Key area electromagnetic spectrum monitoring system based on communication tower group

Abstract

The invention discloses a key area electromagnetic spectrum monitoring system based on a communication tower group. The key area electromagnetic spectrum monitoring system comprises a sensor network module, a data transmission module, a data analysis and processing module, a communication and coordination module, a security and closing scale module, a decision support and report generation module, a user interface and interaction module and a system management and maintenance module. Wherein the sensor network module is used for collecting electromagnetic spectrum data in a key area; the data analysis and processing module is used for analyzing and processing the received electromagnetic spectrum data; the decision support and report generation module is used for generating an analysis report according to the monitored data and generating a decision; based on an existing communication tower, an advanced frequency spectrum monitoring and analyzing function is integrated, and efficient and accurate electromagnetic frequency spectrum monitoring is carried out on a key area. Through the system, spectrum resource allocation can be optimized, national security and communication security are guaranteed, and the quality and efficiency of wireless communication service are improved.

Description

Technical Field

[0001] The present invention relates to the field of radio communication technology, and in particular to a key area electromagnetic spectrum monitoring system based on a communication tower group. Background Art

[0002] With the rapid development of wireless communication technology, electromagnetic spectrum resources are becoming increasingly scarce, and their effective management and utilization have become key issues. Especially in key areas such as urban centers, military bases, and airports, spectrum interference and illegal occupation frequently occur, seriously affecting the normal operation of legal communication services. Traditional spectrum monitoring systems often rely on ground mobile monitoring vehicles or fixed-point monitoring stations, which have disadvantages such as limited coverage, slow response speed, and high monitoring costs. Summary of the invention

[0003] The purpose of the present invention is to provide a key area electromagnetic spectrum monitoring system based on a communication tower group, which aims to integrate advanced spectrum monitoring equipment and intelligent analysis algorithms to achieve all-round and real-time monitoring and management of the electromagnetic environment in key areas.

[0004] To achieve the above object, the present invention provides a key area electromagnetic spectrum monitoring system based on a communication tower group, comprising:

[0005] The spectrum monitoring subsystem is installed on each communication tower in the key area to collect and monitor various wireless communication signals and record the precise geographic location of each monitoring point;

[0006] A data transmission unit, used to transmit the monitoring data collected by the spectrum monitoring subsystem via a wireless network and send it to a central processing and analysis platform;

[0007] The central processing and analysis platform is used to process and analyze the received monitoring data and send alarm information to the user terminal when the monitoring data is abnormal.

[0008] Further, the spectrum monitoring subsystem includes a first sensor module, a second sensor module, a system management and maintenance module, an intelligent sensing module and a power supply module;

[0009] The first sensor module is used to monitor various wireless communication signals and current sensor location information and environmental information near the sensor;

[0010] The second sensor module is used to receive the original monitoring data sent by the first sensor module and pre-process it;

[0011] The system management and maintenance module includes a monitoring and alarm unit and a maintenance and upgrade unit. The monitoring and alarm unit is used to monitor the system status in real time and send alarm information to the user terminal when a fault state is detected. The maintenance and upgrade unit is used to upgrade and update the system.

[0012] Intelligent sensing module, which is used to automatically adjust the receiver's sensitivity and operating frequency band according to the current environmental noise level and signal type;

[0013] The power supply module is used to supply power to the spectrum monitoring subsystem.

[0014] Furthermore, the first sensor module includes:

[0015] Radio frequency sensors, which capture signals in the electromagnetic spectrum;

[0016] GPS / positioning equipment to determine the location of the sensor;

[0017] Environmental sensors are used to detect the ambient temperature and humidity near the sensor.

[0018] Furthermore, the second sensor module includes:

[0019] A data acquisition unit, used to collect the original monitoring data sent by the first sensor network module;

[0020] The data preprocessing unit is used to perform adaptive filtering and denoising operations on the original monitoring data.

[0021] Furthermore, the workflow of the intelligent perception module includes:

[0022] S1: Initialize the parameters of the broadband RF transceiver, including the initial operating frequency band and receiving sensitivity;

[0023] S2: Collect the noise level and signal type of the current environment through sensors;

[0024] S3: Analyze the data collected in step S2 through a machine learning model algorithm to identify noise characteristics and signal types;

[0025] S4: adjusting the operating parameters of the broadband radio frequency transceiver according to the analysis result of step S3, wherein the operating parameters include an operating frequency band and a receiving sensitivity;

[0026] S5: monitoring the performance indicators of the broadband RF transceiver after adjusting the working parameters, wherein the performance indicators include bit error rate and signal quality. If the value of the performance indicator is less than a preset threshold, returning to step S3 for further adjustment;

[0027] S6: Repeat steps S1-S5 according to a preset period, and continuously adjust the receiver parameters according to the new monitoring results.

