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

Abstract

The application discloses a multi-module fault self-diagnosis method and system for a UHF transmitter, the method collects multi-modal operation data such as transmission power and frequency offset of each module of the transmitter, and uses an optimized bat fault positioning algorithm to preliminarily position faults; a multi-modal fault time sequence analysis model is used to determine a fault occurrence time sequence, a fault influence propagation path model is constructed in combination with preset performance parameters, the fault influence range and degree are analyzed, and finally a diagnosis report is generated. The system comprises six units such as a multi-modal data acquisition unit and a preliminary fault positioning unit, and the units are connected and work cooperatively. The method and system break through the limitations of traditional single data and static diagnosis, start from multi-modal data and fault time sequence characteristics, realize accurate positioning and comprehensive diagnosis of faults of the UHF transmitter, improve fault diagnosis efficiency and accuracy, and guarantee stable operation of equipment.

Description

TECHNICAL FIELD

[0001] The present application 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

[0002] With the rapid development of wireless communication technology, UHF transmitters are widely used in broadcast television, mobile communication, aerospace and other fields, and have become the core equipment to ensure stable signal transmission. However, the internal structure of UHF transmitters is complex, including power amplification, frequency synthesis, modulation and demodulation and other functional modules, which work cooperatively and are interrelated. In the long-term operation process, faults are easily caused by factors such as aging and environmental interference. Once a fault occurs, it will not only cause the quality of signal transmission to decline, but also may cause communication interruption, resulting in significant economic losses and social impact. Therefore, it is particularly important to accurately and efficiently diagnose faults of UHF transmitters.

[0003] There are many deficiencies in the existing fault diagnosis technology of UHF transmitters. Firstly, most diagnosis methods can only analyze single modal data, such as monitoring only part of parameters such as transmission power and voltage, which is difficult to fully capture fault characteristics. The fault of UHF transmitter is often the result of multi-factor coupling, and the analysis of single data cannot reflect the complex correlation between modules, resulting in one-sided diagnosis results, easy to miss diagnosis, and unable to timely find potential fault hidden dangers. Secondly, the existing technology focuses on static diagnosis and lacks analysis of the dynamic development process of faults. The propagation of faults among modules of UHF transmitters has time sequence and directionality, while the traditional method cannot accurately determine the specific time of fault occurrence, and it is also difficult to track the propagation path of the fault, making fault positioning ambiguous and unable to provide clear fault handling ideas for maintenance personnel, resulting in low fault repair efficiency.

[0004] To solve the above problems, a multi-module fault self-diagnosis method and system capable of comprehensively analyzing multi-modal data and deeply analyzing fault time sequence characteristics are needed. SUMMARY

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

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

[0007] Step S1: Collecting multi-modal operation data of each module of the UHF transmitter during operation, the multi-modal operation data including 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, an optimized bat fault location algorithm is used to preliminarily locate faults of each module of the UHF transmitter, and a preliminary fault module set is obtained;

[0009] Step S3: The multi-modal operation data corresponding to the preliminary fault module set is input into a multi-modal fault timing analysis model, the timing characteristics of the fault module are analyzed, and a fault occurrence time sequence of the fault module is determined;

[0010] Step S4: According to the fault occurrence time sequence of the fault module and the preset performance parameters of the UHF transmitter, a fault influence propagation path model is constructed, and the preset performance parameters include transmitter rated power, frequency stability index, and phase noise tolerance;

[0011] Step S5: According to the fault influence propagation path model, the connection relationship and signal transmission characteristics between each module of the UHF transmitter are combined to analyze the influence range and degree of the fault module on other modules;

[0012] Step S6: According to the analysis result, a multi-module fault diagnosis report of the UHF transmitter is generated, and the diagnosis report contains fault module name, fault occurrence time, fault influence range and degree information.

[0013] Further, the optimized bat fault location algorithm is as follows: in step S2, the multi-modal operation data of each module of the UHF transmitter is mapped to a multi-dimensional feature space, and a feature vector set X={x1, x2,…,x n} is constructed, wherein x i represents the feature vector of the i-th module; the fault possibility score of each feature vector is calculated by formula , wherein F(x i ) is the fault possibility score of the i-th module, w j is the weight of the j-th feature, f j (x i ) is the feature value 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 the modules with fault possibility scores greater than T are included in the preliminary fault module set.

[0014] Further, the multi-modal fault timing analysis model is as follows: the multi-modal operation data sequence of the UHF transmitter is Y={y1, y2,…,y k}, wherein y t represents the multi-modal data vector at time t; the fault timing feature value at time t is calculated by formula , wherein S(t) is the fault timing feature value at time t, α and β are adjustment parameters, τ is the length of the time window, d(y s , y s+1) is a difference measure of the multi-modal data vectors at time s and s+1; when S(t) exceeds a set timing threshold T s , the time t is determined as the fault occurrence time, thereby obtaining a fault occurrence time sequence of the fault module.

