A base station security monitoring method and system based on data correlation checking

Abstract

The application provides a base station safety monitoring method and system based on data correlation verification, and relates to the technical field of safety management of a telecommunication base station. The method comprises the following steps: obtaining electric parameter data of a telecommunication base station, and performing a preprocessing operation on the electric parameter data; setting a threshold range for each electric parameter data, and comparing the preprocessed electric parameter data with the threshold range to determine whether each electric parameter data is abnormal; when an abnormality occurs, sending an alarm electronic information to an administrator, and sending an alarm sound at the site where the telecommunication base station is located. The application performs accurate verification on data while obtaining node data based on a sensor, establishes a correlation relationship between each monitoring node by using a base station circuit mesh diagram, and then determines the result with the largest probability in the form of voting, so that the false judgment caused by data abnormality due to local and abnormal sensors can be prevented, and the early warning accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of telecommunications base station security management technology, and in particular to a base station security monitoring method and system based on data correlation verification. Background Technology

[0002] With the rapid development of 5G technology, the security of telecommunications base stations, as a critical infrastructure of communication networks, is receiving increasing attention. Traditional base station security monitoring systems largely rely on video surveillance and simple alarm mechanisms, often only issuing alerts after damage has occurred, failing to provide early warnings and making losses difficult to avoid. Furthermore, base stations are often located in remote areas, making manual inspections difficult and monitoring efficiency low. With the development of artificial intelligence, existing technologies utilizing AI to replace manual monitoring and achieve automatic monitoring and early warning functions are now widely used.

[0003] For example, patent publication number CN117437746A discloses an artificial intelligence-based safety management early warning system. This system includes a detection unit, a control and processing unit, an early warning feedback unit, and a safety execution unit. The detection unit detects various environmental elements within the park, refines the detection results, converts them into electrical signals, and transmits them to the control and processing unit. The control and processing unit receives the electrical signals from the detection unit, analyzes and processes the signals based on artificial intelligence, and activates the early warning feedback unit and the safety execution unit accordingly. The early warning feedback unit receives control commands from the control and processing unit, triggers early warning signals, executes early warning operations, and simultaneously feeds back the early warning status to the control and processing unit. The safety execution unit receives control commands from the control and processing unit, executes safety remedial or safety protection operations, and then, in conjunction with the detection unit, re-detects the execution results of the safety execution unit.

[0004] The existing technologies for base station security monitoring mainly have the following problems and shortcomings:

[0005] 1. Sensors are used to acquire node information, and then judgments are made based on the monitoring information. However, when sensors acquire node information, the data acquired by the sensors may be inaccurate due to performance degradation, abnormal damage, etc., which will affect the final decision and result in low early warning accuracy.

[0006] 2. Sensor performance degradation and abnormal damage: When sensors acquire node information, performance degradation or abnormal damage may lead to inaccurate data, affecting the final decision and resulting in low early warning accuracy.

[0007] 3. High false alarm rate: In diverse communication environments, the communication data processed by telecommunications base stations may be subject to noise interference, which can easily be mistaken for security anomalies, leading to incorrect warnings.

[0008] 4. Poor adaptability of denoising techniques: Telecommunication base stations process various types of communication data, requiring denoising techniques to have good adaptive characteristics. Traditional adaptive filtering methods (such as the LMS algorithm) use a fixed learning rate, which cannot adapt to the diverse characteristics of communication data, affecting the denoising effect.

[0009] These problems and shortcomings indicate that existing base station security monitoring technologies need further development and improvement to enhance the accuracy of early warnings, reduce false alarm rates, adapt to diverse communication data, reduce the impact of electromagnetic radiation, enhance the security of physical equipment and operating systems, and improve the intelligent management and control capabilities for construction safety. Summary of the Invention

[0010] This application discloses a base station security monitoring method and system based on data correlation verification.

