A communication detection method and system based on HPLC and HRF fusion of OFDM modulation

Abstract

The application discloses a kind of HPLC and HRF fusion communication detection method and system based on OFDM modulation, including multiple double-mode communication units of detection device carried upper computer, double-mode communication unit is configured as analog master node, subnode and relay node, detection device is connected with upper computer by serial interface, and is connected with double-mode communication unit by hardware interface;Simulation evaluation module is used to simulate the quality change of double-mode communication unit according to preset time-varying model during the detection device test process;Decision evaluation module is used to monitor and record the channel switching and / or routing reconstruction decision behavior made by double-mode communication unit when preset time-varying model simulates the quality change of double-mode communication unit, and evaluates double-mode communication unit based on reconstruction decision behavior, while outputting evaluation report, to solve the technical problems that existing detection method cannot test whether it can adapt in real, variable application scenarios.

Description

Technical Field

[0001] This invention relates to the field of instrument communication technology, specifically to a communication detection method and system based on OFDM modulation and the fusion of HPLC and HRF. Background Technology

[0002] The FDM-modulated HPLC / HRF dual-mode communication unit employs two communication technologies: HPLC for the wired channel and HRF for the wireless channel. HPLC is a high-speed power line carrier communication technology that uses power lines as the communication medium for data transmission; HRF is a high-speed wireless communication technology operating in the 470MHz frequency band, characterized by low power consumption and high anti-interference capabilities. Since HPLC technology relies on the reliability of power lines, while HRF technology has specific requirements for the communication environment, the dual-mode HPLC and HRF technologies complement each other, offering advantages such as flexible networking, low frame loss rate, and strong environmental adaptability. The HPLC and HRF channels support parallel transmission of different service messages on different service channels, maximizing the utilization of extended application layer bandwidth. To improve the pass rate of the dual-mode communication unit, precise testing is required to effectively reduce the failure rate and screen out qualified communication units. In the State Grid technical solution, based on a dual-transmit and dual-receive mechanism, STA nodes can transmit synchronously on both channels; that is, the HPLC and HRF channels support concurrent transmission within the same time period and have dual-receive capability. The physical layer needs to ensure that HPLC and HRF can transmit and receive simultaneously. Through a combination of strategies such as path backup, path optimization, and load balancing, communication reliability and transmission efficiency can be significantly improved.

[0003] Currently, the industry commonly uses automated testing systems, which control testing equipment via a host computer to verify the basic functions of communication units, such as networking and meter reading. However, current testing solutions cannot effectively assess whether communication units can autonomously make optimal decisions and maintain stable communication when faced with channel quality fluctuations and frequent node joining or leaving the network. This makes existing testing methods too simplistic and stable in the testing environment, only able to determine the basic functions of the communication unit, and unable to verify its adaptability in real-world, ever-changing application scenarios. Consequently, when communication units are installed in complex field environments, they are prone to communication interruptions or networking failures due to environmental changes, directly impacting the product's factory quality and long-term operational reliability. Summary of the Invention

[0004] The purpose of this invention is to provide a communication detection method and system based on OFDM modulation of HPLC and HRF, which solves the technical problem that existing detection methods cannot verify their adaptability in real and changing application scenarios.

[0005] The technical solution of this invention is implemented as follows:

[0006] The present invention provides a communication and detection system based on OFDM modulation of HPLC and HRF fusion, including a host computer, at least one detection device, and multiple dual-mode communication units mounted on the detection device. The dual-mode communication units are configured as analog master nodes, sub-nodes, and relay nodes. The detection device is connected to the host computer through a serial interface and to the dual-mode communication units through a hardware interface.

[0007] The simulation evaluation module is used to simulate the quality changes of the dual-mode communication unit according to a preset time-varying model during the testing process of the detection device.

[0008] The decision evaluation module is used to monitor and record the channel switching and / or route reconstruction decision behavior made by the dual-mode communication unit when the preset time-varying model simulates the quality change of the dual-mode communication unit, and to evaluate the dual-mode communication unit based on the reconstruction decision behavior, while outputting an evaluation report.

[0009] A further technical solution is that the simulation evaluation module includes:

[0010] The environment initialization module is used to load the corresponding channel quality parameters from the preset time-varying model library according to the selected test scenario and complete the initial configuration;

[0011] The simulation execution module is used to adjust the attenuation, noise, and interference parameters applied to the dual-mode communication unit according to the preset time-varying model during the testing process of the detection device.

[0012] The monitoring and data acquisition module is used to monitor and record the communication indicators of the dual-mode communication unit in real time under simulation conditions.

[0013] The evaluation result generation module generates an initial inspection report of the dual-mode communication unit under time-varying channel conditions based on the communication indicators.

[0014] A further technical solution is that the steps for building the preset time-varying model include:

[0015] Based on a large amount of field measurement data, the changing quality parameters of the dual-mode communication unit under real working environment were collected, and typical patterns and statistical characteristics describing the changes in channel quality over time were extracted from the changing quality parameters.

[0016] Based on the typical patterns and their statistical characteristics, a mathematical model is constructed to describe channel quality variations.

[0017] Adjustable parameters are configured for the mathematical model to generate multiple time-varying models for different scenarios. These multiple time-varying models are stored in a model library for on-demand use during testing.

[0018] The time-varying model is run in the detection device, and its output is compared with real scene data to verify the accuracy of the model.

[0019] A further technical solution is that the simulation execution module includes:

[0020] The preload module is used to synchronize the time axis of the preset time-varying model with the absolute time axis of the detection device, and preload the channel parameter change sequence within the next time window into the cache according to the synchronized time axis.