[0028] Furthermore, the data transmission unit includes:

[0029] The data transmission module includes a wireless transmission unit and an encryption unit; wherein the wireless transmission unit is used to transmit all data collected by the first sensor network module to the data analysis and processing module through the wireless communication network; the encryption unit is used to encrypt all data monitored by the first sensor network module;

[0030] The data analysis and processing module is used to call the intelligent analysis engine in the central processing and analysis platform to perform pattern recognition, trend prediction, anomaly detection and interference source location on the acquired monitoring data;

[0031] The communication and coordination module includes an interface unit and a communication protocol unit. The interface unit is used to provide an interface to the external system and the data transmission module to ensure effective communication between the system and each module; the communication protocol unit is used to manage the protocols required for communication between the internal and external systems of the monitoring system.

[0032] Furthermore, the data analysis and processing module includes:

[0033] The spectrum analysis unit is used to perform spectrum analysis on the monitoring data obtained by the second sensor network module using an intelligent spectrum analysis algorithm, obtain frequency band occupancy and discover illegal emission sources, thereby providing data support for future spectrum allocation;

[0034] The signal classification unit is used to classify the wireless communication signals collected by the first sensor network module;

[0035] The anomaly detection unit is used to perform anomaly detection on the acquired monitoring data through the interference source rapid location algorithm to discover unusual communication patterns or behaviors.

[0036] Furthermore, the central processing and analysis platform includes:

[0037] The data storage module includes a data storage unit and a data management unit; wherein the data storage unit is used to store the data preprocessed by the second sensor network module; the data management unit is used to add, delete and modify the stored data;

[0038] Intelligent analysis engine, which integrates multiple machine learning models and deep learning algorithms to analyze and process monitoring data;

[0039] Decision support and report generation module, used to formulate new monitoring decisions for the monitoring system based on the analysis results of the intelligent analysis engine;

[0040] The user interface and interaction module is used to provide a web and mobile-friendly visual interface.

[0041] Furthermore, the machine learning models integrated in the intelligent analysis engine include logistic regression model, decision tree model, random forest model, support vector machine, gradient boosting tree, and K-nearest neighbor model; the integrated deep learning algorithms include convolutional neural network, recurrent neural network, long short-term memory network, Transformer network, generative adversarial network, autoencoder and reinforcement learning algorithm.

[0042] Furthermore, the processing steps of the intelligent spectrum analysis algorithm include:

[0043] S1: Obtain the monitored electromagnetic spectrum data after preprocessing;

[0044] S2: Extract the maximum value, average value and standard deviation of signal strength from electromagnetic spectrum data;

[0045] S3: Select the appropriate machine learning model in the intelligent analysis engine;

[0046] S4: Use historical monitoring data to train the selected machine learning model, adjust model parameters through cross-validation, and evaluate the machine learning model;

[0047] S5: Update the model regularly using electromagnetic spectrum data acquired in real time;

[0048] S6: Use the updated model to predict the spectrum occupancy at the specified time and location.

[0049] Therefore, the present invention adopts the above-mentioned key area electromagnetic spectrum monitoring system based on communication tower groups, which has the following beneficial effects:

[0050] First, the present invention sets up a spectrum monitoring subsystem and uses a high-performance broadband receiver to perform spectrum monitoring, which can cover a wide frequency band from MHz to GHz, ensuring comprehensive monitoring of various wireless communication signals (such as mobile communications, broadcasting, satellite communications, etc.); and the GPS module is integrated in this unit to ensure the accurate geographical location information of each monitoring point, which is convenient for the subsequent interference source positioning;

[0051] Secondly, the present invention introduces adaptive tuning technology, so that the monitoring unit can automatically adjust the receiving sensitivity and the working frequency band according to the current environmental noise level and signal type, thereby reducing the false alarm rate while maintaining high monitoring accuracy.

[0052] Third, the present invention utilizes solar panels or wind power generation devices on communication towers to power the monitoring unit, assisted by an intelligent power management system, to achieve efficient use of energy and long-term autonomous operation.

[0053] Fourth, the present invention utilizes the existing optical fiber network as the backbone transmission channel, combined with high-speed wireless backhaul technology (such as 5G or private network communication) to ensure real-time and stable transmission of data.

[0054] Fifth, the present invention improves on existing communication towers, fully utilizes existing communication tower resources, reduces additional infrastructure construction, and reduces implementation costs and complexity.

[0055] Sixth, the present invention adopts adaptive filtering technology and signal denoising algorithm, which can improve the ability to capture weak signals in complex electromagnetic environments and ensure high precision of spectrum monitoring.

