locations for new base stations. MR provides network performance indicators from the perspective of user equipment, while CQT provides coverage and performance data at the macro and micro levels.

[0105] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0107] Figure 1 It is a schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0108] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0109] like Figure 1 As shown, the present invention discloses a base station positioning system based on complex environmental factors of GIS, including the following:

[0110] The indoor environment is complex, and radio waves are blocked by obstacles, causing reflection, refraction or scattering, resulting in non-line of sight (NLOS) propagation, which seriously affects positioning accuracy. In addition, the layout and topology of the indoor production environment are easily affected by human factors, resulting in changes in the propagation of sound, light, electricity, etc., thereby reducing the performance of positioning technology based on the principle of feature matching. Due to the presence of walls, furniture and other obstacles, the propagation path of wireless signals becomes complicated. This leads to the multipath effect. In multipath propagation, wireless signals propagate along multiple different paths to reach the receiver.

[0111] 1. Information acquisition and processing module, which uses periodic test data to acquire MR data and CQT data.

[0112] MR data include reference signal received power, reference signal received quality, and signal-to-interference-plus-noise ratio;

[0113] CQT data needs to be tested at a specific location for fixed-point call quality testing to evaluate network performance by simulating user communication processes;

[0114] The collected MR and CQT data were preprocessed and data cleaning was performed first.

[0115] Missing value processing: Check whether there are missing values ​​in the data set. Use mean filling and interpolation to process.

[0116] Outlier detection and handling: Identify outliers in the data that are obviously unreasonable (e.g., measurements with unusually high or low RSRP), which can be filtered out using statistical methods or thresholds based on domain knowledge.

[0117] The reference signal received power, reference signal received quality, and signal interference plus noise ratio data are normalized or standardized so that subsequent model analysis can be more effective. Normalization usually uses the Z-score method, and normalization can scale the data values ​​to the range of [0,1].

[0118] 1.1 Signal propagation modeling:

[0119] Combining the ray tracing model with the WINNER II model takes full advantage of the two models: the physical accuracy of the ray tracing model and the statistical flexibility of the WINNER II model. The goal of combining the two models is to combine the precise geometric path analysis of ray tracing with the statistical channel characteristics of the WINNER II model to capture all the important characteristics of signal propagation.

[0120] Path tracing: Use the ray tracing model to calculate all paths from the launch point to the receiving point, including direct paths, reflection paths, and diffraction paths;

[0121] For each path i, calculate the path loss due to reflection, refraction and diffraction:

[0122]

[0123] is the total path loss, is the free space loss, and They are the path losses caused by reflection, diffraction and refraction respectively.

[0124] Use the WINNER II model to generate the multipath components, fast fading, and shadow fading characteristics of each path;

[0125] For each path i, calculate the multipath component loss in the WINNER II model:

[0126]

[0127] in, is the calculation result of the superposition of multipath statistical characteristics, A i is the intercept associated with path i, e i is the path loss exponent, which depends on the environment through which the path passes, d i is the distance of the ith path, is the shadow fading factor of the ith path

[0128] Combining the path loss calculated by ray tracing with the statistical properties of the WINNER II model:

[0129]

[0130] is the comprehensive loss of the i-th path, including the accurate physical loss of ray tracing and the statistical loss of WINNER II.

[0131] The losses of all paths are combined and the received signal power is obtained by multi-path superposition.

[0132] The received signal power is calculated by adding up the powers of all paths, taking into account the arrival delays of different paths:

[0133]

[0134] P r is the received signal power, N is the total number of signal measurement points, P t is the transmit power, G t is the gain of the transmitting antenna, G r is the gain of the receiving antenna, λ is the signal wavelength, is the attenuation of the signal on the ith path due to the comprehensive loss;

[0135] In order to simulate the fast fading effect, the multipath interference and fast fading characteristics in WINNER II are used. The Rice distribution is used to calculate the superposition effect of different paths at the receiving point;

[0136] The fast fading part of the received signal is expressed as:

[0137]

[0138] h(t) represents the fast fading value of the signal, a i represents the amplitude of the i-th path, which follows the Rice distribution, φ i is the phase of the ith path. What does the superscript j represent?

