A method and system for spectrum monitoring

Abstract

This application provides a spectrum monitoring method and system, relating to the field of spectrum monitoring technology. The method includes: performing feature analysis on spectrum data collected by multiple monitoring terminals to generate spectrum feature values; calculating anomaly indices for each spectrum data based on the spectrum feature values; generating a first compression scheme for the spectrum data by combining the terminal characteristics and anomaly indices of each monitoring terminal; adjusting the first compression scheme based on the location information of each monitoring terminal to generate a second compression scheme; adjusting the second compression scheme based on the historical data acquisition quality of each monitoring terminal to generate a target compression scheme; compressing the spectrum data collected by each monitoring terminal using the target compression scheme to generate target spectrum data; determining storage priorities based on the spatiotemporal aggregation degree of each target spectrum data; and hierarchically storing the target spectrum data according to the storage priorities. The technical effect of this application is: accurate compression of spectrum data, reducing the waste of storage resources.

Description

Technical Field

[0001] This application relates to the field of spectrum monitoring technology, specifically to a spectrum monitoring method, system, device, and medium. Background Technology

[0002] As the scale of spectrum monitoring systems continues to expand and the number of monitoring terminals surges, spectrum data is growing explosively. How to efficiently store and manage massive amounts of spectrum data has become an urgent technical problem to be solved.

[0003] Currently, a uniform data compression scheme is typically used to process spectrum data, or it is simply categorized based on the importance of the data. However, while these methods alleviate storage pressure to some extent, they cannot differentiate the processing based on the characteristics of spectrum data in different scenarios, resulting in inaccurate compression schemes and wasted storage resources. Summary of the Invention

[0004] This application provides a spectrum monitoring method, system, device, and medium for accurately compressing spectrum data and reducing the waste of storage resources.

[0005] In a first aspect, this application provides a spectrum monitoring method, the method comprising: receiving spectrum data collected by multiple monitoring terminals; performing feature analysis on the spectrum data to generate spectrum feature values; and calculating anomaly indices for each spectrum data based on the spectrum feature values; acquiring terminal features of each monitoring terminal; and generating a first compression scheme for the spectrum data collected by each monitoring terminal by combining the terminal features and the anomaly indices; acquiring location information of each monitoring terminal; calculating a correlation index between each monitoring terminal based on the location information; adjusting the first compression scheme based on the correlation index to generate a second compression scheme; acquiring historical data acquisition quality of each monitoring terminal; generating a stability index for each monitoring terminal based on the historical data acquisition quality; adjusting the second compression scheme based on the stability index to generate a target compression scheme; compressing the spectrum data collected by each monitoring terminal using the target compression scheme to generate target spectrum data; calculating the spatiotemporal aggregation degree of each target spectrum data; determining the storage priority of each target spectrum data based on the spatiotemporal aggregation degree; and storing the target spectrum data hierarchically based on the storage priority.

[0006] By adopting the above technical solutions, the accuracy and efficiency of spectrum data management are significantly improved through multi-level intelligent optimization strategies. First, by performing feature analysis on spectrum data to generate spectrum feature values ​​and calculating anomaly indices, the importance and anomalies of the data can be accurately identified, providing a scientific basis for the subsequent formulation of compression schemes. Second, the first compression scheme generated by combining terminal characteristics and anomaly indices achieves preliminary personalized processing based on data content and terminal performance, avoiding the blindness of traditional uniform compression strategies. Furthermore, the second compression scheme, generated by calculating a correlation index based on location information and adjusting the first compression scheme accordingly, effectively utilizes the spatial correlation between monitoring terminals, reduces data redundancy in geographically proximate areas, and improves storage space utilization. By generating a stability index based on historical data acquisition quality and ultimately forming the target compression scheme, the data of highly stable terminals is better protected, while data from less stable terminals is appropriately compressed, achieving differentiated management based on device reliability. Finally, by calculating the spatiotemporal aggregation degree of the target spectrum data, the storage priority is determined and hierarchical storage is performed. This allows important spatiotemporally concentrated data to receive high-priority storage resources, while data of relatively lower value is archived appropriately. This maximizes the utilization efficiency of overall storage resources while ensuring fast access to critical data, and achieves precise compression of spectrum data, reducing the waste of storage resources.

[0007] Secondly, this application provides a spectrum monitoring system, the system comprising: a receiving module, a first acquisition module, a second acquisition module, a third acquisition module, and a generating module; wherein,

[0008] The receiving module is used to receive spectrum data collected by multiple monitoring terminals, perform feature analysis on the spectrum data, generate spectrum feature values, and calculate anomaly indices for each spectrum data based on the spectrum feature values. The first acquisition module is used to acquire terminal features of each monitoring terminal, and generate a first compression scheme for the spectrum data collected by each monitoring terminal by combining the terminal features and the anomaly index. The second acquisition module is used to acquire location information of each monitoring terminal, calculate a correlation index between each monitoring terminal based on the location information, and adjust the first compression scheme based on the correlation index to generate a second compression scheme. The third acquisition module is used to acquire historical data acquisition quality of each monitoring terminal, generate a stability index for each monitoring terminal based on the historical data acquisition quality, and adjust the second compression scheme based on the stability index to generate a target compression scheme. The generation module is used to compress the spectrum data collected by each monitoring terminal using the target compression scheme to generate target spectrum data, calculate the spatiotemporal aggregation degree of each target spectrum data, determine the storage priority of each target spectrum data based on the spatiotemporal aggregation degree, and perform hierarchical storage of the target spectrum data based on the storage priority.

[0009] Thirdly, this application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to execute a computer program of any of the spectrum monitoring methods described above.

[0010] Fourthly, this application provides a computer-readable storage medium that stores a computer program capable of being loaded by a processor and executing any of the above-mentioned spectrum monitoring methods.

[0011] In summary, this application includes at least one of the following beneficial technical effects:

[0012] A multi-layered intelligent optimization strategy significantly improves the accuracy and efficiency of spectrum data management. First, by performing feature analysis on spectrum data to generate spectrum feature values ​​and calculate anomaly indices, the importance and anomalies of the data can be accurately identified, providing a scientific basis for the subsequent formulation of compression schemes. Second, the first compression scheme, generated by combining terminal characteristics and anomaly indices, achieves preliminary personalized processing based on data content and terminal performance, avoiding the blindness of traditional uniform compression strategies. Furthermore, a second compression scheme, generated by calculating a correlation index based on location information and adjusting the first compression scheme accordingly, effectively utilizes the spatial correlation between monitoring terminals, reduces data redundancy in geographically proximate areas, and improves storage space utilization. By generating a stability index based on historical data acquisition quality and ultimately forming the target compression scheme, better protection is ensured for data from highly stable terminals, while data from less stable terminals is appropriately compressed, achieving differentiated management based on device reliability. Finally, by calculating the spatiotemporal aggregation degree of the target spectrum data, the storage priority is determined and hierarchical storage is performed. This allows important spatiotemporally concentrated data to receive high-priority storage resources, while data of relatively lower value is archived appropriately. This maximizes the utilization efficiency of overall storage resources while ensuring fast access to critical data, and achieves precise compression of spectrum data, reducing the waste of storage resources. Attached Figure Description

[0013] Figure 1 This is a schematic flowchart of a spectrum monitoring method provided in an embodiment of this application;

[0014] Figure 2 This is a schematic diagram of a scenario provided in an embodiment of this application;

[0015] Figure 3 This is a schematic diagram of the structure of a spectrum monitoring system provided in an embodiment of this application;

[0016] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0017] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0019] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0020] Figure 1 This is a schematic flowchart of a spectrum monitoring method provided in an embodiment of this application. Figure 1 As shown, the method includes S101-S105:

[0021] S101 receives spectrum data collected from multiple monitoring terminals, performs feature analysis on the spectrum data, generates spectrum feature values, and calculates the anomaly index of each spectrum data based on the spectrum feature values.

[0022] The system first receives spectrum data from multiple monitoring terminals deployed in different geographical locations. These terminals may be fixed base station monitoring equipment or mobile spectrum analyzers, which continuously collect radio spectrum information within their respective coverage areas. Due to the complex and variable spectrum environment, spectrum data collected at different times and locations varies significantly in value and importance. Therefore, a scientific evaluation mechanism is needed to identify and quantify these differences, providing a basis for decision-making regarding subsequent data processing and storage optimization.