[0011] In a first aspect, this application discloses a base station security monitoring method based on data correlation verification, the method comprising:

[0012] Acquire electrical parameter data of the telecommunications base station and perform preprocessing operations on the electrical parameter data;

[0013] A threshold range is set for each electrical parameter data, and the preprocessed electrical parameter data is compared with the threshold range to determine whether each electrical parameter data is abnormal;

[0014] When an anomaly occurs, an electronic alarm message is sent to the administrator, and an alarm sound is emitted at the location of the telecommunications base station.

[0015] Furthermore, the preprocessing operations for the electrical parameter data include: filling in missing values, removing duplicate values, and verifying accuracy.

[0016] Furthermore, the missing value filling step includes:

[0017] A two-dimensional coordinate system is established with the time axis as the horizontal axis and the electrical parameter data as the vertical axis;

[0018] The corresponding nodes are selected on the horizontal axis based on the period of the frequency at which the sensor collects the electrical parameter data.

[0019] The newly acquired electrical parameter data is filled into the vertical coordinate of each node. If the sensor does not collect the electrical parameter data of the current node, the vertical coordinate of the current node is left blank.

[0020] According to the formula or Fill in the missing electrical parameter data x at the i-th node. i , where x i-1 This represents the electrical parameter data of the (i-1)th node, x i+1 Let x represent the electrical parameter data of the (i+1)th node, m represent the number of samples taken forward, n represent the number of samples taken backward, and x o This represents the electrical parameter data of the o-th node.

[0021] Furthermore, the step of removing duplicate values ​​includes:

[0022] Delete duplicate electrical parameter data to create missing values;

[0023] Fill in the missing values ​​according to the steps described above.

[0024] Furthermore, the accuracy verification steps include:

[0025] Based on the network diagram of the telecommunications base station circuit, the correlation relationship between each monitoring node is established, and the correlation function M of the current monitoring node i and its n related monitoring nodes is obtained. i =f(M1, M2, ..., M) n );

[0026] For the monitoring node whose electrical parameter data is to be verified, obtain the electrical parameter data of n monitoring nodes that are related to it, and use the correlation to calculate the theoretical electrical parameter data of the monitoring node to be verified in reverse, so as to obtain the theoretical electrical parameter data derived from the n monitoring nodes.

[0027] If the electrical parameter data of the monitoring node to be verified is equal to the theoretical electrical parameter data of the currently correlated monitoring node, then the voting result of the currently correlated monitoring node for the monitoring node to be verified is "accurate"; otherwise, the voting result is "incorrect".

[0028] All "accurate" and "incorrect" votes are tallied, and the result that appears most frequently is taken as the final result;

[0029] If the final result is determined to be "accurate", then the actual electrical parameter data is used as the electrical parameter data of the monitoring node to be verified. If the final result is determined to be "incorrect", then the average of the theoretical electrical parameter data derived from all n related monitoring nodes with a voting result of "incorrect" is used as the electrical parameter data of the monitoring node to be verified.

[0030] Furthermore, determining whether each of the electrical parameter data is abnormal includes:

[0031] The areas of all the aforementioned telecommunications base stations are divided into different independent areas;

[0032] Obtain the electrical parameter data and threshold range of all the telecommunications base stations in each independent area;

[0033] If the electrical parameter data is within the threshold range, the output result is "0";

[0034] If the electrical parameter data is less than the lower limit of the threshold range, the output result is:

[0035]

[0036] in, For the electrical parameter data of the i-th node, X max and X min These are the upper and lower limits of the threshold range, respectively;

[0037] If the electrical parameter data is greater than the upper limit of the threshold range, the output result is:

[0038]

[0039] The voting results are weighted and summed, and a threshold value is set for the results. If the sum of the weights is greater than the threshold... If it is not, it is considered abnormal; otherwise, it is considered normal.

[0040] Secondly, this application discloses a base station security monitoring system based on data correlation verification, the system comprising:

[0041] The data preprocessing module is used to acquire electrical parameter data of the telecommunications base station and perform preprocessing operations on the electrical parameter data.

[0042] The data analysis module is used to set a threshold range for each electrical parameter data, and compare the preprocessed electrical parameter data with the threshold range to determine whether each electrical parameter data is abnormal;

[0043] The alarm module is used to send an electronic alarm message to the administrator when an anomaly occurs, and to issue an alarm sound at the location of the telecommunications base station.