[0015] Further, the fault influence propagation path model constructed in step S4 is: assuming that the module connection matrix of the UHF transmitter is A=[a ij ], wherein a ij represents the connection relationship between module i and module j, a ij =1 represents that module i is connected with module j, and a ij =0 represents that they are not connected; the fault influence propagation probability is calculated by the formula , wherein P ij is the probability that the fault propagates from module j to module i, and ΔP i , ΔP j are the power change amounts of module i and module j at the time of fault occurrence; according to the fault influence propagation probability, the fault influence propagation path model is constructed.

[0016] Further, when analyzing the influence range and degree of the fault module on other modules in step S5, the formula is used, wherein I i is the fault influence degree of module i, P ij is the probability that the fault propagates from other modules to module i, V j is the fault severity value of the fault source module j, and n is the number of modules connected with module i; according to the calculated I i value, the influence range and degree of the fault module on other modules are determined.

[0017] Further, when generating the multi-module fault diagnosis report of the UHF transmitter in step S6, a weighted fusion algorithm is used, and the diagnosis report content is generated by the formula , wherein R is the final diagnosis report content vector, R k is the kth diagnosis information vector, u k is the weight of the kth diagnosis information, and l is the number of diagnosis information categories.

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

[0019] Step S31: normalizing the multi-modal operation data input into the multi-modal fault timing analysis model, so as to uniformly map the numerical ranges of different types of data to the [0, 1] interval, facilitating subsequent analysis and calculation;

[0020] Step S32: using the sliding window technique, the normalized multi-modal operation data is segmented according to the preset window size and step size, a plurality of data subsequences are obtained, and local time sequence features of the data are extracted;

[0021] Step S33: feature extraction is performed on each data subsequence, a fault time sequence feature value is calculated, and a fault occurrence time in each subsequence is determined.

[0022] Step S34: the fault occurrence times of all data subsequences are integrated, and repeated times are removed, to obtain an accurate fault occurrence time sequence of the fault module.

[0023] Further, the step S4 specifically includes the following steps:

[0024] Step S41: according to the system architecture diagram of the UHF transmitter, the physical connection relationship between the modules is determined, a module connection matrix is constructed, and the signal transmission path between the modules is determined.

[0025] Step S42: collect the performance parameter data of each module of the UHF transmitter in the normal operation state, establish a normal parameter database, and use it as a reference benchmark for fault analysis.

[0026] Step S43: for each preliminary fault module, analyze the performance parameter change when the fault occurs, compare with the normal parameter database, and determine the fault parameter deviation.

[0027] Step S44: according to the fault parameter deviation and the module connection matrix, calculate the fault influence propagation probability, and construct a fault influence propagation path model.

[0028] Further, the step S5 specifically includes the following steps:

[0029] Step S51: according to the fault influence propagation path model, determine the module set directly connected with the fault module as the initial affected module set.

[0030] Step S52: calculate the fault influence degree of each module in the initial affected module set.

[0031] Step S53: according to the calculated fault influence degree, filter out the modules with larger influence degree and include them in the expanded affected module set.

[0032] Step S54: repeat steps S52 and S53 until the fault influence degree of all affected modules is calculated, and determine the final fault influence range and degree.

[0033] The multi-module fault self-diagnosis system for the UHF transmitter comprises:

[0034] A multi-modal data acquisition unit is configured to acquire multi-modal operation data of each module during operation of the UHF transmitter;

[0035] A preliminary fault positioning unit is connected to the multi-modal data acquisition unit and configured to perform preliminary fault positioning on each module of the UHF transmitter based on the acquired multi-modal operation data by using an optimized bat fault positioning algorithm to obtain a preliminary fault module set;

[0036] A time sequence feature analysis unit is connected to the preliminary fault positioning unit and configured to input the multi-modal operation data corresponding to the preliminary fault module set into a multi-modal fault time sequence analysis model to perform time sequence feature analysis on the fault module and determine a fault occurrence time sequence of the fault module;

[0037] A fault path construction unit is connected to the time sequence feature analysis unit and configured to construct a fault influence propagation path model according to the fault occurrence time sequence of the fault module and preset performance parameters of the UHF transmitter;

[0038] An influence analysis unit is connected to the fault path construction unit and configured to analyze an influence range and degree of the fault module on other modules according to the fault influence propagation path model in combination with a connection relationship and signal transmission characteristics among the modules of the UHF transmitter;

[0039] A diagnosis report generation unit is connected to the influence analysis unit and configured to generate a multi-module fault diagnosis report of the UHF transmitter according to the analysis result.