[0023] When performing feature analysis on the received spectrum data, the system primarily extracts feature information from three core dimensions. First, frequency characteristics: this analyzes the signal distribution in the frequency domain, including parameters such as main frequency components, spectral width, and frequency stability. These parameters reflect compliance with spectrum usage regulations and interference levels. Second, power characteristics: by measuring the signal's power density spectrum and peak power, the system assesses the trend and abnormal fluctuations in signal strength. Power characteristics are directly related to signal quality and transmission efficiency. Third, temporal characteristics: this analyzes the signal's persistence, periodicity, and burstiness over time. Temporal characteristics help identify intermittent interference and abnormal communication behavior.

[0024] Based on the extracted feature information, the system calculates frequency anomalies, power anomalies, and time anomalies, collectively referred to as spectral feature values. Frequency anomalies are calculated by comparing the actual frequency distribution with a preset standard frequency range; this value increases significantly when detected frequency components deviate from the normal operating frequency band or when illegal frequency occupancy occurs. Power anomalies are determined by setting a power threshold; the system marks a power anomaly when signal power exceeds the normal operating range or when abnormal power spikes occur. Time anomalies are based on statistical analysis of signal duration; this value increases accordingly when the signal duration is abnormally prolonged or exhibits irregular timing patterns.

[0025] After obtaining three outliers, the system calculates a comprehensive anomaly index using a weighted summation method. This index is the core indicator for measuring the degree of anomalies in the spectrum data. The calculation process of the anomaly index considers the relative importance of different types of anomalies, balancing the contributions of the three dimensions of frequency, power, and time through pre-set weighting coefficients. A higher anomaly index indicates that the spectrum data contains more anomalous information or potential security threats, and has higher analytical value and necessity for preservation.

[0026] Based on the above embodiments, as an optional implementation, in S101, feature analysis is performed on the spectrum data to generate spectrum feature values, and the anomaly index of each spectrum data is calculated based on the spectrum feature values, specifically including S11-S13:

[0027] S11, acquire the frequency characteristics, power characteristics and time characteristics of each spectrum data.

[0028] The system needs to perform comprehensive feature extraction on the received spectrum data because the value and degree of anomalies of spectrum data can only be accurately assessed through feature analysis across multiple dimensions. Single-dimensional analysis often cannot fully reflect the complex changes and potential anomalies in the spectrum environment. The system first acquires the frequency characteristics of each spectrum data point, converts the time-domain signal into a frequency-domain representation using a Fast Fourier Transform, analyzes the distribution of the signal across different frequency components, and extracts key parameters such as main frequency peaks, spectral width, frequency stability, and frequency offset. These frequency characteristics directly reflect the compliance of spectrum use and whether there is any illegal occupation of frequency bands.

[0029] Simultaneously, the system acquires the power characteristics of the spectrum data, comprehensively describing the signal's performance in the power dimension by calculating parameters such as power spectral density, peak power, average power, and power change rate. Power characteristics are crucial for judging signal strength anomalies and transmission quality; abnormal power changes often indicate equipment failure, interference sources, or illegal signal transmission. Furthermore, the system also acquires the temporal characteristics of the spectrum data, analyzing the signal's persistence, periodicity, burstiness, and intermittency in the time dimension. By statistically analyzing parameters such as signal duration, frequency of occurrence, time interval, and repetition patterns, temporal characteristics help identify abnormal communication behavior and intermittent interference sources.

[0030] S12, Calculate the frequency anomaly value of each spectrum data according to the frequency characteristics; the frequency anomaly value is used to characterize whether the frequency distribution of the spectrum data conforms to the preset frequency range; calculate the power anomaly value of each spectrum data according to the power characteristics; the power anomaly value is used to characterize whether the power of the spectrum data exceeds the preset power threshold; calculate the time anomaly value of each spectrum data according to the time characteristics; the time anomaly value is used to characterize whether the duration of the spectrum data exceeds the preset duration threshold.

[0031] The system calculates outliers across three dimensions based on extracted feature information. This multi-dimensional anomaly assessment method can more accurately identify and quantify different types of spectral anomalies. The system calculates frequency anomalies for each spectral data point based on frequency characteristics. These anomalies characterize whether the frequency distribution of the spectral data conforms to a preset frequency range. During the calculation, the system compares the detected actual frequency components with the pre-defined legal frequency usage range. When a signal frequency exceeds the authorized frequency band, unauthorized frequency occupancy occurs, or an abnormal frequency distribution pattern is detected, the frequency anomaly value increases accordingly. The quantification of frequency anomalies considers the severity and duration of the deviation; significant frequency violations will generate higher anomaly values.

[0032] The system calculates power anomaly values ​​for each spectrum data point based on power characteristics. These anomaly values ​​characterize whether the power of the spectrum data exceeds a preset power threshold. During the calculation, the system compares the measured signal power with preset power control standards. When the signal power exceeds the normal operating range, an abnormal power surge occurs, or the power change pattern does not conform to expectations, the power anomaly value increases significantly. The calculation of power anomaly values ​​not only considers instantaneous power anomalies but also comprehensively analyzes the trends and patterns of power changes to ensure the identification of various types of power anomalies.

[0033] Simultaneously, the system calculates time anomalies for each spectrum data point based on its temporal characteristics. These anomalies characterize whether the duration of the spectrum data exceeds a preset duration threshold. The calculation of time anomalies is based on the analysis of signal behavior patterns over time. When the signal duration is abnormally prolonged, exhibits irregular timing patterns, or deviates from normal communication timing, the time anomaly value increases accordingly. This time-dimensional anomaly detection effectively identifies issues such as malicious signal interference, abnormal transmissions caused by equipment malfunctions, and illegal communication activities.

[0034] S13 uses frequency anomalies, power anomalies, and time anomalies as spectral feature values, and performs a weighted summation of the frequency anomalies, power anomalies, and time anomalies to generate an anomaly index for each spectral data.

[0035] The system defines the calculated frequency anomalies, power anomalies, and time anomalies as unified spectral feature values. These three anomalies together constitute a comprehensive assessment system for the degree of anomalies in spectral data. The system uses a weighted summation method to fuse the three spectral feature values, generating a comprehensive anomaly index for each spectral data point. During the weighted summation process, the system assigns corresponding weight coefficients to the frequency anomalies, power anomalies, and time anomalies based on the regulatory priorities and security requirements of different application scenarios.

[0036] For example, in applications with strict frequency management, frequency anomalies will have relatively high weight to highlight the severity of frequency violations; in applications primarily focused on signal quality monitoring, power anomalies will have increased weight; and in communication security monitoring scenarios, time anomalies may receive higher weight to identify abnormal communication timing patterns. Through this weighted fusion mechanism, the system can flexibly adjust the importance of anomalies in different dimensions according to specific regulatory objectives and application requirements, generating a comprehensive anomaly index that better meets actual needs.

[0037] S102, acquire the terminal characteristics of each detection terminal, and combine the terminal characteristics and anomaly index to generate the first compression scheme for the spectrum data collected by each monitoring terminal.

[0038] The system needs to comprehensively evaluate the hardware performance of each monitoring terminal, as the processing power and resource status of different terminals directly affect their data processing efficiency and compression capabilities. A reasonable compression strategy can maximize the utilization of limited terminal resources and network bandwidth while ensuring data quality. The terminal characteristics acquired by the system include four key dimensions: processing power reflects the terminal's computing performance and data processing speed; storage capacity represents the terminal's available data storage space; network bandwidth represents the terminal's data transmission capability; and battery status reflects the mobile terminal's battery life and power consumption limitations.

[0039] The system converts these raw terminal characteristics into standardized performance coefficients based on preset terminal performance evaluation rules. Processing power is comprehensively evaluated using parameters such as CPU frequency, memory capacity, and processor architecture, and mapped to a processing coefficient; a higher coefficient indicates stronger data processing capabilities. Storage capacity considers available storage space, read / write speed, and storage media type, converting it into a storage coefficient to reflect the terminal's data storage and management capabilities. Network bandwidth combines uplink and downlink rates, network latency, and connection stability to generate a transmission coefficient, quantifying the terminal's data transmission efficiency. Battery status is calculated using remaining battery power, discharge rate, and expected operating time to derive an energy consumption coefficient, which directly relates to the terminal's continuous operating capability.