[0021] The mapping module is used to obtain target parameter values ​​from the channel parameter change sequence and convert the target parameter values ​​into control commands for the programmable attenuator, noise source and interference signal generator through a preset mapping relationship;

[0022] The collaborative application unit is used to synchronously send the control command to the corresponding hardware unit, so that the programmable attenuator, noise source and interference signal generator work together to simultaneously apply the calculated attenuation, noise and interference parameters to the dual-mode communication unit.

[0023] The calibration module is used to compare the actual feedback parameters of the collaborative application unit with the target parameter values, and adjust the control command if the deviation exceeds the limit.

[0024] A further technical solution is that the calibration module comparison step includes:

[0025] Calculate the real-time deviation between the actual feedback parameter and the target parameter value, compare the real-time deviation with the deviation threshold, and determine whether it exceeds the limit;

[0026] If it is determined to be out of limit, then based on the current test scenario, the type of parameter that exceeds the limit and the direction of deviation, the optimal adjustment rule is selected from the strategy library using a PID adjustment algorithm or fuzzy logic matching.

[0027] Based on the prediction of the future short-term parameter change trend by the preset time-varying model, and combined with the optimal adjustment rule, a composite control command of the predictive feedforward component is calculated.

[0028] Before applying the composite control command, perform logic safety verification and collect a new round of actual feedback parameters. Repeat steps 1 to 3 until the deviation reaches the maximum safe iteration number.

[0029] A further technical solution is that the decision evaluation model includes:

[0030] The behavior capture module is used to synchronously capture the channel switching and route reconstruction decision behavior of the dual-mode communication unit when the time-varying model simulates channel quality changes, and associate the decision behavior with the timestamp and the channel state information at the time of triggering to form a decision event sequence;

[0031] The weight allocation module is used to extract decision delay, service interruption duration, handover success rate and routing optimization degree from the decision event sequence, and to assign evaluation weights to the decision delay, service interruption duration, handover success rate and routing optimization degree according to the current test scenario.

[0032] The backtracking analysis module is used to calculate the decision score based on the evaluation weight using a nonlinear scoring function. At the same time, it performs backtracking analysis on low-scoring decision strategies to identify their decision logic bottlenecks.

[0033] The report output module is used to output a final evaluation report that includes at least the decision score, the analysis of the advantages and disadvantages of the decision strategy, and the backtracking analysis.

[0034] A further technical solution is that the weight allocation module includes:

[0035] The scenario priority mapping module is used to identify the mode to which the current test scenario belongs, and to determine the initial priority of decision delay, service interruption duration, handover success rate and routing optimization degree under the current scenario according to the preset scenario priority mapping relationship.

[0036] The objective optimization solution module models the weight allocation problem as a multi-objective optimization problem. Guided by the initial priority, it solves the problem on the Pareto front composed of decision delay, service interruption duration, handover success rate and routing optimization degree, and finds a set of non-dominated optimal weight solutions.

[0037] The weight adjustment module is used to obtain real-time load status information of the network and adjust the optimal weight solution based on the real-time load status information.

[0038] The weight set output module is used to output the final determined weight set for evaluation.

[0039] A further technical solution is that the backtracking analysis module includes:

[0040] The calculation module is used to construct a scoring function, and to calculate the decision score by substituting the decision delay, service interruption duration, handover success rate and routing optimization degree and their assigned evaluation weights into the nonlinear scoring function.

[0041] The low-score decision module is used to compare the decision score with the pass threshold, locate low-score decision events with scores lower than the pass threshold, and extract the channel state sequence, decision triggering conditions, and network state change characteristics from the complete data record corresponding to the low-score decision event.

[0042] The logic bottleneck analysis module is used to input the channel state sequence, decision triggering conditions, and network state change characteristics into the decision tree model, trace the low-scoring decision points, and analyze the deviation between the decision logic based on the dual-mode communication unit and the ideal decision logic at the low-scoring decision points, thereby identifying the decision logic bottleneck.

[0043] A further technical solution is that the computing module includes:

[0044] A scoring function module is built to construct a nonlinear function containing conditional judgment logic as a scoring function. The scoring function sets a qualified threshold for the handover success rate. When the decision delay, service interruption duration, handover success rate, and routing optimization degree are lower than the threshold, the scoring function reduces the comprehensive score through a multiplicative penalty factor.

[0045] The normalization processing module is used to normalize the four performance indicators—decision delay, service interruption duration, handover success rate, and routing optimization degree—to a unified numerical range through a predefined mapping relationship, and inject the output evaluation weight into the corresponding normalized numerical range.

[0046] The weighted calculation module is used to perform a weighted geometric mean calculation on the numerical interval, and the multiplicative penalty factor is applied to the calculation result to produce a preliminary decision score.

[0047] The decision scoring module is used to map the decision score to a preset standard score range, perform output range verification, and finally output the decision score.

[0048] Another aspect of the present invention provides a communication detection method based on OFDM modulation HPLC and HRF fusion, comprising the following steps:

[0049] Initialization and static networking test steps: build the test network and complete the basic networking;

[0050] The simulation evaluation module shows that when the dual-mode communication unit is tested by the detection device for service transmission, the quality of the current primary channel is degraded.

[0051] The decision evaluation module captures and records the entire process data of the dual-mode communication unit from sensing the deterioration of the current primary channel to successfully switching to the backup channel and restoring services.

[0052] An evaluation report is generated based on the test data collected by the host computer from the detection device.