[0040] Beneficial effects: The present application proposes a multi-module fault self-diagnosis method and system for a UHF transmitter. The present application changes the limitation of traditional methods relying on single modal data by acquiring multi-modal operation data such as transmission power, frequency offset, and phase noise, comprehensively captures the operation characteristics of each module, and avoids missed diagnosis caused by one-sided data. The optimized bat fault positioning algorithm is used to map the multi-modal data to a multi-dimensional feature space to calculate a fault possibility score, accurately lock the preliminary fault module set, and greatly improve the diagnosis accuracy compared with the traditional simple threshold judgment. In the fault analysis aspect, the multi-modal fault time sequence analysis model calculates fault time sequence characteristic values according to data sequences to determine a fault occurrence time sequence, which makes up for the deficiency of the prior art that cannot analyze the dynamic development of faults; the fault influence propagation path model is constructed to calculate the fault propagation probability and influence degree in combination with the connection relationship and performance parameters of the modules to accurately track the fault propagation path and range, which provides clear guidance for maintenance. The system units work cooperatively to realize efficient and accurate self-diagnosis of the UHF transmitter fault from data acquisition to generation of a diagnosis report containing fault module, occurrence time, influence range, and other information, which effectively guarantees the stable operation of the UHF transmitter and improves the fault repair efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 A method flowchart of the present application;

[0042] Figure 2 A system unit composition diagram of the present application. DETAILED DESCRIPTION

[0043] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict, and the present application will be further described in detail below with the specific embodiments in combination with the drawings.

[0044] As shown in the figure, the multi-module fault self-diagnosis method for the UHF transmitter comprises the following steps: Figure 1

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

[0046] Specifically, this step focuses on the collection of UHF transmitter operation data, aiming to obtain key information reflecting the running state of the device. As a complex electronic device, the UHF transmitter works collaboratively with its internal modules, and the transmission power data reflects the strength of the transmitter output signal, which is directly related to the coverage range and transmission quality of the signal; the frequency offset data reflects the deviation of the actual working frequency from the nominal frequency, which affects the stability and accuracy of the signal; the phase noise data represents the random fluctuation of the signal phase, which has an important influence on the anti-interference ability and signal quality of the communication; the voltage and current data as basic electrical parameters can reflect the power supply state and working load of the module. Through the collection of these multi-modal operation data, the running status of each module of the transmitter can be comprehensively mastered, providing a rich and reliable data basis for subsequent fault diagnosis.

[0047] In the implementation, high-precision sensors and data acquisition equipment are connected with each module of the UHF transmitter. For the transmission power, a power sensor is used to monitor the power change of the transmitter output port in real time; for the frequency offset and phase noise, a frequency meter and a phase noise analyzer are used for accurate measurement; and the voltage and current data are collected through a voltage transformer and a current transformer. At the same time, in order to ensure the accuracy and real-time performance of data acquisition, a reasonable data acquisition frequency is set, so that the collected data can timely and accurately reflect the running state of the transmitter. These collected multi-modal data will be transmitted to the data processing unit, preparing for the subsequent fault diagnosis step.

[0048] Step S2: Based on the collected multi-modal operation data, using an optimized bat fault positioning algorithm to preliminarily locate the faults of each module of the UHF transmitter, and obtaining a preliminary fault module set; ​

[0049] Specifically, the core of this step is to use the optimized bat fault localization algorithm to deeply analyze the collected multi-modal operation data, so as to realize the preliminary fault localization of each module of the UHF transmitter. Since the operation data of each module of the UHF transmitter contains a large amount of information about the state of the module, and the optimized bat fault localization algorithm can mine potential features and rules 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 state of the module. Thus, the algorithm can filter out the modules that may have faults from the numerous modules and form a preliminary fault module set, providing a clear target range for subsequent more in-depth fault analysis, effectively narrowing the scope of fault troubleshooting, and improving the efficiency of fault diagnosis.

[0050] In the specific implementation process, first, the collected multi-modal operation data is preprocessed to make its format and range conform to the input requirements of the algorithm. Then, the preprocessed data is input into the optimized bat fault localization algorithm, which searches and optimizes in the multi-dimensional data space by simulating the echolocation behavior of bats. During the search process, the algorithm continuously adjusts parameters and calculates the fault likelihood index of each module according to the data characteristics. After multiple iterations of calculation and optimization, the algorithm finally determines the modules with high fault likelihood 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 fault modules in complex data environments, and compared with traditional methods, it has higher fault recognition accuracy and efficiency.

