Method for evaluating reliability of HPLC and HRF dual-mode communication test equipment

Abstract

The invention discloses an HPLC (High Performance Liquid Chromatography) and HRF (High Radio Frequency) dual-mode communication test equipment reliability evaluation method, which comprises the following steps: acquiring operation data of monitoring equipment through an HPLC and HRF communication protocol, including the working state, fault record and environmental parameters of the equipment; analyzing the collected data, identifying a fault mode of the equipment, and performing classification according to the fault mode; mining fault causes by adopting a statistical analysis method; calculating a reliability index of each device by using statistical methods such as two-parameter Weibull distribution; rMSE and NRMSE error evaluation indexes are combined to ensure the accuracy of a fitting effect and an evaluation result; an evaluation result is fed back to a system manager to provide decision support, and the evaluation result is displayed through a visual interface; the problem of evaluation deviation caused by improper model selection in a traditional method is effectively solved, the evaluation result is more credible, and efficient data transmission in a dual-mode communication environment is ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of communication module reliability evaluation, and in particular relates to a reliability evaluation method for HPLC and HRF dual-mode communication test equipment. Background Art

[0002] High-speed power line carrier (HPLC) and high-speed radio frequency (HRF) dual-mode communication technology is an important development in the field of modern communications, aiming to achieve efficient and stable data transmission. HPLC technology uses the existing power line infrastructure for data transmission, with the advantages of wide coverage and low deployment cost, and is suitable for applications such as smart grids and the Internet of Things in home and industrial environments. Through HPLC, users can realize the collection and transmission of power data without adding additional wiring.

[0003] On the other hand, HRF technology uses radio frequencies for data transmission, which has the characteristics of high bandwidth and low latency, making it suitable for mobile devices and real-time communication needs. The flexibility of HRF enables it to be applied in a variety of environments, especially in scenarios that require rapid deployment or mobility.

[0004] In dual-mode communication scenarios, HPLC and HRF can complement each other. HPLC provides stable connections in power line environments, while HRF provides flexibility in scenarios that require wireless connections. This combination can improve the reliability and flexibility of the communication system and meet the needs of different application scenarios. For example, in the power consumption information collection system of power users, dual-mode communication technology can realize the aggregation, transmission and interaction of power consumption information of low-voltage power users, ensuring the accuracy and timeliness of data.

[0005] At present, there are some implementation schemes similar to the present invention on the market. For example, some smart energy meters have adopted a single HPLC or HRF technology for data transmission, but these schemes often have the limitation of a single communication method. In the HPLC scheme, although it performs well in the power line environment, the communication quality may be affected in the case of insufficient wireless coverage or signal interference. Although the HRF scheme has high flexibility and bandwidth, it may face problems of signal attenuation and interference in the power line environment.

[0006] In view of these shortcomings, the existing dual-mode communication module (HPLC+HRF) provides a new acquisition solution by combining the advantages of the two technologies. This solution can not only realize the conversion and transmission of HPLC power line carrier communication protocol and micro-power wireless communication protocol, but also support transparent data transmission of multiple application protocols, ensuring the communication stability and reliability in different environments.

[0007] The TCD0A1 chip of the existing dual-mode communication module (HPLC+HRF) meets the requirements of the State Grid's power user power consumption information collection system, can effectively respond to the challenges brought by the construction of new power systems, and meet customers' development needs in low-voltage intelligent IoT. Through this dual-mode communication technology, power companies can achieve more efficient power consumption information collection and management, and improve overall service quality.

[0008] When conducting reliability analysis of HPLC and HRF dual-mode communication technology, it usually involves:

[0009] Fault distribution analysis: Studying the fault distribution law of equipment is the basis of reliability engineering. Commonly used fault distribution models include exponential distribution, lognormal distribution and Weibull distribution. These models can help analyze the failure probability and life characteristics of equipment under different conditions.

[0010] Parameter estimation method: In fault distribution analysis, commonly used parameter estimation methods include ordinary least squares method and maximum likelihood estimation method. These methods can estimate the reliability parameters of equipment by analyzing fault data.

[0011] Reliability model construction: According to the composition and function of the equipment, a reliability model is constructed. For the HPLC and HRF dual-mode communication system, it can be regarded as a system composed of multiple modules (such as power line module, radio frequency module, control module, etc.), and a comprehensive evaluation is performed through weighted connection.