[0139] Combining ray tracing with the WINNER II model takes advantage of the strengths of both models:

[0140] The ray tracing model provides an accurate physical path analysis of the signal and can capture the reflection, refraction, diffraction and other characteristics in complex geometric environments.

[0141] The WINNER II model can provide actual measurement data support for multipath statistical characteristics, fast fading and shadow fading, and is suitable for statistical modeling of modern communication systems.

[0142] 2. Perform data integration and feature engineering:

[0143] Introducing the joint features of signal strength and location information: Combine signal strength with geographic location information to create more spatial features:

[0144] Flocation =[P r ,x,y]

[0145] F location is a joint location feature, used to represent the combination of signal strength and location, P r is the received signal power, x and y are the geographical coordinates of the signal measurement point;

[0146] Introducing time-varying features: Consider introducing the time dimension. For example, calculate the signal strength variation trend within a window at regular intervals to represent the network performance in different time periods.

[0147]

[0148] Where Δt is the time interval, Indicates the rate of change of signal strength over time, P r (t) is the received signal power at time t, P r (t+1) is the received signal power at time t+1

[0149] Introduce spatial clustering of signal coverage areas: cluster different measurement points to find areas with similar signal strengths, which represent similar signal coverage conditions;

[0150] C = k-means([x,y,P r ]);

[0151] C is the clustering result, indicating similar signal coverage areas. k-means(*) is a clustering algorithm used to divide data points into several clusters.

[0152] Introducing environmental context information: Introducing more environmental information as features, such as road types, building density, etc., obtained through GIS datasets or field surveys;

[0153] F environment =[P r ,B d ,user]

[0154] F environment is the environmental characteristic, indicating the signal strength and its influence by the surrounding environment, B d is the building density, user is the user density,

[0155] Signal stability and fluctuation characteristics: Calculate the standard deviation of the signal strength in the measurement points to indicate the signal's fluctuation and stability;

[0156]

[0157] is the signal strength fluctuation, N is the number of measurement points, represents the received signal power at the nth measurement point, is the mean signal power at the measurement point.

[0158] Frequency domain features of the signal: Perform Fourier transform on the time series data of the signal to extract frequency domain features. These features are used to describe the changing frequency and periodic fluctuations of the signal.

[0159] F frequency =FFT(P r ′(t));

[0160] F frequency It is the frequency characteristic of the signal, which is used to describe the characteristics of the signal in the frequency domain. r ′(t) is a time series signal, which represents the time domain characteristics of the received signal power. FFT(*) is a fast Fourier transform algorithm used to convert the signal from the time domain to the frequency domain;

[0161] Fusion of signal features into a feature matrix:

[0162]

[0163] The matrix X is the fusion feature matrix.

[0164] 3. Deep learning model training and optimization

[0165] Input features include signal propagation characteristics (signal strength, path loss), geographical characteristics (building density, height, etc.), user density, signal fluctuation degree, and frequency characteristics.

[0166] The feature matrix is ​​normalized to ensure that all features are in the value range of [0,1].

[0167] Model construction:

[0168] Convolutional layer: The core of the convolutional neural network is the convolutional layer, which uses different convolution kernels (filters) to scan the input data and automatically learn features. These convolution kernels can extract local spatial features of the data, such as terrain changes around base stations, building layouts, etc.

[0169] Activation layer: Add an activation layer after the convolution layer and use the ReLU (Rectified Linear Unit) activation function to truncate negative values ​​to zero and introduce nonlinear characteristics so that the model can learn more complex features.

[0170] Pooling layer: In order to reduce the amount of calculation and extract the main features in the data, a pooling layer (maximum pooling or average pooling) is added to downsample the convolution output to make the feature map smaller but retain the main information.