[0040] After obtaining the four performance coefficients, the system generates a comprehensive performance index through a weighted calculation method. This index is the core indicator for evaluating the overall performance of the terminal. During the calculation of the comprehensive performance index, the weighting of different coefficients reflects the relative importance of each performance dimension in the spectrum monitoring task. For example, for fixed monitoring sites, processing power and storage capacity may have higher weights, while for mobile monitoring terminals, battery status and network bandwidth will have correspondingly increased weights. A higher comprehensive performance index indicates that the terminal has stronger data processing and transmission capabilities and can handle more complex data processing tasks.

[0041] When determining the compression ratio, the system adopts a two-factor inverse relationship strategy, meaning the compression ratio is inversely proportional to both the anomaly index and the overall performance index. This design logic is based on two important considerations: First, spectral data with a higher anomaly index contains more valuable anomaly information, requiring a lower compression ratio to ensure data integrity and analysis accuracy; second, terminals with a higher overall performance index have stronger processing capabilities, enabling them to execute more sophisticated compression algorithms or maintain higher data fidelity, thus also making a lower compression ratio suitable.

[0042] In the specific compression ratio calculation process, the anomaly index and overall performance index are mapped to a reasonable compression ratio range. When the spectrum data collected by a terminal has a high anomaly index and the terminal has a high overall performance index, the system will assign the lowest compression ratio to the data to ensure the complete preservation of important information. Conversely, for regular spectrum data with a low anomaly index from a terminal with limited performance, the system will apply a higher compression ratio to maximize the saving of storage space and transmission bandwidth while ensuring basic data availability. Based on the calculated compression ratio, the system generates a corresponding first compression scheme for each monitoring terminal.

[0043] Based on the above embodiments, as an optional implementation method, in S102, the terminal characteristics include: processing power, storage capacity, network bandwidth, and battery status. Combining the terminal characteristics and the anomaly index, the first compression scheme for generating the spectrum data collected by each monitoring terminal specifically includes S21-S24:

[0044] S21, based on the preset terminal performance evaluation rules, determine the processing coefficient corresponding to the processing capability of each monitoring terminal, the storage coefficient corresponding to the storage capacity of each monitoring terminal, the transmission coefficient corresponding to the network bandwidth of each monitoring terminal, and the energy consumption coefficient corresponding to the battery status of each monitoring terminal.

[0045] The system requires a comprehensive evaluation of the hardware capabilities and operational status of each monitoring terminal. This is because different terminals exhibit significant differences in key performance indicators such as processing power, storage capacity, network bandwidth, and battery status. These differences directly impact the terminal's ability to process and transmit spectrum data, and must be fully considered in the compression scheme design. Based on pre-defined terminal performance evaluation rules, the system first analyzes the processing capabilities of each monitoring terminal, including parameters such as CPU clock speed, memory capacity, number of computing cores, and floating-point performance. Through standardized performance testing and benchmark comparisons, the system determines the processing coefficient corresponding to the processing capabilities of each monitoring terminal. The processing coefficient is a standardized indicator that quantifies the terminal's computing power; a higher value indicates a stronger data processing and compression algorithm execution capability.

[0046] Simultaneously, the system evaluates the storage capacity of each monitoring terminal, including characteristics such as total local storage space, available space, read / write speed, and storage media type. Through capacity testing and performance benchmark testing, the storage coefficient corresponding to the storage capacity of each monitoring terminal is determined. The storage coefficient reflects the terminal's ability in data caching and temporary storage; terminals with high storage coefficients can handle more data preprocessing and caching tasks, and have greater operational space during compression processing. The system also analyzes the network bandwidth status of each monitoring terminal, including network performance parameters such as uplink bandwidth, downlink bandwidth, network latency, and connection stability. Through network testing and connectivity evaluation, the transmission coefficient corresponding to the network bandwidth of each monitoring terminal is determined. The transmission coefficient reflects the terminal's ability to transmit compressed data to the central system, affecting the efficiency and real-time performance of data transmission.

[0047] In addition, the system monitors the battery status of each monitoring terminal, including energy-related parameters such as remaining power, battery health, power consumption level, and expected usage time. Through status reports and power consumption analysis from the power management module, the system determines the energy consumption coefficient corresponding to the battery status of each monitoring terminal. The energy consumption coefficient is an important indicator for evaluating the energy sustainability of a terminal, and it is particularly critical for battery-powered mobile monitoring terminals. A low energy consumption coefficient means that the terminal needs to adopt more energy-efficient data processing strategies to extend its operating time.

[0048] S22, the processing coefficient, storage coefficient, transmission coefficient and energy consumption coefficient are weighted and calculated to generate the comprehensive performance index of each monitoring terminal.

[0049] The system uses a weighted calculation method to comprehensively process the processing coefficient, storage coefficient, transmission coefficient, and energy consumption coefficient to generate a comprehensive performance index for each monitoring terminal. The comprehensive performance index is a quantitative indicator that comprehensively evaluates the overall capability of the monitoring terminal. It not only considers the terminal's performance in each individual dimension but also reflects the relative importance of different performance dimensions in practical applications through weight allocation. During the weighted calculation process, the system assigns appropriate weight values ​​to the four performance coefficients based on the specific deployment environment and application requirements. For example, in data-intensive application scenarios, the processing coefficient will have a relatively high weight; in regional deployments with complex network environments, the transmission coefficient may receive a greater weight; and in outdoor environments without power supply, the energy consumption coefficient will have a significantly increased weight.

[0050] S23. Combining the anomaly index and the comprehensive performance index, determine the compression ratio of the spectrum data collected by each monitoring terminal. The compression ratio is inversely proportional to the anomaly index and the comprehensive performance index.

[0051] The system combines anomaly index and comprehensive performance index, employing a scientific calculation model to determine the compression ratio of spectrum data collected by each monitoring terminal. This method of determining the compression ratio, which comprehensively considers data value and terminal capabilities, ensures both the quality protection of important data and full utilization of terminal hardware resources. The compression ratio is inversely proportional to the anomaly index, meaning that when the anomaly index of spectrum data is high, the system will reduce its compression ratio, employing a more moderate compression strategy to maintain the integrity and analyzability of the anomaly information. This is because data with a high anomaly index typically contains important regulatory information and security risks, requiring priority protection of its data quality.

[0052] Meanwhile, the compression ratio is inversely proportional to the overall performance index. When the overall performance index of a monitoring terminal is high, the system will reduce its data compression ratio to fully utilize the processing power of high-performance terminals and maintain higher data quality. This strategy allows high-performance terminals to handle more high-quality data processing tasks, while terminals with limited performance can reduce their processing burden through a higher compression ratio, thus optimizing resource allocation and load balancing throughout the monitoring network.

[0053] S24, Based on the compression ratio, generate the first compression scheme for the spectrum data collected by each monitoring terminal.

[0054] Based on the calculated compression ratio, the system formulates a specific first compression scheme for the spectrum data collected by each monitoring terminal. This first compression scheme not only includes a clear compression ratio parameter but also specifies detailed configuration information such as the corresponding compression algorithm selection, quality control standards, and processing priorities. For high-value data with a low compression ratio, the system selects lossless or quasi-lossless compression algorithms to ensure the complete preservation of critical information; for data with a moderate compression ratio, a hybrid compression algorithm balancing compression quality and efficiency is used; and for data with a high compression ratio, an efficient lossy compression algorithm is used to maximize storage space savings while ensuring basic usability.

[0055] S103: Obtain the location information of each monitoring terminal, calculate the correlation index between each monitoring terminal based on the location information, adjust the first compression scheme based on the correlation index, and generate the second compression scheme.

[0056] Based on the acquired location information, the system calculates the spatial distance between each monitoring terminal, employing a geodesic distance calculation method that considers the curvature of the Earth to ensure the accuracy of the distance measurement. After completing the distance calculation, the system determines a set of neighboring monitoring terminals for each monitoring terminal according to a preset proximity threshold. These neighboring terminals typically refer to devices that can monitor similar spectral environments within a certain geographical range. The system further calculates the spatial correlation between each monitoring terminal and its neighboring monitoring terminals. This index comprehensively considers distance, similarity of terrain and geomorphology, and consistency of electromagnetic environment. A higher spatial correlation value indicates that the two terminals are more similar in terms of geographical location and monitoring environment.

