A multi-channel signal power real-time monitoring system based on a match shooting test platform

Abstract

This invention discloses a real-time monitoring system for multi-channel signal power based on a Match-based collaborative testing platform. The system includes an integrated monitoring system for the Match-based collaborative testing platform, comprising a core coordination unit, a multi-channel signal adaptation and acquisition unit, a power data processing and analysis unit, an intelligent early warning push unit, a distributed storage unit, and a platform interaction unit. This invention relates to the field of signal monitoring technology. This real-time monitoring system for multi-channel signal power based on the Match-based collaborative testing platform achieves accurate monitoring of multiple signals from 200K to 100M through wide-band adaptive acquisition, Match protocol synchronous calibration, sliding window trend analysis, and multi-level early warning push. The system supports online and offline dual-mode operation and features multi-channel isolated acquisition, environmental compensation calibration, encrypted storage traceability, and test platform linkage control functions, solving the problems of large interference, poor synchronization, and weak adaptability in traditional monitoring systems.

Description

Technical Field

[0001] This invention relates to the field of signal monitoring technology, specifically to a real-time monitoring system for the power of multiple signals based on a Match test platform. Background Technology

[0002] With the rapid development of electronic testing technology, multi-channel signal power monitoring is playing an increasingly important role in communication equipment testing, radio frequency system verification, and other fields. Traditional signal power monitoring systems mainly adopt a single-channel independent detection mode, measuring each signal individually using a separate power meter or spectrum analyzer. However, this traditional approach has many technical limitations when facing applications that require simultaneous monitoring of multiple signals.

[0003] In the prior art, Chinese patent application CN120847471A discloses a multi-channel signal total power testing system based on a Match test platform. This system acquires multiple signals through a data acquisition module, processes the signals using FFT algorithm and wavelet transform, and calculates the total power using a multi-channel signal power superposition model [CN120847471A]. Chinese patent application CN111510360A discloses a remote multi-channel signal integrated acquisition system for electrical facilities. This system acquires various parameters of electrical facilities through sensors, uses polling for data communication, and realizes remote target data monitoring and alarm handling [CN111510360A]. Chinese patent application CN120934664A discloses a multi-channel FM signal real-time monitoring system. This system simultaneously receives multiple FM signals through a multi-channel RF receiving unit, detects quality parameters such as field strength and signal-to-noise ratio, and stores and analyzes the data through a cloud platform [CN120934664A]. Chinese patent application CN106053990A describes a multi-channel adapter for monitoring the performance indicators of built-in circuit modules in a radio station, providing a physical interface and signal processing functions for monitoring multiple circuit modules [CN106053990A].

[0004] Despite some progress in multi-channel signal monitoring, existing technologies still face several challenges: First, traditional multi-channel power monitoring employs a single-channel independent detection mode, resulting in generally low isolation between monitoring channels, typically only reaching 60-70dB. This leads to severe crosstalk between channels, affecting monitoring accuracy. Second, existing technologies rely on external testing instruments for power measurement, hindering deep integration with integrated testing platforms. This results in monitoring delays of hundreds of milliseconds, poor data synchronization, and an inability to meet real-time monitoring requirements. Third, existing technologies lack dynamic adaptability to multiple signals. When faced with signals from different frequency bands (e.g., the 200K~100M wideband) and power levels, they cannot automatically adjust matching circuits and acquisition parameters, limiting compatibility. Furthermore, existing technologies have weak data storage and traceability capabilities, typically only storing short-term data and lacking comprehensive historical data recording and analysis functions, failing to provide sufficient data support for fault diagnosis. Finally, existing technologies generally lack intelligent early warning mechanisms, unable to perform predictive analysis based on power change trends, making it difficult to identify and mitigate risks of equipment damage or data loss due to power anomalies. Summary of the Invention

[0005] To address the technical challenges of traditional multi-channel signal power monitoring using a single-channel independent detection mode, such as low isolation of monitoring channels, severe signal interference, and high monitoring latency and poor data synchronization due to reliance on external testing instruments and the inability to deeply integrate with the Match testing platform, this paper proposes a real-time multi-channel signal power monitoring system based on the Match testing platform. This system aims to significantly improve the synchronization and accuracy of multi-channel signal monitoring, achieve measurement accuracy better than ±0.1dBm, and shorten fault response time.

[0006] The technical problem this invention aims to solve is that existing technologies for multi-channel signal power monitoring employ a single-channel independent detection mode, resulting in low isolation between monitoring channels and severe signal interference. Existing technologies rely on external testing instruments and cannot be deeply integrated with integrated testing platforms, leading to high monitoring latency and poor data synchronization. They lack dynamic adaptability to multiple signals, making it difficult to meet the monitoring needs of signals with different frequency bands and power levels. Their data storage and traceability capabilities are weak, failing to provide complete data support for fault diagnosis. Furthermore, they lack intelligent early warning mechanisms, failing to proactively mitigate the risk of equipment damage or data loss due to abnormal power levels.