[0051] Step S3: inputting the multi-modal operation data corresponding to the preliminary fault module set into the multi-modal fault time sequence analysis model to perform time sequence feature analysis on the fault modules and determining the fault occurrence time sequence of the fault modules;

[0052] Specifically, this step aims to use the multi-modal fault time sequence analysis model to perform more in-depth analysis on the modules in the preliminary fault module set to determine the specific time sequence of fault occurrence. The fault of the UHF transmitter often has certain time evolution characteristics, and the occurrence of the fault is not instantaneous but gradually develops over time. The multi-modal fault time sequence analysis model can fully consider the change rules of multi-modal operation data over time, and by performing time sequence analysis on the multi-modal data of the preliminary fault modules, it can mine the features and abnormal changes of the data in the time dimension. From these features and changes, the time when the fault module appears and the development trend of the fault in time can be accurately determined, thereby determining the time sequence of fault occurrence. This is of great significance for comprehensively understanding the development process of the fault, accurately judging the fault cause, and formulating effective maintenance strategies.

[0053] In an embodiment, the multi-modal operation data corresponding to the preliminary fault module set is sorted and arranged in chronological order to form complete time series data. Then, the time series data is input into a multi-modal fault time series analysis model, and the model uses specific time series analysis algorithms and techniques to analyze and process the data in segments. During the analysis process, the model calculates the characteristic values of the data at different time points, and identifies abnormal fluctuations and trends in the data by comparing and analyzing these characteristic values. When the characteristic values of the data exceed the normal range or a specific abnormal pattern is detected, it is determined that the time point is the fault occurrence time. Through analysis of the entire time series data, the complete fault occurrence time sequence of the fault module is determined, providing accurate time basis for subsequent fault diagnosis and processing.

[0054] Step S4: According to the fault occurrence time sequence of the fault module and the preset performance parameters of the UHF transmitter, a fault influence propagation path model is constructed, and the preset performance parameters include transmitter rated power, frequency stability index, and phase noise tolerance.

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

[0056] In the implementation process, first, the detailed system architecture information of the UHF transmitter is collected to clearly define the physical connection relationship and signal transmission path between the modules, and a module connection database is established. Then, the fault occurrence time sequence of the fault module is analyzed in association with the preset performance parameters of each module. According to the fault occurrence time, the change of the performance parameters of each module at that time is analyzed, and the possible propagation direction and path of the fault are judged in combination with the module connection relationship. Through step-by-step analysis and reasoning, the influence of the fault on each module at different time points is determined, and a complete fault influence propagation path model is finally constructed. The model is presented in the form of graphics or data structure, which intuitively shows the propagation path of the fault and its influence on each module, providing an important reference for subsequent fault diagnosis and processing.

[0057] Step S5: According to the fault influence propagation path model, combined with the connection relationship and signal transmission characteristics between each module of the UHF transmitter, the influence range and degree of the fault module on other modules are analyzed;

[0058] Specifically, this step is to further analyze the influence range and degree of the fault module on other modules based on the constructed fault influence propagation path model. The fault influence propagation path model only shows the possible propagation path of the fault, but to fully understand the influence of the fault, detailed analysis is needed in combination with the actual connection relationship and signal transmission characteristics between each module of the UHF transmitter. The connection mode between modules determines the physical channel of fault propagation, and different connection modes may lead to different speed and range of fault propagation; signal transmission characteristics affect the degree of influence of fault on signal quality during propagation. By considering these factors comprehensively, the influence range of the fault module on other modules can be accurately evaluated, and it can be determined which modules are directly affected by the fault and which modules are indirectly affected; at the same time, the degree of influence of the fault on the performance of each module can also be quantified, providing accurate basis for formulating targeted maintenance and processing measures.

[0059] In specific implementation, the path information of fault propagation is extracted from the fault influence propagation path model, and then according to the connection relationship between each module, the modules directly connected with the fault module are determined. For these directly connected modules, the signal transmission change after the fault occurs is analyzed, and the degree of influence of the fault on its performance index is evaluated in combination with the signal transmission characteristics. Then, taking the directly affected module as the new starting point, the influence of the fault on the next level module is analyzed step by step according to the fault propagation path. In the analysis process, factors such as signal attenuation and interference in the transmission process are considered, and the influence range and degree of the fault on each module are comprehensively judged. Through this step-by-step analysis, the influence range and degree of the fault module on each module of the entire UHF transmitter are finally determined comprehensively and accurately, providing detailed guidance information for subsequent fault processing.

[0060] Step S6: According to the analysis results, a multi-module fault diagnosis report of the UHF transmitter is generated, which contains the fault module name, fault occurrence time, fault influence range and degree information.

