Multi-module fault self-diagnosis method and system for UHF transmitter

Abstract

The invention discloses a multi-module fault self-diagnosis method and system for a UHF transmitter, and the method comprises the steps: collecting the transmitting power, frequency deviation and other multi-mode operation data of each module of the transmitter, and carrying out the preliminary fault positioning through employing an optimized bat fault positioning algorithm; and determining a fault occurrence time sequence by using a multi-modal fault time sequence analysis model, constructing a fault influence propagation path model in combination with preset performance parameters, analyzing a fault influence range and degree, and finally generating a diagnosis report. The system comprises six units such as a multi-modal data acquisition unit and a preliminary fault positioning unit, and the units are mutually connected and cooperatively work. According to the method and the system, the limitation of traditional single data and static diagnosis is broken through, accurate positioning and comprehensive diagnosis of the UHF transmitter fault are realized from multi-modal data and fault time sequence characteristics, the fault diagnosis efficiency and accuracy are improved, and stable operation of equipment is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of fault diagnosis of UHF transmitters, and in particular to a multi-module fault self-diagnosis method and system for UHF transmitters. Background Art

[0002] With the rapid development of wireless communication technology, UHF transmitters are widely used in broadcasting, mobile communications, aerospace, and other fields, becoming core equipment for ensuring stable signal transmission. However, UHF transmitters have a complex internal structure, including multiple functional modules such as power amplification, frequency synthesis, and modulation and demodulation. These modules work together and are interconnected. Over long-term operation, they are prone to failure due to factors such as aging and environmental interference. Once a failure occurs, it not only degrades signal transmission quality but can even cause communication interruptions, resulting in significant economic losses and social impact. Therefore, accurate and efficient fault diagnosis of UHF transmitters is crucial.

[0003] Existing UHF transmitter fault diagnosis technology has many shortcomings. First, most diagnostic methods can only analyze single-modal data, such as monitoring only some parameters such as transmission power and voltage, which makes it difficult to fully capture the fault characteristics. UHF transmitter failures are often the result of multiple factors coupling. The analysis of a single data cannot reflect the complex correlation between modules, resulting in one-sided diagnostic results, prone to missed diagnoses, and unable to timely discover potential fault hazards. Second, existing technologies focus on static diagnosis and lack analysis of the dynamic development process of faults. The propagation of faults between the modules of the UHF transmitter is time-series and directionally specific, and traditional methods cannot accurately determine the specific time when the fault occurs, nor can they track the propagation path of the fault, making fault location vague and unable to provide maintenance personnel with a clear fault handling idea, resulting in low fault repair efficiency.

[0004] To solve the above problems, there is an urgent need for a multi-module fault self-diagnosis method and system that can integrate multi-modal data and deeply analyze the fault timing characteristics. Summary of the Invention

[0005] In order to overcome the shortcomings and deficiencies of the prior art, the present invention provides a multi-module fault self-diagnosis method and system for a UHF transmitter.

[0006] The technical solution adopted by the present invention is a multi-module fault self-diagnosis method for a UHF transmitter, comprising the following steps:

[0007] Step S1: Collecting multi-modal operation data of each module during the operation of the UHF transmitter, wherein the multi-modal operation data includes transmission power data, frequency offset data, phase noise data, and voltage and current data;

[0008] Step S2: Based on the collected multi-modal operation data, the optimized bat fault location algorithm is used to perform preliminary fault location on each module of the UHF transmitter to obtain a preliminary fault module set;

[0009] Step S3: Input the multimodal operation data corresponding to the preliminary fault module set into the multimodal fault timing analysis model, perform timing feature analysis on the fault module, and determine the fault occurrence time sequence of the fault module;

[0010] Step S4: constructing a fault-affected propagation path model based on the fault occurrence time sequence of the fault module and preset performance parameters of the UHF transmitter, wherein the preset performance parameters include transmitter rated power, frequency stability index, and phase noise margin;

[0011] Step S5: Based on the fault impact propagation path model and in combination with the connection relationship and signal transmission characteristics between the modules of the UHF transmitter, analyze the scope and degree of impact of the faulty module on other modules;

[0012] Step S6: Generate a UHF transmitter multi-module fault diagnosis report based on the analysis results, wherein the diagnosis report includes the name of the fault module, the time when the fault occurred, and the scope and extent of the fault impact.

[0013] Furthermore, the optimized bat fault location algorithm is specifically as follows: in step S2, the multimodal operation data of each module of the UHF transmitter is mapped to a multidimensional feature space, and a feature vector set X = {x1, x2, ..., x n}, where x i Represents the characteristic vector of the i-th module; through the formula Calculate the fault probability score of each feature vector, where F(x i ) is the fault probability score of the i-th module, w j is the weight of the jth feature, f j (x i ) is the eigenvalue of the i-th module in the j-th feature dimension, and m is the number of feature dimensions; a fault threshold T is set, and modules with a fault possibility score greater than T are included in the preliminary fault module set.