[0007] The technical solution adopted by this invention to solve its technical problem is: to provide a real-time monitoring system for multi-channel signal power based on a Match test platform, including a Match test platform integrated monitoring system. The Match test platform integrated monitoring system includes a core coordination unit, a multi-channel signal adaptation and acquisition unit, a power data processing and analysis unit, an intelligent early warning push unit, a distributed storage unit, and a platform interaction unit. The multi-channel signal adaptation and acquisition unit is connected to the core coordination unit. The core coordination unit is connected to the power data processing and analysis unit and the platform interaction unit respectively. The power data processing and analysis unit is connected to the intelligent early warning push unit and the distributed storage unit in sequence.

[0008] Preferably, the multi-channel signal adaptation and acquisition unit is used to achieve compatible acquisition of multiple types of signals in a wide frequency band of 200K~100M, and is equipped with 8 independent acquisition channels with an isolation of not less than 80dB, effectively reducing signal interference.

[0009] Furthermore, the multi-channel signal adaptation and acquisition unit includes a frequency band adaptive module, a multi-channel isolation acquisition module, a signal preprocessing module, an acquisition parameter configuration module, and a signal type identification module. The signal type identification module is used to automatically identify sine wave, square wave, and pulse wave signal types, and optimize the acquisition parameter configuration based on the signal type to improve the monitoring accuracy of different types of signals.

[0010] Preferably, the power data processing and analysis unit includes a Match protocol synchronization module for achieving nanosecond-level synchronization with the test platform. The power data processing and analysis unit has calibration algorithms and trend analysis functions, with a measurement accuracy better than ±0.1dBm, and can predict power anomaly risks in advance.

[0011] Furthermore, the power data processing and analysis unit also includes a data calibration module, an anomaly detection module, and a power trend analysis module. The anomaly detection module supports custom threshold settings and batch import and export of thresholds. Threshold types include upper limit threshold, lower limit threshold, and fluctuation amplitude threshold. The fluctuation amplitude threshold can be set to the maximum allowable value of power change per unit time.

[0012] Preferably, the intelligent early warning push unit is a multi-level intelligent early warning push unit that quickly pushes abnormal information through multiple channels, including an early warning level classification module, a multi-channel push module, and an early warning feedback module. The multi-channel push module includes four push methods: SMS, APP push, platform pop-up, and voice broadcast.

[0013] Furthermore, the distributed storage unit adopts a distributed encrypted storage architecture to ensure data security, including an original data storage module, an analysis result storage module, a configuration information storage module, and a data encryption module. The data encryption module uses the AES-256 encryption algorithm to encrypt the stored data.

[0014] Preferably, the platform interaction unit supports diverse human-computer interaction and test platform linkage control, and has fault self-diagnosis and parameter customization functions, including a visual interface module, an operation instruction receiving module, and a platform linkage control module.

[0015] Furthermore, the core coordination unit includes a unit scheduling module, a status monitoring module, and a fault self-diagnosis module. The fault self-diagnosis module is used to automatically identify the unit fault type and record fault information, supporting fault location accuracy down to the module level.

[0016] Preferably, the system adopts a dual-mode operating architecture, supporting both offline and online operation. In online mode, it communicates with the host computer via Ethernet to achieve remote data transmission and remote control. In offline mode, it exports data through local storage media. The system has a built-in backup power supply to maintain core data storage and basic monitoring functions after a power outage.

[0017] Beneficial effects

[0018] This invention provides a real-time monitoring system for the power of multiple signals based on a Match testing platform. Compared with existing technologies, it has the following advantages:

[0019] The beneficial effects of this invention are as follows: Compared with the prior art, this invention significantly improves the synchronization and accuracy of multi-channel signal monitoring through a multi-channel signal adaptation and acquisition unit and a Match protocol synchronization module; through the calibration algorithm and trend analysis function of the power data processing and analysis unit, the measurement accuracy is better than ±0.1dBm, enabling early prediction of power anomaly risks; through a multi-level intelligent early warning push unit, the fault response time is shortened, avoiding equipment damage and data loss; through distributed encrypted storage and a dual-mode operation architecture, data security and system stability are ensured; and through the diverse human-computer interaction and test platform linkage control of the platform interaction unit, it adapts to different application scenarios, exhibiting strong versatility and practicality, and can be widely applied in multiple fields such as communication testing and industrial electronics. Attached Figure Description

[0020] Figure 1 This is a system principle block diagram of the present invention;

[0021] Figure 2 This is a schematic diagram of the core coordination unit of the present invention;

[0022] Figure 3This is a block diagram illustrating the principle of the multi-channel signal adaptation and acquisition unit of the present invention.

[0023] Figure 4 This is a block diagram illustrating the principle of the power data processing and analysis unit of the present invention.

[0024] Figure 5 This is a schematic diagram of the intelligent early warning push unit of the present invention;

[0025] Figure 6 This is a schematic diagram of the distributed storage unit of the present invention.

[0026] Figure 7 This is a schematic diagram of the interactive unit of the platform of the present invention.