[0061] Specifically, this step is the summary and output stage of the entire fault diagnosis process. Its purpose is to organize and summarize the results obtained from the previous steps to generate a comprehensive and detailed multi-module fault diagnosis report for the UHF transmitter. The previous steps have already completed tasks such as locating the faulty module, determining the time of fault occurrence, constructing the fault propagation path, and analyzing the scope and extent of the fault's impact. This information is crucial for maintenance personnel to accurately understand the fault situation and formulate maintenance plans. 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 implementation, the analysis results obtained from the previous steps are first categorized and organized, with information such as the fault module name, fault occurrence time, fault impact range, and degree arranged according to a specific format. Then, based on a pre-set report template, the organized information is filled into the corresponding locations to generate a preliminary fault diagnosis report. During report generation, the information is double-checked and verified to ensure the accuracy and completeness of the report content. Finally, the report's format and layout are optimized to make it clearer and easier to read. The generated UHF transmitter multi-module fault diagnosis report can be output in electronic or paper format, facilitating access and use by maintenance personnel and providing strong support for UHF transmitter fault repair and maintenance.

[0063] Preferably, the optimized bat fault location algorithm specifically involves: in step S2, mapping the multimodal operation data of each module of the UHF transmitter to a multidimensional feature space, and constructing a feature vector set X = {x1, x2, ..., x...} n}, where x i This represents the feature vector of the i-th module; expressed by the formula... Calculate the fault probability score for each feature vector, where F(x) i ) represents the failure probability score of the i-th module, w j f is the weight of the j-th feature. j (x i Let be the feature value of the i-th module in the j-th feature dimension, and m be the number of feature dimensions; set a fault threshold T, and include modules with a fault probability score greater than T into the initial fault module set.

[0064] Specifically, in step S2, the multi-modal operation data is mapped to a multi-dimensional feature space to construct a feature vector set, a failure possibility score is calculated by assigning different weights to each feature dimension, and quantitative evaluation of the failure possibility of each module is realized. The algorithm filters out modules with high failure possibility by setting a failure threshold, forming a preliminary failure module set, effectively reducing the subsequent failure troubleshooting range and improving the diagnosis efficiency. Among them, the construction of the feature vector set realizes the integrated processing of multi-modal data, and the setting of the weight parameter can be adjusted according to the characteristics of different modules, making the algorithm more targeted and adaptive.

[0065] Preferably, the multi-modal failure time sequence analysis model is: let the multi-modal operation data sequence of the UHF transmitter be Y={y1, y2, …, y k}, wherein y t represents the multi-modal data vector at time t; the failure time sequence characteristic value at time t is calculated by the formula , wherein S(t) is the failure time sequence characteristic value at time t, α and β are adjustment parameters, τ is the length of the time window, and d(y s , y s+1 ) is the difference measure of the multi-modal data vectors at times s and s+1; when S(t) exceeds the set time sequence threshold T s , it is determined that time t is the failure occurrence time, and thus the failure occurrence time sequence of the failure module is obtained.

[0066] Specifically, by analyzing the multi-modal operation data sequence, the failure time sequence characteristic value is calculated to realize accurate positioning of the failure occurrence time. The model introduces adjustment parameters and time window length, which can be adjusted according to different fault types and device characteristics, making the extraction of failure time sequence characteristics more accurate. By setting the time sequence threshold to determine the failure occurrence time, a failure occurrence time sequence is formed, which provides key information in the time dimension for subsequent analysis of the failure propagation path and impact range, and helps to understand the development process and evolution law of the failure.

[0067] Preferably, the failure influence propagation path model constructed in step S4 is: let the module connection matrix of the UHF transmitter be A=[a ij ], wherein a ij represents the connection relationship between module i and module j, a ij =1 indicates that module i is connected to module j, and a ij =0 indicates that they are not connected; the failure influence propagation probability is calculated by the formula , wherein P ij is the probability of failure propagation from module j to module i, ΔP i , ΔP jrespectively, are the power variation amounts of the module i and the module j when the fault occurs; a fault influence propagation path model is constructed according to a fault influence propagation probability.

[0068] Specifically, the fault influence propagation probability is calculated based on the module connection matrix and the power variation amount, and the possibility of the fault propagation between the modules is determined. The module connection matrix is constructed based on the actual physical connection relationship of the UHF transmitter, so that the model can truly reflect the physical path of the fault propagation. Through comparative analysis of the power variation amount, the strength of the fault propagation is quantified, so that the model can more accurately predict the range of the possible influence of the fault, and provide an important basis for comprehensive evaluation of the fault influence.