[0014] Furthermore, the multi-modal fault sequence analysis model is: suppose the multi-modal operation data sequence of the UHF transmitter is Y={y1,y2,…,y k}, where y t Represents the multimodal data vector at time t; through the formula Calculate the fault time series characteristic value at time t, where S(t) is the fault time series characteristic value at time t, α and β are adjustment parameters, τ is the time window length, and d(y s ,y s+1) is the difference measure of the multimodal data vector at time s and s+1; when S(t) exceeds the set timing threshold T s , the time t is determined to be the time when the fault occurs, thereby obtaining the fault occurrence time sequence of the faulty module.

[0015] Furthermore, the fault impact propagation path model constructed in step S4 is: Assume that the module connection matrix of the UHF transmitter is A=[a ij ], where a ij Indicates the connection relationship between module i and module j, a ij =1 means module i is connected to module j, a ij =0 means no connection; through the formula Calculate the probability of fault impact propagation, where P ij is the probability of fault propagation from module j to module i, ΔP i , ΔP j are the power changes of module i and module j respectively when a fault occurs; according to the fault impact propagation probability, a fault impact propagation path model is constructed.

[0016] Furthermore, when analyzing the impact range and degree of the faulty module on other modules in step S5, the formula Among them, I i is the degree of impact of the fault on module i, P ij is the probability of fault propagation from other modules to module i, V j is the fault severity value of fault source module j, n is the number of modules connected to module i; according to the calculated I i value to determine the scope and extent of the impact of the faulty module on other modules.

[0017] Furthermore, when generating the UHF transmitter multi-module fault diagnosis report in step S6, a weighted fusion algorithm is used to calculate the fault diagnosis report by formula Generate diagnostic report content, where R is the final diagnostic report content vector, R k is the k-th diagnostic information vector, u k is the weight of the kth category of diagnostic information, and l is the number of diagnostic information categories.

[0018] Furthermore, the step S3 specifically includes the following sub-steps:

[0019] Step S31: normalizing the multimodal operating data input into the multimodal fault timing analysis model, uniformly mapping the numerical ranges of different types of data to the interval [0, 1] to facilitate subsequent analysis and calculation;

[0020] Step S32: Using the sliding window technology, the normalized multimodal operation data is segmented according to the preset window size and step size to obtain multiple data subsequences to extract local time series features of the data;

[0021] Step S33: extracting features from each data subsequence and calculating the fault time series feature value, thereby determining the time when the fault occurs in each subsequence;

[0022] Step S34: Integrate the fault occurrence times of all data subsequences, remove duplicate times, and obtain an accurate fault occurrence time sequence of the faulty module.

[0023] Furthermore, the step S4 specifically includes the following sub-steps:

[0024] Step S41: According to the system architecture diagram of the UHF transmitter, determine the physical connection relationship between the modules, build a module connection matrix, and clarify the signal transmission path between the modules;

[0025] Step S42: collecting performance parameter data of each module of the UHF transmitter under normal operating conditions and establishing a normal parameter database as a reference benchmark for fault analysis;

[0026] Step S43: for each preliminary fault module, analyzing the performance parameter changes when the fault occurs, comparing with the normal parameter database, and determining the fault parameter deviation;

[0027] Step S44: Calculate the fault impact propagation probability based on the fault parameter deviation and the module connection matrix, and construct a fault impact propagation path model.

[0028] Furthermore, the step S5 specifically includes the following sub-steps:

[0029] Step S51: According to the fault impact propagation path model, determine the module set directly connected to the fault module as the initial affected module set;

[0030] Step S52: Calculate the fault impact degree for each module in the initial affected module set;

[0031] Step S53: Filter out modules with greater impact based on the calculated fault impact, and include them in the extended affected module set;

[0032] Step S54: Repeat steps S52 and S53 until the fault impact levels of all affected modules are calculated, and the final fault impact range and level are determined.

[0033] Multi-module fault self-diagnosis system for UHF transmitters, including:

[0034] Multimodal data acquisition unit, used to collect multimodal operating data of each module during the operation of the UHF transmitter;

[0035] A preliminary fault location unit is connected to the multimodal data acquisition unit and is used to perform preliminary fault location on each module of the UHF transmitter based on the collected multimodal operation data using an optimized bat fault location algorithm to obtain a preliminary fault module set;

[0036] The timing characteristic analysis unit is connected to the preliminary fault location unit and is used to input the multimodal operation data corresponding to the preliminary fault module set into the multimodal fault timing analysis model, perform timing characteristic analysis on the fault module, and determine the fault occurrence time sequence of the fault module;

[0037] The fault path construction unit is connected to the timing characteristic analysis unit and is used to construct a fault impact propagation path model based on the fault occurrence time sequence of the fault module and the preset performance parameters of the UHF transmitter;

[0038] The impact analysis unit is connected to the fault path construction unit and is used to analyze the scope and degree of influence of the fault module on other modules based on the fault impact propagation path model and the connection relationship and signal transmission characteristics between the modules of the UHF transmitter;

[0039] The diagnosis report generating unit is connected to the impact analysis unit and is used for generating a UHF transmitter multi-module fault diagnosis report according to the analysis result.

