Real-time data acquisition and analysis method for 5G power virtual private network test platform
By monitoring the data acquisition rate change curve to select the optimal protocol and performing anomaly verification, the problems of low efficiency, high cost, poor stability and insufficient security of traditional 5G power virtual private network testing platforms are solved. This enables efficient and secure data acquisition and analysis, adapting to the data transmission needs of different business scenarios.
Patent Information
- Application Number
- CN202511048372.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-29
- Publication Date
- 2025-10-31
AI Technical Summary
Traditional 5G power virtual private network testing platforms suffer from low efficiency, high cost, poor stability, and insufficient security in data interaction and system architecture. They are unable to meet the data analysis needs of different business scenarios and lack efficient data collection and anomaly identification mechanisms.
By monitoring the data acquisition rate change curve, selecting the optimal protocol and performing anomaly verification, generating port data anomaly signals, and eliminating abnormal data, the system adopts a B/S architecture and distributed deployment, combined with WebSocket and RESTful API for data interaction, to achieve efficient and secure data acquisition and analysis.
It improves data acquisition efficiency and quality, reduces costs, ensures system stability and security, can adapt to the transmission requirements of different data types, quickly identifies and removes abnormal data, and improves the accuracy of test results.
Smart Images
Figure CN120880930A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data acquisition technology, specifically to a real-time data acquisition and analysis method for a 5G power virtual private network testing platform. Background Technology
[0002] In the field of 5G power virtual private network testing, traditional testing platforms employ a relatively simple data interaction method, failing to fully consider the characteristics of data interaction under different business scenarios. They may not differentiate between data interaction with the front end and interaction with the execution machine, resulting in an inability to balance efficiency and cost control when processing large amounts of real-time data or small amounts of low-frequency interaction data.
[0003] Meanwhile, in terms of system architecture, traditional application servers have relatively dispersed functions and lack integrated design. Each functional module may be relatively independent and rely on many external components, resulting in poor system integrity and stability.
[0004] In terms of data processing and analysis, it may lack customized dashboard functions and multi-dimensional analysis capabilities, making it difficult to meet the diverse analytical needs of different users for test data. Regarding security design, it may not have adopted comprehensive, multi-layered security measures tailored to the high security requirements of the power grid industry, posing certain risks to data security and system stability.
[0005] If a traditional testing platform adopts a single data interaction method, it may experience high data transmission latency and low throughput when processing large amounts of real-time data due to the mismatch between the interaction methods, affecting users' real-time acquisition and analysis of test data; while when processing small amounts of low-frequency interaction data, the complex technical architecture may lead to high development, deployment and maintenance costs. Traditional application servers have fragmented functions and rely on multiple external components to implement functions such as program execution and database connection. This not only increases the complexity of the system, but also reduces the overall integrity and stability of the system. If one of the external components fails, it may affect the normal operation of the entire test platform. Traditional testing platforms lack customized data presentation and analysis functions, making it difficult to meet the needs of different users for horizontal, vertical, or multi-dimensional combined analysis of test data. This limits users' in-depth understanding and effective use of test results, hindering accurate decision-making. Given the extremely high security requirements in the power grid industry, traditional testing platforms may not employ secure communication protocols such as HTTPS and WSS, and may lack multi-layered security measures such as token and user / password dual authentication and regular automatic backup mechanisms. This makes data vulnerable to leakage and tampering, the system susceptible to attacks, and the risk of data loss high. Summary of the Invention
[0006] To address the shortcomings of existing technologies, this invention provides a real-time data acquisition and analysis method for a 5G power virtual private network testing platform, which solves the problem of protocol conflicts that can easily occur during the data acquisition process.