[0057] In addition to spatial dimension analysis, the system also needs to evaluate the correlation at the content level of spectral data. The system acquires the similarity between spectral data collected by each monitoring terminal and spectral data collected by its neighboring monitoring terminals. Through correlation analysis of spectral feature vectors, calculation of power spectral density matching degree, and evaluation of signal mode consistency, a data similarity index is generated. The data similarity index is a key indicator for measuring the degree of correlation between the content of data collected by different terminals; high similarity means that these data have strong consistency in spectral characteristics, signal strength, and temporal domain characteristics.

[0058] The system transforms spatial correlation and data similarity indices into standardized spatial correlation and standardized data similarity indices through normalization, eliminating the influence of different units and numerical ranges on subsequent calculations. Based on standardization, the system assigns spatial weights to the standardized spatial correlation and data weights to the standardized data similarity indices according to preset correlation calculation rules. These weights reflect the relative importance of geographical location and data content in the correlation assessment. The system obtains a weighted spatial correlation by multiplying the standardized spatial correlation by its spatial weights and a weighted data similarity index by multiplying the standardized data similarity index by its data weights. Finally, the two weighted values ​​are summed to generate a comprehensive correlation index among the monitoring terminals.

[0059] The correlation index is a comprehensive quantitative indicator that reflects the degree of correlation between monitoring terminals in both geographic space and data content dimensions. A higher correlation index indicates that the corresponding terminals have strong similarities in both spatial location and data collection, providing an important basis for optimizing data compression strategies. The system identifies monitoring terminal combinations with correlation indices higher than a preset threshold. These combinations are considered highly correlated monitoring units, and their collected data exhibits a certain degree of redundancy.

[0060] Based on this correlation analysis, the system intelligently adjusts the first compression scheme. For terminal combinations with high correlation indices, the system adopts a differentiated compression strategy: increasing the compression ratio of the spectrum data of the monitoring terminal with the lowest correlation index in the combination, because this terminal is relatively independent and its data is highly unique; at the same time, decreasing the compression ratio of the spectrum data of the monitoring terminal with the highest correlation index in the combination, ensuring that highly correlated terminals, as representative data sources, can maintain good data quality. This adjustment strategy avoids excessive preservation of redundant data while ensuring the integrity of spectrum information in important areas.

[0061] Based on the above embodiments, as an optional implementation, in S103, calculating the correlation index between each monitoring terminal according to the location information, and adjusting the first compression scheme according to the correlation index to generate the second compression scheme specifically includes S31-S36:

[0062] S31, calculate the spatial distance between each monitoring terminal based on the location information.

[0063] S32, based on spatial distance, determine the neighboring monitoring terminals of each monitoring terminal, and calculate the spatial correlation between each monitoring terminal and its neighboring monitoring terminals.

[0064] The system establishes a proximity relationship network between monitoring terminals based on calculated spatial distances. A preset distance threshold is set as the criterion for determining proximity relationships, identifying combinations of monitoring terminals with a spatial distance less than this threshold as neighboring monitoring terminal pairs. Neighboring monitoring terminals refer to combinations of devices that are geographically close and may monitor similar spectral environments. After determining proximity relationships, the system further calculates the spatial correlation degree between each monitoring terminal and its neighboring monitoring terminals. This indicator comprehensively considers factors such as distance, geographical similarity, and overlap of monitoring coverage areas. The spatial correlation degree is calculated using a distance attenuation model; terminals that are closer together have a higher spatial correlation degree. The system also considers the consistency of topography and the similarity of the electromagnetic environment to ensure the scientific validity and accuracy of the correlation degree assessment.

[0065] S33, obtain the similarity between the spectrum data collected by each monitoring terminal and the spectrum data collected by its neighboring monitoring terminals, and generate a data similarity index.

[0066] The system analyzes the correlation between monitoring terminals at the data content level. Geographical proximity alone cannot definitively determine the similarity of data content; actual spectral data comparison is needed to verify and quantify this correlation. The system acquires spectral data collected by each monitoring terminal and its neighboring terminals. Through methods such as correlation analysis of spectral feature vectors, calculation of power spectral density distribution matching, and consistency assessment of signal pattern recognition results, a data similarity index is generated. The data similarity index is a crucial indicator for quantifying the correlation between data collected by different terminals. It reflects whether different terminals in similar geographical locations have indeed detected similar spectral characteristics and signal activities. A high similarity index indicates strong consistency in spectral characteristics, signal intensity distribution, and temporal variation patterns among these terminals.

[0067] S34, combining spatial correlation and data similarity index, generates correlation index between monitoring terminals.

[0068] The system uses a scientific fusion method, combining spatial correlation and data similarity indices, to generate a comprehensive correlation index among monitoring terminals. This correlation index is a comprehensive evaluation indicator that simultaneously considers geospatial relationships and data content relevance; it more accurately reflects the true degree of correlation between terminals than simple geographical distance or data similarity. During the fusion calculation process, the system assigns appropriate weights to spatial correlation and data similarity indices. This weighting strategy considers the relative importance of geographical and data factors in different application scenarios. For example, in mountainous deployments with complex and varied geographical environments, the weight of the data similarity index may be higher because terrain changes may cause terminals in similar locations to monitor different spectral environments; while in open plains areas, the weight of spatial correlation may dominate.

[0069] Based on the above embodiments, as an optional implementation method, in S34, combining spatial correlation and data similarity index to generate the correlation index between each monitoring terminal specifically includes S341-S344:

[0070] S341, normalize the spatial correlation degree to generate a standardized spatial correlation degree; normalize the data similarity index to generate a standardized data similarity index.

[0071] The system requires standardization of spatial correlation and data similarity indices because these two indicators differ significantly in numerical range, units of measurement, and distribution characteristics. Direct fusion calculation could lead to the larger indicator having an unreasonable dominant role in the final result, thus affecting the scientific validity and accuracy of the correlation index. The system first normalizes the spatial correlation using a maximum-minimum standardization method, mapping the original spatial correlation values ​​to a standard range of 0 to 1, generating standardized spatial correlation values. During normalization, the system identifies the maximum and minimum values ​​of spatial correlation among all monitoring terminal pairs, and then uses a linear transformation to convert each spatial correlation value into a relatively standardized value. This process ensures that the spatial correlation values ​​calculated under different geographical environments have a unified numerical benchmark and are comparable.

[0072] Simultaneously, the system performs the same normalization process on the data similarity index to generate a standardized data similarity index. Since the calculation of the data similarity index involves a comprehensive evaluation of multiple dimensions, such as the correlation coefficient of spectral eigenvectors, the matching degree of power spectrum distribution, and signal mode consistency, its numerical distribution may exhibit complex nonlinear characteristics. Therefore, the system not only considers the uniformity of the numerical range during the normalization process but also identifies the skewed characteristics of the data distribution through statistical analysis. An adaptive normalization strategy is adopted to ensure that the standardized data similarity index can accurately reflect the correlation strength of the original data. This dual normalization process establishes a unified numerical foundation for subsequent weight allocation and fusion calculations, eliminating calculation biases caused by differences in dimensions.

[0073] S342, based on the preset correlation calculation rules, determine the spatial weight of the standardized spatial correlation and the data weight of the standardized data similarity index.

[0074] The system scientifically determines the spatial weight of standardized spatial correlation and the data weight of standardized data similarity index based on preset correlation calculation rules. The correlation calculation rules are weight allocation criteria formulated based on the application needs of spectrum monitoring and the characteristics of the actual deployment environment. They comprehensively consider the relative importance of geographical and data factors in different application scenarios. When formulating the weight allocation strategy, the system analyzes key factors such as the complexity of the geographical environment of the monitoring area, spectrum usage density, signal propagation characteristics, and regulatory priorities. For example, in mountainous areas with complex terrain or densely populated urban areas, geographical obstacles and building obstructions may cause terminals in similar locations to monitor significantly different spectrum environments. In such cases, the data weight will be relatively high, relying more on the similarity of actual monitoring data to determine the degree of correlation between terminals.

[0075] Conversely, in relatively uniform geographical areas such as plains or sea areas, spatial relationships can often better predict the similarity of the spectral environment, thus spatial weights receive a higher allocation proportion. The weight determination process also considers the deployment density and coverage of monitoring terminals. In areas with higher terminal deployment density, the importance of data weights increases accordingly, as dense deployment makes correlation analysis based on actual data more reliable and meaningful. The system continuously optimizes and adjusts the weight allocation rules through historical data analysis and field verification to ensure that the weight settings reflect real application needs and environmental characteristics.

