LTE equipment protocol data analysis method and device based on rail transit

Abstract

The invention provides an LTE equipment protocol data analysis method and device based on rail transit, and relates to the technical field of data processing, and the method comprises the steps: collecting LTE equipment protocol data through manual execution of a data collection command, setting of an automatic data collection task and monitoring of at least one data collection mode in an LTE network interface; and analyzing the LTE equipment protocol data. According to the LTE equipment protocol data acquisition system and method, the LTE equipment protocol data are comprehensively acquired by adopting an acquisition mode of combining manual acquisition, automatic acquisition and interface acquisition, so that the LTE equipment protocol data are fully analyzed.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and device for analyzing LTE equipment protocol data based on rail transit. Background Art

[0002] With the widespread application of technologies such as the Internet of Things and big data, the Long Term Evolution for Metropolitan Area Networks (LTE) equipment protocol data analysis technology for rail transit will also pay more attention to data mining and utilization, providing strong support for the intelligent and automated development of rail transit systems.

[0003] Due to network data security and privacy protection reasons, the collected LTE device protocol data is not comprehensive enough, resulting in insufficient analysis of the LTE device protocol data. Summary of the invention

[0004] The present invention provides a method and device for analyzing LTE device protocol data based on rail transit, so as to solve the defect that the analysis of LTE device protocol data in the prior art is not sufficient, and realize sufficient analysis of LTE device protocol data.

[0005] The present invention provides a method for analyzing LTE equipment protocol data based on rail transit, comprising: Collect LTE device protocol data by at least one of manually executing data collection commands, setting automatic data collection tasks, and monitoring LTE network interfaces; The LTE device protocol data is analyzed.

[0006] In some embodiments, analyzing the LTE device protocol data includes: Cleaning the LTE device protocol data to obtain valid protocol data; When an abnormality exists in the valid protocol data, the valid protocol data is analyzed based on an abnormality analysis algorithm.

[0007] In some embodiments, the anomaly analysis algorithm is a box plot method; analyzing the valid protocol data based on the anomaly analysis algorithm includes: Display the upper and lower thresholds of the valid protocol data; The valid protocol data exceeding the upper and lower thresholds are regarded as abnormal data.

[0008] In some embodiments, the abnormal analysis algorithm is a standard deviation detection method; analyzing the valid protocol data based on the abnormal analysis algorithm includes: Determining the mean and standard deviation of the valid protocol data; When the distance between the valid protocol data and the mean exceeds a preset number of standard deviations, the valid protocol data is regarded as abnormal data.

[0009] In some embodiments, the method further comprises: Extracting features related to performance degradation and performance anomaly of the LTE device from the valid protocol data; Based on the characteristics, a service life prediction model is constructed; Using historical valid protocol data to train the service life prediction model to obtain a trained service life prediction model; Based on the new valid protocol data, the trained service life prediction model is updated.

[0010] In some embodiments, the LTE device protocol data includes wireless signal data, user behavior data, and network status data.

[0011] The present invention also provides a LTE equipment protocol data analysis device based on rail transit, comprising: A collection module, used to collect LTE device protocol data by at least one of manually executing data collection commands, setting automatic data collection tasks, and monitoring LTE network interfaces; The analysis module is used to analyze the LTE device protocol data.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for analyzing LTE device protocol data based on rail transit as described in any one of the above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for analyzing LTE device protocol data based on rail transit as described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned LTE device protocol data analysis methods based on rail transit.

[0015] The rail transit-based LTE equipment protocol data analysis method and device provided by the present invention realizes comprehensive collection of LTE equipment protocol data by adopting a collection method that combines manual collection, automatic collection and interface collection, thereby achieving full analysis of the LTE equipment protocol data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is one of the flow charts of the LTE equipment protocol data analysis method based on rail transit provided by the present invention; Figure 2 This is the second flow chart of the LTE equipment protocol data analysis method based on rail transit provided by the present invention; Figure 3 It is a structural schematic diagram of a LTE equipment protocol data analysis device based on rail transit provided by the present invention; Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] There are three main purposes for fully analyzing LTE device protocol data: Optimize system performance: Through in-depth analysis of LTE device protocol data, bottlenecks and potential problems in the network can be identified, so as to perform targeted optimization and improve the overall system performance.