[0044] Furthermore, the data preprocessing module is used to: fill in missing values, remove duplicate values, and verify the accuracy of the electrical parameter data.

[0045] Furthermore, the data preprocessing module is specifically used for:

[0046] A two-dimensional coordinate system is established with the time axis as the horizontal axis and the electrical parameter data as the vertical axis;

[0047] The corresponding nodes are selected on the horizontal axis based on the period of the frequency at which the sensor collects the electrical parameter data.

[0048] The newly acquired electrical parameter data is filled into the vertical coordinate of each node. If the sensor does not collect the electrical parameter data of the current node, the vertical coordinate of the current node is left blank.

[0049] According to the formula or Fill in the missing electrical parameter data x at the i-th node. i , where x i-1 This represents the electrical parameter data of the (i-1)th node, x i+1 Let x represent the electrical parameter data of the (i+1)th node, m represent the number of samples taken forward, n represent the number of samples taken backward, and x o This represents the electrical parameter data of the o-th node.

[0050] Furthermore, the data preprocessing module is specifically used for:

[0051] Delete duplicate electrical parameter data to create missing values;

[0052] Fill in the missing values ​​according to the steps described above.

[0053] The technical solution provided in this application may include the following beneficial effects:

[0054] The base station security monitoring method based on data correlation verification provided in this application offers effective protection for the safe operation of base stations through its advantages in improving data accuracy, reducing false alarm rate, rapid response to anomalies, energy saving and consumption reduction, and digital management. Attached Figure Description

[0055] Figure 1 This is a flowchart of the steps of the method described in this application;

[0056] Figure 2 This is a flowchart of the system used in this application;

[0057] Figure 3 This is an architecture diagram of a base station security monitoring system based on data correlation verification provided in this application;

[0058] Figure 4 This is a block diagram of an electronic device according to this application;

[0059] Figure 5 This is a block diagram of a computer-readable storage medium according to this application. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] [Glossary]

[0062] SRv6 SFC (Segment Routing over IPv6 Service Function Chain) is a technology that guides packets through application layer service devices sequentially along a specified path by adding SRv6 path information to the original packets.

[0063] SC (Service Classifier): Located at the edge of the SRV6 SFC service chain network, it is the source node of the service chain path. SC can use different traffic redirection methods to introduce service data into the SRV6 TE Policy tunnel for forwarding.

[0064] Reference Figure 1 This paper illustrates a base station security monitoring method based on data correlation verification, which can be applied to electronic devices. The method includes the following steps:

[0065] Step 101: Obtain electrical parameter data of the telecommunications base station through the sensor module, and perform preprocessing operations on the electrical parameter data. The sensor module includes a current meter, a voltmeter, a power meter, and a wave sensor.

[0066] Specifically, the preprocessing operations for the electrical parameter data include: filling in missing values, removing duplicate values, and verifying accuracy.

[0067] Specifically, the missing value filling step includes:

[0068] A two-dimensional coordinate system is established with the time axis as the horizontal axis and the electrical parameter data as the vertical axis. Specifically, the horizontal axis represents the time point t, and the vertical axis represents the electrical parameter data x.

[0069] Using the sampling frequency of the electrical parameter data collected by the sensor as the period, corresponding nodes are selected on the horizontal axis. Specifically, the sampling frequency of the sensor is set to f. s (For example, if data is collected once per second), then the interval between each node is 1 / f. s Second;

[0070] The newly acquired electrical parameter data is used to fill the ordinate of each node. If the sensor has not collected the electrical parameter data for the current node, the ordinate of the current node is left blank. Specifically, if at a certain time point t... i If no data was collected, then the electrical parameter value x at that point is... i Empty;

[0071] For the missing electrical parameter data x of the i-th node i Calculate x using one of the following two methods. i Value:

[0072] Linear interpolation:

[0073]

[0074] Where, x i-1 This is the electrical parameter data of the (i-1)th node, x i+1 This refers to the electrical parameter data of the (i+1)th node. Linear interpolation is applicable to x. i The situation where there is valid electrical parameter data on both sides.