[0076] S343, the weighted spatial correlation degree is obtained by multiplying the standardized spatial correlation degree by the spatial weight, and the weighted data similarity index is obtained by multiplying the standardized data similarity index by the data weight.

[0077] The system performs weighted calculations, generating weighted indices reflecting different levels of importance by multiplying standardized indicators with their corresponding weights. The system multiplies the standardized spatial correlation degree by the determined spatial weights to obtain the weighted spatial correlation degree, which reflects the contribution of geospatial relationships to terminal relevance in the current application environment. The weighted spatial correlation degree not only retains the numerical characteristics of the original spatial correlation but also reflects the actual importance of geographical factors in specific application scenarios through weight modulation. Similarly, the system multiplies the standardized data similarity index by the data weights to obtain the weighted data similarity index, which quantifies the weighted contribution of the similarity of actual monitored data content in terminal relevance assessment.

[0078] S344 sums the weighted spatial correlation degree and the weighted data similarity index to generate the correlation index between each monitoring terminal.

[0079] S35: Obtain the monitoring terminal combination with a correlation index higher than the preset index, increase the compression ratio of the spectrum data of the monitoring terminal with the lowest correlation index in the monitoring terminal combination, and decrease the compression ratio of the spectrum data of the monitoring terminal with the highest correlation index in the monitoring terminal combination.

[0080] Based on correlation index analysis results, the system implements intelligent compression strategy adjustments. First, the system identifies monitoring terminal combinations with correlation indices exceeding a preset threshold. These highly correlated combinations indicate strong correlation between the corresponding terminals in both geographical location and data content, suggesting a degree of information redundancy in the collected data. For these highly correlated terminal combinations, the system employs a differentiated compression ratio adjustment strategy: increasing the compression ratio of the spectrum data from the monitoring terminal with the lowest correlation index in the combination, as this terminal has strong independence relative to other terminals in the combination, and its data has relatively low unique value, thus saving storage resources through a higher compression ratio; simultaneously, decreasing the compression ratio of the spectrum data from the monitoring terminal with the highest correlation index in the combination, making it a representative data source for the geographical area and maintaining high data quality to ensure the integrity and accuracy of regional spectrum information.

[0081] This differentiated adjustment strategy based on correlation indices avoids excessive storage of redundant data while ensuring coverage quality for spectrum monitoring in important areas. By establishing a gradient allocation of data quality within highly correlated combinations, the system optimizes the utilization of storage resources while guaranteeing the effectiveness and continuity of spectrum monitoring. For terminals with correlation indices below a preset threshold, the system maintains their original compression ratio settings because the data from these terminals is highly independent and irreplaceable, requiring processing according to the original strategy.

[0082] S36. Based on the adjusted compression ratio, update the first compression scheme of each monitoring terminal and generate the second compression scheme.

[0083] S104: Obtain the historical data acquisition quality of each monitoring terminal; generate a stability index for each monitoring terminal based on the historical data acquisition quality; adjust the second compression scheme based on the stability index; and generate a target compression scheme.

[0084] The system first acquires historical data acquisition quality records for each monitoring terminal over a period of time. These records cover the terminal's data acquisition performance under different time periods and environmental conditions. Based on these historical records, the system calculates three key quality indicators. The data integrity indicator reflects the completeness of the data acquired by the monitoring terminal. It is evaluated using parameters such as data packet loss rate, sampling interval deviation, and data missing frequency. A higher indicator indicates that the terminal can consistently and stably provide complete spectrum data. The data accuracy indicator measures the degree of matching between the data acquired by the monitoring terminal and the actual spectrum environment. It is quantified through comparison with standard reference equipment, signal-to-noise ratio analysis, and measurement error statistics. A high accuracy indicator means that the terminal's sensors are well-calibrated and the measurement accuracy is reliable. The data continuity indicator assesses the monitoring terminal's ability to maintain stable acquisition over long-term operation. It is determined by analyzing factors such as the temporal continuity of data acquisition, service interruption frequency, and recovery time. A good continuity indicator indicates that the terminal has reliable long-term operational capabilities.

[0085] Based on the calculated quality indicators, the system further generates corresponding coefficient indicators. The integrity coefficient, derived by standardizing and weighting the data integrity indicators, quantifies the integrity level of terminal data collection. The accuracy coefficient, generated based on statistical analysis and threshold comparison of data accuracy indicators, reflects the reliability of terminal measurement results. The continuity coefficient, derived through time-series analysis and stability assessment of data continuity indicators, reflects the reliability of continuous terminal service. These three coefficients together constitute the core elements of terminal data quality assessment, providing a quantitative basis for subsequent stability index calculations.

[0086] The system employs a weighted calculation method to comprehensively process the integrity coefficient, accuracy coefficient, and continuity coefficient to generate a stability index for each monitoring terminal. The stability index is a comprehensive indicator measuring the long-term reliability and data quality consistency of a monitoring terminal. A higher index indicates greater stability in historical operation, and the data collected has higher reliability and analytical value. During the weighted calculation process, the system assigns appropriate weights to the three coefficients based on the needs of different application scenarios. For example, in scenarios requiring high-precision monitoring, the accuracy coefficient has a relatively higher weight, while in applications requiring long-term continuous monitoring, the continuity coefficient has a higher weight.

[0087] Based on the calculated stability index, the system classifies monitoring terminals into high-stability terminals and low-stability terminals. High-stability terminals are those with a stability index exceeding a preset high-stability threshold; these terminals have performed well historically, provide reliable data, and are capable of undertaking important monitoring tasks. Low-stability terminals are those with a stability index below a preset low-stability threshold; these may suffer from hardware aging, environmental interference, or configuration issues, and their data quality is relatively less reliable. Terminals falling between these two thresholds are classified as medium-stability terminals and processed using standard strategies.

[0088] After determining the terminal stability classification, the system makes targeted adjustments to the second compression scheme. For highly stable terminals, the system reduces the compression ratio of their spectrum data because the high-quality data provided by these terminals has significant analytical value and should be preserved as completely as possible to support accurate spectrum analysis and anomaly detection. For low-stability terminals, the system increases the compression ratio of their spectrum data to reduce the consumption of storage and processing resources while ensuring basic availability, avoiding excessive system resource consumption by low-quality data. This stability-based differentiated processing strategy ensures full utilization of high-quality data sources while avoiding the negative impact of low-quality data on system performance.

[0089] Based on the above embodiments, as an optional implementation, in S104, a stability index for each monitoring terminal is generated according to the historical data acquisition quality. The second compression scheme is then adjusted based on the stability index to generate the target compression scheme, specifically including S41-S45:

[0090] S41, based on the historical data collection quality, calculate the data integrity index, data accuracy index, and data continuity index for each monitoring terminal.

[0091] The system needs to comprehensively evaluate the performance and stability of each monitoring terminal based on historical data acquisition quality. This is because monitoring terminals are affected by various factors during long-term operation, such as hardware aging, environmental interference, network fluctuations, and maintenance status, leading to significant differences in data acquisition quality. These historical performance differences are crucial for predicting future terminal reliability and optimizing compression strategies. The system analyzes the data acquisition records of each monitoring terminal over a past period to calculate quantitative evaluation results for three key quality dimensions: data integrity, data accuracy, and data continuity.

[0092] Data integrity indicators reflect the completeness of data collected by monitoring terminals. The system calculates this indicator by statistically analyzing parameters such as the ratio of actual data collected to expected data collected within a specified time period, data packet loss rate, proportion of missing sampling points, and data field completeness. A high integrity indicator indicates that the terminal can stably complete data collection tasks according to the established plan, with few instances of data loss or collection interruptions. Data accuracy indicators assess the precision and reliability of data collected by monitoring terminals. The system quantifies accuracy levels by comparing and analyzing characteristics such as the deviation of terminal collection results from standard reference values, signal-to-noise ratio trends, measurement error distribution, and frequency of abnormal values. Data continuity indicators measure the temporal consistency and stability of data collection by monitoring terminals. The system evaluates continuity performance by analyzing aspects such as the frequency of service interruptions, data transmission latency fluctuations, regularity of collection timing, and long-term operational sustainability.

[0093] S42, Calculate the integrity coefficient of each monitoring terminal based on the data integrity index; calculate the accuracy coefficient of each monitoring terminal based on the data accuracy index; calculate the continuity coefficient of each monitoring terminal based on the data continuity index.