[0040] Beneficial effects: The present invention proposes a multi-module fault self-diagnosis method and system for UHF transmitters. By collecting multi-modal operating data such as transmission power, frequency offset, and phase noise, the present invention changes the limitation of traditional methods that rely on single-modal data, comprehensively captures the operating characteristics of each module, and avoids missed diagnosis due to one-sided data. Using the optimized bat fault location algorithm, multi-modal data is mapped to a multi-dimensional feature space to calculate the fault possibility score, accurately lock the initial fault module set, and greatly improve the diagnostic accuracy compared to the traditional simple threshold judgment. At the fault analysis level, the multi-modal fault timing analysis model calculates the fault timing characteristic value based on the data sequence and determines the sequence of fault occurrence time, which makes up for the deficiency of the existing technology that cannot analyze the dynamic development of the fault; constructs a fault impact propagation path model, combines the module connection relationship and performance parameters to calculate the fault propagation probability and impact degree, and realizes accurate tracking of the fault propagation path and range, providing clear guidance for maintenance. The various units of the system work together, from data collection to the generation of diagnostic reports containing information such as fault module, occurrence time, and impact range, to achieve efficient and accurate self-diagnosis of UHF transmitter faults, effectively ensure the stable operation of the UHF transmitter, and improve fault repair efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of the method steps of the present invention;

[0042] Figure 2 It is a diagram of the system unit composition of the present invention. DETAILED DESCRIPTION

[0043] It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of this application can be combined with each other. The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] like Figure 1 As shown, the multi-module fault self-diagnosis method for a UHF transmitter includes the following steps:

[0045] Step S1: Collecting multi-modal operation data of each module during the operation of the UHF transmitter, wherein the multi-modal operation data includes transmission power data, frequency offset data, phase noise data, and voltage and current data;

[0046] Specifically, this step focuses on collecting UHF transmitter operating data, aiming to obtain key information reflecting the operating status of the equipment. As a complex electronic device, the UHF transmitter has various internal modules working in coordination. The transmission power data reflects the strength of the transmitter's output signal, which is directly related to the signal coverage and transmission quality; the frequency offset data reflects the deviation between the actual operating frequency and the nominal frequency, affecting the stability and accuracy of the signal; the phase noise data characterizes the random fluctuations in the signal phase, which has a significant impact on the communication's anti-interference ability and signal quality; the voltage and current data, as basic electrical parameters, can reflect the module's power supply status and workload. By collecting these multi-modal operating data, the operating status of each transmitter module can be fully understood, providing a rich and reliable data foundation for subsequent fault diagnosis.

[0047] In terms of implementation, high-precision sensors and data acquisition equipment are connected to each module of the UHF transmitter. For transmit power, a power sensor monitors real-time power changes at the transmitter output port. Frequency offset and phase noise are precisely measured using a frequency meter and a phase noise analyzer. Voltage and current data are collected using voltage and current transformers. To ensure accurate and timely data collection, a reasonable data collection frequency is set so that the collected data can accurately and promptly reflect the transmitter's operating status. This collected multimodal data is then transmitted to the data processing unit to facilitate subsequent fault diagnosis steps.

[0048] Step S2: Based on the collected multi-modal operation data, the optimized bat fault location algorithm is used to perform preliminary fault location on each module of the UHF transmitter to obtain a preliminary fault module set;

[0049] Specifically, the core of this step is to use the optimized bat fault location algorithm to conduct in-depth analysis of the collected multi-modal operating data to achieve preliminary fault location of each module of the UHF transmitter. Since the operating data of each module of the UHF transmitter contains a large amount of information about the status of the module, the optimized bat fault location algorithm can mine the potential features and patterns in the data through a unique search strategy and data analysis mechanism. Through the comprehensive processing of multi-modal data, the algorithm can identify abnormal patterns in the data, which are often associated with the fault status of the module. In this way, modules with the possibility of failure can be screened out from many modules to form a preliminary fault module set, providing a clear target range for subsequent more in-depth fault analysis, effectively narrowing the scope of fault investigation, and improving the efficiency of fault diagnosis.

[0050] During the specific implementation process, the collected multimodal operating data is first preprocessed so that its format and range meet the input requirements of the algorithm. Then, the preprocessed data is input into the optimized bat fault localization algorithm. The algorithm searches and optimizes in the multidimensional data space by simulating the echolocation behavior of bats. During the search process, the algorithm continuously adjusts the parameters and calculates the fault probability index of each module based on the data characteristics. After multiple iterative calculations and optimizations, the algorithm finally determines the modules with a higher probability of failure and includes these modules in the preliminary fault module set. In this process, the optimization characteristics of the algorithm enable it to more accurately locate faulty modules in complex data environments, and compared with traditional methods, it has higher fault identification accuracy and efficiency.

[0051] Step S3: Input the multimodal operation data corresponding to the preliminary fault module set into the multimodal fault timing analysis model, perform timing feature analysis on the fault module, and determine the fault occurrence time sequence of the fault module;

[0052] Specifically, this step aims to use the multimodal fault timing analysis model to conduct a more in-depth analysis of the modules in the preliminary fault module set to determine the specific time sequence of the fault. The fault of the UHF transmitter often has certain time evolution characteristics. The occurrence of the fault is not instantaneous, but gradually develops over a period of time. The multimodal fault timing analysis model can fully consider the change pattern of multimodal operation data over time. By performing timing analysis on the multimodal data of the preliminary fault module, it can mine the characteristics and abnormal changes of the data in the time dimension. From these characteristics and changes, it is possible to accurately determine when the fault module fails and the development trend of the fault in time, thereby determining the time sequence of the fault. This is of great significance for fully understanding the development process of the fault, accurately determining the cause of the fault, and formulating effective maintenance strategies.