[0069] Preferably, in the step S5 of analyzing the influence range and degree of the fault module on other modules, the formula is used, wherein, I i is the influence degree of the fault on the module i, P ij is the probability of the fault propagation from other modules to the module i, V j is the fault severity value of the fault source module j, and n is the number of modules connected to the module i; the influence range and degree of the fault module on other modules are determined according to the calculated I i value.

[0070] Specifically, by comprehensively considering the fault propagation probability and the severity value of the fault source module, the quantitative evaluation of the influence degree of each module is realized. This method not only considers the direct path of the fault propagation, but also comprehensively evaluates the indirect influence through cumulative calculation, so that the evaluation result is more comprehensive and accurate. Through the calculated influence degree value, the range of the fault influence can be clearly divided, and a scientific basis is provided for formulating a targeted maintenance strategy and preferentially processing the modules with severe influence.

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

[0072] Specifically, the weighted fusion algorithm is used to integrate various types of diagnosis information, and by assigning different weights to different types of diagnosis information, it is ensured that the finally generated report can comprehensively and objectively reflect the fault condition. The application of this algorithm realizes the standardized and systematic processing of the diagnosis information, so that the report content is more standardized and orderly. The weight setting can be adjusted according to the importance and reliability of different diagnosis information, which improves the accuracy and practicality of the report, and provides clear and effective fault diagnosis guidance for maintenance personnel.

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

[0074] Step S31: normalizing the multi-modal operation data input into the multi-modal fault time sequence analysis model, so as to uniformly map the numerical ranges of different types of data to the interval [0, 1], facilitating subsequent analysis and calculation;

[0075] Step S32: using a sliding window technique, the normalized multi-modal operation data is segmented according to a preset window size and step size, to obtain a plurality of data subsequences, so as to extract local time sequence features of the data;

[0076] Step S33: feature extraction is performed on each data subsequence, and a fault time sequence feature value is calculated, so as to determine the fault occurrence time in each subsequence;

[0077] Step S34: integrating the fault occurrence times of all data subsequences, removing repeated times, to obtain an accurate fault occurrence time sequence of the fault module.

[0078] Specifically, first, the multi-modal operation data is normalized to eliminate the influence of the dimension difference of different types of data and ensure the accuracy of subsequent analysis. Then, the sliding window technique is used to segment the data, converting long time sequence data into a plurality of short time sequence subsequences, so as to facilitate the extraction of local time sequence features. Feature extraction and fault occurrence time judgment are performed on each subsequence, and finally the results are integrated and repeated times are removed to obtain an accurate fault occurrence time sequence. This step-by-step processing method makes the time sequence feature analysis more detailed and comprehensive, effectively improving the accuracy of fault occurrence time judgment.

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

[0080] Step S41: according to the system architecture diagram of the UHF transmitter, the physical connection relationship between the modules is determined, a module connection matrix is constructed, and the signal transmission path between the modules is clarified;

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

[0082] Step S43: for each preliminary fault module, analyzing the performance parameter change at the time of fault occurrence, comparing with the normal parameter database, and determining the fault parameter deviation;

[0083] Step S44: according to the fault parameter deviation and the module connection matrix, calculating the fault influence propagation probability, and constructing a fault influence propagation path model.

[0084] Specifically, a module connection matrix is ​​first constructed based on the system architecture diagram to clarify the physical connections and signal transmission paths between modules, providing a structural foundation for subsequent analysis. Next, a normal parameter database is established as a reference benchmark for fault analysis, facilitating comparative analysis of parameter changes during fault occurrence. For modules with initial faults, their parameter deviations are analyzed. Combining the module connection matrix and fault propagation probability calculation methods, a complete fault propagation path model is constructed. This step-by-step implementation ensures the scientific rigor and accuracy of the model construction, realistically reflecting the propagation of faults within the UHF transmitter.

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

[0086] Step S51: Based on the fault impact propagation path model, determine the set of modules directly connected to the faulty module, as the initial set of affected modules;

[0087] Step S52: For each module in the initial set of affected modules, calculate the degree of fault impact;

[0088] Step S53: Based on the calculated degree of fault impact, filter out the modules that are more affected and include them in the extended set of affected modules;

[0089] Step S54: Repeat steps S52 and S53 until the fault impact of all affected modules has been calculated, and determine the final fault impact range and extent.

[0090] Specifically, the initial set of affected modules directly connected to the faulty module is first identified as the starting point for analysis. Then, the degree of impact on these initial affected modules is calculated, and modules with significant impact are selected for further analysis. By repeatedly performing calculations and selections, the degree of impact on all affected modules is gradually determined, ultimately clarifying the scope and extent of the fault's influence. This progressively in-depth analysis method ensures a comprehensive assessment of the fault's impact, providing detailed and accurate data for developing effective fault handling measures.