[0007] To achieve the above objectives, the present invention provides the following technical solution: a real-time data acquisition and analysis method for a 5G power virtual private network testing platform, comprising the following steps: Based on the data request instructions associated with the platform requester, the data ports that need to be collected are identified. Pre-collection processing is performed on multiple data ports according to the interaction protocol associated with this platform. Based on the data characteristics during the collection process, the optimal protocol is selected. Specifically: Based on the interaction protocols associated with the current platform, a set of interaction protocols is randomly selected as the execution protocol, and data is collected from multiple data ports for a preset time. The data collection rate of different data ports within the collection time is monitored, and data collection rate change curves for different data ports corresponding to the collection time are generated. The process involves verifying multiple sets of data acquisition rate change curves associated with the current execution protocol: selecting the highest rate point from a single set of data acquisition rate change curves, recording the time associated with different highest rate points as characteristic times, identifying the characteristic time periods of each adjacent characteristic time period based on the order of the characteristic times, determining undetermined curve segments from multiple sets of data acquisition rate change curves based on the determined characteristic time periods, confirming the rate points associated with a single time period from multiple undetermined curve segments, averaging the acquisition rates associated with multiple rate points, locking the average rate, and using the locked average rate as the characteristic rate of this time period. Based on the different characteristic rates associated with different times in the current execution protocol's corresponding characteristic time period, the maximum value is selected as the time period feature of the current characteristic time period. The same processing method is then applied to other characteristic time periods. The time period features of different characteristic time periods are confirmed sequentially, and the maximum value is selected from the time period features associated with several different characteristic time periods. The selected maximum value is used as the protocol feature associated with the current execution protocol. Then, other interaction protocols are treated as execution protocols in turn, and the same processing steps described above are used to lock the protocol characteristics associated with the corresponding execution protocol; Based on the different protocol characteristics associated with different execution protocols, the maximum value is selected from multiple sets of protocol characteristics, and the execution protocol associated with the maximum value is taken as the optimal protocol; Based on the determined optimal protocol, anomaly verification is performed on the port data collected by the current test platform to assess whether the numerical verification process is normal, and based on the assessment results, it is determined whether to generate a port data anomaly signal. The specific method is as follows: Confirm the historical data of the corresponding data port, and based on the running process of the historical data, confirm the data change curve of this historical data, identify the data change value of the data from the generated data change curve, determine the data value associated with the previous data node of the adjacent data node as T1, and mark the data value associated with the next data node as T2, and its data change value = T2-T1, and select the maximum change value Bmax and the minimum change value Bmin from the confirmed set of data change values; Anomaly checks are performed on the data collected at different times from the data port, and the specific data collected at the current time is denoted as J. i Where i represents different times, based on the determined J i Confirm the check interval associated with the next data [J] i +Bmin, J i +Bmax] identifies whether the data collected at the next moment belongs to the current verification interval. If not, the current change period is recorded as an abnormal period, and subsequent abnormal periods are continuously confirmed. If so, the data at the next moment is continuously verified based on the data collected at the current moment. If the duration of the subsequent abnormal period exceeds 1 minute, a port data abnormality signal will be generated and displayed, and the port code of the corresponding data port will be displayed synchronously. Based on the generated port data anomaly signals, the relevant data collected by the corresponding data port during the abnormal period is confirmed, and the relevant data that needs to be tested and processed by the 5G power virtual private network test platform is removed.