[0094] The system converts the three quality indicators into standardized coefficients for unified numerical processing and comparative analysis. Based on the historical distribution characteristics of the data integrity indicators and industry standard requirements, the system calculates the integrity coefficient for each monitoring terminal. This coefficient uses a non-linear mapping function to convert the original integrity indicators into standardized values ​​between 0 and 1. Values ​​close to 1 indicate extremely high data integrity, while lower values ​​reflect significant data loss. During the calculation, the system considers the weight of data from different periods, giving higher weight to recent performance to ensure the coefficient reflects the current trend of the terminal's status.

[0095] Similarly, the system calculates the accuracy coefficient for each monitoring terminal based on data accuracy indicators. This coefficient comprehensively considers factors such as the stability of measurement accuracy, the rationality of error distribution, and the effectiveness of outlier control. The calculation of the accuracy coefficient employs a multi-layered evaluation model, focusing not only on the average accuracy level but also on the stability and predictability of accuracy. This is because, in spectrum monitoring applications, stable and reliable measurement accuracy is more important than occasional high accuracy. The system also calculates the continuity coefficient for each monitoring terminal based on data continuity indicators. This coefficient reflects the degree to which the terminal maintains stable data acquisition capabilities over long-term operation, quantifying continuity performance by analyzing service availability, data flow coherence, and system response timeliness.

[0096] S43 calculates the stability index of each monitoring terminal by weighting the integrity coefficient, accuracy coefficient, and continuity coefficient.

[0097] The system uses a scientific weighted calculation method to comprehensively process the integrity coefficient, accuracy coefficient, and continuity coefficient to generate a stability index for each monitoring terminal. The stability index is a comprehensive indicator that evaluates the historical performance of the monitoring terminal and predicts its future reliability. It not only considers the terminal's performance across various dimensions of data quality but also reflects the relative importance of different quality factors in practical applications through weight allocation. During the weighted calculation process, the system assigns corresponding weight values ​​to the three coefficients based on the specific application requirements and quality requirements of spectrum monitoring.

[0098] For example, in real-time monitoring applications with extremely high requirements for data continuity, the weight of the continuity coefficient will be significantly increased; in scientific research applications mainly based on precision measurement, the accuracy coefficient may receive a larger weight allocation; and in statistical analysis applications involving large-scale data acquisition, the importance of the integrity coefficient will be relatively prominent. The weight allocation strategy also considers the characteristics of different types of monitoring terminals and the differences in deployment environments, ensuring that the stability index can objectively reflect the true performance level and reliability of each terminal. The final generated stability index numerically reflects a comprehensive evaluation of the terminal's historical performance, providing an important decision-making basis for further optimization of the compression strategy.

[0099] S44. Based on the stability index, the monitoring terminals are divided into high-stability terminals and low-stability terminals. The compression ratio of the spectrum data of high-stability terminals is reduced, while the compression ratio of the spectrum data of low-stability terminals is increased.

[0100] S45, based on the adjusted compression ratio, update the second compression scheme of each monitoring terminal and generate the target compression scheme.

[0101] S105: The spectrum data collected by each monitoring terminal is compressed using a target compression scheme to generate target spectrum data. The spatiotemporal aggregation degree of each target spectrum data is calculated. The storage priority of each target spectrum data is determined based on the spatiotemporal aggregation degree. The target spectrum data is then stored hierarchically according to the storage priority.

[0102] After data compression, the system needs to further evaluate the spatiotemporal value characteristics of the target spectrum data in order to formulate a more refined storage strategy. The system acquires temporal and spatial information for each target spectrum data point. Temporal information includes the timestamp of data collection, duration, periodicity, and correlation with important time events. Spatial information covers the geographic coordinates of the data collection points, coverage area, topographic features, and distance from key monitoring areas. This spatiotemporal information provides the foundational data for subsequent aggregation degree analysis.

[0103] Based on time-dimensional information, the system calculates the temporal concentration of each target spectrum data, a crucial indicator of the density of spectrum data distribution along the time axis. The calculation of temporal concentration considers the data acquisition time intervals, variations in acquisition frequency, and temporal overlap with data from other time periods. Higher temporal concentration indicates that the spectrum data is from a period of frequent acquisition or key monitoring, possessing significant temporal value. Simultaneously, the system calculates the spatial concentration of each target spectrum data based on spatial-dimensional information. This indicator reflects the density of spectrum data distribution geographically. Spatial concentration comprehensively considers the geographical distribution of data acquisition points, the density of neighboring data sources, and the spatial relationship with important monitoring areas. High spatial concentration signifies that the area is a key focus area for spectrum monitoring, and its data possesses significant geospatial value.

[0104] The system uses a weighted fusion method to synthesize temporal and spatial concentration into a comprehensive spatiotemporal aggregation index. Spatiotemporal aggregation is a core indicator for evaluating the comprehensive importance of spectral data across both temporal and spatial dimensions. A higher index indicates stronger concentration and representativeness of the corresponding spectral data in its spatiotemporal distribution, thus possessing greater preservation value and analytical significance. During the weighted fusion process, the system assigns appropriate weight coefficients to temporal and spatial concentration based on specific application requirements and monitoring objectives. For example, in applications requiring focused attention on spectral changes over a specific time period, the weight of temporal concentration is relatively higher, while in geographically regional monitoring tasks, the weight of spatial concentration is strengthened.

[0105] Based on the calculated spatiotemporal aggregation degree, the system determines the storage priority of each target spectrum data according to a preset priority division rule. The system sets a first threshold and a second threshold as boundary conditions for priority division. Target spectrum data with a spatiotemporal aggregation degree higher than the first threshold is set as high priority; these data have important concentrated characteristics in spatiotemporal distribution, represent key monitoring information, and need to be saved first and provided with optimal storage conditions. Target spectrum data with a spatiotemporal aggregation degree lower than the second threshold is set as low priority; these data have relatively low importance in the spatiotemporal dimension and can be stored using a more economical method. Target spectrum data with a spatiotemporal aggregation degree between the first and second thresholds is set as medium priority and processed using a standard storage strategy.

[0106] Ultimately, the system tiers the target spectrum data based on the determined storage priorities. High-priority data is stored in a high-performance, high-reliability main storage system, enjoying fast access and multiple backup protections to ensure the security and accessibility of this critical data. Medium-priority data is stored in a standard storage system, providing a balance of performance and cost-effectiveness. Low-priority data is stored in a lower-cost archive storage system; although access speeds are relatively slower, this still meets the needs for long-term preservation and retrieval when necessary.

[0107] Based on the above embodiments, as an optional implementation, in S105, calculating the spatiotemporal aggregation degree of each target spectrum data and determining the storage priority of each target spectrum data according to the spatiotemporal aggregation degree specifically includes S51-S54:

[0108] S51, obtain the time dimension information and spatial dimension information of the spectrum data of each target.

[0109] The system needs to establish a multi-dimensional feature description foundation for the target spectrum data. This is because the value and importance of spectrum data depend not only on its content characteristics but also on its temporal and spatial distribution. Only by comprehensively acquiring this dimensional information can accurate data support be provided for subsequent aggregation analysis and prioritization. The system acquires the temporal dimension information of each target spectrum data point, including the precise timestamp of data acquisition, the duration of the acquisition period, the regularity of acquisition frequency, the coverage of the time span, and detailed characteristics such as its temporal correlation with important events or regulatory activities. This temporal dimension information not only reflects the temporal characteristics of the data but also embodies the distribution pattern and density characteristics of the data along the time axis. This information is crucial for understanding the temporal patterns of spectrum activity and identifying key periods.

[0110] Simultaneously, the system acquires spatial dimension information for each target spectrum data, encompassing key elements such as the geographical coordinates of the data acquisition location, the size of the coverage area, the type and characteristics of the geographical environment, the spatial relationship with important facilities or regulatory areas, and the location distribution within the entire monitoring network. This spatial dimension information reveals the geographical distribution characteristics and regional representativeness of the spectrum data, providing crucial evidence for analyzing the spatial value of the data and determining geographical priorities. This acquisition of spatiotemporal dual-dimensional information establishes a complete data feature description framework for the system, ensuring that subsequent aggregation degree calculations can comprehensively reflect the spatiotemporal distribution characteristics and relative importance of the data.