[0012] The analysis method of the prior art includes the following steps:

[0013] Data collection: Collect the failure time data of each device in the HPLC and HRF systems to ensure the integrity and consistency of the data.

[0014] Assumed distribution type: Assume that the equipment failure time follows a specific distribution type (such as Weibull distribution) for subsequent analysis.

[0015] Parameter estimation: Use ordinary least squares or maximum likelihood estimation to estimate the parameters of the assumed distribution model to obtain the failure rate and reliability parameters of the equipment.

[0016] Model validation: The model fitting effect is verified by calculating the correlation coefficient and error indicators (such as RMSE and NRMSE) to ensure the applicability of the selected distribution model.

[0017] Reliability evaluation: Based on the selected fault distribution model, the overall reliability of the HPLC and HRF systems is calculated, and the reliability evaluation of the system is performed.

[0018] The disadvantages of the above prior art are:

[0019] (1) Limitations of fault distribution models:

[0020] In the prior art, commonly used fault distribution models (such as exponential distribution and lognormal distribution) often fail to accurately reflect the actual fault characteristics of complex devices when describing the fault behavior of the device. Especially in wireless communication hardware systems, the failure mechanism of the device may be affected by many factors, such as environmental conditions, frequency of use, etc. The assumptions of these models are relatively simple, resulting in poor model fitting in actual applications, which in turn affects the evaluation of device reliability.

[0021] (2) Deficiencies of parameter estimation methods:

[0022] In the prior art, parameter estimation methods mainly rely on ordinary least squares and maximum likelihood estimation. These methods may lead to inaccurate estimation results when dealing with small samples or data with outliers, thus affecting subsequent reliability analysis. In addition, when evaluating the model fitting effect, the existing methods often only rely on correlation coefficients and lack a comprehensive assessment of the model's applicability.

[0023] (3) One-sidedness of system reliability assessment:

[0024] When evaluating system reliability, existing technologies often fail to fully consider the mutual influence between various devices. For example, the failure of a certain device may have a chain reaction on the operation of the entire system, but the existing model fails to effectively capture this complex cause-and-effect relationship. This one-sidedness may cause the overall reliability evaluation results of the system to be biased and fail to truly reflect the reliability of the system in actual operation. Summary of the invention

[0025] The technical problem to be solved by the present invention is to provide a reliability evaluation method for HPLC and HRF dual-mode communication test equipment to solve the above-mentioned deficiencies in the prior art for reliability evaluation of HPLC and HRF dual-mode communication modules.

[0026] Technical solution of the present invention:

[0027] A reliability assessment method for HPLC and HRF dual-mode communication test equipment, the method comprising:

[0028] Step 1: Data collection: Collect the operating data of the monitoring equipment through HPLC and HRF communication protocols, including the working status, fault records and environmental parameters of the equipment;

[0029] Step 2: Fault analysis: Analyze the collected data, identify the fault mode of the equipment, and classify it according to the fault mode; use statistical analysis methods to explore the cause of the fault;

[0030] Step 3, Reliability evaluation: Use statistical methods such as two-parameter Weibull distribution to calculate the reliability index of each device; combine RMSE and NRMSE error evaluation indicators to ensure the accuracy of fitting effect and evaluation results;

[0031] Step 4: Result feedback: Feedback the evaluation results to the system administrator to provide decision support and display the evaluation results through a visual interface.

[0032] The data collection includes:

[0033] Signal monitoring: Real-time monitoring of the output signals of HPLC and HRF communication modes, including monitoring of signal strength, signal quality and data integrity, to ensure the stability and reliability of dual-mode communication;

[0034] Environmental parameter collection: By deploying sensors and monitoring equipment on site, environmental parameters related to equipment operation, including temperature, humidity, and voltage fluctuations, are collected in real time.

[0035] Data transmission: The collected data is transmitted to the data processing center via wired or wireless networks for storage and analysis, using DL / T698.45 or DL / T645-2007 / 1997 protocols to ensure fast data transmission and accurate reception;

[0036] Historical data integration: Obtain historical fault data from existing fault work order systems and equipment maintenance records;

[0037] Data processing support: Through the data processing center, using high-performance DSP data processing technology, the collected data is analyzed and processed.