[0008] This invention provides a real-time data acquisition and analysis method for a 5G power virtual private network testing platform. Compared with existing technologies, it has the following advantages: This invention monitors and analyzes the data acquisition rates of different interaction protocols within a preset time period, enabling it to accurately identify the optimal protocol from multiple data acquisition protocols. Specifically, based on the characteristic time period analysis and average processing of the acquisition rate change curve, it can identify the protocol with the highest transmission efficiency in different characteristic time periods, thereby maximizing the data acquisition rate. For example, within a 10-second test period, the average rate of a certain protocol reaches 95Mbps in the characteristic time period, which is significantly higher than the 70Mbps of other protocols, effectively improving the efficiency and quality of data acquisition. This mechanism can adapt to the transmission requirements of different data types for different acquisition processes at multiple data ports on the platform. For large amounts of data with high real-time requirements, such as monitoring indicators, the optimal protocol ensures low latency and high throughput transmission. For small amounts of low-frequency interactive data, such as test task configuration, a lightweight protocol is used to reduce processing costs, achieving a balance between efficiency and cost. The system dynamically generates verification intervals based on the maximum and minimum values of historical data, which can accurately identify data anomalies. When the abnormal period lasts for more than 1 minute, it automatically generates and displays an abnormal signal containing port codes, making it easy for maintenance personnel to quickly locate the source of the fault. This hierarchical processing mechanism avoids false alarms from short-term fluctuations and can respond promptly to persistent anomalies. After generating an abnormal signal, it automatically removes relevant data within the abnormal period to avoid interference from erroneous data with the test results. Attached Figure Description
[0009] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a diagram of the business data interaction architecture of the present invention. Detailed Implementation
[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0011] First Embodiment Please see Figure 1 This application provides a real-time data acquisition and analysis method for a 5G power virtual private network test platform, including the following steps: Step 1: Based on the data request instructions associated with the platform requester, identify the data ports that need to be collected. Perform pre-collection processing on multiple data ports according to the interaction protocol associated with this platform. Based on the data characteristics in the collection process, select the optimal protocol. Specifically, a platform has multiple data collection protocols, and each data collection protocol has a different data collection process. This allows you to identify the optimal data collection rate and achieve the best data collection effect. The specific method for selecting the optimal protocol is as follows: Based on the interaction protocols associated with the current platform, a set of interaction protocols is randomly selected as the execution protocol, and data is collected from multiple data ports. The collection time is a preset time, generally 10 seconds. The data collection rate of different data ports within the collection time is monitored, and data collection rate change curves of different data ports corresponding to the collection time are generated. The process involves verifying multiple sets of data acquisition rate change curves associated with the current execution protocol: selecting the highest rate point from a single set of data acquisition rate change curves, recording the time associated with different highest rate points as characteristic times, identifying the characteristic time periods of each adjacent characteristic time period based on the order of the characteristic times, determining the undetermined curve segments from multiple sets of data acquisition rate change curves (the initial and final times of the undetermined curve segments correspond to the two endpoint times of the characteristic time period), confirming the rate points associated with a single time period from multiple undetermined curve segments, averaging the acquisition rates associated with multiple rate points, locking the average rate, and using the locked average rate as the characteristic rate of this time period. The maximum value is selected based on the different characteristic rates associated with different times in the current execution protocol's corresponding characteristic time period, and used as the time period feature of the current characteristic time period. The same processing method is then applied to other characteristic time periods, and the time period features of different characteristic time periods are confirmed sequentially. Finally, the maximum value is selected from the time period features associated with several different characteristic time periods, and the selected maximum value is used as the protocol feature associated with the current execution protocol. Then, other interaction protocols are treated as execution protocols in turn, and the same processing steps described above are used to lock the protocol characteristics associated with the corresponding execution protocol; Based on the different protocol characteristics associated with different execution protocols, the maximum value is selected from multiple sets of protocol characteristics, and the execution protocol associated with the maximum value is taken as the optimal protocol; Specifically, when different interaction protocols are executed, different data ports have different data acquisition processes, resulting in different data acquisition rates. From the different acquisition rate curves, the corresponding lowest rate point can be identified, and the lowest rate point corresponds to different specific times. The time interval between different times is the corresponding characteristic time period. Within each different characteristic time period, the specific time of the corresponding average speed maximum can be selected, thereby confirming the specific characteristics of the corresponding time period. From the different time period characteristics associated with different characteristic time periods, the specific characteristics of the protocol can be quickly and effectively confirmed, thus completing the specific selection process of the corresponding optimal protocol.