[0171] Fully connected layer: After the convolutional layer and pooling layer, the features are flattened and connected to the fully connected layer for final output prediction. The fully connected layer can integrate the features extracted by the convolutional layer for classification or regression.

[0172] Calculate the gradient of the loss function with respect to the network parameters, use the back-propagation algorithm to pass the gradient back to each neuron, and update the weights in the network to minimize the loss function. Use the gradient descent method to update the parameters.

[0173] Finally, the training result of the convolutional neural network f model (X) Used for subsequent base station optimization.

[0174] The specific advantages of applying CNN models to base station optimization:

[0175] CNN automatically extracts effective features from input data without the need to manually define the parameters of the signal propagation model (such as path loss exponent, etc.), and is particularly suitable for processing complex geographical environments and multivariate features.

[0176] The convolutional layer can effectively identify patterns in spatial data, such as building distribution, terrain changes, etc. The impact of these factors on signal propagation is complex, and CNN can better analyze and understand the relationship between these factors.

[0177] Through a large amount of training data, the CNN model has good generalization ability and can be applied in different environments to adapt to different base station deployment requirements.

[0178] 4. Base station optimization

[0179] Deep learning model output: Base station location suitability score S j and signal coverage prediction

[0180] Base station site optimization uses the data trained by the CNN convolutional neural network to optimize the optimal base station location:

[0181] S=[S1,S2,.....,S j ,.....,S J ];

[0182] S j =f model (X j );

[0183] Where S is the set of base station locations in a given area, J is the total number of base stations in a given area, and f model (X j ) is the base station location suitability score calculated by the CNN model, Xj is the feature matrix at the candidate base station location;

[0184] User resource scheduling:

[0185] Use deep learning models to analyze user mobility patterns and needs and output user density hotspot distribution;

[0186] U m =f model (X user );

[0187] U m is the user density at the mth position, X user It is the user data feature;

[0188] Visualization tool combined with:

[0189] The prediction results of the deep learning model are combined with GIS tools to display the predicted base station site selection and signal coverage on the map.

[0190] Using signal intensity distribution to predict P c , generate a contour map of the coverage to visualize the coverage of each base station:

[0191] P c =f model (X coverage );

[0192] Among them, X coverage are the characteristics of different locations in the region;

[0193] Check in GIS whether the location of each base station is affected by terrain and buildings, and use 3D visualization tools to analyze the propagation effect of base station signals in complex terrain.

[0194] Using signal strength coverage to predict P r (x,y) generates three-dimensional graphics:

[0195] Contour(P r (x,y)=P threshold );

[0196] Contour(P r (x,y)=P threshold ) represents the generation of a three-dimensional graph based on signal strength, P threshold It is the signal strength threshold used to distinguish between effective coverage and unavailable coverage areas.

[0197] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A base station positioning system based on complex environmental factors of GIS, characterized in that: include: Information acquisition and processing module: acquiring MR data and CQT data, and processing the data; Signal propagation modeling: combining the ray tracing model with the WINNER II model to build a comprehensive model using the advantages of both models; Feature engineering module: introduces signal features and environmental features and combines them into a total feature matrix; Model training module: input the feature matrix into the convolutional neural network for deep learning training; Base station optimization module: uses the training results of convolutional neural networks to optimize base station operations.

2. The base station positioning system based on complex environmental factors of GIS according to claim 1 is characterized in that: The information collection and processing module includes: Use periodic test data to perform MR data acquisition and CQT data acquisition operations, MR data include reference signal received power, reference signal received quality, and signal-to-interference-plus-noise ratio; CQT data needs to be tested at a specific location for fixed-point call quality testing to evaluate network performance by simulating user communication processes; Preprocessing the collected MR and CQT data, including missing value processing and / or outlier detection and processing and / or normalization or standardization; Missing value processing: Check whether there are missing values ​​in the data set, and use mean filling, interpolation or discarding missing samples to handle them; Outlier detection and processing: Identify obviously unreasonable outliers in the data and filter them using standard deviation analysis; The reference signal received power, reference signal received quality, and signal interference plus noise ratio data are normalized or standardized so that subsequent model analysis can be more effective. The normalization uses the Z-score method, and the normalization scales the data values ​​to the range of [0,1].