[0111] S52. Based on the time dimension information, calculate the temporal concentration of each target spectrum data. The temporal concentration is used to characterize the density of spectrum data distribution on the time axis. Based on the spatial dimension information, calculate the spatial concentration of each target spectrum data. The spatial concentration is used to characterize the density of spectrum data distribution on the geographical location.

[0112] The system calculates the concentration characteristics of the data from both temporal and spatial dimensions. Concentration is a key indicator of the density and importance of data distribution; high concentration typically signifies greater representativeness and analytical value in the corresponding dimension. Based on the temporal dimension, the system calculates the temporal concentration of each target spectrum data point. This indicator characterizes the density of spectrum data distribution along the time axis. The calculation of temporal concentration comprehensively considers factors such as the temporal density of data collection, the degree of overlap between time periods, the stability of collection frequency, and proximity to key time nodes. For example, data collected during key periods of spectrum regulation will receive a higher temporal concentration score because monitoring data from these periods is more valuable for detecting illegal use and analyzing spectrum trends.

[0113] Temporal concentration also considers the continuity and completeness of data acquisition. Continuously acquired data sequences have higher analytical value and temporal concentration than scattered single-point acquisitions. The system employs a sliding time window analysis method to identify the clustering characteristics of data at different time scales and comprehensively assess the temporal importance of the data. Similarly, based on spatial dimension information, the system calculates the spatial concentration of each target spectral data point. This indicator characterizes the density of spectral data distribution in geographical locations. The calculation of spatial concentration considers factors such as the geographical density of data acquisition points, the importance of the coverage area, distance from key facilities, and strategic location within the monitoring network.

[0114] Data located in areas of high spectrum usage, near important communication facilities, or in key regulatory areas will receive higher spatial concentration scores because spectrum monitoring data from these locations is more valuable for understanding the regional spectrum environment and supporting regulatory decisions. Spatial concentration also considers the geographical representativeness and coverage completeness of the data; data points that can represent the characteristics of a large area have higher spatial value than data that only reflects a local environment.

[0115] The system employs a scientific weighted fusion method to comprehensively process temporal and spatial concentration, generating a spatiotemporal aggregation degree for each target spectrum data. This spatiotemporal aggregation degree is a comprehensive evaluation indicator that considers both temporal and spatial distribution characteristics, fully reflecting the importance and priority of spectrum data in both temporal and spatial dimensions. During the fusion process, the system assigns appropriate weight coefficients to temporal and spatial concentration degrees based on specific application requirements and regulatory priorities. This weighting strategy considers the relative importance of temporal and spatial factors in different application scenarios; for example, temporal concentration may be more important in spectrum interference investigations, while spatial concentration may dominate in frequency planning applications.

[0116] The weighted fusion employs a linear combination method to ensure that the contributions of the two concentration indices to the final result are consistent, avoiding the problem of a single dimension dominating the entire evaluation result. The numerical range of spatiotemporal aggregation degree is standardized to a specific interval to facilitate subsequent comparative analysis and prioritization. High spatiotemporal aggregation degree indicates that the corresponding spectral data has high concentration and importance in both time and space dimensions. This type of data is usually a core information resource in the monitoring network, with high analytical value and decision support.

[0117] S53 weights and fuses temporal and spatial concentration to generate the spatiotemporal aggregation degree of each target spectral data.

[0118] S54, according to the preset priority division rules, target spectrum data with a spatiotemporal aggregation degree higher than the first threshold is set as high priority, target spectrum data with a spatiotemporal aggregation degree lower than the second threshold is set as low priority, and target spectrum data with a spatiotemporal aggregation degree between the first threshold and the second threshold is set as medium priority.

[0119] Figure 2 This is a schematic diagram of a scenario provided in an embodiment of this application, such as... Figure 2 As shown in the figure, the five green monitoring terminals distributed in different geographical locations represent spectrum monitoring equipment deployed in mountainous areas, urban areas, suburbs, urban areas and development zones. Each terminal is equipped with an antenna device to collect spectrum data of the area where it is located, and also has clear geographical coordinate information. These terminals transmit spectrum data to the central control center in real time through wireless signal ripple animation and orange data transmission lines.

[0120] The blue control center building at the heart of the monitoring network integrates four core functional modules: the feature analysis and anomaly detection module, which analyzes the received spectrum data and generates spectrum feature values ​​and anomaly indices; the correlation calculation and scheme optimization module, which calculates the correlation index between each terminal based on their location information and adjusts the compression scheme accordingly; the stability evaluation and compression processing module, which evaluates the stability index by analyzing the historical data acquisition quality of each terminal and generates the final target compression scheme; and the spatiotemporal analysis and priority allocation module, which performs spatiotemporal aggregation analysis on the compressed target spectrum data to determine storage priorities. The purple tiered storage system at the bottom contains three different levels of storage devices: red high-priority SSD storage for storing important data with high spatiotemporal aggregation; orange medium-priority hybrid storage for handling moderately important data; and green low-priority cold storage for archiving historical data with lower value.

[0121] The entire system's workflow is embodied in dynamic data flow. Each monitoring terminal continuously transmits spectrum data to the control center through signal ripple effects. Within the control center, processing indicators in each module show that the system is performing real-time data analysis and processing. The data, after multi-layered optimization, is finally transmitted to a hierarchical storage system via storage connection lines for differentiated management. This deployment scheme fully leverages the advantages of geographical distribution, optimizing data compression strategies by analyzing the spatial correlation between terminals in different locations. Simultaneously, it makes personalized adjustments based on the historical performance and stability of each terminal, ultimately realizing a complete technical chain from data acquisition, feature analysis, multi-round scheme optimization to hierarchical storage, effectively improving the overall efficiency and resource utilization level of the spectrum monitoring system.

[0122] Based on the above method, this application also discloses a spectrum monitoring system, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of a spectrum monitoring system provided in an embodiment of this application. The system includes: a receiving module, a first acquisition module, a second acquisition module, a third acquisition module, and a generating module; wherein,

[0123] The receiving module receives spectrum data collected by multiple monitoring terminals, performs feature analysis on the spectrum data, generates spectrum feature values, and calculates the anomaly index of each spectrum data based on the spectrum feature values. The first acquisition module acquires the terminal features of each monitoring terminal, combines the terminal features and the anomaly index to generate a first compression scheme for the spectrum data collected by each monitoring terminal. The second acquisition module acquires the location information of each monitoring terminal, calculates the correlation index between the monitoring terminals based on the location information, adjusts the first compression scheme based on the correlation index, and generates a second compression scheme. The third acquisition module acquires the historical data acquisition quality of each monitoring terminal, generates a stability index for each monitoring terminal based on the historical data acquisition quality, adjusts the second compression scheme based on the stability index, and generates a target compression scheme. The generation module compresses the spectrum data collected by each monitoring terminal using the target compression scheme to generate target spectrum data, calculates the spatiotemporal aggregation degree of each target spectrum data, determines the storage priority of each target spectrum data based on the spatiotemporal aggregation degree, and performs hierarchical storage of the target spectrum data according to the storage priority.

[0124] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0125] Please see Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0126] The communication bus 1002 is used to realize the connection and communication between these components.

[0127] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0128] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0129] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0130] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 4 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a spectrum monitoring method.

[0131] exist Figure 4 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call an application program of a spectrum monitoring method stored in the memory 1005. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0132] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0133] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0134] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0138] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0139] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A spectrum monitoring method, characterized in that, The method includes: The system receives spectrum data collected from multiple monitoring terminals, performs feature analysis on the spectrum data, generates spectrum feature values, and calculates the anomaly index of each spectrum data based on the spectrum feature values. Obtain the terminal characteristics of each monitoring terminal, and combine the terminal characteristics with the anomaly index to generate a first compression scheme for the spectrum data collected by each monitoring terminal: Obtain the location information of each monitoring terminal, calculate the correlation index between each monitoring terminal based on the location information, adjust the first compression scheme based on the correlation index, and generate a second compression scheme. The process involves obtaining historical data acquisition quality for each monitoring terminal, generating a stability index for each monitoring terminal based on the historical data acquisition quality, adjusting the second compression scheme based on the stability index, and generating a target compression scheme. Generating the stability index for each monitoring terminal based on the historical data acquisition quality includes: calculating a data integrity index, a data accuracy index, and a data continuity index for each monitoring terminal based on the historical data acquisition quality; calculating an integrity coefficient for each monitoring terminal based on the data integrity index; calculating an accuracy coefficient for each monitoring terminal based on the data accuracy index; calculating a continuity coefficient for each monitoring terminal based on the data continuity index; and weighting the integrity coefficient, accuracy coefficient, and continuity coefficient to generate the stability index for each monitoring terminal. The target compression scheme is used to compress the spectrum data collected by each monitoring terminal to generate target spectrum data. The spatiotemporal aggregation degree of each target spectrum data is calculated, and the storage priority of each target spectrum data is determined based on the spatiotemporal aggregation degree. The target spectrum data is then stored hierarchically according to the storage priority. The calculation of the spatiotemporal aggregation degree of each target spectrum data includes: acquiring the time dimension information and spatial dimension information of each target spectrum data; calculating the time concentration degree of each target spectrum data based on the time dimension information, where the time concentration degree is used to characterize the density of spectrum data distribution on the time axis; calculating the spatial concentration degree of each target spectrum data based on the spatial dimension information, where the spatial concentration degree is used to characterize the density of spectrum data distribution on a geographical location; and weightedly fusing the time concentration degree and the spatial concentration degree to generate the spatiotemporal aggregation degree of each target spectrum data.