[0075] Moving average method:

[0076]

[0077] Where m represents the number of samples taken forward (i.e., the number of samples from im to i-1), n ​​represents the number of samples taken backward (i.e., the number of samples from i+1 to i+n), and x o This represents the electrical parameter data for the o-th node. The moving average method can calculate x in a broader context. i This is especially true when there are no directly adjacent valid electrical parameter data on one or both sides.

[0078] Specifically, the step of removing duplicate values ​​includes:

[0079] Delete duplicate electrical parameter data to create missing values;

[0080] Fill in the missing values ​​according to the steps described above.

[0081] Specifically, the accuracy verification steps include:

[0082] Based on the network diagram of the telecommunications base station circuit, the correlation relationship between each monitoring node is established, and the correlation function M of the current monitoring node i and its n related monitoring nodes is obtained. i =f(M1, M2, ..., M) nFor example, in a series circuit, the current at the two nodes is the same, and the voltage is proportional to the resistance of the nodes. In a parallel circuit, the voltage between the two nodes is the same, and the current is inversely proportional to the resistance. In a single link for information transmission, the frequency of the wave signals at the two nodes remains unchanged, and the amplitude difference is proportional to the line length between the two nodes. This is called the matching transmission loss.

[0083] The following example illustrates the correlation function M. i =f(M1, M2, ..., M) n The following description is provided:

[0084] Correlation function M i =f(M1, M2, ..., M) n Considering three types of circuits: series circuits, parallel circuits, and information transmission links, a weighted average is calculated.

[0085] The first type is a series circuit, as shown below:

[0086] In a series circuit, the current at all nodes is the same, and the voltage is proportional to the resistance. Let R... i Let V be the resistance of the i-th node. i Let I be the voltage drop at the i-th node, and I be the current through the circuit. Then:

[0087] V i =I·R i

[0088] Establish a correlation function between the i-th node and its neighboring nodes, using the voltage ratio as the metric, i.e.:

[0089]

[0090] Among them, V j This represents the voltage at the j-th node connected to the i-th node. R represents the sum of the resistances of all relevant nodes. k This represents the resistance of the k-th node.

[0091] The second type is a parallel circuit, as shown below:

[0092] In a parallel circuit, the voltage across all nodes is the same, while the current is inversely proportional to the resistance. Let G... i =1 / R i Let V be the conductance of the i-th node, V be the common voltage, and I be the voltage across the node. i Let be the current flowing through the i-th node, then:

[0093] I i =V·G i

[0094] For the correlation function in parallel circuits, the current ratio can be used:

[0095]

[0096] Among them, I j This represents the current at the j-th node connected to the i-th node. This represents the sum of the conductances of all relevant nodes.

[0097] The third type is the information transmission link, as shown below:

[0098] In a single information transmission link, the frequency of the wave signal remains constant, and the amplitude difference is proportional to the line length between the two nodes. Let A... i Let L be the amplitude at the i-th node. ij Let be the line length from node i to node j, then the amplitude difference ΔA ij The following formula can be used to calculate:

[0099] ΔA ij =A i -A j =k·L ij

[0100] Where k is a constant representing the amplitude attenuation rate per unit length.

[0101] The correlation function on the information transmission link can be defined based on amplitude loss:

[0102]

[0103] in, This represents the sum of the line lengths between the i-th node and all other related nodes.

[0104] Finally, the three circuits are weighted, with a total weight of 1, and M is calculated. i Similarity:

[0105]

[0106] Among them, w1, w2 and w3 are the weighting coefficients set for series circuits, parallel circuits and information transmission links, respectively, to ensure that their sum is 1.

[0107] For the monitoring node whose electrical parameter data is to be verified, obtain the electrical parameter data of n monitoring nodes that are related to it, and use the correlation to calculate the theoretical electrical parameter data of the monitoring node to be verified in reverse, so as to obtain the theoretical electrical parameter data derived from the n monitoring nodes.