[0027] The diagram shows: 1. Match test platform integrated monitoring system; 2. Core coordination unit; 3. Multi-channel signal adaptation and acquisition unit; 4. Power data processing and analysis unit; 5. Intelligent early warning push unit; 6. Distributed storage unit; 7. Platform interaction unit; 8. Frequency band adaptive module; 9. Multi-channel isolation acquisition module; 10. Signal preprocessing module; 11. Acquisition parameter configuration module; 12. Match protocol synchronization module; 13. Data calibration module; 14. Anomaly detection module; 15. Power trend analysis module; 16. Early warning level classification module; 17. Multi-channel push module; 18. Early warning feedback module; 19. Raw data storage module; 20. Analysis result storage module; 21. Configuration information storage module; 22. Data encryption module; 23. Visual interface module; 24. Operation instruction receiving module; 25. Platform linkage control module; 26. Unit scheduling module; 27. Status monitoring module; 28. Fault self-diagnosis module; 29. ​​Signal type identification module. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0029] Example 1

[0030] One technical solution is as follows:

[0031] S1, Multi-channel signal acquisition

[0032] Multiple signal sources (such as communication base station signals and industrial control signals) are connected to the eight independent channels of the multi-channel isolation acquisition module 9 of the multi-channel signal adapter acquisition unit 3 through shielded cables to ensure that the signal transmission is not affected by external interference.

[0033] The signal type identification module 29 of the multi-channel signal adapter acquisition unit 3 automatically identifies the type of input signal, including sine wave, square wave, and pulse wave;

[0034] The frequency band adaptive module 8 of the multi-channel signal adaptation acquisition unit 3 scans the signal frequency band and automatically switches the matching circuit according to the frequency band range.

[0035] The acquisition parameter configuration module 30 of the multi-channel signal adaptation acquisition unit 3 automatically optimizes the acquisition parameters based on the recognition results of the signal type recognition module 29 and the frequency band adaptive module 8. For example, for pulse wave signals, the sampling rate is adjusted to 1.2GSps and the gain is adjusted to 12dB. At the same time, it supports the staff to manually fine-tune the parameters through the touch panel, and the adjustment results are synchronized to the core coordination unit 2 in real time.

[0036] The signal preprocessing module 29 of the multi-channel signal adapter acquisition unit 3 performs three-stage processing on the original signal: low-pass filtering, programmable gain amplification, and analog-to-digital conversion. It filters out high-frequency interference signals and amplifies weak signals to the range of the analog-to-digital conversion chip, and finally converts them into 14-bit digital signals.

[0037] The multi-channel isolation acquisition module 9 of the multi-channel signal adaptation acquisition unit 3 synchronously acquires the raw power data of 8 signals at a frequency of once every 10ms. After the acquisition is completed, it is transmitted to the core coordination unit 2 in real time, and the data transmission delay is controlled within 5ms.

[0038] S2, Data Processing and Anomaly Detection

[0039] The core coordination unit 2 forwards the received collected data to the power data processing and analysis unit 4. The Match protocol synchronization module 12 of the power data processing and analysis unit 4 extracts the data timestamp and aligns it precisely with the test data timestamp of the Match test platform to ensure the time consistency of the two types of data.

[0040] The data calibration module 13 of the power data processing and analysis unit 4 acquires real-time temperature and humidity data through the built-in environmental sensor, and uses the environmental compensation algorithm to correct the deviation of the collected data. The calibration process takes no more than 2ms, and the error of the corrected data is controlled within ±0.08dBm.

[0041] The power trend analysis module 34 of the power data processing and analysis unit 4 uses a sliding window algorithm to perform trend analysis on the calibrated data. The window size can be flexibly set within the range of 10-100 acquisition cycles. Through analysis, a power change trend curve is generated, such as linear rise, stable fluctuation, and sudden drop. When the slope of the trend curve exceeds 0.5dBm / cycle, it is marked as a potential early warning risk.

[0042] The anomaly detection module 33 of the power data processing and analysis unit 4 compares the calibrated data with the preset three-level warning threshold in real time. If the power fluctuation is within ±0.5dBm, it is judged to be normal, and the data is directly stored in the distributed storage unit 6.

[0043] If the fluctuation is between ±0.5dBm and ±1dBm, it is considered a slight abnormality; if it is between ±1dBm and ±2dBm, it is considered a moderate abnormality; if it exceeds ±2dBm or the fluctuation amplitude per unit time exceeds 1dBm per second, it is considered a severe abnormality.

[0044] The power data processing and analysis unit 4 combines the anomaly type, occurrence time, involved channels, and power value information determined by the anomaly detection module 33 with the prediction results of the power trend analysis module 34, packages them, and transmits them to the core coordination unit 2. At the same time, it triggers the intelligent early warning push unit 5 to start the early warning process.

[0045] S3, Intelligent Early Warning Push and Feedback

[0046] The core coordination unit 2 transmits the abnormal information to the intelligent early warning push unit 5. After receiving the abnormal information, the early warning level classification module 35 of the intelligent early warning push unit 5 matches the corresponding response mechanism according to the abnormal level. Minor abnormalities only trigger platform pop-up reminders. If no confirmation is received within 10 minutes, it will automatically be upgraded to a medium abnormality.

[0047] A moderate anomaly will trigger a platform pop-up and SMS notification; if no confirmation is received within 5 minutes, it will be upgraded to a severe anomaly.

[0048] Serious anomalies simultaneously trigger platform pop-ups, SMS push notifications, APP push notifications, and voice broadcasts, continuously reminding users until staff confirm.