[0056] Seventh, the present invention proposes an intelligent scheduling algorithm that can dynamically adjust the spectrum usage strategy according to real-time monitoring results, realize spectrum resource sharing and dynamic allocation, maximize the utilization efficiency of spectrum resources, and at the same time ensure the communication quality of key services.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is the module architecture diagram of this system. DETAILED DESCRIPTION

[0059] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the claims attached to the application.

[0060] The present invention discloses a key area electromagnetic spectrum monitoring system based on a communication tower group, which is deployed on the existing communication tower infrastructure in the key area to achieve all-round and real-time monitoring and management of the electromagnetic environment in the key area.

[0061] The electromagnetic spectrum monitoring system includes a spectrum monitoring unit, a data transmission unit and a central processing and analysis platform;

[0062] The following is an introduction to each module:

[0063] 1. Spectrum monitoring unit

[0064] The spectrum monitoring unit is installed on each communication tower in the key area to monitor various wireless communication signals and record the precise geographic location of each monitoring point;

[0065] The spectrum monitoring unit includes a first sensor module, a second sensor module, a system management and maintenance module, an intelligent sensing module and a power supply module;

[0066] The following is an introduction to each of the above sub-modules:

[0067] (1) First sensor module

[0068] The first sensor module includes a radio frequency sensor, a GPS / positioning device, and an environmental sensor. The radio frequency sensor is used to capture signals in the electromagnetic spectrum, the GPS / positioning device is used to determine the location of the sensor, and the environmental sensor is used to detect environmental factors such as temperature and humidity, which helps to understand the reasons for signal changes;

[0069] The RF sensor can cover a wide frequency band from MHz to GHz, ensuring comprehensive monitoring of various wireless communication signals (such as mobile communications, broadcasting, satellite communications, etc.). The broadband RF transceiver is an AD9375 broadband RF transceiver. The broadband RF transceiver is connected to a high-speed signal processor. The high-speed signal processor is responsible for the preliminary decoding and preprocessing of the signal, and can use a TMS320C55x high-speed signal processor;

[0070] (2) Second sensor module

[0071] The second sensor module includes a data acquisition unit and a data preprocessing unit, wherein the data acquisition unit is used to collect the original monitoring data sent by the first sensor network module, and the data preprocessing unit is used to perform adaptive filtering and denoising operations on the original monitoring data;

[0072] Specifically, the data preprocessing unit uses an adaptive filtering algorithm and a signal denoising algorithm to adaptively filter and denoise the original monitoring data, thereby ensuring high accuracy of spectrum monitoring;

[0073] in:

[0074] The adaptive filtering method includes the following steps:

[0075] ①Data collection

[0076] Signal Capture: Sensor nodes capture signals in the electromagnetic spectrum, which can be continuous or intermittent.

[0077] Data recording: record signal frequency, power, arrival time and other information.

[0078] ②Data preprocessing

[0079] Signal separation: Use filtering techniques and related denoising algorithms to separate signals from different sources.

[0080] Data cleaning: Remove invalid or noisy data to ensure data quality.

[0081] The signal denoising method includes the following steps:

[0082] ① Use a bandpass filter to remove noise outside the signal frequency range.

[0083] ② Use wavelet transform to decompose the signal into multiple scales, and apply threshold processing at each scale to remove noise.

[0084] ③If the signal is non-stationary, you can consider using an adaptive filter for denoising.

[0085] (3) System management and maintenance module

[0086] The system management and maintenance module includes a monitoring and alarm unit and a maintenance and upgrade unit. The monitoring and alarm unit is used to monitor the system status in real time and send alarm information to the user terminal when a fault state is detected; the maintenance and upgrade unit is used to upgrade and update the system.

[0087] (4) Intelligent sensing module

[0088] Intelligent sensing module, which is used to automatically adjust the receiving sensitivity and operating frequency band according to the current environmental noise level and signal type, thereby reducing the false alarm rate while maintaining high monitoring accuracy;

[0089] This module introduces adaptive tuning technology, such as the adaptive tuning method disclosed in the document "Adaptive Antenna Tuning for Improved Cellular Coverage and Battery Life," [IEEE Transactions on Antennas and Propagation, vol. 61, no. 10, pp. 5230-5238, Oct. 2013], which tracks the working state of the antenna through a closed-loop tuning scheme and a mismatch sensor, and provides a feedback signal to maintain the optimal matching state.