[0038] The fault analysis is to process and analyze the fault data of the wireless communication hardware system to determine the cause and type of the fault, and provide a basis for the reliability evaluation of the system; collect the service life data of the wireless communication hardware equipment through field tests, historical records or data provided by the manufacturer, which includes the time from the start of the equipment to the occurrence of a fault, or the operation cycle of the equipment; including:

[0039] According to the characteristics and failure modes of wireless communication hardware equipment, it is assumed that the distribution type that its failure time may follow includes exponential distribution, lognormal distribution or two-parameter Weibull distribution; these assumptions are based on the physical characteristics and failure mechanisms of the equipment, and in a dual-mode communication environment, the different characteristics of HPLC and HRF may lead to different failure modes;

[0040] Using the collected service life data, mathematical statistical methods such as least squares or maximum likelihood estimation are used to estimate the parameters of the assumed failure time distribution;

[0041] The chi-square test correlation test technique is used to evaluate the degree of fit between different distribution hypotheses and actual data. By comparing the correlation coefficient, mean square error (RMSE) and normalized mean square error (NRMSE) indicators, it is determined which distribution hypothesis best describes the failure time distribution of the equipment; the failure time distribution type of the dual-mode communication detection equipment is determined.

[0042] Reliability assessment includes: statistical analysis of equipment failure data and calculation of reliability indicators to evaluate the overall reliability of the equipment; first, based on the failure time distribution type determined by the analysis, the reliability parameters of the equipment are calculated using parameter estimates obtained using the least squares method or maximum likelihood estimation method. These parameters may include failure rate, mean life, scale parameters, and shape parameters.

[0043] The reliability assessment includes:

[0044] Fault tree analysis: Construct a fault tree to identify all paths that may lead to system failure; identify potential failure modes by analyzing the various components of the dual-mode communication module;

[0045] Reliability calculation: Based on the fault tree and the reliability parameters of each module, calculate the reliability indicators of the system, including system failure rate, mean time between failures and system availability;

[0046] Sensitivity analysis: Analyze the impact of changes in the failure probability of each module on system reliability, determine key modules and sensitive parameters, and identify the module with the greatest impact on the overall system reliability by analyzing the failure probability of each communication protocol in the dual-mode communication module;

[0047] Fault prediction: Based on historical data and models, predict the failure probability and reliability trend of the system in the future. Combined with the operating data of the dual-mode communication module, predict the failure rate under different load and environmental conditions.

[0048] Results feedback includes:

[0049] Data visualization: Visualize the assessment results in the form of charts and reports, using color coding, trend charts and pie charts to highlight key information and potential problems to ensure clarity of information transmission;

[0050] Evaluation report generation: Automatically generate detailed evaluation reports, including system reliability evaluation results, fault prediction analysis and maintenance recommendations;

[0051] Real-time warning: When the system reliability is lower than the preset threshold, the warning mechanism is automatically triggered;

[0052] Maintenance recommendations: Provide targeted maintenance recommendations based on the evaluation results;

[0053] Feedback log records: A log of all feedback activities, including feedback time, feedback content, and manager response.

[0054] Beneficial effects of the present invention:

[0055] The present invention introduces more accurate fault analysis:

[0056] The present invention proposes to use a two-parameter Weibull distribution as a fault distribution model for wireless communication hardware devices. Through in-depth analysis of the model, the fault characteristics of the device can be more accurately described, especially in complex environments, thereby improving the accuracy of fault prediction. This model can better adapt to the variability of practical applications and improve the ability to predict device failures.

[0057] The present invention improves the parameter estimation method:

[0058] The present invention proposes a more comprehensive parameter estimation method by combining ordinary least squares and maximum likelihood estimation. The introduction of error evaluation indicators such as RMSE (root mean square error) and NRMSE (normalized root mean square error) can better evaluate the fitting effect of the model and ensure the reliability and accuracy of the selected model. This improvement will make parameter estimation under different conditions more robust and enhance the credibility of the overall analysis.

[0059] The present invention comprehensively considers the mutual influence of various parts of the system:

[0060] The reliability model constructed by the present invention not only takes into account the independent failures of each device, but also the relationship between the devices. By establishing a series and weighted connection model, the overall reliability of the dual-mode communication hardware system can be more comprehensively evaluated to ensure the accuracy and practicality of the evaluation results. This comprehensive consideration will help identify potential system risks and improve the overall stability and reliability of the system.

[0061] Through the above improvements, the present invention overcomes the deficiencies of the prior art and provides a more reliable and efficient reliability analysis method for a dual-mode communication module to meet the needs of new power system construction and diversified power business development. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0063] The present invention proposes a reliability evaluation method for HPLC and HRF dual-mode communication test equipment. The method combines the reliability analysis of the wireless communication hardware system with the characteristics of dual-mode communication to provide a comprehensive evaluation scheme. The technical scheme will be described in detail below.