[0053] In terms of implementation, the multimodal operating data corresponding to the preliminary fault module set are sorted and arranged in chronological order to form complete time series data. These time series data are then input into the multimodal fault timing analysis model. The model uses specific timing analysis algorithms and techniques to analyze and process the data segment by segment. During the analysis process, the model calculates the characteristic values ​​of the data at different time points, and identifies abnormal fluctuations and changing trends in the data by comparing and analyzing these characteristic values. When it is detected that the data characteristic value exceeds the normal range or a specific abnormal pattern appears, the time point is determined to be the time when the fault occurred. By analyzing the entire time series data, the complete fault occurrence time sequence of the fault module is finally determined, providing an accurate time basis for subsequent fault diagnosis and processing.

[0054] Step S4: constructing a fault-affected propagation path model based on the fault occurrence time sequence of the fault module and preset performance parameters of the UHF transmitter, wherein the preset performance parameters include transmitter rated power, frequency stability index, and phase noise margin;

[0055] Specifically, the main task of this step is to construct a model that can reflect the fault impact propagation path based on the fault occurrence time sequence of the faulty module and the preset performance parameters of the UHF transmitter. There are close connections and signal transmission paths between the modules of the UHF transmitter. When a module fails, the fault will propagate along these paths to other modules, thereby affecting the normal operation of the entire transmitter. By combining the fault occurrence time sequence and the preset performance parameters, the propagation pattern and impact range of the fault between the modules can be more accurately analyzed. Preset performance parameters such as transmitter rated power, frequency stability index, and phase noise tolerance provide standards and basis for judging the impact of the fault on the performance of other modules. The fault impact propagation path model constructed based on this information can clearly show how the fault starts from the source module and gradually affects other related modules, which helps to gain a deeper understanding of the fault propagation mechanism and potential hazards.

[0056] During the implementation process, detailed system architecture information for the UHF transmitter was first collected to clarify the physical connections and signal transmission paths between modules, and a module connection database was established. Next, the fault occurrence sequence of the faulty module was correlated with the preset performance parameters of each module. Based on the moment of the fault, the changes in the performance parameters of each module at that moment were analyzed. Combined with the module connection relationships, the possible propagation direction and path of the fault were determined. Through step-by-step analysis and reasoning, the extent of the fault's impact on each module at different time points was determined, ultimately constructing a complete fault impact propagation path model. This model, presented as a graph or data structure, intuitively illustrates the fault's propagation path and its impact on each module, providing an important reference for subsequent fault diagnosis and resolution.

[0057] Step S5: Based on the fault impact propagation path model and in combination with the connection relationship and signal transmission characteristics between the modules of the UHF transmitter, analyze the scope and degree of impact of the faulty module on other modules;

[0058] Specifically, this step is to further analyze the scope and extent of the impact of the faulty module on other modules based on the constructed fault impact propagation path model. The fault impact propagation path model only shows the possible propagation path of the fault. To fully understand the impact of the fault, it is necessary to conduct a detailed analysis based on the actual connection relationship and signal transmission characteristics between the modules of the UHF transmitter. The connection method between the modules determines the physical channel for the fault to propagate. Different connection methods may lead to different speeds and scopes of fault propagation; signal transmission characteristics affect the extent of the fault's impact on signal quality during the propagation process. By comprehensively considering these factors, the scope of the faulty module's impact on other modules can be accurately assessed, and it can be determined which modules are directly affected by the fault and which modules are indirectly affected. At the same time, it is also possible to quantify the extent of the fault's impact on the performance of each module, providing an accurate basis for formulating targeted maintenance and treatment measures.

[0059] During implementation, fault propagation path information is extracted from the fault impact propagation path model. Then, based on the inter-module connectivity, the modules directly connected to the faulty module are identified. For these directly connected modules, changes in signal transmission after the fault occurs are analyzed, and the impact of the fault on their performance indicators is assessed based on signal transmission characteristics. Next, starting with the directly affected module, the fault's impact on the next-level modules is analyzed step by step along the fault propagation path. During the analysis, factors such as signal attenuation and interference during transmission are considered to comprehensively determine the scope and extent of the fault's impact on each module. This step-by-step analysis ultimately comprehensively and accurately determines the scope and extent of the faulty module's impact on all modules of the entire UHF transmitter, providing detailed guidance for subsequent troubleshooting.

[0060] Step S6: Generate a UHF transmitter multi-module fault diagnosis report based on the analysis results, wherein the diagnosis report includes the name of the fault module, the time when the fault occurred, and the scope and extent of the fault impact.