[0053] The beneficial effects of this invention are as follows:

[0054] 1. The testing device can be equipped with a State Grid local dual-mode communication unit as the Concentrator Control Unit (CCO) to simulate a concentrator device; simultaneously, it can be equipped with a State Grid communication unit as the STA to simulate an energy meter. The STA with relay routing function acts as the Proximity Control Unit (PCO) to simulate a relay energy meter. The testing device can simulate a topology scenario consisting of a concentrator and multiple energy meters in the field. Its CCO, PCO, and STA topology is constructed by combining the testing device with the corresponding dual-mode communication units. The testing device adopts the State Grid standard interface, realistically replicating the interface characteristics between the concentrator and the energy meter. The CCO, PCO, and STA communicate with each other via HPLC and HRF dual-mode communication units, while communication with the host computer is via a 232 serial port. Simulation evaluation and decision evaluation modules are used to improve testing in complex environments. Suitable for laboratory and production scenarios, it has the advantages of rapid and stable testing and strong anti-interference capabilities, thus solving the technical problem that existing testing methods cannot adapt to real and changing application scenarios.

[0055] 2. The dual-mode communication unit includes HPLC+HRF dual-channel networking communication. The detection device and the host computer can achieve the following functions: In terms of hardware, it can individually control the I / O of each interface pin, read the status of each interface pin, and receive serial port messages; In terms of software, the host computer has protocol debugging functions, possesses all the function instructions of the main station platform, supports querying the topology map, and can remotely read meters, concurrently read meters, and upgrade.

[0056] 3. The testing device connects to the host computer via a RS-232 serial cable to monitor the messages sent and received by each dual-mode communication unit and their corresponding times in real time. The host computer sends command messages to the testing device via the serial cable, which can accurately simulate actual field application scenarios for performance testing, frequency offset and anti-frequency offset testing, network testing, relay testing, automatic routing testing, data transmission testing, and data reporting testing. It aims to replicate the testing environment of the State Grid Metrology Center, and will subsequently expand related testing content according to actual application needs.

[0057] 4. Based on the testing device and its supported functions, in practical application scenarios, it can communicate with a host computer via a RS-232 serial port. The host computer can automatically issue commands to start the testing process, or it can perform manual debugging to conduct targeted testing on the onboard dual-mode communication unit. Automatic testing significantly improves testing efficiency and reduces interference factors, while manual testing can proactively locate problems and enhance defect detection capabilities. It also supports on-demand expansion of testing content, giving the testing method good flexibility to ensure its practicality and making it suitable for production testing by dual-mode communication unit manufacturers. Attached Figure Description

[0058] Figure 1 This is a wiring topology diagram of the detection device and communication unit of the present invention;

[0059] Figure 2 This is a network communication topology diagram of the detection device of the present invention;

[0060] Figure 3 This is a system block diagram of the simulation evaluation module of the present invention;

[0061] Figure 4 This is a system block diagram of the decision evaluation module of the present invention;

[0062] Figure 5 This is a flowchart of the detection method steps of the detection device of the present invention. Detailed Implementation

[0063] To better understand the technical content of this invention, specific embodiments are provided below, and the invention will be further described in conjunction with the accompanying drawings.

[0064] Example 1

[0065] See Figures 1 to 4 This invention provides a communication and detection system based on OFDM modulation and fusion of HPLC and HRF, including a host computer, at least one detection device, and multiple dual-mode communication units mounted on the detection device. The dual-mode communication units are configured to simulate master nodes, child nodes, and relay nodes. The detection device is connected to the host computer via a serial interface and to the dual-mode communication units via a hardware interface. A simulation evaluation module is used to simulate the quality changes of the dual-mode communication units according to a preset time-varying model during the testing of the detection device. A decision evaluation module is used to monitor and record the channel switching and / or route reconstruction decision behaviors made by the dual-mode communication units when the preset time-varying model simulates the quality changes of the dual-mode communication units, and to evaluate the dual-mode communication units based on the reconstruction decision behaviors, while outputting an evaluation report.

[0066] It should be noted that the host computer can be a PC. The dual-mode communication unit includes HPLC+HRF dual-channel networking communication.

[0067] The detection device and host computer can achieve the following functions: In terms of hardware, they can individually control the I / O of each interface pin, read the status of each interface pin, and receive serial port messages; In terms of software, the host computer has protocol debugging function, all function instructions of the main station platform, supports querying the topology map, and can remotely read meters, concurrently read meters, and upgrade.

[0068] The testing device connects to a host computer via a RS-232 serial cable to monitor the messages sent and received by each dual-mode communication unit and their corresponding times in real time. The host computer sends command messages to the testing device via the serial cable, accurately simulating real-world application scenarios for performance testing, frequency offset and anti-frequency offset testing, network testing, relay testing, automatic routing testing, message delivery testing, and reporting testing. The aim is to replicate the testing environment of the State Grid Metrology Center, with future expansion of related testing content based on actual application needs.

[0069] Based on the testing device and its supported functions, in practical application scenarios, it can communicate with a host computer via a RS-232 serial port. The host computer can automatically issue commands to start the testing process, or it can perform manual debugging to conduct targeted testing on the onboard dual-mode communication unit. Automatic testing significantly improves testing efficiency and reduces interference factors, while manual testing can proactively locate problems and enhance defect detection capabilities. It also supports on-demand expansion of testing content, giving the testing method good flexibility to ensure its practicality and making it suitable for production testing by dual-mode communication unit manufacturers.

[0070] In this embodiment of the invention, the detection device and the dual-mode communication unit form a closed-loop system topology. Through a modular connection method conforming to the State Grid interface standards, power interfaces, data interfaces, and other signal interfaces are configured to simulate the actual working environment of the communication unit on the matching table. The detection device monitors the operating status of the tested dual-mode communication unit in real time and feeds back the detection data to the host computer for visualization, simultaneously generating standardized detection logs. The host computer, acting as the control center, issues test commands to at least one detection device via a serial interface. This detection device drives its multiple dual-mode communication units through a standard hardware interface. The dual-mode communication units are configured to simulate master nodes, child nodes, and relay nodes, respectively, to construct a realistic network topology. Based on this hardware, the simulation evaluation module simulates channel quality changes applied to the master nodes, child nodes, and relay nodes according to a preset time-varying model during the test. Simultaneously, the decision evaluation module monitors and records the channel switching and route reconstruction decisions made by the master nodes, child nodes, and relay nodes in the face of channel changes, and evaluates these decisions. By coordinating hardware systems and software modules, the test environment is upgraded from a static ideal scenario to a high-fidelity simulation, thereby enabling effective testing of the decision-making ability and adaptability of dual-mode communication units in real complex network environments.