[0064] The core of this technical solution is to be able to monitor and evaluate the reliability of each monitoring device in the dual-mode communication system in real time. It includes the following key functional modules:

[0065] Data collection:

[0066] ●Responsible for collecting the operating data of monitoring equipment in the dual-mode communication system, including the equipment’s working status, fault records, and environmental parameters.

[0067] ●Acquire equipment operation information in real time through HPLC and HRF communication protocols to ensure the timeliness and accuracy of data.

[0068] Failure analysis:

[0069] ●Analyze the collected data, identify the failure mode of the equipment, and classify it according to the failure mode.

[0070] ● Use statistical analysis methods such as Fault Tree Analysis (FTA) and Root Cause Analysis (RCA),

[0071] Deeply explore the cause of the failure and provide a basis for subsequent reliability assessment.

[0072] Reliability Assessment:

[0073] ●Based on the fault analysis results, the reliability index of each device is calculated using statistical methods such as the two-parameter Weibull distribution.

[0074] ●This module combines RMSE (root mean square error) and NRMSE (normalized root mean square error)

[0075] Error evaluation indicators such as error (error difference) are used to ensure the fitting effect of the model and the accuracy of the evaluation results. Result feedback:

[0076] ●Feedback the evaluation results to system managers to provide decision support and help optimize equipment configuration and maintenance strategies.

[0077] ●The evaluation results are displayed through a visual interface, allowing managers to quickly understand the reliability status of the equipment.

[0078] Figure 1 The reliability analysis method of the test equipment for remote testing of HPLC and HRF dual-mode modules is demonstrated. First, the output signal and environmental signal of the dual-mode detection module are obtained through the data acquisition module. Then, the fault analysis module is used to perform fault analysis, including fault time distribution analysis and correlation test. Then, the reliability evaluation module is used to calculate the equipment reliability parameters, build a system reliability analysis model, and perform model evaluation. Finally, the result feedback module is used to visualize the data, generate an evaluation report, and implement early warning.

[0079] Data collection includes:

[0080] The dual-mode communication test equipment monitors whether the device under test is operating normally through electrical signals. In order to analyze the reliability of its monitoring data, the data acquisition module needs to simultaneously collect the detection signals sent by the dual-mode communication test modules (HPLC and HRF) and information about the environment in which the device is located.

[0081] Specifically, data collection includes the following aspects:

[0082] Signal monitoring: Real-time monitoring of the output signals of HPLC (power line carrier communication) and HRF (micropower wireless communication) communication modes. This includes monitoring of signal strength, signal quality and data integrity to ensure the stability and reliability of dual-mode communication.

[0083] Environmental parameter collection: By deploying sensors and monitoring equipment on site, environmental parameters related to equipment operation, such as temperature, humidity, voltage fluctuations, etc., are collected in real time. These environmental factors may affect the performance of the dual-mode communication module, so comprehensive monitoring is required.

[0084] Data transmission: The collected data can be transmitted to the data processing center via wired or wireless networks for storage and analysis. The use of efficient communication protocols (such as DL / T698.45, DL / T645-2007 / 1997, etc.) ensures fast data transmission and accurate reception.

[0085] Historical data integration: In addition to real-time data collection, historical fault data should also be obtained from existing fault work order systems, equipment maintenance records, etc. These historical data are crucial for fault pattern identification and reliability analysis.

[0086] The present invention constructs a comprehensive data acquisition system to provide basic data support for reliability analysis of wireless communication hardware systems. Through the data processing center, high-performance DSP data processing technology is used to analyze and process the collected data to provide a basis for subsequent fault analysis and reliability evaluation.

[0087] Through the above design, data acquisition will provide a solid data foundation for the reliability analysis of dual-mode communication test equipment, ensuring that the HPLC and HRF dual-mode modules can operate stably and detect potential faults in time under different working environments.

[0088] Failure analysis includes:

[0089] The main function of fault analysis is to process and analyze the fault data of wireless communication hardware systems to determine the cause and type of faults and provide a basis for reliability assessment. Fault analysis collects service life data of wireless communication hardware equipment (such as mobile base stations and various terminal devices) through field tests, historical records or data provided by manufacturers. These data usually include the time from the start of the equipment to the occurrence of failure, or the operation cycle of the equipment.