[0108] If the electrical parameter data of the monitoring node to be verified is equal to the theoretical electrical parameter data of the currently correlated monitoring node, that is...

[0109]

[0110] Then the voting result of the currently relevant monitoring nodes for the monitoring nodes to be verified is "accurate". If x i for

[0111]

[0112] One of them will result in a "mistake" vote;

[0113] All "accurate" and "incorrect" votes are tallied, and the result that appears most frequently is taken as the final result;

[0114] If the final result is determined to be "accurate", then the actual electrical parameter data will be used as the electrical parameter data of the monitoring node to be verified.

[0115]

[0116] If the final result is determined to be "incorrect", then the mean of the theoretical electrical parameter data derived from the n relevant monitoring nodes whose voting result is "incorrect" is used as the electrical parameter data of the monitoring node to be verified.

[0117]

[0118] Where q represents The number of incorrect votes.

[0119] Step 102: Set a threshold range for each electrical parameter data, and compare the preprocessed electrical parameter data with the threshold range to determine whether each electrical parameter data is abnormal;

[0120] Specifically, determining whether each of the electrical parameter data is abnormal includes:

[0121] The areas of all the aforementioned telecommunications base stations are divided into different independent areas;

[0122] Obtain the electrical parameter data and threshold range of all the telecommunications base stations in each independent area;

[0123] If the electrical parameter data is within the threshold range, the output result is "0";

[0124] If the electrical parameter data is less than the lower limit of the threshold range, the output result is:

[0125]

[0126] in, For the electrical parameter data of the i-th node, X max and X min These are the upper and lower limits of the threshold range, respectively;

[0127] If the electrical parameter data is greater than the upper limit of the threshold range, the output result is:

[0128]

[0129] The voting results are weighted and summed, and a threshold value is set for the results. (For example If the sum of the weights is greater than the threshold... If it is not, it is considered abnormal; otherwise, it is considered normal.

[0130] Preferably, a weight coefficient is set for each node. It represents the weight of the i-th node;

[0131] According to the formula Calculation results, among which The weight of the i-th node This is the output result of the i-th node.

[0132] Step 103: When an anomaly occurs, send an alarm electronic message to the administrator and issue an alarm sound at the location of the telecommunications base station.

[0133] In one scenario, when an abnormal situation occurs, the safety warning module sends an electronic alarm to the administrator and simultaneously uses an audible and visual alarm to issue an on-site alert.

[0134] This application, while acquiring node data based on sensors, performs accuracy verification on the data. It establishes the correlation between each monitoring node using a base station circuit network diagram, and then uses a voting method to determine the result with the highest probability. This prevents misjudgments caused by local or sensor anomalies leading to data anomalies, increasing early warning accuracy. By imputing missing values ​​and removing duplicates from the acquired data, the uniqueness of the acquired data time series is guaranteed, ensuring the reliability of subsequent analysis processes and increasing system reliability. For result analysis, a "voting" method is used to further utilize probability to select the output result, thereby further increasing early warning accuracy. By further weighting and summing the "voting" results and then using threshold control for judgment, the weights can be designed specifically according to the importance of node data to the actual anomaly situation, thus possessing strong practicality and accuracy. Through data preprocessing operations, including missing value imputation, duplicate value removal, and accuracy verification, the completeness and accuracy of electrical parameter data can be improved, thereby improving the accuracy of base station safety monitoring.

[0135] 1. Anomaly Detection and Early Warning: The system can determine whether electrical parameters are abnormal by setting threshold ranges and comparing preprocessed data, and issue an alarm in a timely manner when an anomaly is detected. This helps to discover and respond to potential safety issues in a timely manner.

[0136] 2. Reduce false alarm rate: By using data correlation verification, through correlation function and reverse calculation of theoretical electrical parameter data, combined with a voting mechanism to determine the final result, the false alarm rate can be effectively reduced and the reliability of safety monitoring can be improved.

[0137] 3. Improved response speed: When the system detects an anomaly, it can quickly send an electronic alarm message to the administrator and issue an alarm sound on site, which helps to respond to and handle security incidents quickly.