[0049] The multi-channel push module 36 of the intelligent early warning push unit 5 automatically edits standardized early warning information and pushes it through the corresponding channels. After receiving the early warning information, staff can provide feedback in two ways: local feedback is provided by clicking "Confirm Processing" on the visualization interface module 42 of the platform interaction unit 7 and entering the information of the person in charge; remote feedback is provided by clicking "Confirm" through the dedicated operation and maintenance APP, and the feedback information is synchronized to the core coordination unit 2.

[0050] The early warning feedback module 37 of the intelligent early warning push unit 5 records the feedback time and the information of the person handling the issue, and stops the corresponding early warning push. The feedback record is synchronously stored in the distributed storage unit 6.

[0051] S4, Distributed Storage and Data Management

[0052] The raw data storage module 38 of the distributed storage unit 6 receives the collected raw data forwarded by the core coordination unit 2 and adopts a cyclic overwrite storage mechanism. After the data is stored for 180 days, it automatically overwrites the oldest stored data. The data is classified and stored according to the naming rule of "date-channel number-time stamp" and supports fast retrieval by time period and channel number.

[0053] The distributed storage unit 6's analysis result storage module 39 stores calibrated data, anomaly detection reports, and trend analysis charts. Chart formats support PNG and Excel. All data is linked to the original data through timestamps for subsequent traceability and querying.

[0054] The configuration information storage module 40 of the distributed storage unit 6 uses read-only protection mode to store system parameters, calibration curves, threshold settings, and user permission information, and only the administrator has the right to modify them;

[0055] The data encryption module 41 of the distributed storage unit 6 uses the AES-256 encryption algorithm to encrypt all stored data. The key is updated regularly by the administrator, with a default update cycle of once a month.

[0056] The system automatically performs data backup operations daily, with backup files stored in a separate partition. It also supports manual backups triggered by administrators, and backup files can be exported via USB flash drive and transferred to a host computer via Ethernet.

[0057] Users can use the visualization interface module 42 of the platform interaction unit 7 to search for data by time range, channel number, signal type, and anomaly level. The search results are displayed in the form of data list, trend curve, and anomaly point marker. It supports exporting to Excel or PNG format files. By entering the time of the fault occurrence, users can query the collected data, analysis results, early warning records, and processing procedures for the corresponding time period.

[0058] S5, Core Coordination and Linkage Control

[0059] After the system starts, the unit scheduling module 45 of the core coordination unit 2 automatically loads the preset scheduling process and performs a cyclic monitoring of the operating status of each unit every 1ms. When the multi-channel signal adaptation and acquisition unit 3 completes data acquisition, it immediately issues a "data transmission instruction" to push the data to the power data processing and analysis unit 4.

[0060] Upon receiving the abnormal result from the power data processing and analysis unit 4, the intelligent early warning push unit 5 is triggered to start the push process, and at the same time, the distributed storage unit 6 is instructed to store the relevant data.

[0061] When a data transmission interruption is detected in each unit (such as a communication link being disconnected), a retry mechanism is automatically initiated, with each retry interval being 200ms. If a retry fails, an audible and visual alarm is triggered, and the fault information is recorded.

[0062] The status monitoring module 46 of the core coordination unit 2 monitors the operating status of each unit in real time. The hardware status includes voltage, temperature, and communication link status, while the software status includes data processing progress and remaining storage space.

[0063] The fault self-diagnosis module 47 of the core coordination unit 2 automatically identifies the fault type. Hardware faults can be located at the module level, such as "the multi-channel isolation acquisition module 9 channel 3 of the multi-channel signal adapter acquisition unit 3 is damaged". Software faults, such as abnormal algorithm operation and data storage failure, automatically generate fault logs containing fault codes, occurrence time and troubleshooting suggestions.

[0064] Minor faults are alerted via a pop-up window in the visualization interface module 42 of the platform interaction unit 7, while serious faults trigger voice broadcasts and SMS alerts, and the current data is automatically saved to prevent loss.

[0065] The platform linkage control module 44 of the platform interaction unit 7 establishes a stable connection with the Match test platform via Ethernet, and receives the operating status of the test platform in real time (such as test start / stop and test parameters). When a slight abnormality in the power of multiple signals is detected, it sends a "parameter fine-tuning suggestion" to the Match test platform, such as "suggest adjusting the test signal gain by 0.5dB". When a moderate or severe abnormality occurs, it sends a "pause test command". After the fault is handled, it sends a "resume test command". At the same time, it receives parameter adjustment commands (such as test frequency band switching) from the Match test platform and automatically synchronizes to the multi-channel signal adapter acquisition unit 3 to adjust the acquisition parameters to achieve coordinated linkage between monitoring and testing.

[0066] S6, Dual-mode operation switching

[0067] In online mode, the system connects to the host computer network via Ethernet to achieve remote data transmission. The host computer can view monitoring data, trend curves, and early warning information in real time through the platform interaction unit 7.

[0068] The administrator sends instructions through the host computer, such as adjusting the acquisition parameters, exporting data, and restarting the system. The instructions are received by the operation instruction receiving module 43 of the platform interaction unit 7 and then synchronized to the core coordination unit 2.