[0090] The workflow of the above intelligent perception module is as follows:

[0091] S1: Initialize the parameters of the broadband RF transceiver, including the initial operating frequency band and receiving sensitivity;

[0092] S2: Collect the noise level and signal type of the current environment through built-in or external sensors (such as spectrum analyzer, noise detector);

[0093] S3: Analyze the data collected in step S2 through a machine learning model algorithm to identify noise characteristics and signal types;

[0094] S4: adjusting the operating parameters of the broadband radio frequency transceiver according to the analysis result of step S3, wherein the operating parameters include an operating frequency band and a receiving sensitivity;

[0095] Specifically, the receiver's sensitivity to the signal is adjusted by changing the gain setting, filter parameters, etc., and the optimal operating frequency or frequency band is selected to avoid the frequency band with greater interference;

[0096] S5: monitoring the performance indicators of the broadband RF transceiver after adjusting the working parameters, wherein the performance indicators include bit error rate and signal quality. If the value of the performance indicator is less than a preset threshold, returning to step S3 for further adjustment;

[0097] Specifically, the performance indicators of the broadband RF transceiver are monitored by performing performance tests on the transmitter and receiver. When testing the transmitter performance, it is necessary to measure the transmitter's output power, test the frequency stability, evaluate the modulation quality, and test the spurious radiation; when testing the receiver performance, it is necessary to perform receiver sensitivity, selectivity, blocking capability, dynamic range, linearity tests, and noise figure measurements; when performing the test, auxiliary equipment tests are used to perform performance tests, and the auxiliary test equipment includes a signal generator, a spectrum analyzer, a bit error rate (BER) tester, a noise source, and a vector network analyzer (VNA).

[0098] S6: Establish a continuous monitoring mechanism to check changes in environmental conditions regularly or as needed, and continue to adjust receiver parameters based on new monitoring results.

[0099] The monitoring mechanism includes:

[0100] 1) Determine monitoring objectives: define key performance indicators that need to be monitored, including bit error rate and signal quality;

[0101] 2) Select monitoring equipment: Use spectrum analyzer and bit error rate tester for monitoring;

[0102] 3) Data collection: Install sensors and collect data;

[0103] 4) Data transmission: Set up data transmission channels to ensure that data is transmitted to the central server in real time.

[0104] 5) Data analysis and processing: Store and analyze data on a central server for anomaly detection;

[0105] 6) Alarm and notification: Set thresholds and trigger alarms when performance indicators exceed the thresholds;

[0106] 7) User interface: Provides real-time monitoring and historical data query functions through the user interface.

[0107] 8) On-demand monitoring: allows users to manually trigger specific monitoring tasks as needed.

[0108] 9) Regular maintenance: Regularly check the status of monitoring equipment and update it.

[0109] 10) Security assurance: Implement access control strategies to ensure data security.

[0110] (5) Power supply module

[0111] The power supply submodule is connected to the output end of the solar panel or wind power generation device on the communication tower to power the spectrum monitoring unit, thereby achieving efficient use of energy and long-term autonomous operation.

[0112] 2. Data transmission unit

[0113] The data transmission unit includes a data transmission module, a data analysis and processing module, and a communication and coordination module;

[0114] Each sub-module is introduced below.

[0115] (1) Data transmission module

[0116] The data transmission module includes a wireless transmission unit and an encryption unit; wherein the wireless transmission unit is used to transmit all data collected by the first sensor network module to the data analysis and processing module through the wireless communication network; the encryption unit is used to encrypt all data monitored by the first sensor network module;

[0117] Specifically, the wireless transmission unit uses the existing fiber optic network as the backbone transmission channel, combined with high-speed wireless backhaul technology (such as 5G or private network communication), that is, deploying wireless access points in places where the fiber optic network cannot cover, and using high-speed wireless backhaul technology to connect these wireless access points to the optical network backbone to ensure real-time and stable data transmission. In addition, the transmission network adopts a redundant mechanism, by deploying multiple physical links in the network to connect the same two points. When a link fails, the traffic can be automatically switched to another link, thereby ensuring that a single point failure does not affect the overall data flow. In addition, personal identification information is removed during the transmission of data through the transmission network to protect user privacy.

[0118] The encryption unit efficiently compresses the data before transmission, and encrypts the data using an industry standard encryption algorithm to prevent the data from being intercepted or tampered with during transmission, thereby improving transmission efficiency and ensuring data security. Specifically, the compression method adopted by the present invention is LZ77 / LZ78 or Deflate. The data encryption process uses the AES encryption algorithm. The sender generates an AES key and uses the AES key to encrypt the compressed data, and then sends the encrypted data and the AES key to the receiver. The receiver generates an RSA key pair (public key and private key), and uses its own RSA private key to decrypt the AES key, and finally uses the AES key to decrypt the original data.