[0071] For example, this embodiment covers communication testing between upstream devices (concentrators) and downstream devices (electricity meters). The specific implementation architecture is as follows: the master node simulates the concentrator's detection device and is configured with a local dual-mode communication unit (defined as CCO); the child node simulates the electricity meter's detection device and is configured with a dual-mode communication unit (defined as STA); the relay node simulates an electricity meter detection device with relay routing functionality and is configured with a dual-mode communication unit (defined as PCO). The master node, relay node, and child node communicate via the dual-mode communication unit. The host computer sends control commands and data acquisition instructions to each detection device. After executing the commands, the detection device feeds back the monitoring data to the host computer. The host computer summarizes and analyzes the data and generates a log. The specific detection steps of the detection device are as follows (refer to...). Figure 1 and Figure 2 ):

[0072] (1) Performance test. After initializing the environment, the host computer performs instruction detection: the detection device performs power-on and power-off operations and power-on event detection, sends parameter settings (including address, power consumption, channel link, node IO, CCO, STA, PCO), and reads and compares data consistency to verify that the uplink serial port function of the dual-mode communication unit is normal.

[0073] (2) Frequency offset test and anti-frequency offset test. Initialize the detection device environment, read the parameters and set the initial frequency offset of the detection device, and calibrate the dual-channel frequency offset of the node; after setting the default attenuation value, the host computer sends multiple frames of PLC test messages to the dual-mode communication unit. The detection device transmits the messages to the host computer through the serial port and compares them with the sent content: if they match, the transmission is successful (the number of successful communication is incremented by 1); if they do not match, they are not counted, and the success rate is calculated. If the success rate is less than the threshold, the test ends; otherwise, the frequency offset of the transparent access unit is gradually increased until the success rate is lower than the threshold, and the frequency offset and anti-frequency offset data are recorded.

[0074] (3) Networking Test. Initialize the testing device environment. The host computer controls the local dual-mode communication unit of the CCO and the dual-mode communication unit of the STA to power on through the testing device, enabling HPLC carrier coupling and performing HPLC channel networking test. The CCO will automatically issue instructions and record time T1. The CCO sends a broadcast networking message. The STA receives the broadcast message and returns a networking frame. The testing device checks whether the host computer has received the networking event and counts the number of network nodes. When the number of nodes is equal to the number of actually powered networking communication units N1, record time T2, which means the networking is successful, and record the networking time, i.e., T2-T1. Similarly, after the CCO and STA are powered off, enable the HRF channel, retest, confirm that the networking function of each channel is working properly, and record the time.

[0075] (4) Network Access Detection. After initializing the detection device environment, the host computer sends an instruction: the detection device actively disconnects the 12V and 2.5V power supplies, completely powering down the STA to simulate an offline state. The STA is then re-enabled for initialization, and the "association request message" sent by the STA to the CCO is read via the serial port and its accuracy is verified. Subsequently, the host computer controls the CCO to send an association confirmation message rejecting network access to the STA. After the STA initiates the network access request again, and the time is recorded as T3, the CCO agrees to network formation. The host computer then sends a routing query instruction to obtain the number of child nodes, confirming that the number of STAs joining the network has increased. The time recorded is T4, and the network access time is T4-T3, confirming that the network access detection has passed.

[0076] (5) Relay detection. The detection device initializes the environment, powers on and sets up CCO, STA1 and STA2, with STA1 running as a PCO node; the detection device sends a network command to CCO and PCO to complete the network formation, and then sends a network beacon frame to STA2. The detection device verifies whether STA2 has received the beacon correctly sent by PCO and sent a network entry request. At the same time, it detects whether CCO has received the network entry request forwarded by PCO and received it. Then the host computer sends a routing query sub-node topology command to confirm that STA2 has successfully entered the network and that the link is "CCO-1 level PCO-STA2 structure". The relay function is confirmed to be normal and the network entry time is the time difference between T5 and T4.

[0077] (6) Automatic Routing Detection. Initialize the detection device environment and power on the CCO and STA devices. To verify the automatic routing rapid networking and real-time network optimization functions, it is necessary to simulate channel changes and node removal scenarios. After the detection device and host computer network the CCO and STA, confirm that the link status is in HPLC connection mode and send test messages. After the host computer compares the messages received by the communication unit and finds them to be consistent, it determines that the channel is open. Subsequently, the host computer issues a channel switching test command to disable a specific channel of the communication unit, and the CCO and STA then conduct communication tests through the remaining channel HRF. The detection device captures the messages and transmits them to the host computer, which compares the consistency of the received and transmitted messages before and after the switch. If the comparison is successful, the automatic routing detection is passed.

[0078] (7) Sending the test. Initialize the test device environment and power on CCO and STA. Wait for CCO and STA to complete the network formation. The host computer sends function commands such as "concentrator actively reads meters" to CCO. If STA correctly receives the message sent by CCO and can complete the "STA meter reading response command" and correctly report the meter reading results, then the test is passed.