[0012] Step 2: Based on the determined optimal protocol, perform anomaly verification on the port data collected by the current test platform, assess whether the numerical verification process is normal, and determine whether to generate a port data anomaly signal based on the assessment result. Specifically, monitor the data verification process based on the specific data being monitored, and based on the corresponding monitoring process and historical progress data, anomaly assessment can be performed on the monitored data to identify whether the port data monitoring is normal. The specific methods for evaluation are as follows: Confirm the historical data of the corresponding data port, and based on the running process of the historical data, confirm the data change curve of this historical data, identify the data change value of the data from the generated data change curve, determine the data value associated with the previous data node of the adjacent data node as T1, and mark the data value associated with the next data node as T2, and its data change value = T2-T1, and select the maximum change value Bmax and the minimum change value Bmin from the confirmed set of data change values; Anomaly checks are performed on the data collected at different times from the data port, and the specific data collected at the current time is denoted as J. i Where i represents different times, based on the determined J i Confirm the check interval associated with the next data [J] i +Bmin, J i +Bmax] identifies whether the data collected at the next moment belongs to the current verification interval. If so, the data at the next moment is continuously verified based on the data collected at the current moment. If not, the current change period is recorded as an abnormal period, and subsequent abnormal periods are continuously confirmed. If the duration of the subsequent abnormal period exceeds 1 minute, a port data abnormality signal will be generated and displayed, and the port code of the corresponding data port will be displayed synchronously for external personnel to view. Step 3: Based on the generated port data anomaly signal, confirm the relevant data collected by the corresponding data port during the abnormal period, and remove the relevant data that needs to be tested by the 5G power virtual private network test platform to avoid affecting the test accuracy of the corresponding test platform.
[0013] Second Embodiment Compared to traditional networks, 5G power virtual private networks (VPNs) are specifically designed for the power industry and feature high security, low latency, and high reliability. Therefore, in addition to efficiency and stability, security was also a crucial design consideration for our test platform's data acquisition.
[0014] The testing platform adopts a B / S architecture, and the test case execution scheduling part is deployed and run in a distributed manner. Data interaction between the front-end and back-end is conducted via WebSocket; the testing platform center interacts with each specific execution machine (Agent) via RESTful API.
[0015] Users' subjective experience with the test comes from the WEBUI; WebSocket has the characteristics of low latency and high throughput, and using this data interaction method can ensure the immediate presentation of a large amount of real-time data; while each test task part, a set of test cases runs on an hourly basis, except for a single real-time running case, the real-time interaction with the test platform is characterized by small data volume and infrequent interaction. Adopting the lightweight architecture of Restful API can significantly reduce development, deployment and maintenance costs.
[0016] The testing and business logic layer in this system replaces some functions of traditional application servers. The framework used by the system itself covers functions such as program execution and database connection pooling. This system also involves the recording, storage, and querying of data such as management logs and operation logs, using mature message queues such as RabbitMQ and Apache Kafka in the industry.
[0017] Combination Figure 2 After the large amount of business data accumulated on the platform, it can be classified, presented and analyzed through the dashboard function provided by the platform and customized statistics. The system has been developed in many aspects such as chart style, statistical granularity and data layout customization to meet the horizontal, vertical or other multi-dimensional analysis requirements of different users for test data.
[0018] The interaction process of the two types of business data in this system has been introduced above, and two methods, WebSocket and RESTful API, are used respectively. The data interaction process between the test platform and the execution machine follows a client-server model. Depending on the data initiator, both the test platform and the execution machine play the roles of both server and client.
[0019] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.
[0020] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. A real-time data acquisition and analysis method for a 5G power virtual private network testing platform, characterized in that, Includes the following steps: Based on the data request instructions associated with the platform requester, the data ports that need to be collected are identified, and multiple data ports are pre-collected according to the interaction protocol associated with this platform. Based on the data characteristics in the collection process, the optimal protocol is selected. Based on the determined optimal protocol, anomaly verification is performed on the port data collected by the current test platform to assess whether the numerical verification process is normal, and based on the assessment results, it is determined whether to generate a port data anomaly signal.