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

[0092] The multimodal data acquisition unit is used to acquire multimodal operation data of each module during the operation of the UHF transmitter;

[0093] The preliminary fault location unit, connected to the multimodal data acquisition unit, is used to perform preliminary fault location on each module of the UHF transmitter based on the acquired multimodal operation data and the optimized bat fault location algorithm, and to obtain a preliminary fault module set.

[0094] The time sequence feature analysis unit is connected with the preliminary fault positioning unit, and is configured to input the multi-modal operation data corresponding to the preliminary fault module set into a multi-modal fault time sequence analysis model, analyze time sequence features of the fault module, and determine a fault occurrence time sequence of the fault module.

[0095] The fault path construction unit is connected with the time sequence feature analysis unit, and is configured to construct a fault influence propagation path model according to the fault occurrence time sequence of the fault module and preset performance parameters of the UHF transmitter.

[0096] The influence analysis unit is connected with the fault path construction unit, and is configured to analyze an influence range and degree of the fault module on other modules according to the fault influence propagation path model, in combination with a connection relationship and signal transmission characteristics among the modules of the UHF transmitter.

[0097] The diagnosis report generation unit is connected with the influence analysis unit, and is configured to generate a multi-module fault diagnosis report of the UHF transmitter according to the analysis result.

[0098] The UHF transmitter multi-module fault self-diagnosis method and system provided by the application form a complete fault diagnosis system from data acquisition, analysis to diagnosis report generation, and effectively make up for the defects of the prior art. The prior art often relies on single modal data, which leads to incomplete fault feature capture and easy missed diagnosis. Meanwhile, the static diagnosis method is difficult to track the dynamic development of the fault. The application collects multi-modal operation data such as transmit power, frequency offset and phase noise, breaks through the limitation of single data, maps the multi-modal data to a multi-dimensional feature space, calculates the fault possibility score by using the optimized bat fault positioning algorithm, accurately locks the preliminary fault module, and greatly improves the diagnosis accuracy.

[0099] In the fault analysis link, the prior art cannot effectively analyze the time sequence characteristics and propagation path of the fault, resulting in fuzzy fault positioning. The system calculates fault time sequence feature values by means of a multi-modal fault time sequence analysis model, accurately determines the fault occurrence time sequence, constructs a fault influence propagation path model, calculates the fault propagation probability in combination with the module connection matrix and the power change amount, and then determines the affected degree of each module according to the propagation probability and the fault severity, clearly outlines the fault propagation path and range, provides clear guidance for maintenance personnel, and changes the fuzzy condition of the traditional diagnosis idea.

[0100] On the system architecture, the six units of multi-modal data acquisition unit, preliminary fault location unit, time sequence feature analysis unit, etc. are closely connected and work cooperatively. From data acquisition, through preliminary positioning, time sequence analysis, path construction, influence evaluation, finally a diagnostic report covering detailed information of fault module, occurrence time, influence range, etc. is generated, realizing the full-process automation and precision of UHF transmitter fault diagnosis. Compared with the prior art, the system significantly improves the fault diagnosis efficiency and accuracy, greatly guarantees the stable and reliable operation of the UHF transmitter, and provides strong technical support for equipment maintenance in the communication field.

[0101] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "set", "install", "connect", "connect", "fix" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be connected inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0102] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various equivalent changes, modifications, replacements and variations of the embodiments can be made without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalent scope.

Claims

1. A multi-module fault self-diagnosis method for UHF transmitters, characterized in that, Includes the following steps: Step S1: Collect multi-mode operation data of each module during the operation of the UHF transmitter. The multi-mode operation data includes transmit power data, frequency offset data, phase noise data, and voltage and current data. Step S2: Based on the collected multimodal operation data, the optimized bat fault location algorithm is used to perform preliminary fault location on each module of the UHF transmitter and obtain a preliminary fault module set; Step S3: Input the multimodal operation data corresponding to the preliminary set of fault modules into the multimodal fault time series analysis model, perform time series feature analysis on the fault modules, and determine the fault occurrence time sequence of the fault modules; Step S4: Based on the fault occurrence time sequence of the fault module and the preset performance parameters of the UHF transmitter, construct a fault impact propagation path model. The preset performance parameters include the transmitter's rated power, frequency stability index, and phase noise tolerance. Step S5: Based on the fault propagation path model, and combined with the connection relationship and signal transmission characteristics between the modules of the UHF transmitter, analyze the scope and degree of the impact of the faulty module on other modules; Step S6: Based on the analysis results, generate a multi-module fault diagnosis report for the UHF transmitter. The diagnosis report includes the name of the faulty module, the time of the fault occurrence, and the scope and degree of the fault's impact. The optimized bat fault location algorithm specifically involves: in step S2, mapping the multimodal operation data of each module of the UHF transmitter to a multidimensional feature space to construct a set of feature vectors. ,in, Indicates the first The feature vectors of each module; through the formula Calculate the fault probability score for each feature vector, where, For the first The failure probability score of each module. For the first The weights of each feature, For the first The module in the first Feature values ​​in each feature dimension Set the number of feature dimensions; set the fault threshold. The failure probability score is greater than The modules were included in the initial fault module set; The multimodal fault timing analysis model is as follows: Let the multimodal operation data sequence of the UHF transmitter be... ,in, Indicates time Multimodal data vectors; through formulas Calculation time The fault timing characteristic values, where, For a moment The fault timing characteristic value, To adjust the parameters, The time window length, For a moment and The difference measure of multimodal data vectors; when Exceeding the set timing threshold At that time, determine the moment. The fault occurrence time is given, thus obtaining the fault occurrence time sequence of the faulty module.