[0079] (8) Reporting and Testing. 1) When the CCO receives the confirmation of the STA's active event reporting, the test environment is initialized. After receiving the broadcast networking message from the CCO, the host computer sends an association request and waits for the networking to complete. The host computer then sends a command to allow the child node to report. After the CCO responds with confirmation, the host computer sends an uplink frame for the event reporting message. After receiving it, the CCO should reply with a downlink frame for the event message. After the host computer confirms that the downlink frame is correct, the test is passed. 2) When the STA actively reports the event, the test environment is initialized. The host computer checks whether the STA has received the broadcast networking message from the CCO. After waiting for the networking to complete, the host computer pulls the STA's event pin high. The STA sends an event message. The host computer checks the message reported by the STA through the HPLC and HRF dual channels and checks the message received by the CCO and the reception time. After confirming that the message is correct, the test is passed.

[0080] Specifically, the testing device can be equipped with a State Grid local dual-mode communication unit as the Concentrator Control Unit (CCO) to simulate a concentrator device; simultaneously, it can be equipped with a State Grid communication unit as the STA to simulate an energy meter. The STA with relay routing function acts as the Proximity Control Unit (PCO) to simulate a relay energy meter. The testing device can simulate a topology scenario consisting of a field concentrator and multiple energy meters. Its CCO, PCO, and STA topology is constructed by combining the testing device with the corresponding dual-mode communication units. The testing device adopts the State Grid standard interface, realistically replicating the interface characteristics between the concentrator and the energy meter. The CCO, PCO, and STA communicate with each other via HPLC and HRF dual-mode communication units, while communication with the host computer is via a 232 serial port. Simulation and decision evaluation modules are used to improve testing in complex environments. Suitable for laboratory and production scenarios, it offers advantages such as rapid and stable testing and strong anti-interference capabilities, thus solving the technical problem that existing testing methods cannot adapt to real and changing application scenarios.

[0081] Preferably, the simulation evaluation module includes: an environment initialization module, used to load the corresponding channel quality parameters from a preset time-varying model library according to the selected test scenario and complete the initial configuration; a simulation execution module, used to adjust the attenuation, noise, and interference parameters applied to the dual-mode communication unit according to the preset time-varying model during the testing process of the detection device; a monitoring and data acquisition module, used to monitor and record the communication indicators of the dual-mode communication unit under simulation conditions in real time; and an evaluation result generation module, which generates an initial test report of the dual-mode communication unit under time-varying channel conditions based on the communication indicators.

[0082] It should be noted that communication metrics include bit error rate (BER) which reflects transmission reliability, received signal strength (RSS) which judges link quality, channel handover events which record handover time and direction, handover success rate (HSR) which calculates the proportion of successful handovers, network latency which measures data transmission speed, and throughput which evaluates bandwidth utilization efficiency.

[0083] In this embodiment of the invention, the environment initialization module calls the corresponding channel parameters from the preset time-varying model to complete the scenario setting according to the test requirements. During the test, the simulation execution module adjusts the attenuation, noise and other parameters applied to the dual-mode communication unit according to the preset time-varying model to simulate real channel changes. At the same time, the monitoring and data acquisition module records various communication indicators of the communication unit in real time. Finally, the evaluation result generation module automatically generates a preliminary test report on the performance of the communication unit under dynamic channel conditions based on the data obtained by the monitoring and data acquisition module. This ensures automated testing of the performance of the dual-mode communication unit in complex channel environments, avoids the shortcomings of static testing in simulating real environmental changes, and improves testing efficiency and result reliability.

[0084] Furthermore, the steps for building the pre-defined time-varying model include:

[0085] Step S11: Based on a large amount of field measurement data, collect the changing quality parameters of the dual-mode communication unit in the real working environment, and extract typical patterns and statistical characteristics from the changing quality parameters to describe the changes in channel quality over time.

[0086] Step S12: Based on typical patterns and their statistical characteristics, construct a mathematical model to describe channel quality variations;

[0087] Step S13: Configure adjustable parameters for the mathematical model to generate multiple time-varying models for different scenarios, and store the multiple time-varying models in the model library for use as needed during testing;

[0088] Step S14: Run the variable model in the detection device and compare its output with the real scene data to verify the accuracy of the model.

[0089] It should be noted that typical patterns refer to the regularity, periodicity, or triggering nature of channel quality changes; statistical characteristics refer to the mathematical description of typical patterns, assigning them specific values ​​and probabilities.

[0090] In this embodiment of the invention, by analyzing a large amount of real-world data, the patterns and characteristics of channel quality changes are summarized. Then, a mathematical model is constructed based on these patterns and characteristics. Next, by adjusting the parameters of the mathematical model, multiple channel models capable of simulating different application scenarios are generated, and a model library is established. Finally, the mathematical model is run and verified in a testing device to ensure that it accurately reflects the actual channel conditions. This allows testers to readily utilize various high-fidelity channel scenarios to test the dual-mode communication unit.

[0091] Furthermore, the simulation execution module includes: a preloading module, used to synchronize the time axis of the preset time-varying model with the absolute time axis of the detection device, and preload the channel parameter change sequence within the next time window into the cache according to the synchronized time axis; a mapping module, used to obtain the target parameter value from the channel parameter change sequence, and convert the target parameter value into control commands for the programmable attenuator, noise source, and interference signal generator through a preset mapping relationship; a collaborative application unit, used to synchronously send the control commands to the corresponding hardware units, so that the programmable attenuator, noise source, and interference signal generator work together to simultaneously apply the calculated attenuation, noise, and interference parameters to the dual-mode communication unit; and a calibration module, used to compare the actual feedback parameters of the collaborative application unit with the target parameter value, and adjust the control commands if the deviation exceeds the limit.

[0092] In this embodiment of the invention, on a hardware platform consisting of a detection device controlled by a host computer and its onboard dual-mode communication unit, the model and device are first synchronized in time via a preloading module, and the channel change sequence is preloaded. Then, a mapping module converts the sequence parameters into control commands for hardware units such as programmable attenuators in real time. A collaborative application unit synchronously applies attenuation, noise, and interference parameters to the dual-mode communication unit. Finally, a calibration module compares the deviation between the actual parameters and the target values ​​in real time and makes dynamic adjustments, forming a closed-loop control. Through the synergistic effect of time-synchronized preloading, real-time mapping control, and closed-loop dynamic calibration, high-fidelity and high-precision dynamic reproduction of complex channel environments is achieved, solving the distortion problem present in traditional open-loop simulations.