[0069] In offline mode, the system automatically switches to offline mode after disconnecting the Ethernet connection, and the core functions (data acquisition, processing, early warning, and local storage) operate normally.

[0070] By inserting a USB flash drive into the USB interface of the distributed storage unit 6, and selecting "offline export" on the visualization interface module 42 of the platform interaction unit 7, the raw data and analysis results for a specified time period can be exported.

[0071] The system has a built-in backup power supply, which automatically switches power supply after a power outage, maintaining the core data storage function of the distributed storage unit 6 for 6 hours and the basic monitoring function for 4 hours. After power is restored, it will automatically resume normal operation, and the data that has not been stored will be automatically added to the distributed storage unit 6.

[0072] It should be noted that the core coordination unit 2 uses an ARM Cortex-A9 quad-core industrial-grade embedded controller with a main frequency of 2.0GHz, 8GB of memory and 64GB of storage capacity, and has multiple serial ports and Ethernet expansion capabilities, which can ensure the data transmission rate and continuous stable operation requirements between units.

[0073] The multi-channel signal adapter acquisition unit 3 is equipped with a 200K~100M wideband adaptive circuit, an 8-channel isolation acquisition card, and an inter-channel isolation of 85dB. The signal preprocessing stage uses an AD9625 high-speed analog-to-digital converter chip with a sampling rate of 1.2GSps and a resolution of 14 bits. It is also equipped with a touch-sensitive acquisition parameter configuration panel.

[0074] The power data processing and analysis unit 4 adopts a heterogeneous computing architecture of Xilinx Kintex-7 FPGA and TITMS320C6678DSP, and has a built-in -60dBm to +20dBm standard power calibration module, which can realize nanosecond-level data synchronization and high-speed computing processing.

[0075] The intelligent early warning push unit 5 is equipped with a GSM / 4G dual-mode communication module, a voice broadcast module with an output power of not less than 85dB, and a platform pop-up trigger interface to ensure the real-time push of early warning information from multiple channels.

[0076] Distributed storage unit 6 uses a 2TB industrial-grade solid-state drive with a read / write speed of no less than 500MB / s. It integrates an AES-256 hardware encryption chip and a backup power module, which can maintain data storage function after power failure.

[0077] The platform interaction unit 7 is equipped with a 15-inch industrial touch screen with a resolution of 1920×1080 and a response time of no more than 30ms. It supports external mouse and keyboard expansion and can be connected to the Match test platform via an Ethernet interface, and is compatible with the TCP / IP communication protocol.

[0078] Secondly, deploy the Linux embedded operating system in the core coordination unit 2, and install the corresponding drivers, data processing algorithm libraries (including sliding window analysis algorithm and environmental compensation algorithm) and human-computer interaction software for each unit to complete the software version adaptation and compatibility test.

[0079] The core operating parameters are entered through the visualization interface module 23 of the platform interaction unit 7. The default sampling parameters are set to a sampling rate of 1GSps, a gain of 10dB, and a filter bandwidth of 500MHz. Custom adjustments can be made according to actual needs.

[0080] The warning threshold is divided into three levels according to the degree of abnormality: the threshold for minor abnormality is set at ±0.5dBm of power fluctuation, the threshold for moderate abnormality is ±1dBm, and the threshold for severe abnormality is ±2dBm. The threshold for fluctuation amplitude is set at 1dBm / second.

[0081] The push notification configuration binds to the mobile phone numbers of 3 groups of staff, sets the priority of APP push notifications to be higher than SMS, and enables the linkage function between platform pop-ups and voice broadcasts.

[0082] The Match protocol synchronization module 12 of the power data processing and analysis unit 4 is connected to the clock synchronization signal of the Match test platform to complete the data timestamp alignment between the monitoring system and the test platform, and the synchronization error is controlled within 0.8ns.

[0083] The standard power calibration curve is imported through the data calibration module 13 of the power data processing and analysis unit 4, and the environmental compensation parameters for the temperature range of -20℃ to 60℃ and the humidity range of 10% to 90%RH are initialized. After calibration, the system measurement accuracy reaches ±0.08dBm.

[0084] The system configures three levels of user permissions: administrators have full access to all functions, maintenance personnel can only view data and fault records, and operators can only perform signal acquisition and early warning confirmation operations. At the same time, corresponding account passwords are assigned to users with different permissions, and permission information is synchronously stored in the configuration information storage module 21 of the distributed storage unit 6.

[0085] Example 2

[0086] A real-time monitoring system for multi-channel signal power based on a Match test platform includes an integrated monitoring system for the Match test platform. This integrated monitoring system includes a core coordination unit, a multi-channel signal adaptation and acquisition unit, a power data processing and analysis unit, an intelligent early warning push unit, a distributed storage unit, and a platform interaction unit. The multi-channel signal adaptation and acquisition unit is connected to the core coordination unit. The core coordination unit is connected to both the power data processing and analysis unit and the platform interaction unit. The power data processing and analysis unit is sequentially connected to both the intelligent early warning push unit and the distributed storage unit.