[0020] Improve data transmission efficiency: Analysis of LTE device protocol data helps understand issues such as latency and packet loss during data transmission, and then take measures to reduce these issues and improve data transmission efficiency and reliability.

[0021] Ensure communication security: Analyzing LTE device protocol data can identify potential security vulnerabilities and take timely protective measures to ensure the communication security of the rail transit system.

[0022] Figure 1 This is one of the flow charts of the LTE device protocol data analysis method based on rail transit provided by the present invention, such as Figure 1 As shown, the present invention provides a method for analyzing LTE device protocol data based on rail transit, comprising: Step 110, collecting LTE device protocol data by manually executing a data collection command, setting an automatic data collection task, and monitoring at least one data collection method of an LTE network interface; Step 120: Analyze the LTE device protocol data.

[0023] Specifically, to analyze LTE device protocol data, you must first collect LTE device protocol data. There are three main collection methods: Manual collection: Log in to the maintenance terminal of the base station or core network and manually execute the data collection command to obtain the LTE device protocol data. This method is simple to operate but inefficient and is suitable for small-scale or temporary data collection needs.

[0024] Automatic collection: By setting data collection tasks, the system can automatically collect LTE device protocol data at specified times or conditions. This method can greatly improve the efficiency and accuracy of data collection and is suitable for large-scale or long-term data collection needs. For example, in an LTE network, you can set the PM-Initiated UE Measurements function to automatically collect data such as periodic measurement reports.

[0025] Interface collection: Obtain data streams and data packets containing LTE device protocol data by monitoring interfaces in the LTE network (such as S1 interface, X2 interface, etc.). This method can obtain real-time data and event information in the network in real time, but requires a deep understanding of network protocols and interfaces.

[0026] After collecting the LTE device protocol data by at least one of manual collection, automatic collection, and interface collection, the LTE device protocol data is analyzed.

[0027] The rail transit-based LTE device protocol data analysis method provided by the present invention realizes comprehensive collection of LTE device protocol data by adopting a collection method that combines manual collection, automatic collection and interface collection, thereby achieving full analysis of the LTE device protocol data.

[0028] In some embodiments, the LTE device protocol data includes wireless signal data, user behavior data, and network status data.

[0029] Specifically, LTE device protocol data is very rich, mainly including the following aspects: Wireless signal data: including base station transmit power, receive power, signal quality (such as reference signal received power (RSRP), reference signal received quality (RSRQ), etc.), and signal interference information. These data are important for evaluating wireless coverage quality and optimizing wireless parameters.

[0030] User behavior data: including user movement tracks, access duration, service type, service traffic, etc. This data helps analyze user behavior patterns, optimize network resource allocation, and improve user experience.

[0031] Network status data: including base station load, resource utilization, fault alarms, performance indicators, etc. This data is important for monitoring network operation status and timely discovering and solving problems.

[0032] In some embodiments, analyzing the LTE device protocol data includes: Clean the LTE device protocol data to obtain valid protocol data; When there is an anomaly in the valid protocol data, the valid protocol data is analyzed based on an anomaly analysis algorithm.

[0033] Specifically, data cleaning is performed on the LTE device protocol data, such as normalization, missing value processing, duplicate deletion, outlier processing, etc., to obtain valid protocol data.

[0034] In the case where there is an anomaly in the valid protocol data, the abnormal device data will be detected and the valid protocol data will be input into the anomaly analysis algorithm for anomaly analysis.

[0035] Figure 2 This is a second flow chart of the LTE device protocol data analysis method based on rail transit provided by the present invention, such as Figure 2 As shown, the collection software is connected to the backbone network, and the collection software collects LTE device protocol data according to the interface protocol based on the IP configuration table, and cleans the LTE device protocol data to obtain valid protocol data.

[0036] When the valid protocol data is normal, the acquisition software goes into sleep mode and waits for the next cycle of equipment inspection. When the valid protocol data is abnormal, an abnormal analysis algorithm is used to analyze the valid protocol data.

[0037] In some embodiments, the anomaly analysis algorithm is a box plot method; analyzing the valid protocol data based on the anomaly analysis algorithm includes: Display upper and lower thresholds of valid protocol data; Valid protocol data that exceeds the upper and lower limit thresholds is regarded as abnormal data.