[0090] During the analysis process, the fault analysis will use the collected monitoring data for in-depth analysis. First, assuming the distribution of fault time is a key step. According to the characteristics and failure modes of the wireless communication hardware equipment, it is assumed that its failure time may follow a distribution type, such as exponential distribution, lognormal distribution, or two-parameter Weibull distribution. These assumptions are based on the physical characteristics and failure mechanisms of the equipment, especially in a dual-mode communication environment, where the different characteristics of HPLC (power line carrier communication) and HRF (micropower wireless communication) may lead to different failure modes. Therefore, using multiple distribution assumptions to fit the time distribution law of monitoring equipment failures can improve the accuracy and applicability of fault analysis evaluation.

[0091] Next, using the collected service life data, mathematical statistical methods such as the least squares method or the maximum likelihood estimation method are used to estimate the parameters of the assumed failure time distribution. For example, for the exponential distribution, the failure rate λ needs to be estimated; for the lognormal distribution, the log mean μ and log standard deviation σ need to be estimated; for the two-parameter Weibull distribution, the scale parameter η and shape parameter m need to be estimated. Accurate estimation of these parameters is crucial to understanding the reliability of dual-mode communication modules under different working conditions.

[0092] Then, the chi-square test correlation test technique is used to evaluate the degree of fit between different distribution assumptions and actual data. By comparing indicators such as correlation coefficient, mean square error (RMSE), and normalized mean square error (NRMSE), it is determined which distribution assumption best describes the failure time distribution of the equipment. This process not only helps identify failure modes, but also provides important data support for subsequent reliability assessments.

[0093] Through these steps, fault analysis can effectively determine the fault time distribution type of dual-mode communication detection equipment, thus laying the foundation for subsequent reliability analysis and the establishment of an early warning mechanism.

[0094] Reliability Assessment:

[0095] Reliability assessment statistically analyzes the equipment failure data and calculates reliability indicators to evaluate the overall reliability of the equipment. First, based on the determined failure time distribution type (such as the two-parameter Weibull distribution), the reliability parameters of the equipment are calculated using the parameter estimates obtained by the least squares method or the maximum likelihood estimation method. These parameters may include failure rate, average life, scale parameters, and shape parameters.

[0096] In the dual-mode communication module (HPLC+HRF), reliability assessment is particularly important because the module involves multiple communication protocols and complex hardware components. The system reliability model is constructed by combining the actual operating characteristics and failure criteria of the equipment. This model usually takes into account the series and weighted connection characteristics of the equipment. For example, for a system composed of multiple modules, the failure of each module may have different effects on the entire system, so it is necessary to assign weights to each module. For the dual-mode communication test equipment system, it includes multiple wireless control boards, programmable attenuators, power supply devices, electric energy meters, and dual-mode copy controllers. The failure probability and weight of each module will affect the reliability of the entire system.

[0097] Next, use the constructed reliability model to evaluate the overall reliability of the entire hardware system. This usually involves the following steps:

[0098] (1) Fault tree analysis: Build a fault tree to identify all paths that may lead to system failure.

[0099] By analyzing the various components of the dual-mode communication module, potential failure modes such as power failure and communication module failure are identified.

[0100] (2) Reliability calculation: Based on the fault tree and the reliability parameters of each module, calculate the reliability indicators of the system, such as system failure rate, mean time between failures (MTBF), system availability, etc. For dual-mode communication modules, special attention should be paid to the reliability performance of the two communication modes, HPLC and HRF.

[0101] (3) Sensitivity analysis: Analyze the impact of changes in the failure probability of each module on system reliability and determine key modules and sensitive parameters. By analyzing the failure probability of each communication protocol in the dual-mode communication module, identify the module that has the greatest impact on the overall reliability of the system.

[0102] (4) Fault prediction: Based on historical data and models, predict the failure probability and reliability trend of the system in the future. Combined with the operating data of the dual-mode communication module, predict the failure rate under different load and environmental conditions.

[0103] Test and verify the accuracy of the system reliability model through actual cases. This can be achieved through simulation experiments, field tests, or historical data analysis. Based on the test results and verification results, adjust and optimize the model to improve the accuracy and reliability of the model. Especially in the dual-mode communication environment, ensure that the model can adapt to different communication modes and fault conditions to provide more accurate reliability assessment.