[0061] Specifically, this step summarizes and outputs the entire fault diagnosis process. Its purpose is to organize and summarize the results analyzed in the previous steps to generate a comprehensive and detailed UHF transmitter multi-module fault diagnosis report. The previous steps have already completed the location of the faulty module, the determination of the fault's onset time, the construction of the fault's impact propagation path, and the analysis of the scope and extent of the fault's impact. This information is crucial for maintenance personnel to accurately understand the fault situation and formulate a repair plan. By systematically organizing this key information and generating a standardized fault diagnosis report, maintenance personnel can quickly and clearly obtain all important information related to the fault, avoiding omissions and confusion, and improving the efficiency and accuracy of maintenance work.

[0062] During the implementation process, the analysis results obtained in the previous steps are first categorized and organized, with information such as the faulty module name, fault occurrence time, and the scope and extent of the fault impact organized according to a specific format. Then, according to a pre-set report template, this organized information is entered into the corresponding fields to generate a preliminary fault diagnosis report. During the report generation process, the information is rechecked and verified to ensure the accuracy and completeness of the report content. Finally, the report format is optimized and typeset for clarity and readability. The generated UHF transmitter multi-module fault diagnosis report can be output as an electronic or paper document, making it easy for maintenance personnel to review and use, providing strong support for UHF transmitter troubleshooting and maintenance.

[0063] Preferably, the optimized bat fault location algorithm is specifically as follows: in step S2, the multimodal operation data of each module of the UHF transmitter is mapped to a multidimensional feature space, and a feature vector set X = {x1, x2, ..., x n}, where x i Represents the characteristic vector of the i-th module; through the formula Calculate the fault probability score of each feature vector, where F(x i ) is the fault probability score of the i-th module, w j is the weight of the jth feature, f j (x i ) is the eigenvalue of the i-th module in the j-th feature dimension, and m is the number of feature dimensions; a fault threshold T is set, and modules with a fault possibility score greater than T are included in the preliminary fault module set.

[0064] Specifically, in step S2, multimodal operational data is mapped to a multidimensional feature space to construct a feature vector set. By assigning different weights to each feature dimension, a fault probability score is calculated, achieving a quantitative assessment of the fault probability of each module. This algorithm sets a fault threshold to screen out modules with a high fault probability, forming a preliminary set of faulty modules, effectively narrowing the scope of subsequent troubleshooting and improving diagnostic efficiency. The construction of the feature vector set enables the integrated processing of multimodal data, and the weight parameters can be adjusted according to the characteristics of different modules, making the algorithm more targeted and adaptable.

[0065] Preferably, the multi-modal fault sequence analysis model is: assuming that the multi-modal operation data sequence of the UHF transmitter is Y={y1, y2, ..., y k}, where y t Represents the multimodal data vector at time t; through the formula Calculate the fault time series characteristic value at time t, where S(t) is the fault time series characteristic value at time t, α and β are adjustment parameters, τ is the time window length, and d(y s ,y s+1 ) is the difference measure of the multimodal data vector at time s and s+1; when S(t) exceeds the set timing threshold T s , the time t is determined to be the time when the fault occurs, thereby obtaining the fault occurrence time sequence of the faulty module.

[0066] Specifically, by analyzing multimodal operational data sequences and calculating fault time series characteristics, the model accurately locates the moment of fault occurrence. The model introduces adjustment parameters and time window lengths, which can be adjusted based on different fault types and device characteristics, making the extraction of fault time series characteristics more accurate. By setting time series thresholds to determine the moment of fault occurrence, a sequence of fault occurrence times is generated. This provides critical time-dimensional information for subsequent analysis of fault propagation paths and impact ranges, helping to gain a deeper understanding of the fault's development and evolution.

[0067] Preferably, the fault impact propagation path model constructed in step S4 is: assuming that the module connection matrix of the UHF transmitter is A=[a ij ], where a ij Indicates the connection relationship between module i and module j, a ij =1 means module i is connected to module j, a ij =0 means no connection; through the formula Calculate the probability of fault impact propagation, where P ij is the probability of fault propagation from module j to module i, ΔP i , ΔP jare the power changes of module i and module j respectively when a fault occurs; according to the fault impact propagation probability, a fault impact propagation path model is constructed.

[0068] Specifically, the fault propagation probability is calculated based on the module connection matrix and power variation, clarifying the likelihood of fault propagation between modules. The module connection matrix is ​​constructed based on the actual physical connections of the UHF transmitter, ensuring that the model accurately reflects the physical path of fault propagation. Comparative analysis of power variation quantifies the intensity of fault propagation, enabling the model to more accurately predict the potential impact of a fault, providing an important basis for comprehensive fault impact assessment.

[0069] Preferably, when analyzing the scope and degree of influence of the faulty module on other modules in step S5, the formula Among them, I i is the degree of impact of the fault on module i, P ij is the probability of fault propagation from other modules to module i, V j is the fault severity value of fault source module j, n is the number of modules connected to module i; according to the calculated I i value to determine the scope and extent of the impact of the faulty module on other modules.