[0119] (2) Data analysis and processing module

[0120] The data analysis and processing module is used to call the intelligent analysis engine in the central processing and analysis platform to perform pattern recognition, trend prediction, anomaly detection and interference source location on the acquired monitoring data;

[0121] The data analysis and processing module includes a spectrum analysis unit, a signal classification unit and an anomaly detection unit; wherein the spectrum analysis unit is used to perform spectrum analysis on the monitoring data acquired by the second sensor network module using an intelligent spectrum analysis algorithm, obtain the frequency band occupancy and discover illegal emission sources, thereby providing data support for future spectrum allocation; the signal classification unit is used to classify the wireless communication signals collected by the first sensor network module; the anomaly detection unit is used to perform anomaly detection on the acquired monitoring data through an interference source rapid location algorithm to discover unusual communication patterns or behaviors;

[0122] in:

[0123] ①Intelligent spectrum analysis algorithm

[0124] Intelligent spectrum analysis algorithm predicts spectrum occupancy based on an integrated deep learning model;

[0125] It uses historical monitoring data to train the various models integrated in the intelligent analysis engine to predict spectrum demand at specific times and locations, and assist spectrum management departments in planning and adjusting spectrum allocation in advance.

[0126] The specific steps of the intelligent spectrum analysis algorithm are:

[0127] S1: Data collection, collecting pre-processed electromagnetic spectrum data.

[0128] S2: Feature selection, extracting the maximum value, average value, and standard deviation of signal strength from electromagnetic spectrum data.

[0129] S4: Model selection: select a suitable machine learning algorithm from the deep learning model integrated in the intelligent analysis engine, such as support vector machine (SVM) or neural network (such as LSTM).

[0130] S5: Model training, train the selected model using historical data, adjust model parameters through cross-validation, and evaluate the selected model.

[0131] S6: Model optimization, collect current new electromagnetic spectrum data, and regularly update the model to improve prediction accuracy.

[0132] S7: Model prediction: Use the updated model to predict spectrum demand at a specific time and location.

[0133] ②Interference source fast positioning algorithm

[0134] The present invention combines the time difference of arrival (TDoA), angle of arrival (AoA) positioning technology, and a multi-base station joint positioning algorithm to achieve rapid and accurate positioning of interference sources.

[0135] The working principle of the interference source fast location algorithm is:

[0136] In a typical TDOA / AoA joint positioning scenario, assume that we have three receiving sites A, B, and C, and each site is equipped with an antenna array capable of measuring AoA.

[0137] First, perform TDOA calculation: calculate the time difference ΔtAB between the signal arriving at A and B, and the time difference ΔtAC between the signal arriving at A and C.

[0138] Second, perform AoA measurements: measure the angles θA, θB, and θC at which the signal arrives at sites A, B, and C.

[0139] Again, data fusion: use the least squares method to fuse these time differences and angle information.

[0140] Finally, the position calculation is performed: Based on the TDOA information, a series of hyperbolas are obtained, and the intersection points of these hyperbolas are potential positions. The AoA information is used to further limit the possibilities of the position, and finally the exact position of the signal source is determined.

[0141] Based on the above working principle, the interference source fast positioning algorithm adopted by the present invention includes the following steps:

[0142] S1: Data Collection

[0143] -Multiple receiving sites collect signal data simultaneously.

[0144] - Each station records the timestamp of signal arrival and signal strength.

[0145] -Record the angle information of signal arrival.

[0146] S1: Time difference calculation

[0147] Calculate the time difference of arrival (TDOA) of signals between different receiving sites.

[0148] S3: Angle measurement

[0149] The angle at which the signal arrives at each receiving site is measured.

[0150] S4: Data Fusion

[0151] - Fusion of TDOA and AoA data.

[0152] -Weighted combination of these two types of information.

[0153] S5: Positioning calculation

[0154] -Calculate the distance difference between the signal source and the receiving site based on the TDOA information.

[0155] - Combined with AoA information to further narrow the positioning range.

[0156] -Use the least squares method or maximum likelihood estimation to solve the location coordinates of the signal source.

[0157] S6: Result output

[0158] - Output S5 calculates the interference source location.

[0159] S7: Error Correction

[0160] -According to the actual application scenarios and requirements, error correction or iterative optimization is performed to improve positioning accuracy.

[0161] S8: Live Updates

[0162] Positioning in a dynamic environment requires constant updating of the data from the receiving site and recalculation of the position.

[0163] (3) Communication and coordination module

[0164] The communication and coordination module includes an interface unit and a communication protocol unit. The interface unit is used to provide an interface to the external system and the data transmission module to ensure effective communication between the system and each module. The communication protocol unit is used to manage the protocols required for internal and external communication of the system, ensure that data is encapsulated and decapsulated in the correct protocol format, ensure the integrity and accuracy of the data, and improve the overall performance of the system.

[0165] 3. Central processing and analysis platform

[0166] The central processing and analysis platform is used to analyze and process the data sent by the data transmission unit;

[0167] It includes data storage module, security and compliance module, intelligent analysis engine, decision support and report generation module, user interface and interaction module;

[0168] The above sub-modules are introduced below respectively.