[0138] 4. Energy saving and cost-effectiveness: The system achieves energy saving in the base station's air conditioning power consumption, reduces energy consumption, and reduces operating costs through intelligent management, such as automatic control of air conditioning and fans, and natural cooling.

[0139] 5. Digital and intelligent management: The system supports digital and intelligent remote management of base station power consumption, including monitoring electrical parameters, statistical energy consumption data, and real-time early warning of anomalies, which helps to improve the efficiency and intelligence level of base station management.

[0140] 6. Preventive maintenance: Through real-time monitoring of base station equipment, the system can predict and warn of possible faults, such as overheating or fire, thereby enabling preventive maintenance and reducing unexpected downtime and repair costs.

[0141] 7. Simple structure and low cost: The monitoring system has a simple structure and low cost. While providing security for the base station, it also reduces the cost of deployment and maintenance.

[0142] In summary, this base station security monitoring method and system provides effective protection for the safe operation of base stations through its advantages in improving data accuracy, reducing false alarm rates, rapidly responding to anomalies, saving energy and reducing consumption, and digital management.

[0143] like Figure 3 This application illustrates a base station security monitoring system based on data correlation verification, the system comprising:

[0144] The data preprocessing module is used to acquire electrical parameter data of the telecommunications base station and perform preprocessing operations on the electrical parameter data.

[0145] The data analysis module is used to set a threshold range for each electrical parameter data, and compare the preprocessed electrical parameter data with the threshold range to determine whether each electrical parameter data is abnormal;

[0146] The alarm module is used to send an electronic alarm message to the administrator when an anomaly occurs, and to issue an alarm sound at the location of the telecommunications base station.

[0147] The data preprocessing module is used to: fill in missing values, remove duplicate values, and verify the accuracy of the electrical parameter data.

[0148] The data preprocessing module is specifically used for:

[0149] A two-dimensional coordinate system is established with the time axis as the horizontal axis and the electrical parameter data as the vertical axis. Specifically, the horizontal axis represents the time point t, and the vertical axis represents the electrical parameter data x.

[0150] Using the sampling frequency of the electrical parameter data collected by the sensor as the period, corresponding nodes are selected on the horizontal axis. Specifically, the sampling frequency of the sensor is set to f. s (For example, if data is collected once per second), then the interval between each node is 1 / f. s Second;

[0151] The newly acquired electrical parameter data is used to fill the ordinate of each node. If the sensor has not collected the electrical parameter data for the current node, the ordinate of the current node is left blank. Specifically, if at a certain time point t... i If no data was collected, then the electrical parameter value x at that point is... i Empty;

[0152] For the missing electrical parameter data x of the i-th node iCalculate x using one of the following two methods. i Value:

[0153] Linear interpolation:

[0154]

[0155] Where, x i-1 This is the electrical parameter data of the (i-1)th node, x i+1 This refers to the electrical parameter data of the (i+1)th node. Linear interpolation is applicable to x. i The situation where there is valid electrical parameter data on both sides.

[0156] Moving average method:

[0157]

[0158] Where m represents the number of samples taken forward (i.e., the number of samples from im to i-1), n ​​represents the number of samples taken backward (i.e., the number of samples from i+1 to i+n), and x o This represents the electrical parameter data for the o-th node. The moving average method can calculate x in a broader context. i This is especially true when there are no directly adjacent valid electrical parameter data on one or both sides.

[0159] The data preprocessing module is specifically used for:

[0160] Delete duplicate electrical parameter data to create missing values;

[0161] Fill in the missing values ​​according to the steps described above.

[0162] As the system implementation is basically similar to the method implementation, it is described in a relatively simple way. For relevant details, please refer to the description of the method implementation.