[0070] Specifically, by comprehensively considering the fault propagation probability and the severity of the fault source module, a quantitative assessment of the impact on each module is achieved. This method not only considers the direct fault propagation path but also comprehensively assesses the indirect impact through cumulative calculation, making the assessment results more comprehensive and accurate. The calculated impact value can clearly define the scope of the fault's impact, providing a scientific basis for developing targeted maintenance strategies and prioritizing the most severely affected modules.

[0071] Preferably, when generating the UHF transmitter multi-module fault diagnosis report in step S6, a weighted fusion algorithm is used, through the formula Generate diagnostic report content, where R is the final diagnostic report content vector, R k is the k-th diagnostic information vector, u k is the weight of the kth category of diagnostic information, and l is the number of diagnostic information categories.

[0072] Specifically, a weighted fusion algorithm is used to integrate various types of diagnostic information. By assigning different weights to different types of diagnostic information, the resulting report ensures a comprehensive and objective reflection of the fault situation. This algorithm standardizes and systematizes diagnostic information, making the report more standardized and organized. Weights can be adjusted based on the importance and reliability of different diagnostic information, improving the accuracy and practicality of the report and providing maintenance personnel with clear and effective fault diagnosis guidance.

[0073] Preferably, the step S3 specifically includes the following sub-steps:

[0074] Step S31: normalizing the multimodal operating data input into the multimodal fault timing analysis model, uniformly mapping the numerical ranges of different types of data to the interval [0, 1] to facilitate subsequent analysis and calculation;

[0075] Step S32: Using the sliding window technology, the normalized multimodal operation data is segmented according to the preset window size and step size to obtain multiple data subsequences to extract local time series features of the data;

[0076] Step S33: extracting features from each data subsequence and calculating the fault time series feature value, thereby determining the time when the fault occurs in each subsequence;

[0077] Step S34: Integrate the fault occurrence times of all data subsequences, remove duplicate times, and obtain an accurate fault occurrence time sequence of the faulty module.

[0078] Specifically, the multimodal operating data is first normalized to eliminate the impact of dimensional differences between different data types and ensure the accuracy of subsequent analysis. Then, a sliding window technique is used to segment the data, converting long time series data into multiple short time series subsequences to facilitate the extraction of local time series features. Feature extraction and fault occurrence time determination are performed on each subsequence. Finally, the results are integrated and duplicate moments are removed to obtain an accurate sequence of fault occurrence times. This step-by-step approach makes time series feature analysis more detailed and comprehensive, effectively improving the accuracy of fault occurrence time determination.

[0079] Preferably, the step S4 specifically includes the following sub-steps:

[0080] Step S41: According to the system architecture diagram of the UHF transmitter, determine the physical connection relationship between the modules, build a module connection matrix, and clarify the signal transmission path between the modules;

[0081] Step S42: collecting performance parameter data of each module of the UHF transmitter under normal operating conditions and establishing a normal parameter database as a reference benchmark for fault analysis;

[0082] Step S43: for each preliminary fault module, analyzing the performance parameter changes when the fault occurs, comparing with the normal parameter database, and determining the fault parameter deviation;

[0083] Step S44: Calculate the fault impact propagation probability based on the fault parameter deviation and the module connection matrix, and construct a fault impact propagation path model.

[0084] Specifically, a module connection matrix is ​​first constructed based on the system architecture diagram to clarify the physical connection relationships and signal transmission paths between modules, providing a structural foundation for subsequent analysis. A normal parameter database is then established as a reference for fault analysis, facilitating comparative analysis of parameter changes during fault occurrences. Parameter deviations are analyzed for the initial fault module, and a complete fault impact propagation path model is constructed by combining the module connection matrix and a method for calculating the probability of fault impact propagation. This step-by-step implementation ensures the scientific and accurate nature of model construction, truly reflecting the propagation of faults within the UHF transmitter.

[0085] Preferably, the step S5 specifically includes the following sub-steps:

[0086] Step S51: According to the fault impact propagation path model, determine the module set directly connected to the fault module as the initial affected module set;

[0087] Step S52: Calculate the fault impact degree for each module in the initial affected module set;

[0088] Step S53: Filter out modules with greater impact based on the calculated fault impact, and include them in the extended affected module set;

[0089] Step S54: Repeat steps S52 and S53 until the fault impact levels of all affected modules are calculated, and the final fault impact range and level are determined.

[0090] Specifically, the initial set of affected modules directly connected to the faulty module is identified as the starting point for analysis. The fault impact of these initial affected modules is then calculated, and the most significantly affected modules are selected for further analysis. By repeating this calculation and screening process, the fault impact of all affected modules is gradually determined, ultimately clarifying the scope and extent of the fault's impact. This progressively more in-depth analysis approach ensures a comprehensive assessment of the fault's impact and provides a detailed and accurate basis for developing effective troubleshooting measures.