[0169] (1) Data storage module

[0170] The data storage module uses a cloud service platform provided by an external cloud service provider to receive spectrum monitoring data sent from the data transmission network and store the spectrum monitoring data.

[0171] The data storage and management module includes a data storage unit and a data management unit; wherein the data storage unit is used to store the data preprocessed by the second sensor network module; the data management unit is used to add, delete and modify the stored data;

[0172] (2) Intelligent Analysis Engine

[0173] It integrates multiple machine learning models and multiple deep learning algorithms to perform pattern recognition, trend prediction, anomaly detection and interference source location on massive monitoring data. The model continuously learns and self-optimizes to improve analysis accuracy and response speed.

[0174] Specifically, its integrated machine learning models include:

[0175] ①Logistic Regression Model

[0176] This model is a linear model for binary or multi-classification tasks. It is used to predict the probability of an event and determine which features have the greatest impact on the classification results.

[0177] ②Decision Trees Model

[0178] This model is a tree-structured model that classifies or regresses data through a series of rules. It is used to predict results based on different branches of feature values ​​and generate easy-to-understand decision rules.

[0179] ③ Random Forest Model

[0180] This model is an ensemble learning method based on multiple decision trees, which improves prediction accuracy by majority voting or averaging prediction results. It is used to improve generalization ability by integrating multiple decision trees and to evaluate the importance of features.

[0181] ④Support Vector Machines (SVM)

[0182] This model is a supervised learning model for classification and regression tasks, which divides the data by finding the hyperplane with the maximum margin. It is used to process linear and nonlinear data sets and map data to high-dimensional space to solve nonlinear problems.

[0183] ⑤Gradient Boosting Trees

[0184] The model gradually improves the prediction model by sequentially adding weak learners (usually decision trees). It is used to reduce bias and variance and improve prediction accuracy by iteratively adding models; and to handle unbalanced data sets by giving more weight to minority classes.

[0185] ⑥K-Nearest Neighbors (KNN)

[0186] KNN is an instance-based learning method that predicts results by calculating the distance between the test sample and the sample in the training set. It is used for classification and regression, as well as for standardizing or normalizing features for feature scaling.

[0187] Its integrated deep learning algorithms include:

[0188] ①Convolutional Neural Networks (CNN)

[0189] It is a deep learning model specifically designed to process grid-structured input data (such as images and videos). It can be used for image classification, object detection, and semantic segmentation.

[0190] ②Recurrent Neural Networks (RNN)

[0191] It is a deep learning model that processes sequence data (such as time series data, speech, and text). It can be used for speech recognition, natural language processing (NLP), and time series prediction.

[0192] ③Long Short-Term Memory (LSTM)

[0193] LSTM is a special form of RNN that can solve long-term dependency problems. It can be used for text generation, speech synthesis, and sequence-to-sequence (Seq2Seq) learning.

[0194] ④Transformer

[0195] Transformer is an architecture based on the self-attention mechanism, which was originally used for natural language processing tasks. It can be used for machine translation, generating text summaries, and implementing question and answer.

[0196] ⑤Generative Adversarial Networks (GAN)

[0197] GAN consists of two networks, the generator and the discriminator, which are trained in a competitive manner. They are usually used for image generation, image type conversion, and converting low-resolution images to high-resolution images.

[0198] ⑥Autoencoders

[0199] It is a neural network used for unsupervised learning, which learns efficient data representation by reconstructing input data. It is often used for data dimensionality reduction and anomaly detection.

[0200] ⑦Reinforcement Learning (RL)

[0201] RL is a method for learning policies through trial and error, where an agent takes actions in an environment to maximize cumulative rewards.

[0202] The steps for continuous learning and self-optimization of all the models integrated above are:

[0203] S1: Data Collection: Continuously collect electromagnetic spectrum data.

[0204] S2: Online Learning: Use an online learning algorithm (such as SGD) to update the model.

[0205] S3: Experience Replay: Store recent data and periodically sample from it for retraining.

[0206] S4: Regularization: Add a regularization term to the loss function to prevent the model from overfitting to the latest data.

[0207] S5: Multi-task learning: Simultaneously learn recent spectral features to improve recommendation accuracy.

[0208] (3) Security and Compliance Module

[0209] The security and compliance module includes a security audit unit and a compliance check unit. The security audit unit is used to analyze system log files to find potential security vulnerabilities. The compliance check unit is used to perform compliance checks on the user identities of the system.