[0104] Results feedback:

[0105] The result feedback module is responsible for timely and accurate feedback of the evaluation results and maintenance suggestions to the system manager, so that the manager can take corresponding measures based on this information. The following is the design of the result feedback module, which combines the characteristics of dual-mode communication technology to ensure the efficiency and reliability of information transmission.

[0106] The result feedback module includes:

[0107] Ensure the timeliness and accuracy of evaluation results: Utilize the high-speed data transmission capability provided by the dual-mode communication module (HPLC+HRF) to ensure that the evaluation results can be delivered to managers in the shortest possible time.

[0108] Provide clear, easy-to-understand feedback: Transform complex assessment results into easy-to-understand information through intuitive graphical interfaces and data visualization tools.

[0109] Support multiple feedback methods to meet diverse needs: Combining the dual-mode characteristics of wireless and power line carrier communications, it provides multiple feedback channels, such as SMS, email, APP push, etc., to ensure that managers can receive feedback information in a timely manner.

[0110] Assist managers to make decisions and take actions quickly: Real-time data and analysis results help managers respond quickly at critical moments.

[0111] The feedback module features include:

[0112] Data visualization: Visualize the evaluation results in the form of charts, reports, etc. to facilitate managers' intuitive understanding. Use tools such as color coding, trend charts, pie charts, etc. to highlight key information and potential problems to ensure the clarity of information transmission.

[0113] Evaluation report generation: Automatically generate detailed evaluation reports, including system reliability evaluation results, fault prediction analysis, maintenance recommendations, etc. The report should contain easy-to-understand language and charts, as well as detailed analysis of different devices, to ensure that managers can quickly understand the system status.

[0114] Real-time warning: When the system reliability is lower than the preset threshold, the warning mechanism is automatically triggered. Through the rapid response capability of the dual-mode communication module, the system administrator is notified in time by issuing notifications, sound and light signals, etc., to ensure that timely measures are taken.

[0115] Maintenance recommendations: Based on the evaluation results, provide targeted maintenance recommendations, such as replacing faulty parts, adjusting maintenance cycles, etc. Recommendations should be based on data analysis and take cost-effectiveness into consideration to help managers optimize resource allocation.

[0116] Feedback log records: Logs of all feedback activities, including feedback time, feedback content, manager response, etc. Log records can be used for subsequent audits and improvements to the feedback process to ensure the transparency and traceability of the feedback mechanism.

[0117] Finally, the effective operation and continuous improvement of the feedback module are ensured through user interface, data integration, security and permission control, and performance optimization. Overall, the feedback module provides system managers with an efficient and reliable feedback mechanism by integrating and optimizing various functions and combining dual-mode communication technology, thereby improving the system's responsiveness and management efficiency.

[0118] The technical features of the present invention are as follows:

[0119] Automatic selection of multiple fault distribution models: The present invention can automatically select the most suitable fault distribution model (such as exponential distribution, lognormal distribution and two-parameter Weibull distribution) according to the actual fault data of the wireless communication hardware system to improve the accuracy and applicability of reliability assessment.

[0120] Comprehensive reliability assessment model: A system reliability assessment model combining series and weighted connections is constructed, which can comprehensively consider the reliability of each device and provide reliability assessment results of the overall system. It is particularly suitable for complex hardware systems in dual-mode communication scenarios.

[0121] Optimization of parameter estimation method: Ordinary least squares method and maximum likelihood estimation method are used to estimate the parameters of the fault distribution model to ensure the accuracy of parameter estimation. By comparing the effects of different methods, the optimal parameter estimation method is selected.

[0122] Real-time data collection and analysis: It realizes real-time data collection and fault analysis of wireless communication hardware systems, can timely monitor the working status of equipment, quickly respond to potential faults, and improve system reliability.

[0123] Optimization suggestion generation module: Based on the system evaluation results, it uses data mining technology to analyze potential fault causes and puts forward system optimization suggestions to help improve the overall performance and reliability of the wireless communication hardware system in dual-mode communication scenarios.

[0124] Highly adaptable evaluation framework: The evaluation framework of the present invention has high adaptability and can be adjusted according to different types of wireless communication hardware systems and different working environments to meet diverse application requirements.

[0125] Highly adaptable evaluation framework: The evaluation framework of the present invention has high adaptability and can be adjusted according to different types of wireless communication hardware systems and different working environments to meet diverse application requirements. This feature enables the dual-mode communication module to be flexibly applied in a variety of power user power consumption information collection systems to meet the needs of different customers.

[0126] The present invention provides wider possibilities for the application of dual-mode communication technology and ensures the effectiveness and reliability in practical applications.