[0087] The multi-channel signal adaptation and acquisition unit includes a frequency band adaptive module, a multi-channel isolated acquisition module, a signal preprocessing module, and an acquisition parameter configuration module. The frequency band adaptive module, multi-channel isolated acquisition module, and signal preprocessing module are connected in series. The acquisition parameter configuration module is connected to the frequency band adaptive module. The frequency band adaptive module supports a wide frequency band adaptation of 200K~100M, automatically identifying the input signal frequency band and switching the matching circuit. Through a built-in frequency band detection circuit, it analyzes the spectral characteristics of the input signal in real time and automatically selects the most suitable matching circuit configuration based on the detection results, ensuring optimal transmission matching for signals of different frequency bands. The multi-channel isolated acquisition module contains 8 independent acquisition channels with an isolation of no less than 80dB between channels. Each channel uses... The independent RF front-end and analog-to-digital converter, along with high isolation between channels achieved through physical shielding and circuit isolation technology, effectively prevent signal crosstalk between channels and ensure the independence and accuracy of simultaneous acquisition of multiple signals. The signal preprocessing module processes the raw signal through filtering, amplification, and analog-to-digital conversion, with a sampling rate of no less than 1GSps. The filtering circuit uses an adjustable bandpass filter that can automatically adjust the filtering bandwidth according to signal characteristics. The amplification circuit uses a low-noise amplifier to ensure signal quality. The high-speed analog-to-digital converter realizes the digital processing of the signal. The acquisition parameter configuration module supports manual setting and automatic optimization of acquisition parameters, including sampling rate, gain, and filtering bandwidth. Through intelligent algorithms, it analyzes signal characteristics and automatically optimizes parameter configuration to obtain the best acquisition effect.

[0088] In a preferred embodiment, the multi-channel signal adaptation and acquisition unit further includes a signal type identification module. The signal type identification module is connected to the frequency band adaptive module. The signal type identification module is used to automatically identify sine wave, square wave and pulse wave signal types. By analyzing the time domain and frequency domain characteristics of the signal, the module uses a pattern recognition algorithm to automatically determine the signal type and optimizes the acquisition parameter configuration based on the signal type. The module adopts corresponding acquisition strategies for different signal types to improve the monitoring accuracy of different types of signals.

[0089] The power data processing and analysis unit includes a Match protocol synchronization module, a data calibration module, an anomaly detection module, and a power trend analysis module. The anomaly detection module is connected to the Match protocol synchronization module, which in turn is connected to both the data calibration module and the power trend analysis module. The Match protocol synchronization module, based on the clock synchronization protocol of the Match testing platform, aligns the timestamps of the monitoring data with those of the testing platform. Through a high-precision clock synchronization mechanism, it ensures accurate time-axis correspondence between the monitoring data of multiple signals and the testing platform data, achieving nanosecond-level synchronization accuracy. The data calibration module, through a built-in standard power calibration curve and environmental compensation algorithm, corrects for the effects of temperature and humidity on the measurement results. The calibration curve is established based on a standard power source. The environmental compensation algorithm monitors changes in environmental parameters in real time and automatically adjusts the measurement results to ensure measurement accuracy is better than ±0.1dBm. The anomaly detection module presets multiple power thresholds and compares the monitored data with the threshold range in real time. It supports custom threshold settings and batch import and export of thresholds. Threshold types include upper limit threshold, lower limit threshold, and fluctuation amplitude threshold. The fluctuation amplitude threshold can set the maximum allowable value of power change per unit time. When the monitored data exceeds any threshold, an early warning mechanism is automatically triggered. The power trend analysis module analyzes the power change trend based on the sliding window algorithm and predicts potential anomaly risks. Through historical data analysis and trend prediction algorithms, it identifies possible power anomalies in advance.

[0090] The intelligent early warning push unit includes an early warning level classification module, a multi-channel push module, and an early warning feedback module. The early warning level classification module is connected to the multi-channel push module, which in turn is connected to the early warning feedback module. The early warning level classification module divides abnormal situations into three levels: minor, moderate, and severe, each corresponding to a different response mechanism. Minor abnormalities are only logged, moderate abnormalities send reminder notifications, and severe abnormalities immediately trigger an emergency warning. The multi-channel push module includes four push methods: SMS, APP push, platform pop-up, and voice broadcast. It supports custom push targets and priorities, and automatically selects the appropriate push channel based on the early warning level and user settings to ensure that abnormal information can be promptly conveyed to relevant personnel. The early warning feedback module receives confirmation feedback from personnel, synchronizes it to the core coordination unit, and records the feedback time, forming a complete early warning processing closed loop.

[0091] The distributed storage unit includes a raw data storage module, an analysis result storage module, a configuration information storage module, and a data encryption module. These modules are all connected to the data encryption module. The raw data storage module employs a circular overwrite storage mechanism, supporting the retention of at least 180 days of raw monitoring data. This distributed storage architecture improves data reliability and access efficiency. The analysis result storage module stores calibrated data, anomaly detection reports, and trend analysis charts, providing data support for subsequent data analysis and system optimization. The configuration information storage module stores system parameters, calibration curves, threshold settings, and user permission information, ensuring the integrity and consistency of the system configuration. The data encryption module uses the AES-256 encryption algorithm to encrypt the stored data, ensuring data security and preventing the leakage of sensitive data.