2. The real-time data acquisition and analysis method for a 5G power virtual private network testing platform according to claim 1, characterized in that, The specific method for pre-collecting and processing multiple data ports according to the interaction protocol is as follows: Based on the interaction protocols associated with the current platform, a set of interaction protocols is randomly selected as the execution protocol, and data is collected from multiple data ports for a preset time. The data collection rate of different data ports within the collection time is monitored, and data collection rate change curves for different data ports corresponding to the collection time are generated.
3. The real-time data acquisition and analysis method for a 5G power virtual private network testing platform according to claim 2, characterized in that, The optimal protocol is selected as follows: The process involves verifying multiple sets of data acquisition rate change curves associated with the current execution protocol: selecting the highest rate point from a single set of data acquisition rate change curves, recording the time associated with different highest rate points as characteristic times, identifying the characteristic time periods of each adjacent characteristic time period based on the order of the characteristic times, determining undetermined curve segments from multiple sets of data acquisition rate change curves based on the determined characteristic time periods, confirming the rate points associated with a single time period from multiple undetermined curve segments, averaging the acquisition rates associated with multiple rate points, locking the average rate, and using the locked average rate as the characteristic rate of this time period. Based on the different characteristic rates associated with different times in the current execution protocol's corresponding characteristic time period, the maximum value is selected as the time period feature of the current characteristic time period. The same processing method is then applied to other characteristic time periods. The time period features of different characteristic time periods are confirmed sequentially, and the maximum value is selected from the time period features associated with several different characteristic time periods. The selected maximum value is used as the protocol feature associated with the current execution protocol. Then, other interaction protocols are treated as execution protocols in turn, and the same processing steps are used to lock the protocol features associated with the corresponding execution protocol.
4. The real-time data acquisition and analysis method for a 5G power virtual private network testing platform according to claim 3, characterized in that, Based on the different protocol characteristics associated with different execution protocols, the maximum value is selected from multiple sets of protocol characteristics, and the execution protocol associated with the maximum value is taken as the optimal protocol.
5. The real-time data acquisition and analysis method for a 5G power virtual private network testing platform according to claim 1, characterized in that, The specific method for assessing whether the numerical verification process is normal is as follows: Confirm the historical data of the corresponding data port, and based on the running process of the historical data, confirm the data change curve of this historical data, identify the data change value of the data from the generated data change curve, determine the data value associated with the previous data node of the adjacent data node as T1, and mark the data value associated with the next data node as T2, and its data change value = T2-T1, and select the maximum change value Bmax and the minimum change value Bmin from the confirmed set of data change values; Anomaly checks are performed on the data collected at different times from the data port, and the specific data collected at the current time is denoted as J. i Where i represents different times, based on the determined J i Confirm the check interval associated with the next data [J] i +Bmin, J i +Bmax] identifies whether the data collected at the next moment belongs to the current verification interval. If not, the current change period is recorded as an abnormal period, and subsequent abnormal periods are continuously confirmed. If the duration of the subsequent abnormal period exceeds 1 minute, a port data abnormality signal will be generated and displayed, and the port code of the corresponding data port will be displayed synchronously.
6. The real-time data acquisition and analysis method for a 5G power virtual private network testing platform according to claim 5, characterized in that, If the data collected at the next moment falls within the current verification interval, then the data at the next moment will continue to be verified based on the data collected at the current moment.
7. The real-time data acquisition and analysis method for a 5G power virtual private network testing platform according to claim 6, characterized in that, It also includes the following steps: Based on the generated port data anomaly signals, the relevant data collected by the corresponding data port during the abnormal period is confirmed, and the relevant data that needs to be tested and processed by the 5G power virtual private network test platform is removed.