[0210] The compliance check unit performs compliance checks on user identities through access rights management methods to prevent unauthorized access and operations;

[0211] The access permission management method comprises:

[0212] 1) User Authentication

[0213] Verify the user's identity through username / password verification, multi-factor authentication (MFA), or biometric authentication to ensure that only authorized users can access the system.

[0214] 2) Access Tokens

[0215] Issue certificates to authenticated users through JSON Web Tokens (JWT) or Oauth methods, which are used to prove the user's identity in subsequent requests.

[0216] 3) Password Policies

[0217] According to the password complexity requirements and password expiration policies, rules such as password complexity and validity period are specified.

[0218] (4) Decision support and report generation module

[0219] The decision support and report generation module includes a report generation unit and a decision support unit. The report generation unit is used to summarize abnormal data and signal frequency band usage and generate corresponding reports. The decision support unit is used to formulate new monitoring decisions for the monitoring system based on the analysis results of the intelligent analysis engine. The monitoring decisions include:

[0220] Resource allocation: guides the efficient allocation of frequency resources.

[0221] Task scheduling: Determine the priority and execution order of monitoring tasks.

[0222] System adjustment: Adjust the operating parameters of the monitoring equipment to improve the monitoring quality.

[0223] Alarm management: responding based on the severity of the alarm, such as notifying relevant personnel to take action;

[0224] (5) User interface and interaction module

[0225] The user interface and interaction module includes a web interface and a mobile APP, which are used to provide a web and mobile-friendly visual interface to display real-time spectrum occupancy maps, spectrum usage statistics reports, interference event history records, etc. It can also automatically send SMS, email or APP push notifications to relevant personnel according to preset rules.

[0226] Example

[0227] 1. Sensor network module

[0228] Function: Responsible for collecting electromagnetic spectrum data in key areas.

[0229] composition:

[0230] Radio frequency sensors: Used to capture signals in the electromagnetic spectrum.

[0231] GPS / Positioning Device: Used to determine the location of the sensor.

[0232] Environmental sensors: used to monitor environmental factors such as temperature and humidity, which helps to understand the reasons for signal changes.

[0233] Workflow: The sensor network module continuously monitors signals in the electromagnetic spectrum and records information such as frequency and power.

[0234] 2. Data storage and management module

[0235] Function: Store and manage collected data in a high-performance cloud computing infrastructure.

[0236] composition:

[0237] Data storage unit: Use high-performance storage systems (such as object storage and block storage) to save data.

[0238] Data management unit: provides data indexing, query and other functions to facilitate subsequent data analysis.

[0239] Workflow: The data storage and management module is responsible for storing the collected data and providing data management functions such as indexing and querying.

[0240] 3. User interface and interaction module

[0241] Function: Provides user interface and interactive functions to facilitate users to access system functions and view data.

[0242] composition:

[0243] Web interface: provides users with a browser-based access method.

[0244] Mobile application: supports access on mobile devices.

[0245] Workflow: The user interface and interaction module provides users with an interface to access system functions and view data.

[0246] 4. Communication and coordination module

[0247] Function: Responsible for communication and coordination with other systems (such as data centers).

[0248] composition:

[0249] Interface unit: provides data exchange interface with external systems.

[0250] Communication protocol unit: supports multiple communication protocols to ensure data compatibility and interoperability.

[0251] Workflow: The communication and coordination module is responsible for data exchange and coordination with other systems.

[0252] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A key area electromagnetic spectrum monitoring system based on a communication tower group, characterized in that: include: The spectrum monitoring subsystem is installed on each communication tower in the key area to collect and monitor various wireless communication signals and record the precise geographic location of each monitoring point; A data transmission unit, used to transmit the monitoring data collected by the spectrum monitoring subsystem via a wireless network and send it to a central processing and analysis platform; The central processing and analysis platform is used to process and analyze the received monitoring data and send alarm information to the user terminal when the monitoring data is abnormal.

2. A key area electromagnetic spectrum monitoring system based on a communication tower group as claimed in claim 1, characterized in that: The spectrum monitoring subsystem includes a first sensor module, a second sensor module, a system management and maintenance module, an intelligent sensing module and a power supply module; The first sensor module is used to monitor various wireless communication signals and current sensor location information and environmental information near the sensor; The second sensor module is used to receive the original monitoring data sent by the first sensor module and pre-process it; The system management and maintenance module includes a monitoring and alarm unit and a maintenance and upgrade unit. The monitoring and alarm unit is used to monitor the system status in real time. When a fault state is detected, an alarm message is sent to the user terminal. The maintenance and upgrade unit is used to upgrade and update the system. Intelligent sensing module, which is used to automatically adjust the receiver's sensitivity and operating frequency band according to the current environmental noise level and signal type; The power supply module is used to supply power to the spectrum monitoring subsystem.