[0093] Furthermore, the calibration module comparison steps include:

[0094] Step S21: Calculate the real-time deviation between the actual feedback parameter and the target parameter value, compare the real-time deviation with the deviation threshold, and determine whether it exceeds the limit.

[0095] Step S22: If it is determined to be out of limit, then according to the current test scenario, the type of parameter that exceeds the limit and the direction of deviation, the optimal adjustment rule is selected from the strategy library by using the PID adjustment algorithm or fuzzy logic matching.

[0096] Step S23: Based on the prediction of the future short-term parameter change trend by the preset time-varying model, and combined with the optimal adjustment rule, calculate the composite control command of the predictive feedforward component.

[0097] Step S24: Before applying the composite control command, perform logic safety verification and collect a new round of actual feedback parameters. Repeat steps 1 to 3 until the deviation reaches the maximum safe iteration number.

[0098] In this embodiment of the invention, the deviation between the actual parameters output by the hardware unit and the target value is calculated in real time and it is determined whether the limit is exceeded. If the limit is exceeded, a PID algorithm or fuzzy logic rule is selected as the adjustment strategy according to the current test scenario. Then, combined with the prediction of future changes by the time-varying model, a composite control command containing predictive compensation is generated. Finally, the control command is executed after safety verification, and this process is repeated until the deviation reaches the target or the safety iteration limit is reached. This achieves high precision and high reliability of the simulation environment. It can actively predict and compensate for the error caused by channel changes, and prevent equipment damage through safety mechanisms. Ultimately, the simulated channel environment is highly consistent with the real scenario.

[0099] It is worth noting that when parameter deviations exceed limits are detected, the deviation characteristics are first analyzed: for steady-state parameter deviations with clear patterns and linear characteristics, a PID control algorithm is selected, using its proportional, integral, and derivative functions to achieve rapid and accurate error elimination; for dynamic parameter fluctuations with complex changes and significant nonlinear characteristics, a fuzzy logic algorithm is activated, transforming the fuzzy quantities of "deviation magnitude" and "change trend" into membership functions, and making inference decisions based on preset expert rules to output the most suitable control command. Through adaptive matching, the control accuracy under normal operating conditions can be guaranteed, while effectively coping with complex and sudden changes, enabling the calibration system to maintain optimal control performance under different testing environments.

[0100] Preferably, the decision evaluation model includes: a behavior capture module, used to synchronously capture the channel handover and route reconstruction decision behaviors of the dual-mode communication unit when the time-varying model simulates channel quality changes, and associate the decision behaviors with timestamps and channel state information at the time of triggering to form a decision event sequence; a weight allocation module, used to extract decision delay, service interruption duration, handover success rate and route optimization degree from the decision event sequence, and assign evaluation weights to decision delay, service interruption duration, handover success rate and route optimization degree according to the current test scenario; a backtracking analysis module, used to calculate the decision score based on the evaluation weights through a nonlinear scoring function, and at the same time, perform backtracking analysis on low-scoring decision strategies to identify their decision logic bottlenecks; and a report output module, used to output a final evaluation report that includes at least the decision score, the merits and demerits analysis of the decision strategy and the backtracking analysis.

[0101] In this embodiment of the invention, a behavior capture module records all decision-making behaviors of the dual-mode communication unit in real time under dynamic channel conditions and associates them with environmental data. A weight allocation module then assigns weights based on the current testing focus: decision latency, service interruption duration, handover success rate, and routing optimization. A backtracking analysis module performs a comprehensive scoring using a decision tree and analyzes the root causes of low-scoring decisions. A report output module generates a complete report containing the score and diagnostic conclusions, providing an in-depth diagnosis of the dual-mode communication unit's decision-making capabilities. This not only objectively evaluates its intelligence level but also directly points out specific flaws in the decision-making logic.

[0102] Furthermore, the weight allocation module includes: a scenario priority mapping module, used to identify the mode to which the current test scenario belongs, and determine the initial priorities of decision latency, service interruption duration, handover success rate, and routing optimization degree under the current scenario according to the preset scenario priority mapping relationship; an objective optimization solution module, which models the weight allocation problem as a multi-objective optimization problem, and solves it on the Pareto front composed of decision latency, service interruption duration, handover success rate, and routing optimization degree guided by the initial priorities, to find a set of non-dominated optimal weight solutions; a weight adjustment module, used to obtain real-time network load status information, and adjust the optimal weight solution based on the real-time load status information; and a weight set output module, used to output the finally determined weight set for evaluation.

[0103] In this embodiment of the invention, the scenario priority mapping module identifies the current test mode and determines the initial priorities of decision latency, service interruption duration, handover success rate, and routing optimization. Then, the objective optimization solution module automatically weighs multiple mutually constraining performance objectives to find an optimal weight combination that balances the performance of each factor. Subsequently, the weight adjustment module fine-tunes this weight combination based on real-time network load. Finally, the weight set output module outputs the final weights for evaluation. This achieves intelligent configuration of evaluation weights, enabling the test evaluation criteria to simultaneously consider the core requirements of the test scenario and the real-time network status, thereby ensuring that the final score reflects both the core capabilities of the tested unit in a specific scenario and conforms to actual network operating conditions.