[0092] The platform interaction unit includes a visualization interface module, an operation command receiving module, and a platform linkage control module. The visualization interface module is connected to the operation command receiving module, which in turn is connected to the platform linkage control module. The visualization interface module supports real-time curve display, numerical annotation, and anomaly highlighting of multi-channel signal power data. It can switch between single-channel and multi-channel display modes, providing users with an intuitive data monitoring interface. The operation command receiving module supports mouse, touch, and keyboard operation methods to adapt to different users' operating habits and application scenarios. The platform linkage control module is used to send start / stop and parameter adjustment commands to the Match test platform to achieve linkage control between monitoring and testing, ensuring the coordination and consistency of the testing process.

[0093] The core coordination unit includes a unit scheduling module, a status monitoring module, and a fault self-diagnosis module. Both the status monitoring module and the fault self-diagnosis module are connected to the unit scheduling module. The unit scheduling module coordinates the data transmission and function execution of each unit based on a preset process to ensure synchronous operation of the system. It rationally allocates system resources through task scheduling algorithms to ensure coordinated work of each functional unit. The status monitoring module monitors the operating status of each unit in real time, including voltage, temperature, and communication link status. It ensures stable system operation through continuous monitoring. The fault self-diagnosis module is used to automatically identify the type of unit fault and record fault information. It supports fault location accuracy down to the module level and quickly locates the fault source through built-in diagnostic algorithms, improving system maintenance efficiency.

[0094] The system supports both offline and online operation modes. In online mode, it communicates with the host computer via Ethernet to achieve remote data transmission and remote control. Users can remotely monitor the system status and obtain monitoring data through the network. In offline mode, data is exported through local storage media to ensure that the system can still work normally when the network is unavailable. The system has a built-in backup power supply to maintain core data storage and basic monitoring functions after a power outage, ensuring that critical data is not lost.

[0095] This monitoring system, through its 200K~100M wideband adaptation and high isolation design with 8 independent acquisition channels, achieves compatible acquisition of multiple signal types, effectively reducing signal interference. Combined with the Match protocol synchronization module, it achieves nanosecond-level synchronization with the test platform, significantly improving the synchronization and accuracy of multi-channel signal monitoring. The calibration algorithm and trend analysis function of the power data processing and analysis unit enable measurement accuracy better than ±0.1dBm, allowing for early prediction of power anomaly risks. The multi-level intelligent early warning push unit quickly pushes abnormal information through multiple channels, shortening fault response time and preventing equipment damage and data loss. Distributed encrypted storage and dual-mode operation architecture ensure data security and system stability. The platform interaction unit supports diverse human-machine interaction and test platform linkage control, and has fault self-diagnosis and parameter customization functions. It is highly versatile and practical, and can be widely used in communication testing, industrial electronics, and other fields.

Claims

1. A real-time monitoring system for multi-channel signal power based on a Match test platform, comprising a Match test platform integrated monitoring system (1), characterized in that: The Match test platform integrated monitoring system (1) includes a core coordination unit (2), a multi-channel signal adaptation and acquisition unit (3), a power data processing and analysis unit (4), an intelligent early warning push unit (5), a distributed storage unit (6), and a platform interaction unit (7). The multi-channel signal adaptation and acquisition unit (3) is connected to the core coordination unit (2). The core coordination unit (2) is connected to the power data processing and analysis unit (4) and the platform interaction unit (7) respectively. The power data processing and analysis unit (4) is connected to the intelligent early warning push unit (5) and the distributed storage unit (6) in sequence. The multi-channel signal adaptation and acquisition unit (3) is used to adapt to multi-channel signals of different frequency bands and different types and acquire raw power data. The power data processing and analysis unit (4) completes data calibration and anomaly identification based on the synchronization protocol of the Match test platform. The intelligent early warning push unit (5) is used to push power anomaly information to staff. The distributed storage unit (6) is used to classify and store monitoring data and system configuration information. The platform interaction unit (7) is used to realize human-computer interaction and test platform linkage control. The core coordination unit (2) is used to schedule the coordinated operation of each unit.

2. The multi-channel signal power real-time monitoring system based on the Match test platform according to claim 1, characterized in that: The multi-channel signal adaptation and acquisition unit (3) includes a frequency band adaptive module (8), a multi-channel isolation acquisition module (9), a signal preprocessing module (10), and an acquisition parameter configuration module (11). The frequency band adaptive module (8), the multi-channel isolation acquisition module (9), and the signal preprocessing module (10) are connected in series. The acquisition parameter configuration module (11) is connected to the frequency band adaptive module (8). The frequency band adaptive module (8) is connected to the multi-channel isolation acquisition module (9). The multi-channel isolation acquisition module (9) is connected to the signal preprocessing module (10). The frequency band adaptive module (8) supports 200K~100M wide frequency band adaptation, automatically identifies the input signal frequency band and switches the matching circuit. The multi-channel isolation acquisition module (9) contains no less than 8 independent acquisition channels with an isolation of no less than 80dB between channels. The signal preprocessing module (10) processes the original signal through filtering, amplification and analog-to-digital conversion with a sampling rate of no less than 1GSps. The acquisition parameter configuration module (11) supports manual setting and automatic optimization of acquisition parameters, including sampling rate, gain and filtering bandwidth.