3. A key area electromagnetic spectrum monitoring system based on a communication tower group as claimed in claim 2, characterized in that: The first sensor module comprises: Radio frequency sensors, which capture signals in the electromagnetic spectrum; GPS / positioning equipment to determine the location of the sensor; Environmental sensors are used to detect the ambient temperature and humidity near the sensor.

4. A key area electromagnetic spectrum monitoring system based on a communication tower group as claimed in claim 2, characterized in that: The second sensor module comprises: A data acquisition unit, used to collect the original monitoring data sent by the first sensor network module; The data preprocessing unit is used to perform adaptive filtering and denoising operations on the original monitoring data.

5. A critical area electromagnetic spectrum monitoring system based on a communication tower group as claimed in claim 2, characterized in that: The workflow of the intelligent perception module includes: S1: Initialize the parameters of the broadband RF transceiver, including the initial operating frequency band and receiving sensitivity; S2: Collect the noise level and signal type of the current environment through sensors; S3: Analyze the data collected in step S2 through a machine learning model algorithm to identify noise characteristics and signal types; S4: adjusting the operating parameters of the broadband radio frequency transceiver according to the analysis result of step S3, wherein the operating parameters include an operating frequency band and a receiving sensitivity; S5: monitoring the performance indicators of the broadband RF transceiver after adjusting the working parameters, wherein the performance indicators include bit error rate and signal quality. If the value of the performance indicator is less than a preset threshold, returning to step S3 for further adjustment; S6: Repeat steps S1-S5 according to a preset period, and continuously adjust the receiver parameters according to the new monitoring results.

6. A critical area electromagnetic spectrum monitoring system based on a communication tower group as claimed in claim 2, characterized in that: The data transmission unit includes: The data transmission module includes a wireless transmission unit and an encryption unit; wherein the wireless transmission unit is used to transmit all data collected by the first sensor network module to the data analysis and processing module through the wireless communication network; the encryption unit is used to encrypt all data monitored by the first sensor network module; The data analysis and processing module is used to call the intelligent analysis engine in the central processing and analysis platform to perform pattern recognition, trend prediction, anomaly detection and interference source location on the acquired monitoring data; The communication and coordination module includes an interface unit and a communication protocol unit. The interface unit is used to provide an interface to the external system and the data transmission module to ensure effective communication between the system and each module; the communication protocol unit is used to manage the protocols required for communication between the internal and external systems of the monitoring system.

7. A critical area electromagnetic spectrum monitoring system based on a communication tower group as claimed in claim 6, characterized in that: Data analysis and processing modules include: The spectrum analysis unit is used to perform spectrum analysis on the monitoring data obtained by the second sensor network module using an intelligent spectrum analysis algorithm, obtain frequency band occupancy and discover illegal emission sources, thereby providing data support for future spectrum allocation; The signal classification unit is used to classify the wireless communication signals collected by the first sensor network module; The anomaly detection unit is used to perform anomaly detection on the acquired monitoring data through the interference source rapid location algorithm to discover unusual communication patterns or behaviors.

8. A critical area electromagnetic spectrum monitoring system based on a communication tower group as claimed in claim 7, characterized in that: The central processing and analysis platform includes: The data storage module includes a data storage unit and a data management unit; wherein the data storage unit is used to store the data preprocessed by the second sensor network module; the data management unit is used to add, delete and modify the stored data; Intelligent analysis engine, which integrates multiple machine learning models and deep learning algorithms to analyze and process monitoring data; Decision support and report generation module, used to formulate new monitoring decisions for the monitoring system based on the analysis results of the intelligent analysis engine; The user interface and interaction module is used to provide a web and mobile-friendly visual interface.

9. A critical area electromagnetic spectrum monitoring system based on a communication tower group as claimed in claim 8, characterized in that: The machine learning models integrated in the intelligent analysis engine include logistic regression model, decision tree model, random forest model, support vector machine, gradient boosting tree, and K-nearest neighbor model; the integrated deep learning algorithms include convolutional neural network, recurrent neural network, long short-term memory network, Transformer network, generative adversarial network, autoencoder and reinforcement learning algorithm.

10. The key area electromagnetic spectrum monitoring system based on communication tower group as claimed in claim 8, characterized in that: The processing steps of the intelligent spectrum analysis algorithm include: S1: Obtain the monitored electromagnetic spectrum data after preprocessing; S2: Extract the maximum value, average value and standard deviation of signal strength from electromagnetic spectrum data; S3: Select the appropriate machine learning model in the intelligent analysis engine; S4: Use historical monitoring data to train the selected machine learning model, adjust model parameters through cross-validation, and evaluate the machine learning model; S5: Update the model regularly using real-time electromagnetic spectrum data; S6: Use the updated model to predict the spectrum occupancy at the specified time and location.

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