[0127] The reliability assessment of the present invention specifically targets the complexity and diversity of wireless communication hardware systems in dual-mode communication scenarios, and proposes a systematic assessment scheme to address the deficiencies in traditional assessment methods. First, by automatically selecting the most suitable fault distribution model, the present invention can accurately reflect the actual fault characteristics of the equipment, thereby significantly improving the accuracy of reliability assessment. This process effectively solves the assessment bias problem caused by improper model selection in traditional methods, making the assessment results more credible and ensuring efficient data transmission in a dual-mode communication environment.

[0128] Secondly, the construction of a comprehensive reliability assessment model enables the reliability of each device to be effectively integrated into the overall system. By considering the mutual influence and weight distribution between devices, the present invention can more comprehensively reflect the operating status of the wireless communication hardware system in a dual-mode communication scenario. This method not only improves the reliability assessment efficiency of the system, but also provides a scientific basis for fault warning and maintenance decisions, ensuring stability and reliability in both power line carrier and micro-power wireless communication modes.

[0129] Finally, the implementation of real-time data collection and analysis enables the system to respond to potential faults in a timely manner, reducing the risk of communication interruption due to equipment failure. This feature is particularly important in dual-mode communication scenarios because it requires the system to maintain high availability and stability in different communication modes. Therefore, the present invention not only improves the overall reliability of the wireless communication hardware system, but also provides a solid foundation for the future development of communication technology, and promotes the further development and application of smart power systems.

[0130] The present invention can also adopt:

[0131] For reliability analysis of wireless communication hardware systems in dual-mode communication scenarios, the following alternatives can be used:

[0132] Use different failure distribution models:

[0133] In addition to the traditional exponential distribution, lognormal distribution and two-parameter Weibull distribution, other fault distribution models such as gamma distribution and Poisson distribution can also be considered. These models can be selected according to the actual fault characteristics of dual-mode communication equipment to improve the accuracy of reliability assessment.

[0134] Use different parameter estimation methods:

[0135] In addition to the least squares method and maximum likelihood estimation, Bayesian estimation, moment estimation and other methods can also be used to estimate the parameters of the fault distribution. These methods can better adapt to the complexity and diversity of data in the dual-mode communication environment.

[0136] Introducing machine learning methods:

[0137] Machine learning algorithms, such as support vector machines (SVM), random forests, neural networks, etc., are used to identify fault modes in dual-mode communications and perform fault prediction. This method can process large amounts of data and extract valuable information from it, improving the accuracy and efficiency of fault detection.

[0138] Artificial intelligence-based fault diagnosis system:

[0139] It can automatically identify fault modes and predict faults by learning a large amount of fault data. This intelligent system can monitor the status of equipment in real time in a dual-mode communication environment and respond to potential faults in a timely manner.

[0140] Using Fault Tree Analysis (FTA):

[0141] By constructing a fault tree and analyzing all potential paths that may lead to failure of the dual-mode communication system, the weak links of the system can be identified. This method can help design a more reliable communication system and reduce the probability of failure.

[0142] Using system dynamics models:

[0143] The system dynamics model is used to simulate the behavior of the dual-mode communication hardware system, and the reliability of the system is evaluated through model simulation. This method can help understand the performance of the system in different working modes and optimize the system design.

[0144] Implement real-time monitoring and adaptive learning:

[0145] Implement real-time monitoring of the dual-mode communication hardware system and use an adaptive learning algorithm to dynamically adjust the fault prediction model to adapt to changes in the system's operating status. This approach can improve the system's responsiveness and reduce the risk of communication interruption caused by equipment failure.

[0146] Through the combination and application of the above alternative solutions, the reliability assessment and fault management capabilities of wireless communication hardware systems in dual-mode communication scenarios can be further enhanced, providing a more solid foundation for the future development of communication technology.

Claims

1. A reliability assessment method for HPLC and HRF dual-mode communication test equipment, characterized in that: The method comprises: Step 1: Data collection: Collect the operating data of the monitoring equipment through HPLC and HRF communication protocols, including the working status, fault records and environmental parameters of the equipment; Step 2: Fault analysis: Analyze the collected data, identify the fault mode of the equipment, and classify it according to the fault mode; use statistical analysis methods to explore the cause of the fault; Step 3, Reliability evaluation: Use statistical methods such as two-parameter Weibull distribution to calculate the reliability index of each device; combine RMSE and NRMSE error evaluation indicators to ensure the accuracy of fitting effect and evaluation results; Step 4: Result feedback: Feedback the evaluation results to the system administrator to provide decision support and display the evaluation results through a visual interface.