3. The multi-channel signal power real-time monitoring system based on the Match test platform according to claim 1, characterized in that: The power data processing and analysis unit (4) includes a Match protocol synchronization module (12), a data calibration module (13), an anomaly detection module (14), and a power trend analysis module (15). The anomaly detection module (14) is connected to the Match protocol synchronization module (12), and the Match protocol synchronization module (12) is connected to the data calibration module (13) and the power trend analysis module (15) respectively. The Match protocol synchronization module (12) is based on the clock synchronization protocol of the Match test platform to realize the timestamp alignment between the monitoring data and the test platform data. The data calibration module (13) corrects the influence of temperature and humidity on the measurement results through the built-in standard power calibration curve and environmental compensation algorithm. The anomaly detection module (14) presets multiple power thresholds and compares the monitoring data with the threshold range in real time. The power trend analysis module (15) analyzes the power change trend based on the sliding window algorithm and predicts potential anomaly risks.

4. A multi-channel signal power real-time monitoring system based on a Match test platform according to claim 1, characterized in that: The intelligent early warning push unit (5) includes an early warning level classification module (16), a multi-channel push module (17), and an early warning feedback module (18). The early warning level classification module (16) is connected to the multi-channel push module (17), and the multi-channel push module (17) is connected to the early warning feedback module (18). The warning level classification module (16) classifies abnormal situations into three levels: mild, moderate and severe, each corresponding to a different response mechanism. The multi-channel push module (17) includes four push methods: SMS, APP push, platform pop-up and voice broadcast. It supports custom push objects and priorities. The warning feedback module (18) receives confirmation feedback information from staff, synchronizes it to the core coordination unit (2) and records the feedback time.

5. A multi-channel signal power real-time monitoring system based on a Match test platform according to claim 1, characterized in that: The distributed storage unit (6) includes a raw data storage module (19), an analysis result storage module (20), a configuration information storage module (21), and a data encryption module (22). The raw data storage module (19), the analysis result storage module (20), and the configuration information storage module (21) are all connected to the data encryption module (22). The original data storage module (19) adopts a cyclic overwrite storage mechanism to support the retention of original monitoring data for no less than 180 days. The analysis result storage module (20) stores calibrated data, abnormal detection reports, and trend analysis charts. The configuration information storage module (21) stores system parameters, calibration curves, threshold settings, and user permission information. The data encryption module (22) uses the AES-256 encryption algorithm to encrypt the stored data to ensure data security.

6. A multi-channel signal power real-time monitoring system based on a Match test platform according to claim 1, characterized in that: The platform interaction unit (7) includes a visual interface module (23), an operation instruction receiving module (24), and a platform linkage control module (25). The visual interface module (23) is connected to the operation instruction receiving module (24), and the operation instruction receiving module (24) is connected to the platform linkage control module (25). The visualization interface module (23) supports real-time curve display, numerical annotation, and abnormal point highlighting of multi-channel signal power data. It can switch between single-channel and multi-channel display modes. The operation instruction receiving module (24) supports three operation modes: mouse, touch, and keyboard. The platform linkage control module (25) is used to send start / stop and parameter adjustment instructions to the Match test platform to realize linkage control of monitoring and testing.

7. A multi-channel signal power real-time monitoring system based on a Match test platform according to claim 1, characterized in that: The core coordination unit (2) includes a unit scheduling module (26), a status monitoring module (27), and a fault self-diagnosis module (28), both of which are connected to the unit scheduling module (26). The unit scheduling module (26) coordinates the data transmission and function execution of each unit based on a preset process to ensure the synchronous operation of the system. The status monitoring module (27) monitors the operating status of each unit in real time, including voltage, temperature and communication link status. The fault self-diagnosis module (28) is used to automatically identify the fault type of the unit and record the fault information, supporting fault location accuracy down to the module level.

8. A multi-channel signal power real-time monitoring system based on a Match test platform according to claim 1, characterized in that: The anomaly detection module (14) supports custom threshold settings and batch import and export of thresholds. The threshold types include upper limit threshold, lower limit threshold, and fluctuation amplitude threshold. The fluctuation amplitude threshold can set the maximum allowable value of power change per unit time. When the monitored data exceeds any threshold, an early warning mechanism is automatically triggered.

9. A multi-channel signal power real-time monitoring system based on a Match test platform according to claim 1, characterized in that: The multi-channel signal adaptation and acquisition unit (3) also includes a signal type identification module (29), which is connected to the frequency band adaptive module (8). The signal type identification module (29) is used to automatically identify sine wave, square wave and pulse wave signal types, and optimize the acquisition parameter configuration based on the signal type to improve the monitoring accuracy of different types of signals.

10. A multi-channel signal power real-time monitoring system based on a Match test platform according to claim 1, characterized in that: The system supports both offline and online operation modes. In online mode, it communicates with the host computer via Ethernet to achieve remote data transmission and remote control. In offline mode, it exports data through local storage media. The system has a built-in backup power supply to maintain core data storage and basic monitoring functions after a power outage.

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