[0163] This application presents a base station security monitoring system based on data correlation verification. While acquiring node data from sensors, it performs precise data verification. It establishes the correlation between each monitoring node using a base station circuit network diagram, and then uses a voting method to determine the result with the highest probability. This prevents misjudgments caused by local or sensor anomalies leading to data anomalies, increasing early warning accuracy. By filling missing values ​​and removing duplicates from the acquired data, the uniqueness of the acquired data time series is guaranteed, ensuring the reliability of subsequent analysis processes and increasing system reliability. For result analysis, a "voting" method is used to further utilize probability to select the output result, thereby further increasing early warning accuracy. By further weighting and summing the "voting" results and then using threshold control for judgment, the weights can be designed specifically according to the importance of node data to the actual anomaly situation, thus possessing strong practicality and accuracy. Through data preprocessing operations, including missing value filling, duplicate value removal, and precision verification, the completeness and accuracy of electrical parameter data can be improved, thereby enhancing the accuracy of base station security monitoring.

[0164] Optionally, this application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the above method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0165] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0166] The figure is a block diagram of an electronic device 800 shown in the four applications. For example, the electronic device 800 can be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0167] Reference Figure 4 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0168] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0169] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, images, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0170] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0171] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0172] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0173] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0174] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0175] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast operation information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0176] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0177] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0178] Figure 5 This is a block diagram illustrating a computer-readable storage medium 1900. For example, the computer-readable storage medium 1900 can be provided as a server.

[0179] Reference Figure 5 The computer-readable storage medium 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by memory 1932 for storing instructions executable by the processing component 1922, such as an application program. The application program stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0180] The computer-readable storage medium 1900 may also include a power supply component 1926 configured to perform power management of the computer-readable storage medium 1900, a wired or wireless network interface 1950 configured to connect the computer-readable storage medium 1900 to a network, and an input / output (I / O) interface 1958. The computer-readable storage medium 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0181] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0182] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0183] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0184] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0185] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0186] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods 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 coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0187] 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.

[0188] In addition, 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.

[0189] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium 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 storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0190] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A base station security monitoring method based on data correlation verification, characterized in that, The method includes: Acquire electrical parameter data of the telecommunications base station and perform preprocessing operations on the electrical parameter data; A threshold range is set for each electrical parameter data, and the preprocessed electrical parameter data is compared with the threshold range to determine whether each electrical parameter data is abnormal; In the event of an anomaly, an electronic alarm message will be sent to the administrator, and an alarm sound will be emitted at the location of the telecommunications base station. The preprocessing operations for the electrical parameter data include: filling in missing values, removing duplicate values, and verifying accuracy. The accuracy verification steps include: Based on the network diagram of the telecommunications base station circuit, the correlation relationship between each monitoring node is established, and the correlation function M of the current monitoring node i and its n related monitoring nodes is obtained. i =f(M1, M2, ..., M n ); For the monitoring node whose electrical parameter data is to be verified, obtain the electrical parameter data of n monitoring nodes that are related to it, and use the correlation to calculate the theoretical electrical parameter data of the monitoring node to be verified in reverse, so as to obtain the theoretical electrical parameter data derived from the n monitoring nodes. If the electrical parameter data of the monitoring node to be verified is equal to the theoretical electrical parameter data of the currently correlated monitoring node, then the voting result of the currently correlated monitoring node for the monitoring node to be verified is "accurate"; otherwise, the voting result is "incorrect". All "accurate" and "incorrect" votes are tallied, and the result that appears most frequently is taken as the final result; If the final result is determined to be "accurate", then the actual electrical parameter data is used as the electrical parameter data of the monitoring node to be verified. If the final result is determined to be "incorrect", then the average of the theoretical electrical parameter data derived from all n related monitoring nodes with a voting result of "incorrect" is used as the electrical parameter data of the monitoring node to be verified.

2. The base station security monitoring method based on data correlation verification as described in claim 1, characterized in that, The missing value filling step includes: A two-dimensional coordinate system is established with the time axis as the horizontal axis and the electrical parameter data as the vertical axis; The corresponding nodes are selected on the horizontal axis based on the period of the frequency at which the sensor collects the electrical parameter data. The newly acquired electrical parameter data is filled into the vertical coordinate of each node. If the sensor does not collect the electrical parameter data of the current node, the vertical coordinate of the current node is left blank. According to the formula or Fill in the missing electrical parameter data x at the i-th node. i ,in, Indicates the first Electrical parameter data for each node. Indicates the first The electrical parameter data for each node, where m represents the number of samples taken forward and n represents the number of samples taken backward. Indicates the first Electrical parameter data for each node.