3. The base station positioning system based on complex environmental factors of GIS according to claim 1 is characterized in that: The signal propagation modeling includes: Path tracing: Use the ray tracing model to calculate all paths from the launch point to the receiving point, including direct paths, reflection paths, and diffraction paths; For each path i, calculate the path loss due to reflection, refraction and diffraction: is the total path loss of the ith path, is the free space loss of the ith path, and are the path losses caused by reflection, diffraction and refraction of the i-th path respectively; Use the WINNER II model to generate the multipath components, fast fading, and shadow fading characteristics of each path; For each path i, calculate the multipath component loss in the WINNER II model: in, is the calculation result of the superposition of the multipath statistical characteristics of the i-th path, A i is the intercept associated with path i, e i is the path loss exponent of the ith path, which depends on the environment through which the path passes, d i is the distance of the ith path, is the shadow fading factor of the ith path; Combining the path loss calculated by ray tracing with the statistical properties of the WINNER II model: is the comprehensive loss of the ith path, including the exact physical loss of ray tracing and the statistical loss of WINNER II; Considering the arrival delays of different paths, the losses of all paths are combined, and the received signal power is obtained by multipath superposition: P r is the received signal power, N is the total number of signal measurement points, P t is the transmit power, G t is the gain of the transmitting antenna, G r is the gain of the receiving antenna, λ is the signal wavelength, is the attenuation of the signal on the ith path due to the comprehensive loss; In order to simulate the fast fading effect, the multipath interference and fast fading characteristics in WINNER II are used, and the Rice distribution is used to calculate the superposition effect of different paths at the receiving point; The fast fading part of the received signal is expressed as: h(t) represents the fast fading value of the signal, a i represents the amplitude of the i-th path, which follows the Rice distribution, φ i is the phase of the ith path.

4. The base station positioning system based on complex environmental factors of GIS according to claim 1 is characterized in that: Its feature engineering modules include: Introducing the joint features of signal strength and location information: Combine signal strength with geographic location information to create more spatial features: F location =[P r ,x,y]; F location is a joint location feature, used to represent the combination of signal strength and location, P r is the received signal power, x and y are the geographical coordinates of the signal measurement point; Introducing time-varying features: Introducing the time dimension, calculating the signal strength variation trend within a window at regular intervals to represent network performance in different time periods; Where Δt is the time interval, Indicates the rate of change of signal strength over time, P r (t) is the received signal power at time t, P r (t+1) is the received signal power at time t+1; Introduce spatial clustering of signal coverage areas: cluster different measurement points to find areas with similar signal strengths, which represent similar signal coverage conditions; C=kmeans([x,y,P r ]); C is the clustering result, indicating similar signal coverage areas. kmeans(*) is a clustering algorithm used to divide data points into several clusters. Introducing environmental context information: Introducing more environmental information as features, including road types, building density, and user density, obtained through GIS datasets or field surveys; F environment =[P r ,B d ,user]; F environment is the environmental characteristic, indicating the signal strength and its influence by the surrounding environment, B d is the building density, user is the user density, Signal stability and fluctuation characteristics: Calculate the standard deviation of the signal strength in the measurement points to indicate the signal's fluctuation and stability; is the signal strength fluctuation, n represents the number of measurement points, represents the received signal power at the i-th measurement point, is the mean signal power at the measurement point; Frequency domain characteristics of the signal: Perform Fourier transform on the time series data of the signal to extract frequency domain features. These features are used to describe the changing frequency of the signal and its periodic fluctuations. F frequency =FFT(P r ′(t)); F frequency It is the frequency characteristic of the signal, which is used to describe the characteristics of the signal in the frequency domain. r ′(t) is a time series signal, which represents the time domain characteristics of the received signal power. FFT(*) is a fast Fourier transform algorithm used to convert the signal from the time domain to the frequency domain; Fusion of signal features into a feature matrix: The matrix X is the fusion feature matrix.