[0091] like Figure 2 As shown, a multi-module fault self-diagnosis system for UHF transmitters includes:

[0092] Multimodal data acquisition unit, used to collect multimodal operating data of each module during the operation of the UHF transmitter;

[0093] A preliminary fault location unit is connected to the multimodal data acquisition unit and is used to perform preliminary fault location on each module of the UHF transmitter based on the collected multimodal operation data using an optimized bat fault location algorithm to obtain a preliminary fault module set;

[0094] The timing characteristic analysis unit is connected to the preliminary fault location unit and is used to input the multimodal operation data corresponding to the preliminary fault module set into the multimodal fault timing analysis model, perform timing characteristic analysis on the fault module, and determine the fault occurrence time sequence of the fault module;

[0095] The fault path construction unit is connected to the timing characteristic analysis unit and is used to construct a fault impact propagation path model based on the fault occurrence time sequence of the fault module and the preset performance parameters of the UHF transmitter;

[0096] The impact analysis unit is connected to the fault path construction unit and is used to analyze the scope and degree of influence of the fault module on other modules based on the fault impact propagation path model and the connection relationship and signal transmission characteristics between the modules of the UHF transmitter;

[0097] The diagnosis report generating unit is connected to the impact analysis unit and is used for generating a UHF transmitter multi-module fault diagnosis report according to the analysis result.

[0098] The UHF transmitter multi-module fault self-diagnosis method and system proposed in the present invention forms a complete fault diagnosis system from data collection and analysis to diagnostic report generation, which effectively makes up for the shortcomings of the existing technology. The existing technology often relies on single-modal data, resulting in incomplete capture of fault characteristics and prone to missed diagnosis; at the same time, static diagnosis methods are difficult to track the dynamic development of faults. The present invention breaks through the limitations of single data by collecting multi-modal operating data such as transmission power, frequency offset, and phase noise, mapping multi-modal data to a multi-dimensional feature space, and using an optimized bat fault location algorithm to calculate the fault possibility score, accurately locking the initial fault module, and greatly improving the accuracy of diagnosis.

[0099] In the fault analysis phase, existing technologies are unable to effectively analyze the timing characteristics and propagation paths of faults, resulting in ambiguous fault location. This system utilizes a multimodal fault timing analysis model to calculate fault timing characteristic values ​​and accurately determine the sequence of fault occurrence times. By constructing a fault impact propagation path model, the system combines the module connection matrix and power variation to calculate the fault propagation probability. The system then determines the degree of impact on each module based on the propagation probability and fault severity, clearly outlining the fault propagation path and scope, providing clear guidance for maintenance personnel and overcoming the ambiguity of traditional diagnostic methods.

[0100] The system architecture features six closely interconnected units: a multimodal data acquisition unit, preliminary fault location unit, and time series feature analysis unit. Starting with data acquisition, the system then moves through preliminary location, time series analysis, path construction, and impact assessment, ultimately generating a diagnostic report detailing the faulty module, time of occurrence, and impact range. This system automates and accurately diagnoses UHF transmitter faults throughout the entire process. Compared to existing technologies, this system significantly improves fault diagnosis efficiency and accuracy, greatly ensuring the stable and reliable operation of UHF transmitters and providing strong technical support for equipment maintenance in the communications sector.

[0101] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0102] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-module fault self-diagnosis method for a UHF transmitter, characterized in that: The following steps are involved: Step S1: Collecting multi-modal operation data of each module during the operation of the UHF transmitter, wherein the multi-modal operation data includes transmission power data, frequency offset data, phase noise data, and voltage and current data; Step S2: Based on the collected multi-modal operation data, the optimized bat fault location algorithm is used to perform preliminary fault location on each module of the UHF transmitter to obtain a preliminary fault module set; Step S3: Input the multimodal operation data corresponding to the preliminary fault module set into the multimodal fault timing analysis model, perform timing feature analysis on the fault module, and determine the fault occurrence time sequence of the fault module; Step S4: constructing a fault-affected propagation path model based on the fault occurrence time sequence of the fault module and preset performance parameters of the UHF transmitter, wherein the preset performance parameters include transmitter rated power, frequency stability index, and phase noise margin; Step S5: Based on the fault impact propagation path model and in combination with the connection relationship and signal transmission characteristics between the modules of the UHF transmitter, analyze the scope and degree of impact of the faulty module on other modules; Step S6: Generate a UHF transmitter multi-module fault diagnosis report based on the analysis results. The diagnosis report includes the name of the fault module, the time when the fault occurred, and the scope and extent of the fault impact.

2. The multi-module fault self-diagnosis method for a UHF transmitter according to claim 1, characterized in that: The optimized bat fault location algorithm is specifically as follows: in step S2, the multimodal operation data of each module of the UHF transmitter is mapped to a multidimensional feature space, and a feature vector set X = {x1, x2, ..., x n },in, x i represents the feature vector of the i-th module; through the formula Calculate the fault probability score of each feature vector, where F(x i ) is the fault probability score of the i-th module, w j is the weight of the jth feature, f j (x i ) is the eigenvalue of the i-th module in the j-th feature dimension, and m is the number of feature dimensions; a fault threshold T is set, and modules with a fault possibility score greater than T are included in the preliminary fault module set.