[0038] Specifically, the principle of the box plot method is: the box plot can show the distribution of a set of data, including the minimum value, lower quartile (Q1), median (Q2), upper quartile (Q3), and maximum value. In the box plot, values ​​beyond the upper or lower bounds are considered outliers.

[0039] Based on the box plot method, the upper and lower thresholds of the valid protocol data are displayed, and any valid protocol data exceeding the upper and lower thresholds is regarded as abnormal data.

[0040] The box plot method is applicable to performance indicator data in LTE networks, such as throughput, packet loss rate, etc., and can quickly identify abnormal data points.

[0041] The LTE equipment protocol data analysis method based on rail transit provided by the present invention performs abnormal analysis on effective protocol data through a box plot method, thereby realizing rapid identification of abnormal data points.

[0042] In some embodiments, the anomaly analysis algorithm is a standard deviation detection method; analyzing the valid protocol data based on the anomaly analysis algorithm includes: Determine the mean and standard deviation of valid protocol data; When the distance between the valid protocol data and the mean exceeds a preset number of standard deviations, the valid protocol data is regarded as abnormal data.

[0043] Specifically, the principle of the standard deviation detection method is: when the data follows a normal distribution, 99% of the values ​​should be within 3 standard deviations from the mean, and 95% of the values ​​should be within 2 standard deviations from the mean. If the distance between a value and the mean exceeds this range, it can be regarded as an outlier.

[0044] Based on the standard deviation detection method, the mean and standard deviation of the valid protocol data are determined. If the distance between a valid protocol data and the mean exceeds a preset number of standard deviations, the valid protocol data is considered abnormal data.

[0045] The standard deviation detection method is applicable to continuous data in LTE networks, such as signal strength and latency, and can quantify the degree of abnormality.

[0046] The rail transit-based LTE equipment protocol data analysis method provided by the present invention performs abnormal analysis on valid protocol data through a standard deviation detection method, which is conducive to quantifying the degree of abnormality.

[0047] In some embodiments, the rail transit-based LTE device protocol data analysis method provided by the present invention further includes: Extract features related to performance degradation and performance anomalies of LTE devices from valid protocol data; Based on the features, a service life prediction model is constructed; The service life prediction model is trained based on historical valid protocol data to obtain a trained service life prediction model; Based on the new valid protocol data, the trained service life prediction model is updated.

[0048] Specifically, features related to performance degradation and performance anomaly of LTE devices are extracted from valid protocol data. These features may include operating parameters, performance indicators, failure modes, etc. of the LTE devices.

[0049] The extracted features are used to construct a service life prediction model, and the historical valid protocol data is used to train the constructed service life prediction model, optimize the model parameters, improve the prediction accuracy of the model, and obtain a trained service life prediction model. As new valid protocol data is continuously generated, the service life prediction model is updated regularly or irregularly to maintain the timeliness and accuracy of the model.

[0050] The following is a description of the LTE device protocol data analysis device based on rail transit provided by the present invention. The LTE device protocol data analysis device based on rail transit described below and the LTE device protocol data analysis method based on rail transit described above can be referenced to each other.

[0051] Figure 3 Schematic diagram of the structure of the LTE equipment protocol data analysis device based on rail transit provided by the present invention, such as Figure 3 As shown, the present invention provides a LTE equipment protocol data analysis device based on rail transit, comprising: The collection module 310 is used to collect LTE device protocol data by at least one of manually executing a data collection command, setting an automatic data collection task, and monitoring an LTE network interface; The analysis module 320 is used to analyze the LTE device protocol data.

[0052] In some embodiments, the analysis module 320 includes: A cleaning unit, used for cleaning the LTE device protocol data to obtain valid protocol data; The analyzing unit is used to analyze the valid protocol data based on an abnormality analysis algorithm when the valid protocol data has an abnormality.

[0053] In some embodiments, the anomaly analysis algorithm is a box plot method; the analysis unit is specifically used for: Display the upper and lower thresholds of the valid protocol data; The valid protocol data exceeding the upper and lower thresholds are regarded as abnormal data.