2. The multi-module fault self-diagnosis method for UHF transmitters according to claim 1, characterized in that, The fault propagation path model constructed in step S4 is as follows: Let the module connection matrix of the UHF transmitter be... ,in, Representation module With modules The connection relationship, Representation module With modules Connected, Indicates that they are not connected; through the formula Calculate the probability of fault impact propagation, where, For faults from the module propagation to module The probability, Modules Module The power change at the time of the fault; based on the probability of fault propagation, construct a fault propagation path model.

3. The multi-module fault self-diagnosis method for UHF transmitters according to claim 1, characterized in that, In step S5, when analyzing the scope and extent of the impact of the faulty module on other modules, the formula is used. ,in, For module The extent of the impact of the fault To prevent faults from propagating from other modules to other modules The probability, Fault source module The severity value of the fault. To be compatible with modules The number of connected modules; calculated accordingly. The value determines the scope and extent of the impact of the faulty module on other modules.

4. The multi-module fault self-diagnosis method for UHF transmitters according to claim 1, characterized in that, In step S6, when generating the UHF transmitter multi-module fault diagnosis report, a weighted fusion algorithm is used, through the formula... Generate diagnostic report content, including, This is a vector representation of the final diagnostic report content. For the first Class diagnostic information vector, For the first Weights of diagnostic information This represents the number of diagnostic information categories.

5. The multi-module fault self-diagnosis method for UHF transmitters according to claim 1, characterized in that, Step S3 specifically includes the following sub-steps: Step S31: Normalize the multimodal operation data of the input multimodal fault timing analysis model, and map the numerical range of different types of data to the [0,1] interval to facilitate subsequent analysis and calculation; Step S32: Using the sliding window technique, the normalized multimodal running data is segmented according to the preset window size and step size to obtain multiple data subsequences in order to extract the local temporal features of the data; Step S33: Extract features from each data subsequence and calculate the fault timing feature value to determine the fault occurrence time within each subsequence; Step S34: Integrate the fault occurrence times of all data subsequences, remove duplicate times, and obtain the accurate fault occurrence time sequence of the fault module.

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

7. The multi-module fault self-diagnosis method for UHF transmitters according to claim 1, characterized in that, Step S5 specifically includes the following sub-steps: Step S51: Based on the fault impact propagation path model, determine the set of modules directly connected to the faulty module, as the initial set of affected modules; Step S52: For each module in the initial set of affected modules, calculate the degree of fault impact; Step S53: Based on the calculated degree of fault impact, filter out the modules that are more affected and include them in the extended set of affected modules; Step S54: Repeat steps S52 and S53 until the fault impact of all affected modules has been calculated, and determine the final fault impact range and extent.

8. A multi-module fault self-diagnosis system for UHF transmitters, characterized in that, This system is applied to the multi-module fault self-diagnosis method for UHF transmitters as described in claim 1, comprising: The multimodal data acquisition unit is used to acquire multimodal operation data of each module during the operation of the UHF transmitter; The preliminary fault location unit, connected to the multimodal data acquisition unit, is used to perform preliminary fault location on each module of the UHF transmitter based on the acquired multimodal operation data and the optimized bat fault location algorithm, and to obtain a preliminary fault module set. The timing feature analysis unit, connected to the preliminary fault location unit, is used to 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 modules, and determine the fault occurrence time sequence of the fault modules; The fault path construction unit, connected to the time sequence feature analysis unit, 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, connected to the fault path construction unit, is used to analyze the scope and degree of the impact of a faulty 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 diagnostic report generation unit, connected to the impact analysis unit, is used to generate a multi-module fault diagnostic report for the UHF transmitter based on the analysis results.

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