[0104] Furthermore, the backtracking analysis module includes: a calculation module, used to construct a scoring function, which substitutes decision delay, service interruption duration, handover success rate, and routing optimization degree and their assigned evaluation weights into the nonlinear scoring function to calculate the decision score; a low-score decision module, used to compare the decision score with the pass threshold to locate low-score decision events with scores below the pass threshold; extracting the channel state sequence, decision triggering conditions, and network state change characteristics from the complete data records corresponding to the low-score decision events; and a logic bottleneck analysis module, used to input the channel state sequence, decision triggering conditions, and network state change characteristics into the decision tree model, trace the low-score result decision point, and analyze the deviation between the decision logic based on the dual-mode communication unit and the ideal decision logic at the low-score result decision point, thus identifying the decision logic bottleneck.

[0105] In this embodiment of the invention, the calculation module uses a nonlinear scoring function to comprehensively score the decision-making performance of the communication unit. When the score is lower than the threshold, the low-score decision module immediately locates the specific failure event and extracts complete process data. Subsequently, the logic bottleneck analysis module inputs this data into the decision tree model. By comparing the difference between the actual decision and the ideal decision, the core logical defects that lead to decision failure are located. This not only objectively evaluates the intelligence level of the communication unit, but also directly points out the specific problems in its decision-making logic.

[0106] Furthermore, the calculation module includes: a scoring function construction module, used to construct a nonlinear function containing conditional judgment logic as the scoring function. The scoring function sets a qualified threshold for the handover success rate. When the decision delay, service interruption duration, handover success rate, and routing optimization degree are lower than the threshold, the scoring function reduces the comprehensive score through a multiplicative penalty factor; a normalization processing module, used to normalize the four performance indicators of decision delay, service interruption duration, handover success rate, and routing optimization degree to a unified numerical range through a predefined mapping relationship, and inject the output evaluation weight into the corresponding normalized numerical range; a weighted calculation module, used to perform weighted geometric mean calculation on the numerical range, and apply the multiplicative penalty factor to the calculation result to produce a preliminary decision score; and a decision score module, used to map the decision score to a preset standard score range, perform output range verification, and finally output the decision score.

[0107] In this embodiment of the invention, a nonlinear scoring function with conditional judgment is constructed. When the handover success rate fails to meet the standard, a penalty mechanism is automatically activated to reduce the score. Then, decision delay, service interruption duration, handover success rate, and routing optimization are standardized and weighted. A preliminary score is then calculated using a weighted geometric mean, and a penalty coefficient is applied. Finally, the score is mapped to a standard interval and verified before the final result is output. This approach amplifies the impact of defects in decision delay, service interruption duration, handover success rate, and routing optimization on the total score, solving the problem that single-item shortcomings are masked by the average score in traditional scoring. This allows the evaluation results to reveal the core defects of the dual-mode communication unit, providing a more discriminative basis for quality screening and performance optimization.

[0108] Example 2

[0109] See Figure 5 This invention provides a communication detection method based on OFDM modulation and fusion of HPLC and HRF, comprising the following steps:

[0110] Step 101: Initialization and Static Networking Test Steps, build the test network and complete the basic networking;

[0111] Step 102: Through the simulation evaluation module, when the dual-mode communication unit is tested by the detection device for service transmission, the quality of the current primary channel is degraded;

[0112] Step 103: Through the decision evaluation module, capture and record the entire process data of the dual-mode communication unit from sensing the deterioration of the current primary channel to successfully switching to the backup channel and restoring services;

[0113] Step 104: Generate an evaluation report based on the test data collected by the host computer from the testing device.

[0114] It should be noted that building a test network refers to powering on dual-mode communication units such as master nodes, child nodes, and relay nodes through a testing device and establishing the most basic communication connection between them; completing the basic network setup refers to confirming that the master node can successfully discover the child nodes and that the child nodes can successfully register and connect to the master node's network.

[0115] In this embodiment of the invention, the basic construction and static networking of the test network are completed. Then, the simulation evaluation module actively degrades the quality of the primary channel of the dual-mode communication unit when it is working normally. At the same time, the decision evaluation module captures the entire process data of the communication unit from sensing the channel degradation to switching to the backup channel and restoring services. Finally, the host computer summarizes all the data to generate an evaluation report, realizing the verification of the real adaptability of the dual-mode communication unit in the laboratory and verifying its switching and fault recovery capabilities in simulated real scenarios.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A communication detection system based on OFDM modulation and fusion of HPLC and HRF, characterized in that, The system includes a host computer, at least one detection device, and multiple dual-mode communication units mounted on the detection device. The dual-mode communication units are configured to simulate a master node, a child node, and a relay node. The detection device is connected to the host computer via a serial interface and to the dual-mode communication units via a hardware interface. The simulation evaluation module is used to simulate the quality changes of the dual-mode communication unit according to a preset time-varying model during the testing process of the detection device. A decision evaluation module is used to monitor and record the channel switching and / or route reconstruction decisions made by the dual-mode communication unit when the preset time-varying model simulates the quality changes of the dual-mode communication unit, and to evaluate the dual-mode communication unit based on the reconstruction decision behavior, while outputting an evaluation report. The decision evaluation module includes: The behavior capture module is used to synchronously capture the channel switching and route reconstruction decision behavior of the dual-mode communication unit when the preset time-varying model simulates channel quality changes, and associate the decision behavior with the timestamp and the channel state information at the time of triggering to form a decision event sequence; The weight allocation module is used to extract decision delay, service interruption duration, handover success rate and routing optimization degree from the decision event sequence, and to assign evaluation weights to the decision delay, service interruption duration, handover success rate and routing optimization degree according to the current test scenario. The backtracking analysis module is used to calculate the decision score based on the evaluation weight using a nonlinear scoring function. At the same time, it performs backtracking analysis on low-scoring decision strategies to identify their decision logic bottlenecks. The report output module is used to output a final evaluation report that includes at least the decision score, the merits and demerits of the decision strategy, and the backtesting analysis. The weight allocation module includes: The scenario priority mapping module is used to identify the mode to which the current test scenario belongs, and to determine the initial priority of decision delay, service interruption duration, handover success rate and routing optimization degree under the current scenario according to the preset scenario priority mapping relationship. The objective optimization solution module models the weight allocation problem as a multi-objective optimization problem. Guided by the initial priority, it solves the problem on the Pareto front composed of decision delay, service interruption duration, handover success rate and routing optimization degree, and finds a set of non-dominated optimal weight solutions. The weight adjustment module is used to obtain real-time load status information of the network and adjust the optimal weight solution based on the real-time load status information. The weight set output module is used to output the final determined weight set for evaluation.