2. A reliability assessment method for HPLC and HRF dual-mode communication test equipment according to claim 1, characterized in that: The data collection includes: Signal monitoring: Real-time monitoring of the output signals of HPLC and HRF communication modes, including monitoring of signal strength, signal quality and data integrity, to ensure the stability and reliability of dual-mode communication; Environmental parameter collection: By deploying sensors and monitoring equipment on site, environmental parameters related to equipment operation, including temperature, humidity, and voltage fluctuations, are collected in real time. Data transmission: The collected data is transmitted to the data processing center via wired or wireless networks for storage and analysis, using DL / T698.45 or DL / T645-2007 / 1997 protocols to ensure fast data transmission and accurate reception; Historical data integration: Obtain historical fault data from existing fault work order systems and equipment maintenance records; Data processing support: Through the data processing center, using high-performance DSP data processing technology, the collected data is analyzed and processed.

3. A reliability assessment method for HPLC and HRF dual-mode communication test equipment according to claim 1, characterized in that: The fault analysis is to process and analyze the fault data of the wireless communication hardware system to determine the cause and type of the fault, and provide a basis for the reliability evaluation of the system; collect the service life data of the wireless communication hardware equipment through field tests, historical records or data provided by the manufacturer, which includes the time from the start of the equipment to the occurrence of a fault, or the operation cycle of the equipment; including: According to the characteristics and failure modes of wireless communication hardware equipment, it is assumed that the distribution type that its failure time may follow includes exponential distribution, lognormal distribution or two-parameter Weibull distribution; these assumptions are based on the physical characteristics and failure mechanisms of the equipment, and in a dual-mode communication environment, the different characteristics of HPLC and HRF may lead to different failure modes; Using the collected service life data, mathematical statistical methods such as least squares or maximum likelihood estimation are used to estimate the parameters of the assumed failure time distribution; The chi-square test correlation test technique is used to evaluate the degree of fit between different distribution hypotheses and actual data. By comparing the correlation coefficient, mean square error (RMSE) and normalized mean square error (NRMSE) indicators, it is determined which distribution hypothesis best describes the failure time distribution of the equipment; the failure time distribution type of the dual-mode communication detection equipment is determined.

4. A reliability assessment method for HPLC and HRF dual-mode communication test equipment according to claim 1, characterized in that: Reliability assessment includes: statistical analysis of equipment failure data and calculation of reliability indicators to evaluate the overall reliability of the equipment; first, based on the failure time distribution type determined by the analysis, the reliability parameters of the equipment are calculated using parameter estimates obtained using the least squares method or maximum likelihood estimation method. These parameters may include failure rate, mean life, scale parameters, and shape parameters.

5. A reliability assessment method for HPLC and HRF dual-mode communication test equipment according to claim 1, characterized in that: The reliability assessment includes: Fault tree analysis: Construct a fault tree to identify all paths that may lead to system failure; identify potential failure modes by analyzing the various components of the dual-mode communication module; Reliability calculation: Based on the fault tree and the reliability parameters of each module, calculate the reliability indicators of the system, including system failure rate, mean time between failures and system availability; Sensitivity analysis: Analyze the impact of changes in the failure probability of each module on system reliability, determine key modules and sensitive parameters, and identify the module with the greatest impact on the overall system reliability by analyzing the failure probability of each communication protocol in the dual-mode communication module; Fault prediction: Based on historical data and models, predict the failure probability and reliability trend of the system in the future. Combined with the operating data of the dual-mode communication module, predict the failure rate under different load and environmental conditions.

6. A reliability assessment method for HPLC and HRF dual-mode communication test equipment according to claim 1, characterized in that: Results feedback includes: Data visualization: Visualize the assessment results in the form of charts and reports, using color coding, trend charts and pie charts to highlight key information and potential problems to ensure clarity of information transmission; Evaluation report generation: Automatically generate detailed evaluation reports, including system reliability evaluation results, fault prediction analysis and maintenance recommendations; Real-time warning: When the system reliability is lower than the preset threshold, the warning mechanism is automatically triggered; Maintenance recommendations: Provide targeted maintenance recommendations based on the evaluation results; Feedback log records: A log of all feedback activities, including feedback time, feedback content, and manager response.

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