2. The spectrum monitoring method according to claim 1, characterized in that, The step of performing feature analysis on the spectrum data to generate spectrum feature values, and calculating the anomaly index of each spectrum data based on the spectrum feature values, includes: Acquire the frequency characteristics, power characteristics, and time characteristics of each of the aforementioned spectrum data; Based on the frequency characteristics, frequency anomalies are calculated for each of the spectrum data; the frequency anomalies are used to characterize whether the frequency distribution of the spectrum data conforms to a preset frequency range; based on the power characteristics, power anomalies are calculated for each of the spectrum data; the power anomalies are used to characterize whether the power of the spectrum data exceeds a preset power threshold; based on the time characteristics, time anomalies are calculated for each of the spectrum data; the time anomalies are used to characterize whether the duration of the spectrum data exceeds a preset duration threshold. The frequency anomaly, the power anomaly, and the time anomaly are used as spectral feature values, and the frequency anomaly, the power anomaly, and the time anomaly are weighted and summed to generate an anomaly index for each spectral data.

3. The spectrum monitoring method according to claim 1, characterized in that, The terminal characteristics include: processing power, storage capacity, network bandwidth, and battery status. The first compression scheme for generating the spectrum data collected by each monitoring terminal, combining the terminal characteristics and the anomaly index, includes: According to the preset terminal performance evaluation rules, the processing coefficient corresponding to the processing capability of each monitoring terminal, the storage coefficient corresponding to the storage capacity of each monitoring terminal, the transmission coefficient corresponding to the network bandwidth of each monitoring terminal, and the energy consumption coefficient corresponding to the battery status of each monitoring terminal are determined. The processing coefficient, storage coefficient, transmission coefficient, and energy consumption coefficient are weighted and calculated to generate a comprehensive performance index for each monitoring terminal. Combining the anomaly index and the comprehensive performance index, the compression ratio of the spectrum data collected by each monitoring terminal is determined, wherein the compression ratio is inversely proportional to the anomaly index and the comprehensive performance index. Based on the compression ratio, a first compression scheme is generated for the spectrum data collected by each monitoring terminal.

4. The spectrum monitoring method according to claim 1, characterized in that, The step of calculating the correlation index between each monitoring terminal based on the location information, and adjusting the first compression scheme based on the correlation index to generate a second compression scheme includes: Based on the location information, the spatial distance between each monitoring terminal is calculated; Based on the spatial distance, determine the neighboring monitoring terminals of each monitoring terminal, and calculate the spatial correlation between each monitoring terminal and its neighboring monitoring terminals; The similarity between the spectrum data collected by each monitoring terminal and the spectrum data collected by its neighboring monitoring terminals is obtained, and a data similarity index is generated. By combining the spatial correlation degree and the data similarity index, a correlation index is generated between each of the monitoring terminals; The system obtains a combination of monitoring terminals with a correlation index higher than a preset index, increases the compression ratio of the spectrum data of the monitoring terminal with the lowest correlation index in the combination, and decreases the compression ratio of the spectrum data of the monitoring terminal with the highest correlation index in the combination. Based on the adjusted compression ratio, the first compression scheme of each monitoring terminal is updated to generate a second compression scheme.

5. The spectrum monitoring method according to claim 4, characterized in that, The step of combining the spatial correlation degree and the data similarity index to generate a correlation index between the monitoring terminals includes: The spatial correlation degree is normalized to generate a standardized spatial correlation degree; the data similarity index is normalized to generate a standardized data similarity index. Based on the preset correlation calculation rules, the spatial weight of the standardized spatial correlation and the data weight of the standardized data similarity index are determined. The weighted spatial correlation degree is obtained by multiplying the standardized spatial correlation degree by the spatial weight, and the weighted data similarity index is obtained by multiplying the standardized data similarity index by the data weight. The weighted spatial correlation degree and the weighted data similarity index are summed to generate the correlation index between each monitoring terminal.

6. The spectrum monitoring method according to claim 1, characterized in that, The step of adjusting the second compression scheme according to the stability index to generate the target compression scheme includes: Based on the magnitude of the stability index, the monitoring terminals are divided into high-stability terminals and low-stability terminals. The compression ratio of the spectrum data of the high-stability terminals is reduced, and the compression ratio of the spectrum data of the low-stability terminals is increased. Based on the adjusted compression ratio, the second compression scheme of each monitoring terminal is updated to generate the target compression scheme.

7. The spectrum monitoring method according to claim 1, characterized in that, Determining the storage priority of each target spectrum data based on the spatiotemporal aggregation degree includes: According to the preset priority division rules, target spectrum data with a spatiotemporal aggregation degree higher than the first threshold is set as high priority, target spectrum data with a spatiotemporal aggregation degree lower than the second threshold is set as low priority, and target spectrum data with a spatiotemporal aggregation degree between the first threshold and the second threshold is set as medium priority.

8. A spectrum monitoring system, characterized in that, The system includes: a receiving module, a first acquisition module, a second acquisition module, a third acquisition module, and a generating module; wherein, The receiving module is used to receive spectrum data collected by multiple monitoring terminals, perform feature analysis on the spectrum data, generate spectrum feature values, and calculate the anomaly index of each spectrum data based on the spectrum feature values. The first acquisition module is used to acquire the terminal characteristics of each monitoring terminal, and combine the terminal characteristics and the anomaly index to generate a first compression scheme for the spectrum data collected by each monitoring terminal. The second acquisition module is used to acquire the location information of each of the monitoring terminals, calculate the correlation index between each of the monitoring terminals based on the location information, and adjust the first compression scheme based on the correlation index to generate a second compression scheme. The third acquisition module is used to acquire the historical data acquisition quality of each monitoring terminal, generate a stability index for each monitoring terminal based on the historical data acquisition quality, and adjust the second compression scheme based on the stability index to generate a target compression scheme. Generating the stability index for each monitoring terminal based on the historical data acquisition quality includes: calculating a data integrity index, a data accuracy index, and a data continuity index for each monitoring terminal based on the historical data acquisition quality; calculating an integrity coefficient for each monitoring terminal based on the data integrity index; calculating an accuracy coefficient for each monitoring terminal based on the data accuracy index; calculating a continuity coefficient for each monitoring terminal based on the data continuity index; and weighting the integrity coefficient, accuracy coefficient, and continuity coefficient to generate the stability index for each monitoring terminal. The generation module is used to compress the spectrum data collected by each monitoring terminal using the target compression scheme to generate target spectrum data, calculate the spatiotemporal aggregation degree of each target spectrum data, determine the storage priority of each target spectrum data based on the spatiotemporal aggregation degree, and perform hierarchical storage of the target spectrum data according to the storage priority. The calculation of the spatiotemporal aggregation degree of each target spectrum data includes: acquiring the time dimension information and spatial dimension information of each target spectrum data; calculating the time concentration degree of each target spectrum data based on the time dimension information, where the time concentration degree characterizes the density of spectrum data distribution on the time axis; calculating the spatial concentration degree of each target spectrum data based on the spatial dimension information, where the spatial concentration degree characterizes the density of spectrum data distribution in geographical location; and weightedly fusing the time concentration degree and the spatial concentration degree to generate the spatiotemporal aggregation degree of each target spectrum data.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-7.

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