3. The base station security monitoring method based on data correlation verification as described in claim 2, characterized in that, The step of removing duplicate values ​​includes: Delete duplicate electrical parameter data to create missing values; Fill in the missing values ​​according to the steps described above.

4. The base station security monitoring method based on data correlation verification as described in claim 1, characterized in that, Determining whether each of the electrical parameter data is abnormal includes: The areas of all the aforementioned telecommunications base stations are divided into different independent areas; Obtain the electrical parameter data and threshold range of all the telecommunications base stations in each independent area; If the electrical parameter data is within the threshold range, the output result is "0"; If the electrical parameter data is less than the lower limit of the threshold range, the output result is: in, The electrical parameter data for the i-th node, These are the upper and lower limits of the threshold range, respectively; If the electrical parameter data is greater than the upper limit of the threshold range, the output result is: ； The voting results are weighted and summed, and a threshold value is set for the results. If the sum of the weights is greater than the result threshold If it is, it is judged as abnormal; otherwise, it is judged as normal.

5. A base station security monitoring system based on data correlation verification, characterized in that, The system includes: The data preprocessing module is used to acquire electrical parameter data of the telecommunications base station and perform preprocessing operations on the electrical parameter data. The data analysis module is used to set a threshold range for each electrical parameter data, and compare the preprocessed electrical parameter data with the threshold range to determine whether each electrical parameter data is abnormal; The alarm module is used to send an electronic alarm message to the administrator when an anomaly occurs, and to issue an alarm sound at the location of the telecommunications base station. The preprocessing operations for the electrical parameter data include: filling in missing values, removing duplicate values, and verifying accuracy. The accuracy verification steps include: Based on the network diagram of the telecommunications base station circuit, the correlation relationship between each monitoring node is established, and the correlation function M of the current monitoring node i and its n related monitoring nodes is obtained. i =f(M1, M2, ..., M n ); For the monitoring node whose electrical parameter data is to be verified, obtain the electrical parameter data of n monitoring nodes that are related to it, and use the correlation to calculate the theoretical electrical parameter data of the monitoring node to be verified in reverse, so as to obtain the theoretical electrical parameter data derived from the n monitoring nodes. If the electrical parameter data of the monitoring node to be verified is equal to the theoretical electrical parameter data of the currently correlated monitoring node, then the voting result of the currently correlated monitoring node for the monitoring node to be verified is "accurate"; otherwise, the voting result is "incorrect". All "accurate" and "incorrect" votes are tallied, and the result that appears most frequently is taken as the final result; If the final result is determined to be "accurate", then the actual electrical parameter data is used as the electrical parameter data of the monitoring node to be verified. If the final result is determined to be "incorrect", then the average of the theoretical electrical parameter data derived from all n related monitoring nodes with a voting result of "incorrect" is used as the electrical parameter data of the monitoring node to be verified.

6. The base station security monitoring system based on data correlation verification as described in claim 5, characterized in that, The data preprocessing module is specifically used for: A two-dimensional coordinate system is established with the time axis as the horizontal axis and the electrical parameter data as the vertical axis; The corresponding nodes are selected on the horizontal axis based on the period of the frequency at which the sensor collects the electrical parameter data. The newly acquired electrical parameter data is filled into the vertical coordinate of each node. If the sensor does not collect the electrical parameter data of the current node, the vertical coordinate of the current node is left blank. According to the formula or Fill in the missing electrical parameter data x at the i-th node. i ,in, Indicates the first Electrical parameter data for each node. Indicates the first The electrical parameter data for each node, where m represents the number of samples taken forward and n represents the number of samples taken backward. Indicates the first Electrical parameter data for each node.

7. The base station security monitoring system based on data correlation verification as described in claim 6, characterized in that, The data preprocessing module is specifically used for: Delete duplicate electrical parameter data to create missing values; Fill in the missing values ​​according to the steps described above.

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