2. The system according to claim 1, characterized in that, The simulation evaluation module includes: The environment initialization module is used to load the corresponding channel quality parameters from the preset time-varying model according to the selected test scenario and complete the initial configuration; The simulation execution module is used to adjust the attenuation, noise, and interference parameters applied to the dual-mode communication unit according to the preset time-varying model during the testing process of the detection device. The monitoring and data acquisition module is used to monitor and record the communication indicators of the dual-mode communication unit in real time under simulation conditions. The evaluation result generation module generates an initial inspection report of the dual-mode communication unit under time-varying channel conditions based on the communication indicators.

3. The system according to claim 2, characterized in that, The steps for building the preset time-varying model include: Based on a large amount of field measurement data, the changing quality parameters of the dual-mode communication unit under real working environment were collected, and typical patterns and statistical characteristics describing the changes in channel quality over time were extracted from the changing quality parameters. Based on the typical patterns and their statistical characteristics, a mathematical model is constructed to describe channel quality variations. Adjustable parameters are configured for the mathematical model to generate multiple time-varying models for different scenarios. These multiple time-varying models are stored in a model library for on-demand use during testing. The time-varying model is run in the detection device, and its output is compared with real scene data to verify the accuracy of the model.

4. The system according to claim 2, characterized in that, The simulation execution module includes: The preload module is used to synchronize the time axis of the preset time-varying model with the absolute time axis of the detection device, and preload the channel parameter change sequence within the next time window into the cache according to the synchronized time axis. The mapping module is used to obtain target parameter values ​​from the channel parameter change sequence and convert the target parameter values ​​into control commands for the programmable attenuator, noise source and interference signal generator through a preset mapping relationship; The collaborative application unit is used to synchronously send the control command to the corresponding hardware unit, so that the programmable attenuator, noise source and interference signal generator work together to simultaneously apply the calculated attenuation, noise and interference parameters to the dual-mode communication unit. The calibration module is used to compare the actual feedback parameters of the collaborative application unit with the target parameter values, and adjust the control command if the deviation exceeds the limit.

5. The system according to claim 4, characterized in that, The comparison steps of the calibration module include: Step 1: Calculate the real-time deviation between the actual feedback parameter and the target parameter value, compare the real-time deviation with the deviation threshold, and determine whether it exceeds the limit; Step 2: If it is determined to be out of limit, then according to the current test scenario, the type of parameter that exceeds the limit and the direction of deviation, the optimal adjustment rule is selected from the strategy library by using the PID adjustment algorithm or fuzzy logic matching. Step 3: Based on the prediction of the future short-term parameter change trend by the preset time-varying model, and combined with the optimal adjustment rule, calculate the composite control command of the predictive feedforward component. Step 4: Before applying the composite control command, perform logic security verification and collect a new round of actual feedback parameters. Repeat steps 1 to 3 until the deviation meets the requirements within the maximum number of safe iterations.

6. The system according to claim 1, characterized in that, The backtracking analysis module includes: The calculation module is used to construct a scoring function, and to calculate the decision score by substituting the decision delay, service interruption duration, handover success rate and routing optimization degree and their assigned evaluation weights into the nonlinear scoring function. The low-score decision module is used to compare the decision score with the pass threshold, locate low-score decision events with scores lower than the pass threshold, and extract the channel state sequence, decision triggering conditions, and network state change characteristics from the complete data record corresponding to the low-score decision event. The logic bottleneck analysis module is used to input the channel state sequence, decision triggering conditions, and network state change characteristics into the decision tree model, trace the low-scoring decision points, and analyze the deviation between the decision logic based on the dual-mode communication unit and the ideal decision logic at the low-scoring decision points, thereby identifying the decision logic bottleneck.

7. The system according to claim 6, characterized in that, The computing module includes: A scoring function module is built to construct a nonlinear function containing conditional judgment logic as a scoring function. The scoring function sets a qualified threshold for the handover success rate. When the decision delay, service interruption duration, handover success rate, and routing optimization degree are lower than their respective thresholds, the scoring function reduces the comprehensive score through a multiplicative penalty factor. The normalization processing module is used to normalize the four performance indicators—decision delay, service interruption duration, handover success rate, and routing optimization degree—to a unified numerical range through a predefined mapping relationship, and inject the evaluation weights into the corresponding normalized numerical range. The weighted calculation module is used to perform a weighted geometric mean calculation on the numerical interval, and the multiplicative penalty factor is applied to the calculation result to generate a preliminary decision score. The decision scoring module is used to map the decision score to a preset standard score range, perform output range verification, and finally output the decision score.

8. The detection method of a communication detection system based on OFDM modulation HPLC and HRF fusion according to any one of claims 1-7, characterized in that, Includes the following steps: Initialization and static networking test steps: build the test network and complete the basic networking; The simulation evaluation module degrades the quality of the current primary channel when the detection device performs service transmission tests on the dual-mode communication unit. The decision evaluation module captures and records the entire process data of the dual-mode communication unit from sensing the deterioration of the current primary channel to successfully switching to the backup channel and restoring services. An evaluation report is generated based on the test data collected by the host computer from the detection device.

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