3. The multi-module fault self-diagnosis method for a UHF transmitter according to claim 1, characterized in that: The multi-modal fault sequence analysis model is: suppose the multi-modal operation data sequence of the UHF transmitter is Y={y1,y2,…,y k }, where y t Represents the multimodal data vector at time t; through the formula Calculate the fault time series characteristic value at time t, where S(t) is the fault time series characteristic value at time t, α and β are adjustment parameters, τ is the time window length, and d(y s ,y s+1 ) is the difference measure of the multimodal data vector at time s and s+1; when S(t) exceeds the set timing threshold T s , the time t is determined to be the time when the fault occurs, thereby obtaining the fault occurrence time sequence of the faulty module.

4. The multi-module fault self-diagnosis method for a UHF transmitter according to claim 1, characterized in that: The fault impact propagation path model constructed in step S4 is: Assume that the module connection matrix of the UHF transmitter is A=[a ij ], where a ij Indicates the connection relationship between module i and module j, a ij =1 means module i is connected to module j, a ij =0 means no connection; through the formula Calculate the probability of fault impact propagation, where P ij is the probability of fault propagation from module j to module i, ΔP i , ΔP j are the power changes of module i and module j respectively when a fault occurs; according to the fault impact propagation probability, a fault impact propagation path model is constructed.

5. The multi-module fault self-diagnosis method for a UHF transmitter according to claim 1, characterized in that: When analyzing the scope and degree of influence of the faulty module on other modules in step S5, the formula Among them, I i is the degree of impact of the fault on module i, P ij is the probability of fault propagation from other modules to module i, V j is the fault severity value of fault source module j, n is the number of modules connected to module i; according to the calculated I i value to determine the scope and extent of the impact of the faulty module on other modules.

6. The multi-module fault self-diagnosis method for a UHF transmitter according to claim 1, characterized in that: When generating the UHF transmitter multi-module fault diagnosis report in step S6, a weighted fusion algorithm is used to calculate the fault diagnosis report. Generate diagnostic report content, where R is the final diagnostic report content vector, R k is the k-th diagnostic information vector, u k is the weight of the kth category of diagnostic information, and l is the number of diagnostic information categories.

7. The multi-module fault self-diagnosis method for a UHF transmitter according to claim 1, characterized in that: The step S3 specifically includes the following sub-steps: Step S31: normalizing the multimodal operating data input into the multimodal fault timing analysis model, uniformly mapping the numerical ranges of different types of data to the interval [0, 1] to facilitate subsequent analysis and calculation; Step S32: Using the sliding window technology, the normalized multimodal operation data is segmented according to the preset window size and step size to obtain multiple data subsequences to extract local time series features of the data; Step S33: extracting features from each data subsequence and calculating the fault time series feature value, thereby determining the time when the fault occurs in each subsequence; Step S34: Integrate the fault occurrence times of all data subsequences, remove duplicate times, and obtain an accurate fault occurrence time sequence of the faulty module.

8. The multi-module fault self-diagnosis method for a UHF transmitter according to claim 1, characterized in that: The step S4 specifically includes the following sub-steps: Step S41: According to the system architecture diagram of the UHF transmitter, determine the physical connection relationship between the modules, build a module connection matrix, and clarify the signal transmission path between the modules; Step S42: collecting performance parameter data of each module of the UHF transmitter under normal operating conditions and establishing a normal parameter database as a reference benchmark for fault analysis; Step S43: for each preliminary fault module, analyzing the performance parameter changes when the fault occurs, comparing with the normal parameter database, and determining the fault parameter deviation; Step S44: Calculate the fault impact propagation probability based on the fault parameter deviation and the module connection matrix, and construct a fault impact propagation path model.

9. The multi-module fault self-diagnosis method for a UHF transmitter according to claim 1, characterized in that: The step S5 specifically includes the following sub-steps: Step S51: According to the fault impact propagation path model, determine the module set directly connected to the fault module as the initial affected module set; Step S52: Calculate the fault impact degree for each module in the initial affected module set; Step S53: Filter out modules with greater impact based on the calculated fault impact, and include them in the extended affected module set; Step S54: Repeat steps S52 and S53 until the fault impact levels of all affected modules are calculated, and the final fault impact range and level are determined.

10. A multi-module fault self-diagnosis system for UHF transmitters, characterized in that: include: Multimodal data acquisition unit, used to collect multimodal operating data of each module during the operation of the UHF transmitter; A preliminary fault location unit is connected to the multimodal data acquisition unit and is used to perform preliminary fault location on each module of the UHF transmitter based on the collected multimodal operation data using an optimized bat fault location algorithm to obtain a preliminary fault module set; The timing characteristic analysis unit is connected to the preliminary fault location unit and is used to input the multimodal operation data corresponding to the preliminary fault module set into the multimodal fault timing analysis model, perform timing characteristic analysis on the fault module, and determine the fault occurrence time sequence of the fault module; The fault path construction unit is connected to the timing characteristic analysis unit and is used to construct a fault impact propagation path model based on the fault occurrence time sequence of the fault module and the preset performance parameters of the UHF transmitter; The impact analysis unit is connected to the fault path construction unit and is used to analyze the scope and degree of influence of the fault module on other modules based on the fault impact propagation path model and the connection relationship and signal transmission characteristics between the modules of the UHF transmitter; The diagnosis report generating unit is connected to the impact analysis unit and is used to generate a UHF transmitter multi-module fault diagnosis report according to the analysis result.

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