5. The base station positioning system based on complex environmental factors of GIS according to claim 1 is characterized in that: Its model training modules include: Input features include signal propagation characteristics, geographical characteristics, user density, signal fluctuation degree, and frequency characteristics; Normalize the feature matrix to ensure that all features are in the numerical range of [0,1]; Model construction: Convolutional layer: The core of the convolutional neural network is the convolutional layer, which uses different convolution kernels to scan the input data and automatically learn features. These convolution kernels can extract local spatial features of the data; Activation layer: Add an activation layer after the convolution layer, use the ReLU activation function, truncate negative values ​​to zero, introduce nonlinear characteristics, and enable the model to learn more complex features; Pooling layer: In order to reduce the amount of calculation and extract the main features in the data, a pooling layer is added to downsample the convolution output to make the feature map smaller but retain the main information; Fully connected layer: After the convolutional layer and pooling layer, the features are flattened and connected to the fully connected layer for final output prediction. The fully connected layer can integrate the features extracted by the convolutional layer for classification or regression; Calculate the gradient of the loss function with respect to the network parameters, use the back-propagation algorithm to pass the gradient back to each neuron, update the weights in the network to minimize the loss function, and use the gradient descent method to update the parameters; Finally, the training result of the convolutional neural network f model (X) Used for subsequent base station optimization.

6. The base station positioning system based on complex environmental factors of GIS according to claim 1 is characterized in that: The base station optimization module comprises: Deep learning model output: Base station location suitability score S j and signal strength coverage prediction P r (x,y); Base station site optimization uses the data trained by the CNN convolutional neural network to optimize the optimal base station location: S=[S1,S2,.....,S j ,.....,S J ]; S j =f model (X j ); Where S is the set of base station locations in a given area, J is the total number of base stations in a given area, and f model (X j ) is the base station location suitability score calculated by the CNN model, X j is the feature matrix at the candidate base station location; User resource scheduling: Use deep learning models to analyze user mobility patterns and needs and output user density hotspot distribution; U m =f model (X user ); U m is the user density at the mth position, X user It is the user data feature; Visualization tool combination: Combine the prediction results of the deep learning model with GIS tools to display the predicted base station site selection and signal coverage on the map; Using signal intensity distribution to predict P c , generate a contour map of the coverage to visualize the coverage of each base station: P c =f model (X coverage ); Among them, X coverage are the characteristics of different locations in the region; Check in GIS whether the location of each base station is affected by terrain and buildings, and use 3D visualization tools to analyze the propagation effect of base station signals in complex terrain; Using signal strength coverage to predict P r (x,y) generates three-dimensional graphics: Contour(P r (x,y)=P threshold ); Contour(P r (x,y)=P threshold ) represents the generation of a three-dimensional graph based on signal strength, P threshold It is the signal strength threshold used to distinguish between effective coverage and unavailable coverage areas.

7. A computer system, characterized in that: include: a memory for storing processor-executable instructions; Wherein, the processor is configured to implement a base station positioning system based on complex environmental factors of GIS as described in any one of claims 1 to 6 when executing the executable instructions.

8. A computer-readable storage medium, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the program in the memory to implement a base station positioning system based on complex environmental factors based on GIS as described in any one of claims 1 to 6.

9. A method for positioning base stations based on complex environmental factors based on GIS, characterized in that: include: S1, information collection: obtaining MR data and CQT data; S2, signal propagation modeling: combining the ray tracing model with the WINNER II model to build a comprehensive model using the advantages of both models; S3, feature engineering: introduce signal features and environmental features and combine them into a total feature matrix; S4, model training: input the feature matrix into the convolutional neural network for deep learning training; S5, base station optimization: use the training results of convolutional neural network to optimize the base station operation.

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