[0054] In some embodiments, the abnormal analysis algorithm is a standard deviation detection method; the analysis unit is specifically used to: Determining the mean and standard deviation of the valid protocol data; When the distance between the valid protocol data and the mean exceeds a preset number of standard deviations, the valid protocol data is regarded as abnormal data.

[0055] In some embodiments, the apparatus further comprises: An extraction module, used to extract features related to performance degradation and performance anomaly of the LTE device from the valid protocol data; A construction module, used for constructing a service life prediction model based on the characteristics; A training module, used to train the service life prediction model using historical valid protocol data to obtain a trained service life prediction model; An updating module is used to update the trained service life prediction model based on new valid protocol data.

[0056] In some embodiments, the LTE device protocol data includes wireless signal data, user behavior data, and network status data.

[0057] It should be noted here that the above-mentioned rail transit-based LTE equipment protocol data analysis device provided by the present invention can implement all the method steps implemented by the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects that are the same as the method embodiment in this embodiment will not be described in detail here.

[0058] Figure 4 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the LTE device protocol data analysis method based on rail transit, the method comprising: collecting LTE device protocol data by manually executing data collection commands, setting automatic data collection tasks and monitoring at least one data collection method of the LTE network interface; and analyzing the LTE device protocol data.

[0059] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0060] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the LTE device protocol data analysis method based on rail transit provided by the above methods. The method includes: collecting LTE device protocol data by manually executing data collection commands, setting automatic data collection tasks and monitoring at least one data collection method of the LTE network interface; and analyzing the LTE device protocol data.

[0061] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the LTE device protocol data analysis method based on rail transit provided by the above-mentioned methods. The method includes: collecting LTE device protocol data by manually executing data collection commands, setting automatic data collection tasks and monitoring at least one data collection method of the LTE network interface; and analyzing the LTE device protocol data.

[0062] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0063] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing LTE equipment protocol data based on rail transit, characterized in that: include: Collect LTE device protocol data by at least one of manually executing data collection commands, setting automatic data collection tasks, and monitoring LTE network interfaces; The LTE device protocol data is analyzed.

2. The method for analyzing LTE equipment protocol data based on rail transit according to claim 1, characterized in that: The analyzing the LTE device protocol data includes: Cleaning the LTE device protocol data to obtain valid protocol data; When an abnormality exists in the valid protocol data, the valid protocol data is analyzed based on an abnormality analysis algorithm.

3. The method for analyzing LTE equipment protocol data based on rail transit according to claim 2, characterized in that: The anomaly analysis algorithm is the box plot method; Analyzing the valid protocol data based on an abnormal analysis algorithm includes: Display the upper and lower thresholds of the valid protocol data; The valid protocol data exceeding the upper and lower thresholds are regarded as abnormal data.

4. The method for analyzing LTE equipment protocol data based on rail transit according to claim 2, characterized in that: The anomaly analysis algorithm is a standard deviation detection method; Analyzing the valid protocol data based on an abnormal analysis algorithm includes: Determining the mean and standard deviation of the valid protocol data; When the distance between the valid protocol data and the mean exceeds a preset number of standard deviations, the valid protocol data is regarded as abnormal data.

5. The method for analyzing LTE equipment protocol data based on rail transit according to claim 2, characterized in that: The method further comprises: Extracting features related to performance degradation and performance anomaly of the LTE device from the valid protocol data; Based on the characteristics, a service life prediction model is constructed; Using historical valid protocol data to train the service life prediction model to obtain a trained service life prediction model; Based on the new valid protocol data, the trained service life prediction model is updated.

6. The method for analyzing LTE equipment protocol data based on rail transit according to claim 1, characterized in that: The LTE device protocol data includes wireless signal data, user behavior data and network status data.

7. A LTE equipment protocol data analysis device based on rail transit, characterized in that: include: A collection module, used to collect LTE device protocol data by at least one of manually executing data collection commands, setting automatic data collection tasks, and monitoring LTE network interfaces; The analysis module is used to analyze the LTE device protocol data.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the rail transit-based LTE device protocol data analysis method as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the rail transit-based LTE device protocol data analysis method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the rail transit-based LTE device protocol data analysis method as described in any one of claims 1 to 6 is implemented.

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